1 

Using the Census to Evaluate Administrative Records 

and Vice Versa 

J. David Brown, Jennifer H. Childs, and Amy O’Hara 

U.S. Census Bureau 

4600 Silver Hill Road 

Washington, DC 20233 

Proceedings of the 2015 Federal Committee on Statistical Methodology (FCSM) Research Conference

1. Introduction 

An ideal way of evaluating the accuracy and coverage of administrative records for use in census enumeration 

would be through a comparison to the actual occupancy and number of residents in each housing unit on Ap ril 1, 

2010. While the 2010 Census provides information about this, not all Census enumerations are equally reliable. 

Censuses, like surveys, have some level of unit and item nonresponse as well as measurement error. 

A common way to evaluate the quality of survey response data is by comparing it to information from 

administrative records on the same people. Meyer and Goerge (2011), for example, compare responses on food 

stamp receipt from both the American Community Survey (ACS) and the Current Population Survey (CPS) to 

administrative data on food stamps. With such an approach, however, one must determine the direction of quality 

comparison. Is comparing the two sources a measure of administrative record quality, survey or census response 

quality, both, or neither? 

Sources of error in survey data collection have been well documented in the literature (see Groves et al., 2009). 

More recently, researchers have started documenting systematic errors within administrative records sources as well 

(Groen, 2012). At the Census Bureau, researchers have been using administrative records as a research tool to 

assess survey responses, allowing for the possibility that neither the census nor the records are perfect (Mulry et al., 

2006). This paper follows that vein. 

This paper posits that some census responses are likely of higher quality than a given administrative record, and 

others may be of worse quality. By exploring characteristics of census responses that we hypothesize are related to 

accuracy, we can make inferences about how the census data compare to administrative record data with regard to 

accuracy. 

Our specific problem - how can we evaluate the quality of administrative records for census enumeration when the 

main comparison source (the decennial census) is likely imperfect? - illustrates a general problem: how can 

researchers evaluate data quality when each source is likely imperfect? 

To address this problem, we evaluate the quality, or fitness of use, of administrative records for decennial census 

enumeration purposes by comparing them to census responses. We segregate what we believe are the most 

trustworthy enumerations for comparison. Recognizing that administrative record quality varies both within and 

across sources, we assign quality scores that vary with characteristics within and across sources. We then evaluate 

the soundness of our “trustworthy” approach by comparing census counts in housing units captured in the 

independent Census Coverage Measurement (CCM) evaluation. 

We aim to develop quality scores for administrative records and survey enumerations. The quality scoring can 

support decisions on when and how to use administrative records data in operations for the decennial census or 

surveys. Though there are many interesting aspects of data quality, this study focuses on the number of persons 

residing in a housing unit. For the decennial census, the housing unit population count is the foundation upon which 

higher-level population aggregates are built. Errors in a housing unit’s population count are associated with errors in 

other important data items, such as age, gender, race, and Hispanic origin. Section 2 describes the data, Section 3 

describes the methodology and results, and Section 4 concludes. 
2 

2. Data 

The study employs data from three sources: (1) the 2010 decennial census person and housing unit response files, 

(2) administrative records sources, and (3) the 2010 Census Coverage Measurement (CCM) post-enumeration 

survey. The 2010 decennial census files include data on names, relationships, sex, age, Hispanic origin, race, and 

usual residence elsewhere, how many people lived or stay in the house on April 1, whether there are additional 

people not included in the count, housing tenure, whether there are people included in the count who sometimes live 

elsewhere, telephone number, the enumeration mode, and whether a USPS Undeliverable As Addressed (UAA) 

notice was received. 

Table 1. Administrative Records Data Used in This Study 

Person-Address Sources Years

IRS individual income tax returns (Form 1040)1 2008-2009

IRS information returns (Form 1099/W2) 2008-2009

Department of Housing and Urban Development Computerized Homes Underwriting 

Management System (HUD CHUMS)

2000-2010

Housing and Urban Development Public and Indian Housing Information Center (HUD PIC) 2009-2010

Housing and Urban Development Tenant Rental Assistance Certification System (TRACS) 2009-2010

Selective Service System (SSS) registration records 2009-2010

Medicare Enrollment records 2009-2010

Indian Health Service (IHS) Patient Registration System records 2009-2010

United States Postal Service National Change of Address (NCOA) records 2009-2010

New York Supplemental Nutrition Assistance Program (New York SNAP) records 2009-March 

2010

Supplemental Security Record (SSR) data 2010

Experian End-Dated Records (Experian-EDR) 2010

Experian-Insource Records 2010

InfoUSA Records 2010

Melissa Data Records 2010

Targus-Consumer Records 2010

Targus-Wireless Records 2010

Veteran Service Group of Illinois Name and Address Res ource Consumer file (VSGI-NAR) 

Records

2010

Veteran Service Group of Illinois TrackerPlus (VSGI-TRK) Records 2010

Address-Only Sources

Texas Supplemental Nutrition Assistance Program (Texas SNAP) records 2009

Targus National Address File (Targus NAF) Data

Corelogic Records 2010

The administrative record sources vary in content. Some include marital status, household income, housing tenure, 

length of residence, home value, mortgage information, investment property indicators, types of tax filing, and the 

extent of household roster turnover in the previous year. 

For this analysis, we use the CCM population (P) sample. 2 The CCM survey was conducted to assess the quality of 

the 2010 decennial census, producing measures of net coverage, the components of coverage (erroneous 

 

1 We incorporate information from the 2009 electronic filings, which contain dependents beyond the four included 

in the main 2009 file. 

2 The P sample is a housing unit and person sample obtained independently from the Census for a sample of block 

clusters. See Mule (2008) for details about the survey design. The entire P-sample universe contains 178,696 

observations. The analysis excludes observations from Puerto Rico (7,479 observations), livin g quarters classified as 

group quarters in the Census (nine observations), observations that could not be matched to the Census (6,154 

observations), those with an unresolved P-sample housing unit status (39 observations), those with an unresolved Psample match status (eight observations), those not interviewed in CCM (5,118 observations), those with a blank P-
3 

enumerations and omissions), and coverage for demographic groups, geographic areas, and for key census 

operations. CCM operations make extra efforts to determine each person’s Census Day address by asking detailed 

follow-up questions and conducting additional interviews. It was conducted 4-5 months after Census Day, however, 

introducing error from recall bias and people moving in and out of housing units. Being a survey, it may suffer from 

some of the same issues as the census itself. The primary purpose of the CCM was not to determine the housing unit 

population count, rather focusing on whether individuals were census day residents in the block or not.3 The CCM 

Census Day population count in this analysis is calculated by summing the counts of people reported as living in the 

selected housing units.4 

For all three data sources, the addresses are linked using the Census Bureau’s address identifier called the Mas ter 

Address File ID, or MAFID. Person records in the decennial census, the CCM, and all the administrative record 

sources except Corelogic, Targus NAF, and Texas SNAP have also been assigned a common person ID, called a 

Protected Identification Key (PIK), by the Census Bureau’s Person Identification Validation System (PVS), so we 

can link the person records within and across sources.5

 We merge in demographic information (age, gender, race, 

and Hispanic origin) from a demographic file created by the Census Bureau’s Center for Administrative Records 

Research and Application (CARRA) using the most reliable demographics for each person based on pre-2010 

Census Bureau data, Social Security Administration (SSA) data, and other government sources. Information on 

deaths and citizenship status come from SSA. 

3. Methodology and Results 

This paper aims to evaluate quality in both administrative records and the census. We first divide 2010 census 

responses into more and less reliable groups based on potential observable enumeration errors. Next, we measure 

administrative records data quality using logistic regressions to predict whether the record and more reliable census 

enumerations place a person at the same housing unit. Using various federal, state, and commercial data sources, we 

construct a composite file of persons at the housing unit where he or she has the highest propensity score to reside. 

We sum the number of persons assigned to the housing unit, forming the administrative record population count for 

each address. We assign each housing unit’s administrative records a quality score. 

We then evaluate the quality of census responses with potential observable errors by comparing them to 

administrative records in a set of housing units that both have potential errors and high estimated administrative 

record quality scores, using administrative record characteristics as predictors. Once each census enumeration has 

been assigned a quality score, we use the score as a dependent variable in models predicting census enumeration 

quality, separately estimated by enumeration mode. As a final evaluation of this methodology, we study correlations 

between estimated administrative record quality score, predicted census quality, and agreement rates among the 

CCM, the census, and administrative records. 

3.1 Classifying Census Enumerations by Reliability 

We have developed a list of “potential observable errors”, or POEs, in census enumerations based on research 

conducted for the 2010 Census Program for Evaluations and Experiments and 2020 Research and Testing Program. 

The existence of a POE casts doubt on the validity of an enumeration.6 We assume that enumerations without POEs

are more reliable and use them as the comparison for administrative records. Table 2 contains our list of POEs. Note 

 

sample Census Day housing unit status (5,997 observations), those with unclassified persons (i.e., it could not be 

determined if the person lives at the housing unit on Census Day or not – 5,317 observations), and three errant 

records identified in microsimulation research. The usable P-sample universe for this project contains 148,572 

observations. 

3 As a result, obtaining the Census Day address for persons who moved from one housing unit to another within the 

same block since Census Day was given a lower priority. 

4

 This is calculated as the sum of nonmovers, P-sample outmovers, non-P-sample outmovers, and unclassified 

outmovers. The CCM results are weighted using the unbiased P-sample weights. These have not been adjusted for 

the exclusion of some observations from the analysis. 

5

 See Wagner and Layne (2013) for details about the PVS system. 

6

 We recognize that enumerations without POEs may nonetheless be inaccurate, and those with POEs may actually 

be correct. We are assuming that those without POEs are more likely to be accurate. 
4 

that administrative data help identify several of these POEs. The identification of unvalidated persons and duplicates 

uses the PVS process for assigning PIKs to person records, and the PVS system uses data from SSA, the IRS, and 

other federal government sources thought to be of high quality. Identification of movers is based on NCOA data. 

Persons filling out change of address forms for NCOA have an incentive to do it correctly in order to receive their 

mail at their place of residence. 

Table 2. Potential Observable Census Errors (POEs) 

Not Alive: at least one individual in the response is not alive on Census Day.

Duplicate: at least one individual in the response is found elsewhere in the Census.

Count Imputation: the housing unit’s status and/or household count was count imputed.

Occupied Proxy: the housing unit has a proxy response, and the status is occupied.

Unvalidated Persons: at least one individual in the response is not validated.

Conflicting Responses: the housing unit status or household count differs across responses for this housing 

unit.

Moved In Before Census Day, Not Counted: at least one person moved in during Decembe r 2009-March 

2010 with no move out by this person from this unit before April 2010, according to the U.S. Postal 

Service’s National Change of Address File (NCOA), and the housing unit was classified as unoccupied in 

the decennial.

Moved In After Census Day, Counted: at least one person in the decennial response moved in during April 

2010-July 2010 with no move out of this unit by the person between April and the move in, according to 

NCOA.

Moved Out Before Census Day, Counted: at least one person in the decennial response moved out in 

December 2009-March 2010 (with no subsequent move back in by this person before April 2010), 

according to NCOA.

Moved Out After Census Day, Not Counted: at least one person moved out in April 2010-July 2010 (with 

no move in by this person to this unit between April 2010 and the move out), according to NCOA, and the 

housing unit was classified as unoccupied in the decennial.

Count ≠ Number of Persons, CFU: the response household count (the number provided by the respondent)

differs from the number of listed persons (the number of persons with data captured) in at least one of this 

housing unit’s responses. In other words the household count screener question at the beginning and the 

content filled are different. The case was sent to Coverage Follow-Up (CFU).

Count ≠ Number of Persons, Non-CFU: the response household count (the number provided by the 

respondent) differs from the number of listed persons (the number of persons with data captured) in at least 

one of this housing unit’s responses. In other words the household count screener question at the beginning 

and the content filled are different. The case was not sent to CFU.

Yes to Undercount Question, CFU: at least one of this housing unit’s responses contains a yes ans wer to an 

Undercount question. The case was sent to CFU.

Yes to Undercount Question, Non-CFU: at least one of this housing unit’s responses contains a yes answer 

to an Undercount question. The case was not sent to CFU.

Yes to Overcount Question, CFU: at least one of this housing unit’s responses contains a yes answer to an 

Overcount question. The case was sent to CFU.

Yes to Overcount Question, Non-CFU: at least one of this housing unit’s responses contains a yes answer 

to an Overcount question. The case was not sent to CFU.

We study how well POEs predict disagreement between the census and the CCM and how this varies by the mode of 

data collection (self-responses via the mailout/mailback (MOMB) operation and Nonresponse Follow-up (NRFU) 

fieldwork). Table 3 shows that all cases that were flagged as potential sources of error have lower levels of 

agreement than cases that have no flags, both for MOMB and NRFU enumerations. The number of flags identified 

is also negatively correlated with percent agreement. Not surprisingly, levels of agreement are lowest for housing 

units that are “count imputed” due to nonresponse in the census. The second lowest agreement rate is for households 

that moved out before census day, but were counted there in error. 
5 

Table 3. Percent Agreement between CCM and Census Household Population Counts by Potential Observable Error 

(POE) Type. 

