Understanding Overeatingand Obesity

By Christopher J. Ruhm

University of North Carolina at Greensboro, NBER and IZA

March 2010

Obesity: Major & Growing Health Problem

• Important Cause of Premature Death

• Linked to Many Health Problems

• Higher Medical/Economic Costs

• Rapidly Increasing

Contrasting Policy Perspectives

• Public Health – interventions to reduce weight

• Economics – individual choices optimal without market failures

Are Economic Models Missing Something Important?

Triune Brain

Key Elements of Brain Structure

New capabilities added to rather than replacing existing structures

• Affective (limbic) system rapidly responds to stimuli

• Deliberative system accounts for long-term consequences

• Systems operate in parallel; yield differences in perception/memory

• Emotional feelings separate from rational appraisals of them

Key Elements of Brain (continued)

Affective system often most powerful

• Affective system came first• Responds faster than deliberative system

• More neural connections from limbic system to neocortex than in opposite direction

Eating decisions involve interaction of:

• Higher order rational calculations &• Affective processes based on emotion, chemical

responses & feelings

Dual Decision Model of Eating and Weight

Utility-maximizing deliberative system

• Rational eating (Philipson & Posner, 2003)• Food influences utility directly & through body weight• “Ideal” weight (W0): weight chosen if costless to achieve• Most policy interventions reduce utility

Affective System (Loewenstein & O’Donoghue, 2004)

• Responds to external stimuli• Does not consider consequences of eating on weight

Both systems influence outcomes

Results in deviations from utility-maximizing food intake & weight

Deliberative System: Utility-Maximization

• maxf,c U(W(f),f,c) subject to c + pf = I

W = weight p = food prices

f = food intake I = income

c = other consumption

• First-Order Conditions: UWWf + Uf = pUc

– equalize MU of last $ on food & other consumption– direct utility of food + indirect utility through weight

Deliberative System (continued)

• Optimal food consumption

fd = argmaxf F U(W(f),f,z)

F = feasible food choicesz = preference shifters determining utility from calories

• Food innovations change characteristics (z) to increase utility from consumption

(taste, mouth-feel, texture, complexity, etc.)

Affective System

• Optimal food consumption

fm = argmaxf F M(f,s,z)

M = motivational functions = stimuli (do not affect deliberative system)

• Generally fm > fd

– Affective system does not consider future weight gain

– Food is engineered to appeal to affective system

Dual System Interactions

• Costly for deliberative system to override affective system

Loss Function: L = L(|f- fm|,R) = L(D,R)

D = |f - fm| = deviation from affective system optimum

R = willpower

Losses increase with D, decrease in R

Dual System Interactions (cont.)

• Food Consumption

fc = argmaxf F U(W(f),f,z) – L(D,R)

• FOC: UWWf + Uf + LD = pUc (if fd < fm)

• Therefore: fd < fc < fm

• Food consumption & weight exceed utility-maximizing levels

Dual System Characteristics

• Higher prices reduce eating– Deliberative system still operates– Time prices more powerful than money prices

• Explanation for Quasi-hyperbolic discounting

• Related to models where individuals voluntarily limit choices available– Planner-Doer (Sheffrin & Thaler, 1988)

– Long-run patient self, short-run impulsive selves (Fudenberg & Levine, 2006)

– Temptation Utility (Gul & Pesendorfer

– “Hot/Cool” States (Bernheim & Rangel, 2004)

Strategic behaviors to manage overeating

• Voluntarily increase time/money price of foods triggering affective system (don’t bring ice cream home)

• Limit exposure to stimuli triggering affect system (avoid parties/restaurants where overeating is likely)

• Strategies to increase willpower – stress management– formal diets (to reduce cognitive load)– Weight watchers/weight loss tournaments (public

announcement of weight affects loss function)

• Medical procedures (bariatric surgery) to reduce fm

Food Engineering

• Strategic manipulation of food to raise consumption– Increase utility of food (welfare-increasing)– Promote affective system responses (increase overeating)

• Components of food engineering (Kessler, 2009)– Balance of (high levels of) added fats, sugars & salt– Combinations of flavor, aroma, oral/visual texture, after-taste– Heightened complexity & multi-sensory effects

– Heavy use of processed products, chemical flavorings

– “Edible food-like substances” rather than food itself

– Environmental cues (for food outside home)

• Promoted by new technologies & agricultural policies

Predictions Common to Both Models

• Weight & food prices inversely related

• High BMI individuals more likely to report being above “ideal weight” (W0)

– But utility-maximizers are not above Wd

Dual Decision Model Predictions

• Eating mistakes common & positively related to BMI– High BMI: strong affective system responses, weak willpower

• Weight loss attempts frequent, often unsuccessful– positively correlated with BMI & increasing over time

• Weight increases over time, even with stable prices, due to advances in food engineering

• Largest weight increases occur in upper tail of BMI distribution

• High BMI individuals eat more engineered foods; pattern strengthens with time

Data

• National Health & Nutrition Examination Surveys

– NHES (1960-62); NHANES 2 (1976-80); NHANES 3 (1988-94); NHANES 99 (1999-2006)

