Developing a Residence Candidate File for Use With 

Employer-Employee Matched Data 

Matthew R. Graham, Mark J. Kutzbach, and Danielle H. Sandler∗

U.S. Census Bureau 

4600 Silver Hill Road 

Washington, DC 20233 

May 5, 2016 

1 Introduction 

This paper describes the Longitudinal Employer-Household Dynamics (LEHD) program’s efforts to use administrative records in a 

predictive model that describes residence locations for workers. This project was motivated by the discontinuation of a residence file 

produced elsewhere at the Census Bureau. The discontinued file provided only a single residence per person/year, even when contributing administrative data may have contained multiple residences. The goal of the Residence Candidate File (RCF) process is to 

provide the LEHD Infrastructure Files with residence information that maintains currency with the changing state of administrative 

sources and represents uncertainty in location as a probability distribution. This paper describes the motivation for the project, our 

proposed methodology, the administrative data sources, and the model estimation results. We find that the best prediction of the 

person-place model provides superior accuracy compared with previous methods and and performs well for workers in the LEHD 

jobs frame. Although our model predictions provide an indication of uncertainty, in expectation, the probability weighted model 

is less accurate than either the best prediction or the previous methodology. The paper outlines further work that may improve the 

representation of uncertainty and enhance the model with job timing and location information from the LEHD Infrastucture Files. 

1.1 Background and Motivation 

The LEHD program in the Center for Economic Studies (CES) at the Census Bureau uses job and employer information from states 

along with federal survey and administrative data to produce statistics on labor force dynamics including the Quarterly Workforce 

Indicators, LODES, and Job-to-Job Flows (Abowd et al. [2009]). States provide LEHD with quarterly files supplying the earnings 

of all workers covered by state unemployment insurance programs. These include state and local government employment as well 

as approximately 96% of all private sector wage and salary employment (Stevens [2007]). States also provide quarterly employer 

files listing establishment locations as well as industry, ownership, and size. LEHD combines these files into a Person History 

File, listing the earnings history of each job, and an Employer Characteristics File (ECF). LEHD also produces an Individual 

Characteristics File (ICF) based on federal survey and administrative data that provides demographic information on workers. 

LEHD uses these files to produce the public use datasets as well as for economic research. 

The LEHD program requires place of residence information for several core processes, each of which expects a single, best 

residence for each worker. The Unit-to-Worker (U2W) imputation of establishments to persons uses residence to calculate implied 

commute distance from a workplace. The ICF imputes demographic characteristics based on the observed characteristics of neighbors. Additionally, the LEHD Origin-Destination Employment Statistics (LODES), disseminated through the OnTheMap web tool, 

tabulates the residence location of jobs in origin-destination tables and as a residence margin. 

From its inception, the LEHD program has used the Composite Person Record (CPR) as a source of residence data. The CPR 

containes fields that provided a linkage between a unique person record and the location of a residential housing unit. The Center 

for Administrative Records Research and Administration (CARRA) used the Statistical Administrative Records System (StARS) 

to produce the CPR until the file was discontinued in 2011 (data year 2010).1 CARRA delivered the MAF-ARF (Master Address 

File-Auxiliary Reference File) in place of the CPR in 2012 (data year 2011). The MAF-ARF was found to differ from the CPR 

in a number of ways, including a difference in coverage and a lack of deduplication among PIKs. LEHD was able to produce a 

deduplicated version of the MAF-ARF by defining some very basic business rules that were implemented by the CES Data Staff. 

∗DISCLAIMER: Any opinions and conclusions expressed herein are those of the author(s) and do not necessarily represent the views of the 

U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. 

1See https://www.census.gov/content/dam/Census/about/about-the-bureau/SORNs/CEN-08.pdf 

1 
 

 

 

 

The 2012 process of unduplicating the MAF-ARF was not sustainable, and in creating a process internal to LEHD, we were 

able to address quality and suitability issues specific to the needs of the program. As a first step to developing a permanent 

replacement for the CPR/MAF-ARF, the LEHD program developed a process (RCFv0.5) that replicated the methodology of the 

MAF-ARF. LEHD used the output of this new process to supply the 2013-2015 cycles of LODES processing (data years 2012-

2014) with residential information. The iteration described here (RCFv1.0) develops a predictive model for residence location that 

is customized to the needs of employer-employee matched data. The RCF file contains PIKs and a weighted, preferred list of 

residential locations geocoded to 2010 census tabulation blocks along with metadata on geocoding outcomes. Integration with the 

LEHD Infrastructure Files is scheduled as a next step. 

2 Methodology 

The production specification for the RCF is to transform a set of annual administrative source files listing person/location into a 

file with preference weights for each person/location and with no remaining source information. Construction of the RCF does 

not alter the fundamental principle of how LEHD uses residence data. Namely, LEHD processes requiring a place of residence 

still access a composite file, rather than the source files themselves. Downstream processes currently expect a single residence for 

each worker in a year, but imputations and tabulations could be modified to consider a set of residences with model-determined 

preference weights. Neither the CPR nor the RCF indicate which source provided each record. The present analysis considers a 

baseline, rank-order model that is analagous to the CPR methodology as well as several specifications of a person-place model that 

offers more customization and provide more information on the uncertainty of locations for a person. 