Error Type All Housing Units Mailout/Mailback Nonresponse Follow-up

Percent 

Agreement

No. Obs. Percent 

Agreement

No. Obs. Percent 

Agreement

No. Obs.

All Observations 82.7 148,572 86.2 85,755 75.6 47,195

No POEs 90.3 105,913 91.3 66,745 87.4 28,459

At Least One POE 62.2 42,659 67.1 19,010 56.7 18,736

One POE 70.9 26,741 73.8 13,761 66.5 9,764

Two POEs 51.6 11,615 52.7 4,204 51.0 6,043

Three POEs 37.1 3,568 38.0 830 36.9 2,451

Four or More POEs 23.6 735 21.9 215 24.6 478

Not Alive 75.6 1,036 81.7 675 57.3 217

Duplicate 52.6 12,449 52.4 6,332 52.9 4,785

Count Imputation 16.6 523 N.A. N.A. 16.7 332

Occupied Proxy 53.3 6,856 N.A. N.A. 53.2 6,486

Unvalidated 

Persons

53.8 16,011 63.4 4,144 48.9 9,631

Conflicting 

Responses

44.1 2,528 46.3 155 43.9 2,121

Moved In Before 

4/1, Not Counted

67.0 1,646 70.6 859 62.3 729

Moved In After 

4/1, Counted

66.9 561 75.0 283 57.1 261

Moved Out Before

4/1, Counted

27.1 168 23.3 111 30.0 51

Moved Out After 

4/1, Not Counted 

55.4 1,779 56.5 709 54.8 1,009

Count ≠ Number of 

Persons, CFU

61.5 1,116 62.9 845 56.2 167

Count ≠ Number of 

Persons, Non-CFU

56.5 6,951 66.9 1,507 52.5 4,381

Yes to Undercount 

Question, CFU

67.2 1,628 68.9 1,277 58.4 225

Yes to Undercount 

Question, NonCFU

54.4 1,549 59.4 990 42.8 398

Yes to Overcount 

Question, CFU

69.1 1,910 69.5 1,655 67.1 72

Yes to Overcount 

Question, NonCFU

62.8 6,993 63.1 5,995 61.2 308

Sources: the 2010 Census Decennial Response File (DRF), the 2010 Census Unedited File (CUF), and the 2010 

Census Coverage Measurement survey (CCM). 

Some potential errors are associated with each other. For example, proxy responses often result in duplicate 

enumerations and conflict with NCOA move dates. Neighbors on both sides of a household move may report the 

household, and they may not remember names and birthdates, resulting in unvalidated persons and conflicts between 

the count of persons and the number of person records. 

To see which potential errors have independent predictive power for CCM-Census agreement, we estimate a logistic 

model predicting agreement in the household count between the census and the CCM, including each of the 

potential errors as explanatory variables. Figure 1 shows the odds ratios. Every discrepancy category is a significant 

negative predictor of agreement in the population count between the census and CCM. Count imputation and being 

counted despite moving out before Census Day are most negatively associated with agreement. Duplicate 
6 

enumerations, unvalidated persons, and conflicting responses are also strongly negatively associated with 

agreement. 

Figure 1. Using Potential Error Scenarios to Predict CCM and Census Household Population Count Agreement 

Sources: the 2010 Census Decennial Response File (DRF), the 2010 Census Unedited File (CUF), and the 2010 

Census Coverage Measurement survey (CCM). The oods ratios come from a logistic regression with a dependent 

variable equal to one when the census and the CCM have the same population count for a housing unit and zero 

otherwise. 

We conduct sensitivity analysis involving the move-in/move-out potential error scenarios using the NCOA data. 

We examine the relative incidence of NCOA household moves near Census Day to assess whether enumeration 

errors are more likely to occur in conjunction with moves . These results are shown in Appendix A. We find that 

outmovers have a heightened incidence of potential errors, consistent with there being outmovers just before Census 

Day that neighbors report having lived there on Census Day in proxy responses, while the outmovers themselves or 

their subsequent neighbors also report them living at their destination address. Analogously, inmovers just after 

Census Day may have new neighbors reporting them as having lived there on Census Day, while the inmovers or 

their former neighbors report them living at their previous address. Such patterns provide support for the accuracy of 

the NCOA data and reasonableness of the potential error flags. 

3.2 Estimating Administrative Record Quality Scores 

Next, we produce administrative record quality scores. We drop records that fail to receive a PIK in the PVS process 

to include validated persons and avoid duplication in the administrative record enumeration. Unduplicating persons 

across administrative record sources is critically important as new sources are added, because there is considerable 

overlap in coverage (e.g., same person may be in IRS 1040 and Experian data). It is also necessary to unduplicate 

within sources, as many sources retain historical records in the data.7

 In addition, persons not alive on Census Day 

 

7 There are two drawbacks to the PVS validation constraint. The first is that some U.S. residents cannot be validated, 

because they do not have an SSN or ITIN. Alternatively, they have such an I.D. but do not appear in any of the 

federal administrative sources used as reference files in the PVS process. A second drawback is that the PVS process 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Odds Ratio of CCM-Census Housing Unit 

Population Count Agreement
7 

are removed from the pool of eligible records. Dates of birth and death are checked by linking the PIKs to SSA’s 

Death Master File and Numident data. The one exception is Individual Taxpayer Identification Numbers (ITINs), 

which are not found in SSA data: a being alive requirement is not imposed on ITINs here. 

Taking the unduplicated set of PIKs alive on Census Day, we assess administrative records quality using the 

record’s probability of placing the person at the same address as a decennial census enumeration without POEs. 

Focusing on housing units enumerated during NRFU with none of the POEs,8 we execute this via two stages of 

person-place logistic regressions. 9 The first-stage regressions predict quality variation within individual 

administrative record sources. A separate first-stage regression is run for each administrative record source, using 

the subset of addresses both in the source and which meet the above sample restrictions. The dependent variable is 

equal to one if the administrative record source places the person at the same address as the decennial census, and it 

is zero otherwise. Explanatory variables vary across source regressions depending on availability. All sources except 

Texas SNAP, Targus NAF, and Corelogic contain person-address data, allowing us to include the following as 

explanatory variables: the shares of the persons with different demographic characteristics (deceased, gender, age 

categories, race categories, Hispanic origin, citizenship status, number of validated persons, and number of 

unvalidated persons). Most regressions include variables indicating the data vintage. Some include marital status, 

household income, owner vs. renter, length of residence, home value, mortgage information, investment property 

indicators, types of tax filing, and the extent of household roster turnover in the previous year (IRS 1040). 

Table 4 shows selected results from the first-stage person-place regressions for the IRS 1040, NCOA, and VSGITRK sources; full results for these sources are in Appendix Tables B1, B2, and B3.10 The results suggest that 

administrative records addresses for males and minorities are less likely to match the Census address, while those of 

young children, persons found in 2008 and 2009 IRS 1040 returns at this address, persons on married-filing-jointly 

returns, and those with higher income, owner-occupancy, and longer-term residence are more likely to match. 

NCOA records with a destination address just before Census Day have a very high probability of being a match, 

while a departure address before Census Day and a destination address after Census Day has an extremely low 

probability of being a match, as expected. Scores capturing the reliability of the PVS process identifying the right 

person generally increases the probability that the administrative record’s address matches the Census address.11

Table 4. First-Stage Person-Place Regression Findings for Selected Administrative Records Sources 

Variable Odds Ratio Standard Error

IRS 1040 Male 0.812 0.004

IRS 1040 Age 0-2 2.870 0.039

IRS 1040 Age 3-17 2.884 0.029

IRS 1040 Age 18-24 0.731 0.005

IRS 1040 Age 45-64 1.096 0.007

IRS 1040 Age 65-74 0.677 0.008

IRS 1040 Age 75+ 0.450 0.006

IRS 1040 Hispanic 0.800 0.006

IRS 1040 African-American 0.592 0.003

IRS 1040 American Indian/Alaska Native 0.787 0.015

IRS 1040 Asian 0.967 0.013

IRS 1040 Native Hawaiian/Pacific Islander 0.885 0.047

IRS 1040 Some Other Race 1.020 0.013

 

can sometimes assign multiple persons the same PIK, resulting in the erroneous removal of records when 

unduplicating by PIK.

8

 We limit the sample to NRFU housing units, because we are particularly interested in evaluating administrative 

record fitness for enumerating non-responding housing units. 

9

 Theoretically, this could be done in a single regression, but this is not feasible due to computer processing 

constraints. 

10 Results for the other sources are available upon request. Note that some caution is warranted in interpreting the 

results, since the regressions contain many variables and may thus have some multicollinearity. The purpose of the 

regressions is prediction rather than interpretation of the factors affecting match rates. 

11 The PVS process involves seven different attempts (called passes) to link person records, and the NCOA file 

includes the pass number used for linking each particular record. The table shows results separately by pass. 
8 

IRS 1040 Multi-Race 1.035 0.016

IRS 1040 Married Filing Jointly 2.792 0.020

IRS 1040 Married Filing Separately 1.092 0.016

IRS 1040 Filing as Household Head 1.121 0.008

IRS 1040 Filing as Widow 2.304 0.177

IRS 1040 Both 2008 & 2009 1040 Return Here 2.289 0.011

NCOA Destination Address in May 2009 0.939 0.006

NCOA Destination Address in June 2009 1.001 0.007

NCOA Destination Address in July 2009 1.037 0.007

NCOA Destination Address in August 2009 1.054 0.007

NCOA Destination Address in September 2009 1.069 0.007

NCOA Destination Address in October 2009 1.099 0.008

NCOA Destination Address in November 2009 1.150 0.008

NCOA Destination Address in December 2009 6.171 0.072

NCOA Destination Address in January 2010 6.209 0.072

NCOA Destination Address in February 2010 6.400 0.077

NCOA Destination Address in March 2010 6.792 0.072

NCOA Destination Address in April 2010 0.033 0.0004

NCOA Departure Address in April 2009 0.019 0.0003

NCOA Departure Address in May 2009 0.017 0.0002

NCOA Departure Address in June 2009 0.015 0.0002

NCOA Departure Address in July 2009 0.015 0.0002

NCOA Departure Address in August 2009 0.014 0.0002

NCOA Departure Address in September 2009 0.015 0.0002

NCOA Departure Address in October 2009 0.015 0.0002

NCOA Departure Address in November 2009 0.014 0.0002

NCOA Departure Address in December 2009 0.009 0.0002

NCOA Departure Address in January 2010 0.010 0.0002

NCOA Departure Address in February 2010 0.009 0.0001

NCOA Departure Address in March 2010 0.006 0.0001

NCOA Departure Address in April 2010 0.510 0.006

NCOA PVS Pass 1 2.097 0.073

NCOA PVS Pass 1*PVS Score 0.983 0.001

NCOA PVS Pass 2 0.577 0.448

NCOA PVS Pass 2*PVS Score 1.055 0.041

NCOA PVS Pass 3 1.286 0.049

NCOA PVS Pass 3*PVS Score 1.012 0.002

NCOA PVS Pass 4 1.076 0.036

NCOA PVS Pass 4*PVS Score 1.008 0.001

NCOA PVS Pass 5 0.858 0.159

NCOA PVS Pass 5*PVS Score 1.021 0.008

NCOA PVS Pass 6 0.281 0.035

NCOA PVS Pass 6*PVS Score 1.045 0.005

NCOA PVS Pass 7 0.056 0.187

NCOA PVS Pass 7*PVS Score 1.145 0.190

VSGI-NAR Owner 1.510 0.003

VSGI-NAR Renter 0.583 0.002

VSGI-NAR Log Length of Residence 1.206 0.0008

VSGI-NAR Income <$20,000 0.531 0.002

VSGI-NAR Income $20,000-29,999 0.585 0.002

VSGI-NAR Income $30,000-39,999 0.648 0.002

VSGI-NAR Income $40,000-49,999 0.705 0.002

VSGI-NAR Income $50,000-74,999 0.788 0.002

VSGI-NAR Income $75,000-99,999 0.907 0.003

VSGI-NAR Income $100,000-124,999 0.963 0.003

VSGI-NAR Income $125,000-149,999 0.918 0.004
9 

Notes: Sources include 2008-2009 IRS 1040 records, 2009-2010 USPS NCOA records, and 2010 Veteran Service 

Group of Illinois TrackerPlus (VSGI-TRK) Records. The odds ratios and robust standard errors are from logistic 

regressions with a dependent variable equal to one if the administrative record address is the same as the census 

address, and it is zero otherwise. The base categories are 25-44 for IRS 1040 age, white for IRS 1040 race, single 

filer for IRS 1040 filing status, destination address in April 2010 for NCOA address, $150,000 and above for VSGINAR income, and missing tenure for VSGI-NAR tenure. 

A second-stage regression predicts the person-place match propensity for each person-address pair found in at least 

one of the sources used in the first-stage regressions. The regression incorporates information from the first-stage 

regressions by including variables indicating whether the person record is in each particular administrative record 

source at this address or a different one, plus interactions between these dummy variables and the individual match 

propensities obtained from the first-stage regression corresponding to the variable source for the particular personplace pair.12 In addition, the regression contains variables regarding the housing structure and decennial census 

paradata. Selected findings are presented in Table 5 below; full results are presented in Appendix Table B4. 

