1. 1 FDPIR Direct Certification: 1 NAFDPIR June 7, 2016 Traverse City, Michigan

2. 2 TODAY’s AGENDA FDPIR-DC Overview Where are We? The DC Process - Explained Questions 2

3. 3 What is Direct Certification? 3 Direct certification means direct eligibility determination for National School Lunch Program (NSLP) benefits based on Supplemental Nutrition Assistance Program (SNAP), Temporary Assistance for Needy Families (TANF), or Food Distribution Program on Indian Reservations (FDPIR) eligibility (or through several other allowable categorically eligible designations). Eligibility documentation is obtained directly from the appropriate State or local agency or other authorized individual, without need for application or any action by the household.

4. 4 Benefits of Direct Certification? Free Meal Benefits for Eligible Children Application-Free Process for Households Increased Reimbursement for Local Schools Increased CEP Claiming % Improved Integrity

5. 5 Community Eligibility Basics CEP allows schools, groups of schools, or entire school districts with a high percentage of high poverty students to serve healthy breakfasts and lunches to ALL children at no cost for up to four years Eliminates the use of household income applications School year (SY) 2014-2015 was first year of nationwide availability -Eleven pilot States participated in three year phased-in testing period

6. 6 Which Schools can Participate in CEP? Must participate in both the National School Lunch Program (NSLP) and School Breakfast Program (SBP) Eligibility is based on the number of students who would normally be certified for free school meals without an application Mostly obtained from “Direct certification” (SNAP, TANF, FDPIR.) Must represent at least 40% of student population (Identified Student Percentage or ISP) ISP multiplied by 1.6 is the percentage of meals reimbursed at the Federal “free” rate USDA CEP resource center: http://www.fns.usda.gov/school- meals/community-eligibility-provisionhttp://www.fns.usda.gov/school- meals/community-eligibility-provision

7. 7 Why FDPIR? Simply…There are children who participate in FDPIR who can be helped through direct certification FDPIR has readily-available data for matching FDPIR visibility to in-need households Potential Population

8. 8 What this means by the numbers….FDPIR

9. 9 Where Are We?

10. 10 Tribal Consultation May 4, 2016 Over 50 attendees FDPIR-DC Guidance Package

11. 11 FDPIR-DC Guidance Package FDPIR-DC Q&A Agreement to Share and Exchange Data - MOU- FDPIR Direct Certification AIS Participant By Birthdate/Direct Certification AIS User Guidance

12. 12 State of Alaska Progress Story

13. 13 Collaboration in Alaska

14. 14 Preliminary Data Currently: data in test Receive bi-monthly 20% Children

15. 15 Preliminary Data

16. 16 State of Montana Progress Story

17. 17 Montana – FDPIR Direct Certification Data Sharing Agreements Initial communication with FDPIR State Agency staff Gathered helpful information relative to FDPIR operations Planned road trip to visit the 7 FDPIR agencies Contacted each office prior to traveling Follow-up phone calls while in route to confirm meetings Visited with FDPIR agency staff Most agencies requested we meet with their Tribal Council to discuss data sharing agreements Briefed Tribal Councils on the purpose and benefits of utilizing FDPIR data to directly certify meal eligibility as well as potential associated reimbursement & CEP percentages

18. 18 Montana – FDPIR-DC Data Files Matched Crow FDPIR Data (through DC system) 39% Matched to Students 18% Resulted in FDPIR Direct Certifications Matched Rocky Boy-FDPIR Data (manual match) 25% Resulted in FDPIR Direct Certifications Positive Results - Rocky Boy Public School’s CEP reimbursement will increase next school year to 100% free (previously 90% free)

19. 19 The DC Process - Explained

20. 20 MANUAL VS. AUTOMATED Can be labor intensive for FDPIR staff Long lists and false negatives for schools Missed opportunities for children at more distant schools and transient children

21. 21 Student Data FDPIR Data

22. 22 The DC process depends on matching data –attributes of a real-world child – provided in electric form.

23. 23 Match Results Source Data Match Process Use Match Results to directly certify at local level The Direct Certification Process SNAP DC SNAP Participation Data

24. 24 The Match Engine Match Engines employ logic and algorithms to identify that two records are the same even when they don’t appear to be by the naked eye. Utilizing computer matching can identify children effectively, efficiently, and quickly, often finding additional children.

