Carryover and spillover effects of financial
incentives in health: lab-field evidence
Matteo M Galizzi
LSE Behavioural Research Lab
LSE Health and Social Care
Department of Social Policy
Centre for the Study of Incentives in Health
Paris School of Economics, Hospinnomics, J-PAL Europe
UCL CBC Research Seminar, 13th May 2014
Acknowledgements
Many thanks to CBC, Michelle Baddeley and Antonio Cabrales.
Work within the Centre for the Study of Incentives in Health (CSIH)
•  Behavioural/health economists at LSE
•  Health psychologists at KCL
•  Bioethics/philosophy experts at QMUL
All studies funded by the CSIH from a strategic award by the
Wellcome Trust Biomedical Ethics Programme (086031/Z/08/Z).
Many thanks to the Wellcome Trust and to CSIH people.
Joint work with Paul Dolan (LSE) and Daniel Navarro-Martinez
(UPF).
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Road map
“Unintended behavioural consequences” of incentives in health
•  A frame and taxonomy of ‘behavioural spillovers’
•  A study on ‘carryover’ effects
•  A study on ‘spillover’ effects
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Incentives in health
A key interest to economists is how people react to incentives
(Camerer & Hogarth, 1999; Laffont & Martimort, 2002; Gneezy &
List, 2013)
‘Basic law of behaviour’ (Gneezy et al. 2011): higher incentives lead
to greater effort and performance
Incentives for healthy behaviours (Marteau et al. 2009; Volpp et al.
2011; Loewenstein et al. 2012)
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Weight loss (Volpp et al., 2008; John et al. 2011, 2012; Kullgren et al. 2013)
Smoking cessation (Volpp et al., 2006; 2009)
Gym attendance (Charness & Gneezy, 2009)
Consumption of fruit and veg (Cooke et al. 2011)
Children immunization (Banerjee et al., 2009)
Children health in schools (Vera-Hernandez et al., 2015)
Incentives can change health behaviour in the short-run, when
super-charged by ‘behavioural’ insights (Loewenstein et al. 2007)
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
‘Hidden costs’ of incentives
Mounting evidence on the ‘hidden costs’ of incentives (Fehr & List, 2004)
Especially when high, incentives can
—  Crowd out intrinsic motivation (Frey & Oberholzer-Gee)
—  Change social norms or individual beliefs about social norms (Gneezy
& Rustichini 2000a,b; Fehr & Falk, 2002; Heyman & Ariely, 2004)
—  Interact with reciprocity (Fehr & Gachter, 1997; Rigdon, 2009; Dur et
al. 2010), reputation (Benabou & Tirole, 2006; Ariely et al. 2009), and
social comparison concerns (Gachter & Thoni, 2010; Greiner,
Ockenfels, & Werner, 2011)
—  Lead subjects to ‘choke under pressure’ because of anxiety or arousal
(Ariely, Gneezy, Loewenstein & Mazar, 2009)
Two relatively unexplored aspects of the ‘unintended consequences’ of
(health) incentives…
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
‘Unintended behavioural consequences’ of incentives
Carryover
Once they are removed, incentives can have ‘carryover’ effects
on the same targeted behaviour over time.
—  Incentives cannot be in place forever…
—  Same behaviour over time: today, tomorrow
Spillover
Incentives can have ‘spillover’ effects on health behaviours
other than the ones directly targeted.
Research and policy motivation
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
‘Behavioural spillovers’ of incentives
Spillovers on other behaviours
Incentives can have ‘spillover’ effects on health behaviours
other than the ones directly targeted.