Table 5. Second-Stage Person-Place Match Logistic Regression Findings 

Variable Odds Ratio Standard Error

Mobile or Other Housing Structure 1.030 0.013

2-4-Unit Housing Structure 0.863 0.014

5-9-Unit Housing Structure 1.055 0.020

10-19-Unit Housing Structure 1.101 0.018

20-49-Unit Housing Structure 1.070 0.017

50+-Unit Housing Structure 1.066 0.015

Residential, Excluded from Delivery Statistics 0.472 0.021

In 2000 Census Here 1.168 0.010

In 2000 Census Elsewhere 1.280 0.007

Same Race for All Persons in Housing Unit 1.079 0.007

Same Hispanic Origin for All Persons in Housing Unit 1.022 0.009

Two Adrec PIKs in Housing Unit 0.891 0.009

Three Adrec PIKs in Housing Unit 0.646 0.007

Four Adrec PIKs in Housing Unit 0.608 0.007

Five Adrec PIKs in Housing Unit 0.570 0.007

Six Adrec PIKs in Housing Unit 0.514 0.006

Seven Adrec PIKs in Housing Unit 0.466 0.006

Eight Adrec PIKs in Housing Unit 0.439 0.007

Nine Adrec PIKs in Housing Unit 0.391 0.007

Ten or More Adrec PIKs in Housing Unit 0.271 0.007

IRS1040 Here 1.751 0.014

IRS 1040 Here*1st-Stage Match Propensity 3.522 0.036

IRS 1040 Elsewhere 0.537 0.004

IRS 1040 Elsewhere*1st-Stage Match Propensity 0.421 0.004

NCOA Here 0.102 0.002

NCOA Here*1st-Stage Match Propensity 90.056 2.736

NCOA Elsewhere 1.454 0.013

NCOA Elsewhere*1st-Stage Match Propensity 0.143 0.003

VSGI-NAR Here 1.511 0.056

VSGI-NAR Here*1st-Stage Match Propensity 0.850 0.047

VSGI-NAR Elsewhere 1.136 0.043

VSGI-NAR Elsewhere*1st-Stage Match Propensity 0.844 0.048

 

12 The rationale for the interactions is that the location where a source lists a person should carry more weight if the 

first-stage match propensity is high. For the three sources without person information in 2010, dummy variables are 

included for whether the source has at least one record for the housing unit and interactions between those dummy 

variables and their first-stage occupancy probability from a housing unit status multinomial logit model. 
10

Notes: Sources include all those listed in Table 1, the 2010 Census Unedited File (CUF), and the January 2011 

Master Address File (MAF). This is a logistic regression with a dependent variable equal to one if the administrative 

record address is the same as the census address for the person, and it is zero otherwise. The base categories include 

single-unit structure for housing structure type and not in the 2000 Census for 2000 Census person categories. The 

first-stage occupancy propensities for Texas SNAP, Targus National Address File, and Corelogic come from the 

occupancy models described in footnote 16. The first-stage match propensity is the person-place pair’s predicted 

value from the first-stage regression corresponding to the source the propensity is being interacted with. A 10 

percent random sample of person-place pairs is drawn, and the ones that are at addresses with no U.S. Postal Service 

Undeliverable As Addressed (UAA) received after the questionnaire mailing and with 2010 NRFU fieldwork with 

no POEs are used in the regression. A random sample is taken due to computer processing constraints. The standard 

errors are cluster-adjusted at the housing-unit level. 

Characteristics predicting a discrepancy between a person being at the administrative record address versus the 

census address include being in a small, multi-unit housing structure, an address excluded from the USPS Delivery 

Sequence File (DSF) delivery statistics, reporting mixed races or Hispanic origins across persons assigned to the 

housing unit by administrative records, persons not found in the 2000 Census, and large numbers of persons with 

this address in administrative records. 

For most administrative record sources for a person, having a record from that source at this address is a more 

powerful predictor of an administrative record-census person-place match when this person-place’s match 

propensity from that source’s first-stage regression is high. In addition, if the person has a record from the source at 

a different address from the one being examined, and the person-place match propensity at the other address is high 

(low), then the person’s match propensity at the examined address is reduced (raised). The fact that these results for 

individual sources remain highly significant even when controlling for other sources suggests that agreement among 

the sources improves the probability that the person is enumerated at that address. Each source contributes predictive 

power despite the large number of sources with heterogeneous perceived quality ex ante.13

Using out-of-sample predictions, the second-stage regression produces a propensity for the person to be at a 

particular address for all PIKs alive on Census Day and at an address in the census.14 We use these results to create 

an administrative records composite, selecting the address with the highest propensity for each person’s PIK.15 We 

sum these records to construct the administrative record population count for each housing unit. We use the 

minimum propensity among persons assigned to the housing unit as the housing unit’s administrative records quality 

score.16 

3.3 Predicting Census Enumeration Quality 

With these preparations complete – POEs flagged on the census records and quality scores on the administrative 

records – we can calculate a quality score for each census enumeration. The score is set to one if the enumeration 

has no POEs. For enumerations with POEs, the score equals the mean agreement rate between the census and highquality administrative records17 for the particular combination of POEs the housing unit has in 2010.18

 

 

13 For example, one might assume prior to study that tax records are more reliable than commercial records. 

14 This implicitly assumes that the administrative record characteristics predicting the address match propensity at 

addresses where the census enumeration has no potential errors are the same as the ones predicting the propensity 

for the administrative records to place the person at the correct Census Day address in cases where the census 

enumeration has potential errors and/or had a self-response. 

15 Each person is assigned a single address, because the decennial census aims to count each person once in a single 

residence. For datasets with multiple implicates, such as the Longitudinal Employer-Household Dynamics (LEHD) 

program, one could consider assigning fractions of persons to each of the addresses found for the person in 

administrative records, with weights based on the relative propensities to match to the census. 

16 We have also tried using the mean propensity among persons assigned to the housing unit to rank housing units, 

and that ranking is highly correlated with the minimum propensity score ranking. 

17 High-quality administrative records are defined as follows. High-quality USPS Undeliverable As Addressed 

(UAA) for vacancy reasons (UAA-vacant) and non-UAA housing units have an occupancy probability of two 

percent or less or a likelihood that the administrative record population count matches the census count of 90 percent 

or more, while high-quality UAA for other reasons (UAA-other) housing units have an occupancy probability of 
11

We then use these enumeration quality scores as the dependent variable in models predicting the quality of census 

enumerations by mode (self-response or NRFU fieldwork). 19 We employ a quasi-likelihood function, using a 

binomial family variance with a logistic link, since the dependent variable takes on values in the 0-1 interval.20

Housing units with a self-response in 2010 are eligible to be included in the self-response logistic regression models 

for this dependent variable. The explanatory variables are aggregated to the housing unit level, using shares of 

individuals having each characteristic (e.g., in a particular age category). The coefficients are applied to all housing 

units. Analogously, NRFU housing units are eligible to be in the NRFU logistic regression models. As is the case in 

the person-place models above, the second-stage models include dummy variables for whether each source has any 

records for the housing unit, plus interactions between those variables and the first-stage propensities from those 

sources. 

Full results are shown in Appendix C. In the first-stage self-response enumeration quality regressions for IRS 1040, 

NCOA, and VSGI-NAR, we find the following variables are positively associated with a high-quality census 

enumeration via self-response: 

 Persons aged 65-74, 

 Married couples, 

 High stability of the household roster across the 2008 and 2009 IRS 1040 filings, and 

 Middle income. 

The following variables are associated with low-quality census enumeration via self-response: 

 Deceased individuals, 

 Males, 

 Persons aged 18-24, 

 Minorities, 

 Persons with Schedule C filings, 

 Persons on an IRS 1040 return as a dependent at one address and on another return as a non-dependent at a 

second address, 

 Unvalidated records, 

 Frequent moves, and 

 particularly moves near Census Day. 

Results for the second-stage regression are shown in Appendix Table C4. The following characteristics are 

associated with poor-quality self-responses: 

 mobile homes and small multi-unit structures, 

 addresses deleted or with imputed responses in the 2000 Census, 

 

five percent or less or a population count match likelihood of 80 percent or more. The values are less strict for UAAother, because too few UAA-other housing units meet the more strict criteria to be able to produce reliable 

estimates. Occupancy probabilities come from a series of multinomial logit regression models using occupied vs. 

vacant vs. delete in the Census as the dependent variable, focusing on housing units without potential errors. As with 

the person-place models, we first run separate occupancy regressions by administrative record source to obtain 

propensities for each source-address pair, then run a second-stage regression using dummies for present at this 

address, present interacted with the vacant propensity from the first-stage regression, and present interacted with the 

delete propensity from the first-stage regression as explanatory variables, along with various characteristics from the 

MAF. 

18 We calculate means for each pairwise combination of potential errors, provided they have at least 100 

observations. For housing units with more than two potential errors, we use the minimum value from among their 

pairwise potential error combinations’ means. 

19 Not reported here, we have also estimated separate models by NRFU fieldwork contact attempt number and for 

proxy responses. The NRFU results shown here are for all NRFU contact attempt numbers, and they include 

household member and proxy responses. 

20 Wedderburn (1974) was the first to suggest this model for such dependent variables. Hardin and Hilbe (2007) 

show how to implement it in Stata. 
12

 excluded from DSF delivery statistics, 

 2010 address canvassing or otherwise added addresses, 

 addresses with an additional questionnaire sent, 

 bilingual questionnaires, and 

 low first-stage response quality propensities. 

Appendix D displays regression results for fieldwork enumeration quality. Unlike with self-response quality, 

deceased persons and addresses not in the DSF delivery statistics are highly positively associated with fieldwork 

quality. People may self-respond in March, then pass away before Census Day, leading to an enumeration error. In 

contrast, NRFU fieldwork occurs after the person’s death, and neighbors are likely to know about the person’s 

passing. Persons 75 or over are more strongly positively associated with fieldwork quality than self-response quality, 

possibly because they are more homebound than other age groups. Higher-income and owner-occupied households 

are also more strongly positively associated with fieldwork quality than self-response quality. Otherwise, the 

patterns are similar to those for self-response quality. 

3.4 Comparing Administrative Record and Enumeration Quality Predictions Against a Post-Enumeration 

Survey 

We calculate agreement rates among the administrative record count, census count, and the CCM count for housing 

units grouped by potential errors, focusing on housing units with high-quality administrative records. These results, 

displayed in Table 6, exhibit higher CCM-census agreement rates than those in Table 3 that also include housing 

units with lower-quality administrative records, suggesting that survey-style enumeration quality is positively 

correlated with administrative record quality. As is the case in Table 3, these results show that all cases flagged as 

potential sources of error have lower levels of agreement across sources than cases that have no flags. The number 

of potential errors is also negatively correlated with percent agreement. 21 Of special interest is that addresses with 

household moves have lower agreement rates between either the CCM or the census and administrative records than 

between the CCM and the census. The CCM and the census, which are both survey-style sources, may well suffer 

from the same measurement error; the CCM appears to have particular difficulty handling moves, possibly due to 

the several month lag between Census Day and the fieldwork. 

Application of the average CCM-census agreement rates for each POE or combination of POEs to non-CCM 

housing units with those POEs could be considered as an alternative approach to assessing housing unit-level census 

enumeration quality. The CCM is a relatively s mall survey, however, resulting in a s mall number of observations for 

each particular type of POE and thus estimates with a low level of confidence. And the apparent correlation in 

census and CCM enumeration difficulties may make administrative records with high predicted quality a preferable 

benchmark. 

 

21 Agreement here means the two sources have the same housing unit population count. 
13

Table 6. Percent Agreement between CCM, Census, and Administrative Record Household Counts by Potential 

Observable Error (POE) Type, High-Quality Administrative Records Sample 

Error Type CCMAdministrative 

Record Agreement 

Rate

CensusAdministrative 

Record Agreement 

Rate

CCM-Census

Agreement Rate

Number of 

Observations

All Observations 93.6 94.9 94.9 15,743

No POEs 95.6 97.7 97.2 13,773

At Least One POE 78.6 74.6 79.0 1,970

One POE 81.6 81.8 86.3 1,451

Two or More POEs 69.2 52.4 56.6 519

Not Alive 90.7 86.7 83.5 124

Duplicate 68.0 55.2 65.1 410

Occupied Proxy 77.1 69.2 69.4 293

Unvalidated Persons 70.4 52.8 66.5 481

Conflicting 

Responses

66.4 60.9 60.8 79

Moved In Before 

4/1, Not Counted

47.0 56.7 73.5 78

Moved Out After 

4/1, Not Counted 

73.6 79.3 81.8 50

Count ≠ Number of 

Persons, CFU

85.7 71.2 72.0 61

Count ≠ Number of 

Persons, Non-CFU

85.0 79.6 79.2 264

Yes to Undercount 

Question, CFU

71.3 66.5 78.8 84

Yes to Overcount 

Question, CFU

86.9 71.9 69.8 90

Yes to Overcount 

Question, Non-CFU

82.4 84.8 82.8 512

Sources: all person-address administrative record sources in Table 1, the 2010 Census Decennial Response File 

(DRF), the 2010 Census Unedited File (CUF), and the 2010 Census Coverage Measurement survey (CCM). These 

are weighted using CCM weights. Only housing units with high-quality administrative records and in the CCM are 

included here. 

