# Presentation: 266465

## Improving Internal Migration Estimates:   Update

- Esther R. Miller, Hyo Park, and Barbara van der Vate
- Population Division
- Census Bureau
- Presentation to Spring FSCPE Meeting
- Philadelphia, PA   March 28,  2005

## Overview of Presentation

- Enhancements for July 1, 2004 Estimates
- Migration Update System for Estimates (MUSE)
- Planned Enhancements for July 1, 2005 Estimates
- Planned Enhancements for July 1, 2006 Estimates
- Planned Enhancements for post July 1, 2006 Estimates

## Enhancements for July 1, 2004 Estimates

- Increased consistency and efficiency by consolidating two IRS 1040 sub-processing systems
- State and County Totals
- State and County Characteristics

## Production Enhancement 

- Used identical selection criteria to prepare base for internal migration files
- 	-	Removed all foreign returns
- 	-	In-migrants will equal Out-migrants at the 	national level
- Removed Y2 only 1040NR (non-resident) returns
- Removed Y1-Y2 matched returns that did not match to the Person Characteristics File (PCF)
- -	Represents 0.4 percent of all matched returns

**Notes:**
BULLET #2	 Treats Y2 only 1040NR returns as foreign returns

Explain the PCF File - Social Security Numident File – with all transactions represented etc. etc.

Collapse records to Census Numident Person Characteristics File (PCF)

File contains modeled age, sex, race, and Hispanic origin for all persons with a valid  SSN

## Production Enhancement Led to a Modification of Migration Review Procedures

**Pre-production enhancement**
- Reviewed county level data and adjusted county level migration rates
- Adjustments only affected state and county level total migration rates
**Post-production enhancement**
- Reviewed county level data and adjusted Geocodes at the individual level record
- Adjustments affect migration rates for state and county totals AND state and county characteristics

**Notes:**
It was a few years ago – but I think some of you may have attended a workshop that Barbara Van der vate and Jason Devine presented at the Bureau.  At that time they walked you through some of the review procedures that we conduct at the county level and stepped you through how we implement any adjustments to flows at the county level which in turn adjusts the migration rates.  I know that you review the net migration rates as well and some of you have provided feedback to us that we have been able to incorporate in the past. 

Just as an aside - 

This year - during what I am calling the post-production enhancement – we received comments from a few of the co-ops and we were able to incorporate their comments into our review and adjustments.  

We will continue to be able to incorporate comments as long as they meet the timing requirements.

Note – they were GA and NC

Esther – remember – we send NET rates – not ins, outs, nons, OR flows to the co-ops.

## Migration Update System for Estimates (MUSE)

- Joint project between Population Division (POP) and Planning Research and Evaluation Division (PRED)
- Move from return-based processing to person-based processing approach

**Notes:**
In fact our match rate for the TY02 – TY03 files was 89.1 percent – meaning that there is a 10 percent undercoverage rate.

However, the 10 percent undercoverage is random across all counties.  

We are looking forward to decreasing the bias in our estimates.

## Main Objectives:

**Improve migration rates**
**Improve accuracy of demographic characteristics of dependents**
** **
**Improve the estimates**
**Increase coverage**
**Reduce bias in the migration rates by:**
- Increasing the set of administrative records we currently work with
- Incorporating models

## MUSE (cont)

- Long Term Goals:
- Complete Project for July 1, 2008 Estimates
- Measure and document incremental impact of adding Administrative Records
- Improve Geocoding
- Share results with FSCPE Methodology Research sub-committee

**Notes:**
BULLET #2:		Which in turn Increases the accuracy of our estimates.

BULLET # 4		Looking forward to sharing our results and methodologies with the FSCPE methodology research sub-committee and the FULL FSCPE AND the Greater User Community.

## MUSE (cont.)

- Collaborate with PRED to incorporate full set of administrative records
- Electronic Filing File (ELF)
- Medicare
- Other Administrative Record Files

**Notes:**
Bullet #1  The ELF file is generated by electronic tax filers – 			such as direct IRS online filers, people who use 			(name of electronic software), etc.

As we incorporate the full set of administrative records – we are also incorporating the full set of demographic characteristics.

