Uab 28june 12

Background & Motivation Previous Research Data Methods Results Discussion

SNAP and Diet Quality: A Treatment Eﬀects
Approach

Christian A. Gregory*1 , Shelly Ver Ploeg1 , Margaret Andrews1 ,
Alisha Coleman-Jensen1
presented at
Lister Hill Center for Health Policy
The University of Alabama at Birmingham
The analysis and views expressed are the authors’ and do not represent the
views of the Economic Research Service or USDA.

1 Economic Research Service, USDA

*contact author: cgregory@ers.usda.gov

Gregory, Ver Ploeg, Andrews, Coleman-Jensen June 27, 2012
Economic Research Service, USDA *contact author: cgregory@ers.usda.gov
SNAP and Diet Quality: A Treatment Eﬀects Approach


Background: Intent of Program

SNAP authorizing legislation: ”To alleviate such hunger and
malnutrition, a supplemental nutrition assistance program is
herein authorized which will permit low-income households to
obtain a more nutritious diet through normal channels of
trade by increasing purchasing power ...”
food security and nutrition declared goals of SNAP

Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.gov


Background: Public Perceptions

”As I look at what this card is paying for in the orders being
scanned at the register, I see T-bone steaks, thick-cut sirloins,
thick-cut pork chops (all expensive cuts of meat). I see crab
legs, bags of shrimp, and box after box of pastries, cakes and
doughnuts from the bakery department, and bagged candy,
chips and cookies from the snack aisles. Then come the
sodas, energy drinks and Starbucks coﬀee drinks... The people
using this card are eating better than most families that have
two incomes.” -Letter to Frederick News Post



Background: SNAP & Food Security

recent research: SNAP ⇓ food insecurity
Yen et al. (2008); DePolt et al. (2009); Shaefer and Gutierrez
(2012); Nord and Golla (2009); Nord and Prell (2011);
Ratcliﬀe et al. (2011)
estimates suggest SNAP participation ⇓ food insecurity 33 -
40 percent



Background: SNAP & Diet Quality

recently–a good deal of concern
many expensive chronic illnesses associated with low-income
populations
public bears sizable fraction of cost
policy suggestions:
1. restrict foods eligible for SNAP (as in WIC)
2. Wholesome Wave Double Coupon
3. Healthy Incentives Pilot



Motivation

large extant literature (detail below)
some–improved intakes (Devaney and Moffitt, 1991; Wilde
et al., 1999)
some–poorer intakes (Butler and Raymond, 1996; Yen, 2010)
difficult to identify treatment effects
selection on unobservables
selection: adverse or beneficial?



Our Contribution

use individual data (NHANES) matched to state-level data
identify SNAP selection
estimate treatment effects by isolating unobservables in SNAP
and diet
show that marginal effect of SNAP is positive and significant
for some HEI components; adverse selection accounts for
worse diet outcomes



Preview of Results

as measured by HEI total and component scores
1. SNAP participants comparable diets
2. total effect of SNAP (including selection): slightly lower HEI
scores
3. economically significant?
4. selection is adverse for many components
5. effect of SNAP on marginal participant is positive
6. in particular, SNAP gets participants to consume some whole
fruit and whole grains
results corroborated by nutrient intakes
robust to specification choice?
suggest policy caution: tradeoff improving nutritional quality,
changing selection into the program



Previous Research

comprehensive review of literature (Fox et al., 2004)
wrt intakes, few find significant impact ↑, ↓
highlight Gleason et al. (2000)–array of outcomes including
HEI–rule out large effects in either direction
studies that find positive effects: Wilde et al. (1999);
Kramer-LeBlanc et al. (1997); Basiotis et al. (1998)
more recent studies: Cole and Fox (2008); Yen (2010)
Waehrer and Deb (2012) used latent factor model/IV–SNAP
participants ↑ caloric sweetened beverages ↓ fruits/vegetables



Data: NHANES 2003-04, 2005-06, 2007-08

individual: NHANES 2003-04, 2005-06, 2007-08
dependent variable: Healthy Eating Index Score (HEI) (day 1), total and
component
total = sum of 12 elements
total fruit, whole fruit, total veg, dark green and orange veg,
total grains, whole grains, milk, meat and beans, oils, sat fat,
sodium, SoFAAS
for food groups and oils: zero intake = score of zero;
meet/exceed dietary recommendation = perfect score; linear
interpellation b/w



Data: NHANES 2003-04, 2005-06, 2007-08 (continued)
dependent variable: Healthy Eating Index Score (HEI) (day 1), total and
component (continued)
how to score “moderation” components? (i.e. things you should eat less
of)
85th pctile of consumption = score of zero; meet Dietary
Guidelines recommendation = score of 8; meet somewhat
higher standard, below dietary rec = score of 10; linear
interpellation b/w amounts at 0 and 8, 8 and 10.
example: sat fat. – fraction of total energy (2001-2002
NHANES data)
85th pctile: 15 % : score of 0
DG: less than 10 %: score of 8
below 7% : score of 10
weights: milk, meat/beans, oils, sat fat, sodium = 10; total
fruit, whole fruit, total veg, dark green and orange veg, total
grains, whole grains =5 ; SoFAAS = 20