Finally, we examine the usefulness of our administrative record quality scores for predicting agreement among 

administrative records, Census, and CCM housing unit population counts. We do this by sorting housing units by 

their predicted administrative record-census agreement rates. Here the predicted administrative record-census 

agreement rate is the mean agreement between administrative records and Census enumerations without POEs 

separately for 100 administrative record quality score one percentage point bins, using all housing units with at least 

one administrative record and no POEs for these calculations.22 For 17 groups of these predicted agreement rates (0-

9.99, 10-19.99, 20-24.99,..., 85-89.99, 90-100), 23 we calculate the actual agreement rates among administrative 

record counts, census counts, and CCM counts for the housing units in our CCM sample, and we display them in 

Figures 2-4. The X-axis represents the 17 predicted administrative record-census agreement rate groups in ascending 

order (each value on the X-axis is displayed at the upper value of the range for each group). The Y-axis is the 

percent of the housing units with the same population count across the two or three sources. In addition to pair-wise 

 

22 These agreement rates are monotonically increasing in the quality score. 

23 We use five-percentage-point groups here, as single-percentage-point bins have too few observations. Values in 

the tails are particularly scarce, so we group together 0-9.99, as well as 90-100.
14

and three-way agreement among the administrative record composite, the CCM, and the census, we also display 

predicted Census enumeration quality produced by the model in the previous subsection.24

 

Figure 2, which includes housing units both with and without census POEs, shows that the agreement rates 

involving administrative records range from the teens to the 90’s, increasing monotonically with the administrative 

record quality score. The CCM -census agreement rate also increases with administrative record quality, with a 

variation of over 30 percentage points across the administrative record quality score distribution. 

Predicted census enumeration quality is also monotonically increasing in administrative record quality scores, again 

suggesting that census enumeration and administrative record enumeration both tend to be more difficult in the same 

housing units. The census quality line has a much more gradual slope than that of the CCM-census agreement rate, 

reflecting the difficulty the models have at predicting which housing units are likely to have poor-quality census 

enumerations. The gap between the two lines is roughly half the distance between the CCM -census agreement rate 

and 100 percent in the lower part of the administrative record quality range. If one were to assume that when the 

CCM and the census disagree, each is “correct” half the time (rather than both being “incorrect”), then this gap is 

about right. 

Predicted census enumeration quality and especially the CCM-census agreement rate are much lower when the 

census enumeration has at least one POE (Figure 3) than it is for those with none (Figure 4). The actual 

administrative record agreement rates are less strongly associated with predicted administrative record-census 

agreement when the census enumeration has at least one POE. At the 90 percent predicted administrative recordcensus agreement level, the CCM-administrative record agreement rate is 96 percent without POEs in the census 

enumeration, but it is only 80 percent when there is at least one potential error in the Census. This again suggests 

that the census and the CCM tend to have enumeration difficulties in the same housing units. 

Note, however, that the models are estimated using census enumerations without POEs, so the predictions in Figure 

3 are all out of sample. A potential weakness of our application of non-POE housing units to study associations 

between various characteristics and census-administrative record agreement to POE housing units is that there may 

be unobservable systematic differences between POE and non-POE housing units (e.g., POE housing units may 

have a higher rate of household moves not captured in administrative records than non-POE housing units do). The 

fact that all the agreement rates in Figure 3 are monotonically increasing in administrative record quality suggests 

that the models’ administrative record-census predicted agreement rates are still highly relevant for POE housing 

units. 

 

24 This is predicted self-response quality for housing units with a self-response in 2010 and predicted fieldwork 

quality for all other housing units. 
15

Figure 2. Variation in Housing Unit Population Count Agreement by Administrative Record Quality: Housing Units with Persons in Administrative Record 

Sources, Census Enumerations with or without POEs 

Notes: Sources include the 2010 Census Unedited File (CUF), the 2010 Census Coverage Measurement survey (CCM), all administrative re cord sources listed in 

Table 1, and the January 2011 Census Master Address File (MAF). These numbers exclude USPS Undeliverable As Addressed (UAA) housing units, as many of 

them are unoccupied. 

0

10

20

30

40

50

60

70

80

90

100

10 20 30 40 50 60 70 80 90

Percent HU Population Count 

Agreement

Predicted AR-Census HU Population Count Agreement Rate

Predicted Census

Quality

CCM-Census

Census-AR

CCM-AR

CCM-Census-AR
16

Figure 3. Variation in Housing Unit Population Count Agreement by Administrative Record Quality: Housing Un its with Persons in Administrative Records, 

Census Enumerations with At Least One POE

Notes: Sources include the 2010 Census Unedited File (CUF), the 2010 Census Coverage Measurement survey (CCM), all administrative re cord sources listed in 

Table 1, and the January 2011 Census Master Address File (MAF). These numbers exclude USPS Undeliverable As Addressed (UAA) housing units, as many of 

them are unoccupied. 

0

10

20

30

40

50

60

70

80

90

100

10 20 30 40 50 60 70 80 90

Percent HU Population Count 

Agreement

Predicted AR-Census HU Population Count Agreement Rate

Predicted Census

Quality

CCM-Census

Census-AR

CCM-AR

CCM-Census-AR
17

Figure 4. Variation in Housing Unit Population Count Agreement by Administrative Record Quality: Housing Units with Persons in Administrative Records, 

Census Enumerations with No POEs 

Notes: Sources include the 2010 Census Unedited File (CUF), the 2010 Census Coverage Measurement survey (CCM), all administra tive record sources listed in 

Table 1, and the January 2011 Census Master Address File (MAF). These numbers exclude USPS Undeliverable As Addressed (UAA) housing units, as many of 

them are unoccupied. 

0

10

20

30

40

50

60

70

80

90

100

10 20 30 40 50 60 70 80 90

Percent HU Population Count 

Agreement

Predicted AR-Census HU Population Count Agreement Rate

Predicted Census

Quality

CCM-Census

Census-AR

CCM-AR

CCM-Census-AR
18

4. Conclusion 

This paper demonstrates multiple methods to assess data quality and to explore the accuracy of respondent-provided 

data, proxy-provided data, and administrative records. Our findings focus on the decennial census, but our 

processes can be applied to other surveys or evaluations.25 Future research may extend our study of potential 

observable errors, particularly for the timing of enumerations. Potential errors associated with move timing can help 

understand whether respondents (or administrative records) engage in a de jure vs. de facto census. Our 

administrative record quality scores can serve as housing-unit-level hard-to-count scores. These scores may inform 

decisions about whether to use administrative records or fieldwork to enumerate individual housing units. Finally, 

our approach to forming an administrative record composite can assist research and planning for decennial census 

and adaptive design applications, providing a rigorous, repeatable process to compile multiple sources . 

 

25 Note that our evaluation of these methods is limited to housing unit population count, while surveys collect many 

other types of data as well. We leave analysis of how well administrative records can help with collection of other 

data items to future research. 
19

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Methodology (2nd edn). Hoboken, NJ: John Wiley & Sons. 

Hardin, James W., and Joseph M. Hilbe, 2007. Generalized Linear Models and Extensions, Second Edition. College 

Station, Texas: Stata Press. 

Mulry, M.H., Bean, S.L., Bauder, D.M., Wagner, D., Mule, T., and Petroni, R.J. (2006). Evaluation of Estimates of 

Census Duplication Using Administrative Records Information. 

Mule, Tom, 2008. “2010 Census Coverage Measurement Estimation Methodology.” DSSD 2010 Census Coverage 

Measurement Memorandum Series #2010-E-18. 

Pritts, Mary, 2013. “2010 Census: Comparisons of Unit Status and Household Size Between Census and Census 

Coverage Measurement Addresses.” DSSD 2010 Decennial Census Memorandum Series #J-15.

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Opinion. Downloaded from http://www.law.cornell.edu/supct/html/01-714.ZS.html on June 7, 2013. 

Meyer, Bruce D., and Robert M. Goerge, 2011. “Errors in Survey Reporting and Imputation and their Effects on 

Estimates of Food Stamp Program Participation,” working paper. 

Mule, Thomas, 2012. “Census Coverage Measurement Estimation Report: Summary of Estimates of Coverage for 

Persons in the United States,” DSSD 2010 Census Coverage Measurement Memorandum Series #2010-G-01. 

Rastogi, Sonya, and Amy O’Hara. 2012. “2010 Cens us Match Study.” 2010 Census Planning Memorandum Series, 

No. 247. 

Wedderburn, Robert W.M., 1974. “Quasi-likelihood Functions, Generalized Linear Models, and the Gauss -Newton 

Method.” Biometrika 61(3): 439-447. 
20

Appendix A: Associations Between Potential Observable Errors (POEs) and NCOA Moves 

One would expect enumeration to be more difficult when household moves occur near Census Day. If the NCOA 

data accurately record moves, and if our list of potential observable enumeration errors (POEs) capture actual 

enumeration problems, then NCOA moves near Census Day should be associated higher rates of potential errors. 

We examine these correlations both as a way to judge the quality of the NCOA data and for further exploration of 

the reasonableness of the potential observable enumeration error flags. Figure A1 shows the variation in the share of 

nonresponding housing units by moving activity as recorded in the NCOA, focusing on housing units classified as 

occupied in the decennial census. Housing units containing people moving out just prior to Census Day and moving 

in soon after Census Day are in the NRFU universe more often than other units. Such housing units should have 

high rates of enumeration error; if the NCOA data are accurate, these housing units are particularly likely to have 

been vacant on Census Day (and hence the nonresponse), while they are classified as occupied in the Census. In 

contrast, housing units with inmover before Census Day or post-Census-Day outmovers experience similar 

nonresponse rates to nonmovers, which is to be expected given that those housing units were apparently occupied on 

Census Day. 

Figure A1. Percent of Housing Units in NRFU by Month of Arrival/Departure 

 

Sources: 2009-2010 USPS NCOA records and the 2010 Census Decennial Response File (DRF). 

The percentage of non-POE enumerations among occupied NRFU housing units by NCOA move status is shown in 

Figure A2. Departures are associated with higher rates of potential errors than non-mover housing units, especially 

when they occur in the months straddling Census Day, with similar rates for departures before and after Census Day. 

Post-Census-Day arrivals experience more potential errors than non-movers do, while pre-Census-Day arrivals do 

not, consistent with the hypothesis that the housing units with post-Census-Day arrivals are particularly likely to be 

vacant on Census Day, despite being classified as occupied in the Census. The associations between NCOA move 

timing and individual POEs exhibit similar patterns, with POE rates peaking for March or April NCOA departures 

and May-June NCOA arrivals, as shown in Figures A3-A7. 

0

10

20

30

40

50

60

70

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Percent in NRFU

Month of Arrival/Departure

No Move

Arrival

Departure
21

Figure A2. Percent of Non-POE NRFU Housing Units by Move Type and Month 

Sources: 2009-2010 USPS NCOA records and the 2010 Census Decennial Response File (DRF). 

 

0

5

10

15

20

25

30

35

40

45

50

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Percent Non-POE

Month of Arrival/Departure

No Move

Arrival

Departure
22

Figure A3. Percent of NRFU Housing Units with Persons Duplicated Elsewhere by Move Type and Month 

Sources: 2009-2010 USPS NCOA records, the 2010 Census Decennial Response File (DRF), and the 2010 Census 

Unedited File (CUF). 

Figure A4. Percent of NRFU Housing Units with Occupied Proxy Response by Move Type and Month 

Sources: 2009-2010 USPS NCOA records, the 2010 Census Decennial Response File (DRF), and the 2010 Census 

Unedited File (CUF). 

0

5

10

15

20

25

30

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Percent of Housing Units with Persons 

Responding Elsewhere in Census

Month of Arrival/Departure

No Move

Arrival

Departure

0

5

10

15

20

25

30

35

40

45

50

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Percent of Housing Units with 

Proxy Response

Month of Arrival/Departure

No Move

Arrival

Departure
23

Figure A5. Percent of NRFU Housing Units with Unvalidated Persons by Move Type and Month 

Sources: 2009-2010 USPS NCOA records, the 2010 Census Decennial Response File (DRF), and the 2010 Census 

Unedited File (CUF). 

Figure A6. Percent of NRFU Housing Units with Conflicting Responses by Move Type and Month 

Sources: 2009-2010 USPS NCOA records and the 2010 Census Decennial Response File (DRF). 

0

5

10

15

20

25

30

35

40

45

50

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Percent of Housing Units with 

Unvalidated Persons

Month of Arrival/Departure

No Move

Arrival

Departure

0

2

4

6

8

10

12

14

16

18

20

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Percent of Housing Units with 

Conflicting Responses

Month of Arrival/Departure

No Move

Arrival

Departure
24

Figure A7. Percent of NRFU Housing Units with Different Household Count and Number of Listed Persons by 

Move Type and Month 

Sources: 2009-2010 USPS NCOA records, the 2010 Census Decennial Response File (DRF), and the 2010 Census 

Unedited File (CUF). 