—  Sequence of two different behaviours:
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behaviour 1, behaviour 2
—  Linked, at conscious or unconscious level, by a ‘motive’
—  After all, no behaviour sits in a vacuum
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And we should capture all ‘ripples’ in the pond (Dolan & Galizzi, 2015a)…
—  Promoting, permitting, purging spillovers
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Dolan & Galizzi (2015a)
Contrast/assimilation (Cialdini et al. 1995), highlight/balance (Dhar &
Simonson, 1999; Fishbach et al. 2013)…
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Behavioural spillovers
Second behaviour
A run after
work
First
behavior Eat healthily
Eat less healthily 1. Promoting
2. Permitting
I ran an hour, let’s
keep up the good
work
I ran an hour, I
deserve a big slice of
cake
Sofa-sitting
after work 3. Purging
4. Promoting
I’ve been lazy today,
best not eat so much
tonight
I’ve been lazy today,
so, what the heck,
let’s have a big slice
of cake
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Promoting spillovers in health
Preference for consistency (Muller, Dijksterhuis et al. 2009)
—  If you write down as many arguments you can of why smoking is
bad, once outside the lab, you wait significantly longer before you
smoke a cigarette
Survey effects (Zwane et al., 2011)
Survey HHs either biweekly (18 times) or every 3 months (3 times)
¡  Some questions on health status and behaviours
¡  18 months after, Kenya’s HHs surveyed more often had higher
chlorine in the stored drinking water
¡  Borrowers from a rural bank in Philippines…
¡  9 months later were 25% more likely to take up government
sponsored health insurance sold door to door
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Permitting spillovers in health (I)
Licensing
•  Subjects with healthy options as default were more likely to order
healthy sandwiches but then have more side dishes/drinks/
desserts: (Wisdom et al., 2010)
•  Subjects who had healthy main dishes more likely to have side
dishes/drinks/desserts: (Chandon & Wansink, 2007)
•  Subjects asked to read a scenario there they walked 30 minutes:
then serve +51.8-59.8% more snacks than reading a neutral
scenario (Werle et al., 2010)
•  Subjects given placebo pills and said they were either
multivitamins supplements or placebo: subjects told they were
multivitamins then expressed higher preferences for unhealthy
activities and walked less to return a pedometer than subjects told
they were placebo (Chiou et al., 2010, 2011)
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Permitting spillovers in health (II)
Ego depletion (Baumeister et al., 1998)
After exerting high levels of physical or cognitive self-control in
behaviour 1, in behaviour 2 you exert lower levels self control
¡  Two bowls in front of you: hot cookies and radishes.
¡  You are told to taste only the radishes, for 5 minutes.
¡  The experimenter leaves for 5 minutes
¡  Back with a puzzle, in fact impossible to solve.
¡  After having resisted the temptation of indulging in cookies you
quit sooner on trying to solve the puzzle
To exert self-control we draw from a limited pool of mental energy
—  Physical and mental effort consumes energy: blood glucose drops
¡  If after task 1 you drink a sweet milkshake, you performed better
in another effortful task 2! (Gaillot, Baumeister et al., 2007)
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
‘All negative’ promoting spillovers in health
What-the-hell effect (Policy & Herman, 1985; Wilcox et al., 2009)
—  Once you decide upon a course of actions that is inconsistent with a
goal/motive…
—  and thus abandon goal–directed behaviour (behaviour 1), then…
—  Instead of taking the middle ground, you are...
—  More likely to exacerbate extent of your failure to behave in line
with the goal (behaviour 2)
¡  Once you abandon the diet to eat a cookie, instead of selecting a
low-fat cookie you eat the most unhealthy/whole bag of cookies
Abstinence violation effects
—  Similar effects have been documented among alcoholics (Collins &
Lapp, 1991), smokers (Shiffman, Hickcox, Paty, Gnys, Kassel, &
Richards, 1996), and drug users (Stephens & Curtin, 1994).
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Do ‘spillovers’ also occur with incentives?
Spillovers are everywhere, and health is not an exception
Most evidence on behavioural spillovers has been documented
in absence of financial incentives over time.
—  Traditional experimental economics view:
—  Most of these ‘behavioural anomalies’ disappear when real
money is on the table
Do incentives in health also have carryover/spillover effects?
Two exploratory lab-field experiments
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Carryover effects
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Dolan, Galizzi, Navarro-Martinez (2015) in 30’’…
Randomised controlled ‘nested’ lab experiment to explore:
“Carryover” effects of financial incentives over time
On the same target health behaviour: sweets eating
—  Compare incentives ‘to eat’ versus ‘not to eat’
—  Incentives ‘not to eat’ have stronger carryover effects 2 days later
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Key questions
Do incentives have ‘carryover’ effects once they are removed?
First experiment to compare head-to-head monetary
incentives ‘to act’ or ‘to abstain to act’
Consider ‘ambivalent’ health activity: sweets eating
Example of pleasurable behaviour, that is potentially harmful (and
unwanted at a deeper level)
—  Which incentives have stronger carryover effects?
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Subjects’ pool and experimental set up
—  Research ethics: protocol approved
¡
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LSE REC
CSIH
—  Behavioural Research Lab
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Cross-departments lab at LSE
1 “proper” lab + 6 small rooms
Experiments run between end of June and September 2012
—  Invitation to BRL mailing list (about 5,000 subjects)
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¡
¡
¡
Under- and post-graduate students
Staff members, alumni working in London area
Mailing list managed by SONA system software
Subjects identified by SONA ID code
—  Payment was £30, if they participated to 3 sessions in a week
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Monday, Wednesday, Friday
35 sessions run, 5 sessions a day
10 am, 11.30 am, 1 pm, 2.30 pm, 4 pm
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Randomisation and overall structure
—  Arrived to BRL, read and sign informed consent form
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A total of 353 subjects participated
Anonymously identified by their SONA ID code
Use SONA ID code to link observations across days
Asked to pick a number to be assigned to their cubicle in the lab
Random cubicle assigned to one of 3 groups: Eat, Don’t Eat, Control.
