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Title: Temptation, Impulsiveness and Committment


1
Temptation, Impulsiveness and Committment
  • Daniel Houser
  • Professor of Economics
  • Director, Interdisciplinary Center for Economic
    Science
  • George Mason University, Fairfax, VA

2
Temptation and Impulsiveness
3
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4
Impulse Buying
Impulse buying is a sudden and powerful urge that
arises within the consumer to buy immediately
(Beatty and Ferrell 1998 Rook 1987).
Impulsive purchasing is defined as involving
spontaneous and unreflective desires to buy,
without thoughtful consideration of why and for
what reason a person should have the product
(Rook 1987 Rook and Fisher 1995 Verplanken and
Herabadi 2001).
5
Why Investigate Impulse Buying?
  • Impulse buying is easier now than it has ever
    been.
  • In past times, one would usually need to wait
    several hours between seeing a product advertised
    and actually purchasing the product.
  • This can be useful in reducing the impact of
    impulsivity on purchase decisions.
  • Now cash machines, television shopping and the
    internet make it easy to respond to any urge
    immediately.

6
Some Evidence
  • Self-regulatory resources play an important role
    in affecting many types of behaviors (Baumeister
    et. al,1998 Baumeister and Ciarocco 2000 and
    many others, see Vohs 2006 for review.)
  • Overeating
  • Procrastination
  • Underachievement
  • Vohs and Heatherton (2000) find that dieters
    sitting next to a bowl of candy are subsequently
    less able to perform arithmetic as well as
    dieters who were seated away from a bowl of
    candy.
  • Exerting self-control on one task renders it
    harder to exert self-control on a subsequent
    task.

7
The famous marshmallow test.
  • Mischel and Ebbeson, with 4-year-old subjects.
  • Here is a marshmallow for you. I have to leave
    the lab for 10 minutes. If you can refrain from
    eating the marshmallow until I return, you can
    have a second marshmallow.
  • Results put children into three categories
  • Some children did wait for the delayed reward.
  • A predictor of later academic success!
  • Many children chose to take the lesser reward
    immediately.
  • A third group of children waited several minutes,
    only to end up eating the marshmallow before the
    researcher returned.

8
The Marshmallow Experiment
  • Attention to the rewards strongly influenced the
    outcomes in the experiment.
  • Children who managed to distract themselves from
    the marshmallow (or other reward) were much more
    likely to pass the marshmallow test.
  • Follow-up research found that putting the
    marshmallow inside a desk drawer helped the
    subjects become much more successful at waiting.

9
Marshmallow Experiment with Aduluts?
  • Goal is to discover a naturally occurring
    economic environment where
  • Temptation plays an important role
  • Effects of exposure to temptation can be tested

10
Checking Out Temptation
  • A Natural Experiment
  • at the Grocery Register

Daniel Houser, George Mason University David H.
Reiley, University of Arizona Michael B.
Urbancic, UC-Berkeley
11
Grocery-store innovations have increased the time
and attention customers spend with products.
  • 1800s General stores kept goods behind the
    counter.
  • Individual consumers presented their shopping
    list to the clerk.
  • Simple product packaging, for the clerks benefit
    only.

12
  • 1916 Self-service stores invented.
  • At first, cramped shelves through which customers
    navigated one-way through a predetermined
    pattern.
  • Consumer packaging became important.
  • 1936 Shopping carts invented.
  • Carts (along with automobiles) increased the
    feasible size of grocery purchases.
  • Customers could spend much more time comfortably
    browsing. Previously, only hand-carried baskets
    were available.

13
  • Behavioral psychologist John Watson made a second
    career of consulting on product placement in
    stores, including impulse items at the checkout
    counter.

14
Predictions to Test
  • Assuming different individuals are characterized
    by different cases, the model predicts the
    following.
  • (i) the frequency of tempting purchases
    increases as exposure duration increases
  • (ii) some people will not purchase tempting goods
    even with long exposure
  • (iii) some people will purchase tempting goods
    even with short exposure
  • (iv) If there is uncertainty regarding exposure
    duration then the model predicts delay in
    consumption just as observed in the marshmallow
    task.