2.1 Rank-Order Model 

The CPR limits residence data to a “best” record for each person in a year.2 Administrative data handlers chose a best record by 

developing a source priority order. The LEHD program has little information on the development of the priority order for residence 

data in earlier years. For processing the 2010 MAF-ARF residence data, CES used a priority order based on findings from the 2010 

Census Match Study [Rastogi et al., 2012]. The study linked responses to the 2010 Decennial Census with administrative records 

to identify the sources that corresponded best with a person’s response location on the census reference date, April 1, 2010. (The 

April 1 date is useful because it is coincident with the LEHD Beginning-of-Quarter employment definition for Quarter 2, used for 

the LODES snapshot of jobs.) CES deduplicated the MAF-ARF according to the priority order and retained the highest priority 

residence source for each person. 

There are several drawbacks with using a priority order to create a deduplicated series of addresses for LEHD processes: 

• First, deduplication disposes of information on the distribution of possible residential locations that may apply to a worker 

at a point in time. For some other missing data problems, LEHD reflects uncertainty by the tabulating statistics with weights 

associated with the probability of any piece of information. Furthermore, a worker may have multiple residences over a 

period of time, each of which might be associated with a different job. Prioritizing one address over another degrades the 

interpretation of residence as the home of a worker while employed at a particular job. 

• Second, the priority order developed in the 2010 Census Matching Study is based on an April 1 reference date for a single 

year, but LEHD produces a quarterly series of job statistics beginning in 1985 for some states. Seasonal or longitudinal 

variation in the quality of the sources might alter the priority order. 

• Third, the availability of sources varies longitudinally and not all sources included in the study are available for the RCF. 

The priority order of the sources might change if it were limited to those available for the RCF in each year. 

• Fourth, this priority list does not reflect the relative strength of each source for different populations. Although the 2010 

match study used demographic information in its predictive model, we do not have disaggregated priority lists. 

We compare this baseline methodology (RCFv0.5) with proposals for a person-place methodology (RCFv1.0) described below. 

2.2 Person-Place Model 

As a basis for the RCF, we build upon a two stage process that was originally developed by David Brown at the Census Bureau 

in order to create a model for estimating the occupancy of households that did not respond to the 2010 Decennial Census [Brown, 

2013]. Brown [2013] uses responses to the 2010 Census as a truth set for training and validating the model.3 Brown [2013] uses 

a two-stage logistic model of location agreement. The first stage estimates an equation for each adminstrative data source and the 

second stage, pooling residences from all sources, makes use both of indicators for the presence of each source as well as predicted 

probabilities from the first stage. These probabilities give the expected validity of each source for each person. The predicted 

2As an exception, the 2001 CPR included multiple residences for some persons. 

3Other studies at the Census Bureau that trains and validates an administrative data model using the 2010 Census include [Rastogi et al., 2012] 

and [Steeg Morris et al., 2016, forthcoming] 

2 
 

 

 

 

 

 

 

 

 

probabilities from this second stage are used to create preference weights for each person at each reported residence. We build on 

and adapt this model in several respects, described in more technical detail below. 

First, given that LEHD produces quarterly jobs data, it is necessary to train the residence model with greater frequency than is 

possible with the Decennial Census. We use the American Community Survey (ACS), a continuous household survey with national 

coverage since 2003. Both the American Community Survey (ACS) and Decennial Census require respondents to reside at an 

address in the mailing frame, which provides a concept of residence that is fairly consistent across time, geography, and populations. 

Using the ACS allows us to train and validate the model independently for any given year. For this study, we implement the model 

for a single study year. 

Second, to make the residence file more robust to the gaps in the reporting of administrative sources for a person in any year, we 

supplement the residence list with addresses appearing in prior and subsequent years in the model. Lagged or later residences may 

have less predictive power for where someone is in the study year, so along with including these records, we add parameters that 

capture the longitudinal history of reporting for each source. This model-based solution will be more informed than the longiduinal 

edits currenly used in LEHD processing. 

Lastly, we limit the model estimation to working-age persons ever appearing in LEHD job histories and use demographic 

information already included the LEHD Infrastructure Files. We also evaluate the model specifically for persons employed in 

LEHD during the study year. 

2.3 Model Description 

We first make eligibility restrictions on both the survey and administrative residence files based on address reference dates, person 

characteristics, and the availability of linking information. We use identifiers encoding personal identifying information to link 

residence candidates from the administrative sources to a person’s survey based residence location. We designate one portion of 

this linked set as a training sample and the remainder as a validation sample. Note that in this case, the vast majority of administrative 

records eligible for inclusion in the residence file do not link to any person in the survey file. 

For individual i with location l from source s, we specify an agreement, Γils as 

Γils = I(Surveyil = Adminils) (1) 

where I is an indicator function for agreement of the residence location between the person’s survey response and administrative 

source at some level of geography. The model could be specified for a range of geographic precision for Γils, including by address 

identifier, Census tabulation geography, county, or state. For the present analysis, we use Census block, the most detailed geographic 

tabulation of residence published by LEHD in LODES. 

Starting with the training sample, we explain the variation of this binary agreement variable with a logistic model estimated 

separately for each of the S sources. We specify the model as 

exp(αs + βsXils)

Γils = (2)

1 + exp(αs + βsXils)

where Xils is a vector of individual and source characteristics including demographic information as well as the reference date of 

the source. For the same observations, we predict Γˆils given our estimates of αˆs and βˆs as well as the characteristics Xils. We use 

these expected values for each person/location/source in the second stage. 

For the second stage, we deduplicate the data by person/location (collapsing cases of multiple sources for the same person/location). We add indicator variables Sil and the predicted probabilities Γˆils for each source appearing for that person/location. 