Enter the main room in the lab and given written instructions
—  Session 1 (Monday): a questionnaire, 3 videos: 1, 2, 3
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In video 2 the experimental manipulation: incentives
—  Session 2 (Wednesday): ‘filler’ task (completely
unrelated), then 1 video: 4
—  Session 3 (Friday): a control questionnaire (other tasks)
¡
Big 5 Inventory, Health & Taste Attitudes, sweets intakes, BMI, diet
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Key design features
—  Subjects in LSE BRL watched different videos on
computer screens…
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A total of 4 videos during two sessions set 2 days apart (Monday,
Wednesday)
Mildly boring 10’ videos: bus journey in London, animals
documentaries, video 2 was 5’ about sweets-making (cover story)
—  Leave bowls of sweets next to participants:
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Jelly Beans: 2.2 Kcal, 1.14 gr per Jelly Bean…
—  Sweet eating monitored throughout all videos:
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¡
After each video subjects moved to a different room allegedly to answer
a questionnaire on the videos
Meanwhile, bowls of sweets were weighted with high-precision scales…
—  Monetary incentives introduced in a video in first session
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Videos and treatments
3 videos in session 1, 1 in session 2
—  Video 1: ‘could eat as they like’: baseline ‘preference’ for
sweets eating
—  Video 2: incentive manipulation (depends on group)
—  Video 3: incentives are removed, could keep eating as they
like: first indicator of carryover
—  Video 4: could eat as they like, cleaner indicator for carryover
effects
Randomly allocate subjects to
—  Eat (n=133): get £3 if would eat at least 10 Jelly Beans
—  Don’t Eat (n=112): get £3 if would do not eat any Jelly Bean
—  Control (n=108): could eat as they like
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Summary of treatments
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Main results
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Summary of main results
—  Randomization worked, no sample bias:
¡
No significant differences in sweet-eating across conditions in Video 1.
—  Financial incentives worked, as expected: Video 2.
—  Subjects in Don’t Eat condition ate less sweets in Video 3, despite
—
—
—
—
having eaten less in Video 2: carryover effects.
Subjects in Don’t Eat condition ate less sweets also in Video 4,
two days later!
Clear carryover effects.
Significant difference between sweets eaten in Videos 4 and 1.
Regression analysis confirms significant effect of Don’t Eat
condition
¡
In general, subjects more anxious, with higher BMI, and who usually eat more
sweets, also ate more Jelly Beans
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Conclusions and limitations
—  First study to compare head-to-head incentives ‘to act’ versus ‘to
—
—
—
—
—
abstain to act’ in the same health context/environment
An ambivalent health behaviour: incentives ‘to eat’ vs ‘not to eat’
Both incentives were effective in changing target behaviour
Incentives ‘not to eat’ also had significant carryover effects
Carryover effects persisted until 2 days after incentives were
removed
Consistent with the ‘bad is stronger than good’ effect in
psychology (Baumeister et al., 2001):
¡
Negative messages are easier to retain than positive ones.
—  Limitations make difficult to generalise results: students, 2 days…
—  Potentially extendable to other ‘ambivalent’ behaviours
¡
Drinking, unsafe sex, reckless driving?
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Spillover effects
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Dolan and Galizzi (2015b) in 30’’…
Conduct a randomised controlled ‘lab-field’ experiment to explore
“unintended spillovers” of incentives in health
Consider incentives for real-effort physical task
—  High financial incentives can “spillover” on eating behaviour
—  Key driver seems to be level of satisfaction with the task
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Key questions
Incentives are usually effective in changing target behaviour.
Can incentives also have spillovers?
No RCTs evidence on long-term spillovers (Mantzari et al. 2014)
An exploratory lab-field experiment focusing on short-term effects
3 questions:
1.  Whether spillovers occur when real money is on the table
2.  Whether they depend on the size (high versus low) and nature
(financial versus non-financial) of the incentives
3.  Which behavioural mechanisms are most likely to ‘drive’ the
spillover effects
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Key design features
Spillovers relevant for both research and policy purposes
—  Stylised incentives for physical activity: ‘calories out’
¡
¡
¡
¡
Della Vigna & Malmendier (2006), Acland & Levy (2013), Cawley et al.
