Uab 28june 12
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Uab 28june 12 Uab 28june 12 Presentation Transcript

  • Background & Motivation Previous Research Data Methods Results Discussion SNAP and Diet Quality: A Treatment Effects Approach 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 the views of the Economic Research Service or USDA. 1 Economic Research Service, USDA *contact author: cgregory@ers.usda.govGregory, Ver Ploeg, Andrews, Coleman-Jensen June 27, 2012 Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionBackground: Intent of Program SNAP authorizing legislation: ”To alleviate such hunger and malnutrition, a supplemental nutrition assistance program is herein authorized which will permit low-income households to obtain a more nutritious diet through normal channels of trade by increasing purchasing power ...” food security and nutrition declared goals of SNAPGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionBackground: Public Perceptions ”As I look at what this card is paying for in the orders being scanned at the register, I see T-bone steaks, thick-cut sirloins, thick-cut pork chops (all expensive cuts of meat). I see crab legs, bags of shrimp, and box after box of pastries, cakes and doughnuts 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 PostGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionBackground: 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) estimates suggest SNAP participation ⇓ food insecurity 33 - 40 percentGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionBackground: SNAP & Diet Quality recently–a good deal of concern many expensive chronic illnesses associated with low-income populations public bears sizable fraction of cost policy suggestions: 1. restrict foods eligible for SNAP (as in WIC) 2. Wholesome Wave Double Coupon 3. Healthy Incentives PilotGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionMotivation large extant literature (detail below) some–improved intakes (Devaney and Moffitt, 1991; Wilde et al., 1999) some–poorer intakes (Butler and Raymond, 1996; Yen, 2010) difficult to identify treatment effects selection on unobservables selection: adverse or beneficial?Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionOur Contribution use individual data (NHANES) matched to state-level data identify SNAP selection estimate treatment effects by isolating unobservables in SNAP and diet show that marginal effect of SNAP is positive and significant for some HEI components; adverse selection accounts for worse diet outcomesGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionPreview of Results as measured by HEI total and component scores 1. SNAP participants comparable diets 2. total effect of SNAP (including selection): slightly lower HEI scores 3. economically significant? 4. selection is adverse for many components 5. effect of SNAP on marginal participant is positive 6. in particular, SNAP gets participants to consume some whole fruit and whole grains results corroborated by nutrient intakes robust to specification choice? suggest policy caution: tradeoff improving nutritional quality, changing selection into the programGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionPrevious Research comprehensive review of literature (Fox et al., 2004) wrt intakes, few find significant impact ↑, ↓ highlight Gleason et al. (2000)–array of outcomes including HEI–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–SNAP participants ↑ caloric sweetened beverages ↓ fruits/vegetablesGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionData: NHANES 2003-04, 2005-06, 2007-08 individual: NHANES 2003-04, 2005-06, 2007-08 dependent variable: Healthy Eating Index Score (HEI) (day 1), total and component 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/exceed dietary recommendation = perfect score; linear interpellation b/wGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionData: NHANES 2003-04, 2005-06, 2007-08 (continued) dependent variable: Healthy Eating Index Score (HEI) (day 1), total and component (continued) how to score “moderation” components? (i.e. things you should eat less of) 85th pctile of consumption = score of zero; meet Dietary Guidelines recommendation = score of 8; meet somewhat higher standard, below dietary 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, whole fruit, total veg, dark green and orange veg, total grains, whole grains =5 ; SoFAAS = 20Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionData: NHANES 2003-04, 2005-06, 2007-08 independent variable of interest: HH SNAP participation 2003, 2005 waves: 2 questions HH SNAP participation: number of persons authorized to receive SNAP, whether HH receive SNAP 12 mos. 2007 wave: HH receive SNAP 12 mos we use whether HH receive SNAP 12 mos 2003, 2005, 2007 robustness check: sample person currently receiving SNAP other rhs variables: race/ethnicity, income, education, SR weight 1 year ago, age, marital status, employment status, vigorous ex./week, nutrition ed per poor person, hh size, state fixed-effects 200% FPLGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionData: SNAP Policy Database in model (following) we need exogenous variables to identify participation in SNAP state-month level variation in three policies: expanded categorical eligibility–relaxed asset and/or income requirements biometric info needed to enroll–usually a fingerprint certification period–median certification period for households with earnings calculated from the QC data valid: the policies affect SNAP participation but not diet quality/HEIGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionSelection Model one might begin with HEIi = Xi β + SNAPi δOLS + i (1) problem: SNAP is endogenous to HEI another way to proceed HEIi = Xi β + SNAPi δZ + i (2) SNAPi∗ = Zi γ + Xi θ + υi (3) Z exogenous variables for SNAP SNAP ∗ latent index of SNAP participation X other variables correlated w/ SNAP, HEI and υ bivariate normal w/covariance matrix σ 2 ρσ V = ρσ 1Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionIdentification & Marginal Effects model is theoretically identified by functional form imposed by distribution of and υ. we use exogenous policy variables to identify SNAP participation total effects of SNAP : φ(Zi γ + Xi θ) µi = δZ + ρσ (4) Φ(Zi γ + Xi θ) ∗ [1 − Φ(Zi γ + Xi θ)] this is what δOLS will estimateGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionIdentification & Marginal Effects without selection: µi = δOLS ; with selection δZ + difference in expected value of errors conditional on participation (See Greene, 2011) unconditional on selection, δZ measures marginal affects of SNAP on participants standard errors (of total effects) (ν) by delta method: let α = [γ, θ] ∂µ ∂µ νµ = M , (5) ∂α ∂α where M is the covariance matrix of the selection equationGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionDescriptive HEI Score and SNAP Participation Data: NHANES, 2003−08 53 51.8 52 51 HEI Score 49 50 47.8 No SNAP SNAP Participants SNAP Participation Status Figure: Differences in HEI over SNAP ParticipationGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionDescriptive Total Food Energy and SNAP Participation 2044 2074 2104 2134 Data: NHANES, 2003−08 2124.3 2094 Total Energy Intake No SNAP SNAP Participants SNAP Participation Status Figure: Differences in Energy over SNAP ParticipationGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionDescriptive 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 5,105Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionDescriptive 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 5,105Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionTotal 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 5,105Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionCorrelation, IV Strength Table: Selection Paramter: ρ HEI TotalFruit WholeFruit TotalVeg DkGOrVeg ρ 0.082 -0.107 -0.648*** 0.071 0.040 νρ (0.169) (0.223) (0.203) (0.129) (0.301) TotGrain WholeGrain Milk MeatBeans Oils ρ -0.059 -1.032*** -0.017 -0.000 0.066 νρ (0.048) (0.069) (0.096) (0.084) (0.106) SatFat Sodium SoFAAS ρ -0.035 0.003 0.082 νρ (0.127) (0.117) (0.169) All F-tests of instruments > 15.Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionMarginal Effects of SNAP Table: Marginal Effects of SNAP=δ HEI TotalFruit WholeFruit TotalVeg DkGOrVeg δ -1.429 0.270 1.981*** -0.301 -0.236 νδ (1.916) (0.757) (0.624) (0.382) (0.870) TotGrain WholeGrain Milk MeatBeans Oils δ 0.041 1.940*** 0.116 -0.338 -0.425 νδ (0.133) (0.095) (0.598) (0.392) (0.697) SatFat Sodium SoFAAS δ 0.273 0.357 -1.429 νδ (0.908) (0.670) (1.916) N 5,105Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionQuestions δs seem too large to be believed δwf = 1.98, x = 1.39 ¯ δwg = 1.94, x = .66 ¯Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionDistribution of Components Kernel Density WholeFruit Component Score Kernel Density WholeGrain Component Score Data: NHANES 2003−08, 200% FPL Data: NHANES 2003−08, 200% FPL 1.5 .5 .4 1 .3 Density Density .2 .5 .1 0 0 0 1 2 3 4 5 0 1 2 3 4 5 Score Score Figure: Distribution of Whole Fruit, Whole Grain Components modewf = 0, modewg = 0Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionDistributional 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 as function of SNAP participation (in process)Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionSolution: 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 effect on SNAP is to increase by 40 percentage points points prob of eating any whole fruit or whole grains too large? less than 30% of sample eat any whole fruit or whole grainGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionTotal Effects: Current Recipients Table: Total Effects of SNAP (Current) on HEI/Component Scores HEI TotalFruit WholeFruit TotalVeg DkGOrVeg µ -2.371*** -0.301*** -0.570*** -0.059*** -0.019 νµ (0.601) (0.093) (0.137) (0.013) (0.017) TotGrain WholeGrain Milk MeatBeans Oils µ -0.089*** -0.357*** 0.0570*** -0.352*** -0.076*** νµ (0.007) (0.102) (0.004) (0.019) (0.005) SatFat Sodium SoFAAS µ 0.179*** 0.337*** -0.712*** νµ (0.007) (0.028) (0.139) N 5,105Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionMarginal Effects: Current Recipients Table: Marginal Effect of SNAP (Current) = δ HEI TotalFruit WholeFruit TotalVeg DkGOrVeg δ 5.245 0.897 2.981*** -0.690 -0.674*** νdelta (11.316) (1.102) (0.200) (0.514) (0.180) TotGrain WholeGrain Milk MeatBeans Oils δ 0.053 1.984*** 0.554 -0.264 -0.277 νdelta (0.158) (0.073) (0.614) (0.302) (0.934) SatFat Sodium SoFAAS δ 0.108 -0.313 0.203 νdelta (0.951) (0.542) (2.326) N 5,105 similar marginal effects of SNAP on score > 0.Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionRobustness: 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 5,105Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionDiscussion Results SNAP participants slightly lower HEI scores than comparable non-participants total effects statistically significant, though not economically so total effects for current recipients somewhat larger–same directions corroborated by nutrient intake results however: adverse selection into SNAP SNAP has positive effect on whole fruit and whole grain consumption of SNAP participants ⇑ in P(Score) > 0. but participants in general have slightly less healthy diets compared to similar non-participantsGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionDiscussion Further Questions controlled for endogeneity fully? distribution of error terms–alternative distributions how might SNAP improve DQ w/o adversely affecting selection/effectiveness? subsidies instead of restrictions? (Wholesome Wave, Healthy Incentives)Gregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach
  • Background & Motivation Previous Research Data Methods Results DiscussionFurther Discussion? Thank YouGregory, Ver Ploeg, Andrews, Coleman-Jensen Economic Research Service, USDA *contact author: cgregory@ers.usda.govSNAP and Diet Quality: A Treatment Effects Approach