uab 28june 12

Background & Motivation Previous Research Data Methods Results Discussion

SNAP and Diet Quality: A Treatment EffectsApproach

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 theviews of the Economic Research Service or USDA.

1Economic Research Service, USDA*contact author: [email protected]

June 27, 2012Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: [email protected]

SNAP and Diet Quality: A Treatment Effects Approach


Background: Intent of Program

I 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 oftrade by increasing purchasing power ...”

I food security and nutrition declared goals of SNAP

Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: [email protected]



Background: Public Perceptions

I ”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 thesodas, 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

I recent research: SNAP ⇓ food insecurity

I Yen et al. (2008); DePolt et al. (2009); Shaefer and Gutierrez(2012); Nord and Golla (2009); Nord and Prell (2011);Ratcliffe et al. (2011)

I estimates suggest SNAP participation ⇓ food insecurity 33 -40 percent




Background: SNAP & Diet Quality

I recently–a good deal of concern

I many expensive chronic illnesses associated with low-incomepopulations

I public bears sizable fraction of cost

I policy suggestions:

1. restrict foods eligible for SNAP (as in WIC)2. Wholesome Wave Double Coupon3. Healthy Incentives Pilot




Motivation

I large extant literature (detail below)

I some–improved intakes (Devaney and Moffitt, 1991; Wildeet al., 1999)

I some–poorer intakes (Butler and Raymond, 1996; Yen, 2010)

I difficult to identify treatment effects

selection on unobservables

I selection: adverse or beneficial?




Our Contribution

I use individual data (NHANES) matched to state-level dataidentify SNAP selection

I estimate treatment effects by isolating unobservables in SNAPand diet

I show that marginal effect of SNAP is positive and significantfor some HEI components; adverse selection accounts forworse diet outcomes




Preview of Results

I as measured by HEI total and component scores

1. SNAP participants comparable diets2. total effect of SNAP (including selection): slightly lower HEI

scores3. economically significant?4. selection is adverse for many components5. effect of SNAP on marginal participant is positive6. in particular, SNAP gets participants to consume some whole

fruit and whole grains

I results corroborated by nutrient intakes

I robust to specification choice?

I suggest policy caution: tradeoff improving nutritional quality,changing selection into the program




Previous Research

I comprehensive review of literature (Fox et al., 2004)

I wrt intakes, few find significant impact ↑, ↓

I highlight Gleason et al. (2000)–array of outcomes includingHEI–rule out large effects in either direction

I studies that find positive effects: Wilde et al. (1999);Kramer-LeBlanc et al. (1997); Basiotis et al. (1998)

I more recent studies: Cole and Fox (2008); Yen (2010)

I Waehrer and Deb (2012) used latent factor model/IV–SNAPparticipants ↑ caloric sweetened beverages ↓ fruits/vegetables




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

I individual: NHANES 2003-04, 2005-06, 2007-08

I dependent variable: Healthy Eating Index Score (HEI) (day 1), total and

component

total = sum of 12 elementstotal fruit, whole fruit, total veg, dark green and orange veg,total grains, whole grains, milk, meat and beans, oils, sat fat,sodium, SoFAASfor food groups and oils: zero intake = score of zero;meet/exceed dietary recommendation = perfect score; linearinterpellation b/w




Data: NHANES 2003-04, 2005-06, 2007-08 (continued)

I dependent variable: Healthy Eating Index Score (HEI) (day 1), total andcomponent (continued)

I how to score “moderation” components? (i.e. things you should eat less

of)

85th pctile of consumption = score of zero; meet DietaryGuidelines recommendation = score of 8; meet somewhathigher standard, below dietary rec = score of 10; linearinterpellation b/w amounts at 0 and 8, 8 and 10.example: sat fat. – fraction of total energy (2001-2002NHANES data)

85th pctile: 15 % : score of 0DG: less than 10 %: score of 8below 7% : score of 10

weights: milk, meat/beans, oils, sat fat, sodium = 10; totalfruit, whole fruit, total veg, dark green and orange veg, totalgrains, whole grains =5 ; SoFAAS = 20




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

I independent variable of interest: HH SNAP participation

2003, 2005 waves: 2 questions HH SNAP participation: number ofpersons authorized to receive SNAP, whether HH receive SNAP 12mos.2007 wave: HH receive SNAP 12 moswe use whether HH receive SNAP 12 mos 2003, 2005, 2007robustness check: sample person currently receiving SNAP