(2013)
Incentives are effective: Charness & Gneezy (2009)
We directly observe outcomes
Stepping for 2 minutes
—  Non-targeted behaviour where spillovers can occur is
healthy eating: ‘calories in’
¡
¡
¡
As in Subway experiment by Wisdom et al. (2010)
Werle et al. (2010), Van Kleef et al. (2011), Chiou et al. (2011a)
Choice of food for lunch
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Key design features (II)
Randomly allocate subjects to
¡
either treatment incentives groups (H, L, E) or control group (C)
Spillovers if eating in H, L, E significantly different than C
Both types of spillovers are possible in principle:
—  Promoting spillovers: lower ‘calories in’ than C
¡
Priming: healthy stepping leads to healthy eating (Muller et al. 2010)
—  Permitting: higher ‘calories in’ than C
Licensing: rewards give sense of ‘deservingness’ that ‘licenses’
indulging more in tastier, but also more energy-dense, food
¡  (Ego depletion: having exercised harder under incentives’ strain,
subjects felt more ‘depleted’ in their physical/mental energy: glucose)
Disentangle drivers of permitting spillovers
¡
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Subjects’ pool and experimental set up
—  Research ethics: protocol approved
¡
¡
LSE REC
CSIH
—  Behavioural Research Lab
¡
¡
¡
Cross-departments lab at LSE
1 “proper” lab + 6 small rooms
Experiments run between end of April and September 2012
—  Invitation to BRL mailing list (about 5,000 subjects)
¡
¡
¡
¡
Under- and post-graduate students
Staff members, alumni working in London area
Mailing list managed by SONA system software
Subjects identified by SONA ID code
—  Payment was £10, possibly plus amount depending on tasks
¡
¡
¡
156 subjects
24 sessions run, 5 sessions a day
11 am, noon, 1 pm, 2 pm, 3 pm
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Randomisation and Task A
—  Arrived to BRL, read and sign informed consent form
¡
¡
¡
¡
Anonymously identified by their SONA ID code
Asked to pick a number to be assigned to their cubicle in the lab
Random draw of cubicle assigned them to either C (n=38) or one of
3 treatment groups H (n=40), L (n=39), E (n=39)
Enter the main room in the lab and given written instructions
—  Going to participate into 3 tasks: A, B, C
¡
Tasks A and C questionnaires, B simple physical task, to be
explained
—  Task A ‘filler’ questionnaire, identical for all groups
¡
¡
Subjective perception of time, light, temperature, hunger... using
0-180 mm slider scales
While doing Task A, subjects approached individually to do Task B
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Task B
Subjects move individually to other room in the BRL
Height and weight are measured on scale (no shoes)
¡  Professional heart rate meter: heart rate measured (bpm)
¡  Questions on life satisfaction: 0-10 Likert scale (Dolan et al. 2011)
¡
÷  Including
question on ‘how happy’/ ‘how full of energy’ they feel
Room has a gym stepper
¡  Invited to step “as many times as they can in 2 minutes”:
¡
÷  Stepping
up and down with both feet on the stepper
÷  Experimenter shows how to do it
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
A note on stepping
Stepping for 2 minutes
Picked for several reasons
¡  Very simple and familiar physical task
¡  Moderate physical task with no major physiological effect
÷  Stepping
¡
2’ at quiet pace (about 60 steps) is climbing up 4 floors
Amount of calories burnt is minimal:
÷  Depending
¡
on HR, gender, age, and weight about 15-25 Kcal
Ethics approval
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Experimental treatments
•  In all groups experimenter keeps time and counts steps
•  C: at the end of Task B, subjects draw a number
¡
¡
If the same number randomly drawn at the end of the experiment
They get an extra-payment of £20
—  H: end of session, paid 10p per step they do in 2 minutes
—  L: paid 2p per step they do in 2 minutes
—  E: ‘nudged’ to work hard through verbal encouragement
¡
¡
¡
No money at all
Every 20 seconds verbal encouragements
‘well done, keep going, you’re doing really well, only another 40
seconds to go...’
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Immediately after Task B
—  Immediately after subjects finished stepping
Experimenter take again heart rate measure
¡  Asked how satisfied with just accomplished task: 0-10 scale
¡  Questions on life satisfaction, e.g. ‘how full of energy’: 0-10 scale
¡  Told number of steps and payments, and draw number f0r C
¡  Experimenter say to take a rest before going back to Task A\C
¡
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
‘Obfuscation’
—  RA bring subjects to one of 2 other small rooms
Rooms were prepared ostensibly for subjects to take a rest
¡  Room has chairs and a table
¡  Told to relax for as long as they like
¡
On table, various foods and drinks were arranged
¡  Could help themselves with whatever drink or food they like
¡
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Resting between tasks...
—  Same foods and drinks in C and T groups and for
subjects
Every subject finds exactly same types and quantities of foods/
drinks
¡  Arranged in the same order and presentation
¡  Unbeknownst to subjects, number and type of food or drink item
consumed in the lunch was recorded...
¡  When a subject leaves the room to go back to Task A...
