The Impact of Long Term
Participation in the Supplemental
Nutrition Assistance Program on
Child Obesity
Maximilian D. Schmeiser
University of Wisconsin-Madison
Motivation
• Rapid increase in obesity over past 30 years
• Obesity is most prevalent among low-income
individuals
• One suggested cause is participation in the
Supplemental Nutrition Assistance Program
(SNAP) (formerly Food Stamps)
• SNAP participation has increased substantially
due to recession and benefits were increased
Research Question
• Does participation in the SNAP program
increase obesity among children ages 5 to 18?
• Focus on percent of time over past 5 years
child participated in SNAP since obesity is a
stock measure which takes time to adjust to
changes in behavior
• Use an Instrumental Variables (IV) strategy to
identify causal effect of SNAP participation on
child obesity
Background on SNAP
• The Supplemental Nutrition Assistance
Program is the new name for the Food Stamps
Program
– Emphasize nutrition aspect of program
• In 2008, the program served 28 million lowincome persons, providing total benefits
worth almost $35 billion
• Provides benefits for the purchase of
unprepared food to individuals below 130
percent of the poverty line
SNAP and Obesity
• How can SNAP affect obesity?
• Increase Obesity:
• A dollar of SNAP benefits increases the consumption
of food by more than does a dollar of unrestricted
cash benefits (Fox et al., 2004)
• Approximately a quarter of SNAP recipients would
spend less than their SNAP allocation on food were
the benefits provided as cash (Whitmore, 2002)
• The monthly lump sum distribution of SNAP could
also contribute to obesity if it results in binge eating
(Townsend et al., 2001; Shapiro, 2003)
SNAP and Obesity
• Decrease Obesity:
• Improve nutrition by increasing family’s food
budget allowing the purchase of more
expensive fruits and vegetables and less
processed food
• Encourage eating food at home rather than
food at restaurants or fast food outlets
• Reduce food insecurity
SNAP and Obesity
• Previous research has consistently found SNAP
participation to be associated with an increase
in obesity for adult women (Townsend et al.,
2001; Gibson, 2003, 2006; Chen et al., 2005;
Baum, 2007) and female children (Gibson,
2004, 2006)
• However, no previous study has effectively
addressed the endogeneity between SNAP
participation and obesity
Data
• National Longitudinal Survey of Youth 1979
cohort (NLSY79) and Children and Young Adults of
NLSY79
– NLSY79 Nationally representative sample of
individuals who were between the ages of 14 and 21
on December 31, 1978
– CYA of NLSY79 are data on all children born to women
of the NLSY79 collected biannually since 1986
– Contains detailed demographic and economic data
– Use 1986 through 2006 waves
– Children ages 5 to 18
Table 1. Descriptive Statistics: Children Ages 5 to 11
Ages 5 to 11
Variable
Boys
Girls
Obese
0.1436
0.1286
(0.3507)
(0.3347)
Overweight
0.2804
0.2676
(0.4492)
(0.4427)
Body Mass Index (BMI)
17.3381
17.3753
(3.8011)
(3.8677)
5 Year Food Stamps Exposure (Percent)
16.8902
16.4918
(32.1606) (31.7049)
Indicator for Self-Reported Weight and/or Height
0.2686
0.259
(0.4432)
(0.4381)
Observations
8684
8394
Weighted means with standard deviations in parentheses.
Ages 12 to 18
Boys
Girls
0.162
0.1247
(0.3685)
(0.3304)
0.3253
0.298
(0.4686)
(0.4574)
21.0579
21.3392
(4.5987)
(4.7808)
16.3866
16.0447
(31.9109) (31.9971)
0.4508
0.4677
(0.4976)
(0.4990)
8000
7648
Ages 5 to 18
Boys
Girls
0.1495
0.1273
(0.3566)
(0.3333)
0.2947
0.2774
(0.4559)
(0.4477)
18.5243
18.6515
(4.4259)
(4.5750)
16.7296
16.3478
(32.0807)
(31.7986)
0.3267
0.3262
(0.4690)
(0.4688)
16684
16042
Empirical Methods
• LPM Model:
Fist = α + β1 SNAPit + β 2 X it + β 3 Pst + ε ist
i=individuals, t=time, s=state
F: Obese/Overweight
SNAP: Percent of time spent on SNAP over previous 5 years
X: Vector of demographics (age, race/ethnicity, birth order, birth
weight, whether breast-fed, mother’s age at birth of the child, number
of children in the family, mother’s highest grade completed, mother’s
Armed Forces Qualifying Test (AFQT) percentile score, mother’s
marital status, mother’s employment status, region of residence and
residence in an MSA, an indicator for mother-reported weight and/or
height, and year dummies)
– Time invariant demographics dropped when FEs estimtated
– P: State level food price index
–
–
–
–
Identification
• To generate causal estimates use exogenous variation
in income, which affects SNAP eligibility, generated
by changes in the parameters of state and federal
Earned Income Tax Credit programs
• EITC is the largest anti-poverty program in the U.S.
