1

Investigating the Use of Administrative Records in the 

Consumer Expenditure Survey1

Quentin Brummet, Denise Flanagan-Doyle, Joshua Mitchell, and John Voorheis

Center for Administrative Records Research and Applications

U.S. Census Bureau

September 25, 2017

1 Introduction

The Consumer Expenditure Survey (CE) is the most important data source on the income and 

expenditure habits of American households. CE data are used, among other purposes, to update 

the basket of goods and services underlying the Consumer Price Index (CPI). The CPI in turn is 

used to calculate cost of living adjustments both for workers at many firms and for recipients of 

several government transfer programs (such as Social Security). Maintaining the high quality of 

this survey data is thus of the utmost importance. 

The Bureau of Labor Statistics (BLS) has expressed interest in investigating if alternative data 

sources–such as administrative records from other federal government agencies, or data provided 

by commercial vendors–can be used to improve the Consumer Expenditure Survey. These 

alternative data sources have the potential to reduce respondent burden, reduce measurement 

error, and improve BLS processes for imputation and non-response adjustment. Of particular 

interest is the possibility of enhancing CE questions about income with administrative income 

records from the Internal Revenue Service (IRS).

In collaboration with BLS, the Center for Administrative Records Research and Applications 

(CARRA) at the US Census Bureau has completed a series of exploratory analyses to provide 

information on these topics. The Census Bureau’s data linkage infrastructure allows for the direct 

linking of survey responses to administrative records, and is ideally suited for these types of 

analyses. In this project, responses to the Consumer Expenditure Survey from 2013 and 2014 are 

linked to three sources of administrative records from the IRS: the universe of Form 1040 tax 

returns, the universe of Form W-2 wage reports, and the universe of other information returns. 

We perform two types of record linkage: a person-based linkage, using anonymous individual 

identifiers, and a household-based linkage, using address and location information. Using these 

linked datasets, we perform a series of analyses, which provide information about measurement 

 

1 This paper is released to inform interested parties of research and to encourage discussion. The views expressed are 

those of the authors and not necessarily those of the U.S. Census Bureau.
2

error, non-response bias, and the quality of CE imputation and weighting processes. These 

analyses compare CE data about wages, Social Security payroll deductions, self-employment 

income and retirement income to administrative data on these same topics.

The remaining analysis proceeds as follows. We first describe the data sets to be linked, the 

matching processes used to link the survey and administrative data, and the results of the 

matching processes, summarized by match rates. We then provide further analysis relevant for 

the matching process to examine how match rates would differ if the CE collected more fine 

grained information on date of birth. We then compare individual CE responses to income 

questions with linked administrative data on the same income concepts. We link responding and 

non-responding households to administrative records by address and analyze non-response bias. 

We conclude with a summary of how these linked data inform current and future efforts to 

improve the quality of the CE.

2 Data and Matching

To analyze survey measurement error, we need to compare survey responses to administrative 

records covering the same content and the same individuals. Doing so requires internal CE 

research files, high quality administrative records on the universe of people, and a method for 

linking survey responses to administrative records. In this section, we describe each of these 

three items. 

2.1 Survey Data

The Consumer Expenditure Survey program consists of two surveys: the Quarterly Interview 

Survey, which is designed to measure income and expenditures for lower-frequency purchases;

and the Diary Survey, which is designed to measure expenditures on high frequency purchases. 

We focus on the Quarterly Interview Survey, which asks more detailed questions about income. 

During 2013-14, the Quarterly Interview Survey contacted approximately 12,000 sample units

each quarter, and responding consumer units (CUs) were interviewed 5 times in total. 

Questions about income (including wage receipt, presence of social security payroll deductions, 

self-employment, and retirement income) were asked of all CU members over age 14 on the 

second interview. These responses were carried over to the third and fourth interviews for 

individuals who remained in the CU; individuals who entered a CU after the second interview or 

who previously reported not working, but now were working, were asked the income questions 

in the third or fourth interviews. All CU members were then asked the income questions once 

more in their fifth and final interview. If a CU member did not report an amount to an income 

question, while acknowledging receipt of such income to a previous screening question in the 

second interview, BLS imputed a response using a multiple regression based imputation method

for interviews 2-4. Our analysis will proceed by comparing responses from the first time an 
3

individual responded to an income question to the administrative records (for a vast majority of 

CU members, this occurs in the second interview).

CARRA received two CE files. The first file contains personally identifiable information and 

location information collected by the Census Bureau on behalf of BLS in the administration of 

the CE survey (frame data). This file contains PII for all individuals in CUs who participated in 

all interviews conducted in July-December 2013 and all first interviews conducted in 2014. The 

second file, delivered by BLS, is an extract of the CE research microdata file, containing nontopcoded (but edited and imputed) income responses, as well as a variety of other demographic 

and family-level variables. Both files contain unique identifiers for CUs and individuals within 

CUs, and so our analysis in section 4 will proceed using the set of individuals present in both 

files (the address-matching based analysis in section 5, however, is conducted on all sample units 

in the frame data).

2.2 IRS Administrative Records

The administrative records used in our analysis are collected by the Internal Revenue Service

(IRS) from three types of tax filing forms–Form 1040 tax returns, Form W-2 wage statements, 

and Form 1099 information returns (filed in reference to various types of non-wage payments).

Forms W-2 and 1099 are filed by third parties (employers, pension funds or other payers) and 

sent both to the IRS and to the taxpayer, while Form 1040 is filed by a taxpayer and sent to the 

IRS. These three types of administrative records represent slightly different universes. Form W-2 

data represents the universe of individual wage earners, while the Form 1099 data represents the 

universe of other non-wage payees (e.g. retired individuals) for payments covered on these 

forms. These two datasets contain individual level data, while the Form 1040 data represents the 

universe of tax filing units, and contains tax unit-level data.