¡  Foods & drinks consumed are registered and replaced for next
¡
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Food and drink items
Foods & drinks selected
To guarantee that all subjects have their own “cup of tea”
¡  All food from a leading UK supermarket
¡  On each item nutritional label upfront: “pie” + “traffic lights”
¡  Also, a full GDA nutritional label in the back of item
¡
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
The menu...
Foods & drinks selected
Drinks: water, coke, orange juice
¡  3 “full fat” sandwiches: BLT, chicken & bacon, ham & cheese
¡  3 “low fat” sandwiches (<3% fat): chicken salad, tuna &
cucumber, chicken & bacon “light” (Be Good To Yourself)
¡  3 vegetarian sandwiches: cheese & tomato, hummous & carrots,
cheese & celery
¡  Sweets: Apples, brownie bites, chocolate & cornflakes bites,
strawberries and chocolate muffins
¡
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Menu unpacked...
KCal
Fats
Sat Fats
Sugars
Salt
BLT
407
16.7
3.5
5
1.99
Ham & Cheese
456
15.6
3.4
5.3
2.11
Chicken Triple
509
16.8
2.8
4.1
2.04
Roast Chicken Salad
(BGtY)
273
3.8
0.7
2.9
0.95
Chicken & Bacon (BGtY)
333
6
1.9
3.3
1.54
Tuna & cucumber (BGtY)
283
4.9
0.8
4
1.12
Cheese & Tomato (V)
401
19,6
12,7
2,9
1,74
Cheese & celery (V)
438
22,9
10,7
3,5
1,53
Hummous & carrots (V)
321
6,1
1,4
3,5
1,44
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Back to Task A, Task C
After lunch, subjects go back to complete questionnaire in Task A
¡  After finishing Task A, subjects also go through Task C
¡  Brief control questionnaire on
¡
÷  when
is the last time they have eaten before coming to the lab
÷  what they have eaten
÷  what they have had for breakfast, lunch and dinner the day before
÷  physical activities they had last 24 hours...
Also, questionnaire asks how many calories they think to have
burnt in the physical exercise task
¡  And how many calories they think to have eaten in the lunch
¡  How much satisfied they felt with their performance in task B
¡  Payment phase takes place for all subjects
¡
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Constructing variables and indexes
—  Number of performed steps: Steps
—  Difference in
Heart rate before and after steps: DiffHR (bpm)
¡  Feeling ‘full of energy’ before and after steps: DiffEnergy (0-10)
¡
—  Satisfaction with task immediately after: ImSatisf
(0-10)
¡
And at the end of experiment: EndSatisf (0-10)
—  Number of Kcal consumed in lunch in total: KcalIn
Types of foods: KcalSandw, KcalCrisps, KcalSweets, KcalDrinks
¡  Nutritional intakes: Fats, SatFats, Sugars, Salt
¡
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Calculating Kcal Out
—  State-of-art physiology models fitted by Keytel et al. (2005)
—  Behind most popular software, apps, and online calculators. Two variants:
¡  A measure for maximal oxygen consumption measure (VO2max) is available
÷
¡
Subjects need to breath into a mask during exercise
VO2max is not available
—  When no VO2max, estimated KcalOut are functions of:
¡  Gender
¡  Age
¡  Weight
¡  Duration of physical exercise
¡  Heart rate (immediately after task)
—  Gender-specific models for KcalOut:
•  Male subjects
KcalOut = [-55.0969 + 0.630 * Heart Rate (in bpm) – 0.1988 * Weight (in Kg) +
0.2017 * Age (in Years)] * Time (in minutes) / 4.184
•
Female subjects
KcalOut = [-20.4022 + 0.4472 * Heart Rate (in bpm) – 0.1263 * Weight (in Kg) +
0.074 * Age (in years)] * Time (in minutes) / 4.184
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Variables, again
—  Excess calories
—  Extent to which, in meal, subjects ‘replenished’ the calories
