FoodAPS: Innovations and challenges in surveying the SNAP population

Transcription

1 FoodAPS: Innovations and challenges in surveying the SNAP population Jessica E. Todd Economic Research Service, USDA Presentation to the Council of Professional Associations on Federal Statistics September 16, 2016

2 Outline Overview of FoodAPS Selected SNAP findings Access to Data ERS-funded extramural studies Expansions planned for FoodAPS-2 2

3 FoodAPS: Overview FoodAPS = National Food Acquisition and Purchase Survey Jointly sponsored by the Economic Research Service and the Food and Nutrition Service Designed in consultation with academics and government experts Goal: Expand possibilities for research on food choices, especially research on SNAP participants and comparable nonparticipants 3

4 FoodAPS: Overview Mon Tues Wed Thu Fri Sat Sun Mon Tue Initial Interview (CAPI) Household tracks food acquisitions in booklets and with scanner Final Interview (CAPI) Call to report Call to report Call to report 4 4

5 FoodAPS: Overview Food Acquisitions Food-at-home (FAH) and Food-away-from-home (FAFH) acquisitions for all household members for one week Includes acquisitions of foods obtained for free (school, food pantries, gardens, etc.) Where food was obtained and by whom Expenditures and quantities of items purchased 5 5

6 Food Books 6

7 Food Books 7

8 FoodAPS: Overview Detailed household and individual information Primary food store, reasons for usual food shopping locations, usual mode of transportation Household expenses Participation in other food assistance programs Individual income, body size, allergies, school enrollment and school meals Enriched by extant data Locations of food stores (SNAP-authorized and other) and restaurants Food prices SNAP administrative data (Rosters and transactions) 8

9 Oversampling SNAP participants SNAP/income group Estimated share of population Sample size goal % of sample SNAP participants , nonsnap, <100% PL nonsnap, % PL nonsnap, >=185% PL , , Total ,

10 Oversampling SNAP participants and SNAP/income group comparison groups Estimated share of population Sample size goal % of sample SNAP participants , nonsnap, <100% PL nonsnap, % PL nonsnap, >=185% PL , , Total ,

11 Oversampling SNAP participants SNAP/income group Estimated share of population Sample size goal % of sample SNAP participants , nonsnap, <100% PL nonsnap, % PL nonsnap, >=185% PL , , Total ,

12 Oversampling SNAP participants SNAP/income group Estimated share of population Sample size goal % of sample SNAP participants , nonsnap, <100% PL nonsnap, % PL nonsnap, >=185% PL , , Total ,

13 How to find SNAP and other lowincome households? PSU selection (counties or group of counties) Adjust sampling probabilities (Measure of Size, MOS) using estimated SNAP population in counties, and estimated share of population in other income groups Stratified the list of PSUs by metro status and 7 FNS regions SSUs (Census Block groups) also selected proportional to size using same MOS 13

14 How to find SNAP and other lowincome households? Third stage was selecting addresses SNAP address list obtained from FNS offices was used to sort full address frame into SNAP and nonsnap addresses Received SNAP lists for PSUs in 22 of 27 states 14

15 SNAP participation and income information Asked about SNAP and annual income during an initial screener Asked Primary Respondent about SNAP in initial interview Collected monthly income for each individual age 16+ in final interview Matched households to SNAP administrative rosters and EBT transaction records to confirm participation and nonparticipation 97.5% consented to matching to admin data Imputed income when received don t know or refused responses to income questions 15

16 How did we do? SNAP/income group Sample size goal (%) Actual Sample size (%) SNAP participants 1,500 (30) 1,581 (33) nonsnap, <100% PL 800 (16) 346 (7) nonsnap, % PL 1,200 (24) 851 (18) nonsnap, >=185% PL 1,500 (30) 2,048 (42) Total 5,000 4,826 16

17 What we have learned so far About finding our target (SNAP/income) groups Misclassifications of households at screening (SNAP/income group) Not small and resulted in variation of probabilities of selection (and sampling weights) Increases design effect and reduces effective sample size (varies by outcome) 17

