Does SNAP Improve Your Health? (SEA Slides)

An Apple A Day Can Keep the Doctor Away, But

Does SNAP Improve Your Health?

Christian A. Gregory*1, Partha Deb2, Geetha Waehrer3

1Economic Research Service, USDA2Hunter College

3Pacific Institute for Research and Evaluation

The analysis and views expressed are the authors’ and do not represent the

views of the Economic Research Service or USDA.

Southern Economic Association

Tampa, Florida November 25, 2013

Background and Introduction

Background and Motivation

• SNAP largest food assistance program of USDA

• 2012: $80 billion, 48 million participants

• participation has doubled since 2007

• policy concerns:

– does it reduce food security?– does it support healthy diets?

Gregory, Deb, Waehrer SNAP Health November 25, 2013



• Empirical Work

– food security: Nord and Prell (2011), DePolt et al. (2009), Yenet al. (2008), Shaefer and Gutierrez (2012), Ratcliffe et al. (2011),Cole and Fox (2008), Gregory et al. (2013a), Mabli et al. (2013);encouraging but mixed findings (natural experiments ⇑, binormalerror structure ⇑,⇓ cross sectional data ⇑,⇓)

– nutrition: Fox et al. (2004), Yen (2010), Waehrer and Deb(2012),Gregory et al. (2013b)

• Why would SNAP have any effect on health?

– ⇓ food insecurity = ⇑ health

• but Bhattacharya et al. (2004)• obesity ?

– SNAP as income transfer: ⇑ income ⇑ health (Deaton and Paxson(2001))




• What are possible other avenues for SNAP’s effect?

– by relaxing income constraint, SNAP makes resources and timeavailable for activities that are conducive to well-being but notnecessarily related to diet

– relaxation of budget constraint relieves stress that includes butgoes beyond food hardship

– as in Oregon Medicaid experiment–increase in income ⇒improvement in self-regard, feelings of well-being

• where do we look for evidence of SNAP’s effects?

– self assessed health (SAH)• has strong objective validity• contains “private” information about well-being not captured inother measured outcomes

– healthy time• Grossman (1972, 2000): principle measure of health is healthytime; healthy time is both investment in labor market activitiesand home production, and consumption: sick days bringdisutilityGregory, Deb, Waehrer SNAP Health November 25, 2013



• where do we look for evidence of SNAP’s effects?

– healthcare utilization

• Meyerhoefer and Pylypchuk (2008) finds increasedspending as effect of SNAP (pathway through obesity)

• doesn’t control for utilization–ie. services or Rx–or marketheterogeneity

• Grossman (1972, 2000) Ht = Ht−1(1− δ) + It−1:instantaneous recalibration of health capital through It−1

⇒ ⇑ It−1(Mt−1) ⇑ Ht ; higher utilization = better health• Galama and Kapteyn (2011) consumers have a thresholdof H: above threshold (healthy state), refrain from I (M);below, increase I ; better health ⇒ ⇓ utilization



Other Research, Our Contribution

• 3 issues: selection, distribution of outcomes, sample (data)

• previous (non-diet-outcome related) research; data/methodslimitations

– Nicholas (2011) SNAP and Medicare expenditures,diabetics; FE methods, no accounting for skeweddistribution of outcomes (count, expenditure)

– Fey-Yensan et al. (2003) convenience sample of elderlypersons in CT public housing (SAH), descriptive statistics

– Gibson (2001) SNAP, SAH, 4 chronic conditions, singlewave of NLSY97

– Yen et al. (2012) participants in TN welfare program; SAH,full switching model, copula approach⇓ Pr(Excellent,VeryGood) health

• we use nationally rep. sample of non-elderly adults, methodstake into account selection and distribution of outcomes



Preview of Results

• SNAP improves SAH ⇑ Pr(Excellent,VeryGood) health,⇓ Pr(Good ,Fair ,Poor) health

