Does SNAP Improve Your Health?

An Apple a Day Helps Keep the Doctor Away, but

Does SNAP Improve Your Health?

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

1Economic Research Service, USDA

*contact author [email protected] College 3Pacific Institute for Research and Evaluation

November 14, 2013

Gregory, Deb, Waehrer (ERS) Does SNAP Improve Your Health? November 14, 2013 1 / 23

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?




◮ Empirical Work

◮ food security: Nord and Prell (2011), DePolt et al. (2009), Yen et al.(2008), Shaefer and Gutierrez (2012), Ratcliffe et al. (2011), Cole andFox (2008), Gregory et al. (2013a), Mabli et al. (2013); encouragingbut mixed findings (natural experiments ⇑, binormal errorstructure–meh, cross sectional data–meh)

◮ 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)◮ but: the whole obesity thing

◮ 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 but goesbeyond food hardship

◮ 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 in other

measured outcomes

◮ healthy time◮ Grossman (1972, 2000): principle measure of health is healthy time;

healthy time is both investment in labor market activities and homeproduction, and consumption: sick days bring disutility




◮ where do we look for evidence of SNAP’s effects?◮ healthcare utilization

◮ Meyerhoefer and Pylypchuk (2008) finds increased spending as effectof SNAP (pathway through obesity)

◮ doesn’t control for utilization–ie. services or Rx–or market heterogeneity◮ 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 threshold of H: abovethreshold (healthy state), refrain from I (M); below, increase I ; betterhealth ⇒ ⇓ utilization



Other Research, Our Contribution

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

◮ previous (non-diet-outcome related) research; data/methods limitations

◮ Nicholas (2011) SNAP and Medicare expenditures, diabetics; FEmethods, no accounting for skewed distribution of outcomes (count,expenditure)

◮ Fey-Yensan et al. (2003) convenience sample of elderly persons in CTpublic housing (SAH), descriptive statistics

◮ Gibson (2001) SNAP, SAH, 4 chronic conditions, single wave ofNLSY97

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

◮ parenthetically: Meyerhoefer and Pylypchuk (2008)–and literature onobesity–do not control for state-level unobservables

◮ we use nationally rep. sample of non-elderly adults, methods take intoaccount selection and distribution of outcomes, control for state-levelunobservables



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 significant fraction


Data: MEPS

Data: MEPS 1999-2008

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

◮ rolling panel: 5 interviews over 2 years; demographic, labor market, healthinsurance, health condition, health expenditure and utilization data for allrespondents

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

◮ because we use yearly measures of utilization, we use year’s last recordedSAH response (3rd and 5th interview)

◮ sick days = sum of work days, school days, and days of other activities lostdue to illness, respondent spent at least half of the day in bed

◮ utilization measures are in consolidated yearly data file

◮ models include state, year dummies, gender, race, education, marital status,employment status, participation in medicaid, medicare, whether or not oneis insured, income from government programs, SSI, and family size.


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 1: Treatment Effects Ordered Probit

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 factorseffecting both SNAP and health, Z are instruments: 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 2: Treatment Effects Count Models

◮ 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 and Yang,Autumn 2011)

Treatment Effects: Count Models

To 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/w selectionand the outcome; g is a negative-binomial 1 density


Methods

Methods 2: Treatment Effects Count Models

Treatment Effects: Count Models, con’t

◮ assume that li have a normal distribution

◮ could get joint distribution (Ci , Si |Xi ,Zi) by integrating over the distributionof 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 draws


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


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–public income,medicaid, SSI etc.

◮ consistent with Galama and Kapteyn (2011): persons in better healthdecrease utilization

◮ ρsah < 0 implies SNAP participants have better unobserved health status“before” enrolling

◮ λdays > 0, λobv < 0, λopv < 0


Discussion

Why?

◮ digression, with a point: media coverage of the Oregon Medicaidexperiment: dismissive of the effect of Medicaid on mentalhealth–secondary to measured outcomes like cholesterol,hypertension–and self-assessed health

◮ using NHIS (sampling frame for MEPS), we look at the effect ofsmall changes in income on affective states: feelings of worthlessness,depression, anxiety–even relatively small changes in income make adifference in how people feel; this can account for a lot of what isobserved as improved health


Discussion

Next Steps

◮ robustness checks: poverty status, gender, instruments

◮ use other data (NHANES?, NHIS) to examine mechanisms (doesSNAP promote exercise?)

◮ sobiprobit command

◮ descriptive analysis: SNAP and Medicaid, SNAP and utilization moregenerally.


Discussion

Thanks

Thank you!


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Does SNAP Improve Your Health?