Does SNAP Improve Your Health? (SEA Slides)
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Does SNAP Improve Your Health? (SEA Slides)

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Does SNAP Improve Your Health? (SEA Slides) Presentation Transcript

  • 1. An Apple A Day Can Keep the Doctor Away, But Does SNAP Improve Your Health? Christian A. Gregory*1 , Partha Deb2 , Geetha Waehrer3 1 Economic Research Service, USDA College 3 Pacific Institute for Research and Evaluation 2 Hunter 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
  • 2. 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
  • 3. Background and Introduction Background and Motivation • 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 and Fox (2008), Gregory et al. (2013a), Mabli et al. (2013); encouraging but mixed findings (natural experiments ⇑, binormal error 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)) Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 4. Background and Introduction Background and Motivation • What are possible other avenues for SNAP’s effect? – – – by relaxing income constraint, SNAP makes resources and time available for activities that are conducive to well-being but not necessarily related to diet relaxation of budget constraint relieves stress that includes but goes 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 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 home production, and consumption: sick days bring Gregory, Deb, Waehrer SNAP Health November 25, 2013 disutility –
  • 5. Background and Introduction Background and Motivation • where do we look for evidence of SNAP’s effects? – healthcare utilization • Meyerhoefer and Pylypchuk (2008) finds increased spending as effect of 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: above threshold (healthy state), refrain from I (M); below, increase I ; better health ⇒ ⇓ utilization Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 6. Background and Introduction 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; FE methods, no accounting for skewed distribution of outcomes (count, expenditure) – Fey-Yensan et al. (2003) convenience sample of elderly persons in CT public housing (SAH), descriptive statistics – Gibson (2001) SNAP, SAH, 4 chronic conditions, single wave 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, methods take into account selection and distribution of outcomes • Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 7. Background and Introduction 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 Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 8. Data: MEPS Data: MEPS 1999-2008 • • • • • • data from 10 years (1999-2008) of MEPS rolling panel: 5 interviews over 2 years; demographic, labor market, health insurance, health condition, health expenditure and utilization data for all respondents 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 recorded SAH response (3rd and 5th interview) sick days = sum of work days, school days, and days of other activities lost due to illness, respondent spent at least half of the day in bed utilization measures are in consolidated yearly data file Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 9. Data: MEPS Data: MEPS 1999-2008 SNAP 1999−2008 .06 Participation Rate .07 .08 .09 .1 Data: MEPS, FNS 1998 2000 2002 2004 2006 2008 Year FNS Gregory, Deb, Waehrer SNAP Health MEPS November 25, 2013
  • 10. Methods Methods Treatment Effects Ordered Probit Si∗ = Xi βS + Zi δ + εi Hi∗ (1) = Xi βH + Si ζ + υi . • S ∗ and H ∗ latent variables, utility of SNAP, underlying health, X are factors effecting 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. Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 11. 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 and Yang, Autumn 2011) Treatment Effects: Count Models To fix ideas, let: Si∗ E (Ci |Xi , Si , li ) = Xi βS + Zi δ + li λ + ǫi = g (Xi βC + Si ζ + li λ). (2) • S ∗ , S, X , Z , β, δ, and ζ are defined as above. • Ci count outcome, li latent characteristic underlies correlation b/w selection and the outcome; g is a negative-binomial 1 density Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 12. 