Understanding the Quality of Alternative Citizenship Data Sources for the 2020 Census

by

J. David Brown

U.S. Census Bureau

Misty L. Heggeness

U.S. Census Bureau

Suzanne M. Dorinski

U.S. Census Bureau

Lawrence Warren

U.S. Census Bureau

Moises Yi

U.S. Census Bureau

CES 18-38 August, 2018

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Abstract

This paper examines the quality of citizenship data in self-reported survey responses compared to 

administrative records and evaluates options for constructing an accurate count of resident U.S. 

citizens. Person-level discrepancies between survey-collected citizenship data and administrative 

records are more pervasive than previously reported in studies comparing survey and 

administrative data aggregates. Our results imply that survey-sourced citizenship data produce 

significantly lower estimates of the noncitizen share of the population than would be produced 

from currently available administrative records; both the survey-sourced and administrative data 

have shortcomings that could contribute to this difference. Our evidence is consistent with 

noncitizen respondents misreporting their own citizenship status and failing to report that of other 

household members. At the same time, currently available administrative records may miss some 

naturalizations and capture others with a delay. The evidence in this paper also suggests that adding 

a citizenship question to the 2020 Census would lead to lower self-response rates in households 

potentially containing noncitizens, resulting in higher fieldwork costs and a lower-quality 

population count.

*

* We thank career staff and statistical experts within the Bureau who graciously gave their time and effort to review, 

comment, edit, and make improvements to this document. The analysis, thoughts, opinions, and any errors presented 

here are solely those of the authors and do not necessarily reflect any official position of the U.S. Census Bureau. All 

results have been reviewed to ensure that no confidential information is disclosed. The Disclosure Review Board 

release numbers are DRB-B0093-CDAR-20180621, DRB-B0103-CDAR-20180712, and DRB-B0113-CDAR20180806. Republication in whole or part must be cleared with the authors. J. David Brown is a Senior Economist in 

the Center for Economic Studies at the U.S. Census Bureau and the corresponding author on this paper, 4600 Silver 

Hill Road, Washington, DC 20233, j.david.brown@census.gov. Misty L. Heggeness is Senior Advisor for Evaluations 

and Experiments in the Research and Methodology Directorate at the U.S. Census Bureau. Suzanne M. Dorinski is a 

Mathematical Statistician currently on detail with the Social, Economic, and Housing Statistics Division at the U.S. 

Census Bureau. Lawrence Warren is an Economist in the Center for Economic Studies at the U.S. Census Bureau. 

Moises Yi is an Economist in the Center for Economic Studies at the U.S. Census Bureau.

 
 

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1. Introduction

National statistical agencies are charged with collecting and reporting accurate information about 

society, including individuals, households, and businesses. This information is used to produce

official statistics about the demographic composition of persons living in the nation – including 

information about migration, citizenship, and mobility. For decades, the United States has relied 

on household survey questionnaires to collect data on migration and immigration status (Census 

Bureau 2002). Generally, the focus is on whether an individual has lived in that current location 

for more than one (or five) years, a date for their last move, citizenship status, and year of 

naturalization. To date, the collection of this information via survey vehicles has been sufficient 

for general statistical reporting on immigrants living in the U.S.; however, very few studies have 

examined the extent to which individuals answer these sensitive questions accurately, how 

inclusion of these questions affects overall response rates, or how item nonresponse on these 

questions compares to other questions. 

In this paper, we study the quality of self-reported citizenship questions by comparing responses 

in the American Community Survey (ACS), the Census, the Survey of Income and Program 

Participation (SIPP), and administrative records on citizenship from the Social Security 

Administration. There are now multiple survey and administrative sources of data to study 

immigration and citizenship status. We examine the strengths and weaknesses of these sources for 

the development of future statistics on citizenship status. We focus on both the accuracy and 

completeness in all options. The alternatives we consider for constructing a count of resident 

citizens are the following: (A) no change in current data collection, combined with small area 

estimation using the ACS and administrative citizenship data sources, (B) add a citizenship 

question to the 2020 Census, (C) obtain citizenship status from administrative records for the entire

2020 Census population, and (D) combine alternatives (B) and (C). Factors to consider when 

evaluating these alternatives include the quality of the data sources, comprehensiveness and biases

in data coverage, cost, and the effects on the quality of the 2020 full population count. We analyze 

each of these aspects.

We find that discrepancies between survey-collected citizenship data and administrative records 

are more extensive than discrepancy estimates from previous research. The degree to which 

persons who are noncitizens in administrative records self-report being citizens in surveys is 

greater for non-Hispanics than Hispanics. Most of the people with these discrepancies report being 

citizens from birth or naturalized long ago, regardless of ethnicity. The discrepancy patterns imply

that the ACS estimate of the noncitizen share of the population is lower than comparable estimates 

based on currently available administrative records.

The remainder of the paper is structured as follows. Section 2 provides general background and 

history of the current issue. Section 3 documents the coverage of survey and administrative record 

citizenship data. The quality of the data from survey and administrative record sources is analyzed 

in Section 4. Section 5 contains regression analyses of item response and data quality. Section 6

estimates the effects of inclusion of a citizenship question on survey response rates. Estimates of 

the citizenship question’s effects on the cost and quality of the 2020 Census in general are provided 

in Section 7. Forecasts of the number of people for whom citizenship is sourced by the 2020 Census 
 

4

citizenship question, administrative records, and model imputation when using each of the 

alternatives are given in Section 8. Section 9 concludes. 

2. Background

2.1 History of Citizenship Data Collection through Household Surveys and Censuses

The Census Bureau has collected and preserved citizenship data since 1820 via historical full count 

censuses, household surveys, and administrative records (AR), but the practice of asking 

citizenship and migration-related questions on censuses has varied over time. The 1820 and 1830 

Censuses asked for a tally of the total number of non-naturalized foreigners in the household. The 

1870 Census asked citizenship status of all male persons aged 21 and older (Census Bureau 2002). 

The federal government did not ask citizenship status during the 1880 Census, but reintroduced it 

in the 1890 Census, and the question stayed on full-count Census questionnaires through 1950. 

The 1950 Census was the last full-count Census to ask the citizenship status of every resident in 

the U.S. if he or she reported a foreign birthplace (Census Bureau 2002).

While the 1960 Census did not ask about citizenship throughout the country, it was reintroduced 

on the long form (which sampled approximately one-in-six households across the country) in the 

1970 Census and remained on the long form until 2000 (Census Bureau 2002). The question never 

reappeared on the short form after 1950. After the 2000 Census, citizenship data collection moved 

to the American Community Survey (ACS), which replaced the Census long form. The ACS 

collects responses from approximately 1.6 percent of households annually (American Community 

Survey 2016a, American Community Survey 2016b).

2

Since the advent of the long form and continuing with the ACS, the Census Bureau has focused 

Census enumeration on obtaining only the data necessary for a concise and condensed fullpopulation count (Weinberg 2011). It also prioritizes the collection of data mandated by Public 

Law 94-171 (PL94), which instructs the Census Bureau to cooperate with state redistricting offices 

in support of their efforts to redraw legislative districts in compliance with the Constitution, 

Supreme Court, and the 1965 Voting Rights Act. The questionnaire asks only the core 

demographic, race, ethnicity, and housing questions, not including citizenship.

2.2 The Citizen Voting Age Population by Race and Ethnicity (CVAP) Table

On December 12, 2017, the Census Bureau received a request from the Department of Justice to 

include a citizenship question on the 2020 Census of Population and Housing (Department of 

Justice 2017). The request prompted the Census Bureau to conduct a study of the feasibility and 

best options for meeting this request. This paper summarizes the technical analysis conducted for

alternative options for obtaining citizenship data for the entire population to produce the Citizen 

Voting Age Population by Race and Ethnicity (CVAP) table at the census block level. CVAP is 

 2 We calculate this number using American Fact Finder (AFF) Tables B98001 and B25001. 
 

5

currently produced at the census block-group level using estimates from the five-year American 

Community Survey (ACS) data.

Since 1975, the Census Bureau has provided population estimates by detailed geography to support 

redistricting under Public Law 94-171 (PL94). For the 2000 Census, the Citizen Voting Age 

Population (CVAP) estimates, tabulated at the block-group level, were produced from the long 

form citizenship question. Since 2011, the CVAP estimates have been tabulated annually at the 

block-group level from the most recent 5-year ACS data. The 2011 publication was based on the 

2005-2009 ACS surveys. These data were released in the same time frame as the 2010 PL94 

redistricting estimates.3 The redistricting data must be released before April 1st of the year 

following a census under the authority of 13 U.S.C. Section 141.

The difficulty in integrating these two tables for redistricting and enforcement of the Voting Rights 

Act was cited by the Department of Justice in its December 12, 2017 letter. The Department of 

Justice requested block-level citizen voting-age population estimates by the U.S. Office of 

Management and Budget (OMB)-approved race and ethnicity categories4 directly from the 2020 

Census of Population and Housing, which would require the addition of a citizenship question 

directly onto the full count 2020 Census enumeration form.

2.3 Prior Research on Citizenship Data Quality

We build on past research on Census citizenship data quality. Prior studies have suggested that 

citizenship is inaccurately estimated in Census Bureau surveys. Passel and Clark (1997) document 

that the 1990 Census and 1996 Current Population Survey (CPS) estimates of the number of 

naturalized persons are much higher than the numbers from Immigration and Naturalization 

Services (INS) administrative data.5 The study suggests that about 75 percent of those who report 

having lived in the U.S. fewer than five years and being naturalized citizens probably are not

citizens, at least at the time of the survey. Furthermore, one-third of longer-resident Central 

American and Mexican origin individuals who self-reported naturalization were probably not

citizens at the time of the survey. These discrepancies were attributed to incorrect reporting, 

possibly because respondents were confused about their status or had an incentive to misreport it 

to enumerators and interviewers.

Camarota and Capizzano (2004) conducted focus groups with over 50 field representatives (FRs) 

for the Census 2000 Supplemental Survey (a pilot for the ACS). FRs reported that foreign-born 

respondents living in the country illegally or from countries where there is distrust in government 

were less likely to participate. Some foreign-born respondents failed to list all household members. 

FRs suspected that some foreign-born respondents misreported citizenship status, and they 

 3 For more information, see: https://www.census.gov/programs-surveys/decennial-census/about/votingrights/cvap.html and https://www.census.gov/rdo/data/2010_census.html . 4 See Office of Management and Budget (1997).

5 This comes from Van Hook and Bachmeier’s (2013) summary of Passel and Clark (1997).
 

6

believed this was due to “recall bias, a fear of the implications of certain responses or a desire to 

answer questions in a socially desirable way.”

More recently, Van Hook and Bachmeier (2013) compared 2010 ACS and Office of Immigration 

Statistics (OIS) naturalizations data, finding that the ACS produced higher naturalization estimates

than OIS for those residing in the U.S. less than five years, as well as for longer-resident Mexicanorigin persons. Several papers have studied the effects of state immigration laws on the number 

and locational choices of immigrants (see, for example, Amuendo-Dorantes and Lozano 2014 and 

2015, Bohn et al. 2014, Ellis et al. 2014, Good 2013, and Orrenius and Zavodny 2016). They have 

generally found reductions in the immigrant population after the introduction of these laws.6

Deterioration in survey data quality during periods of stronger immigration enforcement could 

help explain the measured reductions. We contribute to the literature on Census citizenship data 

quality by directly linking Census and household survey data to administrative records. We not 

only examine the quality of survey-collected citizenship data, but also the effect of including a 

citizenship question on the quality of other data via their consequences for response rates and 

nonresponse follow-up. 

3.1 Survey Coverage

In addition to the full count Census of Population and Housing that collects a limited amount of 

information on the entire population once every ten years, the Census Bureau also collects 

information on individuals and households in both legally-mandated and sponsored (reimbursable)

surveys. These surveys collect more detailed demographic, social, and economic characteristics of 

people living in the United States, including information on citizenship status and migration 

variables. 

The Census Bureau currently conducts four surveys that ask citizenship questions. The American 

Community Survey (ACS), the Current Population Survey (CPS), the American Housing Survey 

(AHS), and the Survey of Income and Program Participation (SIPP) all collect data on citizenship 

status. The universe for citizenship questions on these surveys is all persons living in the 

household. The ACS, CPS, SIPP, and AHS distinguish between citizens born in the United States, 

those born in U.S. territories, those born abroad to U.S. citizen parents, and those of foreign nativity 

but naturalized. Additionally, the SIPP asks about more nuanced naturalizations, including 

becoming a citizen through one’s own or a spouse’s military service or via adoption by U.S. citizen 

parents. 7

To assess the citizenship coverage of existing Census Bureau survey data, we link all of the 

household surveys measuring citizenship status to the 2010 Census. The person-level linkage to 

 6 For more information, see https://www.troutman.com/files/FileControl/89dad504-6be0-4335-aa1a35a433102d63/7483b893-e478-44a4-8fedf49aa917d8cf/Presentation/File/Survey%20of%20state%20and%20federal%20laws%20requiring%20E-Verify.pdf

and table 1 in Orrenius and Zavodny (2016) for the list of states with mandatory E-Verify laws. 7 This information is from the Master Demographic Pilot Feasibility Study.
 

7

the 2010 Census is based on the Protected Identification Key or PIK (the Census Bureau’s internal 

unique person identifier) appended to person records using the Person Identification Validation 

System (PVS). To implement the record linkage, we first compiled an unduplicated list of 

individuals surveyed by the Census Bureau in Title 13 mandated surveys (ACS and SIPP) and 

reimbursable surveys (CPS8 and AHS9). We link this unduplicated list of individuals to the 2010 

Census (see Appendix Table A1).

Household surveys linked to the 2010 Census contain self-reported citizenship status for 44.6 

million people, or 14.4 percent of the 2010 Census population. Of these, 43.1 million report being 

citizens (see Appendix Table A2). We conclude that the population coverage from existing survey 

data is a relatively small share of the total population, consistent with the sampling rates of these 

surveys. 

Figure 1 Panel A shows item nonresponse in the 2016 ACS for sex, age, and citizenship.10 We 

show nonresponse rates for the full sample, as well as for select subgroups by race/ethnicity and 

relationship to the householder.11,12 Sex has the lowest nonresponse rates across the entire sample, 

as well as within subgroups with all recording less than 1 percent nonresponse, except for 

nonrelatives. Nonresponse rates for age are higher, and for some subgroups it has the highest level 

of nonresponse among the three items shown here. This is true for non-Hispanic white, nonHispanic black, reference person, and relative of the reference person.13 Hispanics and nonHispanic other race14 have higher rates of nonresponse for citizenship than for sex or age,

providing some preliminary evidence that these groups could be disproportionately impacted by 

the addition of citizenship on the 2020 Census questionnaire.

 8 The CPS is sponsored by the Department of Labor’s Bureau of Labor Statistics. 9 The AHS is sponsored by the Department of Housing and Urban Development. 10 Appendix Table A3 shows item nonresponse rates for questions on the 2000 Census short form and the 2010 Census. 

We choose sex and age as benchmarks, since they are on the Census questionnaire. As shown in Appendix Table A3, 

item allocation rates (including both nonresponses and responses that are edited) are higher for many ACS questions

than for sex, age, or citizenship, but they are not being considered for inclusion on the Census questionnaire and are 

thus less relevant.

11 Throughout the paper, we show results not only by citizenship, but also by race and ethnicity for two main reasons. 

The CVAP data provide counts not just by citizenship, but also race and ethnicity, so differential effects on race/ethnic 

groups from adding a citizenship question are relevant. In addition, our administrative record noncitizen measure has 

incomplete coverage (it does not cover noncitizens without SSNs), while a significant percentage of noncitizens 

without SSNs are Hispanic (Bond et al., 2014). Thus, to some extent the Hispanic category captures noncitizens 

excluded from the measured noncitizen category.

12 The householder, also referred to as the reference person or person 1, is the first person listed on the household 

roster. The reference person typically is the primary or sole respondent to the survey. The relative and nonrelative 

categories are based on the person’s relationship to the householder. The relative category includes husband or wife, 

biological son or daughter, adopted son or daughter, stepson or stepdaughter, brother or sister, father or mother, 

grandchild, parent-in-law, son-in-law or daughter-in-law, other relative, unmarried partner, and foster child. The 

nonrelative category includes roomer or boarder, housemate or roommate, and other nonrelative.

13 We treat all persons in group quarters as reference persons. The results are qualitatively similar if group quarters 

are excluded.

14 Non-Hispanic other race includes non-Hispanic Asian, non-Hispanic American Indian and Alaskan Native, nonHispanic Native Hawaiian and Other Pacific Islander, and non-Hispanic two or more races.
 

8

Figure 1. American Community Survey (ACS) Nonresponse, 2016

Panel A. Item Nonresponse

Panel B. Item Nonresponse for Census Numident-Identified Noncitizens

Source: American Community Survey (ACS) and Census Numident, 2016.

Given item nonresponse to the citizenship question as shown in Figure 1 Panel A, we are 

particularly interested in understanding the potential sensitivity of response specifically for 

noncitizens. Figure 1 Panel B shows the same information as Panel A, restricted to those 
 

9

individuals who are identified as noncitizens in the Census Numident,

15 meaning that 

administrative records show their status as noncitizen. Panel B illustrates the heightened sensitivity 

associated with collecting citizenship data for noncitizens through surveys. Item nonresponse to 

the citizenship question is particularly high for nonrelative household members, where one-in-ten 

do not have a citizenship response in the ACS.

Next, we study whether nonresponse rates have been changing over time. Figure 2 has the same 

layout as Figure 1. It displays the difference in item nonresponse rates between the 2013 and 2016 

ACS for the indicated variable.

16 A positive value indicates an increase in the item nonresponse 

rate, while a negative value indicates a decrease in the same rate. Figure 2, Panel A reports the 

difference in rates for the entire survey population as well as subgroups (see also Appendix Table 

A3 for the rates in the 2000 and 2010 Census short forms). Notice that item nonresponse rates for 

sex have gone down over time. However, item nonresponse for age and citizenship have increased, 

and, in particular, the increase in citizenship item nonresponse is largest for Hispanics and 

nonrelatives.

Figure 2. Difference in American Community Survey (ACS) Item Nonresponse between

2013 and 2016

Panel A. Difference in Item Nonresponse

 15 The Census Numident, which contains all Social Security card applications, is currently the Census Bureau’s most 

complete and reliable administrative record source of citizenship data. For more details, see Section 3.2. 16 Appendix Table A5 shows citizenship item nonresponse rates in 2013 and 2016 separately for mail-in and internet 

responses.
 

10

Panel B. Difference in Census Numident-Identified Noncitizen Item Nonresponse

Source: American Community Survey (ACS) and Census Numident, 2013 and 2016.