15
We made over 2800 direct observations of
customers at the checkout aisle to test
predictions (i)-(iv).
  • During spring 2002, undergraduate research
    assistants watched and recorded 2827 observations
    of customers in grocery checkout aisles in
    Tucson, Arizona.
  • Three stores
  • Store 1 a large national grocery chain, located
    in a middle-income area of the city. (2042
    observations)
  • Store 2 a more upscale chain store, located in a
    wealthier part of town. (423 observations)
  • Store 3 a local, independent grocery in a
    lower-income neighborhood. (326 observations)

16
Observations included an array of descriptive and
quantitative statistics.
  • Location, day of the week, time of day
  • Length of time spent in line (until checkout
    began)
  • Whether or not an impulse item was purchased
    (binary variable multiple impulse items counted
    the same as a single itemat least one impulse
    purchase)
  • Some demographic data (always gender kids,
    sometimes race age)

17
Following are some descriptive statistics of the
observations
Distribution of Sex and Purchases by Store (N
2827)
  Store 1 Store 1 Store 2 Store 2 Store 3 Store 3 Aggregate Aggregate
  Total Purchases Total Purchases Total Purchases Total Purchases
Males 872.67 53 (6.1) 155 6.5 (4.2) 216.833 20 (9.2) 1244.5 79.5 (6.4)
Females 1169.33 114 (9.7) 268 14.5 (5.4) 145.167 21 (14.5) 1582.5 149.5(9.4)
Total 2042 167 (8.2) 423 21 (5.0) 362 41 (11.3) 2827 229 (8.1)
Distribution of Observations Where Children Were
Present and Purchases by Store (N 2827)
  Store 1 Store 1 Store 2 Store 2 Store 3 Store 3 Aggregate Aggregate
  Total Purchases Total Purchases Total Purchases Total Purchases
With Males 52 6 (11.5) 4 0 (0) 7 2.5 (35.7) 63 8.5 (13.5)
With Females 141 27 (19.1) 24 4 (16.7) 22 6.5 (29.5) 187 37.5(20.1)
Overall 193 33 (17.1) 28 4 (14.3) 29 9 (31.0) 250 46 (18.4)
Note In each of the above tables groups of
customers of mixed gender were treated as an
appropriately proportioned fractional sex
observation. Though the percentages above suggest
that females were more likely to make impulse
purchases, t-tests show that there is no
significant difference due to gender. Instead,
this effect reflects the fact that 74.8 of
observations with children involved female
customers.
18
As in the marshmallow test, customers often
waited before picking up an impulse item in the
checkout aisle.
The behavior seen above is consistent with
temptation theory.
19
The data directly suggest that time spent in line
may affect the incidence of impulse purchases.
  • Time in Line for All Observations and Given
    Purchase, by Store
  • (N 2827, times in mmss)

  Store 1 Store 1 Store 2 Store 2 Store 3 Store 3 Aggregate Aggregate
  All Given Purchase All Given Purchase All Given Purchase All Given Purchase
Min Time 003 029 026 026 009 021 003 021
Max Time 1835 1403 1624 720 1242 645 1835 1403
Mean Time 429 540 228 244 238 310 356 457
Mean time in line given purchase is a full minute
(25.8) longer than the mean for all
observations, suggesting that longer wait times
influence positively the frequency of impulse
purchases. Could the direction of causation be
the opposite of what we think? No, because we
measure time until the cashier begins to ring up
ones purchases. Spending time to pick up an
item would not increase my wait time, though it
might possibly increase the wait time of those
who come after me.
20
Logistic regressions confirm the positive effect
of time in line on the frequency of impulse
purchases.
FEMALE KIDS STOR2 STOR3 TIME FEM TIME KIDS TIME STR2 TIME STR3 TIME FEM KIDS Constant
Reg I 0.332 1.134     0.174 -0.015 -0.035       -3.473
Reg I 0.316 0.369     0.048 0.060 0.065       0.246
Reg II 0.497 1.092 0.230 1.044 0.220 -0.036 -0.027 -0.113 -0.064   -3.839
Reg II 0.318 0.376 0.441 0.365 0.052 0.060 0.066 0.126 0.090   0.245
Reg III 0.481 1.001 0.229 1.040 0.220 -0.036 -0.029 -0.112 -0.064 0.128 -3.829
Reg III 0.324 0.516 0.441 0.366 0.052 0.060 0.067 0.126 0.090 0.489 0.285
Significant at the 5 level
Significant at the 1 level
Standard errors in italics
  • Dependent variable Purchase of an impulse item
    (0/1).
  • Note the positive coefficient on time in line.
  • The presence of kids also tends to increase the
    purchase probability.
  • Kids and females tend to reduce the impact of
    time on purchase relative to males, though these
    effects are not statistically significant.
  • Store 3 has more impulse purchases.
  • Probit and linear-probability specifications
    produce qualitatively similar results.