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 the distribution of li : Pr (Ci , Si |Xi , Zi ) = {f (Xi βC +Si ζ+li λ)×Φ(Xi βS +Zi δ+li λ)φ(li )dli }. (3) • no closed form solution; really hard • we use MSM: N lnℓ(Ci , Si |Xi , Zi ) ≈ ln[ i =1 1 S S {f (Xi βC +Si ζ+˜is λ)×Φ(Xi βS +Zi δ+˜is λ)}]. l l i =1 (4) • 400 Halton sequence draws–efficiency properties compared to pseudo random draws Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 13. Methods Methods, cont’d • instruments: simplified reporting of earners in SNAP; others have worked well (more below) Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 14. Results Summary Statistics Non-SNAP 0.45 (0.00) Black 0.17 (0.00) Hispanic 0.25 (0.00) Other Race 0.05 (0.00) Age 39.10 (0.12) Married 0.39 (0.00) HSGrad 0.54 (0.00) College Grad 0.07 (0.00) Grad Deg 0.08 (0.00) Unemployed in Last 12 Months 0.51 (0.00) Medicaid in Last 12 Months 0.22 (0.00) Uninsured All Year 0.39 (0.00) Public Insurance 0.02 (0.00) Number of Health Conditions 3.25 (0.03) N 33423 Gregory, Deb, Waehrer SNAP Health Female SNAP 0.34 (0.01) 0.29 (0.01) 0.20 (0.00) 0.05 (0.00) 37.66 (0.14) 0.31 (0.01) 0.55 (0.01) 0.02 (0.00) 0.04 (0.00) 0.68 (0.01) 0.63 (0.01) 0.26 (0.01) 0.02 (0.00) 4.52 (0.05) November 25, 2013
  • 15. Results Summary Statistics cont’d Wage Income ($) Unemployent Income Other Program Income SSI Income ($) Family Size Excellent Health Very Good Health Good Health Fair Health Poor Health Total Sick Days Office Based Visits Outpatient Visits N Gregory, Deb, Waehrer Non-SNAP 6199.11 (59.86) 88.29 (5.67) 21.18 (2.14) 362.25 (13.03) 2.86 (0.01) 0.19 (0.00) 0.27 (0.00) 0.32 (0.00) 0.15 (0.00) 0.06 (0.00) 9.80 (0.29) 4.64 (0.10) 0.46 (0.03) 33423 SNAP Health SNAP 4261.07 (69.83) 123.33 (8.40) 490.94 (15.88) 1016.25 (29.49) 3.44 (0.02) 0.13 (0.00) 0.20 (0.00) 0.33 (0.01) 0.22 (0.00) 0.12 (0.00) 17.90 (0.54) 6.73 (0.17) 0.89 (0.07) November 25, 2013
  • 16. Results SAH Results Table : Marginal Effects of SNAP on SAH, 130% FPL Parameter (se) : -.446*** (.08) Excellent Very Good Good Fair Poor 0.11 .04 -.04 -.06 -.05 N 33423 Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 17. Results Count Outcomes Predicted Difference in Sick Days SNAP−Non−SNAP 0 .1 Density .2 .3 .4 .5 Data: Non−Elderly Adults < 130 % FPL, MEPS −15 −10 −5 Predicted Difference in Sick Days 0 Median Difference = −1.54 Figure : Distribution of Marginal Effects: Sick Days Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 18. Results Count Outcomes Predicted Difference in Office Visits SNAP−Non−SNAP 0 .1 Density .2 .3 .4 .5 Data: Non−Elderly Adults < 130 % FPL, MEPS −10 −8 −6 −4 −2 Predicted Difference in Office Visits 0 Median Difference = −1.62 Figure : Distribution of Marginal Effects: Office-Based Visits Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 19. Results Count Outcomes Predicted Difference in Outpatient Visits SNAP−Non−SNAP 0 2 4 Density 6 8 10 Data: Non−Elderly Adults < 130 % FPL, MEPS −.3 −.2 −.1 Predicted Difference in Outpatient Visits 0 Median Difference = −.08 Figure : Distribution of Marginal Effects: Outpatient Visits Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 20. Results Median Effects • η.5,days = −1.54 • η.5,obv = −1.62 η.5,opv = −.08 ˆ • p-values on β < .001 • Table : Ancillary Parameters ρ χ2 IV SAH -0.337*** (0.05 ) 9.563*** (.008) Gregory, Deb, Waehrer λ Sick Days 2.113*** (0.021) 7.276** (.026) OB Visits -1.310*** (0.020) 12.202*** (.002) SNAP Health OP Visits 0.324*** (0.107) 10.070*** (.007) November 25, 2013
  • 21. 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 health decrease utilization • ρsah < 0 implies SNAP participants have better unobserved health status “before” enrolling • λdays > 0, λobv < 0, λopv < 0 Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 22. Discussion Why? • as with Medicaid Oregon experiment–subjective states are affected by changes in income • how much is enough? • using NHIS (sampling frame for MEPS), we look at the effect of small changes in income on affective states: feelings of worthlessness, depression, anxiety–even relatively small changes (< $500/yr ) in income make a difference in how people feel; this can account for a lot of what is observed as improved health Gregory, Deb, Waehrer SNAP Health November 25, 2013
  • 23. Discussion Next Steps • robustness checks: poverty status, gender, instruments • interactions between SNAP and Medicaid • latent factor in SAH specification–flexible specifications in SAH • what can we learn by linking to NHIS and using panel component Gregory, Deb, Waehrer SNAP Health November 25, 2013