Note: Administrative record noncitizens make up 6.7 percent of the overall 2016 ACS sample.

Figure 2 Panel B shows the same differenced rates, but for those who are identified as noncitizens

in the Census Numident. The trends over time are relatively similar for sex and age, with minimal 

changes. However, item nonresponse to the ACS citizenship question increased for all noncitizen 

groups, rising by 1.5 percentage points for nonrelatives and 1.8 percentage points for Hispanics. 

Hispanics, nonrelatives, and noncitizens are particularly sensitive to answering the citizenship 

question in the ACS, and that sensitivity has increased in recent years.

Table 1 shows break-off rates for the 2016 ACS internet self-responses (ISR) separately by 

question screen. Using this table, we examine which questions are subject to higher break-off rates.

Higher break-off rates indicate potentially sensitive items. They are used as an indicator to inform

when the respondent might stop answering the rest of the questions on a survey (Census Bureau 

2013). A break-off is the moment in time during which a respondent decides not to continue with 

the survey and leaves the on-line survey. Break-off rates are highest for Hispanics and lowest for 

non-Hispanic whites in all question screens. Citizenship-related questions have the most 

heterogeneous rates across race/ethnicity groups: the ratio of break-off rates for Hispanics versus 

non-Hispanic whites is much higher for year of entry and citizenship than any of the other question 

screens in the ACS, except for English proficiency (included in Table 1 for reference purposes). 

In contrast, financial and work-related questions are sensitive for all groups. This again suggests 

that citizenship-related questions are more sensitive for Hispanics.
 

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Table 1. 2016 ACS Internet Self-Response Break-off Rates (%) by Screen

Non-Hispanic White Non-Hispanic Other Hispanic

(%) S.E. (%) S.E. (%) S.E.

Work Location 0.642 0.011 1.045 0.032 1.246 0.038

Place of Birth 0.448 0.009 0.766 0.026 0.961 0.039

Wage Amount 0.589 0.006 0.691 0.029 0.751 0.032

Work Last Week 0.257 0.006 0.407 0.010 0.597 0.024

Work for Wages 0.365 0.009 0.459 0.019 0.590 0.028

Type of Employee 0.221 0.007 0.367 0.011 0.399 0.026

Verify Income 0.198 0.007 0.263 0.016 0.368 0.021

Citizenship 0.035 0.002 0.268 0.016 0.363 0.026

Health Insurance 0.188 0.006 0.331 0.015 0.336 0.019

Highest Level of 

 Education

0.167 0.005 0.257 0.015 0.298 0.019

Work Duties 0.143 0.005 0.223 0.015 0.266 0.020

Year of Entry into U.S. 0.022 0.002 0.119 0.009 0.260 0.021

Taxes 0.164 0.005 0.182 0.014 0.259 0.019

Interest, Dividends 

 Income

0.209 0.006 0.179 0.013 0.242 0.020

Residence Last Year 0.104 0.004 0.182 0.014 0.232 0.016

English Proficiency 0.003 0.001 0.020 0.005 0.036 0.007

Total Non-Breakoff 90.52 0.040 85.93 0.109 82.41 0.145

Source: 2016 ACS.

Notes: These are the top fifteen screens, sorted by Hispanic break-off rate. English proficiency and total non-breakoff 

are also included for reference. The rates are unweighted. The standard errors are calculated using Fay's balanced 

repeated replication variance estimation method, with 80 replicate weights, adjusting the original weights by a 

coefficient of 0.5.

Another alternative for measuring sensitivity of response is to examine the extent to which unit 

nonresponse changes. Unit nonresponse refers to a situation where no one in the household (or 

unit) responds to the survey. Figure 3 shows ACS unit nonresponse rates from 2010 to 2016 for 

housing units in the decile of tracts with the highest percent of noncitizens (25.5 percent 

noncitizens or more), and those in the decile of tracts that have the lowest percent of noncitizens

(0.6 percent or less).

17 Tracts with noncitizen shares in the top decile have lower levels of unit 

response. In tracts with the highest concentrations of noncitizens, unit response rates have 

decreased over time and show a sharper drop between 2015 and 2016 than for units in tracts with 

the lowest concentrations of noncitizens.

 17 An internet response option was introduced to the ACS in 2013. Baumgardner, Griffin, and Raglin (2014) show 

that this was associated with an increase in self-response rates for economically advantaged groups and a decrease

for economically disadvantaged groups, which could help explain the widening of the gap between these two tract 

groups in 2013. It cannot explain the further widening of the gap in 2016, however.
 

12

Figure 3. ACS Unit Response Rate by Tract-Level Share of Noncitizens

Source: American Community Survey (ACS), 2010-2016. The deciles of the distribution for noncitizen share of the 

tract population are 2011-2015 5-year ACS estimates.

Notes: The noncitizen share is 0.0 to 0.6 percent in the bottom decile and 25.5 to 100 percent in the top decile. The

confidence intervals (CI) are at the 90 percent level, calculated via the successive differences replicate methodology, 

using 80 ACS replicate weights (see American Community Survey (2014)).

The data shown in this section provide preliminary evidence that unit nonresponse and citizenship 

item nonresponse rates are low in the population as a whole. The very low unit and item 

nonresponse rates among citizens and non-Hispanics mask increasingly higher noncitizen and 

Hispanic nonresponse rates, however.

3.2 Administrative Record Coverage

An alternative way to obtain citizenship information is to use data collected in the administration 

of government programs or by commercial data resellers. Respondent sensitivity to answering the 

question should be less of an issue with administrative sources, since proof of citizenship status is 

required to determine eligibility for a passport, a job, or government benefits. However,

administrative data have incomplete coverage for other reasons, as discussed in this subsection.

Among the sources in Table 2, the Census Numident is the most complete and reliable 

administrative record source of citizenship data currently available to the Census Bureau. The 

Numident file is a record of individual applications for Social Security cards and certain 

subsequent transactions for those individuals. Unique, life-long Social Security Numbers (SSNs)

are assigned to individuals based on these applications. In addition, a full record of all changes to 

the account information (such as change of name) is also maintained. To obtain an SSN, the 
 

13

applicant must provide documented identifying information to the Social Security Administration 

(SSA). Through the “enumeration at birth” program, children can be issued an SSN when they are 

born.18 Examples of data elements on a Numident record include name, date and place of birth, 

parents’ names, and date of death. The SSA began requiring evidence of citizenship in 1972. 

Hence, citizenship data for more recently issued SSNs should be reliable as of the time of 

application.

19 SSA is not automatically notified when previously noncitizen SSN holders become 

naturalized citizens, however, so some naturalizations may be captured with a delay or not at all. 

To change citizenship status on an individual’s SSN card, naturalized citizens must apply for a 

new card, showing proof of the naturalization (U.S. passport or certificate of naturalization).

20

Naturalized citizens wishing to work have an incentive to apply for a new card showing their U.S. 

citizenship, because noncitizen work permits expire, and the Numident is used in combination with 

U.S. Citizenship and Immigration Services (USCIS) data in the E-Verify program that confirms 

that job applicants are eligible to work.

Whether or not citizenship data are collected on the 2020 Census questionnaire, administrative 

records may be useful for editing and imputing the citizenship variable, when necessary.21

 18 A parent can apply for the infant’s SSN at the hospital where the infant is born. Otherwise, applications for U.S.-

born persons require an original or certified copy of a birth record (birth certificate, U.S. hospital record, or religious 

record before the age of five including the date of birth), which SSA verifies with the issuing agency, or a U.S. 

passport. Foreign-born U.S. citizen applications require certification of report of birth, consular report of birth abroad, 

a U.S. passport, a certificate of citizenship, or a certificate of naturalization. Noncitizen applications require a lawful 

permanent resident card, machine readable immigrant visa, arrival/departure record or admission stamp in an 

unexpired foreign passport, or an employment authorization document. See 

https://www.ssa.gov/ssnumber/ss5doc.htm. The enumeration at birth was rolled out starting in 1987, and 45 states, 

Puerto Rico, the District of Columbia, and New York City had signed agreements to offer it by 1991. Today over 90 

percent of parents use this process in all 50 states plus Puerto Rico and the District of Columbia. See 

https://www.ssa.gov/policy/docs/ssb/v69n2/v69n2p55.html. 19 A detailed history of the SSN is available at https://www.ssa.gov/policy/docs/ssb/v69n2/v69n2p55.html (Exhibit 

1). For some categories of persons, the citizenship verification requirements started a few years later, but all were in 

place by 1978.

20 For more information, see https://www.ssa.gov/ssnumber/ss5doc.htm. 21 Data edits refer to updating data when there is a clear error either in data entry or in response. Imputations occur 

when the individual or household did not answer a survey or questions on a survey. They involve modeling a most 

likely response for that individual or household using other available data.
 

14

Table 2. Administrative Record (AR) Sources Currently Held and/or Under Negotiation 

for Acquisition

Administrative Records Data 

with Citizenship Info. Currently Held

Universe 

Census Numident National-level file of SSA transactions

HHS TANF National Level (not full content for all 

states)

Alaska Permanent Fund Alaska residents

Colorado Leap Colorado low income energy assistance 

program

Some State SNAP/TANF State-level program participants 

Army Active duty and retired soldiers and family 

members

Bureau of Prisons Federal prison inmates 

Commercial Files Purchased data from data resellers

Administrative Records Data 

with Citizenship Info Under Negotiation for 

Acquisition 

Universe 

Department of Homeland Security United 

States Citizenship and Immigration Services

National-level file of Lawful Permanent 

Residents, Naturalizations

Department of Homeland Security United 

States Customs and Border Protection

National-level file of Customs and Border 

transaction data

Department of State Passport Services National-level passport transaction data

Table 3 shows the coverage of the 2010 Census population by the 2010 Numident and ITINs.

22

Ninety-one percent of persons in the 2010 Census can be assigned a Protected Identification Key 

(PIK) by the Person Identification Validation System (PVS).

23 Once a PIK is assigned, the vast 

majority of records are matched to the 2010 Numident (98.2 percent in Table 3). Most of the PIKs 

associated with persons not in the 2010 Numident are derived from linkage to Individual Taxpayer 

Identification Numbers (ITIN), issued by the Internal Revenue Service to persons who do not have 

 22 Rastogi and O’Hara (2012) used an earlier version of the crosswalk between the Numident and ITINs and the 2010 

Census, and we show results using that version in Table A6. The enhanced crosswalk in Table 3 uses additional 

household and geospatial information to increase person linkage, and it has much greater coverage of ITINs. See Bond 

et al. (2014) for details.

23 See NORC (2011) and Layne, Wagner and Rothhaas (2014) for details about the process used to assign and the 

quality of the PIKs used in data linkage at the Census Bureau.
 

15

and are not eligible to obtain SSNs, but are required to file a federal individual income tax return

(4.3 million person links derived from ITINs vs. 804,000 person links that are not derived from 

ITINs). Among persons with nonmissing citizenship in the 2010 Numident, 91.3 percent are U.S. 

citizens. 

Approximately 20.9 percent, or 57.6 million of the 2010 Numident records have missing

citizenship status. Many older persons did not report citizenship when applying for an SSN, which 

was not required prior to 1972. Of these older persons with missing citizenship, 7.0 million have 

either passed away by 2017 or are likely to do so by 2020 (since they would be over 100 years 

old). Of the remaining 50.7 million persons with missing citizenship in the 2010 Numident, it 

becomes nonmissing for 5.8 million of them by 2017, nearly all switching to U.S. citizens. About 

42.5 million of those still missing citizenship in 2017 were born in the U.S. We treat U.S.-born 

persons missing citizenship as administrative record citizens in our analysis.

24 This leaves just 2.5

million foreign-born persons with missing citizenship, some of whom could be noncitizens. In the 

analysis, we treat foreign-born persons with missing citizenship as having missing administrative 

record citizenship.

Appendix Table A7 shows that among persons who are missing citizenship, alive in 2017, and 

born after 1919, those who are foreign-born have a much lower propensity to be linked to the 2010 

Census (36.3 percent vs. 74.5 percent for U.S.-born persons). Many of the foreign-born people 

missing citizenship in the Numident are presumably residing outside the U.S. and thus will not be 

counted in the 2020 Census.25

 24 Analysis in later sections of this paper labeled “initial assumptions” instead treats all persons with missing Numident 

citizenship values as AR citizens, whether they are U.S.- or foreign-born. This includes Table 6, Figures 10B, 11A, 

12A, and 12C and Appendix Tables A8 and A9. 25 An example is persons who received temporary work visas prior to when evidence of citizenship was required to 

receive an SSN and who have since returned to their home countries.
 

16

Table 3. Administrative Record (AR) Coverage of the 2010 Census

Count Percent of 

2010 Census 

Population

Percent of 

Matched 

Sample

No PIK, not sent to PVS 10,260,000 3.3

No PIK, failed in PVS 17,490,000 5.7

PIK, but not in 2010 Numident, not an 

ITIN

804,000 0.3

PIK, but not in 2010 Numident, is an 

ITIN

4,326,000 1.4 1.5

2010 Numident U.S. Citizen 199,300,000 64.6 71.1

2010 Numident Noncitizen 18,970,000 6.1 6.8

2010 Numident Missing Citizenship 57,620,000 18.7 20.6

 Of which:

 Alive in 2017, born after 1919 50,670,000 16.4 18.1

 Of which:

 2017 Numident U.S. Citizen 5,678,000 1.8 2.0

 2017 Numident Noncitizen 70,500 0.0 0.0

 2017 Numident Missing 

 Citizenship

44,920,000 14.5 16.0

 Of which:

 U.S.-born 42,460,000 13.8 15.2

 Foreign-born 2,464,000 0.8 0.9

Total 308,745,538 100.00 100.00

Source: 2010 Census Unedited File (CUF) and 2010 and 2017 Census Numident Files.

Notes: The 2010 Census Numident File is used for all calculations with “Numident” in the label. The 2017 Census 

Numident File is used to calculate the number alive in 2017 and born after 1919 and the foreign-born share of them. 

PVS is the Person Identification Validation System used to assign PIKs. PIK is Protected Identification Key, which 

is a unique person identifier.

Figure 4 shows the share of persons in the 2016 ACS for whom administrative record citizenship 

status is not available, as well as the ACS citizenship allocation rate (including both item 

nonresponse and edits to original responses; i.e., the share of persons for whom the value tabulated 

is not the respondent’s answer). The missing data rates are higher for administrative records (AR)

than the ACS, and both sources’ rates are higher for minorities and nonrelatives. The variability in 

coverage is higher for AR than the ACS.
 

17

Figure 4. Percent without Administrative Record or ACS Citizenship in 2016

Source: American Community Survey (ACS) and Census Numident, 2016.

Note: For the ACS this is the citizenship item allocation rate, which includes both item nonresponses and edited values. 

As shown in Appendix Table A8, the percent of persons in the ACS who cannot be linked to 

citizenship in AR increases from 8.5 to 10.9 percent between 2010 and 2016. Note that the linkage 

between the ACS and administrative data from the SSA Numident and IRS ITIN tax filings 

depends on two factors: (a) the quality of the personally identifiable information (PII) on the ACS 

response and (b) whether the ACS respondent is in the SSN/ITIN universe. 

With respect to the quality of the PII on the ACS, there may be insufficient information on the 

ACS due to item nonresponse to allow a successful match using the production record linkage 

system. There may also be more than one record in the Numident or ITIN IRS tax filings that 

matches the person’s PII. Finally, there may be a discrepancy between the PII provided to the ACS 

and the PII in the administrative records. 

Alternatively, the person may not be in the Numident or ITIN IRS tax filing databases, because 

they are out of the universe for those administrative systems. This happens when the person is a 

citizen without an SSN, or when the person is a noncitizen who has not obtained an SSN or ITIN. 

Very few of the unlinked cases are due to insufficient PII in the ACS or multiple matches with 

administrative records. The vast majority of unlinked ACS persons have sufficient PII, but fail to 

match any administrative records sufficiently closely. This means that most of the nonmatches are 

because the ACS respondent is not in the administrative record universe.

The incidence of ACS persons with sufficient PII but no match with administrative records 

increased between 2010 and 2016. One contributing factor is that the number of persons linked to 

ITIN IRS tax filings in 2016 was only 35 percent as large as in 2010,26 suggesting that either fewer 

 26 This percentage uses survey weights. Unweighted, it is 39 percent.
 

18

of the noncitizens in the 2016 ACS had ITINs, or more of them provided PII in the ACS that was 

inconsistent with their PII in IRS records.

There is an important caveat to the conclusion that survey-based citizenship data are more 

complete than currently held administrative records. The methods used to adjust the ACS weights 

for survey nonresponse and to allocate citizenship status for item nonresponse assume that the 

citizenship status distribution of the sampled non-respondents is statistically the same as that of 

respondents with similar related characteristics. They might not actually be similar, however, even 

when selecting the allocation of citizenship status using basic characteristics. For example, 

Hispanics who respond to the survey might be different from Hispanics who do not respond in 

various characteristics (including immigration status). Additionally, our unit and item nonresponse 

analysis in Section 3.1 above casts serious doubt on this assumption, suggesting that those who do 

not respond to either the entire ACS or the citizenship question on the ACS are not statistically 

similar to those who do. In particular, their responses to the citizenship question would not be well 

predicted by the answers of those who did respond.

To reduce the AR coverage gaps, the Census Bureau is considering the possibility of acquiring 

access to several other national citizenship-related files listed in Table 2. United States Customs 

and Immigration Services (USCIS) administrative records on naturalizations and lawful permanent 

residents (LPR), and Customs and Border Protection transaction records on border entries can 

partially address the weaknesses of the Numident. Through preliminary project development 

discussions with USCIS, we were informed that USCIS records provide up-to-date information 

since 2001 (and possibly back to 1988, but with incomplete records prior to 2001). These will fill 

some gaps for naturalized citizens, lawful permanent residents, and persons with extended visa 

applications without SSNs, as well as naturalized citizens who did not inform SSA about their 

naturalization. These data do not cover naturalizations occurring before 1988, and they miss some 

between 1988 and 2000. USCIS records do not always cover children under 18 at the time a parent 

became a naturalized U.S. citizen. These children automatically become U.S. citizens under the 

Child Citizenship Act of 2000. The USCIS receives notification of some, but not all, of these child 

naturalizations. Others inform the U.S. government of their U.S. citizenship status by applying for 

U.S. passports, which are less expensive than the application to notify the USCIS. USCIS visa 

applications list people’s children but the information may not be in electronic form.

U.S. passport administrative records available from the State Department can help plug the gaps 

for child naturalizations, missing status on the Numident, and out-of-date citizenship information 

on the Numident. Since U.S. citizens are not required to have a passport, however, these records

will also have coverage gaps.