0

2

4

6

8

10

12

14

16

18

20

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Percent of Housing Units where Count 

and Number of Listed Persons are 

Unequal

Month of Arrival/Departure

No Move

Arrival

Departure
25

Appendix B: Person-Place Logistic Regressions 

Table B1. Person-Place Logistic Regression with IRS 1040 Data 

Variable Odds Ratio Standard Error

Male 0.812 0.004

Age 0-2 2.870 0.039

Age 3-17 2.884 0.029

Age 18-24 0.731 0.005

Age 45-64 1.096 0.007

Age 65-74 0.677 0.008

Age 75+ 0.450 0.006

Hispanic 0.800 0.006

African-American 0.592 0.003

American Indian/Alaska Native 0.787 0.015

Asian 0.967 0.013

Native Hawaiian/Pacific Islander 0.885 0.047

Some Other Race 1.020 0.013

Multi-Race 1.035 0.016

Married Filing Jointly 2.792 0.020

Married Filing Separately 1.092 0.016

Filing as Household Head 1.121 0.008

Filing as Widow 2.304 0.177

Return has Secondary Filer 0.692 0.008

Return has At Least One Dependent 1.606 0.015

Is Secondary Filer 0.735 0.005

Is Dependent 0.423 0.004

Return has Child Away 0.766 0.026

Is Dependent*Return has Child Away 0.262 0.014

Return Contains Schedule C 1.023 0.009

Return Contains Schedule D 1.028 0.009

Return Contains Schedule E 0.901 0.008

Return Contains Schedule F 0.859 0.018

Return Contains Schedule SE 0.848 0.008

U.S. Citizen 0.866 0.007

Legal Alien, Authorized to Work 0.760 0.014

Legal Alien, Not Authorized to Work 0.534 0.021

Other Alien 0.225 0.012

Alien Student, Restricted Work Authorized 0.951 0.067

Conditionally Legalized Alien 0.645 0.044

Ever Alien 0.885 0.013

ITIN 1.448 0.764

Both 2008 & 2009 1040 Return Here 2.289 0.011

Electronic Filer 0.974 0.007

IRS Processing Week 4 0.519 0.006

IRS Processing Week 5 0.610 0.006

IRS Processing Week 6 0.693 0.006

IRS Processing Week 7 0.759 0.007

IRS Processing Week 8 0.820 0.010

IRS Processing Week 9 0.879 0.011

IRS Processing Week 10 0.909 0.012

IRS Processing Week 11 0.958 0.014

IRS Processing Week 12 1.030 0.016

IRS Processing Week 13 1.067 0.018

IRS Processing Week 14 1.087 0.018

IRS Processing Week 15 1.084 0.018

IRS Processing Week 16 1.164 0.015
26

IRS Processing Week 17 1.153 0.017

IRS Processing Week 18 1.198 0.019

IRS Processing Week 19 1.136 0.018

IRS Processing Week 20 1.144 0.020

IRS Processing Week 21 1.112 0.024

IRS Processing Week 22 1.111 0.027

IRS Processing Week 23 1.145 0.033

IRS Processing Week 24 1.122 0.029

IRS Processing Week 25 1.061 0.029

IRS Processing Week 26 0.859 0.032

IRS Processing Week 27 0.813 0.033

IRS Processing Week 28 0.851 0.039

IRS Processing Week 29 0.815 0.036

IRS Processing Week 30 0.822 0.037

IRS Processing Week 31 0.805 0.039

IRS Processing Week 32 0.743 0.037

IRS Processing Week 33 0.821 0.044

IRS Processing Week 34 0.821 0.043

IRS Processing Week 35 0.766 0.040

IRS Processing Week 36 0.749 0.040

IRS Processing Week 37 0.686 0.046

IRS Processing Week 38 0.782 0.046

IRS Processing Week 39 0.708 0.041

IRS Processing Week 40 0.784 0.043

IRS Processing Week 41 0.784 0.038

IRS Processing Week 42 0.791 0.033

IRS Processing Week 43 0.870 0.027

IRS Processing Week 44 0.932 0.026

IRS Processing Week 45 0.956 0.025

IRS Processing Week 46 0.808 0.035

IRS Processing Week 47 0.792 0.052

IRS Processing Week 48 0.867 0.066

IRS Processing Week 49 0.827 0.058

IRS Processing Week 50 0.833 0.058

IRS Processing Week 51 0.806 0.061

IRS Processing Week 52 0.802 0.067

Pseudo-R2 0.101

Number of Observations 1,927,105

Notes: Sources include 2008-2009 IRS 1040 records and the 2010 Census Unedited File (CUF). The base categories 

are 25-44 for age, white for race, missing citizenship for citizenship, single filer for filing status, and missing for 

Internal Revenue Service (IRS) processing week. Dummy variables for missing gender, Hispanic origin, and race 

are also included. A 10 percent random sample of 2009 IRS 1040 person-place pairs is drawn, and the ones at 

addresses with 2010 NRFU fieldwork with no POEs are used in the regression. Predicted values are applied to all 

2009 IRS 1040 person-place pairs. A random sample is taken due to computer processing constraints. The standard 

errors are robust. 

Table B2. Person-Place Logistic Regression with NCOA Data 

Variable Odds Ratio Standard Error

Male 0.889 0.003

Age 0-2 2.124 0.030

Age 3-17 1.858 0.015

Age 18-24 0.728 0.003

Age 45-64 1.034 0.004

Age 65-74 0.995 0.009

Age 75+ 0.852 0.010

Hispanic 0.914 0.005
27

African-American 0.850 0.004

American Indian/Alaska Native 0.850 0.011

Asian 1.080 0.010

Native Hawaiian/Pacific Islander 1.189 0.040

Some Other Race 1.028 0.010

Multi-Race 1.071 0.011

Destination Address in May 2009 0.939 0.006

Destination Address in June 2009 1.001 0.007

Destination Address in July 2009 1.037 0.007

Destination Address in August 2009 1.054 0.007

Destination Address in September 2009 1.069 0.007

Destination Address in October 2009 1.099 0.008

Destination Address in November 2009 1.150 0.008

Destination Address in December 2009 6.171 0.072

Destination Address in January 2010 6.209 0.072

Destination Address in February 2010 6.400 0.077

Destination Address in March 2010 6.792 0.072

Destination Address in April 2010 0.033 0.0004

Departure Address in April 2009 0.019 0.0003

Departure Address in May 2009 0.017 0.0002

Departure Address in June 2009 0.015 0.0002

Departure Address in July 2009 0.015 0.0002

Departure Address in August 2009 0.014 0.0002

Departure Address in September 2009 0.015 0.0002

Departure Address in October 2009 0.015 0.0002

Departure Address in November 2009 0.014 0.0002

Departure Address in December 2009 0.009 0.0002

Departure Address in January 2010 0.010 0.0002

Departure Address in February 2010 0.009 0.0001

Departure Address in March 2010 0.006 0.0001

Departure Address in April 2010 0.510 0.006

U.S. Citizen 0.884 0.005

Legal Alien, Authorized to Work 0.915 0.011

Legal Alien, Not Authorized to Work 0.714 0.024

Other Alien 0.479 0.035

Alien Student, Restricted Work Authorized 0.914 0.035

Conditionally Legalized Alien 0.783 0.049

Ever Alien 1.116 0.012

ITIN 0.874 0.258

Family Move 1.234 0.004

Undeliverable Flag F 0.137 0.028

Undeliverable Flag G 0.119 0.016

Undeliverable Flag K 0.814 0.011

Changed Address (vs. Added Address) 0.259 0.0008

PVS Pass 1 2.097 0.073

PVS Pass 1*PVS Score 0.983 0.001

PVS Pass 2 0.577 0.448

PVS Pass 2*PVS Score 1.055 0.041

PVS Pass 3 1.286 0.049

PVS Pass 3*PVS Score 1.012 0.002

PVS Pass 4 1.076 0.036

PVS Pass 4*PVS Score 1.008 0.001

PVS Pass 5 0.858 0.159

PVS Pass 5*PVS Score 1.021 0.008

PVS Pass 6 0.281 0.035

PVS Pass 6*PVS Score 1.045 0.005
28

PVS Pass 7 0.056 0.187

PVS Pass 7*PVS Score 1.145 0.190

IRS Family Member 4.141 0.041

CHUMS Family Member 1.358 0.021

HUD PIC Family Member 1.590 0.027

HUD TRACS Family Member 1.708 0.067

Medicare Family Member 0.867 0.019

SSR Family Member 0.624 0.017

Experian-EDR Family Member 0.747 0.008

Experian-Insource Family Member 0.971 0.010

InfoUSA Family Member 0.604 0.007

Targus-Consumer Family Member 1.060 0.011

VSGI-NAR Family Member 1.029 0.011

Pseudo-R2 0.5447

Number of Observations 6,653,884

Notes: Sources include 2009-2010 NCOA records and the 2010 Census Unedited File (CUF). The base categories 

are 25-44 for age, white for race, missing citizenship for citizenship, destination address in April 2010 for address, 

and added address for changed vs. added address. Dummy variables for missing gender, Hispanic origin, and race 

are also included. Person-place pairs in 2009-2010 National Change of Address (NCOA) data at addresses with 

2010 NRFU fieldwork with no POEs are used in the regression. Predicted values are applied to all person-place 

pairs in 2009-2010 NCOA data. The standard errors are robust. 

Table B3. Person-Place Logistic Regression with VSGI-NAR Data 

Variable Odds Ratio Standard Error

Male 0.889 0.001

Age 0-17 1.781 0.022

Age 18-24 0.588 0.003

Age 45-64 1.275 0.002

Age 65-74 0.957 0.003

Age 75+ 0.609 0.002

Hispanic 0.978 0.003

African-American 0.864 0.002

American Indian/Alaska Native 0.940 0.007

Asian 0.996 0.005

Native Hawaiian/Pacific Islander 1.020 0.022

Some Other Race 1.036 0.005

Multi-Race 1.022 0.006

Missing Race 0.786 0.004

Owner 1.510 0.003

Renter 0.583 0.002

Number of Persons 0.835 0.0007

Log Length of Residence 1.206 0.0008

Income <$20,000 0.531 0.002

Income $20,000-29,999 0.585 0.002

Income $30,000-39,999 0.648 0.002

Income $40,000-49,999 0.705 0.002

Income $50,000-74,999 0.788 0.002

Income $75,000-99,999 0.907 0.003

Income $100,000-124,999 0.963 0.003

Income $125,000-149,999 0.918 0.004

U.S. Citizen 0.891 0.002

Legal Alien, Authorized to Work 0.976 0.006

Legal Alien, Not Authorized to Work 0.592 0.010

Other Alien 0.483 0.017

Alien Student, Restricted Work Authorized 0.853 0.019

Conditionally Legalized Alien 0.864 0.024
29

Ever Alien 1.017 0.006

ITIN 0.967 0.018

Pseudo-R2 0.0480

Number of Observations 9,388,414

Notes: Sources include 2010 Veteran Service Group of Illinois Name and Address Resource Consumer file (VSGINAR) records and the 2010 Census Unedited File (CUF). The base categories are 25-44 for age, white for race, 

missing citizenship for citizenship, $150,000 and above for income, and missing tenure for tenure. Dummy variables 

for missing race and length of residence are also included. Person-place pairs in VSGI-NAR records at addresses 

with 2010 NRFU fieldwork with no POEs are used in the regression. Predicted values are applied to all person-place 

pairs in 2010 VSGI-NAR records. The standard errors are robust. 