for the non-elderly
– Federal expenditures in excess of $41.5 billion and over 22
million recipients in tax year 2004 (CBPP, 2007)
• Functions as a wage supplement for those who have
earned income
Identification
• Instruments generated by running entire NLSY
sample through NBER TAXSIM program for
each state by year combination and
calculating average EITC value for EITC eligible
individuals
– Use state by year average value of EITC and its
square as instruments for SNAP participation
– Captures changes in EITC benefits that are
unrelated to individual weight
Empirical Methods
• IV (2SLS) Model:
– First Stage:
SNAPist = δ + γ EITCit + λ EITCsqit + φ X it + ϕPst + υist
– Second Stage:
Fist = α + β1 SNAPit + β 2 X it + β 3 Pst + ε ist
• Instruments
– EITC: State by year average value of the combined federal and state
EITC benefit for entire NLSY79 corresponding year sample
– EITCisq: Square of EITC
Table 2. Linear Probability Estimates of the Effect of SNAP Participation on obesity status
for boys ages 5 through 11
Obese
Model 1 Model 2 Model 3 Model 4
Percent of Time Receiving SNAP in Past 5 Years 0.0003* -0.0005** -0.0003 -0.0070**
(0.0001) (0.0002) (0.0003) (0.0034)
Individual Fixed-Effects
X
X
Sibling Fixed-Effects
X
IV
X
First Stage F-Statistic
Observations
8684
8684
8684
8684
Robust Standard Erros in Parentheses. *** p<0.01, ** p<0.05, * p<0.1
Table 3. Linear Probability Estimates of the Effect of SNAP Participation on obesity status
for girls ages 5 through 11
Obese
Model 1 Model 2 Model 3 Model 4
Percent of Time Receiving SNAP in Past 5 Years 0.0001
0.0001
0.0001 0.0076**
(0.0001) (0.0003) (0.0003) (0.0035)
Individual Fixed-Effects
X
X
Sibling Fixed-Effects
X
IV
X
First Stage F-Statistic
Observations
8394
8394
8394
8394
Robust Standard Erros in Parentheses. *** p<0.01, ** p<0.05, * p<0.1
Table 4. Linear Probability Estimates of the Effect of SNAP Participation on obesity status
for boys ages 12 through 18
Obese
Model 1 Model 2 Model 3 Model 4
Percent of Time Receiving SNAP in Past 5 Years 0.0002
-0.0003 -0.0003
0.0056
(0.0001) (0.0002) (0.0002) (0.0040)
Individual Fixed-Effects
X
X
Sibling Fixed-Effects
X
IV
X
First Stage F-Statistic
Observations
8000
8000
8000
8000
Robust Standard Erros in Parentheses. *** p<0.01, ** p<0.05, * p<0.1
Table 5. Linear Probability Estimates of the Effect of SNAP Participation on obesity status
for girls ages 12 through 18
Obese
Model 1 Model 2 Model 3 Model 4
Percent of Time Receiving SNAP in Past 5 Years 0.0004*** -0.0005** -0.0001 0.0165**
(0.0001) (0.0002) (0.0002) (0.0065)
Individual Fixed-Effects
X
X
Sibling Fixed-Effects
X
IV
X
First Stage F-Statistic
Observations
7648
7648
7648
7648
Robust Standard Erros in Parentheses. *** p<0.01, ** p<0.05, * p<0.1
Summary
• Additional year of SNAP participation reduces
the probability of being obese for boys ages 5
to 11 by 14 percentage points
• Boys 12 to 18: 11.2 percentage point increase
• Girls 5 to 11: 15.2 percentage point increase
• Girls 12 to 18: 33 percentage point increase
Conclusion
• Consistent with previous research I find that
SNAP participation differentially affects
obesity for boys and girls ages 5 to 11
– Positive effect on girls 12 to 18
• However, the magnitude of the effect is much
larger once the IV is used
• Recent increase in SNAP participation and
SNAP benefits may increase child obesity
Thank You