25. 25 Fun With Algorithms Don Miller, Director Oneida Tribe of Wisconsin FDP Green Bay Area Resident Packer Fan Guy-on-the Go Do-it-Yourselfer

26. 26 Fun With Algorithms Capital One Venture Mr. Don Miller 3322 5544 6767 4254 Exp. 12/15/2017

27. 27 Fun With Algorithms Culver’s Restaurant$ 9.34 Home Depot$ 34.67 Green Bay Packers Fan Store$ 65.45 Culver’s Restaurant$ 7.14 Green Bay Bike Shop$ 45.00 Culver’s Restaurant$ 11.00 Chicago Bear’s Season Tickets.com$800.00

28. 28 Matching Process Overview First NameLast NameDate of BirthGuardian First Name Zip Code JamesMason10/03/1996Mary01234 JamesMason10/03/1996Mary01234 Exact Match JamesMason03/10/1996Mary12340 Similar Match Primary Elements Secondary Elements 28

29. 29

30. 30 Fun With Algorithms - Too State Student Information System (SSIS) Robert Littlebear10/10/2006 Doug Sprow04/02/2009 Margaret Williams04/05/2007 Lilly Smith-Rondon 02/10/2005 William Emory09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy06/07/2006 Brady Jo Watkins06/04/2008 Chad Fourust05/05/2005 Sherry O’Leary11/03/2001

31. 31 Manual Match SSIS Robert Littlebear10/10/2006 Doug Sprow04/02/2009 Margaret Williams04/05/2007 Lilly Smith-Rondon 02/10/2005 William Emory09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy06/07/2006 Brady Jo Watkins06/04/2008 Chad Fourust05/05/2005 Sherry O’Leary11/03/2010 FDPIR Participant Report Chad Forrest05/05/2005 Doug Sprow04/02/2009 Peggy Williams05/04/2007 Missy Goodfeather06/05/2008 B.J. Watkins06/04/2008 Billy Emerie09/15/2010 Bobby Littlebear10/10/2006 Sherry Leary11/03/2011 L. Rondon-Smith02/10/2005 Molly Bigboy07/06/2006

32. 32 Manual Match SSIS Robert Littlebear10/10/2006 Doug Sprow04/02/2009 Margaret Williams04/05/2007 Lilly Smith-Rondon 02/10/2005 William Emory09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy06/07/2006 Brady Jo Watkins06/04/2008 Chad Fourust05/05/2005 Sherry O’Leary11/03/2010 FDPIR Participant Report Doug Sprow04/02/2009 Missy Goodfeather06/05/2008

33. 33 Match Engine DOB Tools: Flip Flop Month/Day +/- day tolerance Year of Birth Logic

34. 34 DOB TOOLS SSIS Robert Littlebear10/10/2006 Doug Sprow04/02/2009 Margaret Williams04/05/2007 Lilly Smith-Rondon 02/10/2005 William Emory09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy06/07/2006 Brady Jo Watkins06/04/2008 Chad Fourust05/05/2005 Sherry O’Leary11/03/2010 FDPIR Participant Report Chad Forrest05/05/2005 Doug Sprow04/02/2009 Peggy Williams05/04/2007 Missy Goodfeather06/05/2008 B.J. Watkins06/04/2008 Billy Emerie09/15/2010 Bobby Littlebear10/10/2006 Sherry Leary11/03/2011 L. Rondon-Smith02/10/2005 Molly Bigboy07/06/2006

35. 35 DOB TOOLS SSIS Robert Littlebear10/10/2006 Doug Sprow04/02/2009 Margaret Williams04/05/2007 Lilly Smith-Rondon 02/10/2005 William Emory09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy06/07/2006 Brady Jo Watkins06/04/2008 Chad Fourust05/05/2005 Sherry O’Leary11/03/2010 FDPIR Participant Report Molly Bigboy07/06/2006

36. 36 Match Engine Nickname Matching – Treats two attributes as similar if one is the common nickname of the other. This is done by using a supplemental lookup table containing common nicknames and determining similarity based on entries in the table.

37. 37 NICKNAME MATCHING SSIS Robert Littlebear10/10/2006 Doug Sprow04/02/2009 Margaret Williams04/05/2007 Lilly Smith-Rondon 02/10/2005 William Emory09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy06/07/2006 Brady Jo Watkins06/04/2008 Chad Fourust05/05/2005 Sherry O’Leary11/03/2010 FDPIR Participant Report Chad Forrest05/05/2005 Doug Sprow04/02/2009 Peggy Williams05/04/2007 Missy Goodfeather06/05/2008 B.J. Watkins06/04/2008 Billy Emerie09/15/2010 Bobby Littlebear10/10/2006 Sherry Leary11/03/2011 L. Rondon-Smith02/10/2005 Molly Bigboy07/06/2006

38. 38 NICKNAME MATCHING SSIS Robert Littlebear10/10/2006 Doug Sprow04/02/2009 Margaret Williams04/05/2007 Lilly Smith-Rondon 02/10/2005 William Emory09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy06/07/2006 Brady Jo Watkins06/04/2008 Chad Fourust05/05/2005 Sherry O’Leary11/03/2010 FDPIR Participant Report Peggy Williams05/04/2007 B.J. Watkins06/04/2008 Bobby Littlebear10/10/2006

39. 39 Match Engine Phonetic Algorithm: Assigns a value to each word that represents the way it would sound if spoken. Similar sounding words are represented by the same value.

