Gregory etal aaea_12

SNAP and Diet Quality: A Treatment Eﬀects
Approach

Christian A. Gregory* Shelly Ver Ploeg Margaret Andrews
Alisha Coleman-Jensen

Economic Research Service, USDA
*contact author: cgregory@ers.usda.gov.

Annual Meetings AAEA
Seattle, WA
August 13, 2012

The views expressed are those of the authors and should
not be attributed to ERS or USDA.

Background & Motivation

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, et al. SNAP and Diet Quality Aug. 13, 2012


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:
– restrict foods eligible for SNAP (as in WIC)
– Wholesome Wave Double Coupon
– 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
– SNAP participants comparable diets
– average treatment effect of SNAP (ATE): slightly lower HEI
scores
– economically significant?
– selection is adverse for many components
– effect of SNAP on marginal participant is positive
– 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

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

Data: NHANES 2003-08

• 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 interpolation b/w


Data

Data: NHANES 2003-08 (cont)

• 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

Data: NHANES 2003-08

• 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

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


Methods

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

HEIi = Xi β + SNAPi δZ + ϵi (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
• ϵ and υ bivariate normal w/covariance matrix

[ ]
σ 2 ρσ
V =
ρσ 1

Methods

Identification & Marginal Effects

• model is theoretically identified by functional form imposed by
distribution of ϵ and υ.
• we use exogenous policy variables to identify SNAP
participation
• ATE 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


Methods

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


Results

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


Results

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


Results

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


Results

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


Results

ATE of SNAP

Table : ATE 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


Results

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.


Results

Marginal Eﬀects of SNAP

Table : Marginal Eﬀects of SNAP=δZ
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


Results

Questions

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


Results

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


Results

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)


Results

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


Results

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


Results

Marginal Effects: Current Recipients

Table : Marginal Effect of SNAP (Current) = δZ
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.


Results

Robustness: 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

Discussion

• Results
– SNAP participants slightly lower HEI scores than
comparable non-participants
– ATE statistically signiﬁcant, though not economically so
– ATE 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

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)


Discussion

Further Discussion?

Thank You


Gregory etal aaea_12

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