• SNAP reduces sick days– between 1 and 2 a year

• SNAP reduces office based visits–between 1 and 2 a year

• SNAP reduces outpatient visits – a statistically significantfraction


Data: MEPS

Data: MEPS 1999-2008

• data from 10 years (1999-2008) of MEPS

• rolling panel: 5 interviews over 2 years; demographic, labormarket, health insurance, health condition, health expenditureand utilization data for all respondents

• frequency of info differs: health insurance (monthly), BMI(yearly), SNAP (yearly), ability status (at interview), priorityconditions (at interviews), SAH (at interviews), expenditure(yearly), utilization (yearly)

• because we use yearly measures of utilization, we use year’slast recorded SAH response (3rd and 5th interview)

• sick days = sum of work days, school days, and days of otheractivities lost due to illness, respondent spent at least half ofthe day in bed

• utilization measures are in consolidated yearly data file


Data: MEPS

Data: MEPS 1999-2008

.06

.07

.08

.09

.1 P

artic

ipat

ion

Rat

e

1998 2000 2002 2004 2006 2008Year

FNS MEPS

Data: MEPS, FNS

SNAP 1999−2008


Methods

Methods

Treatment Effects Ordered Probit

S∗

i = XiβS + Ziδ + εi (1)

H∗

i = XiβH + Siζ + υi .

• S∗ and H∗ latent variables, utility of SNAP, underlying health,X are factors effecting both SNAP and health, Z areinstruments: simplified reporting, β, ζ parameters

• S is binary, H ∈ (1, 2, 3, 4, 5)

• ε and υ ∼ Φ2, model estimates ρ, correlation of unobservables

• parameters estimated by maximum likelihood

• Greene and Hensher (2010): semi-ordered bivariate probit.


Methods

Methods, cont’d

• joint normal distribution of errors for count models not available

• use latent factor structure, developed in treatment effects literature(Heckman and Vytlacil, 2005; Aakvik et al., 2005; Meyerhoefer andYang, Autumn 2011)

Treatment Effects: Count ModelsTo fix ideas, let:

S∗

i = XiβS + Ziδ + liλ+ ǫi (2)

E (Ci |Xi , Si , li) = g(XiβC + Siζ + liλ).

• S∗, S ,X ,Z , β, δ, and ζ are defined as above.

• Ci count outcome, li latent characteristic underlies correlation b/wselection and the outcome; g is a negative-binomial 1 density


Methods

Methods, cont’d

Treatment Effects: Count Models

• assume that li have a normal distribution

• could get joint distribution (Ci , Si |Xi ,Zi) by integrating over thedistribution of li :

Pr(Ci , Si |Xi ,Zi) =

∫{f (XiβC+Siζ+liλ)×Φ(XiβS+Ziδ+liλ)φ(li )dli}.

(3)

• no closed form solution; really hard

• we use MSM:

lnℓ(Ci , Si |Xi ,Zi ) ≈

N∑i=1

ln[1

S

S∑i=1

{f (XiβC+Siζ+l̃isλ)×Φ(XiβS+Ziδ+l̃isλ)}].

(4)

• 400 Halton sequence draws–efficiency properties compared to pseudorandom drawsGregory, Deb, Waehrer SNAP Health November 25, 2013

Methods

Methods, cont’d

• instruments: simplified reporting of earners in SNAP; othershave worked well (more below)