40. 40 PHONETIC ALGORITHM SSIS Robert Littlebear10/10/2006 Doug Sprow04/02/2009 Margaret Williams04/05/2007 Lilly Smith-Rondon 02/10/2005 William Emory09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy06/07/2006 Brady Jo Watkins06/04/2008 Chad Fourust05/05/2005 Sherry O’Leary11/03/2010 FDPIR Participant Report Chad Forrest05/05/2005 Doug Sprow04/02/2009 Peggy Williams05/04/2007 Missy Goodfeather06/05/2008 B.J. Watkins06/04/2008 Billy Emerie09/15/2010 Bobby Littlebear10/10/2006 Sherry Leary11/03/2011 L. Rondon-Smith02/10/2005 Molly Bigboy07/06/2006

41. 41 PHONETIC ALGORITHM SSIS Robert Littlebear10/10/2006 Doug Sprow04/02/2009 Margaret Williams04/05/2007 Lilly Smith-Rondon 02/10/2005 William Emory09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy06/07/2006 Brady Jo Watkins06/04/2008 Chad Fourust05/05/2005 Sherry O’Leary11/03/2010 FDPIR Participant Report Chad Forrest05/05/2005 Billy Emerie09/15/2010

42. 42 Match Engine String Matching: Treats two data entries as “strings” of letters or numbers and determines the physical similarity between them.

43. 43 STRING MATCHING SSIS Robert Littlebear10/10/2006 Doug Sprow04/02/2009 Margaret Williams04/05/2007 Lilly Smith-Rondon 02/10/2005 William Emory09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy06/07/2006 Brady Jo Watkins06/04/2008 Chad Fourust05/05/2005 Sherry O’Leary11/03/2010 FDPIR Participant Report Chad Forrest05/05/2005 Doug Sprow04/02/2009 Peggy Williams05/04/2007 Missy Goodfeather06/05/2008 B.J. Watkins06/04/2008 Billy Emerie09/15/2010 Bobby Littlebear10/10/2006 Sherry Leary11/03/2011 L. Rondon-Smith02/10/2005 Molly Bigboy07/06/2006

44. 44 STRING MATCHING SSIS Robert Littlebear10/10/2006 Doug Sprow04/02/2009 Margaret Williams04/05/2007 Lilly Smith-Rondon 02/10/2005 William Emory09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy06/07/2006 Brady Jo Watkins06/04/2008 Chad Fourust05/05/2005 Sherry O’Leary11/03/2010 FDPIR Participant Report Sherry Leary11/03/2001 L. Rondon-Smith02/10/2005

45. 45 Automated Match SSIS Robert Littlebear10/10/2006 Doug Sprow04/02/2009 Margaret Williams04/05/2007 Lilly Smith-Rondon 02/10/2005 William Emory09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy06/07/2006 Brady Jo Watkins06/04/2008 Chad Fourust05/05/2005 Sherry O’Leary11/03/2010 FDPIR Participant Report Bobby Littlebear10/10/2006 Doug Sprow04/02/2009 Peggy Williams05/04/2007 L. Rondon-Smith02/10/2005 Billy Emerie09/15/2010 Missy Goodfeather06/05/2008 Molly Bigboy07/06/2006 Chad Forrest05/05/2005 B.J. Watkins06/04/2008 Sherry Leary11/03/2011

46. 46 The FDPIR DC Process – Explained: Recap Reduces labor for FDPIR staff Increases Effectiveness of Match Process Identifies Kids Throughout the State Provides Meal Benefits for Community Families and Assists Local Schools.

47. 47 Questions? 47

48. 48 Contact Info Sarah E. Smith-Holmes Division Director Program Monitoring and Operational Support Child Nutrition Programs USDA - Food and Nutrition Service Phone: 703-305-2063 sarah.smith-holmes@fns.usda.gov Joseph Templin Program Analyst Operational Support Branch Child Nutrition Programs USDA- Food and Nutrition Service (312) 353-6883 Joseph.templin@fns.usda.gov