21
implications for both academics and the grocery
industry
  • This study quantifies the effect of increased
    time in line on impulse purchases.
  • A measurable, real-world implication of
    temptation theory.
  • Though we did not attempt to measure intention,
    the choice data suggest that some purchasers
    changed their decisions and behavior over time
    due to temptation.
  • Future research might benefit from choice data
    with surveys of impulse-item purchasers.
  • These results may have concrete applications for
    grocers, especially since impulse items often
    earn stores their highest profit margins.
  • Stores may wish to staff checkout aisles so that
    customers spend slightly longer before reaching
    the register (though not if it drives customers
    to competing stores).
  • Since kids tend to increase impulse purchases,
    stores may wish to encourage the presence of
    children with their parents on shopping trips.
  • Or, can stores distinguish themselves by having
    checkout lanes without these items?

22
  • Can temptation be controlled by using a
    commitment device?

23
Temptation and Commitment in the Laboratory
  • Daniel Houser Daniel Schunk Joachim Winter
    Erte Xiao

24
Background
  • Long literature on individual decision-making in
    dynamic choice situations
  • Recent focus on temptation
  • A critical feature of most theory is the
    possibility to commit to avoid a temptation
  • There is little empirical data to inform the
    theory

25
Purpose of this paper
  • To design a laboratory experiment that includes a
    good that is tempting in formal sense.
  • To investigate how commitment costs affect
    decisions to consume tempting goods.
  • To learn whether established measures of
    psychological disposition help to predict how
    people behave in tempting situations.

26
Definition of a tempting good
  • Gul and Pesendorfer (2001, Econometrica) argue
    that Set Betweenness describes the preferences
    of an individual who struggles with temptation.
  • Standard decision maker x gt y ? x x,y gt
    y
  • Gul and Pesendorfer y is a temptation if we
    observe some individuals for whom x gt x,y.
  • Set Betweenness Axiom x x,y y

27
Specific Goals (1)
  • Design a laboratory experiment in which Set
    Betweenness is satisfied.
  • Come up with two alternatives x and y, each of
    which can be freely chosen, but such that some
    subjects will pay to avoid having y in their
    choice set.
  • That is, some subjects will make a costly
    commitment to x and avoid the tempting good y.

28
Specific Goals (2)
  • Study the link between commitment and consumption
    of the tempting good.
  • Characterize the effect of commitment.
  • Is the tempting good consumed less, in aggregate,
    when commitment is less expensive?
  • Connect behavior to scores on personality surveys.

29
Instructions
  • Thank you for coming. You have already earned
    five dollars for arriving on time. These
    instructions explain how you can earn more money
    during the experiment.
  • Todays experiment involves counting. From time
    to time you will see displayed on your computer
    screen nine digits, either zeros or ones. Your
    task is to count the number of ones, and report
    that number in a box provided. You will have 15
    seconds to provide an answer. Not providing an
    answer, or providing an incorrect answer, is
    counted as a mistake. If at the end of the
    experiment you have made mistakes on less than
    30 of the counting tasks, then you earn 15 in
    addition to your show-up fee. If at the end of
    the experiment you have made mistakes on more
    than 30 of the counting tasks, then you earn 3
    in addition to your show-up fee.
  • This experiment will end at different times for
    different participants. Please do not leave the
    room, talk or otherwise distract other
    participants in any way until you are told that
    all participants have completed the experiment
    and you have exited the laboratory.

30
120 minutes of boredom
  • Between the counting tasks, subjects face an
    empty screen with only a digital clock. The time
    between counting tasks is equally likely to be
    1min, 2min, or 3min.
  • The counting experiment lasts for 120 minutes.
  • The experiment consists of three phases, but
    subjects were not informed (but this at the
    beginning of the experiments).