## MUSE (cont.)

**Collaborate with PRED to :**
- Model Characteristics for PCF non-matches
- Model for potential bias for non-filers
- Incorporate ACS into the system

**Notes:**
BULLET #3     And of course – incorporate ACS into any model and into the greater system as well.

## Planned Enhancements for July 1, 2005 Estimates

- Utilize Geography Division to produce the ZIP/CRSS file
- Coding Zip Codes to County
- Research outlier migration data prior to production of the migration rates

## ZIP/CRSS Procedures

- Currently the ZIP/CRSS file is based on the postal services Delivery Sequence File code to ZIP+2-to-county codes
- Geography Division will create the  ZIP/CRSS file
- Reflects quarterly updates from the postal services Delivery Sequence File
- Cross reference file relates most up-to-date ZIP+4-to- county codes
- More accurate and consistent with TIGER and MAF
- Consistent with GUSSIE base

**Notes:**
Bullet #1 – Again assignes zip codes to counties

## Research Outlier Migration Data Prior to Production

- Pre-2005 enhancement
- Receive a 1% sample file representing data through the end of June from IRS to build ZIP/CRRS coding guide
- Post-2005 Enhancement
- Receive all data through the end of June for early research and review

**Notes:**
Header -   Production meaning production of IRS Migration Rates – the process which begins in October.
Bullet #2 – Negotiating with IRS for a number of years in order to receive all of the data

Bullet #2 – thorough review meaning an in-depth review earlier in the process as we currently only have a 2-3 week turn around to incorporate changes, including the time for your review

Esther – remember – we send NET rates – not ins, outs, nons, OR flows.

IF and ONLY IF asked – (Can you share the results with us at an earlier point in time for our review).

This year is the first time we will be able to conduct an in-depth review of the migration data at an early point in time. We will need to go through the process at least once before we can approach whether it would be feasible to share the data with you at an early point in time.  But we can certainly work toward that goal.  

## Advantages to Planned Enhancement

- Build the Zip/CRSS file earlier
- Ability to conduct thorough review prior to production
- Identify possible Geocoding problems in time to be resolved prior to producing the IRS Migration Rates
- Easier to incorporate Geocoding changes at individual record level
- Speed up review process during production

## Planned Enhancements for July 1, 2006 Estimates

- Incorporate Person Based Approach
- Integrate the Electronic File (ELF) from IRS

**Notes:**
Bullet #1
The TY 04-05 match – meaning tax returns filed in approximately April 1, 2006 and April 1, 2005

## Assign Characteristics:Return Based Methodology

- Age
- Filer and spouse exemptions are assigned filer’s age from PCF
- Child exemptions are assigned to a 19 and under age category
- Parent exemptions are assigned to a 65+ age category
- Sex
- Filer is assigned filer’s sex from PCF
- Spouse exemption is assigned opposite sex of filer
- Child and parent exemptions are assigned sex by random number generator

**Notes:**
Many of you attended our workshop last fall – but I am just going to walk you through the differences between the return-based (which is our current methodology) and person-based approaches so you can once again see why we are going toward a person-based approach.

## Assign Characteristics:Return Based Methodology  (cont.)

- Race and Hispanic Origin (HO)
- Filer is assigned filer’s race and HO from PCF
- Spouse is assigned filer’s race and HO from PCF
- Child and parent exemptions are assigned filer’s race and HO

## Assign Characteristics:  		 Person Based Methodology

- Age, sex, race, Hispanic Origin
- Filer is assigned filer’s characteristics from PCF
- Spouse is assigned spouse’s characteristics from PCF
- Child exemptions are assigned child’s characteristics from PCF
- Parent exemptions are assigned parent’s characteristics from PCF

## Assign Migration Status: Compare FIPS code Y1-Y2

- Return Based
- Filer, spouse, child, and parent exemptions are all assigned filer’s Y1 and Y2 State and County FIPS codes
- Person Based
- Filer, spouse, child, and parent exemptions are all assigned individual Y1 and Y2 State and County FIPS codes

## Advantage to Person-Based

- Match rates across Y1-Y2 IRS files will increase
- Preliminary evaluation shows that the match rates across Y1-Y2 increased
- Improve migration data by picking up additional filers
- -	Students who move out of the house
- -	Divorce or Separated

**Notes:**
Bullet #2  ”. 

Improvement of Migration Data

 By picking up additional individuals who otherwise are dropped in the returned-based, the migration data will improve to the extent that a lot of these individuals are movers (e.g., married, moving to college, divorce, etc.,).