The acquisition of these sources would also improve record linkage for noncitizens by allowing 

the construction of a supplementary record linkage master list for such people, who are currently 

only in scope for receiving a PIK if they apply for and receive either an SSN or ITIN. Improved 

record linkage would not only facilitate greater use of administrative record citizenship data, but 

it could also permit other uses of these administrative records in 2020 Census operations to lower 

costs and raise quality. Noncitizens are a hard-to-count population (as evidenced by the lower ACS 
 

19

unit response rates in tracts with more noncitizens in Figure 3), making having reliable 

administrative records on them particularly valuable. 

If the Census Bureau were to obtain each of these files, the remaining AR citizenship data gaps 

would include the following categories:

1. U.S. citizens from birth with no SSN or U.S. passport. They will not be processed by the 

production record linkage system used for the 2020 Census, because their PII won’t be found in 

the PVS reference files.

2. U.S. citizens from birth born outside the U.S., who do not have a U.S. passport, and either 

applied for an SSN prior to 1974 and were 18 or older, or applied before the age of 18 prior to 

1978. These people will be assigned PIKs, but none of the administrative sources discussed above 

will reliably generate a U.S. citizenship variable.

3. U.S. citizens who were naturalized prior to 2001 and did not inform SSA of their 

naturalization, because they originally applied for an SSN after they were naturalized, and it was 

prior to when citizenship verification was required for those born outside the U.S. (1974). These 

people either already had an SSN when they were naturalized, and they didn’t inform SSA about 

the naturalization, or they never applied for an SSN. The former group has inaccurate data in the 

Numident. The latter group will not be assigned a PIK.

4. U.S. citizens who were automatically naturalized if they were under the age of 18 when 

their parents became naturalized in 2000 or later, and they did not inform USCIS or receive a U.S. 

passport. Note that such persons would not be able to get an SSN with U.S. citizenship on the card 

without either a U.S. passport or a certificate from USCIS. These people will also not be assigned 

a PIK.

5. Lawful permanent residents (LPR) who received that status prior to 2001 and either do not 

have an SSN, or they applied for an SSN prior to when citizenship verification was required for 

those born outside the U.S. (1974). The former group will not be found in the PVS reference files. 

The latter group has inaccurate data in the Numident.

6. Noncitizen, non-LPR, residents who do not have an SSN or ITIN and who did not apply 

for a visa extension. These persons will not be found in PVS.

7. Persons with citizenship information in administrative data, but the administrative and 

Census data cannot be linked due to missing or discrepant PII.

It is uncertain whether Census Bureau household survey data could reliably fill the above gaps 

when their person record cannot be assigned a PIK or when they have a PIK but the administrative 

record lacks up-to-date citizenship information. Persons in Category 6 have a strong incentive to 

provide an incorrect survey answer, if they answer at all, due to concerns about the data being used 

for enforcement.

27 Presumably a significant, but unknown, fraction of persons without PIKs are in 

 27 Title 13, U.S.C. prohibits the use of Census data for enforcement purposes, but respondents may still have this 

concern. 
 

20

Category 6. Distinguishing these people from the other categories of persons without PIKs is 

inherently inexact, because there is no feasible method of independently verifying their citizenship

status.

 

4. Data Reliability

To assess the reliability of citizenship data, we compare the responses to the 2000 Census long 

form and 2010 and 2016 ACS citizenship questions with the administrative record (AR) citizenship 

variable (from the 2002, 2010, and 2016 Numidents and ITINs for the latter two years).

28 Since 

previous studies suggest that Census survey-AR discrepancies are greater for Hispanics, and the 

CVAP tables show citizen counts by race/ethnicity and voting age, we show discrepancies 

separately by race/ethnicity and the voting-age population (age 18 and over). Appendix Tables A8 

and A9 show a full set of results for all three years, while the discussion in this section focuses on 

the 2016 comparison.

Discrepancies between AR and ACS citizenship could be due to several causes: (1) Linkage errors 

result in the administrative records not matching to the right people in the ACS. The relative 

discrepancy rates would vary depending on whether AR citizens or noncitizens have more linkage 

errors. One might expect unrelated persons in the household to have more linkage errors than 

relatives of reference persons, since PII quality is likely to improve with familiarity. (2) AR 

incorrectly report that the person is a citizen. This would appear as AR citizen-ACS noncitizen 

discrepancies. (3) AR are out of date, missing some naturalizations captured by the ACS. This 

would show up as AR noncitizen-ACS citizen discrepancies.29 (4) The respondent does not know 

the person’s citizenship status and guesses wrong. This is most plausible for unrelated persons and 

least so for the reference person. (5) The respondent misunderstands the question and answers 

incorrectly, despite actually knowing the citizenship status. It is not clear whether this would lead 

to more AR citizen-ACS noncitizen or AR noncitizen-ACS citizen discrepancies, but it should not 

vary across reference person, related persons, and unrelated persons. (6) The respondent knows 

the person’s citizenship status and misreports it. Here the reference person may have a harder time 

justifying item nonresponse (implying (s)he does not know her/his own citizenship), so the way to 

keep from attracting attention is to say (s)he is a U.S. citizen. When asked about others, the

respondent can more easily say (s)he does not know. This factor is likely to be more relevant when 

people have heightened concerns that the data will be used for immigration enforcement.

Of the candidate reasons (1) through (3) relevant for administrative records, linkage errors (reason 

1) would be the most difficult to overcome. If linked to the wrong people, even perfect 

administrative records will produce inaccurate statistics. Though improvements can be made to

record linkage methods, the linkage quality also depends on the quality of PII supplied by the 

sources being linked. In contrast, the acquisition of more timely administrative record sources 

 28 The 2002 Numident is the closest available Numident to the 2000 Census.

29 Note that as the Census Bureau receives more administrative record sources of citizenship data, the probability that 

the administrative records are incorrect should fall.
 

21

should reduce missing naturalizations problems (reason 3). The use of additional administrative 

record sources can also help illuminate instances where currently held administrative records are 

more likely to be incorrect (reason 2).

30

Guessing wrong (reason 4) and misunderstanding the question (reason 5) would reduce precision

(i.e., increase statistical variability), but it is not clear that either would result in biased estimates. 

In contrast, intentional misreporting (reason 6) is likely to result in reduced accuracy (more bias), 

since citizens and noncitizens may have different incentives to misreport status. Of these three 

reasons, the extent of intentional misreporting is most likely to vary across geographical areas and 

over time, depending on the degree of concern about personal security.

Figure 5 Panel A shows that a remarkably high 99.6 percent of U.S. citizens (according to 

administrative records) report being U.S. citizens in the 2016 ACS.31 This suggests that when AR 

report the person is a citizen, (s)he is actually a citizen, and reason (2) is not an important factor. 

The discrepancy rate is higher for Hispanics (2.0 percent) and other minorities (1.3 percent) than

for non-Hispanic white individuals. The discrepancy rate is higher for nonrelatives than relatives

of the respondent, and for relatives than reference persons, consistent with the reference person 

knowing other people’s status less well than his/her own.

Discrepancy rates are higher for those individuals identified as U.S. noncitizens in administrative 

records: 37.6 percent report being U.S. citizens in the ACS, as shown in Figure 5 Panel B. This 

implies that ACS estimates of the U.S. citizen population are higher than they would be if one 

were to use currently available administrative records.32 The ordering of rates across groups is

reversed compared to the AR citizen-ACS noncitizen rates. Here non-Hispanic white individuals 

have the highest discrepancy rate and Hispanic individuals the lowest. This means that the 

difference between ACS citizen and AR citizen population estimates is greatest for non-Hispanic 

white individuals and lowest for Hispanic individuals. This contrasts with Van Hook and 

Bachmeier’s (2013) conclusion based on aggregates that self-reported naturalizations by persons 

of Mexican origin are most likely to be incorrect.33,34 

The AR noncitizen-ACS citizen discrepancy rate is highest for the reference person, followed by 

relatives and then nonrelatives. This pattern is not a clear outcome of out of date administrative 

 30 For example, if a person is a foreign-born citizen in one administrative record source, but other administrative 

records and the survey response each say the person is a noncitizen, one might have more confidence in selecting

noncitizen than when having only the first administrative record source and the survey response. 31 This is even higher than the agreement rate for sex in the 2010 Census vs. the Numident, which is 99.4 percent. See 

Rastogi and O’Hara (2012).

32 Note that since we are unable to compare records that are missing in one or both sources, the estimates provided in 

this section may understate the difference between the ACS estimate of the U.S. citizen population and the true value, 

especially since most unauthorized persons (other than the small fraction with ITINs) are missing AR citizenship data

here. 33 Hispanics make up the largest number of AR noncitizen-ACS citizen persons (2.6 million), compared to 2.5 million 

non-Hispanic other minorities, 1.7 million non-Hispanic whites, and 800,000 non-Hispanic blacks, which may be why 

previous studies’ analysis of aggregated data find the largest administrative record-survey differences to be among 

Hispanics. But the discrepancy rate is more relevant for evaluating quality than the absolute number of discrepancies. 34 According to 2016 1-year ACS data in American Factfinder Table S0201 (American Community Survey 2016c), 

63.2 percent of Hispanics are of Mexican origin. 
 

22

records (reason 3), lack of knowledge about others’ status (reason 4), or misunderstanding the 

question (reason 5). Recall that citizenship item nonresponse is highest for nonrelatives and lowest 

for reference persons (see Figure 1). This suggests respondents behave differently when asked 

about their own status versus that of others. It may be easier for respondents to say they do not

know the status of someone else (particularly a nonrelative) than their own status. They thus 

misreport their own status (reason 6), while they say they do not know the status of others.
 

23

Figure 5. Administrative Records-ACS Survey Response Citizenship Agreement

Panel A. Percent of Administrative Record Citizens who respond as 2016 ACS Noncitizens

Notes: Administrative record citizens make up 81.1 percent of the overall 2016 ACS sample, 90.1 percent for nonHispanic white, 81.5 percent of non-Hispanic black, 60.2 percent of Hispanic, 62.5 percent of non-Hispanic other 

race, 81.1 percent of reference persons, 82.1 percent of relatives, and 64.8 percent of nonrelatives. See Appendix 

Table A10.

Panel B. Percentage of Administrative Record Noncitizens who respond as 2016 ACS Citizens

Notes: Administrative record noncitizens make up 6.7 percent of the overall 2016 ACS sample, 1.9 percent for nonHispanic white, 5.1 percent of non-Hispanic black, 16.2 percent of Hispanic, 22.0 percent of non-Hispanic other race, 

6.9 percent of reference persons, 6.5 percent of relatives, and 7.1 percent of nonrelatives. See Appendix Table A10.

Source: American Community Survey (ACS) 1-year file and Census Numident, 2016.
 

24

We show the AR citizen-ACS noncitizen and AR noncitizen-ACS citizen discrepancies separately 

for higher- and lower-quality linkages and by reference person vs. relative vs. nonrelative 

categories in Figure 6. For AR citizen-ACS noncitizen discrepancies, the rates are lowest for the 

reference person and highest for nonrelatives, likely due to people being able to report their own 

PII more accurately than that of others. Records with high-quality links have lower discrepancy 

rates, consistent with linkage errors being a contributing factor to these discrepancies. The patterns 

reverse for AR noncitizen-ACS citizens. Higher-quality linked records actually have higher 

discrepancy rates, so linkage errors (reason 1) do not appear to explain the AR noncitizen-ACS 

citizen discrepancies. This pattern holds regardless of the type of person the reference person is 

responding about (oneself, a relative, or a nonrelative).
 

25

Figure 6. Quality of the Citizenship Question Responses by Relation to Reference Person 

and Higher- vs. Lower-Quality Linkage

Panel A. AR Identifies as a Citizen and 2016 ACS Identifies as a Noncitizen

Panel B. AR Identifies as a Noncitizen and 2016 ACS Identifies as a Citizen

Source: American Community Survey (ACS) and Census Numident, 2016.

Notes: High-quality linkage is defined as having an above-median linkage confidence score on the first linking attempt 

(pass), and lower-quality is all others. The weighted sample shares of the ACS are 18.1 percent for reference person 

high-quality linkage, 23.9 percent for relative high-quality linkage, 0.6 percent for nonrelative high-quality linkage, 

20.3 percent for reference person low-quality linkage, 33.8 percent for relative low-quality linkage, and 3.2 percent 

for nonrelative low-quality linkage. See Appendix Table A11.

To evaluate further the hypothesis that AR are out of date (reason 3), we make comparisons to 

USCIS statistics. In the AR-ACS citizenship status comparison above, we estimate 7,605,000 

persons are AR noncitizens-ACS citizens. This is equivalent to the Numident missing all the 
 

26

naturalizations reported by USCIS back to 2007, plus some of 2006. Figure 7 shows the annual 

number of persons who first entered the Numident as noncitizens and switch to U.S. citizenship in 

each particular year, as well as the number of naturalizations according to USCIS statistics.35

USCIS reports significantly more naturalizations prior to 2010, but there is little difference 

subsequently. This suggests that if the main reason for the discrepancies were out-of-date 

Numident citizenship, the Numident would have to be missing many naturalizations that occurred 

long ago. 

Figure 7. Estimated Annual Naturalizations in Census Numident Data versus USCIS 

Statistics

Source: USCIS Immigration Yearbooks and 2017 Census Numident.

We compare the ACS naturalization year and the year when citizenship switched to U.S. citizen 

in the Numident among persons with naturalized citizen status in both sources in Figure 8.

36 For 

67.4 percent of these persons, the ACS naturalization year is earlier than the Numident citizenship 

change year, and 33.1 percent have an ACS naturalization year that is more than five years prior. 

Just 11.3 percent have a later ACS naturalization year. This is consistent with tardy notification to 

SSA about naturalizations.

 35 The Numident switches do not include persons who did not have an SSN prior to being naturalized. According to 

USCIS officials, the percentage of persons naturalized in 2014 who did not previously have an SSN is 0.33 percent, 

and it is 0.40 percent in 2015, suggesting that this type of Numident omission is negligible, at least recently. 36 The Numident citizenship change year is the year when citizenship changed from noncitizen to citizen in the data.
 

27

Figure 8. Difference between ACS Naturalization and Numident Citizenship Change Years

Source: American Community Survey (ACS) and Census Numident, 2016. The sample is persons who are naturalized 

citizens in both sources, and the ACS citizenship value is as reported by the respondent.

Figure 9 shows the distribution of AR noncitizen-ACS citizens by naturalization year. 

Approximately 15.9 percent report being citizens from birth, which, if true, would mean that the 

Numident is not out of date for these people, but incorrect from the first SSN application. This 

possibility seems unlikely, given that proof of citizenship status must be presented to SSA when 

applying for an SSN, whereas the ACS citizenship response is not checked. A third of the ACSreported naturalizations (2.1 million) occurred between 2010 and 2016, while the total gap between 

USCIS naturalizations and Numident switches from noncitizen to citizen between 2010 and 2016

is several times less than that, at 288,000.

Figure 9 shows that the AR noncitizen-ACS citizen naturalization distributions are very similar 

for Hispanics and non-Hispanics. The results are contrary to Van Hook and Bachmeier’s (2013) 

finding that citizenship misreporting by persons saying they were naturalized more than five years 

ago primarily occurs among persons of Mexican origin, and Passel and Clark’s (1997) finding that 

it is among those of Mexican or Central American origin.

We also explore whether the AR noncitizen-ACS citizen naturalization distributions vary with 

linkage quality. One might expect that if linkage quality is driving the discrepancies, then persons 

with higher quality links would be recently naturalized, reflecting out-of-date Numident data. In 

contrast, more of the persons with low quality links would be ACS citizens from birth or 

naturalizations long ago, since the Numident and ACS records could be for different people, and 

the Numident should be less likely to be out of date for citizens from birth and earlier 

naturalizations. Figure 9 does show a higher share of ACS citizens from birth among those with 
 

28

lower quality links, but also for more recent naturalizations. This is further evidence that linkage 

errors are probably not an important explanation for these discrepancies.

Figure 9. Distribution of ACS Citizenship Receipt Timing for Administrative Record 

Noncitizen-ACS Citizens by Linkage Quality and Ethnicity

Source: American Community Survey (ACS) and Census Numident, 2016.

As a final data quality check, we calculate the 2016 ACS citizenship distribution for persons with 

ITINs. Though only noncitizens may have ITINs, 6.6 percent say they were born citizens, and 11.1

percent report being citizens in the ACS (see Appendix Table A12).

5. Item Response and Data Quality Regression Analysis

We estimate multivariate regressions predicting item response in Table 4 and AR-ACS 

discrepancies in Table 5. The item response and citizenship status disagreement regressions test 

whether the associations shown above are statistically significant and robust to inclusion of 

controls. These analyses also provide an opportunity to study other potentially relevant factors. 

The item response regressions are estimated separately for AR citizens, AR noncitizens, and those 

missing AR citizenship. The item response variables are equal to one if there is a response for the 

item (whether it was later edited or not), and zero otherwise. The ACS noncitizen-AR citizen 

dependent variable is equal to one if the person is an as-reported noncitizen in the ACS and an AR 

citizen, and it is zero if both sources say the person is a citizen. Analogously, the ACS citizen-AR 

noncitizen dependent variable is equal to one if the person is an as-reported citizen in the ACS and 

an AR noncitizen, and it is zero if both sources say the person is a noncitizen. The last specification 

in Table 5 investigates determinants of the difference between the ACS naturalization year and the 

year in which the status changed to citizen in the Numident among persons who were noncitizens 

in their first SSN application. 
 

29

Besides relationship to the reference person, we include several other factors that theoretically 

could drive differences observed in both survey response and data quality. These include 

demographic characteristics such as sex, race/ethnicity, log one plus age, and its square. We also 

include socioeconomic characteristics such as educational attainment, working in the last week, 

and searching for a job in the last four weeks. Educational attainment is classified as less than high 

school diploma (base category), at least high school but less than a bachelor’s degree, bachelor’s 

degree, and graduate degree. Time since entry to the U.S. and reference person English language 

variables are included, since these variables may influence item response and discrepancies in 

citizenship status reporting. For our analysis, those variables are log of one plus the number of 

years since entering the U.S. (or since birth if born in the U.S.) and its square37 and English 

language ability for those speaking another language at home (speaking only English at home is 

the base category). We include an indicator for better or worse quality person linkage, since it may 

also drive differences in survey response and data quality. An indicator for whether the response 

is via mail or internet (i.e., without participation by an interviewer) vs. a personal or telephone 

interview. According to Camarota and Capizzano (2004), item nonresponse rates are lower in inperson interviews, and foreign-born persons are more likely to take the survey via personal 

interview, so controlling for mode could be particularly important when comparing the behavior 

of citizens and noncitizens.