spent in physical task
¡
¡
Individually-calculated balance between ‘calories in’ and ‘calories out’:
ExcessKcal = KcalIn - KcalOut
—  Other variables are standard:
¡
¡
Dummy for female Female
Dummies for treatment groups: TreatH, TreatL, TreatE
—  Self-reported level of hunger from Task A: Hunger (0-180)
¡
Self-reported time from last meal from Task C: LastEat (minutes)
—  Subjective estimates of number of Kcal
¡
¡
Burnt in stepping: EstKcalOut
Consumed in lunch: EstKcalIn
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Age
Female
Weight
Weekly Rent Expense
SAH
HR Before
Happy
Hunger
Observations
H
L
E
C
Total
25.62
26.51
25.12
24.63
25.47
(5.69)
(6.53)
(7.87)
(4.72)
(6.28)
0.60
0.65
0.67
0.71
0.65
(0.49)
(0.48)
(0.47)
(0.46)
(0.47)
63.4
65.32
63.14
63.21
63.77
(12.44)
(15.87)
(11.29)
(13.88)
(13.37)
175.81
158.21
173.61
160.73
167.23
(133.76)
(131.51)
(149.71)
(98.61)
(128.47)
3.6
3.62
3.59
3.71
3.62
(0.87)
(0.92)
(0.82)
(0.65)
(0.81)
82.67*
78.05
75.80
77.16
78.52
(12.95)
(16.42)
(10.43)
(14.21)
(13.78)
7.10
7.14
6.97
7.21
7.11
(1.69)
(1.31)
(1.58)
(1.71)
(1.57)
48.17
62.05
52.77
49.39
52.97
(39.41)
(35.27)
(35.24)
(33.11)
(35.94)
40
39
39
38
156
Steps
DiffHR
DiffEnergy
ImmSatisf
EndSatisf
KcalIn
KcalOut
ExcessKcal
Observations
H
102.5***
L
105.5***
E
92.49
C
89.42
Total
97.58
(13.35)
(19.15)
(18.55)
(16.91)
(18.24)
64.4**
68.67**
63.39*
51.66
61.95
(22.93)
(28.66)
(25.25)
(27.18)
(26.53)
0.225
1.128**
0.243
-0.132
0.368
(1.746)
(2.105)
(1.716)
(2.462)
(2.059)
8.42***
7.064*
7.487***
6.394
7.355
(1.059)
(1.857)
(1.519)
(1.701)
(1.712)
7.9***
6.807**
7.128***
6.039
6.981
(1.516)
(1.768)
(1.417)
(1.817)
(1.753)
433***
319.23*
350.12*
233.04
335.10
(297.81)
(283.49)
(286.4)
(306.3)
(299.44)
16.95**
16.61**
15.402
13.27
15.58
(5.817)
(6.424)
(6.279)
(7.385)
(6.587)
415.9***
302.62
334.72*
219.77
319.52
(299.69)
(284.07)
(286.23)
(305.34)
(299.5)
40
39
39
38
156
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
KcalSandw
KcalCrisps
KcalSweets
KcalDrinks
Fats
SatFats
Sugars
Salt
Observations
H
L
E
C
Total
169.52
155.49
173.15
143.83
160.66
(222.02)
(186.69)
(198.35)
(230.35)
(208.31)
79.2***
20.31
25.38
13.89
35.11
(77.94)
(45.81)
(51.60)
(41.05)
(61.56)
97.72*
95.54
109.67
38.32
85.69
(131.72)
(153.33)
(156.08)
(87.47)
(136.69)
86.45***
47.89
41.92
37.00
53.63
(54.25)
(71.37)
(55.35)
(62.74)
(63.78)
14.80***
9.08**
11.23*
6.431
10.440
(11.39)
(9.449)
(9.834)
(10.353)
(10.646)
3.932***
2.91**
3.401***
2.016
3.078
(4.517)
(3.448)
(3.493)
(4.206)
(3.970)
31.52***
23.60*
22.39
14.40
23.09
(17.02)
(25.91)
(22.79)
(19.48)
(22.19)
0.843**
0.654
0.754
0.595
0.713
(0.934)
(0.754)
(0.809)
(0.954)
(0.864)
40
39
39
38
156
Second look at the data
—  Significant differences in ExcessKcal across treatments
are due to two, conceptually distinct, factors
—  How many subjects choose to have lunch
% KcalIn>0
Observations
H
90.00**
40
L
84.62*
39
E
76.92
39
C
71.05
38
—  How much to eat/drink given choice to have lunch
# KcalIn|KcalIn>0
Observations
H
481***
L
377.28
E
455**
C
327.98
(273.91)
(269.92)
(240.95)
(318.03)
40
39
39
38
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Modelling two decisions
—  Two-part (hurdle) model
¡
Two decisions are generated by potentially different underlying
mechanisms: simple and flexible
—  First part: choose whether or not to have lunch
Binary choice probit model
¡  Dependent variable: =1 if KcalIn>0, =0 otherwise
¡
—  Second part: how many excess calories to consume,
given the option to have lunch
Linear regression model, in logs, with heteroskedastic-robust SE
