1. Evaluation of Person-based Migration Methodology Presented to FSCPE Meeting Internal Migration Processing Team Local Area Estimates and Migration Processing Branch U.S. Census Bureau September 26, 2006

2. Contents of Presentation 1.Description of Return-based and Person- based 2.Summary of Issues and Recommendations 3.Evaluations 4.Future R/Ds

3.  Internal Revenue Service sends tax extract file to Census Bureau  Drop names and assign unique Person Identification Keys (PIK) derived from SSNs  Run edit process and assign county code to each return based on ZIP+4  Two consecutive years of tax data are matched on primary filer’s PIK Return-Based Method

4.  Compare county codes on matched returns to define migration  Tally exemptions for in-, out-, and non- migration components  Compute Net Internal Migration Rate (NMR) for Under 65 household population: NMR = (In-migrants – Out-migrants) / (Non-migrants + Out-migrants) Return-Based Method (Cont’d)

5.  Match Year-1/Year-2 matched IRS file to PCF to obtain demographic characteristics for primary filers  Demographic characteristics for the spouse and dependents are imputed based on the characteristics of the primary filer  Migration status is assigned based on the migration status of the primary filer  Produce state and county migration data by age, race, sex, and Hispanic origin Return-Based Method (Cont’d)

6. Limitations of Return-Based  Underestimate the moves associated with life-events (e.g., divorce, marriage, first job etc.,)  Demographic characteristics of spouse and dependents are imputed based on the characteristics of the filers  Migration status of spouse and dependents depends on the filer.

7. Person-Based Method  Start with the return-based edited file  Records created for filer, spouse, and all dependents (up to 4); one record per each individual on the tax return  Unduplicate the records by applying selection rules  Assign county code to each record  Matched across two consecutive tax years on PIK

8. Person-Based Method (Cont’d)  Compare county codes on matched returns to define migration  Tally exemptions for in-, out-, and non- migrants  Compute Net Migration Rate (NMR) for Under 65 household population: NMR = (in-migrants – out-migrants) / (non-migrant + out-migrants)

9. Person-Based Method (Cont’d)  Match Year-1/Year-2 matched IRS file to PCF to obtain demographic characteristics for filer, spouse, and dependents (No imputation!!)  Migration status is individually assigned to filer, spouse, and dependents based on the assigned county codes (No imputation!!)  Produce state and county migration data by age, race, sex, and Hispanic origin

10. Issues requiring decision making rules Issue 1. Duplicate Records/Zero Exemptions:  Multiple records are created for one person if the person’s SSN is claimed on more than one tax return, including zero exemption returns  Need to decide which records to keep

11. Zero Exemption  Filed when a dependent child has enough income to report to the IRS  The parent claims separately the dependent on his or her tax return  87 percent of the duplicate records involve zero exemptions returns

12. Issues requiring decision making rules Issue 2. Excess exemptions:  The number of SSNs recorded on a tax return does not match the number of exemptions claimed on the same return  We need to decide whether we create a dummy record for each excess exemption

13. Summary of Issues Zero exemptions  Retain the zero exemption record and drop the dependent record  Addresses on zero exemption returns are likely to be more accurate

14. Summary of Issues Other duplicate records  Filer record trumps all!  Retain primary filer records and drop spouse and dependents records

15. Summary of Issues Excess Exemptions 1.Fewer SSNs than exemptions claimed  Exclude excess exemptions 2. More SSNs than exemptions claimed (i.e., negative excess exemptions)  Include the provided SSNs and ignore negative negative excess exemptions

16. Divorce Scenario Return-Based Person-Based 1 Non-Migrant Non-Match 4 Migrants 1 Filer Cty A 1 Spouse 1 Filer 3 Deps Cty A Cty B Year 1 Year 2

17. 1 Deps 1 Filer 1 Non-Match 1 Migrant Cty ACty B Year 1 Year 2 Return-Based Person-Based Student Scenario

18. Evaluation Match Rates - Definition Year-1/Year-2 Match Rate = (Year-1 and Year-2 Matched Record Count) * 100 / Total Year-1 Record Count PCF Match Rate = (Year1,Year2, and PCF Matched Count) * 100 / (Year1 andYear2 Matched Count)

19. The 10 Lowest Year1-Year2 Match Rates from Return-Based Records from Years 2000 through 2004 (National Average = 90.5%) County and StateYearMatch Rate (%) Loving County, TX Los Alamo County, NM Loving County, TX Santa Fe County, NM Lincoln County, NM Loving County, TX Taos County, NM Bernalillo, County, NM Sandoval County, NM Rio Arriba County, NM 2003 2001 2004 2001 2000 2001 76.36 77.71 77.78 78.73 81.74 81.97 83.87 83.91 84.62 84.68

20. The 10 Lowest Year1-Year2 Match Rates from Person-Based Records from Years 2000 through 2004 (National Average = 94%) County and StateYearMatch Rate (%) Loving County, TX Shannon County, SD Santa Fe County, NM Lincoln County, NM Charlton County, GA Loving County, TX North Slope Borough, AK Taos County, NM San Miguel County, NM Loving County, TX 2004 2001 2004 2003 2001 2000 81.97 88.24 87.72 88.53 88.12 87.74 90.06 87.80 91.25 92.79

21. PCF Match Rates  The match rates from the person-based records were almost the same as the match rates from the return-based records (> 99%).

22. Total Number of Exemptions and Duplicate Records: 2001-2004

23. Matched Y1-Y2 Under-Age-65 Exemptions: Percent of Exemptions Migrating by Exemption Status (10 Percent Sample) Y2 FilerY2 SpouseY2 Dependent Y1 Filer7.10% (679,220)19.16% (38,315)11.10% (17,024) Y1 Spouse16.70% (24,397)4.28% (158,015)30.01% (1,119) Y1 Dependent11.90% (43,663)30.24% (3,488)6.91% (404,213)

24. Migration Base: Person-based vs. Return-based

25. Coverage Analysis by State 1.Coverage patterns are consistent across states and years 2.Person-based coverage was consistently lower than return-based coverage 3.The states with the most extreme coverage rates under return-based processing maintained the same pattern under person-based processing 4.The difference in coverage declined for every state between 2000 and 2004. The highest difference was –5.30 in 2000 and –0.48 in 2004

28. Number of Inter-county Migrants: Person-based vs. Return-based

29. Inter-county Migration Percent: Person-based vs. Return-based

30. Race and Hispanic Origin Distribution: Person-based vs. Return-based

31. Age Distribution: Person-based vs. Return-based

32. Outliers 95% Confidence Interval Migration Rate Outliers Definition

33. Findings from Outlier Analysis  The person-based method had significant effect on the migration flows from the counties with small population to the counties with large population  The new method had the largest impact on individuals in their early 20s

36. Summary of Findings  The person-based method will produce more accurate migration estimates.  The characteristics from the person-based records will be more accurate than the return- based.

37. Future R/Ds 1.Integration of Electronic File to enhance the coverage of child dependent 2.Integration of Medicare data at the micro level to produce the migration data for the 65+