While the IRS extracts contain the universe of individuals (W-2 and 1099) or tax filing units 

(1040) for a given year, CARRA only receives a subset of the fields reported on these forms. 

Relevant for this analysis, CARRA’s Form 1040 data extract contains information on filing 

status, the amount of wage and salary income and adjusted gross income, the amount of interest 

and dividend income, and flags for whether a tax unit filed schedule C or SE, which we use as an 

indicator for the receipt of self-employment income. CARRA’s Form W-2 data contain an 

employer identifier, the amount of wages and tips, the amount of wages subject to FICA taxes

(which we use as an indicator for whether an individual has Social Security or Medicare 

deductions taken out from their paycheck), as well as the amount of deferred compensation. 

CARRA’s Form 1099 data contain only an indicator for whether a form was filed for most types 

of 1099 filings, with the exception of Form 1099-R for retirement income. CARRA’s Form 

1099-R data contain information on the amount of pension or retirement fund distribution 

received by a taxpayer, as well as the type of distribution (pension, IRA, etc.). We de-duplicate 

IRS administrative records to the extent possible given the information available on CARRA’s 

data within each file.
4

2.3 Matching Procedure

The analysis requires that we match the CE to IRS records. The two matching processes we will 

use are a person-based match, where Protected Identification Keys (PIKs) are assigned to 

individual survey or administrative records based on available PII, and an address based match, 

where Master Address File identifiers (MAFIDs) are assigned to survey households or 

administrative tax units based on available address information. Once a file has been assigned 

these identifiers, it is possible to link this file with any other file that has been assigned MAFIDs 

and or PIKs. 

CARRA’s person-based matching process, the Person Validation System (PVS), is a 

probabilistic matching algorithm that assigns protected identification keys (PIKs) to individuals 

in administrative and survey data based on available personally identifying information. If an 

individual’s Social Security number is included in a dataset, then records can be directly linked 

to the SSA Numerical Identification File (Numident) file. If no SSN is available on a file (as in 

CE), then information including name, address and date of birth (or year of birth if exact date is 

unavailable) is used to probabilistically match survey records to the SSA Numident. If only age 

is available (as in CE), PVS searches over a range of years of birth consistent with the response. 

See Wagner and Layne (2014) for an in-depth discussion of PVS.

CARRA’s address matching methodology (MAF match) consists of an initial file edit process, 

then processing addresses through a commercial product to clean the addresses. The first step in 

the MAF match process is to standardize and clean addresses using SAS Dataflux, which updates 

zip codes, adds zip4 if needed, returns a preferred street name, corrects minor misspellings, and 

outputs the address in one string. The cleaned addresses are then parsed and standardized using 

the US Census Bureau’s Geography Division (GEO) Address Standardization routine. The match 

process uses a generalized probabilistic matching routine to compare information from the 

parsed fields to the Census Bureau’s Master Address File. Successful matches are assigned a 

MAFID. 

As part of regular CARRA operations, Form 1040 tax returns, Form W-2 wage statements, and 

Form 1099 information returns have all undergone the PVS and MAF match processes (and new 

deliveries of the IRS data are subsequently processed upon receipt). CE data had not been 

processed through PVS before the start of this project, although the 2013 CE had previously been 

assigned MAFIDs as part of a separate project. CARRA received a file from the CE office at 

Census which contained detailed survey frame information, including address information for 

both respondents and non-respondents, and PII including name, age at survey response, sex, race 

and ethnicity, for all CE interviews conducted from July-December 2013 and all wave 1 

interviews conducted (or attempted) in 2014. CARRA staff then processed this file through PVS 

and MAF match. 

Once PIKs and MAFIDs have been assigned to the CE, it is possible to match CE observations to 

administrative records using these unique identifiers. As noted, BLS sent CARRA a file 

containing information on income and demographics. This file, and the frame data sent by the 
5

CE office at Census, both contain internal CE identifiers. We proceed with our analysis using the 

first interview in which the respondent provides income information for all individuals who 

appear in both CE files.

2

We create two linked datasets. We first link the CE data to the three IRS administrative records 

datasets by PIK to create a PIK-linked dataset. Since it is possible for an individual to appear on 

more than one Form 1040 or Form W-2, we de-duplicate these cases as follows. If an individual 

appears on multiple 1040s as both a dependent and a primary filer, we select the 1040 in which 

the individual appears as a primary filer. If an individual appears on multiple unique W-2s, we 

treat these as distinct income sources, and sum all W-2 wages, FICA wages and deferred 

compensation to form total W-2 amounts for these fields. We also create a MAFID-linked 

dataset. Again, since multiple 1040s or W-2 can be matched to a given MAFID, we de-duplicate 

matched records. We omit any CE MAFIDs which are matched to 10 or more 1040s, and then 

aggregate 1040 records for a given MAFID by averaging across all linked 1040s for each 1040 

field. Since CE income questions ask about income received in the past 12 months, we match CE 

records to IRS administrative records from the previous tax year (i.e. matching the 2013 CE to 

tax year 2012 IRS data).

Table 1 presents PIK rates and PIK-based match rates to each of the three IRS administrative 

records datasets, broken out by CE-derived demographic characteristics. PIK assignment rates 

are a bit lower than MAFID assignment. Overall, about 77 percent of CE respondents can be 

assigned a PIK. About 70 percent of CE respondents can be matched to the universe of 1040s, 39 

percent can be matched to a W-2, and about 59 percent can be matched to the 1099 data. The 

relatively lower match rates to the universe of W-2s are to be expected, as not everyone is 

employed in a given year.3 There are patterns across race, ethnicity and age groups that are 

consistent with other linkage research (O’Hara et al., 2012, Luque and Bhaskar, 2014). Nonwhites and Hispanics are less likely to be assigned a PIK, as are younger individuals and 

individuals with lower incomes.4 Thus, these groups are also less likely to be matched to the 

administrative records. 

