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

Contents of Presentation

1. Description of Return-based and Person-based

2. Summary of Issues and Recommendations

3. Evaluations

4. Future R/Ds

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

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)

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)

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.

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

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)

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

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

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

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

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

 

Summary of Issues Other duplicate records

Filer record trumps all! Retain primary filer records and drop

spouse and dependents records

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

Divorce Scenario

Return-Based Person-Based

1 Non-Migrant 1 Non-Migrant

Non-Match 4 Migrants

1 Filer 1 FilerCty A Cty A

1 Spouse 1 Filer3 Deps 3 Deps Cty A Cty B

Year 1 Year 2

1 Deps 1 Filer 1 Non-Match 1 MigrantCty A Cty B

Year 1 Year 2 Return-Based Person-Based

Student Scenario

EvaluationMatch 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)

The 10 Lowest Year1-Year2 Match Rates from Return-Based Records from Years 2000 through 2004 (National Average = 90.5%)

County and State Year Match 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

2001

2000

2001

2001

2001

2001

76.36

77.71

77.78

78.73

81.74

81.97

83.87

83.91

84.62

84.68

The 10 Lowest Year1-Year2 Match Rates from Person-Based Records from Years 2000 through 2004 (National Average = 94%)

County and State Year Match 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

2001

2001

2004

2003

2003

2001

2001

2000

81.97

88.24

87.72

88.53

88.12

87.74

90.06

87.80

91.25

92.79

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%).

Total Number of Exemptions and Duplicate Records: 2001-2004

0

2

4

6

8

10

12

14

16

2001 2002 2003 2004

Du

pli

cate

s (

in M

illi

on

s)

243

244

245

246

247

248

249

250

251

252

253

254

Exem

pti

on

s (

in M

illi

on

s)

Duplicate

Total Exemptions

Matched Y1-Y2 Under-Age-65 Exemptions: Percent of Exemptions Migrating by Exemption Status

(10 Percent Sample)

0

5

10

15

20

25

30

35

Y1 Filer Y1 Spouse Y1 Dependent

Per

cen

t Y2 Filer

Y2 Spouse

Y2 Dependent

Y2 Filer Y2 Spouse Y2 Dependent

Y1 Filer 7.10% (679,220) 19.16% (38,315) 11.10% (17,024)

Y1 Spouse 16.70% (24,397) 4.28% (158,015) 30.01% (1,119)

Y1 Dependent 11.90% (43,663) 30.24% (3,488) 6.91% (404,213)

Migration Base:Person-based vs. Return-based

180,000

185,000

190,000

195,000

200,000

205,000

1999-2000 2000-2001 2001-2002 2002-2003 2003-2004

Tho

usan

ds

Return-Based Person-Based

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

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

11,500

12,000

12,500

13,000

13,500

14,000

1999-2000 2000-2001 2001-2002 2002-2003 2003-2004

Tho

usan

ds

Return-Based Person-Based

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

5.45.6

5.86.06.2

6.46.66.8

7.07.2

1999-2000 2000-2001 2001-2002 2002-2003 2003-2004

Per

cent

Return-based Person-based

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

01020

30405060

708090

White Black AIAN API Hispanic

Per

cent

Return Person

Age Distribution:Person-based vs. Return-based

0

5

10

15

20

25

30

35

40

1-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+

Per

cent

Return Person

0123456789

0 1 2 3 4 5 6 7 8 9Return-based Migration Rate (%)

Per

son

-bas

ed M

igra

tion

rat

e (%

)

Outliers

Outliers

95% Confidence Interval

Migration Rate Outliers Definition

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

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.

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+