We then estimate a second logistic model, specified as 

exp(γ +

PS ˆ

s=1(φilsSil + λilsSilΓils))

Γil = PS

(3)

1 + exp(γ + (φ ˆ ilsSil + λilsSilΓils))

s=1

Note that the predicted probabilities are set to zero in the case where there is no corresponding source, so we only write them as an 

interaction with the source indicators. 

Turning to the validation sample, we apply the parameter estimates from equations (2) and (3) to compute expected agreement 

for each person/location, where 

exp(ˆγ + ( PSφˆ ˆ ˆ ilsSil + λilsSilΓils))

ˆ s=1 Γil =

1 + exp(ˆγ +

PS ˆ ˆ ˆ

s=1(φilsSil + λilsSilΓils))

We then compute preference weights for each individual i. For a person with Li locations across all sources, we create an aggregated 

value of the predicted probabilities for the denominator of the weight. For each location, we use the predicted probability for the 

person/location for the numerator, such that each person/location is assigned a weight, Wil, specified as 

Γˆ

il Wil = PLi

(4) 

(ˆ

l=1 Γil)

3 
The
same
methodology
used
to
calculate
preference
weights
for
the
validation
sample
is
then
applied
to
the
entire
frame
of


persons
with
administraive
residence
data.
The
resulting
RCF
file,
which
is
annual
at
this
stage,
gives
preference
weights
for
each


person/location
and
retains
no
source
indicators.


3 Data 

This
study
focuses
on
the
year
2012
for
presentation
of
the
data
infrastructure
and
model
estimation.


3.1 Administrative data sources 

This
project
uses
administrative
data
on
residence
location
from
the
Internal
Revenue
Service
(IRS),
the
Department
of
Housing


and
Urban
Development
(HUD),
the
Department
of
Health
and
Human
Services
(HHS),
the
Selective
Service
System
(SSS),
and


the
U.S.
Postal
Service
(USPS).
The
RCF
uses
the
following
administrative
source
files:


IRS 1040 Individual
Tax
Returns
(1040/IMF)


IRS 1099 Information
Returns
Master
File
(1099-R/IRMF)


HUD PIC Multi-Family
Tenant
Characteristics
System
/
PIH
Information
Center
(MTCS/PIC)


HUD TRACS Tenant
Rental
Assistance
Certification
System
(TRACS)


HUD CHUMS Computerized
Home
Underwriting
Management
System
(CHUMS)


HHS IHS Indian
Health
Service
- Patient
Registration


HHS CMS Medicare
Enrollment
Database
- 100
percent
Production
File


SSS Selective
Service
System
Registration
Files


USPS NCOA National
Change
of
Address
File
(NCOA)


Detailed
descriptions
of
each
data
source
are
available
in
the
appendix.


3.2 Definitions 

The
RCF
is
meant
to
be
integrated
with
jobs
data
defined
by
earnings
of
a
person
at
a
job
in
a
quarter.
 The
LEHD
earnings
files


identify
job
holders
with
a
Protected
Identification
Key
(PIK).
Likewise,
the
administrative
source
files
identify
persons
with
a
PIK.


The
Census
Bureau
maps
administrative
and
survey
data
records
to
a
PIK
using
personal
identifying
information
Wagner
and
Layne


[2014].
In
cases
where
records
could
not
be
mapped
to
a
PIK,
data
integration
is
not
possible.


We
use
the
Master
Address
File
ID
(MAFID)
to
define
a
residence
location,
an
identifier
used
throughout
demographic
survey


areas
at
the
Census
Bureau.
The
MAF
is
the
residence
frame
for
both
the
Decennial
Census
and
the
American
Community
Survey.


CARRA
has
already
geocoded
address
fields
in
administrative
source
files
to
MAFIDs,
where
possible.


As
with
the
CPR,
the
RCF
defines
the
period
of
residence
based
on
the
reference
data
of
a
source
file.
 Each
source
file
has


an
independent
schedule
of
when
it
is
collected,
produced,
and
delivered
to
the
Census
Bureau.
 Where
possible,
the
RCF
uses


reference
date
fields
to
define
the
year
of
residence.
In
the
absence
of
a
reference
date,
the
RCF
uses
metadata
on
the
origin
of
the


file
to
infer,
at
a
minimum,
the
year
of
the
residence
records.


3.3 Source Summary 

Table
1:
Administrative
records
with
a
MAFID,
2012 

Address
source
(detailed)
 Records
(millions) 

IRS
1040
 262


IRS
1099
 624


HUD
PIC
 7


HUD
TRACS
 2


IHS
 5


Medicare
 48


SSS
 15


NCOA
 37


Total
 1,000


4

When reading in the source files, we only retained records with a PIK. Of those, 85 percent overall had a MAFID, almost all 

of which had complete 2010 Tabulation Geography. Some sources were notable in having a lower percentage of records with valid 

MAFIDs. In 2012, these include IHS (73 percent) and IRS 1099 weeks 42-52 (77 percent). Table 1 lists the totals of PIKed records 

with a MAFID before any deduplication within or between sources. 

Table 2: Source availability for PIKs, 2012 

Address source (detailed) Percent with source 

IRS 1040 85.3 

IRS 1099 70.3 

HUD PIC 2.2 

HUD TRACS 0.8 

IHS 0.9 

Medicare 16.4 

SSS 5.0 

NCOA 4.8 

Table 2 gives the share of all PIKs that have at least one residence record provided by a source. Almost all PIKs have an IRS 

sourced address available, with 85 percent having a 1040 address and 70 percent having a 1099 address. Availability of the other 

sources is fairly consistent with the scope of the programs providing data. 