– Measured height & weight (not self-reports)– 24-hour food diaries– Weight history & weight loss attempts (NHANES 99 best)– Inconsistent questions on weight loss attempts, so use

• Behavioral Risk Factor Surveillance System– 1991/94, 2000/03– Self-reported height & weight– Common questions on weight loss attempts

Data (continued)

• 25-60 year olds

• Demographics– Age (quadratic)– Race/ethnicity (black, Hispanic, other)– Education (<high school grad, some college, college grad)– Marital Status (married, widowed, separated/divorced)– Tobacco use (ever smoked, current smoker)– Additional covariates experimented with

•Sampling weights incorporated

Body Mass Index & Weight Classes

• Widely used, easy to measure

• Not a direct measure of body fat (or distribution)

• BMI Categories– Underweight <18.5– Healthy weight 18.5 – 24.9– Overweight 25.0 – 29.9– Mild Obesity 30.0 – 34.9– Severe Obesity ≥ 35.0

2

( )

( )

Weight kgBMI

Height m

Fig 1. Trends in Body Mass Index

0.0

2.0

4.0

6.0

8.1

Pro

bab

ility

15 20 25 30 35 40 45BMI: 25-60 Year Olds

1960-1962 1976-19801988-1994 1999-2006

Fig 2. Food Prices, Eating Time & Restaurants/Capita

80

90

10

011

012

013

0N

orm

aliz

ed V

alu

e (1

967

= 1

00

)

1967 1972 1977 1982 1987 1992 1997 2002 2007Year

Food Price Eating TimeRestaurants/Capita

Specific Hypothesis 1

Disproportionate weight growth (over time) in upper-tail of BMI distribution, even during periods of stable food prices

Table 1. Quantile Regression Estimates of Trends in BMI

PercentileBaseline

BMI(1976-80)

Change in BMI Since Baseline1988-94 1999-2006

(a) (b) (a) (b)

Males10 21.2 0.60 0.33 1.00 0.80

25 23.1 0.70 0.55 1.50 1.34

50 25.4 0.60 0.66 2.10 1.97

75 27.9 1.20 1.08 3.10 2.90

90 30.7 2.00 1.90 4.50 4.30

95 33.3 2.40 2.34 5.50 5.41

Additional Controls No Yes No Yes

Table 1. (continued)

PercentileBaseline

BMI(1976-80)

Change in BMI Since Baseline1988-94 1999-2006

(a) (b) (a) (b)

Females

10 19.5 0.40 0.37 1.20 0.84

25 21.3 0.50 0.61 1.80 1.62

50 23.9 1.20 1.35 3.10 2.92

75 28.0 2.30 2.32 4.90 4.75

90 33.1 2.80 3.04 5.50 5.74

95 36.5 2.90 2.60 5.90 5.51

Additional Controls No Yes No Yes

Specific Hypothesis 2

Weight mistakes are common, particularly at high BMI, & have increased over time

– Heavy individuals think they weigh too much

– Desired weight has not increased over time

– But actual weight & dieting have become more common

Table 2. Weight Perceptions & Dieting Behavior, 1999-2006

Full Sample

Under-weight

Healthy Weight

Over-weight

Mild Obesity

Severe Obesity

MalesWeight Self-Perception

Underweight 6.0 73.6 17.0 1.5 0.5 0.0

About Right 42.6 26.4 74.5 45.8 14.0 4.6

Overweight 51.4 0.0 8.5 52.7 85.5 95.4

Preferred Weight

More 8.9 83.4 24.9 3.0 0.8 0.0

About Same 33.4 16.6 60.9 34.9 10.3 3.3

Less 57.6 0.0 14.2 62.1 88.9 96.7

Attempted Weight Loss

25.1 0.0 7.5 27.1 37.3 41.1

Population Share

100.0 0.9 27.2 41.0 20.3 10.7

Table 2. (continued)