Table B4. Second-Stage Person-Place Match Logistic Regression 

Variable Odds Ratio Standard Error

Update/Leave 0.845 0.015

Military 1.032 0.081

Urban Update/Leave 0.781 0.018

City-Style, No DSF 0.363 0.102

City-Style, Some DSF 0.394 0.108

City-Style, All DSF 0.421 0.116

City-Style and Noncity-Style, no DSF 0.229 0.061

City-Style (95-99.99%) and Noncity-Style, some DSF 0.408 0.112

City-Style (90-94.99%) and Noncity-Style, some DSF 0.388 0.106

City-Style (85-89.99%) and Noncity-Style, some DSF 0.379 0.103

City-Style (80-84.99%) and Noncity-Style, some DSF 0.358 0.097

City-Style (75-79.99%) and Noncity-Style, some DSF 0.338 0.092

City-Style (70-74.99%) and Noncity-Style, some DSF 0.348 0.094

City-Style (<70%) and Noncity-Style, some DSF 0.282 0.075

Assorted Noncity-Style, No DSF 0.232 0.062

Mobile or Other Housing Structure 1.030 0.013

2-4-Unit Housing Structure 0.863 0.014

5-9-Unit Housing Structure 1.055 0.020

10-19-Unit Housing Structure 1.101 0.018

20-49-Unit Housing Structure 1.070 0.017

50+-Unit Housing Structure 1.066 0.015

Housing Unit Not in 2000 Decennial 1.031 0.009

Housing Unit Unoccupied in 2000 Decennial 0.979 0.020

Spring 2010 DSF Deliverable Flag 1.267 0.142

Spring 2010 DSF X Flag 1.368 0.190

6-Month Periods Since Last DSF Deliverable Flag 1.011 0.005

Never Had DSF Deliverable Flags 1.012 0.067

Had DSF Deliverable Flag Every Time Since Fall 2008 0.793 0.014

2000 LUCA Address 1.059 0.022

Post-2000 LUCA Address 1.035 0.070

2010 Address Canvassing Address 1.930 0.066

2010 Decennial Added Address 1.180 0.155

Targeted Block, Additional Form Sent 0.933 0.008

Targeted Block, Additional Form Not Sent 1.075 0.010

Block Blanketed with Second Forms 0.879 0.007

Bilingual Form 0.959 0.009

Business Address 1.029 0.204

Residential, Excluded from Delivery Statistics 0.472 0.021

Built After 2000 1.199 0.097

Has Location Description in MAF 0.934 0.018

Missing DSF Route 1.193 0.110

MAF Valid Unit Status 3.940 0.215

Texas SNAP Here 0.731 0.172
30

Texas SNAP Here*Occupancy Propensity 1.570 0.573

Targus National Address File Here 0.530 0.018

Targus National Address File Here*Occupancy Propensity 0.911 0.044

Corelogic Here 0.804 0.013

Corelogic Here*Occupancy Propensity 1.112 0.032

In 2000 Census Here 1.168 0.010

In 2000 Census Elsewhere 1.280 0.007

Same Race for All Persons in Housing Unit 1.079 0.007

Same Hispanic Origin for All Persons in Housing Unit 1.022 0.009

Two Adrec PIKs in Housing Unit 0.891 0.009

Three Adrec PIKs in Housing Unit 0.646 0.007

Four Adrec PIKs in Housing Unit 0.608 0.007

Five Adrec PIKs in Housing Unit 0.570 0.007

Six Adrec PIKs in Housing Unit 0.514 0.006

Seven Adrec PIKs in Housing Unit 0.466 0.006

Eight Adrec PIKs in Housing Unit 0.439 0.007

Nine Adrec PIKs in Housing Unit 0.391 0.007

Ten or More Adrec PIKs in Housing Unit 0.271 0.007

IRS1040 Here 1.751 0.014

IRS 1040 Here*1st-Stage Match Propensity 3.522 0.036

IRS 1040 Elsewhere 0.537 0.004

IRS 1040 Elsewhere*1st-Stage Match Propensity 0.421 0.004

IRS 1099 Here 0.724 0.010

IRS 1099 Here*1st-Stage Match Propensity 2.028 0.048

IRS 1099 Elsewhere 0.562 0.007

IRS 1099 Elsewhere*1st-Stage Match Propensity 1.133 0.027

HUD CHUMS Here 0.232 0.010

HUD CHUMS Here*1st-Stage Match Propensity 24.905 1.893

HUD CHUMS Elsewhere 1.785 0.076

HUD CHUMS Elsewhere*1st-Stage Match Propensity 0.200 0.015

HUD PIC Here 0.584 0.220

HUD PIC Here*1st-Stage Match Propensity 11.670 5.701

HUD PIC Elsewhere 32.105 13.124

HUD PIC Elsewhere*1st-Stage Match Propensity 0.002 0.001

HUD TRACS Here 0.736 0.154

HUD TRACS Here*1st-Stage Match Propensity 7.892 2.509

HUD TRACS Elsewhere 1.287 0.432

HUD TRACS Elsewhere*1st-Stage Match Propensity 0.134 0.057

SSS Here 0.156 0.004

SSS Here*1st-Stage Match Propensity 15.850 1.043

SSS Elsewhere 0.813 0.025

SSS Elsewhere*1st-Stage Match Propensity 1.144 0.107

Medicare Here 0.363 0.024

Medicare Here*1st-Stage Match Propensity 10.525 0.971

Medicare Elsewhere 0.406 0.059

Medicare Elsewhere*1st-Stage Match Propensity 1.415 0.265

IHS Here 0.283 0.027

IHS Here*1st-Stage Match Propensity 16.094 4.979

IHS Elsewhere 1.009 0.116

IHS Elsewhere*1st-Stage Match Propensity 0.333 0.141

NCOA Here 0.102 0.002

NCOA Here*1st-Stage Match Propensity 90.056 2.736

NCOA Elsewhere 1.454 0.013

NCOA Elsewhere*1st-Stage Match Propensity 0.143 0.003

NY SNAP Here 0.212 0.053

NY SNAP Here*1st-Stage Match Propensity 10.636 4.029
31

NY SNAP Elsewhere 0.736 0.143

NY SNAP Elsewhere*1st-Stage Match Propensity 0.368 0.117

SSR Here 0.392 0.035

SSR Here*1st-Stage Match Propensity 13.139 1.867

SSR Elsewhere 0.562 0.071

SSR Elsewhere*1st-Stage Match Propensity 0.323 0.073

Experian-EDR Here 0.428 0.012

Experian-EDR Here*1st-Stage Match Propensity 8.806 0.926

Experian-EDR Elsewhere 0.907 0.023

Experian-EDR Elsewhere*1st-Stage Match Propensity 1.342 0.133

Experian-Insource Here 0.636 0.017

Experian-Insource Here*1st-Stage Match Propensity 2.760 0.113

Experian-Insource Elsewhere 0.881 0.018

Experian-Insource Elsewhere*1st-Stage Match Propensity 0.767 0.024

InfoUSA Here 0.339 0.004

InfoUSA Here*1st-Stage Match Propensity 6.932 0.129

InfoUSA Elsewhere 0.876 0.007

InfoUSA Elsewhere*1st-Stage Match Propensity 0.588 0.010

Melissa Here 0.424 0.007

Melissa Here*1st-Stage Match Propensity 4.618 0.133

Melissa Elsewhere 0.817 0.011

Melissa Elsewhere*1st-Stage Match Propensity 1.227 0.033

Targus-Consumer Here 2.200 0.078

Targus-Consumer Here*1st-Stage Match Propensity 0.966 0.050

Targus-Consumer Elsewhere 0.640 0.018

Targus-Consumer Elsewhere*1st-Stage Match Propensity 1.592 0.070

Targus-Wireless Here 0.614 0.025

Targus-Wireless Here*1st-Stage Match Propensity 1.794 0.146

Targus-Wireless Elsewhere 1.329 0.057

Targus-Wireless Elsewhere*1st-Stage Match Propensity 0.653 0.057

VSGI-NAR Here 1.511 0.056

VSGI-NAR Here*1st-Stage Match Propensity 0.850 0.047

VSGI-NAR Elsewhere 1.136 0.043

VSGI-NAR Elsewhere*1st-Stage Match Propensity 0.844 0.048

VSGI-TRK Here 0.571 0.012

VSGI-TRK Here*1st-Stage Match Propensity 0.904 0.042

VSGI-TRK Elsewhere 0.974 0.023

VSGI-TRK Elsewhere*1st-Stage Match Propensity 0.414 0.021

Pseudo-R2 0.433

Number of Observations 2,487,841

Notes: Sources include all those listed in Table 1, the 2010 Census Unedited File (CUF), and the January 2011 

Master Address File (MAF). The base category for address characteristic type includes the following: non -

residential only, description, assorted noncity-style with some U.S. Postal Service Delivery Sequence File (DSF), 

assorted noncity-style with all DSF, P.O. Box, rural route with some DSF, rural route with all DSF, and no 

addresses found. Other base categories include single-unit structure for housing structure type, no spring 2010 DSF 

flag for spring DSF flag type, other source (mainly addresses in the Master Address File (MAF) prior to 2000) for 

address origin, and not in the 2000 Census for 2000 Census person categories. The first-stage occupancy 

propensities for Texas SNAP, Targus National Address File, and Corelogic come from the occupancy models 

described in footnote 11. The first-stage match propensity is the person-place pair’s predicted value from the firststage regression corresponding to the source the propensity is being interacted with. A 10 percent random sample 

person-place pairs is drawn, and the ones that are at addresses with no U.S. Postal Service Undeliverable As 

Addressed (UAA) received after the questionnaire mailing and with 2010 NRFU fieldwork with no POEs are used 

in the regression. A random sample is taken due to computer processing constraints. The standard errors are clusteradjusted at the housing unit level. 
32

Appendix C: Self-Response Quality Quasi-Likelihood Regressions 

Table C1. Self-Response Quality Quasi-Likelihood Regression with IRS 1040 Data 

Variable Coefficient Standard Error

Deceased -0.437 0.020

Male -0.192 0.004

Age 0-2 0.404 0.014

Age 3-17 0.047 0.008

Age 18-24 -0.548 0.005

Age 45-64 0.021 0.004

Age 65-74 0.085 0.005

Age 75+ -0.071 0.006

Hispanic -0.299 0.005

African-American -0.242 0.004

American Indian/Alaska Native -0.286 0.013

Asian -0.205 0.007

Native Hawaiian/Pacific Islander -0.272 0.039

Some Other Race -0.137 0.008

Multi-Race -0.181 0.010

U.S. Citizen -0.213 0.004

Legal Alien, Authorized to Work -0.204 0.013

Legal Alien, Not Authorized to Work -0.059 0.034

Other Alien -0.130 0.054

Alien Student, Restricted Work Authorized 0.121 0.052

Conditionally Legalized Alien -0.406 0.057

Ever Alien -0.249 0.011

ITIN -0.082 0.026

Married Filing Jointly 0.442 0.004

Married Filing Separately 0.134 0.009

Filing as Household Head -0.368 0.005

Filing as Widow 0.124 0.039

Return Contains Schedule C -0.076 0.004

Return Contains Schedule D 0.073 0.003

Return Contains Schedule E -0.169 0.003

Return Contains Schedule F -0.067 0.009

Return Contains Schedule SE 0.024 0.005

IRS Processing Week 0.028 0.001

IRS Processing Week Squared -0.002 0.00006

IRS Processing Week Cubed 0.00002 0.000001

One PVSed Person 0.221 0.045

Two PVSed Persons -0.012 0.045

Three PVSed Persons -0.236 0.045

Four PVSed Persons -0.288 0.045

Five PVSed Persons -0.445 0.045

Six PVSed Persons -0.584 0.045

Seven or More PVSed Persons -0.735 0.045

One Non-PVSed Record -0.282 0.010

Two Non-PVSed Records 0.017 0.014

Three Non-PVSed Records -0.315 0.026

Four Non-PVSed Records -0.115 0.023

Five or More Non-PVSed Records 0.200 0.017

Dependent PIK Elsewhere for Non-Dependent PIK Here -0.208 0.010

Non-Dependent PIK Elsewhere for Dependent PIK Here -0.238 0.016

2008 IRS 1040 Return at HU -0.128 0.004

Share of IRS 2008, 2009 PIKs in Both Years 0.541 0.003

Electronic Filer -0.008 0.002
33

Pseudo-R2 6,566,713

Number of Observations

Notes: Sources include 2008-2009 IRS 1040 records, the 2010 Census Unedited File (CUF), and the 2010 Census 

Decennial Response File (DRF). This is a quasi-likelihood function using a binomial family variance with a logistic 

link. The base categories are 25-44 for age, white for race, missing citizenship for citizenship, and single filer for 

filing status. Dummy variables for missing gender, age, Hispanic origin, and race are also included. A 10 percent 

random sample of housing units containing 2009 Internal Revenue Service (IRS) 1040 records is drawn. Of those, 

responding housing units are included in the regression. Predicted values are applied to all housing units with 2009 

IRS 1040 records. A random sample is taken due to computer processing constraints. The standard errors are robust. 