The associations highlighted in Figures 1-6 above are robust to inclusion of other variables and 

are highly statistically significant.38 Item nonresponse and ACS noncitizen-AR citizen discrepancy 

rates are higher for nonrelatives, but the ACS citizen-AR noncitizen propensity is much lower, 

again consistent with reference persons misreporting their own citizenship, but not reporting that 

of others at all, especially nonrelatives. Like nonrelatives, Hispanics have a lower propensity to 

provide citizenship, a higher propensity to have ACS noncitizen-AR citizen discrepancies, and a 

lower propensity to have ACS citizen-AR noncitizen discrepancies. Better linkage is strongly 

associated with ACS citizen-AR noncitizen discrepancies, inconsistent with the hypothesis that 

these discrepancies are driven by linkage errors.

Now turning to factors not investigated in previous sections, labor market activity is positively 

associated with having a citizenship answer; especially for AR noncitizens (see Table 4). However, 

as Table 5 shows, working is also associated with both types of citizenship status disagreements, 

particularly ACS citizen-AR noncitizen. Reference persons who speak another language at home 

have a higher propensity to respond about sex, especially when their English language ability is 

less strong. This is also true for AR citizens for the citizenship question, but when asked to report 

about AR noncitizens, those speaking another language at home have much lower citizenship item 

response rates. Those speaking English less well also have a higher propensity to report ACS 

noncitizen when the person they are responding about is an AR citizen, perhaps reflecting 

misunderstanding of the question. However, the reference person’s English language ability is 

positively associated with ACS citizen-AR noncitizen discrepancies, again suggesting that 

 37 In cases where the person came to live in the U.S. more than once, respondents are instructed to give the latest year. 38 In results not shown here, we also estimate item response regressions with the full sample, regardless of AR 

citizenship status. The patterns are similar to those described in this paragraph, except that Hispanics have higher 

propensity to have item response for age in the full sample. 
 

30

misunderstanding the question is an important factor behind ACS noncitizen-AR citizen, but not 

ACS citizen-AR noncitizen discrepancies. Responding without the participation of an interviewer 

results in lower item response (except for age for AR noncitizens), consistent with Camarota and 

Capizzano (2004), and this effect is particularly strong for citizenship item response among AR 

noncitizens. ACS noncitizen-AR citizen discrepancies are more prevalent with interviewer 

participation, but ACS citizen-AR noncitizen discrepancies are much less prevalent. Interviewers 

may develop a rapport that encourages noncitizens to truthfully respond to what is a sensitive 

question for them.39 It could also be more difficult psychologically for a respondent to misreport 

to another person than when they fill out a questionnaire on their own.

As shown in Table 4, the associations with citizenship item response tend to be several times 

stronger for AR noncitizens than for citizens, with those missing AR citizenship falling in between 

the other two categories. Such differences are much more muted for sex and age. This again 

highlights the nonrandom nature of citizenship item nonresponse.

 39 This effect may be weaker in the Census than in the ACS, however, since ACS interviewers have much more 

experience than most Census enumerators.
 

31

Table 4. Item Response Regressions

Sex Item Response Age Item Response Citizenship Item Response

AR 

Citizen

AR 

Noncitizen

AR

Missing

AR 

Citizen

AR 

Noncitizen

AR

Missing

AR 

Citizen

AR 

Noncitizen

AR

Missing

Relative -0.159 -0.079 -0.759 -0.234 -0.224 -4.446 -0.057 -0.480 -0.106

(0.007) (0.016) (0.039) (0.013) (0.060) (0.123) (0.010) (0.082) (0.066)

Nonrelative -0.455 -0.309 -1.146 -2.353 -3.509 -9.533 -1.141 -7.395 -4.808

(0.035) (0.072) (0.084) (0.080) (0.307) (0.300) (0.047) (0.390) (0.200)

Non-Hispanic -0.136 -0.160 -0.003 -0.142 -0.227 -0.225 -0.122 -3.092 -0.979

African Amer. (0.014) (0.050) (0.082) (0.029) (0.143) (0.247) (0.012) (0.171) (0.078)

Hispanic 0.128 0.002 0.147 0.033 0.075 2.068 -0.391 -4.432 -1.692

(0.013) (0.030) (0.069) (0.032) (0.103) (0.210) (0.024) (0.140) (0.119)

Other Non- 0.050 0.038 0.230 -0.100 -0.108 1.229 -0.177 -2.320 -1.885

Hispanic (0.017) (0.028) (0.072) (0.034) (0.092) (0.230) (0.031) (0.129) (0.152)

Worked in 0.174 0.073 0.694 0.334 0.149 1.872 0.915 8.687 3.773

Last Week (0.008) (0.024) (0.037) (0.017) (0.081) (0.132) (0.013) (0.141) (0.088)

Searched for 0.045 0.017 0.668 0.457 0.466 3.834 0.769 7.414 3.494

Job (0.020) (0.046) (0.063) (0.033) (0.126) (0.252) (0.016) (0.185) (0.114)

English Very 0.116 0.101 0.690 0.084 0.068 1.823 0.087 -1.036 -0.580

Well (0.014) (0.029) (0.064) (0.035) (0.088) (0.224) (0.028) (0.133) (0.129)

English Well 0.141 0.050 0.703 0.306 0.074 3.044 0.390 -1.688 -0.892

(0.023) (0.034) (0.073) (0.052) (0.102) (0.247) (0.056) (0.159) (0.191)

English Not 0.125 -0.006 0.523 0.056 -0.148 1.728 0.475 -2.115 -0.441

Well (0.024) (0.041) (0.082) (0.073) (0.128) (0.270) (0.070) (0.191) (0.190)

English Not 0.117 0.070 0.599 -0.179 -0.155 3.178 0.571 -1.241 0.846

At All (0.035) (0.036) (0.072) (0.143) (0.189) (0.272) (0.122) (0.236) (0.178)

Better 1.022 0.338 2.502 1.384 1.193 9.002 0.127 1.766 2.078

Linkage (0.010) (0.019) (0.061) (0.015) (0.040) (0.122) (0.008) (0.115) (0.125)

Mail or Internet -0.967 -0.449 -2.703 -0.083 0.708 -3.527 -0.397 -5.923 -2.329

Response (0.010) (0.024) (0.068) (0.019) (0.073) (0.156) (0.011) (0.122) (0.092)

Weighted Obs. 264,700,000 21,910,000 39,950,000 264,700,000 21,910,000 39,950,000 264,700,000 21,910,000 39,950,000

Unweighted Obs. 4,418,000 280,000 558,000 4,418,000 280,000 558,000 4,418,000 280,000 558,000

Source: American Community Survey (ACS) and Census Numident, 2016. Notes: These regressions are estimated by linear probability models (LPM), weighted 

by ACS person weights. Standard errors are clustered by household. The base categories are reference person for relationship, non-Hispanic white for race/ethnicity, 

speaks only English at home for English ability, and in-person or phone interview for response mode. We also include educational attainment (less than high school, 

high school but less than bachelor’s degree, bachelor’s degree, and graduate degree), log of one plus age and its square, and log of one plus the number of years in 

the U.S. and its square, but do not report them here. 
 

32

Table 5. Citizenship Status and Naturalization Year Disagreement Regressions

ACS Noncitizen-AR 

Citizen

ACS Citizen-AR 

Noncitizen

ACS – Numident 

Natural. Year

Relative 0.028 -0.753 -0.343

(0.011) (0.215) (0.068)

Nonrelative 0.571 -5.461 -0.852

(0.045) (0.613) (0.282)

Non-Hispanic -0.137 2.744 0.683

African Amer. (0.013) (0.546) (0.128)

Hispanic 0.621 -16.00 1.129

(0.030) (0.417) (0.104)

Other Non- -0.327 0.755 0.144

Hispanic (0.034) (0.376) (0.093)

Worked in 0.398 1.992 0.631

Last Week (0.015) (0.260) (0.095)

Searched for 0.302 -0.620 0.136

Job (0.029) (0.542) (0.157)

English Very -0.452 1.983 0.517

Well (0.031) (0.373) (0.096)

English Well 0.114 1.063 0.712

(0.081) (0.426) (0.107)

English Not 1.461 -4.927 0.997

Well (0.113) (0.480) (0.129)

English Not 3.391 -8.282 1.656

At All (0.260) (0.592) (0.210)

Better 0.060 4.586 0.006

Linkage (0.009) (0.308) (0.067)

Mail or Internet -0.262 3.810 0.365

Response (0.012) (0.285) (0.077)

Weighted Obs. 250,300,000 20,220,000 6,407,000

Unweighted Obs. 4,165,000 254,000 89,000

Source: American Community Survey (ACS) and Census Numident, 2016. 

Notes: These regressions are estimated by linear probability models (LPM), weighted by ACS person weights. 

Standard errors are clustered by household. The base categories are reference person for relationship, non-Hispanic 

white for race/ethnicity, speaks only English at home for English ability, and in-person or phone interview for response 

mode. We also include educational attainment (less than high school, high school but less than bachelor’s degree, 

bachelor’s degree, and graduate degree), log of one plus age and its square, and log of one plus the number of years 

in the U.S. and its square, but do not report them here.

The last specification of Table 5 shows that the ACS naturalization-Numident citizenship change 

gap is larger when reporting for a relative or especially a nonrelative, which could indicate lack of 

respondent knowledge about others’ naturalization years.

40 Lack of English language ability is 

associated with a smaller gap between the ACS and Numident years, suggesting that 

misunderstanding the question is not an important explanatory factor. Employed people have 

smaller gaps, reflecting the incentive to promptly tell SSA about the naturalization to facilitate 

their employment eligibility verification. 

 40 Since very few observations have Numident citizenship change years before the ACS naturalization year, a positive 

coefficient generally means a smaller gap.
 

33

6. Effect of Citizenship Question on Unit Self-Response Rates

To forecast the effect of adding a citizenship question to the 2020 Census, we compare mail 

response rates in the 2010 Census and the 2010 American Community Survey (ACS) for the same 

housing units. By comparing the self-response behavior of the same housing unit across two 

surveys, we control for the household’s propensity to self-respond to mandatory Census Bureau 

household surveys in general.

The Census Bureau randomly selected a sample of households to receive the ACS questionnaire 

in 2010. The questionnaire included 75 questions and asked individuals to report their citizenship 

status. These households also received the full-count Census questionnaire in the same year, a list 

of 10 questions that did not include citizenship. We focus on Census housing units that received 

both questionnaires by mail. In the 2010 Census, these are the housing units from the initial mailing 

that did not have the questionnaire returned as Undeliverable as Addressed (UAA) and which were 

not classified as a vacant or delete (meaning uninhabitable or cannot be found). We define a 2010 

Census self-response as a returned questionnaire from the first mailing that is not blank. For the 

2010 ACS, a self-response is a mail response, also from the first contact mailing.

The presence of a citizenship question is not the only potential reason why a household may be 

less inclined to self-respond to the ACS than the Census. Census self-response is bolstered by a 

media campaign and intensive community advocacy group support, and the ACS questionnaire 

involves much greater respondent burden (OMB 2008, OMB 2009). To distinguish the citizenship 

question effect, we compare the actual ACS-Census difference in response rates for households 

that are likely to be more sensitive to the citizenship question to the ACS-Census difference for 

households less likely to be sensitive to the question. We assume that any reduction in selfresponse to the ACS vs. the Census for households unsensitive to the citizenship question is due 

to factors other than the presence of a citizenship question. We use two ways to divide the sample 

into sensitive and non-sensitive groups. The first is to define the sensitive group as households

where at least one person is an AR noncitizen and has been assigned to this housing unit in Rastogi 

and O’Hara’s (2012) administrative records person-address crosswalk (AR noncitizen 

households), and the less sensitive group is households where all of the persons assigned to the 

address are AR citizens (AR all-citizen households).

41 AR citizenship status is established using 

the 2010 Numident and ITINs, as described in Section 3.2.42 The choice of noncitizens as the 

sensitive group is motivated by the results in Section 3.1 that AR noncitizens have much higher 

item nonresponse rates for the citizenship question, both relative to their nonresponse rates for 

other demographic questions and compared to other people for citizenship. The use of an 

independent source for where noncitizens are located avoids the potential problem that households 

with noncitizens may be less likely to provide PII on household members, preventing linkage to 

 41 Here we impose a restriction that all household members have nonmissing AR citizenship for the less sensitive 

group, but we do not impose that restriction on the sensitive group. 42 The initial definition of citizenship (treating all persons in the Numident but with missing citizenship as citizens) is 

used for this first set of groups. In the second set of groups, U.S.-born persons with missing citizenship in the Numident 

are treated as citizens, while foreign-born persons with missing citizenship in the Numident are treated as missing AR 

citizenship.
 

34

their AR citizenship data. The remaining noncitizen households where AR linkage is done may be 

relatively more cooperative, potentially biasing the results.

We examine a second set of groups for several reasons. We would like to project the citizenship 

self-response effect forward in time, since population characteristics associated with this effect 

may be changing. No administrative records person-place crosswalk is available after 2010, 

however, so we instead use the ACS household roster to define which people are living in the 

household.43 AR noncitizens are probably not the people most sensitive to a citizenship question, 

since most of them are legal residents. Those lacking an SSN should presumably be even more 

sensitive to a citizenship question, so the AR noncitizen definition may exclude much of the 

sensitive population.44 In our second dichotomy the less sensitive group is “AR & ACS all-citizen

households”, those households where all persons reported in the ACS to be living in the household 

at the time of the survey are AR citizens, and all are self-reported as being citizens in the ACS as 

well. The more sensitive group is “all other households”, including those households where some 

residents are both AR citizens and self-reported citizens but at least one is not; there is a mismatch 

between the survey report and administrative record response; or citizenship status is not reported 

in one or both sources. We assume AR & ACS all-citizen households are less sensitive to a 

citizenship question than all other households, since they have demonstrated a willingness to 

provide citizenship status answers for all household members, those answers are consistent with 

administrative records and thus likely truthful responses,45 and citizens presumably have less to 

fear about revealing their status than noncitizens. In comparison to others, more of this group’s 

reluctance to self-respond to the ACS should be due to reasons other than the citizenship question, 

such as unwillingness to answer a longer questionnaire. Note that if some of the reluctance by AR 

& ACS all-citizens households to self-respond is due to the citizenship question in the ACS, then 

our analysis will underestimate the citizenship question unit self-response effect. 

The sample size for the second set of groups is significantly larger than that for the first set of 

groups, because the first set excludes households where no persons are AR noncitizens at the 

address, but at least one person assigned to that address by administrative records cannot be linked 

to the Numident.

Table 6 displays unweighted 2010 Census and ACS response rates for the AR all-citizen 

households and AR noncitizen household groups. The self-response rate is higher for the 2010 

Census than for the ACS for both citizenship categories, presumably reflecting the higher burden 

of the ACS. The all-citizen response rate is greater than the noncitizen rate in each survey, 

suggesting that noncitizen households have a lower participation rate in general. Most important 

for this study is understanding how the difference in self-response rate across groups varies 

 43 Another reason to use the survey household roster rather than the AR crosswalk is that the AR crosswalk often 

places people in different locations. Rastogi and O’Hara (2012) report that among the 279.2 million persons in the 

2010 Census who could be assigned a PIK, 27.2 percent are assigned to an address in the AR crosswalk that differs 

from their Census address.

44 This is consistent with Camarota and Capizzano (2004), who say field representatives reported that illegal 

immigrants were less likely to respond than other foreign-born persons. Illegal immigrants are ineligible for SSNs. 45 As shown in Section 4 above, when an administrative record shows that someone is a citizen, the ACS response is 

nearly always citizen as well, giving us a high degree of confidence that the person truly is a citizen.
 

35

between the 2010 Census and ACS. While the self-response rate for citizen households is 13.8 

percentage points lower in the ACS than in the 2010 Census, the self-response rate for households 

with at least one noncitizen is 18.9 percentage points lower for the ACS than the self-response rate 

to the 2010 Census, which is a 5.1 percentage point difference between the two categories.

Table 6. Comparison of 2010 ACS to 2010 Census Response Rates with Initial Assumptions

Self-Response Rate (%) Difference

2010 ACS 2010 Census

Households with at least 52.6 71.5 -18.9

one AR noncitizen (0.21) (0.19) (0.26)

AR all-citizen households 66.1 79.9 -13.8

(0.05) (0.04) (0.06)

Difference-in-differences -5.1

(0.26)

Source: 2010 ACS 1-year file, 2010 Census Unedited File (CUF), and 2010 Numident. 

Notes: 2010 CUF self-response is non-blank response to the first mailing, and only NRFU-eligible housing units are 

included. ACS self-response is mail response. All persons in the 2010 Numident that are missing citizenship are treated 

as citizens here. Robust standard errors are in parentheses, calculated from regressions. The estimates are unweighted.

Around 5.9 percent of the households have at least one noncitizen. The sample size is 929,000. DRB clearance number 

CBDRB-2017-CDAR-001.

Using survey weights can facilitate comparisons of results across years, since sampling can 

change, and we would like to be able to project results forward in time. We thus display weighted 

response rates in Table 7, now both for the first and second sets of groups. As expected, the 

restriction to being a citizen in both the AR and ACS results in higher self-response rates in the 

AR & ACS all-citizen household group compared to the AR all-citizen household group. The 

response rates for the two noncitizen groups differ little from each other. The difference-indifferences estimate for the first set of groups increases to 8.9 percentage points compared to the 

unweighted gap in Table 6. It is three percentage points higher (11.9) across the second set of 

groups. 
 

36

Table 7. Comparison of 2010 ACS to 2010 Census Response Rates (Weighted)

Self-Response Rate (%) Difference

2010 ACS 2010 Census

Households with at least 42.4 62.1 -19.7

one AR noncitizen (0.32) (0.18) (0.26)

AR all-citizen households 62.0 72.8 -10.8

(0.34) (0.11) (0.24)

Difference-in-differences -8.9

(0.35)

All other households 42.0 62.7 -20.7

(0.32) (0.14) (0.25)

AR & ACS all-citizen 65.6 74.4 -8.9

households (0.33) (0.11) (0.24)

Difference-in-differences -11.9

(0.34)

Source: 2010 ACS 1-year file, 2010 Census Unedited File (CUF), and 2010 Numident. 