31
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32
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33
Phase 0
  • Lasts 30 minutes, 15 counting tasks.
  • Subjects count
  • 1, 2, or 3 minutes elapse between counting tasks
  • Subjects stare at blank screen between tasks

34
Phase 0
  • After 30 minutes, at the end of phase 0
  • Some subjects have earned 8 (thus, 7 left to
    earn)
  • Some subjects have earned 10 (thus 5 left to
    earn)

35
Phase 1
  • Lasts 45 minutes (12 counting tasks)
  • Counting screens as before.
  • Temptation screen (see next slide) is present 6
    times, offering these choices
  • Continue Have a chance to earn the maximum 15
    (i.e. 5 or 7 more) and keep
    the option to surf.
  • Commit Have a chance to earn the maximum 15
    (i.e. 5 or 7 more) but without
    the option to surf. The cost
    for this commitment is either 0 or 1.
  • Surf Go to the internet.

36
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37
Phase 1
  • At the end of phase 1 subjects are told they have
    earned either 10 to that point (leaving 5 left
    to earn) or 15 (no more earning possible.)
  • The 15 treatment is a check to ensure that our
    task is not more pleasurable than
    surfing.Finding All subjects for whom no more
    earnings were possible immediately went surfing.

38
Phase 2
  • Lasts 45 minutes (12 counting tasks).
  • Only for those who didnt choose to surf at some
    point in the second period.
  • Subjects who committed only see counting screen.
  • Subjects who continued without commitment see
    counting and temptation screens.

39
The phases of the experiment
40
Hypotheses
  • H0 Surfing is a tempting item for some subjects.
    That is, some subjects pay a positive value to
    remove the temptation screens from their choice
    set.
  • H1 Allowing for decision error, most surfing and
    commitment decisions occur at the first
    opportunity, and then monotonically decline.
  • H2 In phase 2, when the benefit to counting is
    lower (sometimes zero), there are a greater
    number of decisions to surf.
  • H3 The frequency of commitment is lower when the
    commitment cost is higher.

41
Data
  • 88 subjects, equal numbers in each treatment.
  • Subjects are GMU undergrads recruited using
    standard procedures
  • 42 female
  • 37 soc. science, 35 econ/business, 17 nat.
    science, 11 others
  • Subjects were in the lab for 2.5 to 3 hours,
    earned
  • 5 for participation.
  • 3 to 15 for the temptation task, less any
    commitment costs.
  • 3 for the survey.

42
Results count count, surf surf
  • 32 subjects (36) chose to commit to counting for
    the entire two hours.
  • 10 of these subjects paid to make this
    commitment.
  • 38 subjects (43 of all) succumbed to the
    temptation.
  • 11 of those succumbed right at the beginning.
  • 18 subjects (21) neither committed, nor
    succumbed to the temptation (i.e. resisted
    without commitment).
  • Counting High accuracy rates, did not vary over
    treatments, or with time. No subject below 70
    correct.

43
Results commitment cost
  • Commitment is less frequent when there is a cost
    to it

Fraction of subjects who chose to commit (at
first opportunity)
44
Results value of commitment
  • Commitment is more likely when its value is
    higher

Fraction of subjects who chose to commit (at
first opportunity)
45
Results consumption decisions
  • Consumption of the tempting good is independent
    of commitment costs.

Fraction of subjects who chose to surf (at first
opportunity)
46
Results consumption decisions
  • Consumption of the tempting good is less frequent
    when its cost is higher.

Fraction of subjects who chose to surf (at first
opportunity)
47
Psychological Measures
  • Four disposition measures are elicited for
    each subject
  • Big 5 10 items, Costa et al. (1980)5
    personality dimensions
  • Need for Cognition 18 items, Cacioppo et al.
    (1984)Tendency to engage in effortful cognitive
    tasks
  • CFC 8 items, Strathman et al. (1994) Tendency
    to consider the future
  • Mach IV scale 20 items, Christie
    (1970)Machiavellism ? not analyzed here

? Do these measures help to predict behavior in
tempting situations?
48
Results Commitment Decision
Probit regression Number of obs
88 Pseudo R2
0.22 ---------------------------------------------
------- Commitment Decision Coef. Pgtz
------------------------------------------------
--- Remaining value .656 0.000
Commitment cost -.824 0.014 Female
.470 0.191 NatScience-Major -.303
0.622 EconBusiness-Major .428
0.452 SocScience-Major .701
0.210 CFC-Score 1.372 0.281 NC-Score
.435 0.738 Big5-Extraversion -.978
0.263 Big5-Agreeableness .404
0.734 Big5-Conscientiousness -.445
0.652 Big5-Neuroticism .547
0.493 Big5-Openness -.286
0.806 Constant -5.312 0.010 ------------
-----------------------------------------
  • Commitment cost and remaining value of resisting
    temptation are significantly related to
    commitment decision.
  • Psychometric dispositional measures are not
    related to commitment decision.