We produce residence frames for 2011, 2012, and 2013 consisting of the set of unique source/address records per PIK, where 

each residence has a valid MAFID. Table 3 lists the record count for the 2012 file. Over 99% of these records have residence 

information precise enough to be geocoded to a Census block.4 

Table 3: Record counts in millions, 2012 

Record Type Count (Millions) 

Unique person/source/MAFID 593 

Unique person/MAFID 376 

Unique person 296 

After applying the priority rules similar to the CPR to deduplicate by PIK, we retain 296 million records for 2012 (and a similar 

count for the other years). These totals are in line with the 2010 Census Match Study, which found 302 million records with a PIK 

and MAFID using both federal and commercial source data. 

3.4 Training and Validation Sample 

To train the predictive residence model, we draw a sample of 5.3 million respondents from the ACS in 2012. Because we are 

focused on residence prediction for a jobs frame, we first limit the sample to the 5.0 million respondents who can be linked to a 

PIK. The PIK rate for the ACS is 94.0%. The only information we retain from the ACS is the quarter of response within 2012 and 

the place of residence Census block. 

We further limit the sample to those persons recorded as ever having worked in LEHD, or the set of persons in the ICF. 

Although the ACS includes demographic information, we obtain variables for age, sex, race, ethnicity, educational attainment, and 

native birth from the ICF, which is based on NUMIDENT, 2000 Census responses, and imputations. We calculate age from the 

ICF at the response date to ACS and only retain persons from aged 14 to 74. Limiting the sample to this set of likely workers 

improves the applicability of the model estimates to the frame of LEHD workers. We partition this set of 3.4 million persons and 

their associated records randomly to the 70 percent training sample and 30 percent validation sample. 

As is described in the methodology, we then link the ACS file with the administrative residence data described above. We use 

data from 2011 to 2013 as potential address matches for 2012 ACS responses. For just 2012, the merging resulting in 21.5 million 

unique PIK/source/MAFID records and 10.0 million unique PIK/MAFID records. The first stage of the model will draw from the 

first quantity while the second stage will draw from the latter. 

Table 4 shows the number of MAFIDs available per PIK, both in 2012 and for all three years. While 72.2% of PIKs have one 

and only one MAFID from our source files in 2012, 21.1% have two or more potential addresses. These are the individuals for 

which we need a deduplication methodology. We only use persons with a link to at least one administrative residence record for 

the model estimation, but retain the remaining 6.2% for overall evaluation of the model in linking residence locations to persons 

or workers. When we expand the set of candidate records to include locations from sources in a lagged or later year, the share of 

persons with multiple MAFIDs rises to 38.1% and the share with no MAFID falls to 2.6%. 

4For the present analysis, we retain this small set of records with a missing or incomplete geocode and code them as lacking agreement with the 

survey location. 

5 
Table 4: Availability of administrative address for PIKs (quantities in thousands) 

MAFIDs per PIK Count 2012 Percent 2012 Count all years Percent all years 

0 213 6.18 91 2.64 

1 2,503 72.72 2,039 59.24 

2 575 16.71 766 22.25 

3 122 3.53 320 9.30 

4 24 0.70 135 3.93 

5+ 5 0.15 91 2.64 

Total 3,442 100.00 3,442 100.00 

Table 5 shows the longitudinal history trend of addresses by source file. The header gives a triad that indicates whether the 

address was present in 2011, 2012, and 2013. So, if an address is present in 2011 in the Medicare file for a individual, but is not 

present for 2012 or 2013 in the Medicare file, then that individual would have a triad value of “100”, a pattern for 5.5 percent of 

addresses linked to the sample from the Medicare file. Overall, almost half of all addresses appearing in the three year window occur 

in all three years (111). The least common pattern is a one year hole (101), which could be due to either a temporary move, a gap 

in the administrative data for the person, or geographic measurement error in the administrative data. Some of the patterns reflect 

the nature of the source files. For example, the NCOA file, based on change of address, has very few records with a PIK/MAFID in 

all three years, but many one year records. 

Table 5: Longitudinal residence histories (triads), pattern percentages by source 

Source PIK/MAFID/Sources (thousands) 000 100 010 001 110 101 011 111 

None 91 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

IRS1040 3,947 0.0 13.5 5.9 11.8 7.6 1.7 8.2 51.5 

IRS1099 4,555 0.0 12.3 5.6 13.4 7.9 1.8 10.2 48.8 

HUDPIC 78 0.0 14.3 5.9 17.9 12.3 2.8 13.2 33.6 

HUDTRACS 25 0.0 14.9 8.5 18.9 12.3 0.8 10.4 34.2 

IHS 74 0.0 9.5 4.1 8.7 4.4 2.8 9.8 60.8 

Medicare 641 0.0 5.5 2.3 16.6 4.0 0.9 12.6 58.1 

SSS 227 0.0 11.6 1.5 12.1 9.8 0.5 14.0 50.5 

NCOA 403 0.0 40.7 34.0 13.3 7.6 1.5 2.3 0.6 

Total 10,041 0.9 13.3 6.5 12.8 7.5 1.6 9.3 48.0 

4 Model Analysis 

This section summarizes the model estimation and evaluation. 