Notes: 2010 CUF self-response is non-blank response to the first mailing, and only NRFU-eligible housing units are 

included. ACS self-response is mail response. The standard errors are in parentheses. The standard errors for the selfresponse rates and differences are calculated using Fay's balanced repeated replication variance estimation method, 

with 80 replicate weights, adjusting the original weights by a coefficient of 0.5. The difference-in-differences (DiD) 

standard errors (SE) are calculated as DDDDDD SSEE = �SS (EEEEEE1)2 + SS (EEEEEE2)2, where the two estimates (Est) are the 

2010 Census – 2010 ACS differences for the two groups. The estimates use ACS housing unit weights. 88.2 percent 

of households are in the AR all-citizen household group vs. 11.8 percent in the households with at least one AR 

noncitizen group. 74.9 percent are in the AR & ACS all-citizen household group vs. 25.1 percent are in the all other 

households group. The number of observations is 1,418,000.

The larger decline in self-response rates for the AR noncitizen household and all other households

groups may not actually be due to greater sensitivity. Other characteristics besides citizenship 

status could be associated with lower ACS self-response, and the AR noncitizen household and all 

other households groups could have a higher propensity to have such characteristics. To explore 

this possibility, we perform Blinder-Oaxaca decompositions (Blinder 1973 and Oaxaca 1973).46

Households may belong to one of two groups GG ∈ (SS,UU), where the S group is thought to be 

potentially sensitive to a citizenship question, while the U group is not. We set the self-responses 

RRGGiiAAAAAAtt and RRGGii CCCCCCCCCCtt equal to 100 if household i in group G self-responds in year t to the ACS 

and Census, respectively, and zero otherwise.47 The difference between the survey responses is

∆RRGGiitt = RRGGiiAAAAAAtt − RRGGii CCCCCCCCCCtt (1)

The vector of predictors X includes household size and reference person characteristics (sex, 

race/ethnicity, age, educational attainment, household income, working in the last week, job search 

 46 This method was initially developed to study the extent to which the gender wage gap is due to different distributions 

of characteristics associated with wages by gender (explained variation) vs. differing behavior across gender for a 

given set of characteristics (unexplained variation). The unexplained variation is usually attributed to discrimination, 

but it also captures any effects of differences in unobserved variables.

47 We use 100 for response so that the results are expressed in percentages.
 

37

in the last four weeks, and English language ability among those speaking a language other than 

English at home). β contains the slope parameters and intercept, and ε is an error term with mean 

zero.

We estimate OLS models for each household group

∆RR ii = XX ii

′ ββ tt + εε ii (2)

∆RRUUii = XXUUii

′ ββUUtt + εεUUii (3)

The difference-in-differences in expected self-response rates across the two surveys for the two 

groups S and U in year t is

∆∆RR tt = EE�∆RR tt � − EE�∆RRUUtt � (4)

We decompose this as follows:

∆∆RR tt = �EE�XX tt� − EE�XXUUtt ��

′

ββUUtt + �EE�XX tt�

′

�ββ tt − ββUUtt �� (5)

The first term (explained variation) applies the coefficients for the unsensitive group to the 

difference between the expected value of the sensitive group’s predictors and those of the 

unsensitive group. The second (unexplained variation) is the difference between the expected value 

of the sensitive group’s predictors applied to the sensitive group’s coefficients and the same 

predictors applied to the unsensitive group’s coefficients. The interpretation that the unexplained 

variation represents the citizenship question effect is dependent on the assumption that there are 

no unobserved variables relevant to the difference-in-differences in self-response across the two 

surveys.

Table 8 shows the results of the Blinder-Oaxaca decomposition for the two sets of groups. In the 

AR all-citizen vs. AR noncitizen comparison, virtually all the difference-in-differences is 

explained by differences in predictors across the two groups. Thus, it appears that the larger fall in 

self-response to the ACS vs. the Census for AR noncitizen households is not due to sensitivity to 

the citizenship question, but rather that AR noncitizen households have a greater propensity to 

have other characteristics that are associated with lower ACS self-response. In contrast, about half 

(6.1 percentage points) of the difference-in-differences for the AR & ACS all-citizen vs. all other 

household comparison is unexplained, suggesting that the larger drop-off in ACS self-response for 

all other households is partly due to sensitivity to the citizenship question. Appendix Table A13

shows the regression coefficients for equations (2) and (3), and the explained variation and 

unexplained variation coefficients for each predictor are shown in Appendix Table A14.

 
 

38

Table 8. Blinder-Oaxaca Decomposition of Comparison of Predicted 2010 ACS 

to 2010 Census to Response Rates by Households Citizenship Type

2010 ACS – 2010 Census

Households with at least -19.7

one AR noncitizen (0.13)

AR all-citizen households -10.8

(0.12)

Difference-in-differences -8.9

(0.09)

Explained -8.7

(0.11)

Unexplained -0.2

(0.13)

All other households -20.7

(0.12)

AR & ACS all-citizen households -8.9

(0.12)

Difference-in-differences -11.9

(0.07)

Explained -5.8

(0.14)

Unexplained -6.1

(0.16)

Source: 2010 ACS 1-year file, 2010 Census Unedited File (CUF), and 2010 Numident. 

Notes: 2010 CUF self-response is non-blank response to the first mailing, and only NRFU-eligible 

housing units are included. ACS self-response is mail response. The standard errors are in parentheses. 

The standard errors are bootstrapped using 80 ACS replicate weights. The number of observations is 

1,418,000.

To see how changes in predictors over time affect the magnitude of the unexplained variation (UV) 

in the decomposition, we apply the coefficients from the 2010 models to the predictors in the 2016 

ACS 

UUUU2016 = EE�XX 2016�

′

ββ 2010 − EE�XX 2016�

′

ββUU2010 (6)

Table 9 shows that the unexplained variation is still insignificant for the AR all-citizen vs. AR 

noncitizen comparison. It is of a similar magnitude in 2016 as in 2010 (5.8 percentage points vs. 

6.1) for the AR & ACS all-citizen vs. all other household comparison. Note that this does not 

capture changes over time in the degree of sensitivity to a citizenship question for a housing unit 

with a fixed set of characteristics. That would require estimating models on fresher data of surveys 

with and without a citizenship question for the same households. 
 

39

Table 9. Comparison of Predicted 2016 ACS to 2010 Census Response Rates 

for AR Noncitizen and All Other Households with Their Own vs. All-Citizen 

Models

2016 ACS – 2010 Census 

Model\Sample AR noncitizen household sample

AR noncitizen household model -19.7

(0.47)

AR all-citizen household model -20.5

(0.34)

Difference-in-differences 0.8

(0.58)

Model\Sample All other household sample

All other household model -21.7

(0.33)

AR & ACS all-citizen household -15.9

model (0.39)

Difference-in-differences -5.8

(0.51)

Source: 2016 ACS 1-year file and 2016 Numident. 

Notes: 2010 Census self-response is non-blank response to the first mailing, and only NRFU-eligible 

housing units are included. ACS self-response is mail response. The standard errors are in parentheses. 

The standard errors for the 2010 Census – 2016 ACS response differences are calculated using Fay's 

balanced repeated replication variance estimation method, with 80 replicate weights, adjusting the 

original weights by a coefficient of 0.5. The difference-in-differences (DiD) standard errors (SE) are 

calculated as DDDDDD SS = �SS (EEEEEE1)2 + SS (EEEEEE2)2, where the two estimates (Est) are the 2010 Census 

– 2016 ACS differences for the two groups. The estimates use ACS housing unit weights. 28.6 percent 

are in the all other households group in 2016. The standard errors are in parentheses. They are the 

standard errors of the model predictions, based on the bootstrapped regressions in Appendix Table A12

that use 80 ACS replicate weights. The number of observations is 163,000 for the AR noncitizen 

household sample and 477,000 for the all other household sample.

Though suggestive, these exercises and the ones performed below are not perfect laboratories for 

studying the self-response effect of inclusion of a citizenship question on the 2020 Census. The 

ACS contains 75 questions, so any one question is unlikely to stand out, whereas an added question 

will be more visible in the 2020 Census questionnaire, which contains just 10 other questions.48

Thus, we would ideally want to compare response rates on a short questionnaire without a 

citizenship question to one adding just the citizenship question. Second, the level of concern about 

using citizenship data for enforcement purposes may be very different in 2020 than it was in 2000

or 2010, so a more recent test would be preferable. These factors suggest the estimated effect on 

self-response from the exercise in Table 9 is conservative.

 48 A preferable test would be a randomized control trial (RCT) comparing self-response rates where some households 

are randomly chosen to have an 11-question Census questionnaire with a citizenship question (the treated group), and 

a randomly chosen set of control households receive a 10-question Census questionnaire without citizenship.
 

40

As robustness checks we do similar exercises below with the 2000 Census and the 2014 Survey of 

Income and Program Participation (SIPP), a longitudinal survey that follows the same individuals 

over time. Unlike the decennial census and the ACS, individuals respond for themselves in the 

SIPP. The 2000 Census long form (sent to one of every six housing units, selected randomly) 

contained a citizenship question among many other additional questions, while the short form (sent 

to the remaining housing units) did not. As in the first set of groups above, we divide housing units 

into those with all citizens and those with at least one noncitizen, based on citizenship data from 

the 2002 Numident for persons enumerated at those housing units in the 2000 Census.49 As with 

the 2010 ACS and Census exercises, Table 10 shows that self-response rates are higher in the short 

form than the long form, and they are higher in households with all citizens. The short- vs. longform difference in response rates is greater for households with at least one noncitizen by 3.3 

percentage points, again consistent with the possibility that households with noncitizens are more 

sensitive to the inclusion of citizenship questions. 

Table 10. 2000 Census Long Form and Short Form Analysis

Self-response rate (%)

Households by Citizen Long Form Short Form Difference

At Least One Noncitizen 62.5 71.0 -8.5

(0.017) (0.016) (0.023)

All Citizens 76.1 81.3 -5.2

(0.005) (0.004) (0.006)

Difference 13.6 10.3 -3.3

(0.017) (0.016) (0.024)

Source: 2000 Census short and long forms.

Notes: These are weighted using housing unit weights. The number of short forms is 105.5 

million, and the number of long forms is 16.4 million. The definition of self-response is mail 

response here. Robust standard errors are in parentheses, generated from weighted regressions

of response on an interaction of the household citizenship status with short form. The standard 

errors for the differences are calculated as SS (EEEEEE1 − EEEEEE2) = �SS (EEEEEE1)2 + SS (EEEEEE2)2.

Longitudinal data provide another means for understanding response sensitivity to questions of 

citizenship. Using the 2014 Survey of Income and Program Participation (SIPP) longitudinal panel 

waves 1 and 2, we show how nonresponse changes from Wave 1 to Wave 2 for noncitizen

respondents, as well as for households with at least one noncitizen. The first row in Table 11 shows 

nonresponse rates for noncitizens from the 2014 Survey of Income and Program Participation 

(SIPP) Waves 1 and 2. Noncitizens made up around 6 percent of the 2014 SIPP survey in Wave 1.

The proportion of noncitizens in Wave 2 decreased slightly, implying that noncitizens were more 

 49 To be classified as a housing unit with all citizens in this exercise, all persons must be linked to the Numident. A 

housing unit can be classified as having at least one noncitizen if there is at least one person linked to the Numident 

who is a Numident noncitizen, whether or not all the other persons in the housing unit could be linked to the Numident 

or not.
 

41

likely to leave the survey due to attrition or other factors than citizens. In addition, the rate of 

nonresponse among those households with at least one noncitizen increased from Wave 1 to Wave 

2, from 7.9 percent to 8.5 percent. While noncitizens were more likely to drop out of the survey, 

those who stayed were more likely to live in households where at least one member did not 

respond. These data provide additional hints of the potential future impact to nonresponse for 

noncitizens in surveys that ask about citizenship status.

Table 11. Noncitizens and Nonresponse in the 2014 Survey of Income and Program 

Participation

Wave 1 Wave 2

(%) (se) (%) (se)

Noncitizens 6.1 (0.144) 5.7 (0.174)

At least one member in the 

noncitizen household did not 

respond 7.9 (0.473) 8.5 (0.537)

Source: 2014 SIPP, Waves 1 and 2

Notes: Citizenship status refers to status in Wave 1. The standard errors are clustered in Wave 2. These estimates are 

run on the internal run 16 version of the 2014 SIPP.

7. Effects of Citizenship Question on Nonresponse Follow-up Costs and Enumeration Quality

A drop in the self-response rate from adding a citizenship question in Alternatives B (obtaining 

citizenship from the 2020 Census only) and D (obtaining citizenship from the 2020 Census and 

administrative records) results in increased costs in the Nonresponse Follow-up (NRFU) operation 

and affects the quality of the population count. Households deciding not to self-respond because 

of the citizenship question are likely to refuse to cooperate with enumerators coming to their door 

in NRFU, resulting in the use of neighbors as proxy respondents on their behalf. 50 As shown in 

Table 12, Mule (2012) reports that the correct enumeration rate is 27.1 percentage points lower for 

proxies than mail in self-responses based on data from the 2010 Census Coverage Measurement 

(CCM) survey. The person linkage rate is 62.9 percentage points lower for proxies than for mail 

in self-responses in the 2010 Census, according to Rastogi and O’Hara (2012). Both these studies 

provide suggestive evidence that proxies supply poor quality individual demographic and 

socioeconomic characteristic information about the person on behalf of whom they are responding. 

 50 A proxy response is a response about the household by someone outside the household, such as a neighbor or 

property manager. The enumerator will seek a proxy response for households that don’t mail back their Census 

questionnaire or give an in-person interview after several attempts.
 

42

Table 12. Enumeration Quality in Mailout/Mailback and Nonresponse Follow-up (NRFU)

Proxy Responses

Mailout/Mailback Response NRFU Proxy

Correct Enumerations 97.3 70.2

Erroneous Enumerations 2.5 6.7

Whole-Person Census 

Imputations

0.3 23.1

Person Linkage Rate 96.7 33.8

Source: Mule (2012) for correct enumerations, erroneous enumerations, and whole-person Census imputations, and 

Rastogi and O’Hara (2012) for the person linkage rate.

We provide two sets of estimates, the first based on our initial assumptions (in parentheses), and a 

second based on revised assumptions. The main changes in the revised assumptions are an 

expansion of the group of housing units considered potentially sensitive to a citizenship question 

and the estimated percentage of them who will not respond to a questionnaire due to the presence 

of a citizenship question (5.8 percent in Table 9 vs. 5.1 percent in Table 6).

Using these estimates as well as the data in Table 12, we can develop cautious estimates of the 

data quality and cost consequences of adding the citizenship question to the enumeration form. We 

assume that all-citizen households are unaffected by the change and that an additional 5.8 percent

(5.1 percent) of households that possibly have noncitizens go into NRFU because they do not selfrespond.51 We expect 320 million persons in 126 million occupied households in the 2020 

Census.

52 Based on a combination of administrative records from the 2016 Numident and ITINs 

and the 2016 ACS, we estimate that 28.6 percent (9.8 percent) of all households could potentially 

contain at least one noncitizen. Combining these assumptions implies an additional 2,090,000 

households (630,000 households) and 6.5 million persons (1.6 million persons) in NRFU.53 If the 

NRFU data for those households have the same quality as the average NRFU data in the 2010 

Census, then the result would be 561,000 (139,000) fewer correct enumerations, of which 185,000 

(46,000) are additional erroneous enumerations and 376,000 (93,000) are additional whole-person 

census imputations. This analysis assumes that during the NRFU operations a cooperative member 

of the household supplies data 79.0 percent of the time, and 21.0 percent receive proxy responses. 

If all of these new NRFU cases go to proxy responses instead,54 the result would be 1,750,000 

 51 Recall that the initial estimate is based on households with at least one AR noncitizen, which is only a fraction of 

the housing units in the all other households category, which also includes persons with missing citizenship in AR or 

the ACS or citizenship values that conflict between AR and the ACS. 52 We assume 10 million residents of group quarters. Group quarters are not included in either mailout/mailback or 

NRFU operations, and here we assume no effect of a citizenship question on their enumeration.

53 The initial assumption here is that average household size for households with at least one noncitizen is the same as 

the forecast for all households in the 2020 Census (2.54 persons). The revised assumption is that average household 

size for all other households is the same as its average in the 2016 ACS, 3.1 persons. 

54 If a household declines to self-respond due to the citizenship question, we suspect it would also refuse to cooperate 

with an enumerator coming to their door, resulting in a need to use a proxy.
 

43

(432,000) fewer correct enumerations, of which 272,000 (67,000) are erroneous enumerations, and 

1,477,000 (365,000) are whole-person census imputations.55 The number of persons who are 

linkable to administrative records would fall by 4.1 million (1 million). 

Our estimate of the incremental cost proceeds as follows. Using the analysis in the paragraph 

above, the estimated NRFU workload will increase by approximately 2,090,000 households

(630,000 households), or approximately 1.66 percentage points (0.5 percentage points). We 

currently estimate that for each percentage point increase in NRFU, the cost of the 2020 Census 

increases by approximately $55 million. Accordingly, the addition of a question on citizenship 

could increase the cost of the 2020 Census by at least $91.2 million ($27.5 million). It is worth 

stressing that this cost estimate is a lower bound. Our estimate of $55 million for each percentage 

point increase in NRFU is based on an average of three visits per household. We expect that many 

more of these noncitizen households would receive six NRFU visits. 

8. Distribution of 2020 Citizenship Data Sources by Collection Method

Figures 10-12 provide forecasts of how many U.S. residents in the 2020 Census acquire their 

citizenship data from survey responses, administrative records, and model-based imputation 

methods in Alternatives B, C, and D. Once again we provide forecasts based on initial and revised 

assumptions, with initial forecasts in parentheses.56 A reduction in self-response rates and increase 

in proxy responses from adding the citizenship question in Alternatives B and D is likely to affect 

the number of persons with survey responses for citizenship. As shown above, reference persons 

are much less likely to answer the citizenship question for nonrelatives in the household than for 

themselves, so they may be even less likely to answer it for neighbors. In order to obtain a range 

of estimates based on best and worst case scenarios, Figure 10 Panel A and Figure 12 Panels A

and B assume that proxies report citizenship at the same rate as they do in the 2010 ACS relative 

to all persons in the 2010 ACS,

57 while Figure 10 Panels B and C and Figure 12 Panels C and D

assume none of the proxies report citizenship.