¡  Dependent variable: E (Ln Excess Kcal | KcalIn>0 )
¡
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Pr(KcalIn)>0
TreatH
m1
0.727**
(0.345)
TreatL
0.465
(0.325)
TreatE
0.181
(0.309)
Female
Hunger
LastEat
Steps
DiffEnergy
DiffHR
Constant
0.555***
(0.215)
Obs
Pseudo R2
156
0.0352
Pr(KcalIn)>0
TreatH
TreatL
TreatE
m1
0.727**
m2
0.693**
m3
0.768**
m4
0.792**
(0.345)
(0.349)
(0.358)
(0.364)
0.465
0.548
0.473
0.458
(0.325)
(0.342)
(0.351)
(0.356)
0.181
0.157
0.155
0.270
(0.309)
(0.311)
(0.318)
(0.330)
-0.447
-0.340
-0.469
(0.273)
(0.282)
(0.298)
0.0096**
0.0116***
(0.0038)
(0.0041)
Female
Hunger
LastEat
-0.00029
(0.00058)
Steps
DiffEnergy
DiffHR
Constant
Obs
Pseudo R2
0.555***
0.885***
0.359
0.414
(0.215)
(0.299)
(0.362)
(0.377)
156
0.0352
154
0.0579
154
0.104
152
0.124
Pr(KcalIn)>0
TreatH
TreatL
TreatE
m1
0.727**
m2
0.693**
m3
0.768**
m4
0.792**
m5
0.792**
m6
0.788**
m7
0.747**
(0.345)
(0.349)
(0.358)
(0.364)
(0.377)
(0.363)
(0.368)
0.465
0.548
0.473
0.458
0.458
0.497
0.360
(0.325)
(0.342)
(0.351)
(0.356)
(0.372)
(0.362)
(0.365)
0.181
0.157
0.155
0.270
0.270
0.279
0.271
(0.309)
(0.311)
(0.318)
(0.330)
(0.331)
(0.331)
(0.341)
-0.447
-0.340
-0.469
-0.469
-0.505*
-0.463
(0.273)
(0.282)
(0.298)
(0.298)
(0.304)
(0.300)
0.0096**
0.0116***
0.0116***
0.0117***
0.0106**
(0.0038)
(0.0041)
(0.0042)
(0.0041)
(0.0042)
-0.00029
-0.00029
-0.00030
-0.00026
(0.00058)
(0.00058)
(0.00058)
(0.00059)
Female
Hunger
LastEat
Steps
-0.00001
(0.00750)
DiffEnergy
-0.0405
(0.0625)
DiffHR
0.00340
(0.00514)
Constant
Obs
Pseudo R2
0.555***
0.885***
0.359
0.414
0.415
0.436
0.276
(0.215)
(0.299)
(0.362)
(0.377)
(0.775)
(0.380)
(0.446)
156
0.0352
154
0.0579
154
0.104
152
0.124
152
0.124
152
0.127
146
0.116
Ln(ExcessKcal)
TreatH
m12
0.599**
(0.257)
TreatL
0.305
(0.261)
TreatE
0.625**
(0.246)
Female
Hunger
LastEat
Constant
5.27***
(0.207)
Obs
126
R2
0.0695
Adj R2
0.0467
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Ln(ExcessKcal)
TreatH
TreatL
TreatE
m12
m13
m14
m14
m16
0.599**
0.568**
0.596**
0.568**
0.593**
(0.257)
(0.259)
(0.254)
(0.260)
(0.255)
0.305
0.281
0.244
0.246
0.220
(0.261)
(0.265)
(0.272)
(0.265)
(0.271)
0.625**
0.625**
0.614**
0.625**
0.614**
(0.246)
(0.245)
(0.249)
(0.246)
(0.251)
-0.365**
-0.313*
-0.373**
-0.331**
(0.164)
(0.172)
(0.158)
(0.165)
Female
Hunger
0.00399
0.00388
(0.0025)
(0.0027)
LastEat
Constant
0.00006
-0.00006
(0.0005)
(0.0005)
5.27***
5.51***
5.25***
5.5***
5.28***
(0.207)
(0.242)
(0.277)
(0.253)
(0.271)
126
125
125
124
124
R2
0.0695
0.105
0.127
0.110
0.130
Adj R2
0.0467
0.0751
0.0903
0.0720
0.0849
Obs
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Ln(ExcessKcal)
TreatH
m17
0.596**
(0.254)
TreatL
0.244
(0.272)
TreatE
0.614**
(0.249)
Female
-0.313*
(0.172)
Hunger
0.0039
(0.0025)
Steps
DiffEnergy
DiffHR
Constant
5.25***
(0.277)
Obs
R2
Adj R2
125
0.127
0.0903
Ln(ExcessKcal)
TreatH
TreatL
TreatE
Female
Hunger
m17
0.596**
m18
0.635**
m19
0.636**
m20
0.613**
m21
0.661**
m22
0.650**
m23
0.634**
(0.254)
(0.265)
(0.256)
(0.253)
(0.266)
(0.255)
(0.264)
0.244
0.295
0.305
0.296
0.337
0.357
0.326
(0.272)
(0.299)
(0.278)
(0.287)
(0.300)
(0.291)
(0.308)
0.614**
0.620**
0.650**
0.642**
0.651**
0.678**
0.638**
(0.249)
(0.254)
(0.255)
(0.263)
(0.257)
(0.265)
(0.263)
-0.313*
-0.323*
-0.346**
-0.312*
(0.172)
(0.171)
(0.168)
(0.178)
(0.169)
(0.174)
(0.177)
0.0039
0.004
0.0043*
0.00405
0.0043*
0.0043
0.0039
(0.0025)
(0.0025)
(0.0025)
(0.0027)
(0.0025)
(0.0026)
(0.0027)
Steps
-0.0022
-0.0025
(0.0051)
(0.0052)
(0.006)
-0.0374
-0.0333
-0.0386
(0.0439)
(0.0453)
(0.0453)
DiffHR
Obs
R2
Adj R2
-0.321*
-0.0029
DiffEnergy