4.1 Specifications 

We execute both the baseline, rank-order methodology and the person-place methodology described in Section 2. For each model, 

we use agreement by residence Census block as the outcome variable to be explained. In order to better understand the contribution 

of various parameters to the person-place model relative to the rank-order model, we estimate and evaluate several person-place 

model specifications across different sets of source years: 

1. Rank-Order model (2012 MAFIDs) 

2. Person-place model (2012 MAFIDs) 

3. Person-place model (2011-2013 MAFIDs) 

4. Person-place model with first stage triads (2011-2013 MAFIDs) 

5. Person-place model with first and second stage triads (2011-2013 MAFIDs) 

The first model is simply the application of the source rank-order rule to the administrative sources. All of the person-place 

models include the demographic variables from the ICF and the response quarter indicators from the ACS. The second item is a 

person-place model estimated on the same record set as the rank-order model. The third model is the same as the second, but with 

the addition of lagged and later year source files. The fourth model adds indicators for the longitudinal pattens (triads) of each 

source to the first stage as additional characteristics. The fifth model not only includes the triads in the first stage, but also adds 

6 
indicators for each source/triad in the second stage as well as predicted probabilities for each source/triad. Considering Table 5, the 

fourth model adds a parameter to the first stage for each triad column (excluding “000” and “111”). The fifth model adds those in 

the first stage, but also adds parameters for each cell in the table to the second stage. Thus, the fifth model makes the most use of 

longitudinal information. 

4.2 Estimation 

We present results for estimating the fourth model listed above (person-place with longitudinal triads in the first stage) on the 70 

percent training sample. This model explains Census block agreement with our ACS extract from 2012 using MAFIDs from 2011 

to 2013. Table 6 gives the log odds ratios for the first stage regression, as discussed in Section 2, for a single source, the IRS 1040. 

Reviewing the log odds ratios, all of the longitudinal triad estimates are less than one, indicating that sources not appearing in all 

three years (the omitted class) are inferior predictors of location. The reference quarter variables indicate worse correspondence 

for ACS respondents in the 4nd quarter, relative to the first quarter (as shown by the log odds ratio of less than one). The match is 

more accurate for middle aged responents than younger respondents (64 to 74 omitted), with those aged 18 to 24 having the worst 

match rate. White and Asian respondents, non-Hispanics, and those with higher educational attainment tend to have better address 

correspondence.5 

Table 6: First stage person-place estimation for IRS 1040 addresses with first stage triads 

Variable Log odds ratio w.r.t. omitted 95% C.I. lower bound 95% C.I. upper bound 

001 vs 111 0.05 0.05 0.05 

010 vs 111 0.13 0.13 0.13 

011 vs 111 0.38 0.38 0.39 

100 vs 111 0.03 0.03 0.03 

101 vs 111 0.29 0.28 0.29 

110 vs 111 0.23 0.23 0.23 

ACS Quarter 2 0.99 0.98 1.00 

ACS Quarter 3 0.98 0.98 0.99 

ACS Quarter 4 0.95 0.94 0.96 

Age <18 0.64 0.63 0.65 

Age 18 to 24 0.47 0.46 0.47 

Age 25 to 34 0.65 0.64 0.66 

Age 35 to 44 0.84 0.83 0.85 

Age 45 to 54 0.97 0.96 0.99 

Age 55 to 64 0.99 0.98 1.01 

Female 0.97 0.96 0.98 

Black 1.04 1.03 1.05 

AIAN 0.46 0.45 0.47 

Asian 1.10 1.08 1.12 

NHPI 1.17 1.09 1.25 

Two or more 1.02 1.00 1.05 

Hispanic 1.02 1.01 1.03 

High School 1.03 1.02 1.04 

Some college 1.05 1.04 1.06 

Bachelor’s + 1.08 1.07 1.10 

Native born 0.86 0.85 0.87 

Table 7 gives the log odds ratios for the second stage regression (also of the fourth model listed above), which includes 

indicator variables for each of the source files and the probability weights from the first stage. While the log odds ratios provide 

some indication of the correspondence of the sources with ACS residences, interpretation would require considering both the 

indicator and interaction effect of each source. While some sources, such as IRS 1040 have strong effects for both the indicator and 

interaction variables, others, such as SSS and HUDPIC are most accurate for sub-populations identified in the interaction effect. 

4.3 Evaluation 

Having estimated the person-place model on the 70 percent training sample, we now apply the model to make predictions for the 

30 percent validation sample. For evaluation purposes, we define a weighted agreement rate for N persons in the validation sample 

5Similar tables exist for the other source files as well. Since there is no causal interpretation to these tables, we only choose to show this one for 

illustrative purposes. 

7 
 

 

 

Table 7: Second stage person-place estimation with first stage triads 

Variable Log odds ratio w.r.t. omitted 95% C.I. lower bound 95% C.I. upper bound 

IRS1040 0.33 0.33 0.33 

IRS1040*Prob 46.39 45.79 47.01 

IRS1099 0.48 0.48 0.49 

IRS1099*Prob 13.55 13.38 13.73 

HUDPIC 0.21 0.20 0.23 

HUDPIC*Prob 110.58 99.84 122.47 

HUDTRACS 0.36 0.32 0.39 

HUDTRACS*Prob 57.45 48.96 67.42 

IHS 0.26 0.24 0.28 

IHS*Prob 4.77 3.58 6.36 

Medicare 0.50 0.48 0.52 

Medicare*Prob 4.53 4.32 4.75 

SSS 0.13 0.12 0.14 

SSS*Prob 39.11 36.08 42.39 

NCOA 0.16 0.15 0.17 

NCOA*Prob 25.25 23.47 27.18 

as X X N Li

Agreement Rate = 100 ∗ [

1

ILi>0(

1

ΓilWil)]. (5) N Li

i=1 l=1

We include in N all persons in the validation sample, even if they have no candidate MAFID. The indicator function ILi>0

designates those persons with no MAFID as contributing zero to the match rate. We present the agreement rate in percentage terms. 