 

2 Throughout, we refer to each CU member as a respondent, regardless of whether they personally responded to an 

interview question, or a proxy response was recorded.

3 For comparison, 100*(0.39/0.766)=50.9 percent of individuals with PIKs can be matched to the universe of W-2, 

while the employment-to-population ratio was 58.5 percent in January 2013. For the prime age 25-44 age group, 

78.9 percent of individuals with a PIK can be linked to the W-2’s, compared to a prime age EPOP of 75.6 percent.

4 We split the sample into high and low income at the median CU wage (FSALARYM). The median CU wage for 

the pooled 2013-2014 sample is about $45,000.
6

Table 1: PVS Match Rates

Obs.

CE 

PIK 

Rate

CE-IRS 

1040 

Match Rate

CE-IRS W-2 

Match Rate

CE-IRS 1099 

Match Rate

CE-Any IRS 

Match Rate

All CE 

Respondents 36,437 0.766 0.704 0.39 0.586 0.752

Sex

Female 18,761 0.761 0.700 0.374 0.575 0.747

Male 17,676 0.771 0.709 0.408 0.597 0.757

Race

White Alone 28,905 0.778 0.719 0.402 0.607 0.765

Black Alone 4,341 0.752 0.662 0.354 0.53 0.732

AIAN Alone 177 0.768 0.655 0.362 0.559 0.734

Asian Alone 2,108 0.656 0.612 0.348 0.476 0.637

Pacific 

Islander 

Alone

236 0.665 0.64 0.36 0.492 0.665

Two or more 

races

670 0.743 0.678 0.261 0.415 0.722

Age

0-13 6,763 0.675 0.655 0.006 0.073 0.659

14-24 5,353 0.733 0.702 0.369 0.502 0.717

25-44 9,280 0.763 0.721 0.602 0.695 0.75

45-64 9,597 0.824 0.750 0.579 0.77 0.809

65+ 5,444 0.814 0.658 0.195 0.794 0.804

Hispanic Origin

Hispanic 6,628 0.688 0.626 0.295 0.422 0.664

Not 

Hispanic 29,809 0.783 0.721 0.412 0.622 0.771

Income

Above 

Median CU 

Wages

18,682 0.79 0.768 0.491 0.602 0.784

Below 

Median CU 

Wages

17,755 0.741 0.637 0.284 0.569 0.718

Source: CE 2013-2014, IRS 1040s, W-2s and 1099s 2012-2013

Note: All Rates are calculated as # Matches/N, where N is the number of unique 

individual CE respondents from July 2013-December 2014 in a given category
7

Table 2 presents MAFID assignment rates (i.e. CE-MAF match rates) and match rates to the 

1040 and W-2 data.5 Overall, 93 percent of sample unit can be assigned a MAFID, while 68 

percent can be matched to the universe of 1040s, and about 51 percent can be matched to the W2 data. However, there are substantial differences across participation in these match rates. Type 

A non-interviews (non-interviews at valid, non-vacant addresses) and CE participants have 

similar patterns of match rates. About 95 percent of CE participants can be assigned a MAFID, 

while 77 percent can be matched to 1040s and 58 percent to W-2s, compared to 94 percent, 76 

percent and 57 percent for type A (valid) noninterviews, respectively. Type B and C 

noninterviews represent attempted interviews at addresses that are either vacant (type B) or 

invalid (type C). These noninterviews have much lower match rates, as expected – less than 10 

percent of type C noninterviews can be matched to the W-2 data, for instance.6

Table 2: MAF Match Rates by Nonresponse type

Obs.

CE-MAF 

Match 

Rate

CE-IRS 1040 

Match Rate

CE-IRS W-2 

Match Rate

All Sample Units 30,917 0.931 0.682 0.508

Participation

CE Participant 19,677 0.950 0.774 0.582

Valid Non-interview 5,761 0.942 0.761 0.565

Vacant Non-interview 4,159 0.867 0.307 0.209

Invalid Non-interview 1,320 0.789 0.132 0.098

Source: CE 2013-2014, IRS 1040s and W-2s 2012-2013

3 Date of Birth Analysis

The PVS match rates for the CE are somewhat lower than the PIK assignment found for several 

other demographic surveys such as the Current Population Survey’s Annual Social and 

Economic Supplement has been processed via PVS with PIK assignment rate of about 91 percent 

in 2013. It is likely that the relatively low rate of PIK assignment for CE is at least partly due to 

the coarse PII collected in CE. Unlike other comparable surveys, the CE asks the respondent’s 

age only rather than the full date of birth. Day and month of birth are generally useful fields for 

linking person records.

 

5 A MAFID match is defined as a case where at least one 1040 or W-2 can be matched to the MAFID assigned to a 

sample unit (some sample units are matched to multiple 1040s or W-2s, as we discuss in Section 5).

6 Although it may be somewhat surprising that these match rates are nonzero at all, note that the Type C 

noninterview subtypes with the highest match rates are OUTCOME codes 246, 252 and 341, which all correspond to 

locations which could be valid addresses for form 1040 purposes, but would not be valid addresses for CE.
8

To estimate the difference it might make to the PIK rate were CE to collect full date of birth, we 

performed an additional matching analysis. We used the American Community Survey (ACS), 

since CARRA staff have access to the full PII collected in the ACS. We compared the PIK 

assignment rate for the 2013 ACS that was obtained using the full date of birth to an alternative 

procedure that uses age but not date of birth.

The overall PIK assignment rate for the 2013 ACS is 94 percent. Using the alternative 

procedure, we obtained a rate of about 84 percent. If collecting full date of birth would make a 

similar difference for CE, that would bring the CE PIK rate into the neighborhood of the PIK 

rates of other surveys and censuses that collect date of birth information, including the 2010 

Decennial Census (86.4 percent), the CPS ASEC (87 percent in 2016) and the Survey of Income 

and Program Participation (91 percent in 2014).