Table C2. Self-Response Quality Quasi-Likelihood Regression with National Change of Address Data 

Variable Coefficient Standard Error

Deceased -0.201 0.345

Male 0.015 0.002

Age 0-2 -0.496 0.026

Age 3-17 -0.487 0.010

Age 18-24 -0.105 0.003

Age 45-64 -0.071 0.003

Age 65-74 -0.036 0.006

Age 75+ -0.205 0.006

Hispanic -0.305 0.004

African-American -0.373 0.003

American Indian/Alaska Native -0.162 0.010

Asian -0.164 0.007

Native Hawaiian/Pacific Islander -0.231 0.027

Some Other Race -0.176 0.007

Multi-Race -0.122 0.008

U.S. Citizen -0.039 0.004

Legal Alien, Authorized to Work -0.044 0.009

Legal Alien, Not Authorized to Work 0.007 0.026

Other Alien -0.175 0.047

Alien Student, Restricted Work Author. 0.196 0.034

Conditionally Legalized Alien -0.240 0.044

Ever Alien -0.161 0.007

ITIN -0.735 0.014

One PVSed Person -0.223 0.004

Two PVSed Persons -0.237 0.005

Three PVSed Persons -0.214 0.006

Four PVSed Persons -0.237 0.007

Five or More PVSed Persons -0.238 0.009

Number of Non-PVSed Records -0.088 0.001

Departure Address in April 2009 2.076 0.011

Departure Address in May 2009 2.057 0.011

Departure Address in June 2009 2.082 0.011

Departure Address in July 2009 2.079 0.011

Departure Address in August 2009 2.055 0.011

Departure Address in September 2009 2.063 0.011

Departure Address in October 2009 2.077 0.011

Departure Address in November 2009 2.085 0.011

Departure Address in December 2009 1.939 0.011

Departure Address in January 2010 1.867 0.011

Departure Address in February 2010 1.796 0.011

Departure Address in March 2010 0.804 0.011

Departure Address in April 2010 0.782 0.012

Destination Address in April 2009 2.410 0.016

Destination Address in May 2009 2.575 0.015
34

Destination Address in June 2009 2.664 0.014

Destination Address in July 2009 2.696 0.014

Destination Address in August 2009 2.675 0.014

Destination Address in September 2009 2.644 0.014

Destination Address in October 2009 2.676 0.014

Destination Address in November 2009 2.671 0.015

Destination Address in December 2009 1.904 0.013

Destination Address in January 2010 1.769 0.013

Destination Address in February 2010 1.866 0.013

Destination Address in March 2010 1.483 0.013

Num. Moves, April 2009-March 2010 -0.103 0.001

Family Move -0.123 0.002

Undeliverable Flag F -0.116 0.023

Undeliverable Flag G 0.790 0.029

Undeliverable Flag K 0.099 0.003

Changed Address (vs. Added Address) -0.084 0.002

Number of Observations 7,349,003

Notes: Sources include 2009-2010 NCOA records, the 2010 Census Unedited File (CUF), and the 2010 Census 

Decennial Response File (DRF). This is a quasi-likelihood function using a binomial family variance with a logistic 

link. The base categories are 25-44 for age, white for race, missing citizenship for citizenship, destination address in 

April 2010 for address, and added address for changed vs. added address. Dummy variables for missing gender, 

Hispanic origin, and race are also included. Housing units with a self-response and with 2009-2010 National 

Change of Address (NCOA) records are used in the regression. Predicted values are applied to all housing units with 

NCOA data. The standard errors are robust. 

Table C3. Self-Response Quality Quasi-Likelihood Regression with VSGI-NAR Data 

Variable Coefficient Standard Error

Deceased -0.351 0.006

Male -0.040 0.002

Age 0-17 -0.522 0.014

Age 18-24 -0.479 0.007

Age 45-64 -0.059 0.002

Age 65-74 0.199 0.003

Age 75+ 0.126 0.003

Hispanic -0.425 0.004

African-American -0.493 0.003

American Indian/Alaska Native -0.336 0.010

Asian -0.215 0.005

Native Hawaiian/Pacific Islander -0.387 0.026

Some Other Race -0.227 0.006

Multi-Race -0.282 0.008

Married 0.102 0.002

U.S. Citizen -0.233 0.002

Legal Alien, Authorized to Work -0.226 0.007

Legal Alien, Not Authorized to Work -0.157 0.021

Other Alien -0.212 0.034

Alien Student, Restricted Work Authorized 0.154 0.027

Conditionally Legalized Alien -0.390 0.027

Ever Alien -0.263 0.006

ITIN -0.969 0.025

Income <$20,000 0.006 0.004

Income $20,000-29,999 0.027 0.004

Income $30,000-39,999 0.069 0.004

Income $40,000-49,999 0.108 0.003

Income $50,000-74,999 0.144 0.003

Income $75,000-99,999 0.162 0.003
35

Income $100,000-124,999 0.136 0.003

Income $125,000-149,999 0.088 0.004

Log Length of Residence 0.027 0.0007

Owner 0.122 0.002

Renter 0.139 0.004

Number of Persons 0.053 0.002

One PVSed Person 0.357 0.013

Two PVSed Persons 0.426 0.014

Three PVSed Persons 0.232 0.016

Four or More PVSed Persons 0.053 0.017

One Non-PVSed Record -0.035 0.009

Two or More Non-PVSed Records -0.077 0.012

Number of Observations 11,420,245

Notes: Sources include 2010 Veteran Service Group of Illinois Name and Address Resource Consumer file (VSGINAR) records, the 2010 Census Unedited File (CUF), and the 2010 Census Decennial Response File (DRF). This is 

a quasi-likelihood function using a binomial family variance with a logistic link. The base categories are 25-44 for 

age, white for race, missing citizenship for citizenship, $150,000 and above for income, and missing tenure for 

tenure. Dummy variables for missing gender, Hispanic origin, race, and length of residence are also included. A 20 

percent random sample of housing units with VSGI-NAR records is drawn, and those housing units with a selfresponse are used in the regression. Predicted values are applied to all housing units with 2010 VSGI -NAR records. 

The standard errors are robust. 

Table C4. Second-Stage Self-Response Quality Quasi-Likelihood Regression 

Variable Coefficient Standard Error

Update/Leave 0.215 0.004

Military -0.411 0.015

Urban Update/Leave -0.041 0.006

City-Style, No DSF 0.128 0.020

City-Style, some DSF 0.087 0.018

City-Style, all DSF 0.136 0.018

City-Style and Noncity-Style, no DSF 0.045 0.019

City-Style (95-99.99%) and Noncity-Style, some DSF 0.102 0.018

City-Style (90-94.99%) and Noncity-Style, some DSF 0.074 0.018

City-Style (85-89.99%) and Noncity-Style, some DSF 0.050 0.019

City-Style (80-84.99%) and Noncity-Style, some DSF 0.042 0.019

City-Style (75-79.99%) and Noncity-Style, some DSF 0.023 0.020

City-Style (70-74.99%) and Noncity-Style, some DSF -0.003 0.021

City-Style (<70%) and Noncity-Style, some DSF -0.028 0.019

Assorted Noncity-Style, No DSF 0.064 0.022

Mobile or Other Housing Structure -0.052 0.004

2-4-Unit Housing Structure -0.060 0.004

5-9-Unit Housing Structure 0.126 0.005

10-19-Unit Housing Structure 0.174 0.005

20-49-Unit Housing Structure 0.214 0.005

50+-Unit Housing Structure 0.250 0.004

Housing Unit Not in 2000 Decennial -0.017 0.005

Housing Unit Vacant in 2000 Decennial 0.016 0.003

Housing Unit Deleted in 2000 Decennial -0.054 0.008

Housing Unit Imputed Response in 2000 Decennial -0.081 0.015

Housing Unit Self-Response in 2000 Decennial 0.076 0.002

Spring 2010 DSF Deliverable Flag 0.317 0.015

Spring 2010 DSF X Flag 0.265 0.015

6-Month Periods Since Last DSF Deliverable Flag 0.012 0.0009

Never Had DSF Deliverable Flags 0.239 0.013

Had DSF Deliverable Flag Every Time Since Fall 2008 0.014 0.005

2000 LUCA Address -0.028 0.005
36

Post-2000 LUCA Address -0.019 0.008

2010 Address Canvassing Address -0.103 0.007

2010 Decennial Added Address -0.364 0.010

Targeted Block, Additional Form Sent -0.247 0.003

Targeted Block, Additional Form Not Sent 0.027 0.002

Block Blanketed with Second Forms -0.148 0.002

Bilingual Form -0.140 0.003

Business Address -0.088 0.020

Residential, Excluded from Delivery Statistics -0.121 0.010

Built After 2000 0.076 0.029

Has Location Description in MAF -0.036 0.003

Missing DSF Route 0.051 0.013

MAF Valid Unit Status -0.282 0.014

Mean Number of AR Addresses Per Person -0.026 0.0007

HUD CHUMS Here -0.723 0.079

HUD CHUMS Here* Quality Response Propensity 0.896 0.087

HUD PIC Here -3.575 0.128

HUD PIC Here* Quality Response Propensity 4.356 0.143

HUD TRACS Here -5.483 0.308

HUD TRACS Here* Quality Response Propensity 6.401 0.332

IRS1040 Here -4.656 0.020

IRS1040 Here* Quality Response Propensity 5.482 0.022

IRS 1099 Here -2.106 0.030

IRS 1099 Here* Quality Response Propensity 2.494 0.033

SSS Here 2.072 0.066

SSS Here* Quality Response Propensity -2.573 0.074

Medicare Here -0.905 0.054

Medicare Here* Quality Response Propensity 1.031 0.058

IHS Here -1.472 0.338

IHS Here* Quality Response Propensity 1.555 0.378

NCOA Here -0.470 0.003

NCOA Here* Quality Response Propensity 0.217 0.004

SSR Here -1.573 0.078

SSR Here* Quality Response Propensity 1.745 0.089

NY SNAP Here -2.960 0.122

NY SNAP Here* Quality Response Propensity 3.347 0.142

Texas SNAP Here -3.438 0.088

Texas SNAP Here* Quality Response Propensity 3.975 0.105

Experian-EDR Here 1.140 0.055

Experian-EDR Here* Quality Response Propensity -1.373 0.061

Experian-Insource Here -1.275 0.034

Experian-Insource Here* Quality Response Propensity 1.459 0.038

InfoUSA Here -1.555 0.032

InfoUSA Here* Quality Response Propensity 1.896 0.036

Melissa Here -0.422 0.043

Melissa Here* Quality Response Propensity 0.475 0.047

Targus-Consumer Here -1.144 0.039

Targus-Consumer Here* Quality Response Propensity 1.319 0.043

Targus National Address File Here 1.268 0.118

Targus National Address File Here* Quality Response Propensity -1.448 0.129

Targus Wireless Here -0.052 0.054

Targus Wireless Here* Quality Response Propensity -0.022 0.059

VSGI-NAR Here -0.067 0.042

VSGI-NAR Here* Quality Response Propensity 0.091 0.046

VSGI-TRK Here -0.936 0.040

VSGI-TRK Here* Quality Response Propensity 1.109 0.044
37

Corelogic Here 0.364 0.106

Corelogic Here* Quality Response Propensity -0.464 0.115

Number of Observations 8,694,606

Notes: Sources include all those listed in Table 1, the 2010 Census Unedited File (CUF), and the January 2011 

Master Address File (MAF). This is a quasi-likelihood function using a binomial family variance with a logistic link. 

The base category for address characteristic type includes the following: non-residential only, description, assorted 

noncity-style with some U.S. Postal Service Delivery Sequence File (DSF), assorted noncity-style with all DSF, 

P.O. Box, rural route with some DSF, rural route with all DSF, and no addresses found. Other base categories 

include single-unit structure for housing structure type, no spring 2010 DSF flag for spring DSF flag type, and other 

source (mainly addresses in the Master Address File (MAF) prior to 2000) for address origin. The 1st-stage 

nondiscrepant response propensity is the predicted value for the housing unit from the 1st-stage regression 

corresponding to the source the propensity is being interacted with. A 10 percent random sample of housing units is 

drawn, and those housing units with a self-response are used in the regression. Predicted values are applied to all 

housing units. The standard errors are robust. 

Appendix D: Fieldwork Response Quality Quasi-Likelihood Regressions 

Table D1. Fieldwork Response Quality Quasi-Likelihood Regression with IRS 1040 Data 

Variable Coefficient Standard Error

Deceased 0.424 0.022

Male -0.028 0.003

Age 0-2 0.101 0.011

Age 3-17 -0.003 0.007

Age 18-24 -0.142 0.005

Age 45-64 0.046 0.004

Age 65-74 0.027 0.007

Age 75+ 0.046 0.008

Hispanic -0.134 0.004

African-American -0.116 0.003

American Indian/Alaska Native -0.101 0.012

Asian -0.127 0.008

Native Hawaiian/Pacific Islander -0.190 0.030

Some Other Race -0.081 0.008

Multi-Race -0.075 0.009

U.S. Citizen -0.047 0.005

Legal Alien, Authorized to Work -0.049 0.013

Legal Alien, Not Authorized to Work 0.046 0.032

Other Alien 0.082 0.052

Alien Student,Restricted Work Authorized 0.040 0.047

Conditionally Legalized Alien -0.145 0.056

Ever Alien -0.122 0.011

ITIN -0.020 0.021

Married Filing Jointly 0.244 0.004

Married Filing Separately 0.103 0.009

Filing as Household Head -0.022 0.004

Filing as Widow 0.132 0.041

Return Contains Schedule C -0.026 0.005

Return Contains Schedule D 0.039 0.004

Return Contains Schedule E -0.048 0.004

Return Contains Schedule F 0.003 0.012

Return Contains Schedule SE -0.001 0.005

IRS Processing Week -0.005 0.001

IRS Processing Week Squared 0.0002 0.00006

IRS Processing Week Cubed -0.0000016 0.0000009

One PVSed Person 0.135 0.034

Two PVSed Persons 0.059 0.034
38

Three PVSed Persons 0.037 0.034

Four PVSed Persons 0.042 0.034

Five PVSed Persons -0.056 0.034

Six PVSed Persons -0.465 0.034

Seven or More PVSed Persons -0.422 0.034

One Non-PVSed Record -0.107 0.009

Two Non-PVSed Records 0.010 0.011

Three Non-PVSed Records -0.164 0.020

Four Non-PVSed Records -0.065 0.018

Five or More Non-PVSed Records 0.135 0.013

Dependent PIK Elsewhere for Non-Dependent PIK Here -0.067 0.009

Non-Dependent PIK Elsewhere for Dependent PIK Here -0.050 0.017

2008 IRS 1040 Return at HU -0.097 0.003

Share of IRS 2008, 2009 PIKs in Both Years 0.269 0.003

Electronic Filer -0.004 0.003

Number of Observations 1,837,972

Notes: Sources include 2008-2009 IRS 1040 records, the 2010 Census Unedited File (CUF), and the 2010 Census 

Decennial Response File (DRF). This is a quasi-likelihood function using a binomial family variance with a logistic 

link. The base categories are 25-44 for age, white for race, missing citizenship for citizenship, and single filer for 

filing status. Dummy variables for missing gender, Hispanic origin, and race are also included. A 10 percent 

random sample of housing units with 2009 Internal Revenue Service (IRS) 1040 records is drawn, and of those that 

are also NRFU housing units are included in the regression. Predicted values are applied to all housing units with 

2009 IRS 1040 records. A random sample is taken due to computer processing constraints. The standard errors are 

robust. 