## Advantage to Person-Based (cont.)

- Accurate ages for all exemptions
- Migration status is correct
- Accurate sex, race, and HO for all exemptions

**Notes:**
BULLET #1   Moved “child” to “adult” stats

Migration status is correct” is partially true because the dependents still carry the filer’s addresses.  So, for example, a dependent who moved to the college will have the same address, therefore non-migration status, as long as the student is claimed on the filer’s return.   The person-base method will code the same person as migrant if he or she files tax return at the college location.   

## Preliminary Results Comparing Return-based and Person-based

## (Match rates are for person-based returns only)

**Notes:**
The match rate categories are divided in thirds – based on differences – each reflects a difference of 4 percentage points.
As you can see the match rates are higher in the east and the midwest.  The rates appear to be lower in the west and the south.  We are speculating that some of the differences in the match rates may be due to characteristics of filers.

Just mention 1) that the map shows a geographical pattern – low match rates in the south and the west, and higher match rates in the midwest and the northeast; 2) we are exploring the cause of the pattern.

Just as an aside – we do not have the match rates for return-based filers – but what I can tell you is that when we move to return based – we have fewer outliers –with low match rates.

## Age-Specific Match Rates for 1999-2000 Migration Year Using Person-based Records

**Notes:**
Page 22: Mention the match rates are greater than 90% for all age groups, except age 1-2, that the IRS data cover in the production.

A small increase from 17 to 19, followed by a slightly downward slope from 19 to the mid-20s.  
A gradual increase from mod-20s:  steady inflows into labor force
A gradual decrease from mid-60s: retirement from the labor force

## Match Rates by Race and Hispanic origin for 1999-2000 Migration Year using Person-based Records

**Notes:**
2 percent PCF non-match
Higher match rates for non-Hispanics

## Migration Rates(%) for 1999-2000 Migration Year: Return-based vs. Person-based

## Inter-county Migration Rates (%) by Age Group for 1999-2000 Migration Year: Return-based vs. Person-based

**Notes:**
Discussion on base for age groups – due to age assigned to < 19. 

Better job with characteristics and pick up first time filers – meaning will decrease the number of Y2 only returns and INCREASE the denominator.

## Inter-county Migration Rates (%) by Race and Hispanic origin for 1999-2000 Migration Year: Return-based vs. Person-based

## Scatterplots for the Person-based and Return-based In-Migration Rates(%) for 1999-2000 Migration Year

- State Level

- County Level

**Notes:**
You will notice that person-based approach replicates the return based.

Largest outlier at state level  is DC.

Hyo – what about county??

## Slide 30

**Notes:**
Given the 95% confidence interval – person based rates – differences in predicted.

## Scatterplots for the Person-based and Return-based Out-Migration Rates(%) for 1999-2000 Migration Year 

- State Level

- County Level

## Slide 32

## Scatterplots for the Person-based and Return-based Net-Migration Rates(%) for 1999-2000 Migration Year

- State Level

- County Level

## Slide 34

## Some Findings from Analysis of Outlier Counties for Net-Migration

- 60% of the outliers had the population less then 10,000
- 	71% of the outliers less than pop < 10,000, the person-based method increased the out-rates.
- 20% of the outliers was associated with major colleges and universities.
- 	97% of the outliers associated w/ college, the person-based method increased the in-rates.

## Future Research Questions

- Is there a relationship between the outflows from small counties and the inflows into the counties w/ major colleges?
- Is the relationship stronger for the intra-state migration than for the inter-state migration?
- Who are the individuals that move out of the counties w/ small population?
- Who are the individuals that move into the counties with colleges?

## Enhancements for post July 1, 2006 Estimates

- Integrate Full Set of Administrative Records
- Integrate Full Model to Reduce Bias due to differences in coverage rates