Constant
-0.350** -0.349**
-0.0011
-0.001
-0.0003
(0.0035)
(0.0035)
(0.004)
5.25***
5.52***
5.23***
5.31***
5.44***
5.28***
5.50***
(0.277)
(0.489)
(0.277)
(0.312)
(0.510)
(0.317)
(0.514)
125
0.127
0.0903
125
0.130
0.0856
125
0.132
0.0881
120
0.123
0.0763
125
0.134
0.0821
120
0.128
0.0740
120
0.125
0.0698
Alternative models/robustness checks
Results are robust to broad set of alternative specifications:
—  Using ExcessKcal in levels
—  Focusing analysis on subsample where few dummies were
‘trimmed’ off:
¡
3 subjects consumed more than 1,000 Kcal, 1 each for H, L, and C
—  Using KcalIn in levels or logs in the second step
—  Using as dependent variable
¡
¡
Either the number of calories in each food category (e.g. KcalSandw, KcalCrisps)
Or more refined nutritional intakes (e.g. fats, sugars)
—  Using discrete choice models for the likelihood to consume
‘healthy’ versus ‘energy-dense’ food and drink items
General message: more satisfied subjects, i.e. in H, tended to have
more: (excess) calories, intakes of fats/sugars, energy dense items
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Discussion: spillovers
—  In H and E (but not L) 200 more excess Kcal than C
¡
¡
A can of coke: 143 Kcal; pack of crisps: 112 Kcal
About 25 minutes stepping...
—  More excess calories rule out any ‘promoting’ spillovers:
¡
¡
Consistent with Thogersen and Crompton (2009), Evans et al. (2013):
Incentives decrease likelihood of ‘promoting spillovers’ in environmental behaviours
—  Left with ‘permitting’ spillovers. Which one?
¡
¡
¡
Licensing
(Ego-depletion)
(Take-the-most-out-of-it)
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Discussion: Licensing
—  Most plausible explanation
—  H manifested higher levels of satisfaction with the task
¡
Higher sense of ‘deservingness’
—  Higher satisfied subjects tend to eat more excess calories in lunch
—  Excess calories mainly took form of tasty and self-indulging
energy-dense side dishes:
¡
¡
Sweetened drinks
Crisps
—  Crisps hard to explain with ‘glucose’ repletion
—  Go along well with ‘licensing’: feeling entitled to treat myself
¡
Feel I’m ‘worth it’
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Conclusions and limitations
—  Major limitation: a sample of students
¡
Instead of subjects who suffer from actual health problems related to risky behaviour
(e.g. overeating).
—  Need more confirmations, but main finding that…
—  High (but not low) financial incentives have ‘licensing’ spillovers
—  Have number of practical implications:
—  Research methodology: caution is due when interpreting results
of experiments involving sequences of tasks, in particular
¡
¡
Incentivised (e.g. games, eliciting preferences) and
Non-incentivised tasks (e.g. questionnaires, field)
—  Policy: in health contexts, modest financial incentives can work as
well as large financial rewards, while
¡
¡
Being more cost-effective
Presenting lower risks of unintended spillovers
Matteo M Galizzi, LSE: m.m.galizzi@lse.ac.uk
Thank you very much
m.m.galizzi@lse.ac.uk
Thank you very much
m.m.galizzi@lse.ac.uk
•  Dolan P, Galizzi MM. Like Ripples on a Pond: Behavioural Spillovers and Their Consequences for
Research and Policy, Journal of Economic Psychology, 2015, 47, 1-15.
•  Dolan P, Galizzi MM, Navarro-Martinez D. Paying People to Eat or Not to Eat? Carryover Effects of
Monetary Incentives on Eating Behaviour, Social Science and Medicine, 2015, 133, 153-158.
•  Dolan P, Galizzi MM. Because I’m Worth it: a Lab-Field Experiment on Spillover Effects of Incentives in
Health, LSE CEP Discussion Paper CEPDP1286.