4 Imputation and Measurement Error Analysis

We begin with a set of analyses using the individual PIK linkages, which provide useful 

information about measurement error in CE data and the quality of CE imputations for item nonresponse. These analyses compare the distributions of wages, self-employment income receipt, 

retirement income, and social security payroll deductions in the survey and administrative data. 

In each of these analyses, we present results for the sub-sample of CE respondents who receive a

PIK and who appear in both the frame data and cleaned CE internal research files. Some 

analyses will further subset this data to consider only individuals who can be linked to the 

administrative records, or who have non-zero income in both the survey and administrative data.

4.1 Wage Receipt

We first validate the CE wage and salary receipt question. The SALARYST variable in the CE 

research file is a binary variable, coded from the survey question “Did you receive any wages, 

salary, tips bonuses or commissions?” with a reference period of the previous 12 months. Note 

that although BLS imputation procedures theoretically may impute values for non-response, 

there are a minimal number of imputed cases in our sample for SALARYST. We compare this to 

an administrative records analogue, a binary variable coded based on whether an individual 

appears in the universe of Form W-2’s from the previous tax year. The universe of Form W-2’s 

is not necessarily the same as the universe of all wage earners. The two largest populations of 

wage earners not covered in the form W-2 universe are workers in the informal sector and 

independent contractors (or some other non-traditional employment relationship) who may 

consider their earnings to be wages rather than self-employment income, but who receive 

earnings reports via Form 1099 and not Form W-2. Work in the informal sector is fundamentally 
9

unmeasurable in the administrative records. The incidence of independent contractor work, 

however, can partially be examined in the 1099 file to get a sense of misclassification error.7 

Table 3 presents summary statistics for the two measures of wage receipt, broken down by sociodemographic categories. The second and third columns present the unweighted average of the 

binary variable for CE wage receipt, and the binary variable for appearing on a W-2 respectively. 

Since the underlying variables are binary, we interpret these averages as proportions. The final 

two columns present weighted averages of the two binary variables. Both the CE wage receipt 

variable and the W-2 wage receipt variable are weighted by the final CE weights (FINLWT21) 

multiplied by the inverse probability of PIK assignment.8 This re-weighting is done to address 

potential systematic differences in PIK assignment across demographic groups.

The proportion of individuals who report CE wages corresponds well with the proportion of 

individuals who appear on W-2’s. Overall, about 50 percent of the linked sample reports W-2 

wages and an almost identical proportion report wages in the CE. There is variation across 

different demographic categories, both in the proportion of individuals who report wages in 

either data source and in the differences in proportions across data sources. Asians, whites, and 

Pacific Islanders are more likely to report wages in either data source than are the other race 

groups, and also have larger differences in the proportion of individual reporting CE vs. W-2 

wages. For instance, about 54.6 percent of Asians report CE wages, whereas 51.4 percent have 

W-2 wage reports. On the other hand, 44.5 percent of blacks report CE wages and 46.5 percent

have W-2 wage reports.

The CE survey question and the administrative records have inconsistent time periods – the W-2 

records report wages earned in the previous tax year (i.e. the previous calendar year), while the 

survey question asks about wages earned in the past twelve months. These two time periods 

might have a very high or very low degree of overlap, depending on which month a respondent 

answered the income questions. For instance, a respondent in January 2014 would have a high 

degree of overlap (11 months), while a respondent in December 2014 would have low overlap (1 

month). Despite this concern, there is no clear pattern of an increasing or decreasing gap 

depending on the degree of overlap between the two income concepts’ time periods.

Unsurprisingly, the two age groups roughly corresponding to “prime working age” – 25 to 44 

and 45 to 64 – have the highest proportion of wage receipt in either data source. Interestingly, 

although these middle two age groups have higher proportions of CE wage receipt than W-2 

wage receipt, the pattern is reversed for the oldest and youngest age groups – 49.8 percent of 

individuals aged 14 to 24 report W-2 wages, while only 47.5 percent report CE wages. 

 

7 CARRA’s extract of the information returns master file (IRMF) contains 1099-MISC, which are filed for some 

contractors, but not 1099-K, a form that has become more common (filed, i.e. on behalf of drivers for Uber and 

Lyft). CARRA’s extract also does not currently have dollar amounts, only the PII on the form.

8 The probability of PIK assignment is estimated from a probit model of the form P(PIK|X) = Φ(Xβ), where X is a 

matrix of the demographic variables presented in the tables: dummy variables for sex, race, age group and Hispanic 

