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Slides for presentation to Human Nutrition Coordinating Committee, 8 May 2014.
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SNAP and Diet Quality: A New Approach
Christian A. Gregory Shelly Ver Ploeg Margaret AndrewsAlisha Coleman-Jensen
Economic Research Service, USDA
Human Nutrition Coordinating CommitteeWashington, DCMay 8, 2014
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 andmalnutrition, a supplemental nutrition assistance program isherein authorized which will permit low-income households toobtain a more nutritious diet through normal channels of tradeby increasing purchasing power ...”
• food security and nutrition declared goals of SNAP
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Background & Motivation
Background: Public Perceptions
• ”As I look at what this card is paying for in the orders beingscanned at the register, I see T-bone steaks, thick-cut sirloins,thick-cut pork chops (all expensive cuts of meat). I see crablegs, bags of shrimp, and box after box of pastries, cakes anddoughnuts 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
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Background & Motivation
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); Mabli et al. (2013)
• estimates suggest SNAP participation ⇓ food insecurity 33 -40 percent
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Background & Motivation
Background: SNAP & Diet Quality
• recently–a good deal of concern
• many expensive chronic illnesses associated with low-incomepopulations
• public bears sizable fraction of cost
• policy suggestions:
– restrict foods eligible for SNAP (as in WIC)– Wholesome Wave Double Coupon– Healthy Incentives Pilot
• interim report 1/5 c ⇑ in TFV for HIP/SNAP participants
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Background & Motivation
Motivation
• large extant literature (detail below)
• some–improved intakes (Devaney and Moffitt, 1991; Wildeet al., 1999)
• some–poorer intakes (Butler and Raymond, 1996; Yen, 2010)
• difficult to identify treatment effects
selection on unobservables
• selection: adverse or beneficial?
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Background & Motivation
Our Contribution
• use individual data (NHANES) matched to state-level dataidentify SNAP selection
• estimate treatment effects by isolating unobservables in SNAPand diet
• show that marginal effect of SNAP is positive and significantfor some HEI components; adverse selection accounts forworse diet outcomes
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Background & Motivation
Preview of Results
• as measured by HEI total and component scores
– SNAP participants comparable diets– total effect of SNAP (including selection): slightly lower HEIscores
– economically significant?– effect of SNAP on marginal participant is positive for wholefruit, negative for darkgreen veg
• robust to specification choice?
• suggest policy caution: tradeoff improving nutritional quality,changing selection into the program
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
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 includingHEI–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–SNAPparticipants ↑ caloric sweetened beverages ↓ fruits/vegetables
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Data
Data: NHANES 2001-08
• individual: NHANES 2001-02, 2003-04, 2005-06, 2007-08
• dependent variable: Healthy Eating Index Score (HEI) (day 1), total andcomponent
– 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/exceeddietary recommendation = perfect score; linear interpellation b/w
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Data
Data: NHANES 2003-08 (cont)
• dependent variable: Healthy Eating Index Score (HEI) (day 1), total andcomponent (continued)
• how to score “moderation” components? (i.e. things you should eat lessof)
– 85th pctile of consumption = score of zero; meet Dietary Guidelines
recommendation = score of 8; meet somewhat higher standard, belowdietary 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, wholefruit, total veg, dark green and orange veg, total grains, whole grains=5 ; SoFAAS = 20
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Data
Data: NHANES 2003-08
• independent variable of interest: HH SNAP participation
– 2001, 2003, 2005 waves: 2 questions HH SNAP participation: numberof persons authorized to receive SNAP, whether HH authorized toreceive SNAP now.
– 2007 wave: time since HH receive SNAP (less than 31 days)– robustness check: sample person currently receiving SNAP
• other rhs variables: race/ethnicity, income, education, SR weight 1 yearago, age, marital status, employment status, vigorous ex./week, nutritioned per poor person, hh size, state fixed-effects
• 200% FPL
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Data
Data: SNAP Policy Database
• in model (following) we need exogenous variables to identifyparticipation in SNAP
– state-month level variation in two policies:– expanded categorical eligibility–relaxed asset and/orincome requirements
– vehicle exemption whether state exempts 1 vehicle fromcalculation of total assets to determine eligibility
• valid: the policies affect SNAP participation but not dietquality/HEI (except through SNAP)
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Methods
Treatment Effects Model
• one might begin with
HEIi = Xiβ + SNAPiδOLS + ǫi (1)
• problem: SNAP is endogenous to HEI
• another way to proceed
HEIi = Xiβ + SNAPiδZ + ǫi (2)
SNAP∗
i = Ziγ + Xiθ + υi (3)
• Z exogenous variables for SNAP• SNAP∗ latent index of SNAP participation• X other variables correlated w/ SNAP, HEI• ǫ and υ bivariate normal w/covariance matrix
V =
[
σ2 ρσ
ρσ 1
]
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Methods
Identification & Marginal Effects
• model is theoretically identified by functional form imposed bydistribution of ǫ and υ.