I 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

I 200% FPL




Data: SNAP Policy Database

I in model (following) we need exogenous variables to identifyparticipation in SNAP

state-month level variation in three policies:expanded categorical eligibility–relaxed asset and/or incomerequirementsbiometric info needed to enroll–usually a fingerprintcertification period–median certification period forhouseholds with earnings calculated from the QC data

I valid: the policies affect SNAP participation but not dietquality/HEI




Selection Model

I one might begin with

HEIi = Xiβ + SNAPiδOLS + εi (1)

I problem: SNAP is endogenous to HEI

I another way to proceed

HEIi = Xiβ + SNAPiδZ + εi (2)

SNAP∗

i = Ziγ + Xiθ + υi (3)

I Z exogenous variables for SNAPI SNAP

∗ latent index of SNAP participationI X other variables correlated w/ SNAP, HEII ε and υ bivariate normal w/covariance matrix

V =

[

σ2 ρσ

ρσ 1

]




Identification & Marginal Effects

I model is theoretically identified by functional form imposed bydistribution of ε and υ.

I we use exogenous policy variables to identify SNAPparticipation

I total effects of SNAP :

µi = δZ + ρσ

[

φ(Ziγ + Xiθ)

Φ(Ziγ + Xiθ) ∗ [1− Φ(Ziγ + Xiθ)]

]

(4)

this is what δOLS will estimate




Identification & Marginal Effects

I without selection: µi = δOLS ; with selection δZ + differencein expected value of errors conditional on participation (SeeGreene, 2011)

I unconditional on selection, δZ measures marginal affects ofSNAP on participants

I 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




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




Descriptive

2094

2124.320

4420

7421

0421

34T

ota

l En

erg

y In

take

No SNAP SNAP Participants

Data: NHANES, 2003−08

SNAP Participation Status

Total Food Energy and SNAP Participation

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 0.040νρ (0.169) (0.223) (0.203) (0.129) (0.301)


ρ -0.059 -1.032*** -0.017 -0.000 0.066νρ (0.048) (0.069) (0.096) (0.084) (0.106)


ρ -0.035 0.003 0.082νρ (0.127) (0.117) (0.169)

I 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 -0.236νδ (1.916) (0.757) (0.624) (0.382) (0.870)


δ 0.041 1.940*** 0.116 -0.338 -0.425νδ (0.133) (0.095) (0.598) (0.392) (0.697)


δ 0.273 0.357 -1.429νδ (0.908) (0.670) (1.916)

N 5,105




Questions

I δs seem too large to be believed

I δwf = 1.98, x̄ = 1.39

I δwg = 1.94, x̄ = .66




Distribution of Components0

.1.2

.3.4

.5D

ensi

ty

0 1 2 3 4 5Score

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

0.5

11.

5D

ensi

ty

0 1 2 3 4 5Score

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

Figure: Distribution of Whole Fruit, Whole Grain Components

I modewf = 0, modewg = 0




Distributional Concerns

I need to address the violation of distributional assumptions

I GMM, 2SLS, larger std errs, size of δZ still a concern

I finite mixture model (latent class model) – probabilities asfunction 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

I effect on SNAP is to increase by 40 percentage points pointsprob of eating any whole fruit or whole grains

I too large? less than 30% of sample eat any whole fruit orwhole grain




Total Effects: Current Recipients

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


µ -2.371*** -0.301*** -0.570*** -0.059*** -0.019νµ (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 -0.277νdelta (0.158) (0.073) (0.614) (0.302) (0.934)


δ 0.108 -0.313 0.203νdelta (0.951) (0.542) (2.326)

N 5,105

I 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

I Results

SNAP participants slightly lower HEI scores than comparablenon-participantstotal effects statistically significant, though not economically sototal effects for current recipients somewhat larger–samedirectionscorroborated by nutrient intake resultshowever: adverse selection into SNAPSNAP has positive effect on whole fruit and whole grainconsumption of SNAP participants ⇑ in P(Score) > 0.but participants in general have slightly less healthy dietscompared to similar non-participants




Discussion

I Further Questions

controlled for endogeneity fully?distribution of error terms–alternative distributionshow might SNAP improve DQ w/o adversely affectingselection/effectiveness?subsidies instead of restrictions? (Wholesome Wave, HealthyIncentives)




Further Discussion?

Thank You



uab 28june 12

Economy & Finance

treatment eects approach

contact author

nutritious diet

treatment eectsselection

snap food insecurityyen

nutritional quality

alisha colemanjensen1

shelly ver ploeg1