Data: NHANES 2003-04, 2005-06, 2007-08

independent variable of interest: HH SNAP participation
2003, 2005 waves: 2 questions HH SNAP participation: number of
persons authorized to receive SNAP, whether HH receive SNAP 12
mos.
2007 wave: HH receive SNAP 12 mos
we use whether HH receive SNAP 12 mos 2003, 2005, 2007
robustness check: sample person currently receiving SNAP
other rhs variables: race/ethnicity, income, education, SR weight 1 year
ago, age, marital status, employment status, vigorous ex./week, nutrition
ed per poor person, hh size, state ﬁxed-eﬀects
200% FPL



Data: SNAP Policy Database

in model (following) we need exogenous variables to identify
participation in SNAP
state-month level variation in three policies:
expanded categorical eligibility–relaxed asset and/or income
requirements
biometric info needed to enroll–usually a fingerprint
certification period–median certification period for
households with earnings calculated from the QC data
valid: the policies affect SNAP participation but not diet
quality/HEI



Selection Model
one might begin with
HEIi = Xi β + SNAPi δOLS + i (1)
problem: SNAP is endogenous to HEI
another way to proceed

HEIi = Xi β + SNAPi δZ + i (2)
SNAPi∗ = Zi γ + Xi θ + υi (3)
Z exogenous variables for SNAP
SNAP ∗ latent index of SNAP participation
X other variables correlated w/ SNAP, HEI
and υ bivariate normal w/covariance matrix

σ 2 ρσ
V =
ρσ 1


Identification & Marginal Effects

model is theoretically identified by functional form imposed by
distribution of and υ.
we use exogenous policy variables to identify SNAP
participation
total effects of SNAP :
φ(Zi γ + Xi θ)
µi = δZ + ρσ (4)
Φ(Zi γ + Xi θ) ∗ [1 − Φ(Zi γ + Xi θ)]

this is what δOLS will estimate



Identification & Marginal Effects

without selection: µi = δOLS ; with selection δZ + difference
in expected value of errors conditional on participation (See
Greene, 2011)
unconditional on selection, δZ measures marginal affects of
SNAP on participants
standard errors (of total effects) (ν) by delta method: let
α = [γ, θ]
∂µ ∂µ
νµ = M , (5)
∂α ∂α
where M is the covariance matrix of the selection equation



Descriptive

HEI Score and SNAP Participation
Data: NHANES, 2003−08

53
51.8
52 51
HEI Score
49 50

47.8

No SNAP SNAP Participants

SNAP Participation Status

Figure: Diﬀerences in HEI over SNAP Participation



Descriptive

Total Food Energy and SNAP Participation

2044 2074 2104 2134
Data: NHANES, 2003−08
2124.3

2094
Total Energy Intake

No SNAP SNAP Participants

SNAP Participation Status

Figure: Diﬀerences in Energy over SNAP Participation



Descriptive

Table: Means of HEI Components by SNAP Participation

HEI Component No SNAP SNAP Diﬀerence
TotalFruit 2.11 1.73 -0.38***
(0.07) (0.07) (0.12)
WholeFruit 1.93 1.39 -0.54***
(0.06) (0.06) (0.10)
TotalVeg 3.00 2.63 -0.37***
(0.04) (0.07) (0.08)
DkGOrVeg 1.17 0.83 -0.34***
(0.05) (0.05) (0.08)
TotGrain 4.27 4.07 -0.20***
(0.03) (0.04) (0.06)
WholeGrain 0.93 0.66 -0.27***
(0.04) (0.03) (0.05)
N 5,105



Descriptive

Table: Means of HEI Components by SNAP Participation, cont’d

HEI Component No SNAP SNAP Diﬀerence
Milk 4.77 4.39 -0.38**
(0.09) (0.11) (0.15)
Sodium 4.12 4.52 0.40***
(0.07) (0.09) (0.11)
SoFAAS 9.47 7.96 -1.51***
(0.20) (0.25) (0.41)
N 5,105



Total Eﬀects of SNAP

Table: Total Eﬀects of SNAP on HEI/Components: 200% FPL
HEI TotalFruit WholeFruit TotalVeg DkGOrVeg
µ -1.241*** -0.144*** -0.520*** -0.069*** -0.103***
νµ (0.049) (0.016) (0.082) (0.009) (0.005)
TotGrain WholeGrain Milk MeatBeans Oils
µ -0.094*** -0.307*** 0.004 -0.340*** 0.039**
νµ (0.005) (0.078) (0.004) (0.000) (0.017)
SatFat Sodium SoFAAS
µ 0.0290*** 0.376*** -0.388***
νµ (0.009) (0.001) (0.039)
N 5,105



Correlation, IV Strength

Table: Selection Paramter: ρ
ρ 0.082 -0.107 -0.648*** 0.071 0.040
νρ (0.169) (0.223) (0.203) (0.129) (0.301)
ρ -0.059 -1.032*** -0.017 -0.000 0.066
νρ (0.048) (0.069) (0.096) (0.084) (0.106)
ρ -0.035 0.003 0.082
νρ (0.127) (0.117) (0.169)

All F-tests of instruments > 15.