18 What we have learned so far Reported SNAP participation (and matching to SNAP admin data to confirm participation) Reported SNAP No match Match = Participant Match to Admin data Match = nonparticipant Did not Consent to matching Total No 3, ,361 Yes 239 1, ,461 Don t know/refused Total 3,252 1, ,826 18

19 What we have learned so far Reported SNAP participation (and matching to SNAP admin data to confirm participation) Reported SNAP No match Match = Participant Match to Admin data Match = nonparticipant Did not Consent to matching Total No 3, ,361 Yes 239 1, percent 16 agreed to be 26 1,461 Don t 2 2 matched 0 to know/refused admin data 0 4 Total 3,252 1, ,826 19

20 What we have learned so far Reported SNAP participation (and matching to SNAP admin data to confirm participation) Match to Admin data One percent that reported No Match = Match = nonparticipant to matching Did not Consent Reported SNAP SNAP were Total match Participant found to be not No 3, on SNAP 96 3,361 Yes 239 1, ,461 Don t know/refused Total 3,252 1, ,826 20

21 What we have learned so far Reported SNAP participation (and matching to SNAP admin data to confirm participation) Reported SNAP No match Match = Participant Match to Admin data Match = nonparticipant Did not Consent to matching Total No 3, ,361 Yes 239 1,180 4 percent 16 that 26 1,461 reported not on Don t 2 2 SNAP were know/refused found to be on Total 3,252 1,316 SNAP ,826 21

22 What we have learned so far Reported SNAP participation (and matching to SNAP admin data to confirm participation) Match to Admin data No 16.4 Match percent = Match = nonparticipant to matching Did not Consent Reported SNAP match that Total Participant reported SNAP No 3, ,361 participation Yes 239 were 1,180 not ,461 matched to Don t 2 admin 2 data know/refused Total 3,252 1, ,826 22

23 What we have learned so far Reported SNAP participation (and matching to SNAP admin data to confirm participation) Not all households that reported SNAP could be matched to admin data Not all states provided administrative data that could be used for matching Administrative data is variable in format and content Most households reported accurately Matching did improve accuracy 23

24 Selected SNAP comparisons Percent of Households using travel mode to get to primary store SNAP Own Vehicle Someone Else's Car Walk, Bike, Public Transit, Other Non-SNAP PIR<=100% FPL Non-SNAP % FPL Non-SNAP PIR > 185% Nearly all households identify a Supermarket or Supercenter as their primary store. Source: ERS estimates using FoodAPS data. Notes: Weighted means reported. Lighter bars indicate difference from SNAP households is not statistically significant at p<

25 Selected SNAP comparisons The primary store is not usually the store closest to home. Miles Distance to Nearest SS/SM Distance to Primary store 0.0 All Households SNAP Participants WIC Participants Food Insecure Source: ERS estimates using FoodAPS data. Notes: Weighted means reported. 25

26 Data access Public-Use Files to be posted on ERS s website in October Restricted-Use Files available through a webbased secure portal operated by the National Opinion Research Center (NORC) Users must agree to the Confidential Information Protection and Statistical Efficiency Act (CIPSEA) of 2002 Fee of about $4,400/year per project All output is reviewed for risk of disclosure before it can be exported and disseminated 26

27 ERS-funded extramural research University of Kentucky Center for Poverty Research (UKCPR) grants 12 projects began in projects began in National Bureau of Economic Research (NBER) grants 10 projects began in

28 Looking ahead to FoodAPS-2 Refinements planned for Screener to better identify SNAP and household income types Collecting information about food items Questionnaire instruments Expansions planned/desired Over-sample WIC and school meal participants 28

29 Find out More FoodAPS webpage Survey instruments and other documents Public use data (October) Information about restricted-use data and how to access ERS reports and other publications Currently funded external research projects 29

30 Contact Us Mark Denbaly, John Kirlin, Jessica Todd, Elina Page, Liz Larimore 30