origin.
10

Table 3: Proportion of Individuals Who Received Wages by Data Source

Unweighted Weighted

Obs. CE 

Wages

W-2 

Wages CE Wages W-2 Wages

All individuals 27,914 0.508 0.509 0.502 0.502

Sex

Female 14,278 0.476 0.491 0.485 0.481

Male 13,636 0.543 0.529 0.548 0.517

Race

White Alone 22,477 0.517 0.517 0.512 0.512

Black Alone 3,264 0.449 0.471 0.445 0.465

AIAN Alone 136 0.456 0.471 0.453 0.45

Asian Alone 1,382 0.556 0.531 0.546 0.514

Pacific 

Islander Alone 157 0.573 0.541 0.568 0.52

Two or more 

races

498 0.357 0.351 0.327 0.317

Age

14-24 3,923 0.477 0.503 0.475 0.498

25-44 7,078 0.815 0.79 0.814 0.79

45-64 7,911 0.715 0.702 0.716 0.701

65+ 4,434 0.2 0.239 0.201 0.24

Hispanic Origin

Hispanic 4,561 0.45 0.428 0.435 0.413

Not Hispanic 23,353 0.52 0.525 0.515 0.52

Income

Above Median 

CU Wages 14,760 0.652 0.622 0.637 0.606

Below Median 

CU Wages 13,154 0.347 0.383 0.35 0.385

Second Interview Month

1 1,917 0.514 0.505 0.506 0.499

2 2,120 0.51 0.502 0.503 0.495

3 2,087 0.515 0.516 0.511 0.509

4 2,124 0.534 0.517 0.526 0.506

5 2,072 0.49 0.497 0.482 0.491

6 2,145 0.502 0.51 0.491 0.498

7 2,152 0.495 0.51 0.49 0.505

8 2,147 0.501 0.509 0.496 0.505

9 2,090 0.517 0.507 0.516 0.503

10 3,094 0.516 0.518 0.514 0.512

11 3,099 0.494 0.502 0.48 0.484

12 2,867 0.513 0.517 0.508 0.515

Source: CE 2013-2014, IRS W-2s 2012-2013
11

Note that Table 3 simply compares the distributions of the two wage receipt concepts. 

Comparing the averages of these two distributions is instructive, but it should be noted that the 

distributions can differ for two reasons: an individual may report CE wages despite not appearing 

in the universe of W-2’s, or an individual may appear in the universe of W-2 despite reporting no 

wages on the CE. To examine the relative sizes of these two sources of inconsistency, Table 4 

presents a cross-tabulation of wage receipt for the two data sources in Panel A. Over 85 percent 

of the linked sample has concordant responses between the two sources – 44.3 percent report 

both CE and W2 wages, while 42.5 percent report neither CE nor W2 wages. The two types of 

inconsistencies are roughly balanced in size: 6.7 percent of linked respondents report W-2 but 

not CE wages, while 6.6 percent report CE but no W-2 wages.

Table 4: Wage Receipt Agreement by Data Source (%)

Panel A: W-2 Wages

W-2 Wages No W-2 

Wages

CE Wages 44.28 6.56

No CE Wages 6.67 42.5

Panel B: W-2 or 1099-MISC Wages

W-2 or 1099-MISC 

Wages

No W-2 or 

1099-

MISC 

Wages

CE Wages 46.29 4.55

No CE Wages 9.41 39.75

Source: CE 2013-2014, IRS W-2s and 1099-MISC 2012-2013

As noted above, comparing reported CE wage receipt with receipt of a Form W-2 potentially 

leaves out independent contractors or other workers who may not be present in the W-2 universe. 

To examine the degree to which this could be driving the proportion of CE respondents who 

report CE wages, but are not in the W-2s, Panel B of Table 4 reports an additional agreement 

exercise. This lower panel shows the degree of agreement between CE wage receipt and either 

having a form W-2 or having a form 1099-MISC, an information return filed on behalf of some 

but not all independent contractors. When considering this additional administrative records 
12

source of wage receipt, the proportion of individuals with CE wages but no wages in the W-2 or 

1099-MISC decreases to 4.55 percent. However, the proportion of individuals who report no CE 

wages but received a W-2 or 1099-MISC increases to 9.41 percent.

4.2 Reported Wage Amounts

Next, we turn our analysis to a comparison of the amount of wages reported on the CE with the 

amount of wages reported in the Form W-2 linked sample. We begin by comparing the central 

tendency of the two wages distributions using summary statistics (average and median), and then 

compare the distributions beyond the mean through the use of data visualization (density plots). 

For brevity, we focus our discussion of the summary statistics on average wages, but additional 

tables summarizing median wages across income sources are available in the appendix.9 We will 

separately analyze the measurement error properties of the two data sources—by comparing the 

non-imputed CE responses to the W-2 Wage data—as well as analyzing CE imputation 

procedures by comparing CE imputed and non-imputed responses to the W-2 wage data.

We begin with an analysis of the non-imputed responses to the CE question asking “How much 

did you earn before taxes?” which is asked to all individuals who responded affirmatively to the 

wage receipt question analyzed previously. We compare this total (variable SALARYX) to the 

total of all unique W-2 wages for each linked individual in the sample. As with previous 

analyses, there are slight differences between the two wage concepts. CE wages refer to all 

wages before taxes; we define W-2 wages as the sum of all taxable wages and deferred 

compensation. The major wedge between the two wage concepts is due to pre-tax payroll 

deductions (e.g. for 401k retirement contributions). W-2 wages will be smaller than pre-tax 

wages by the amount of any pre-tax deductions.10

Table 5 presents comparisons of average wages in the two data sources, broken out by sociodemographic categories. These summary statistics are calculated for all individuals with PIKs 

who either report wages on the CE or can be linked to the universe of form W-2s. As with the 

previous comparison tables, the first two columns report unweighted averages of CE and W-2 

wages respectively, while the second two columns report weighted averages, with weights equal 

to the CE final weights multiplied by the inverse probability of linkage. Our discussion will 

focus on the weighted statistics, but most patterns are consistent with the unweighted statistics.

 

9 See Appendix Tables A1-A3.

10 To give a rough sense of the size of this wedge, consider that the average insurance premium paid by employee 

was about $1200 in 2014 (AHRQ, 2014), and that the average participant in a defined contribution benefit plan 

contributed about 6 percent of pre-tax earnings to a 401k or equivalent plan. For someone earning the unweighted 

average wage in Table 5 ($46,149), these two deductions would amount to about $4000.
13