For the full ACS sample (with no employment restriction), Table 8 gives the agreement rates for the baseline, rank-order model 

and each of the person-place specifications. For each person-place model, we present the agreement rate under two weighting 

assumptions. First, we set the weight Wil from equation (4) equal to one for the MAFID with the highest Γˆil. We term this 

weight as the “Best” prediction. Second we report estimates based on weights from equation (4). We term this weight as the 

“Weighted” prediction. We compare these with several bounding models in the second panel. The upper bound of the agreement 

rate is dictated by the share of persons in the sample with at least one MAFID, which we compute both for 2012 and all years 

(2011-2013). Within those bounds, some persons may have no MAFID that agrees with the ACS residence. As an upper bound to 

the modeling predictions, we compute the maximum agreement rate assuming that Wil = Γil|Li > 0 (so that the weight is one 

for an agreeing MAFID and zero otherwise). We also report an uninformed agreement rate, placing equal weight on each MAFID 

with Wil = 1/Li|Li > 0. 

Table 8: Model comparison, full ACS validation sample 

Preference weight Rank-Order PP-One Year PP-Multi-Year PP-Multi-Year PP-Multi-Year 

Best prediction 

Weighted prediction 

78.4 

N.A. 

78.5 

76.0 

79.3 

74.2 

81.8 

76.6 

81.9 

76.8 

Has MAFID (any year) 97.4 97.4 97.4 

Has MAFID in 2012 93.8 93.8 

Perfect matching 82.4 82.4 89.2 89.2 89.2 

Uninformed matching 74.6 74.6 71.9 71.9 71.9 

Longitudinal History Triads 1st stage 1st & 2nd stage 

ACS Observations 1,033,000 1,033,000 1,033,000 1,033,000 1,033,000 

Using information only from the reference year, the most basic person-place model has a Best prediction agreement rate of 

78.5 (in column two), only slightly higher than the rank-order rate of 78.4 (in column one). Both of these are higher than the 

Weighted rate of 76.0. Note that both models fall between uninformed and perfect rates of 74.6 and 82.4. The lack of substantial 

improvement for the person-place model relative to the rank-order model suggests that the relative importance of the various 

sources has not changed much from when the source order was determined and that there is relatively little gained from using 

demographic characteristics in the first stage of the person-place model. The relative contribution of this model might improve with 

the inclusion of additional characteristics, if the sources changed in nature, or of the set of sources were updated. The relatively 

worse performance of the weighted model suggests that there may be some mis-specification of the model, resulting in the predicted 

order being more reliable than the exact value of each prediction. 

8 
Expanding the set of candidate location/sources to 2011 and 2013 substantially improves the performance of the person-place 

model relative to the rank-order model. As can be seen from Table 4, expanding the record set introduces many more cases of 

multiple MAFIDs. This complexity could potentially make modeling more difficult, as is apparent from the drop in the agreement 

rate for uninformed matching to 71.9. However, it also provides more opportunities to identify the ACS response location, as is 

apparent from the rise in the agreement rate for perfect matching to 89.2. Simply adding these sources to the person-place model, 

in the third column, increases the agreement rate to 79.3. 

Adding the first-stage longitudinal triads, in the fourth column, further increases the Best prediction to 81.8. Thus, expanding 

to additional years improves the agreement rate, but the inclusion of the longitudinal history triads is important for appropriately 

discounting the contribution of those years relative to the reference year. The final person-place model adds more complexity, with 

additional history parameters for each source in the second stage, but these have only a minor contribution. The agreement rate of 

the Best and Weighted predictions rise from 81.8 to 81.9 and 76.6 to 76.8. 

Because the goal of this project is to predict administrative residences for workers in employee-employer matched data, we 

next restrict the ACS sample to those with at least one LEHD job in the year they responded. We present the results for employed 

respondents in Table 9.6 For this worker sample, we find the same pattern as before, but with higher agreement rates. The highest 

rate we achieve is for the last person-place model, with an agreement rate of 83.4 (in column five) compared to uninformed and 

perfect rates of 72.0 and 91.3, as well as a rank-order rate of 80.3 (in column one). 

Table 9: Model comparison, ACS validation sample employed in LEHD 

Preference weight Rank-Order PP-One Year PP-Multi-Year PP-Multi-Year PP-Multi-Year 

Best prediction 

Weighted prediction 

80.3 

N.A. 

80.4 

77.5 

80.7 

75.2 

83.2 

77.9 

83.4 

78.2 

Has MAFID (any year) 98.5 98.5 98.5 

Has MAFID in 2012 95.8 95.8 

Perfect matching 84.5 84.5 91.3 91.3 91.3 

Uninformed matching 75.6 75.6 72.0 72.0 72.0 

Longitudinal History Triads 1st stage 1st & 2nd stage 

ACS Observations 659,000 659,000 659,000 659,000 659,000 

4.4 Release of RCF 

The final step in production is to release the RCF for use in LEHD infrastructure processes and LODES. The estimation and 

prediction model described above is applied to the entire set of residences described in Table 3. Applying the model requires 

replicating the steps of linking each PIK with demographic information from the ICF as well as longitudinal administrative data on 

place of residence. 