• we use exogenous policy variables to identify SNAPparticipation
• total effects of SNAP :
µi = δZ + ρσ
[
φ(Ziγ + Xiθ)
Φ(Ziγ + Xiθ) ∗ [1− Φ(Ziγ + Xiθ)]
]
(4)
this is what δOLS will estimate
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Methods
Identification & Marginal Effects
• without selection: µi = δOLS ; with selection δZ + difference inexpected value of errors conditional on participation (SeeGreene, 2011)
• unconditional on selection, δZ measures marginal affects ofSNAP on participants
• standard errors (of total effects) (ν) by delta method: letα = [γ, θ]
νµ =
√
∂µ
∂αM
∂µ
∂α
′
, (5)
where M is the covariance matrix of the selection equation
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Results
Descriptive
51.8
47.8
4950
5152
53H
EI S
core
No SNAP SNAP Participants
Data: NHANES, 2003−08
SNAP Participation Status
HEI Score and SNAP Participation
Figure: Differences in HEI over SNAP Participation
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Results
Descriptive
2094
2124.3
2044
2074
2104
2134
To
tal E
ner
gy
Inta
ke
No SNAP SNAP Participants
Data: NHANES, 2003−08
SNAP Participation Status
Total Food Energy and SNAP Participation
Figure: Differences in Energy over SNAP Participation
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
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 6,668
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
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 6,668
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Results
Marginal Effects of SNAP
Table: Marginal Effects of SNAP=δ
HEI TotalFruit WholeFruit TotalVeg DkGOrVeg
δ -1.441 0.270 2.795*** -0.660 -0.735***νδ (4.503) (0.710) (0.209) (0.592) (0.187)
TotGrain WholeGrain Milk MeatBeans Oils
δ -0.039 -0.383 -0.054 -0.248 0.372νδ (0.147) (0.233) (0.590) (0.310) (0.741)
SatFat Sodium SoFAAS
δ -0.193 -0.046 -0.820νδ (0.987) (0.740) (1.423)
N 6,668
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Results
Questions
• δs seem too large to be believed
• δwf = 2.795, x̄ = 1.39
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Results
Distribution of Components
0.1
.2.3
.4.5
Den
sity
0 1 2 3 4 5Score
Data: NHANES 2003−08, 200% FPL Kernel Density WholeFruit Component Score
Figure: Distribution of Whole Fruit Component
• modewf = 0, modewg = 0
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
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 asfunction of SNAP participation
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Results
Solution: Bivariate Probit
Table: Bivariate Probit: Effect of SNAP on Score >0
Whole Fruit
Parameter Marginal Effect
SNAP 0.548** 0.238(0.32)
N 6,668
• effect on SNAP is to increase by 24 percentage points pointsprob of eating any whole fruit
• too large? less than 30% of sample eat any whole fruit orwhole grain
• SNAP increases likelihood that those eating no whole fruit willeat some.
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Results
Correlation, IV Strength
Table: Selection Paramter: ρ
HEI TotalFruit WholeFruit TotalVeg DkGOrVeg
ρ 0.007 -0.112 -0.979*** 0.199 0.216***νρ (0.195) (0.201) (0.084) (0.203) (0.052)
TotGrain WholeGrain Milk MeatBeans Oils
ρ -0.049 0.125 -0.054 -0.033 0.372νρ (0.051) (0.092) (0.096) (0.067) (0.115)
SatFat Sodium SoFAAS
ρ 0.073 0.057 0.050νρ (0.145) (0.136) (0.121)
• All F-tests of instruments < 10. (Reasons and caveats.)
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Results
Expected Differences
Table: Total Effects of SNAP (Current) on HEI/Component Scores
HEI TotalFruit WholeFruit TotalVeg DkGOrVeg
µ -1.249*** -0.186*** -0.678*** 0.023 0.030νµ (0.006) (0.013) (0.105) (0.021) (0.023)
TotGrain WholeGrain Milk MeatBeans Oils
µ -0.157*** -0.031*** -0.376*** -0.431*** -0.123***νµ (0.003) (0.011) (0.010) (0.005) (0.015)
SatFat Sodium SoFAAS
µ 0.344*** 0.325*** -0.163***νµ (0.016) (0.011) (0.020)
N 6,668
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Results
How Big Are These Differences?
• Saturated Fat: SNAP participants 5.5 (12) calories less insaturated fat per day than low-income non-participants (highincome persons).
• Sodium: SNAP participants 75 (160) mg less in sodium thanlow-income non-participants (high income persons).
• statistically significant, economically so?
• 1-ounce bag of potato chips – about 80 mg sodium
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Discussion
Discussion
• SNAP has positive effect on whole fruit and negative effect on darkgreen and orange vegetables of SNAP participants ⇑ inP(Score) > 0 (whole fruit)
• expected differences (after taking SNAP into account) statisticallysignificant, though uncertain in health/economic significance
• adverse selection into SNAP whole fruit models, beneficial selectionin dark green orange vegetable models
• participants in general have slightly less healthy diets compared tosimilar non-participants–SNAP participation alone does not close gapin diet quality.
• suggests caution when thinking about restricting SNAP benefits
– most households consume more in food than they get in SNAPbenefits–changing what can be included not likely to changebehavior
– however, changing foods that can be purchased could change mixof participants–change ameliorative affect on food insecurity
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Discussion
Discussion
• Further Questions
– controlled for endogeneity fully?– distribution of error terms–alternative distributions– how might SNAP improve DQ w/o adversely affectingselection/effectiveness?
– subsidies instead of restrictions? (Wholesome Wave, HealthyIncentives)
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014
Discussion
Further Discussion?
Thank You
Gregory, Ver Ploeg, et al. SNAP and Diet Quality February 13, 2014