Results

Summary Statistics

Non-SNAP SNAPFemale 0.45 0.34

(0.00) (0.01)Black 0.17 0.29

(0.00) (0.01)Hispanic 0.25 0.20

(0.00) (0.00)Other Race 0.05 0.05

(0.00) (0.00)Age 39.10 37.66

(0.12) (0.14)Married 0.39 0.31

(0.00) (0.01)HSGrad 0.54 0.55

(0.00) (0.01)College Grad 0.07 0.02

(0.00) (0.00)Grad Deg 0.08 0.04

(0.00) (0.00)Unemployed in Last 12 Months 0.51 0.68

(0.00) (0.01)Medicaid in Last 12 Months 0.22 0.63

(0.00) (0.01)Uninsured All Year 0.39 0.26

(0.00) (0.01)Public Insurance 0.02 0.02

(0.00) (0.00)Number of Health Conditions 3.25 4.52

(0.03) (0.05)N 33423


Results

Summary Statistics cont’d

Non-SNAP SNAPWage Income ($) 6199.11 4261.07

(59.86) (69.83)Unemployent Income 88.29 123.33

(5.67) (8.40)Other Program Income 21.18 490.94

(2.14) (15.88)SSI Income ($) 362.25 1016.25

(13.03) (29.49)Family Size 2.86 3.44

(0.01) (0.02)Excellent Health 0.19 0.13

(0.00) (0.00)Very Good Health 0.27 0.20

(0.00) (0.00)Good Health 0.32 0.33

(0.00) (0.01)Fair Health 0.15 0.22

(0.00) (0.00)Poor Health 0.06 0.12

(0.00) (0.00)Total Sick Days 9.80 17.90

(0.29) (0.54)Office Based Visits 4.64 6.73

(0.10) (0.17)Outpatient Visits 0.46 0.89

(0.03) (0.07)N 33423


Results

SAH Results

Table : Marginal Effects of SNAP on SAH, 130% FPL

Parameter (se) : -.446*** (.08)Excellent Very Good Good Fair Poor0.11 .04 -.04 -.06 -.05N 33423


Results

Count Outcomes

0.1

.2.3

.4.5

Den

sity

−15 −10 −5 0Predicted Difference in Sick Days

Data: Non−Elderly Adults < 130 % FPL, MEPS

Median Difference = −1.54

Predicted Difference in Sick Days SNAP−Non−SNAP

Figure : Distribution of Marginal Effects: Sick Days


Results

Count Outcomes

0.1

.2.3

.4.5

Den

sity

−10 −8 −6 −4 −2 0Predicted Difference in Office Visits


Median Difference = −1.62

Predicted Difference in Office Visits SNAP−Non−SNAP

Figure : Distribution of Marginal Effects: Office-Based Visits


Results

Count Outcomes

02

46

810

Den

sity

−.3 −.2 −.1 0Predicted Difference in Outpatient Visits


Median Difference = −.08

Predicted Difference in Outpatient Visits SNAP−Non−SNAP

Figure : Distribution of Marginal Effects: Outpatient Visits


Results

Median Effects

• η.5,days = −1.54

• η.5,obv = −1.62

• η.5,opv = −.08

• p-values on β̂ < .001

Table : Ancillary Parameters

SAH Sick Days OB Visits OP Visits

ρ -0.337*** λ 2.113*** -1.310*** 0.324***(0.05 ) (0.021) (0.020) (0.107)

χ2IV 9.563*** 7.276** 12.202*** 10.070***

(.008) (.026) (.002) (.007)


Discussion

Discussion

• find that SNAP has unequivocally positive effect on SAH

• find that SNAP increases “healthy time,” reduces utilization

• might argue that ⇓ utilization index of material hardship

• but all other measures of program participation are positive–publicincome, medicaid, SSI etc.

• consistent with Galama and Kapteyn (2011): persons in betterhealth decrease utilization

• ρsah < 0 implies SNAP participants have better unobserved healthstatus “before” enrolling

• λdays > 0, λobv < 0, λopv < 0


Discussion

Why?

• as with Medicaid Oregon experiment–subjective states areaffected by changes in income

• how much is enough?

• using NHIS (sampling frame for MEPS), we look at the effectof small changes in income on affective states: feelings ofworthlessness, depression, anxiety–even relatively smallchanges (< $500/yr) in income make a difference in howpeople feel; this can account for a lot of what is observed asimproved health


Discussion

Next Steps

• robustness checks: poverty status, gender, instruments

• interactions between SNAP and Medicaid

• latent factor in SAH specification–flexible specifications inSAH

• what can we learn by linking to NHIS and using panelcomponent


Technology

Does SNAP Improve Your Health? (SEA Slides)