We begin with the estimated 2020 Population of 330 million, the total number of persons we expect 

to count in the 2020 Census. Under Alternative B with complete citizenship data from proxy 

 55 These enumeration errors may not be avoidable simply by spending more money on fieldwork. Once a household 

decides not to cooperate, it may not be possible to obtain an accurate enumeration no matter how many times an 

enumerator knocks on their door. 56 In addition to the differences between the initial and revised assumptions mentioned in Section 7, two others are 

relevant here. One is that the initial assumptions classify foreign-born persons with missing citizenship in the 

Numident and without an ITIN as AR citizens, while the revised assumptions classify them as having missing AR 

citizenship. A second is that instead of showing the difference in the AR linkage rate with and without a citizenship 

question in the 2020 Census as an increase in the AR linkage rate in Alternative C, the revised assumptions show it as

a decrease in the AR linkage rate in Alternative D. 57 Within 2010 ACS households that have NRFU proxy responses in the 2010 Census, the nonmissing citizenship rate 

is 96.7 percent, vs. 97.1 percent for all ACS households. We apply this proxy to total sample ratio to the 93.7 percent 

nonmissing citizenship rate in the 2016 ACS to get an estimated 92.9 percent nonmissing citizenship rate for proxies 

in 2020.
 

44

responses, 309.1 million citizenship responses are obtained from the Census. Applying the missing 

citizenship rate of 6.3 percent in the 2016 ACS, we expect 20.9 million to have missing data for 

the citizenship question, either because the respondent skipped the question, or because a proxy 

response in nonresponse follow-up (NRFU) did not deliver information on that question.

Citizenship is imputed using models for these 20.9 million persons.58 With no citizenship data 

from proxy responses, the number of citizenship responses drops to 290 million (294.6 million), 

with 40 million (35.4 million) modeled.59 The accuracy of this imputation system is unknown at 

this time. As discussed above, the imputation will be challenging due to the fact that nonresponse 

is highly correlated with citizenship.

Under Alternative C, we expect to link 289.6 million (295.0 million) to administrative records 

containing citizenship data, applying the linkage rate for the 2016 ACS to currently available 

administrative records.60 The remaining 40.4 million (35.0 million) will have citizenship imputed 

using models based on the variables common to the linked and non-linked portions of the data. At 

this time, the accuracy of that imputation system is not known, but it would be based on the 

administrative record citizenship variable, so it would not be subject to the biases caused by survey 

citizenship reporting issues.

Of the 309.1 million who provide valid responses to the Census citizenship question in Alternative 

D, we expect to link 269.6 million (272.5 million) records to the administrative data.61 Of these, 

the vast majority, 260.9 million (263.0 million), will have administrative record and Census 

responses that agree (applying the 2016 ACS-AR agreement rate of 96.8 percent), and since the 

agreement is with the same administrative record system as in Alternative C, these people will 

have the same citizenship status under either alternative. Of the 269.6 million (277.4 million) 

linked Census responses with a valid answer to the 2020 Census question, we expect the 

administrative record and the Census response to disagree for 8.7 million (9.7 million). These are 

the persons for whom we have two choices: (1) accept the Census questionnaire answer or (2) 

 58 General imputation models develop a response for those who did not respond using all available relevant data.

59 Based on the analysis in Table 9, under our revised assumptions we project 6.5 million additional proxy responses 

due to the citizenship question, of which an estimated 840,000 already have missing citizenship (applying the 

allocation rate of 13.0 percent from the 2016 ACS among persons who do not both report being citizens and are AR 

citizens). This is in addition to an estimated 14.5 million proxy responses in 2020 without a citizenship question, of 

which an estimated 1,030,000 already have missing citizenship (applying the 2016 ACS citizenship item allocation 

rate of 6.3 percent among all ACS-AR citizenship groups, adjusted by the ratio of the 2010 ACS citizenship allocation 

rate for 2010 Census proxy respondents (3.3 percent) to the 2010 ACS citizenship allocation rate for the whole 2010 

ACS sample (2.9 percent)). Note that the proxy responses that are anticipated to occur in 2020 regardless of presence 

of a citizenship question may happen in households containing people in any ACS-AR citizenship group, whereas the 

additional proxies due to the citizenship question are assumed to come from housing units where people are not in the 

group with both ACS and AR citizen responses.

60 As discussed in Section 7, our initial estimate of the effect of a citizenship question on the number of linkable 

persons is 1 million, and the revised estimate is 4.1 million. Our initial estimate adds 1 million to the number of linked 

persons when no citizenship question is included in the questionnaire. We incorporate the change in the number of 

linkable persons as a reduction in AR linkage in Alternative D for our revised estimate, as discussed below. 61 When applying the 2016 ACS linkage to administrative record citizenship rate, the estimate is 273.4 million persons 

with linked citizenship. Of the 4.1 million anticipated reduction in linkage due to the citizenship question in our revised 

estimate, about 3.9 million are applied to the group with observed 2020 citizenship, as 93.7 percent of persons are 

anticipated to have observed 2020 citizenship (applying the missing citizenship rate in the 2016 ACS).
 

45

replace the questionnaire answer with the administrative answer. If we do the former, all of these 

cases will differ from the Alternative C answer. The estimated direct response is U.S. citizen for 

7.6 million (7.7 million) of these persons, compared to 1.1 million (2.0 million) in the 

administrative records. Use of direct responses for those with disagreement would result in a 

projected 6.5 million (5.7 million) more U.S. citizens than when using administrative records.62

Continuing with Alternative D, we would process the 20.9 million responses where we did not get 

a valid answer to the Census citizenship question as in Alternative C. This would result in 16.0

million (16.6 million) persons for whom we expect to find an answer in the administrative records, 

and 4.9 million (4.3 million) for whom we would use a modeled answer.63 The models would be 

developed using the same methods as in Alternative C, but not the same input data, because of the 

change in response behavior associated with asking the citizenship question. 

When 2020 citizenship is observed in Alternative D, but the record cannot be linked to 

administrative data, we would accept the survey response for an expected 39.5 million (31.7

million) people. The number of persons whose records can be linked to administrative data is lower 

by 4.1 million (10.7 million) in Alternative D than in Alternative C due to poorer linkage quality 

from proxy responses, which would have been self-responses without a citizenship question (see 

Table 10). This captures the negative effect of inclusion of the citizenship question on the ability 

to use administrative data for citizenship. 

When we assume that none of the proxy responses report citizenship, the number where 2020 

citizenship is observed falls to 289.5 million (294.6 million) in Alternative D, just as in Alternative 

B. 263.4 million (272.5 million) of these are linked to administrative record citizenship, 255.6

million (263.0 million) of those answers agree between sources, and 7.8 million (9.5 million) 

disagree. The direct response for the latter group is U.S. citizen for 6.8 million (7.5 million) vs. 

1.0 million (2.0 million) U.S. citizens in administrative records, leading to a 5.8 million (5.6

million) higher count of U.S. citizens if direct responses are used.

Of the 26.6 million (22.2 million) persons for whom 2020 citizenship is observed, but the record 

cannot be linked to administrative data, we estimate that about 560,000 (500,000) noncitizens will 

respond as citizens, based on the AR noncitizens reporting as ACS citizens share of the 2016 ACS 

(2.3 percent in the initial estimates and 2.1 percent in the revised estimates). 

These results show that there is a tendency for persons missing citizenship in one source to also be 

missing it in the other. Among persons with observed 2020 Census citizenship in Figure 12 Panel 

D, 90.8 percent have AR citizenship, while only 55.5 percent of those without 2020 Census 

citizenship have AR citizenship. Of those with AR citizenship, 92.2 percent have 2020 Census 

citizenship, but just 59.9 percent of those without AR citizenship have 2020 Census citizenship. 

The correlated missingness reduces the coverage gain from using multiple sources. Only 22.2 

million persons’ citizenship values can be covered by AR among those without 2020 Census 

 62 To put this in context, the 2016 ACS estimates that 22.5 million U.S. residents are noncitizens, or 7.0 percent of the 

population. 63 Here we apply the remainder of the anticipated 4.1 million reduction in linkage to administrative record citizenship 

due to increased proxy response to the group for which 2020 citizenship is not observed.
 

46

citizenship, whereas AR coverage would be 34.6 million if the missingness correlation were zero. 

Analogously, just 26.6 million persons missing AR citizenship have 2020 Census citizenship, vs. 

39.0 million if the correlation were zero.

Across the three alternatives, the data for at least 255.6 million (263.0 million) persons would be 

identical, and it would be identical for at least 276.9 million (284.3 million) between alternatives 

C and D. If the administrative record response is used when the cases disagree, then the data for 

alternatives C and D would agree for 285.6 million (294.0 million) linked cases. 

Alternative C results in more persons with modeled citizenship responses, while Alternative D has 

fewer imputations. If no proxy respondents report citizenship, then Alternative B has about the 

same number of imputations as Alternative C, but otherwise its level is in between that of 

Alternatives C and D.

As mentioned above, the estimated reduction in self-response due to the inclusion of a citizenship 

question is based on a comparison of a long 2010 ACS questionnaire to a short 2010 Census 

questionnaire. The visibility of the citizenship question may be more prominent when added to a 

short questionnaire, resulting in a larger reduction in self-response than what we have estimated 

here. If the assumption that all proxy responses result in citizenship item nonresponse is accurate, 

every additional person without Census citizenship will have to have modeled citizenship in 

Alternative B. With Alternative D, fewer of the additional nonresponses will be modeled, as some 

can be linked to administrative record citizenship data. The option to use administrative records in 

Alternative D thus partially mitigates the citizenship question self-response effect. 

These estimates are based on currently available administrative record citizenship data and linkage 

capability. The Census Bureau may obtain several additional sources by 2020 and develop better 

linkage, in which case administrative record coverage may be higher than that shown here. This 

would lead to fewer imputations in Alternative D and especially Alternative C. The number of 

imputations in Alternative C is not much higher than in Alternative B, so even a small 

improvement in administrative record citizenship data coverage would lead to a lower imputation 

rate in Alternative C than B. Alternative D’s advantage in coverage over Alternative C would 

shrink, though it is unlikely to vanish completely.

A key question when comparing Alternatives C and D is whether the data quality is higher for the 

2020 Census or for imputed values for the persons with imputations in Alternative C and observed 

2020 Census data in Alternative D. Survey citizenship data exhibit a markedly higher U.S. citizen 

share compared to administrative records for persons with both sources, but it is unknown whether 

that tendency also applies to persons without links to administrative records.

A second question is what data source(s) to use when administrative records and the survey 

response disagree in Alternative D. Citizenship status is verified via documentation from the 

issuing government agencies in the administrative records data, but not in the survey, and the 

analysis in Section 4 above exhibits patterns suggesting that the survey responses are more often 

inaccurate when they disagree. On the other hand, using administrative records when the sources 

disagree would mean that the survey response contribution to the citizenship statistics would be 

minor – it would only be necessary for persons without linked administrative record citizenship 
 

47

data. The 2020 Census citizenship data is the sole source for 8.1 percent (6.7 percent) of persons 

in Figure 12 Panel D (Panel C), and this share could be smaller if administrative record coverage 

improves or survey coverage is lower than estimated. It could be difficult to justify burdening 

respondents with this question if needed for only a small fraction of the population. 

Figure 10. Alternative B

Panel A. Alternative B, Proxies Report Citizenship

Panel B. Alternative B, Proxies Don’t Report Citizenship, Initial Assumptions

2020 

Census

330,000,000

2020 

Citizenship 

Observed

2020 

Citizenship 

Not 

Observed

309,100,000

Model

20,900,000

2020 

Census

330,000,000

2020 

Citizenship 

Observed

2020 

Citizenship 

Not 

Observed

294,600,000

Model

35,400,000
 

48

Panel C. Alternative B, Proxies Don’t Report Citizenship, Revised Assumptions

2020 

Census

330,000,000

2020 

Citizenship 

Observed

2020 

Citizenship 

Not 

Observed

290,000,000

Model

40,000,000
 

49

 Figure 11. Alternative C

Panel A. Initial Assumptions

Panel B. Revised Assumptions

2020 Census

330,000,000

Linked Not linked

Adrec Model

295,000,000 35,000,000

2020 

Census

330,000,000

Linked Not linked

Adrec Model

289,600,000 40,400,000
50

 Figure 12. Alternative D

Panel A. Alternative D, Proxies Report Citizenship, Initial Assumptions

2020 

Census

330,000,000

2020 

Citizenship 

Observed

2020 

Citizenship 

Not 

Observed

309,100,000 20,900,000

Linked Not linked Linked Not linked

277,400,000

Agree Disagree Adrec Model

16,600,000 4,300,000

Adrec ??

2020 

Citizenship

267,700,000 9,700,000 31,700,000
51

Panel B. Alternative D, Proxies Report Citizenship, Revised Assumptions

2020 

Census

330,000,000

2020 

Citizenship 

Observed

2020 

Citizenship 

Not 

Observed

309,100,000 20,900,000

Linked Not linked Linked Not linked

269,600,000

Agree Disagree Adrec Model

16,000,000 4,900,000

Adrec ??

2020 

Citizenship

260,900,000 8,700,000 39,500,000
52

Panel C. Alternative D, Proxies Don’t Report Citizenship, Initial Assumptions

2020 

Census

330,000,000

2020 

Citizenship 

Observed

2020 

Citizenship 

Not Observed

294,600,000 35,400,000

Linked Not linked Linked Not linked

272,500,000

Agree Disagree Adrec Model

21,500,000 13,800,000

Adrec ??

2020 

Citizenship

263,000,000 9,500,000 22,200,000
53

 

Panel D. Alternative D, Proxies Don’t Report Citizenship, Revised Assumptions

2020 

Census

330,000,000

2020 

Citizenship 

Observed

2020 

Citizenship 

Not 

Observed

290,000,000 40,000,000

Linked Not linked Linked Not linked

263,400,000

Agree Disagree Adrec Model

22,200,000 17,800,000

Adrec ??

2020 

Citizenship

255,600,000 7,800,000 26,600,000
54

9. Conclusion

This paper analyzes general issues of data quality in self-reported citizenship data and examines 

the coverage and quality of survey-collected and administrative records data available to produce 

block-level estimates of the Citizen Voting Age Population (CVAP). Our descriptive and 

regression analyses suggest that many noncitizens misreport their own citizenship on the American 

Community Survey (ACS), and, in many cases, they do not provide it at all for other noncitizens 

in the household. The evidence also suggests some naturalized persons either do not notify the 

Social Security Administration (SSA) about their change in citizenship status or they do so with 

delay. This potential weakness in SSA data illustrates the desirability of obtaining more timely and 

complete citizenship data from the U.S. Customs and Immigration Services (USCIS), Customs 

and Border Protection (CBP), and the State Department. Addressing survey misreporting would 

be more difficult, however. In the absence of 100 percent complete, accurate, and up to date 

administrative records, one cannot rule out the possibility that the self-reported citizenship status 

is correct. Conceptually, it would be challenging to decide which answer to use when sources 

conflict. Asking respondents to provide proof of citizenship status could reduce misreporting, but 

this would significantly increase respondent burden and the cost of administering the survey, and 

it could result in additional unit nonresponse.

This paper’s examination of several Census Bureau surveys with and without citizenship questions 

suggests that households that may contain noncitizens are more sensitive to the inclusion of 

citizenship in the questionnaire than all-citizen households. The implication is that adding a 

citizenship question to the 2020 Census would lead to lower self-response rates in households 

potentially containing noncitizens, resulting in more nonresponse follow-up (NRFU) fieldwork, 

more proxy responses, and a lower-quality population count. 
55

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on June 28, 2018, https://www.federalregister.gov/documents/1997/10/30/97-28653/revisions-tothe-standards-for-the-classification-of-federal-data-on-race-and-ethnicity. 

Office of Management and Budget, 2009, “The American Community Survey,” OMB Control No. 

0607-0810/ICR Reference No. 200910-0607-005, Washington DC, accessed on July 5, 2018, 

https://www.reginfo.gov/public/do/PRAViewICR?ref_nbr=200910-0607-005#. 

Office of Management and Budget, 2008, “2010 Census,” OMB Control No. 0607-0919/ICR 

Reference No. 200808-0607-003, Washington DC, accessed on July 5, 2018, 

https://www.reginfo.gov/public/do/PRAViewICR?ref_nbr=200808-0607-003#. 

Orrenius, Pia, and Madeline Zavodny, 2016, “Do State Work Eligibility Verification Laws Reduce 

Unauthorized Immigration?” IZA Journal of Migration, 5(5). https://doi.org/10.1186/s40176-016-

0053-3

Passel, Jeffrey S., and R.L. Clark, 1997, “How Many Naturalized Citizens Are There: An 

Assessment of Data Quality in the Decennial Census and the Current Population Survey,” Paper 

presented at the 1997 Annual Meeting of the Population Association of America.

Rastogi, Sonya, and Amy O’Hara, 2012, “2010 Census Match Study,” 2010 Census Planning 

Memoranda Series No. 247.

Rothhaas, C., Lestina, F. and Hill, J. (2012) “2010 Decennial Census Item Nonresponse and 

Imputation Assessment Report” 2010 Census Program for Evaluations and Experiments, 

Census Bureau: Washington DC, accessed July 6, 2018, 

https://www.census.gov/2010census/pdf/2010_Census_INR_Imputation_Assessment.pdf. 

Van Hook, Jennifer, and James D. Bachmeier, 2013, “How Well Does the American Community

Survey Count Naturalized Citizens?” Demographic Research, 29(1), pp. 1-32.

https://doi.org/10.4054/DemRes.2013.29.1

Weinberg, Daniel H., 2011, “Management Challenges of the 2010 U.S. Census,” Census Bureau 

Working Paper: Washington, DC, accessed on June 27, 2018, 

https://www.census.gov/history/pdf/weinberg-managing2010.pdf. 
58

Appendix I: Tables & Figures

Table A1. Linkage Rates to the 2010 Census by Household Survey and Year, 2000 to 2015

ACS SIPP CPS AHS

Sample 

(N)

Linked 

(N) (%)

Sample 

(N)

Linked 

(N) (%)

Sample 

(N)

Linked 

(N) (%)

Sample 

(N)

Linked 

(N) (%)

2000 33,500 27,500 0.8114

2001 1,301,000 1,097,000 0.8432 46,500 38,500 0.8328 44,500 37,500 0.8381

2002 1,110,000 948,000 0.8539 71,000 61,000 0.8564

2003 1,225,000 1,060,000 0.8652 68,500 59,000 0.8635

2004 1,222,000 1,072,000 0.8770 86,000 76,000 0.8847 62,000 54,000 0.8716 6,700 5,700 0.8397

2005 4,068,000 3,609,000 0.8870 61,000 53,500 0.8783

2006 4,259,000 3,792,000 0.8904 78,000 69,000 0.8853

2007 4,159,000 3,754,000 0.9026 77,500 69,000 0.8909

2008 4,123,000 3,774,000 0.9155 94,500 84,500 0.8927 75,500 68,000 0.9021

2009 4,068,000 3,759,000 0.9241 77,500 70,500 0.9059

2010 4,206,000 3,868,000 0.9195 77,000 70,500 0.9115

2011 4,571,000 4,205,000 0.9200 77,000 70,000 0.9068 298,000 270,000 0.9047

2012 5,096,000 4,615,000 0.9055 75,500 67,000 0.8869

2013 4,801,000 4,275,000 0.8903 75,000 65,500 0.8747 133,500 115,500 0.8683

2014 5,014,000 4,385,000 0.8745 66,000 57,500 0.8717

2015 4,966,000 4,273,000 0.8605 173,500 146,500 0.8455

Source: 2010 Census and Master Demographics, U.S. Census Bureau.