The final RCF does not list any source information and contains the fields listed in Table 10. The RCF is unique by person, 

year, and residence, with person defined by a PIK, the period of residence defined as the calendar year for which the address is 

valid. Based on the prediction methodology, each residence is assigned both a preference weight (that sums to one by PIK) and a 

preference rank (starting at one, for the highest preference weight and proceeding with no ties). Each person/year/residence record 

includes the MAFID (where available), Census tabulation geography up to block level (where available), the length, in digits, of 

the geocode, and the last year in which the geocode was observed (to help with updating RCF geography to any later vintage of 

tabulation geography). There is an indicator for whether a residence actually was observed in the RCF address year. 

4.5 Further work 

Job related information from LEHD may provide further opportunities to improve this application of the person-place model. 

The present analysis described agreement at the person level, but it might also be calculated at the person/job level. Because 

administrative data are joined together from several sources, the reference date of each source may lack the coordination inherent in 

a household survey. The timing of jobs relative to the administrative source reference dates as well as implied commute distances 

between a residence and a workplace location could be used to further improve the model. For example, in the case of long distance 

moves, such information would favor shorter, more realistic commutes. Even if these improvements only impact a small set of 

records, those cases may contribute substantially to the longer, average commute distance documented for LODES relative to ACS. 

6

In practice, we link the ACS sample, by PIK to a file containing the quartely earnings histories from all jobs held in 2012 and retain only those 

with a match. 

9 
Table 10: RCF fields 

Field Type Length Label 

pik Char 9 Protected Indentification Key (PIK) 

rcf year Num 4 Address year of RCF 

addyear obs Num 4 Indicates observation in address year 

mafid Char 9 MAFID from best source 

pref rank Num 8 Rank order of residential location for PIK*Year 

pref weight Num 8 Preference weight 

geocodefull Char 15 2010 Census tabulation geography 

stfidlen Num 4 Length of geocodefull 

geo year Num 4 Last year observed for residence geocode 

5 Conclusions 

The RCF methodology described in this document will be well suited for use with employer-employee matched data. The administrative data contributing to the RCF provide a high degree of coverage for the employed population and represent a range of 

demographics. Using the American Community Survey as training data provides a longitudinally and nationally consistent definition of residence that is strongly based on a person’s regular home location. The two stage model, which is estimated for each 

source, includes person characteristics, and is re-estimated for each year of data, will provide highly customized predictions. 

Further development of the model will allow for customization of predictions by person/job/quarter, which will provide residence predictions for each job when someone moves during a year. The LEHD program, which already makes use of multiple 

imputation for demographic characteristics and workplace at multi-unit employrs, will now also be able to fully represent the uncertainty of residential location in the Infrastructure Files. Public use statistics, such as LODES, will directly incorporate data from 

the RCF into the place of residence synthetic data model. LEHD staff will continued to develop plans for crafting these goals into 

a Version 1.0 for the RCF methodology. 

Acknowledgments 

Thanks to David Brown for sharing his previous work on residential location from administrative and other sources. Thanks to 

Katharine Abraham for discussion and comments at FCSM. Thanks also to the staff of CARRA for providing expertise on the CPR 

and MAF-ARF. Thanks to the CES data staff for preparing and providing source data for this project and to the LEHD program 

staff for helping to set up the development environment. 

10 
Appendix: Data Sources 

5.1 Master Address File 

The Geography division (GEO) at the Census Bureau maintains the Master Address File (MAF) as the frame of all residential 

addresses in the United States. Every year GEO releases an extract of the current MAF, consisting of a list of MAFIDs for 

addresses with associated characteristic information, Census tabulation geocodes, and coordinates. Because the MAF is meant to 

be cumulative, with addresses being added but not removed, the RCF should only need to use the most recent extract. For example, 

the RCF for sources up through 2013 should use the 2013 MAF extract. 

5.2 American Community Survey 

The ACS is the Census Bureau’s continuous demographic survey, including approximately 3.5 million households annually. The 

sampling frame of the ACS is the MAF and the definition of residence is the home location where a household responds to the 

survey. Because response to the ACS is tied to occupancy, the frame of respondents is well suited to serve as a truth set for estimating 

a residency model based on administrative data. Respondents to the ACS provide a name, sex, and date of birth. The Census Bureau 

uses this information along with the residence MAFID to link respondents to a PIK. The ACS PIK rate is approximately 95 percent. 

The ACS microdata file also lists the response date, which makes it feasible to consider seasonal variability in a residence model. 

5.3 LEHD Infrastructure Files 

The Longitudinal Employer-Household Dynamics program at the Census Bureau uses job and employer information from states 

along with federal survey and administrative data to produce statistics on labor force dynamics including the Quarterly Workforce 

Indicators, LODES, and Job-to-Job Flows (Abowd et al. [2009]). States provide LEHD with quarterly files giving the earnings 

of all workers covered by state unemployment insurance programs. These include state and local government employment as 

well as approximately 96% of all private sector wage and salary employment. States also provide quarterly employer files listing 

establishment locations as well as industry, ownership, and size. LEHD combines these files into a Person History File, giving the 

earnings history of each job, and an Employer Characteristics File. LEHD also produces an Individual Characteristics file based 

on federal survey and administrative data that provides demographic information on workers. LEHD uses thes files to produce the 

public use datasets as well as for economic research. 

5.4 IRS 1040 

The IRS 1040 data come from the IRS 1040 income tax filings and are formatted in the source data as one record per family. Since 

the RCF is an individual-based, not family-based, dataset, the IRS 1040 data are transposed to create one observation for every 

person in an IRS 1040 household with a valid PIK. There are up to 6 PIKs per household. 