Table D2. Fieldwork Response Quality Quasi-Likelihood Regression with National Change of Address Data 

Variable Coefficient Standard Error

Deceased 0.099 0.310

Male 0.024 0.002

Age 0-2 -0.124 0.019

Age 3-17 -0.027 0.007

Age 18-24 -0.141 0.002

Age 45-64 0.119 0.002

Age 65-74 0.247 0.005

Age 75+ 0.416 0.005

Hispanic -0.166 0.003

African-American -0.137 0.002

American Indian/Alaska Native -0.082 0.007

Asian -0.153 0.005

Native Hawaiian/Pacific Islander -0.199 0.018

Some Other Race -0.107 0.005

Multi-Race -0.075 0.006

Ever Alien -0.124 0.005

ITIN -0.592 0.009

One PVSed Person -0.037 0.003

Two PVSed Persons -0.022 0.003

Three PVSed Persons -0.034 0.004

Four PVSed Persons -0.053 0.005

Five or More PVSed Persons -0.063 0.007

Number of Non-PVSed Records -0.028 0.0008

Departure Address in April 2009 1.355 0.007

Departure Address in May 2009 1.312 0.007

Departure Address in June 2009 1.334 0.007

Departure Address in July 2009 1.348 0.007

Departure Address in August 2009 1.356 0.007

Departure Address in September 2009 1.410 0.007
39

Departure Address in October 2009 1.453 0.007

Departure Address in November 2009 1.498 0.007

Departure Address in December 2009 1.483 0.007

Departure Address in January 2010 1.492 0.007

Departure Address in February 2010 1.539 0.007

Departure Address in March 2010 1.410 0.007

Departure Address in April 2010 -0.576 0.008

Destination Address in April 2009 1.161 0.011

Destination Address in May 2009 1.228 0.011

Destination Address in June 2009 1.216 0.010

Destination Address in July 2009 1.200 0.010

Destination Address in August 2009 1.179 0.010

Destination Address in September 2009 1.157 0.010

Destination Address in October 2009 1.148 0.010

Destination Address in November 2009 1.135 0.011

Destination Address in December 2009 -0.275 0.010

Destination Address in January 2010 -0.333 0.010

Destination Address in February 2010 -0.349 0.010

Destination Address in March 2010 -0.428 0.009

Num. Moves, April 2009-March 2010 -0.085 0.001

Family Move 0.203 0.001

Undeliverable Flag F 0.384 0.019

Undeliverable Flag G 0.705 0.023

Undeliverable Flag K -0.009 0.002

Changed Address (vs. Added Address) -0.060 0.001

Number of Observations 7,669,545

Notes: Sources include 2009-2010 NCOA records, the 2010 Census Unedited File (CUF), and the 2010 Census 

Decennial Response File (DRF). This is a quasi-likelihood function using a binomial family variance with a logistic 

link. The base categories are 25-44 for age, white for race, missing citizenship for citizenship, destination address in 

April 2010 for address, and added address for changed vs. added address. Dummy variables for missing gender, 

Hispanic origin, and race, as well as six citizenship categories are also included in the regression. Housing units with 

both 2010 NRFU fieldwork and 2009-2010 National Change of Address (NCOA) records are used in the regression. 

Predicted values are applied to all housing units with NCOA data. The standard errors are robust. 

Table D3. Fieldwork Response Quality Quasi-Likelihood Regression with VSGI-NAR Data 

Variable Coefficient Standard Error

Deceased 0.305 0.006

Male 0.009 0.002

Age 0-17 -0.229 0.011

Age 18-24 -0.119 0.006

Age 45-64 0.012 0.002

Age 65-74 0.111 0.004

Age 75+ 0.170 0.004

Hispanic -0.208 0.004

African-American -0.216 0.003

American Indian/Alaska Native -0.163 0.010

Asian -0.173 0.006

Native Hawaiian/Pacific Islander -0.273 0.024

Some Other Race -0.145 0.006

Multi-Race -0.124 0.008

Married 0.025 0.002

U.S. Citizen -0.061 0.003

Legal Alien, Authorized to Work -0.049 0.007

Legal Alien, Not Authorized to Work 0.022 0.021

Other Alien 0.043 0.032

Alien Student, Restricted Work Authorized 0.122 0.028
40

Conditionally Legalized Alien -0.114 0.027

Ever Alien -0.177 0.006

ITIN -0.479 0.026

Income <$20,000 -0.049 0.005

Income $20,000-29,999 -0.071 0.004

Income $30,000-39,999 -0.060 0.004

Income $40,000-49,999 -0.067 0.004

Income $50,000-74,999 -0.058 0.004

Income $75,000-99,999 -0.038 0.004

Income $100,000-124,999 -0.034 0.004

Income $125,000-149,999 -0.010 0.006

Log Length of Residence 0.018 0.0008

Owner 0.097 0.002

Renter 0.002 0.004

Number of Persons -0.003 0.002

One PVSed Person -0.135 0.014

Two PVSed Persons -0.118 0.015

Three PVSed Persons -0.231 0.017

Four or More PVSed Persons -0.290 0.018

One Non-PVSed Record 0.012 0.011

Two or More Non-PVSed Records 0.288 0.013

Number of Observations 3,378,193

Notes: Sources include 2010 Veteran Service Group of Illinois Name and Address Resource Consumer file (VSGINAR) records, the 2010 Census Unedited File (CUF), and the 2010 Census Decennial Response File (DRF). This is 

a quasi-likelihood function using a binomial family variance with a logistic link. The base categories are 25-44 for 

age, white for race, missing citizenship for citizenship, $150,000 and above for income, and missing tenure for 

tenure. Dummy variables for missing gender, Hispanic origin, race, and length of residence are also included. A 20 

percent random sample of housing units with VSGI-NAR records is drawn, and those housing units also with 2010 

NRFU fieldwork are used in the regression. Predicted values are applied to all housing units with 2010 VSGI-NAR 

records. The standard errors are robust. 

Table D4. Second-Stage Fieldwork Response Quality Quasi-Likelihood Regression 

Variable Coefficient Standard Error

Update/Leave 0.261 0.004

Military 0.072 0.016

Urban Update/Leave 0.070 0.005

City-Style, No DSF 0.029 0.014

City-Style, some DSF -0.051 0.010

City-Style, all DSF -0.016 0.010

City-Style and Noncity-Style, no DSF 0.052 0.013

City-Style (95-99.99%) and Noncity-Style, some DSF -0.039 0.011

City-Style (90-94.99%) and Noncity-Style, some DSF -0.025 0.011

City-Style (85-89.99%) and Noncity-Style, some DSF 0.006 0.012

City-Style (80-84.99%) and Noncity-Style, some DSF 0.015 0.013

City-Style (75-79.99%) and Noncity-Style, some DSF 0.033 0.014

City-Style (70-74.99%) and Noncity-Style, some DSF 0.014 0.015

City-Style (<70%) and Noncity-Style, some DSF 0.094 0.012

Assorted Noncity-Style, No DSF 0.202 0.018

Mobile or Other Housing Structure -0.042 0.003

2-4-Unit Housing Structure -0.137 0.003

5-9-Unit Housing Structure -0.098 0.004

10-19-Unit Housing Structure -0.082 0.004

20-49-Unit Housing Structure -0.040 0.004

50+-Unit Housing Structure -0.025 0.003

Housing Unit Not in 2000 Decennial -0.022 0.004

Housing Unit Unoccupied in 2000 Decennial 0.089 0.002
41

Spring 2010 DSF Deliverable Flag 0.113 0.012

Spring 2010 DSF X Flag 0.176 0.012

6-Month Periods Since Last DSF Deliverable Flag 0.004 0.0007

Never Had DSF Deliverable Flags 0.119 0.011

Had DSF Deliverable Flag Every Time Since Fall 2008 -0.005 0.005

2000 LUCA Address 0.022 0.005

Post-2000 LUCA Address 0.122 0.007

2010 Address Canvassing Address -0.038 0.006

2010 Decennial Added Address -0.433 0.007

Targeted Block, Additional Form Sent -0.100 0.002

Targeted Block, Additional Form Not Sent 0.130 0.007

Block Blanketed with Second Forms -0.106 0.002

Bilingual Form -0.131 0.002

Business Address 0.645 0.017

Residential, Excluded from Delivery Statistics 0.329 0.008

Built After 2000 -0.054 0.027

Has Location Description in MAF 0.017 0.003

Missing DSF Route 0.143 0.010

MAF Valid Unit Status -0.836 0.007

Mean Number of AR Addresses Per Person -0.047 0.0006

HUD CHUMS Here -0.678 0.093

HUD CHUMS Here* Quality Fieldwork Propensity 0.858 0.110

HUD PIC Here -3.300 0.122

HUD PIC Here* Quality Fieldwork Propensity 4.132 0.148

HUD TRACS Here -3.871 0.347

HUD TRACS Here* Quality Fieldwork Propensity 4.827 0.410

IRS1040 Here -3.289 0.022

IRS1040 Here* Quality Fieldwork Propensity 3.977 0.027

IRS 1099 Here -1.520 0.033

IRS 1099 Here* Quality Fieldwork Propensity 1.739 0.039

SSS Here 0.114 0.082

SSS Here* Quality Fieldwork Propensity -0.208 0.099

Medicare Here 0.172 0.066

Medicare Here* Quality Fieldwork Propensity -0.212 0.079

IHS Here -1.965 0.381

IHS Here* Quality Fieldwork Propensity 2.256 0.455

NCOA Here -0.546 0.003

NCOA Here* Quality Fieldwork Propensity 0.425 0.004

SSR Here -1.367 0.083

SSR Here* Quality Fieldwork Propensity 1.576 0.102

NY SNAP Here -2.405 0.142

NY SNAP Here* Quality Fieldwork Propensity 2.969 0.179

Texas SNAP Here -3.576 0.143

Texas SNAP Here* Quality Fieldwork Propensity 4.325 0.178

Experian-EDR Here -0.755 0.054

Experian-EDR Here* Quality Fieldwork Propensity 0.832 0.064

Experian-Insource Here -0.420 0.037

Experian-Insource Here* Quality Fieldwork Propensity 0.449 0.043

InfoUSA Here -0.746 0.032

InfoUSA Here* Quality Fieldwork Propensity 0.890 0.036

Melissa Here -0.331 0.045

Melissa Here* Quality Fieldwork Propensity 0.381 0.053

Targus-Consumer Here 0.120 0.045

Targus-Consumer Here* Quality Fieldwork Propensity -0.120 0.053

Targus National Address File Here -1.020 0.108

Targus National Address File Here* Quality Fieldwork Propensity 1.218 0.125
42

Targus Wireless Here -0.236 0.067

Targus Wireless Here* Quality Fieldwork Propensity 0.252 0.079

VSGI-NAR Here 0.109 0.049

VSGI-NAR Here* Quality Fieldwork Propensity -0.096 0.058

VSGI-TRK Here -0.226 0.044

VSGI-TRK Here* Quality Fieldwork Propensity 0.258 0.052

Corelogic Here -2.049 0.054

Corelogic Here* Quality Fieldwork Propensity 2.423 0.062

Number of Observations 3,554,729

Notes: Sources include all those listed in Table 1, the 2010 Census Unedited File (CUF), and the January 2011 

Master Address File (MAF). This is a quasi-likelihood function using a binomial family variance with a logistic link. 

The base category for address characteristic type includes the following: non-residential only, description, assorted 

noncity-style with some Delivery Sequence File (DSF), assorted noncity-style with all DSF, P.O. Box, rural route 

with some DSF, rural route with all DSF, and no addresses found. Other base categories include single-unit structure 

for housing structure type, no spring 2010 DSF flag for spring DSF flag type, and other source (mainly addresses in 

the Master Address File (MAF) prior to 2000) for address origin. The 1st-stage vacant and delete status propensities 

are the predicted values for the housing unit from the 1st-stage regression corresponding to the source the propensity 

is being interacted with. A 10 percent random sample of housing units at risk of 2010 NRFU fieldwork is drawn, 

and those housing units also with no USPS Undeliverable as Addressed notification (UAA) and with 2010 NRFU 

fieldwork are used in the regression. The standard errors are robust.