Marginal Eﬀects of SNAP

Table: Marginal Eﬀects of SNAP=δ
δ -1.429 0.270 1.981*** -0.301 -0.236
νδ (1.916) (0.757) (0.624) (0.382) (0.870)
δ 0.041 1.940*** 0.116 -0.338 -0.425
νδ (0.133) (0.095) (0.598) (0.392) (0.697)
δ 0.273 0.357 -1.429
νδ (0.908) (0.670) (1.916)
N 5,105



Questions

δs seem too large to be believed
δwf = 1.98, x = 1.39
¯
δwg = 1.94, x = .66
¯



Distribution of Components

Kernel Density WholeFruit Component Score Kernel Density WholeGrain Component Score
Data: NHANES 2003−08, 200% FPL Data: NHANES 2003−08, 200% FPL

1.5
.5
.4

1
.3
Density

Density
.2

.5
.1
0

0
0 1 2 3 4 5 0 1 2 3 4 5
Score Score

Figure: Distribution of Whole Fruit, Whole Grain Components

modewf = 0, modewg = 0



Distributional Concerns

need to address the violation of distributional assumptions
GMM, 2SLS, larger std errs, size of δZ still a concern
ﬁnite mixture model (latent class model) – probabilities as
function of SNAP participation (in process)



Solution: Bivariate Probit

Table: Bivariate Probit: Effect of SNAP on Score >0
Whole Fruit Whole Grain
Parameter Marginal Effect Parameter Marginal Effect
SNAP 0.672** 0.409 .699*** 0.409
(0.29) (0.22)
N 5,105

effect on SNAP is to increase by 40 percentage points points
prob of eating any whole fruit or whole grains
too large? less than 30% of sample eat any whole fruit or
whole grain



Total Eﬀects: Current Recipients

Table: Total Eﬀects of SNAP (Current) on HEI/Component Scores
µ -2.371*** -0.301*** -0.570*** -0.059*** -0.019
νµ (0.601) (0.093) (0.137) (0.013) (0.017)

µ -0.089*** -0.357*** 0.0570*** -0.352*** -0.076***
νµ (0.007) (0.102) (0.004) (0.019) (0.005)

µ 0.179*** 0.337*** -0.712***
νµ (0.007) (0.028) (0.139)
N 5,105



Marginal Effects: Current Recipients

Table: Marginal Effect of SNAP (Current) = δ
δ 5.245 0.897 2.981*** -0.690 -0.674***
νdelta (11.316) (1.102) (0.200) (0.514) (0.180)
δ 0.053 1.984*** 0.554 -0.264 -0.277
νdelta (0.158) (0.073) (0.614) (0.302) (0.934)
δ 0.108 -0.313 0.203
νdelta (0.951) (0.542) (2.326)
N 5,105

similar marginal effects of SNAP on score > 0.



Robustness: Total Eﬀects of SNAP on Nutrient Intake

Table: Total Eﬀects of SNAP on Nutrient Intake
Energy (Kcal) Protein Total Fat Sat Fat Carbs
µ -19.78*** -0.047*** -1.810*** -0.221*** 0.711***
νµ (1.87) (0.02) (0.31) (0.05) (0.129)

Vitamin C Niacin Folate Sodium Frac FAFH
µ 8.220*** 0.166*** -0.063*** -0.208*** -0.029***
νµ (0.08) (0.02) (0.01) (0.00) (0.00)
N 5,105



Discussion

Results
SNAP participants slightly lower HEI scores than comparable
non-participants
total effects statistically significant, though not economically so
total effects for current recipients somewhat larger–same
directions
corroborated by nutrient intake results
however: adverse selection into SNAP
SNAP has positive effect on whole fruit and whole grain
consumption of SNAP participants ⇑ in P(Score) > 0.
but participants in general have slightly less healthy diets
compared to similar non-participants



Discussion

Further Questions
controlled for endogeneity fully?
distribution of error terms–alternative distributions
how might SNAP improve DQ w/o adversely aﬀecting
selection/eﬀectiveness?
subsidies instead of restrictions? (Wholesome Wave, Healthy
Incentives)



Further Discussion?

Thank You


Uab 28june 12

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