The date variable is set to (datafileyear) + 1, since the data file is named according to the tax year, which is the previous 

calendar year, but the address date is as of the date of filing during the subsequent year. In this version of the RCF, only the year 

information is used, although additional information regarding the reference date is available in some years, notably the week of 

filing (the posting cycle date). This information may be used in future iterations of the RCF. 

5.5 IRS 1099 

The IRS 1099 data come from IRS 1099 income tax filings. Individuals that have a 1099 filing do not necessarily file a IRS 1040 

tax return, thus the 1099 addresses supplement the 1040 addresses. Each 1099 filing is associated with only one individual, but 

an individual may have more than one filing if filing with multiple companies and/or multiple times a year. Thus, the 1099 data 

contains a large number of duplicates by PIK. 

There are two 1099 files per year, one for the first 41 weeks of the year, the other for weeks 42 through 52. The observations 

are at the individual level, not the family level as in the 1040 files. The data variable is set to (datafileyear) + 1, since the data 

file is named according to the tax year, which is the previous calendar year, but the address data are as of the date of filing during 

the subsequent year. There is no specific filing date available in the 1099 data. 

5.6 HUD PIC 

The HUD PIC data come from the Department of Housing and Urban Development’s Multi-Family Tenant Characteristics system. 

This system is used by public housing agencies to record the information from form HUD-50058, the family report. It records 

information on all families and the units they occupy. The HUD PIC data contain low-income individuals who may not file tax 

returns and thus may not be captured in the IRS 1040 or IRS 1099 data. 

11 
The input data are provided with one observation per person for every person residing in public or voucher housing. There is 

often more than one individual per address. The input data are delivered yearly. The reference date used is the year the file was 

delivered. The data contain a move-in date that could be used in future versions of the RCF to provide either an address further 

back in time or more longitudinally accurate address assignment. 

5.7 HUD TRACS 

The HUD TRACS data come from the Department of Housing and Urban Development’s Tenant Rental Assistance Certification 

System. This is used to improve financial controls over assisted housing programs, including voucher programs. Thus, the population contained in HUD TRACS is a low income population that may not be covered in the HUD PIC data. 

The input data are provided with one observation per person for every person residing in assisted housing. There are often more 

than one individual per address. The input data are delivered yearly. The reference date used is the year the file was delivered. The 

data contain a move-in date that could be used in future versions of the RCF to provide either an address further back in time or 

more longitudinally accurate address assignment. 

5.8 HHS IHS 

The Health and Human Services Indian Health Service Patient Registration provides data on the Native American population, who 

may not file a 1040 income tax return if they earn income exclusively on reservation land. 

Observations in the IHS file are at the individual level. The reference data is the date the file was delivered, however a more 

detailed date is available on the file, which could be used in the future. 

5.9 SSS 

The Selective Service System Registration files provide data for the male population at time of registration. This may be a more 

comprehensive dataset of addresses of young men than the other sources, but will not capture changes in address. 

5.10 HUD CHUMS 

The HUD CHUMS data come from the Department of Housing and Urban Development’s Computerized Home Underwriting 

Management System. The individuals in this input dataset are those receiving HUD mortgage assistance. Thus, they are a population 

of low-income individuals who are not captured in the public or voucher housing data. 

The HUD CHUMS input data can contain both a borrower and a co-borrower on each observation. When there is a coborrower, the data are reformatted to provide one person per observation with identical addresses for the borrower and co-borrower. 

The reference year on the HUD CHUMS data is the closing date of the loan. The input dataset contains all loans from 2000 to 

2010. Since the end data of this file is 2010, HUD CHUMS data is not used in the first iteration of the RCF. 

5.11 Medicare 

The Medicare data provide information for the elderly population, who may not be captured in the other datasets if they are no 

longer working, and thus do not file income taxes and are not included in the other address datasets because they are not part of 

those populations. 

The Medicare data are at the individual level. The reference date used is the date the input file was delivered. There is a 

residence change date in the file that may be used in future versions of the RCF to more accurately assign a date to the address. 

5.12 USPS NCOA 

The US Postal Service’s National Change of Address (USPS NCOA) file contains records from filing change of address forms 

with the United States Postal Service. The most recent change of address for each individual and/or family that filed a change of 

address form between 2009 and 2013 is recorded in the NCOA file, with both the “to” address and the “from” address. The RCF 

currently uses the move effective date and the “to” address, but the “from” address can be added for later versions, creating a short, 

two-address panel for each observation. For the present version of the RCF, addresses are assigned to the move year if the move 

occurred from January to April of that year, otherwise the following calendar year is assigned to the address. 

There are also some address categories within the NCOA data that are not currently used, but could provide information on the 

quality and/or timing of the address. These include indicators for whether an address was a temporary or permanent move and if the 

addresses were residential or business addresses. The address is currently only associated with a single PIK per record, but some 

change of address filings are for full families and others are for individuals. This information may also be used in future versions. 

The street address in the NCOA file is not a single field, but 4 separate fields for the number (ncoa input new prim num), 

the prefix direction (ncoa input new pre dir), the street name (ncoa input new prim name), and the suffix (ncoa input new suffix). 

12 
These are concatenated together to form a single street address for the output file. Some of the addresses are foreign addresses, 

which have a foreign address indicator. These addresses are the most frequent addresses with no assigned MAFID. 

5.13 CPR/MAF-ARF 

The original CPR file, produced at the Census Bureau from StARS covered the years 1999 to 2010. In 2011, LODES used a file 

that supplemented the 2011 MAF-ARF with the 2010 CPR. While the 2011 MAF-ARF was composed of records with a PIK and 

MAFID, the 2010 CPR also includes records with less precise geographic information. 

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