HNCC Presentation 8 May 2014

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: 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



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



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



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?



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



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


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


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


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


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


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)


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

]


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


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


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


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


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


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


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


Results

Questions

• δs seem too large to be believed

• δwf = 2.795, x̄ = 1.39


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


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


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.


Results

Correlation, IV Strength

Table: Selection Paramter: ρ


ρ 0.007 -0.112 -0.979*** 0.199 0.216***νρ (0.195) (0.201) (0.084) (0.203) (0.052)


ρ -0.049 0.125 -0.054 -0.033 0.372νρ (0.051) (0.092) (0.096) (0.067) (0.115)


ρ 0.073 0.057 0.050νρ (0.145) (0.136) (0.121)

• All F-tests of instruments < 10. (Reasons and caveats.)


Results

Expected Differences

Table: Total Effects of SNAP (Current) on HEI/Component Scores


µ -1.249*** -0.186*** -0.678*** 0.023 0.030νµ (0.006) (0.013) (0.105) (0.021) (0.023)


µ -0.157*** -0.031*** -0.376*** -0.431*** -0.123***νµ (0.003) (0.011) (0.010) (0.005) (0.015)


µ 0.344*** 0.325*** -0.163***νµ (0.016) (0.011) (0.020)

N 6,668


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


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


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)


Discussion

Further Discussion?

Thank You

[email protected]


HNCC Presentation 8 May 2014

Economy & Finance

Transcript of HNCC Presentation 8 May 2014