Table 5: Mean Individual Wages by Data Source 

Universe: Individuals with Non-imputed CE Wage Responses or W-2 Wages

Unweighted Weighted

Obs Mean 

Wages, 

CE

Mean 

Wages, 

W-2

Mean Wages, 

CE

Mean Wages, 

W-2

Net 

Difference

All individuals 27,914 $46,149 $44,372 44,240 42,463 1,777

Sex

Female 14,278 37,331 34,586 36,116 33,573 2,543

Male 13,636 54,641 53,896 52,178 51,332 846

Race

White Alone 22,477 47,539 45,847 45,511 43,789 1,722

Black Alone 3,264 33,169 30,322 32,291 29,631 2,660

AIAN Alone 136 33,046 35,256 52,540 55,983 -3,443

Asian Alone 1,382 52,687 55,381 29,589 30,250 -661

Pacific Islander 

Alone 157 34,991 36,644 35,851 38,333 -2,482

Two or more 

races

498 33,536 30,523 32,084 29,712 2,372

Age

14-24 3,923 13,847 10,814 13,562 10,602 2,960

25-44 7,078 47,882 45,183 46,303 43,495 2,808

45-64 7,911 56,102 57,553 54,273 55,981 -1,708

65+ 4,434 39,442 34,905 37,444 33,719 3,725

Hispanic Origin

Hispanic 4,561 31,450 29,989 30,852 29,481 1,371

Not Hispanic 23,353 48,759 46,663 46,604 44,558 2,046

Income

Above Median 

CU Wages 14,760 59,556 57,322 57,441 54,985 2,456

Below Median 

CU Wages 13,154 17,943 20,798 17,710 20,347 -2,637

Second Interview Month

1 1,917 47,461 44,611 44,772 41,769 3,003

2 2,120 43,966 44,899 42,362 43,518 -1,156

3 2,087 43,302 44,318 41,833 42,700 -867

4 2,124 49,258 44,877 47,377 43,272 4,105

5 2,072 46,417 43,514 44,490 41,821 2,669

6 2,145 51,515 47,582 50,499 45,831 4,668

7 2,152 42,254 43,590 40,501 41,626 -1,125

8 2,147 42,870 42,397 41,951 40,812 1,139

9 2,090 45,535 45,829 44,346 45,184 -838

10 3,094 46,369 41,677 44,468 39,915 4,553

11 3,099 47,988 45,117 46,010 43,004 3,006

12 2,867 46,108 44,857 42,558 41,590 968

Source: CE 2013-2014, IRS W-2s 2012-2013
14

CE wages are, on average, slightly higher than W-2 wages–for the full linked sample, average 

CE wages are 44,240, compared to an average W-2 wage of 42,463; this gap is relatively small 

compared to average wages, representing a discrepancy of 3.3 percent of CE wages. There is 

some variation in the difference in wages across data sources between different sociodemographic groups. Whites and Asians earn higher wages, on average, than do blacks and other 

races. Whites, Blacks and multi-race individuals report higher CE wages than W-2 wages while 

the exception of Asians, Native Americans and Pacific Islanders have higher W-2 wages than CE 

wages on average.

Looking across age groups, there is the hump-shaped pattern in wages with wages rising through 

the prime working age groups, and then falling for the elderly. The 45-64 cohort report lower CE 

wages than W-2 wages, while the other cohorts report higher CE wages relative to W-2 wages.

The largest errors (i.e. the difference between CE and W-2 wages) occur for the oldest and 

youngest cohorts, both in absolute and relative terms: the difference between CE and IRS wages 

for individuals aged 14-24 is about $2,960 on average, which is more than 20 percent of the 

average CE wage for this group (the difference for the oldest cohort is over $3,700, but this is 

only about 11 percent of their average wages). Once again, there is no consistent pattern in the 

difference between CE and W-2 wages across second interview months. This suggests that 

individuals respond to the wage question as if it referred to the previous tax year and not to the 

past twelve months.11 While leveraging administrative tax data seems to be consistent with 

current survey responses, these responses may in fact not be working as intended, as BLS 

requires a twelve-month income figure for to overlap with the expenditure reporting period.

The results in Table 5 present summary statistics of the unconditional distributions of CE and W2 wages. The differences in average wages observed in Table 5 can occur for two reasons: 

individuals who report income from both sources may report different values, or individuals may 

report income on one source but not the other (and the wages they do report are systematically 

different from those for individuals who report both sources of wages). We explore each of these 

possibilities in Tables 6 and 7. 

Table 6 repeats the structure of Table 5, reporting average wages from both the CE survey 

responses and IRS W-2 records, but restricts the sample to only individuals who report both CE 

and W-2 wages. For the sample of individuals with either CE or W-2 wages (Table 5), average 

weighted CE wages are about $1,777 higher than weighted W-2 wages. On the other hand, 

average weighted CE wages are about $624 higher than weighted W-2 wages for the sample of 

individuals who report both CE and W-2 wages (Table 6).

 

11 Since nominal wages tend to rise over time, we might expect a systematic wedge between “past 12 months” and 

previous tax year income in later months. Consider two individuals responding in January and December 2014. If 

both saw increasing wages from 2013-2014 and provided their 12 month true wages, we would expect a higher 

discrepancy for the individual responding in December (who would have earned 11 months of 2014 salary and 1 

month of 2013 salary) than in January (who would have earned 1 month of 2014 salary and 11 months of 2013 