Notes: Household survey data unweighted. All counts have been rounded. 
59

Table A2. Citizenship in Household Surveys Linked to the 2010 Census by Demographics

Source: 2010 Census and Master Demographics, U.S. Census Bureau.

Notes: The household survey data are unweighted. The reported population total is the official count from the 2010 Census. All other counts have been rounded. 

CBDRB-2017-CDAR-001.

Household Surveys Linked to 2010 Census 2010 Census

Noncitizen Citizen Missing Total

N (%) N (%) N (%) (%) N (%)

Total Population 1,523,000 43,090,000 1,192,000 100.0 308,745,538 100.0

Coverage 14.4

Sex

Female 785,000 1.7 22,380,000 48.9 613,000 1.3 51.9 157,000,000 50.8

Male 738,000 1.6 20,710,000 45.2 579,000 1.3 48.1 151,800,000 49.2

Race

White 729,000 1.6 35,320,000 77.1 837,000 1.8 80.5 227,200,000 73.6

Black 127,000 0.3 4,157,000 9.1 173,000 0.4 9.7 40,400,000 13.1

American Indian, Aleut Eskimo 15,000 0.0 562,000 1.2 16,000 0.0 1.3 4,007,000 1.3

Asian or Pacific Islander 364,000 0.8 1,688,000 3.7 93,000 0.2 4.7 16,770,000 5.4

Other 287,000 0.6 1,358,000 3.0 74,500 0.2 3.8 20,400,000 6.6

Ethnicity

Hispanic/Spanish 675,000 1.5 4,046,000 8.8 198,000 0.4 10.7 50,480,000 16.4

Non-Hispanic/Spanish 848,000 1.9 39,040,000 85.2 994,000 2.2 89.3 258,300,000 83.7
60

Table A3. Item Nonresponse Rates for 2000 and 2010 Short Form Person Questionnaires 

Relationship Sex Age Hispanic 

Origin

Race Tenure

2000 1.3 1.1 3.7 3.1 2.9 4.1

2010 1.5 1.5 3.5 3.9 3.3 4.5

Source: Rothhaas, Lestina, and Hill (2012) Tables.

Notes: Rothhaas, Lestina, and Hill (2012) state “the INR rate is essentially the proportion of missing responses 

before pre-editing or imputation procedures for a given item (i.e. the respondent did not provide an answer to the 

item). For INR, missing values are included in the rates, but inconsistent responses (i.e. incompatible with other 

responses) are considered non-missing responses.”
61

Table A4. ACS Item Allocation Rates

Item 2010 2013 2016

Overall housing allocation rate

 occupied and vacant housing units

5.2 5.6 4.9

Overall person allocation rate

 total population

5.8 8.4 9.5

Vacancy status

 vacant housing units

2.9 3.5 3.9

Tenure

 occupied housing units

1.2 1.3 1.2

Units in structure

 occupied and vacant housing units

1.5 1.5 1.5

Year moved in

 occupied housing units

3.4 3 3

Month moved in

 occupied housing units into which households move 

 in the last two years

0.7 0.7 0.7

Year built

 occupied and vacant housing units

16.2 17.1 18.2

Lot size

 occupied and vacant single family and mobile homes

4.2 3.9 3.9

Agricultural sales

 occupied and vacant single family and mobile homes 

 with lot size greater than or equal to 1 acre

4.4 4.2 4

Business on property

 occupied and vacant single family and mobile homes

3 2.4 **

Number of rooms

 occupied and vacant housing units

5.2 5.5 5

Number of bedrooms

 occupied and vacant housing units

4.3 4.6 5.5

Running water

 occupied and vacant housing units

2 2.1 2.4

Flush toilet

 occupied and vacant housing units

2 2.2 **

Bathtub or shower

 occupied and vacant housing units

2 2.2 2.6

Sink with a faucet

 occupied and vacant housing units

2 2.2 2.6

Stove or range

 occupied and vacant housing units

2.5 2.8 3.1

Refrigerator

 occupied and vacant housing units

2.7 2.9 3.2

Telephone

 occupied housing units

1.1 1.2 1.5
62

Table A4. ACS Item Allocation Rates Continued

Item 2010 2013 2016

Number of vehicles

 occupied housing units

1.3 1.4 1.2

Heating fuel, occupied housing units 3.3 3.4 3.4

Monthly electricity cost

 occupied housing units

7.3 8.2 8.1

Monthly gas cost

 occupied housing units

9.8 9.9 9.6

Yearly water and sewer cost

 occupied housing units

8.1 8.8 8.5

Yearly other fuel cost

 occupied housing units

10.6 8.3 7.3

Yearly food stamp recipiency

 household occupied housing units

1.3 1.7 1.7

Yearly real estate taxes

 owner-occupied housing units

16.3 18.5 16.7

Yearly property insurance

 owner-occupied housing units

23.2 25.6 23.9

Mortgage status

 owner-occupied housing units

2.1 2.5 2.2

Monthly mortgage payment

 owner-occupied housing units with a mortgage 

 payment

10.7 12.4 10.5

Mortgage payment incl. real estate taxes

 owner-occupied housing units with a mortgage

(X) 6.9 6.2

Mortgage payment incl. insurance

 owner-occupied housing units with a mortgage

(X) 7.4 6.8

Second mortgage

 owner-occupied housing units

3.4 3.7 3.2

Home equity loan

 owner-occupied housing units

4.2 4.3 3.7

Other monthly mortgage payment(s)

 owner-occupied housing units with second mortgage 

 or home equity loan

17.9 21.7 23.3

Property value

 owner-occupied housing units and vacant housing 

 units for sale

12.3 12.9 11.6

Yearly mobile home costs

 Occupied mobile homes and other units

19.9 21.5 21.7

Monthly condominium fee

 owner-occupied housing units

0.7 0.8 0.8

Monthly rent

 occupied housing units rented for cash rent and 

 vacant housing units for rent

9.3 9.8 10.5
63

Table A4. ACS Item Allocation Rates Continued

Item 2010 2013 2016

Meals included in rent

 occupied housing units rented for cash rent and 

 vacant housing units for rent 

2 2.1 2.1

Desktop/laptop/notebook computer

 occupied housing units

** 3.2 1.3

Handheld computer/smart mobile phone

 occupied housing units

** 3.3 **

Tablet or other portable wireless computer

 occupied housing units

  1.6

Smartphone

 occupied housing units

**  ** 1.6

Other computer

 occupied housing units

** 3.7 1.7

Household has internet access

 occupied housing units

** 4.4 3.3

Dial-up internet service

 occupied housing units with internet access

** 5.7 3.8

DSL internet service

 occupied housing units with internet access

** 5.7 **

Cable modem internet service

 occupied housing units with internet access

** 5.7 **

Fiber-optic internet service

 occupied housing units with internet access

** 5.7 **

Cellular data plan (formerly mobile broadband)

 occupied housing units with internet access

** 26.7 7.6

Satellite internet service

 occupied housing units with internet access

** 5.7 3.8

High speed internet service

 occupied housing units with internet access

  3.8

Some other internet service

 occupied housing units with internet access

**  5.7 3.8

Race

 total population

1.5 1.6 1.5

Hispanic origin

 total population

1.8 2.1 1.8

Sex

total population

0.1 0.1 0.1

Age

 total population

1.3 1.6 1.7

Relationship

 total household population

1.2 1.1 1.2

Marital status

 total population 15 years and over

3 4.8 5.3
64

Table A4. ACS Item Allocation Rates Continued

Item 2010 2013 2016

Married past 12 months

 total population 15 years and over, except those 

 never married

4.7 6.6 6.9

Widowed past 12 months

 total population 15 years and over, except those 

 never married

4.5 7 7.4

Divorced past 12 months

 total population 15 years and over, except those 

 never married

4.5 7 7.4

Times married

 Total population 15 years and over, except those 

 never married

5.1 7.8 8.1

Year last married

 total population 15 years and over, except those 

 never married

11.4 13.3 13.5

Place of birth

 total population

6.5 8.6 9.1

Citizenship

 total population

2.7 5.2 6

Year of naturalization

 total population naturalized citizens

16.6 22.5 22.5

Year of entry

 total population not born in U.S.

10.3 13.2 14.8

Speaks another language at home

 total population 5 years and over

3.4 5.9 6.8

Language spoken

 total population 5 years and over who speak another 

 language at home

5.7 7 8.3

English ability

 total population 5 years and over who speak another 

 language at home

4 5.9 7.1

School enrollment

 total population 3 years and over

3.7 6 6.7

Grade level attended

 total population 3 years and over enrolled

6 8.9 10.2

Educational attainment

 total population 3 years and over

5.6 8 8.5

Field of degree

 total population 25 years and over with a bachelor’s 

 degree or higher

9.8 12.4 13.5

Mobility status

 total population 1 year and over

4 6.5 7.2
65

Table A4. ACS Item Allocation Rates Continued

Item 2010 2013 2016

Migration state/foreign county

 total population 1 year and over movers

7.1 11.3 13.2

Migration county

 total population 1 year and over movers within U.S.

8.3 12.5 14.6

Migration minor civil division

 total population 1 year and over movers within U.S.

8.4 12.1 14.2

Migration place

 total population 1 years and over movers within U.S.

8.8 12.9 15

Health insurance through employer/union

 total population

6.2 9 10.7

Health insurance purchased directly

 total population

6.9 9.7 11.3

Health insurance through Medicare

 total population

5.2 8.1 9.5

Health insurance through Medicaid

 total population

7.9 10.5 12.2

Health insurance through TRICARE

 total population

8.1 10.8 12.5

Health insurance through VA

 total population

8.1 10.7 12.3

Health ins. Through Indian Health Service

 total population

8.5 11.1 12.8

Visual difficulty

 total population

3.4 6.1 7.1

Hearing difficulty

 total population

3.2 5.9 6.8

Physical difficulty

 total population 5 years and over

3.5 6.7 7.5

Difficulty remembering

 total population 5 years and over

3.5 6.7 7.5

Difficulty dressing

 total population 5 years and over

3.5 6.7 7.5

Difficulty going out

 total population 16 years and over

3.4 6.5 7.3

Grandchildren living in home

 noninstitutionalized population 30 years and over

0.9 1 1.1

Responsibility for grandchildren

 noninstitutionalized population 30 years and over 

 who are grandparents with grandchildren in the 

 home

12 15.7 17.7
66

Table A4. ACS Item Allocation Rates Continued

Item 2010 2013 2016

Months responsible for grandchildren

 noninstitutionalized population 30 years and over 

 who are grandparents with grandchildren in the 

 home that have responsibility 

14.9 16.1 17.2

Fertility status

 female total population 15-50

3.7 6.7 7.8

Veteran status

 total population 17 years and over

3.8 6.8 7.3

Periods of military service

 total population 17 years and over on active duty 

 now or previously

6.3 9.3 9.7

Service-connected disability rating

 total population 17 years and over, except those who 

 never served in the Armed Forces

3.9 6.6 6.8

Employment status recode

 noninstitutionalized population 16 years and over

0.7 0.2 0.2

When last worked

 noninstitutionalized population 16 years and over

5.1 8.1 8.7

Weeks worked in the past 12 months

 noninstitutionalized population 16 years and over 

 who worked in the past 12 months

6.9 9.7 10.6

Hours worked per week

 noninstitutionalized population 16 years and over 

 who worked in the past 12 months

7.7 10.8 11.9

Place of work state/foreign county

 noninstitutionalized population 16 years and over at 

 work last week

6.3 10.4 11.8

Place of work county

 noninstitutionalized population 16 years and over at 

 work last week

7 11 12.5

Place of work minor civil division

 noninstitutionalized population 16 years and over at 

 work last week

2.1 3.3 3.6

Place of work place

 noninstitutionalized population 16 years and over at 

 work last week

7.6 11.6 13.1

Transportation to work

 noninstitutionalized population 16 years and over at 

 work last week

5.7 8.8 9.6

Carpool size

 noninstitutionalized population 16 years and over at 

 work last week who drive to work

6.8 9.9 10.9
67

Table A4. ACS Item Allocation Rates Continued

Item 2010 2013 2016

Time of departure

 noninstitutionalized population 16 years and over at 

 work last week who don’t work at home

12.8 18.5 20.2

Commuting time

 noninstitutionalized population 16 years and over at 

 work last week who don’t work at home

9.7 13.3 14.5

Class of worker

 total population 16 years and over who worked in

 the last 5 years

7.2 10.7 11.7

Industry

 total population 16 years and over who worked in 

 the last 5 years

7.8 11.4 12.7

Occupation

 total population 16 years and over who worked in 

 the last 5 years

8.1 11.8 13.4

Wages/salary income

 total population 15 years and over

16 19 19.1

Self-employment income

 total population 15 years and over

5.9 9.3 10.5

Interest, dividends, etc. income

 total population 15 years and over

8.8 12.6 15.2

Social security or railroad retirement

 total population 15 years and over

8.9 12.3 14.5

Supplemental security income

 total population 15 years and over

6.7 10.3 12.7

Public assistance

 total population 15 years and over

6.8 10.5 13.2

Retirement income

 total population 15 years and over

7.5 11.1 13.6

Other income

 total population 15 years and over

7.4 10.8 13.2

Some or all income allocated

 total population 15 years and over

22.4 25.3 28.4

Source: American Community Survey (ACS) 1-year files in 2010, 2013, and 2016. 

Notes: Item allocation includes nonresponses and responses that were edited. See ACS (2018a and 2018b) for more 

information about ACS item allocation rates. ** Item was not asked in this year. (X) Some instances where no response 

to this question was required were incorrectly tallied as allocations, overstating the true level of item allocation 

required. The incorrect rates have been removed.
68

Table A5. Citizenship Item Allocation Rate by Response Mode, 2013-2016

Source: 2013 & 2016 ACS 1-year files.

Note: Item allocation includes nonresponses and responses that were edited.

Table A6. Administrative Record (AR) Coverage of the 2010 Census, Using Initial ARCensus Crosswalk

Count Percent of 

Decennial 

Population

Percent of 

Matched 

Sample

No PIK, not sent to PVS 10,370,000 3.4

No PIK, failed in PVS 19,200,000 6.2

PIK, but not in Numident, not ITIN 8,900 0.0

PIK, but not in Numident, is ITIN 1,567,000 0.5

Blank Citizenship 57,910,000 18.8 20.9

U.S. Citizen 200,400,000 64.9 72.2

Noncitizen 19,270,000 6.2 6.9

Total 308,745,538 100.00 100.00

Source: 2010 Numident and initial administrative record-2010 Census crosswalk.

Note: This is the crosswalk used by Rastogi and O’Hara (2012).

Table A7. Percent Linked to 2010 Census among 2017 Numident Records with Missing

Citizenship 

Foreign-Born U.S.-Born

Percent Linked to 2010 

Census

36.3 74.5

Total 6.8 million 57.0 million

Source: 2010 Census and 2017 Numident

Notes: These are persons in the 2017 Numident with missing citizenship, born after 1919, and with no date of death.

Our preliminary analysis reported 6.6 million foreign-born persons, which excluded some relevant records. 

(%) s.e. (%) s.e. (%) s.e. (%) s.e.

NH White 6.1 (0.023) 6.3 (0.024) 6.2 (0.019) 6.2 (0.018)

NH Black 12.3 (0.090) 12.6 (0.100) 12.3 (0.100) 13.1 (0.091)

NH Asian/NHPI 10.3 (0.126) 12.7 (0.151) 9.4 (0.083) 9.6 (0.075)

NH Other 8.4 (0.143) 8.4 (0.154) 10.0 (0.128) 10.2 (0.114)

Hispanic/Latino 11.8 (0.080) 12.3 (0.088) 13.0 (0.078) 15.5 (0.071)

Mail-in Response Internet Self-Response

2013 2016 2013 2016
69

Table A8 Panel A: Citizenship Agreement between 2000 Census Long Form and Administrative Records 

AR Citizen AR Noncitizen AR Missing Percent by ACS 

Category

All (N=42,580,000)

Census Citizen 98.8 29.9 71.6 93.0

Census Noncitizen 0.9 66.4 23.8 6.2

Census Missing 0.3 3.7 4.6 0.8

Percent by AR Cat. 86.9 5.4 7.7 100.0

Non-Hispanic White (N=31,690,000)

Census Citizen 99.4 31.8 92.4 97.9

Census Noncitizen 0.4 65.8 5.9 1.8

Census Missing 0.2 2.4 1.7 0.3

Percent by AR Cat. 93.4 1.8 4.8 100.0

Non-Hispanic Black (N=4,543,000)

Census Citizen 99.3 36.3 92.5 96.1

Census Noncitizen 0.4 59.2 5.4 3.3

Census Missing 0.2 4.5 2.1 0.6

Percent by AR Cat. 85.3 4.0 10.8 100.0

Hispanic (N=4,534,000)

Census Citizen 94.3 25.6 35.9 69.3

Census Noncitizen 4.7 69.7 55.2 27.3

Census Missing 1.0 4.7 9.0 3.3

Percent by AR Cat. 60.6 19.1 20.3 100.0

Non-Hispanic Other Race (N=1,821,000)

Census Citizen 93.4 33.3 53.1 71.2

Census Noncitizen 5.1 63.7 37.0 26.0

Census Missing 1.4 3.0 9.9 2.9

Percent by AR Cat. 59.2 29.3 11.6 100.0

Reference Person (N=16,450,000)

Census Citizen 98.7 32.9 76.9 94.0

Census Noncitizen 0.9 63.3 19.3 5.3

Census Missing 0.4 3.9 3.8 0.8

Percent by AR Cat. 89.4 5.5 5.1 100.0

Relative (N=24,980,000)

Census Citizen 98.9 28.6 71.7 92.9

Census Noncitizen 0.8 68.0 24.3 6.4

Census Missing 0.2 3.4 4.1 0.7

Percent by AR Cat. 86.4 5.3 8.3 100.0

Nonrelative (N=1,153,000)

Census Citizen 97.2 20.4 58.6 80.4

Census Noncitizen 1.9 72.0 31.3 15.6

Census Missing 0.9 7.6 10.1 4.1

Percent by AR Cat. 63.7 7.4 28.9 100.0

Age 18+ (N=31,260,000)

Census Citizen 98.5 30.3 67.1 91.8

Census Noncitizen 1.1 65.5 26.8 7.2

Census Missing 0.4 4.1 6.1 1.1

Percent by AR Cat. 86.2 6.5 7.2 100.0

Source: 2000 Census Long Form and 2002 Census Numident.