Full Sample

Under-weight

Healthy Weight

Over-weight

Mild Obesity

Severe Obesity

FemalesWeight Self-Perception

Underweight 2.5 58.0 3.3 0.2 0.2 0.0

About Right 28.0 40.6 60.2 17.7 4.0 1.4

Overweight 69.5 1.4 36.5 82.0 95.8 98.6

Preferred Weight

More 2.6 51.1 3.7 0.4 0.1 0.0

About Same 19.7 47.4 41.9 11.0 3.1 1.6

Less 77.7 1.4 54.4 88.6 96.9 98.4

Attempted Weight Loss

43.3 0.2 30.9 48.9 55.3 53.0

Population Share

100.0 2.2 35.7 24.4 17.8 17.9

Fig 3A. Desired and Attempted Weight Loss, 1999-2006

0.2

.4.6

.81

Pro

bab

ility

20 22 24 26 28 30 32 34 36 38 40BMI

Weigh Less, females Diet, femalesWeigh Less, males Diet, males

Fig 3B. Desired Weight Loss by Survey Year

0.2

.4.6

.81

Pro

bab

ility

20 22 24 26 28 30 32 34 36 38 40BMI

1988-1994, females 1999-2006, females1988-1994, males 1999-2006, males

Fig 3C. Attempted Weight Loss, BRFSS

0.2

.4.6

.8P

rob

ab

ility

20 22 24 26 28 30 32 34 36BMI

1991 & 1994, females 2000 & 2003, females1991 & 1994, males 2000 & 2003, males

Specific Hypothesis 3

Severe obesity results from uncontrolled eating & unintended weight gains

– Heavy individuals more likely to have gained weight in last year

– & particularly likely to have large unintended weight gains

Table 3. Weight Gain During Last Year, 1999-2006Weight Gain

Full Sample

Healthy Weight

Over-weight

Mild Obesity

Severe Obesity

MalesAny Gain 25.7 19.7 26.2 24.4 28.1

≥5 lbs 23.1 16.8 23.3 23.5 27.5

≥10 lbs 17.3 10.8 16.8 18.7 25.6

≥10 lbs & Unintended

10.7 4.0 11.5 13.4 21.9

FemalesAny Gain 28.2 28.2 29.3 25.2 26.3

≥5 lbs 24.6 21.5 27.1 23.4 24.7

≥10 lbs 18.6 14.3 20.4 18.2 22.4

≥10 lbs & Unintended

12.7 8.2 15.4 14.1 18.9

Table 4. Econometric Estimates of Weight Gain in Last Year, 1999-2006

Weight ClassUnconditional

Any Gain ≥5 lbs ≥10 lbs≥10 lbs &

Unintended

Males

Severe Obesity .090 .115 .159 .182

Mild Obesity .053 .074 .089 .099

Overweight .069 .071 .069 .080

Baseline .211 .178 .112 .040

Females

Severe Obesity -.033 .020 .073 .108

Mild Obesity -.040 .012 .035 .062

Overweight .004 .051 .059 .076

Baseline .297 .225 .149 .077

Table 4. (continued)

Weight ClassConditional on Some Weight Gain

≥5 lbs ≥10 lbs ≥10 lbs & Unintended

Males

Severe Obesity .134 .380 .579

Mild Obesity .118 .243 .357

Overweight .038 .106 .248

Baseline .842 .529 .179

Females

Severe Obesity .174 .346 .443

Mild Obesity .170 .229 .301

Overweight .160 .192 .262

Baseline .751 .496 .255

Specific Hypothesis 4

Obese individuals disproportionately eat engineered foods

– Particularly true in recent years as food engineering has advanced

– Focus on fat & sodium intake

Table 5. Econometric Estimates of Food Consumption, 1999-2006

Weight Class

% Calories From Sodium/Day(mg)

Calories/Day # Eating Occasions/

DayFatSaturated

Fat (a) (b)

MalesSevere Obesity

3.42 1.42 292.6 23.0 114.5 -0.20

Mild Obesity 2.26 0.97 334.5 68.5 41.9 -0.18

Overweight 0.46 0.30 -35.6 -5.0 21.2 0.01

Baseline 31.5 10.0 4026 2695 4.76

FemalesSevere Obesity

1.78 0.85 322.3 81.5 116.2 -0.26

Mild Obesity 1.73 0.66 159.4 22.1 47.0 -0.05

Overweight 0.84 0.35 89.1 -16.3 18.9 -0.07

Baseline 32.3 10.5 2834 1873 4.81

FIG 4A. Fat Consumption by BMI

01

23

4N

orm

aliz

ed

% C

alo

ries

fro

m F

at

20 22 24 26 28 30 32 34 36 38 40BMI

1976-1980, females 1999-2006, females1976-1980, males 1999-2006, males

FIG 4B. Sodium Consumption by BMI

-400

-200

02

00

400

600

Norm

aliz

ed

So

diu

m In

take

(m

g)

20 22 24 26 28 30 32 34 36 38 40BMI

1976-1980, females 1999-2006, females1976-1980, males 1999-2006, males

Conclusion

• Brain Structure suggests food consumption & weight often deviate from utility-maximizing levels

– Eating “mistakes” common & difficult to correct– Weight loss attempts increase with BMI– Food engineering increases overeating & obesity

• Empirical evidence more consistent with dual decision-making than utility-maximization

Expanded/different role for policy?

• Time costs more effective than money prices– Availability constraints not fat taxes

• Information less useful– Not used by affective system

• Default options to exploit systematic decision errors (nudging)– Put healthy menu choices first

• Do advances in food engineering increase scope for intervention?

Future Research Topics

• Role of physical activity to offset overeating

• More detailed analysis of food engineering– Consumption of specific foods & food products

• Heterogeneity in affective system effects– Across individuals & the lifecycle (e.g. children)

• Incorpore cue-conditioned responses (addiction)

• Dual decision-making & other types of consumption