salary).
15

Table 6: Comparing Mean Individual Wages by Data Source

Universe: Nonimputed CE Responses, All Individuals With Both CE and W-2 Wages

Unweighted Weighted

Obs

Mean 

Wages, 

CE

Mean Wages, 

W-2

Mean Wages, 

CE

Mean Wages, 

W-2

Net Difference

All individuals 9,124 $48,328 $47,942 46,397 45,773 624

Sex

Female 4,504 39,564 38,859 38,383 37,609 774

Male 4,620 56,872 56,797 54,340 53,866 474

Race

White Alone 7,647 49,739 49,296 47,675 46,992 683

Black Alone 833 35,031 33,675 54,648 56,091 -1,443

AIAN Alone 41 36,110 41,809 34,117 32,859 1,258

Asian Alone 442 54,266 56,389 35,236 33,582 1,654

Pacific Islander 

Alone 62 36,598 36,373 37,234 38,972 -1,738

Two or more 

races

99 37,115 35,465 33,296 35,192 -1,896

Age

14-24 1,070 15,624 12,036 15,278 11,687 3,591

25-44 3,841 49,696 47,351 48,065 45,514 2,551

45-64 3,711 57,201 59,405 55,379 57,574 -2,195

65+ 502 41,977 44,249 39,838 41,864 -2,026

Hispanic Origin

Hispanic 1,259 33,819 33,603 33,269 33,024 245

Not Hispanic 7,865 50,651 50,237 48,488 47,805 683

Income

Above Median 

CU Wages 6,359 61,073 59,590 58,980 57,178 1,802

Below Median 

CU Wages 2,765 19,019 21,152 18,857 20,814 -1,957

Second Interview Month

1 667 48,015 48,962 45,338 45,349 -11

2 744 45,837 45,532 44,771 44,125 646

3 722 45,611 46,129 44,111 44,639 -528

4 704 51,693 50,178 49,698 49,227 471

5 683 48,177 47,785 46,221 45,932 289

6 721 53,005 53,849 51,847 51,857 -10

7 682 44,048 45,187 42,211 42,629 -418

8 684 45,468 44,878 44,133 43,201 932

9 660 48,490 48,662 47,455 48,672 -1,217

10 994 48,816 45,711 46,876 43,933 2,943

11 940 50,644 50,691 48,949 47,592 1,357

12 923 48,865 47,754 44,865 43,554 1,311

Source: CE 2013-2014, IRS W-2s 2012-2013
16

Since the differences between average CE wages and average W-2 wages are smaller for the 

sample of individuals who report both CE and W-2 wages, it is unlikely that misreporting by 

these individuals is the dominant source of the CE-W-2 differences in the unconditional averages 

in Table 5. Table 7 examines another possibility: that individuals who report CE wages but not 

W-2 wages (or vice-versa) are systematically different from the rest of the sample. The first two 

columns of Table 7 report average W-2 wages and the proportion of individuals who report no 

CE wages, but can be linked to the universe of form W-2s. Likewise, the final two columns 

report average wages and the proportion of individuals who report CE wages but cannot be 

linked to the universe of W-2s. Note that the proportions in Table 7 differ slightly from Table 4, 

because Table 7 only includes non-imputed wage amounts, while Table 4 reports percentages 

who reported wage receipt.

Approximately 20 percent of individuals with PIKs can be linked to the universe of W-2s but 

report no CE wages, while about 5 percent of individuals report CE wages but cannot be linked 

to the universe of W-2s. Both of these groups have lower wages, on average, than the overall 

sample reported in Table 5 or in the sample of dual-reporters in Table 6. Individuals who report 

CE wages but not W-2 wages have lower wages, on average, than individuals who report W-2 

wages but not CE wages. This is consistent with the differences between CE and W-2 wages 

reported in Table 5 being largely a result of item non-response by individuals who have lower 

than average wages.12

Tables 5-7 summarize differences between reported CE and W-2 wages at the mean of the wage 

distribution. While there appear to be some substantial differences between the two data sources, 

the average difference could arise from any number of patterns across the distribution. To further 

examine how reported CE wages and W-2 wages compare across the entire wage distribution, 

we examine the entire distributions graphically. Figures 1 and 2 show two visualizations which 

compare these two distributions. In Figure 1, kernel density plots for the CE and W-2 wage 

distributions are overlaid. For a smooth distribution (e.g. wages) kernel density plots provide a 

more informative visualization than do histograms. Figure 2 depicts the average difference 

between CE and W-2 wages by percentiles of the W-2 wage distribution, among individuals 

reporting wages from both sources.

 

12 Note that this would not directly affect the breakouts shown for the “Above/Below Median CU Wage” categories 

in Table 5, since these categories are based on survey wages 
17

Table 7: Mean Wages for Respondents who Reported Wages in only one Data Source

Universe: Nonimputed CE Wages or W-2 Wages

Mean W-2 Wages, 

Observations with 

no CE Wages

Proportion 

with W-2 

but no CE 

Wages

Mean CE Wages, 

Observations with 

no W-2 Wages

Proportion 

with CE but 

no W-2 

Wages

All individuals $37,975 0.18 $30,304 0.04

Sex

Female 26,911 0.18 20,227 0.04

Male 48,710 0.19 39,186 0.05

Race

White Alone 39,213 0.18 31,038 0.05

Black Alone 26,339 0.21 21,051 0.04

AIAN Alone 23,576 0.17 7,920 0.04

Asian Alone 53,855 0.21 42,854 0.05

Pacific Islander 

Alone 37,375 0.15 18,383 0.04

Two or more 

races

24,086 0.15 19,363 0.05

Age

14-24 9,370 0.23 7,615 0.08

25-44 40,417 0.25 30,670 0.06

45-64 53,816 0.23 46,660 0.05

65+ 26,514 0.13 27,874 0.02

Hispanic Origin

Hispanic 23,432 0.15 21,702 0.07

Not Hispanic 40,271 0.19 33,076 0.04

Income

Above Median 

CU Wages 52,200 0.19 45,287 0.05

Below Median 

CU Wages 20,368 0.17 12,808 0.04

Second Interview Month

1 34,970 0.16 43,490 0.05

2 35,082 0.15 27,374 0.05

3 36,597 0.17 25,080 0.05

4 40,065 0.19 26,223 0.05

5 43,432 0.17 31,859 0.05

6 40,635 0.17 25,755 0.04

7 35,405 0.19 32,453 0.04

8 35,083 0.19 33,626 0.05

9 35,436 0.19 40,087 0.06

10 40,965 0.2 27,153 0.04

11 38,228 0.2 25,105 0.04

12 41,143 0.19 28,722 0.04

Source: CE 2013-2014, IRS W-2s 2012-2013
18

Figure 1: Kernel Density Plot of CE vs. W-2 Wages 

Source: CE 2013-2014, IRS W-2s 2012-2013

Two patterns are immediately apparent from these visualizations. First, the kernel density clearly 

show excess mass at low wage amounts for the W-2 wage distribution relative to the CE wages: 

this is consistent with there being a large number of individuals who have W-2 but do not report 