49
Results Succumbing to Temptation
Probit regression Number of obs
88 Pseudo R2 0.16 --------------------
------------------------------------- Succumbing
to Temptation Coef. Pgtz -------------------
------------------------------------- Remaining
value -.276 0.107 Commitment cost
-.165 0.611 Female -.443 0.200
NatScience-Major -.418 0.507
EconBusiness-Major -.210 0.722
SocScience-Major .201 0.729
CFC-Score -3.613 0.004 NC-Score .740
0.573 Big5-Extraversion -.617
0.465 Big5-Agreeableness -.001 1.000
Big5-Conscientiousness .632
0.500 Big5-Neuroticism -.203 0.806
Big5-Openness -.599 0.596 Constant
3.888 0.040 -------------------------------
---------------------------------
  • Commitment cost and remaining value of resisting
    temptation are not related to succumbing to
    temptation.
  • CFC-score The higher your consideration for
    future consequences, the less likely you succumb
    to the temptation.

50
Summary
  • Our laboratory design includes a good that
    satisfies Set Betweenness Some subjects pay a
    commitment cost to avoid temptation.
  • Commitment is sensitive to its cost, but the
    likelihood of succumbing to temptation is not
    related to commitment costs. This is predicted by
    the G-P model.
  • Patterns of commitment and consumption over time
    are also in line with G-P predictions.
  • Dispositional measures are correlated with
    behavior in situations that require self-control.

51
Temptation at WorkA Field Experimenton
Willpower and Productivity
  • Alessandro Bucciol
  • University of Amsterdam and Netspar
  • Daniel Houser
  • George Mason University
  • Marco Piovesan
  • University of Copenhagen

52
MOTIVATION
  • Temptations are a largely unavoidable part of
    life.
  • Resisting temptation is usually seen as a virtue.
  • However, delaying gratification can detrimentally
    impact performance on subsequent tasks (Vohs and
    Heatherton, 2000)
  • We develop a simple model connecting temptation,
    willpower and worker productivity.
  • We test that model using a field experiment with
    children

August, 2010
53
GOAL
  • Examine the role of willpower in determining the
    effect of a prohibited tempting item on work
    productivity
  • Novelty of our approach
  • Labor output is the outcome variable
  • Productivity is rewarded
  • Participants are children

August, 2010
53
54
CLOSELY RELATED LITERATURE
  • Baumeister et al, (1998)
  • Vohs and Heatherton (2000)
  • Burger, Charness Lynham (2009)
  • Bryan, Karlan and Nelson (2009)

54
August, 2010
55
CHILD DEVELOPMENT
  • Child development literature argues children
    seven and younger typically find it difficult to
    delay gratification
  • Children 11 and older have developed delay of
    gratification strategies (Mischel and Metzner,
    1962)

August, 2010
56
PREDICTIONS
  • Productivity of young children (under age eight)
    will be detrimentally impacted by resisting a
    temptation
  • Resisting temptation depletes willpower, leaving
    them with less psychic energy to focus on the
    productivity task
  • Productivity of children age 11 or older will be
    positively impacted by resisting a temptation
  • Older children distract themselves from the
    temptation by focusing on how to perform task
    better

August, 2010
57
FIELD EXPERIMENT
  • Who
  • Children aged 6-13
  • Where
  • In the summer camp of CUS Padua (Italy)
  • Outdoors
  • When
  • Two warm days of July 2008 (temperature 70-88o
    F)
  • 11 sessions between 9.00 am and 5.30 pm local
    time
  • Groups in each session roughly homogeneous in
    age
  • How
  • Participants complete a repetitive paper-folding
    task

57
August, 2010
58
1. SPLIT AND INSTRUCTIONS
  • We split each group randomly into two sub-groups
  • Control Treatment (CT) group
  • Food Treatment (FT) group
  • We seat the two sub-groups separately
  • We provide them with identical instructions on
  • how to complete the game
  • Subjects were not attending to the tempting
    items while participating in the instructions.
  • We leave them seated in their separate areas for
    five more minutes in (FT) need to resist!