Notes: These are weighted percentages. The first three rows of each panel contain percentages by survey group within 

the AR category, and the last row contains percentages by AR category of the sample as a whole. Here AR citizen 

includes Numident records with missing citizenship, regardless of their country of birth.
70

Table A8 Panel B: Citizenship Agreement between 2010 ACS and Administrative Records 

AR Citizen AR Noncitizen AR Missing Percent by ACS 

Category

All (N=4,520,000)

ACS Citizen 96.9 32.7 74.8 90.2

ACS Noncitizen 0.8 63.2 17.5 6.9

ACS Missing 2.4 4.1 7.7 2.9

Percent by AR Cat. 83.9 7.5 8.5 100.0

Non-Hispanic White (N=3,152,000)

ACS Citizen 97.8 42.4 87.4 96.1

ACS Noncitizen 0.2 53.9 4.0 1.5

ACS Missing 2.0 3.7 8.6 2.4

Percent by AR Cat. 92.4 2.0 5.7 100.0

Non-Hispanic Black (N=434,000)

ACS Citizen 96.3 40.1 85.0 92.4

ACS Noncitizen 0.5 54.5 6.5 3.8

ACS Missing 3.2 5.4 8.6 3.8

Percent by AR Cat. 86.2 5.2 8.6 100.0

Hispanic (N=609,000)

ACS Citizen 93.9 23.8 61.1 72.7

ACS Noncitizen 2.9 72.4 32.9 23.4

ACS Missing 3.2 3.9 6.0 3.9

Percent by AR Cat. 59.8 21.5 18.8 100.0

Non-Hispanic Other Race (N=326,000)

ACS Citizen 93.7 41.3 59.0 77.3

ACS Noncitizen 2.8 54.3 31.8 18.5

ACS Missing 3.5 4.4 9.1 4.2

Percent by AR Cat. 65.7 25.4 8.8 100.0

Reference Person (N=1,770,000)

ACS Citizen 97.7 37.2 80.8 91.9

ACS Noncitizen 0.7 59.9 14.1 6.2

ACS Missing 1.6 2.9 5.1 1.9

Percent by AR Cat. 85.4 7.7 6.9 100.0

Relative (N=2,504,000)

ACS Citizen 96.5 30.6 75.3 89.7

ACS Noncitizen 0.7 64.7 16.4 6.9

ACS Missing 2.8 4.6 8.2 3.4

Percent by AR Cat. 84.0 7.5 8.5 100.0

Nonrelative (N=102,000)

ACS Citizen 94.1 20.0 53.0 77.0

ACS Noncitizen 1.9 72.2 34.8 16.6

ACS Missing 4.0 7.8 12.2 6.4

Percent by AR Cat. 66.0 9.4 24.6 100.0

Age 18+ (N=3,505,000)

ACS Citizen 97.0 33.1 71.7 89.1

ACS Noncitizen 0.9 62.9 20.9 8.2

ACS Missing 2.1 4.0 7.3 2.7

Percent by AR Cat. 82.6 9.0 8.4 100.0

Source: 2010 American Community Survey (ACS) and 2010 Census Numident.

Notes: These are weighted percentages. The first three rows of each panel contain percentages by survey group within 

the AR category, and the last row contains percentages by AR category of the sample as a whole. Here AR citizen 

includes Numident records with missing citizenship, regardless of their country of birth.
71

Table A8 Panel C: Citizenship Agreement between 2016 ACS and Administrative Records 

AR Citizen AR Noncitizen AR Missing Percent by ACS 

Category

All (N=5,255,000)

ACS Citizen 93.8 34.7 70.4 87.3

ACS Noncitizen 0.7 57.6 17.7 6.4

ACS Missing 5.5 7.7 11.9 6.3

Percent by AR Cat. 82.4 6.7 10.9 100.0

Non-Hispanic White (N=3,579,000)

ACS Citizen 95.0 44.7 81.8 93.1

ACS Noncitizen 0.2 48.8 4.4 1.4

ACS Missing 4.8 6.6 13.7 5.5

Percent by AR Cat. 91.1 1.9 7.0 100.0

Non-Hispanic Black (N=495,000)

ACS Citizen 93.2 42.0 82.5 89.3

ACS Noncitizen 0.4 49.7 5.8 3.6

ACS Missing 6.4 8.4 11.7 7.2

Percent by AR Cat. 82.3 5.1 12.6 100.0

Hispanic (N=732,000)

ACS Citizen 90.5 26.6 58.0 73.1

ACS Noncitizen 2.7 65.2 32.3 19.3

ACS Missing 6.8 8.2 9.7 7.7

Percent by AR Cat. 62.0 16.2 21.8 100.0

Non-Hispanic Other Race (N=449,000)

ACS Citizen 90.3 39.1 54.2 74.6

ACS Noncitizen 2.4 53.5 32.2 17.3

ACS Missing 7.3 7.4 13.6 8.1

Percent by AR Cat. 65.8 22.0 12.2 100.0

Reference Person (N=2,037,000)

ACS Citizen 96.7 39.1 71.6 90.6

ACS Noncitizen 0.7 56.4 20.1 6.1

ACS Missing 2.7 4.5 8.3 3.2

Percent by AR Cat. 85.5 7.3 7.2 100.0

Relative (N=2,789,000)

ACS Citizen 92.3 32.5 68.4 86.0

ACS Noncitizen 0.7 58.2 18.6 6.3

ACS Missing 6.9 9.3 13.0 7.7

Percent by AR Cat. 83.5 6.5 10.0 100.0

Nonrelative (N=135,000)

ACS Citizen 85.3 21.5 52.4 71.8

ACS Noncitizen 1.5 61.5 23.6 11.8

ACS Missing 13.2 17.0 23.9 16.4

Percent by AR Cat. 65.6 7.1 27.3 100.0

Age 18+ (N=4,178,000)

ACS Citizen 94.3 34.8 68.0 86.6

ACS Noncitizen 0.9 57.7 20.3 7.6

ACS Missing 4.8 7.6 11.7 5.8

Percent by AR Cat. 81.0 8.2 10.7 100.0

Source: 2016 American Community Survey (ACS) and 2016 Census Numident. 

Notes: These are weighted percentages. The first three rows of each panel contain percentages by survey group within 

the AR category, and the last row contains percentages by AR category of the sample as a whole. Here AR citizen 

includes Numident records with missing citizenship, regardless of their country of birth.
72

Table A9. Citizenship Agreement Rates (%) Between Census Surveys and Administrative 

Records

Consistent Inconsistent Missing in 

One or Both 

Sources

Consistent, 

Conditional 

on 

Nonmissing

Inconsistent, 

Conditional 

on 

Nonmissing

2000 Census 89.4 2.4 8.2 97.4 2.6

2010 ACS 86.1 3.1 10.8 96.5 3.5

2016 ACS 81.1 2.9 15.9 96.5 3.5

Source: 2000 Census long form, 2002 Census Numident, 2010 American Community Survey (ACS), 2010 Census 

Numident, 2016 ACS, and 2016 Census Numident.

Notes: These are weighted percentages. Here AR citizen includes Numident records with missing citizenship, 

regardless of their country of birth. The original estimate for inconsistent in the 2000 Census was 2.3 percent, for 

consistent in the 2010 ACS was 86.0 percent, consistent conditional on nonmissing in the 2010 ACS was 96.4 percent, 

inconsistent conditional on nonmissing in the 2010 ACS was 3.6 percent, and consistent in the 2016 ACS was 81.2 

percent. 

Table A10. AR Citizen and Noncitizen Percentages of the 2016 ACS by Race/Ethnicity and 

Relationship to Reference Person

AR Citizens AR Noncitizens

All 81.1 6.7

Non-Hispanic White 90.1 1.9

Non-Hispanic Black 81.5 5.1

Hispanic 60.2 16.2

Non-Hispanic Other Race 62.5 22.0

Reference Person 81.1 6.9

Relative 82.1 6.5

Non-Relative 64.8 7.1

Source: 2010 ACS 1-year file

Notes: These are weighted percentages. The omitted category is persons missing AR citizenship.

Table A11. Percentages of the 2016 ACS Sample by Relationship to Reference Person and 

Record Linkage Quality

High-Quality Linkage Low-Quality Linkage

Reference Person 18.1 20.3

Relative 23.9 33.8

Non-Relative 0.6 3.2

Source: 2016 ACS 1-year file

Notes: These results are weighted. This excludes persons missing AR citizenship.
73

Table A12. 2016 ACS Citizenship Distribution for ITINs

Percent of All ITINs

U.S. Citizens 11.1

Born Citizens 6.6

Source: 2016 ACS 1-year file

Note: These results are weighted.
74

Table A13. Comparison of 2010 ACS and 2010 Census Response Rates: Regressions by 

Household Citizenship Type

AR all-citizen

households

AR noncitizen 

households

AR & ACS allcitizen

households

All other 

households

Log Household Size -3.184 -8.237 -0.4762 -7.185

(0.1476) (0.5100) (0.1737) (0.3067)

Log Household Size 

Squared -0.0998 1.565 -1.929 2.944

(0.0899) (0.2304) (0.1159) (0.1423)

Female -6.665 -6.687 -6.263 -8.167

(0.0557) (0.1809) (0.0578) (0.1289)

Non-Hispanic -10.53 -8.422 -11.48 -3.573

African Amer. (0.1143) (0.3952) (0.1275) (0.2064)

Hispanic -7.532 -20.55 -7.145 -14.07

(0.1585) (0.2962) (0.1640) (0.2123)

Other Non- 0.8338 -0.1256 0.4897 4.129

Hispanic (0.1809) (0.2904) (0.2162) (0.2158)

Age 25-34 -4.052 -3.101 -4.658 -1.380

(0.2078) (0.5207) (0.2602) (0.3202)

Age 35-44 -9.122 -4.746 -9.582 -3.653

(0.2117) (0.5048) (0.2704) (0.3181)

Age 45-54 -11.83 -6.676 -12.26 -5.095

(0.2418) (0.5313) (0.3082) (0.3113)

Age 55-64 -12.78 -5.792 -13.20 -5.395

(0.2715) (0.5466) (0.3530) (0.3315)

Age 65+ -13.06 -4.225 -13.76 -3.617

(0.3121) (0.6672) (0.4051) (0.3857)

High School 0.7658 -1.195 1.641 -1.866

(0.1055) (0.2641) (0.1097) (0.1828)

Bachelor’s Degree 3.864 2.383 5.116 0.1112

(0.1197) (0.3549) (0.1262) (0.2316)

Graduate Degree 7.685 7.098 8.448 6.310

(0.1330) (0.3923) (0.1387) (0.2661)

HH Income $1-

$25,000 -1.854 -1.525 -2.249 -1.537

(0.3130) (0.9868) (0.3665) (0.5480)

HH Income $25,001 -2.759 -1.995 -3.002 -3.304

-$50,000 (0.3158) (0.9549) (0.3604) (0.5348)

HH Income $50,001 -3.093 -0.6062 -3.454 -2.555

-$75,000 (0.3164) (0.9907) (0.3563) (0.5494)

HH Income $75,001 -3.037 -0.4054 -3.300 -2.435

-$100,000 (0.3091) (1.004) (0.3590) (0.5505)

HH Income -2.272 1.035 -2.499 -0.9051

$100,001+ (0.3183) (1.016) (0.3672) (0.5719)
75

Table A13. Continued

AR all-citizen

households

AR noncitizen 

households

AR & ACS allcitizen

households

All other 

households

Worked in 2.741 1.204 3.027 -1.012

Last Week (0.0644) (0.2549) (0.0742) (0.1613)

Searched for 8.495 8.559 8.565 6.753

Job (0.1282) (0.3652) (0.1357) (0.2629)

Log Number of 

Years in -11.17 -10.34 -14.09 -1.286

U.S. (0.5538) (0.5499) (1.434) (0.4572)

Log Number of 

Years in 2.845 1.997 3.315 -0.1304

U.S. Squared (0.0904) (0.1044) (0.2063) (0.0823)

English Very 0.9990 0.7404 0.7193 5.302

Well (0.1669) (0.2508) (0.1760) (0.1927)

English Well 3.823 0.4760 6.686 3.160

(0.3037) (0.3007) (0.3449) (0.2369)

English Not -4.707 -7.014 -0.5008 -6.007

Well (0.3595) (0.3431) (0.4334) (0.3088)

English Not -13.87 -14.00 -15.19 -13.50

At All (0.6209) (0.5355) (1.070) (0.4750)

Weighted Obs. 85,100,000 11,400,000 72,300,000 24,200,000

Unweighted Obs. 1,280,000 139,000 1,112,000 306,000

Source: 2010 ACS 1-year file, 2010 Census Unedited File (CUF), and 2010 Numident. 

Notes: The 2010 Census self-response is non-blank response to the first mailing, and only NRFU-eligible housing 

units are included. ACS self-response is mail response. These regressions are estimated by linear probability models 

(LPM), weighted by ACS person weights. The standard errors are in parentheses. The standard errors are bootstrapped 

using 80 ACS replicate weights. The base categories are non-Hispanic white for race/ethnicity, less than high school 

for educational attainment, no income for household income, and speaks only English at home for English ability. 
76

Table A14. Blinder-Oaxaca Decomposition Coefficients for Comparison of ACS 2010 and 

Census 2010 Self-Response Rates by Household Citizenship 

AR all-citizen vs. AR noncitizen 

households

AR & ACS all-citizen vs. all 

other households

Explained Unexplained Explained Unexplained

Log Household Size 1.347 5.799 0.1479 6.758

(0.0624) (0.5861) (0.0539) (0.3311)

Log Household Size 0.0778 -2.687 1.108 -6.613

Squared (0.0700) (0.3702) (0.0664) (0.2305)

Female -0.4591 0.0090 -0.2351 0.8355

(0.0076) (0.0761) (0.0048) (0.0619)

Non-Hispanic White 2.261 -0.6934 1.612 0.4970

(0.0419) (0.0480) (0.0311) (0.0566)

Non-Hispanic -0.2343 -0.4469 -0.0307 -0.7966

African Amer. (0.0042) (0.0230) (0.0030) (0.0217)

Hispanic 1.196 4.487 0.6141 2.391

(0.0420) (0.0904) (0.0280) (0.0515)

Other Non- -0.9869 -0.4652 -0.6266 -0.3870

Hispanic (0.0258) (0.0396) (0.0202) (0.0298)

Age Below 25 0.0184 0.1686 -0.0735 0.2663

(0.0029) (0.0160) (0.0031) (0.0156)

Age 25-34 -0.3132 0.7466 -0.1863 0.4588

(0.0091) (0.0536) (0.0065) (0.0402)

Age 35-44 0.0819 0.0026 0.0487 -0.0507

(0.0107) (0.0539) (0.0068) (0.0396)

Age 45-54 0.1199 -0.1936 0.0323 -0.3278

(0.0036) (0.0537) (0.0017) (0.0315)

Age 55-64 -0.2849 -0.3316 -0.1938 -0.3167

(0.0069) (0.0331) (0.0059) (0.0295)

Age 65+ -0.7552 -0.2910 -0.4319 -0.6361

(0.0229) (0.0218) (0.0165) (0.0354)

Below High School 0.4911 -0.2640 0.4301 -0.5491

(0.0130) (0.0609) (0.0094) (0.0338)

High School -0.3682 0.4025 -0.1901 0.4193

(0.0084) (0.0735) (0.0048) (0.0557)

Bachelor’s Degree 0.0169 0.0827 0.0341 0.4077

(0.0013) (0.0356) (0.0018) (0.0255)

Graduate Degree -0.0978 -0.0594 -0.0036 -0.0646

(0.0030) (0.0320) (0.0018) (0.0217)

HH Income = $0 0.0017 0.0190 -0.0191 0.0117

(0.0005) (0.0099) (0.0024) (0.0096)

HH Income $1 0.0072 0.2648 0.0003 -0.0193

-$25,000 (0.0021) (0.0567) (0.0002) (0.0453)

HH Income $25,001 0.0122 0.2196 0.0042 0.2364

-$50,000 (0.0017) (0.0619) (0.0007) (0.0424)
77

Table A14. Continued

AR all-citizen vs. AR noncitizen 

households

AR & ACS all-citizen vs. all other 

households

Explained Unexplained Explained Unexplained

HH Income 0.0030 -0.1651 -0.0063 -0.0477

$50,000-$75,000 (0.0006) (0.0470) (0.0007) (0.0288)

HH Income -0.0032 -0.1245 -0.0060 -0.0278

$75,001-$100,000 (0.0005) (0.0339) (0.0007) (0.0221)

HH Income 0.0003 -0.3579 -0.0001 -0.1981

$100,001+ (0.0003) (0.0581) (0.0007) (0.0408)

Worked in -0.3684 1.134 -0.0440 2.545

Last Week (0.0089) (0.1959) (0.0024) (0.1203)

Searched for -0.1195 -0.0050 -0.0399 0.1230

Job (0.0041) (0.0286) (0.0025) (0.0198)

Log Number of. -10.19 -2.434 -8.077 -42.62

Years in U.S (0.5032) (2.068) (0.8219) (4.835)

Log Years in 16.68 7.861 12.22 40.24

U.S. Squared (0.5292) (1.126) (0.7610) (2.490)

Only English 1.876 -0.2650 0.7321 -0.2700

(0.1384) (0.0675) (0.1293) (0.1763)

English Very -0.9976 -0.3151 -0.4244 -1.183

Well (0.0486) (0.0873) (0.0481) (0.0699)

English Well -1.270 0.4510 -1.010 0.3912

(0.0491) (0.0669) (0.0360) (0.0468)

English Not 0.3237 0.1948 -0.1211 0.5456

Well (0.0444) (0.0541) (0.0393) (0.0414)

English Not 0.6369 -0.0655 0.5055 -0.0864

At All (0.0272) (0.0377) (0.0308) (0.0358)

Source: 2010 ACS 1-year file, 2010 Census Unedited File (CUF), and 2010 Numident. 

Notes: The 2010 Census self-response is non-blank response to the first mailing, and only NRFU-eligible housing 

units are included. ACS self-response is mail response. These regressions are estimated by linear probability models 

(LPM), weighted by ACS person weights. The standard errors are in parentheses. The standard errors are bootstrapped 

using 80 ACS replicate weights. The base categories are non-Hispanic white for race/ethnicity, less than high school 

for educational attainment, no income for household income, and speaks only English at home for English ability. The 

number of observations is 1,418,000 (unweighted) and 96,540,000 (weighted).