5 min.
5 min.
58
August, 2010
59
FOOD TREATMENT
  • Only the sub-group in FT is seated near a table
    with snacks and drinks
  • Prior to the instructions the children in FT are
    informed that the snacks and drinks have been
    reserved for a different event in the same day
  • The children in FT are tempted by the snack
    items only during the final five minutes!

59
August, 2010
60
2. REJOIN AND GAME
  • The two sub-groups rejoin
  • and go to a long table to complete the task
  • The task is
  • To fold a pre-printed sheet in three parts
  • Highlight the star
  • Attach a label
  • Close the sheet with a paper clip

10 min.
60
August, 2010
61
August, 2010
62
THE GAME
62
August, 2010
63
REWARD
  • 1 token for each sheet accurately folded
  • At the end of the game each kid receives a
    certificate showing the number of tokens she won
  • Children use their certificates to get items from
    a menu of food, ice cream and drinks available at
    the summer camps clubhouse
  • 1 token 10 eurocents

63
August, 2010
64
OBSERVATIONS
  • Prior to conducting the experiment we received
    informed consent from the parents
  • Acceptance rate 82.27
  • In such occasion we collected basic information
    on each child

Average statistics Whole sample CT FT
N. Sheets folded 5.79 5.86 5.71
Age 8.95 9.05 8.84
Female 27.56 32.10 22.67
Better at school 47.44 59.26 34.67
Number of siblings 0.97 0.93 1.01
Body Mass Index (BMI) 16.82 16.95 16.84
N. Observations 156 81 75
64
August, 2010
65
FINDING 1
  • The exposure to a prohibited tempting item
    significantly reduces productivity for children
    younger than 8, and significantly increases it
    for children older than 10

CT CT FT FT Test
(1) Obs. (2) Obs. (1) (2)
Age under 8 4.3200 25 3.0870 23 2.0604
Age between 8 and 10 5.4118 34 5.1875 32 0.3370
Age over 10 8.3182 22 9.5500 20 -1.1402
Whole sample 5.8642 81 5.7067 75 0.2847
reject at 5 in favor of (1) gt (2)
65
August, 2010
66
FINDING 2
  • The increasing relation between age and FT is
    robust to demographic and experimental controls

N. Sheets folded
FT 0.3858
Age under 8 -0.0817
Age over 10 0.5401
Female 0.1304
Better at school 0.0153
N. Siblings 0.1536
BMI 0.0196
FTAge under 8 -0.3625
FTAge over 10 0.3013
FTFemale 0.3423
FTBetter at school 0.2109
FTN. Siblings -0.1914
FTBMI -0.0319
Day 2 0.1471
FTSessions before break 0.2329
FTSessions before meal 0.1477
Constant 1.0118
Alpha 0.0000
N. Observations 123
Log-Pseudo-likelihood -276.4334
Method negative binomial regression
significant at 10 significant at 5
significant at 1
66
August, 2010
67
FINDING 3
  • The elasticity of FT on productivity is
    significantly negative for children younger than
    8, and is significantly positive for children
    older than 10

12.28
4.72
-49.03
67
August, 2010
68
FINDING 4
  • The elasticity of FT on productivity is
    significantly negative for boys, and is
    significantly positive for girls

21.18
-16.57
68
August, 2010
69
DISCUSSION
  • Temptations are a largely unavoidable part of
    todays workplace (e.g., Internet) but are
    detrimental to productivity
  • Offices prohibit them
  • Employees have to wait until the workday ends
  • The use of willpower to delay gratification can
    impact performance on subsequent tasks (Vohs and
    Heatherton, 2000)
  • What is optimal office policy for promoting
    productivity?

August, 2010
70
SUMMARY
  • Our results are consistent with predictions
    implied by the child development literature
    combined with our simple model of temptations
    effect on productivity
  • Resisting temptation reduces productivity among
    youngest children
  • Older children use the repetitive task to
    distract themselves from the tempting items
  • Prohibiting a tempting activity may eliminate the
    productivity cost of the temptation

70
August, 2010
71
Thank You!
71
August, 2010
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