CE wages and who have relatively low W-2 wages. Second, among people who report wages 

from both sources, there is some evidence of over-reporting CE wages at the bottom of the (W-2) 

wage distribution, and substantial under-reporting at the very top of the wage distribution.
19

Figure 2: Average Difference between CE and W-2 Wages by Percentile of the W-2 Wage 

Distribution

Source: CE 2013-2014, IRS W-2s 2012-2013

4.3 Imputed Wage Amounts

The previous analysis comparing reported CE wage amounts to wage amounts from the linked

W-2 sample showed that, although the two sources have similar distributions, there is evidence 

of disparities, particularly at the bottom of the wage distribution. Some of these disparities could 

be related to item nonresponse. We next compare the two wage distributions, including imputed 

wage values, to examine how item non-response and imputation process might affect the 

previous results.
20

For individuals who report receiving wages but do not report a wage amount, BLS imputes a 

value using a multiple imputation method similar to other population surveys such as the Current 

Population Survey. This imputation process uses other information reported by the respondent 

(such as age, sex and whether they report a wage bracket) to impute another similar response for 

the item nonresponse. For wages, the variable SALARYXM contains both the raw responses in 

SALARYX and imputed responses for individuals who did not report a wage amount.

Table 8 reports average CE wages (including imputed and non-imputed responses) compared 

with average W-2 wages. As with previous tables, the second and third columns are unweighted 

averages, while the final two columns report weighted averages. Note that Columns 3 and 5 are 

identical to Columns 3 and 5 from Table 5 since both tables report unconditional averages of the 

CE and W-2 wage distributions.

Overall, the distribution of CE wages including imputed values is closer to the distribution of W2 wages, at least on average. This suggests that the imputation process is reducing the 

discrepancy between survey and administrative records, at least in the middle of the wage 

distribution. There is some heterogeneity across some demographic groups, however. The gap 

between CE and W-2 wages is larger for men when including imputed values relative to nonimputed wages and all race groups have smaller gaps between CE and W-2 wages when 

including imputed values except for Native Americans and Pacific Islanders. Individuals ages 

25-44 have gaps between CE and W-2 wages that are much smaller when including imputed 

values; on the other hand, the youngest cohort has close to no change, and the cohort aged 45-64 

actually has a larger gap on average when including imputations. 

As with the analysis of the reported CE wage values, one source of difference between the CE 

and W-2 wage distributions may arise from individuals who report CE wages but cannot be 

linked to the universe of W-2s, or vice versa. To investigate this possibility, Table 9 reports 

average wages for individuals who report CE wages but not W-2 wages and vice versa, as well 

as the proportion of individuals who report wages in one but not the other dataset. The structure 

of Table 9 mirrors that of Table 7 above. 
21

Table 8: Mean Individual Wages by Data Source 

Universe: All Responses with CE wages (imputed and non-imputed responses) or W-2 Wages

Unweighted Weighted

Obs. Mean 

Wages, CE

Mean 

Wages, W-2

Mean Wages, CE Mean Wages, W2Net Difference

All individuals 27,914 $43,458 $44,372 41,753 42,463 -710

Sex

Female 14,278 36,562 34,586 35,545 33,573 1,972

Male 13,636 49,858 53,896 47,627 51,332 -3,705

Race

White Alone 22,477 45,043 45,847 43,268 43,789 -521

Black Alone 3,264 30,461 30,322 29,721 29,631 90

AIAN Alone 136 36,359 35,256 52,479 55,983 -3,504

Asian Alone 1,382 52,194 55,381 30,035 30,250 -215

Pacific 

Islander Alone 157 31,006 36,644 31,910 38,333 -6,423

Two or more 

races

498 32,419 30,523 31,162 29,712 1,450

Age

14-24 3,923 13,725 10,814 13,288 10,602 2,686

25-44 7,078 45,214 45,183 43,911 43,495 416

45-64 7,911 53,704 57,553 52,299 55,981 -3,682

65+ 4,434 36,862 34,905 35,429 33,719 1,710

Hispanic Origin

Hispanic 4,561 29,648 29,989 29,179 29,481 -302

Not Hispanic 23,353 46,118 46,663 44,235 44,558 -323

Income

Above Median 

CU Wages 14,760 56,459 57,322 54,503 54,985 -482

Below Median 

CU Wages 13,154 18,018 20,798 17,709 20,347 -2,638

Second Interview Month

1 1,917 44,254 44,611 42,152 41,769 383

2 2,120 41,561 44,899 40,854 43,518 -2,664

3 2,087 42,583 44,318 40,327 42,700 -2,373

4 2,124 44,299 44,877 42,382 43,272 -890

5 2,072 45,521 43,514 43,857 41,821 2,036

6 2,145 47,726 47,582 46,849 45,831 1,018

7 2,152 40,201 43,590 38,340 41,626 -3,286

8 2,147 40,994 42,397 40,281 40,812 -531

9 2,090 43,368 45,829 42,483 45,184 -2,701

10 3,094 43,526 41,677 41,252 39,915 1,337

11 3,099 43,870 45,117 42,217 43,004 -787

12 2,867 43,576 44,857 40,782 41,590 -808

Source: CE 2013-2014, IRS W-2s 2012-2013
22

Table 9: Mean Wages for Respondents who Reported Wages in only one Data Source

Universe: All Responses with CE wages (imputed and non-imputed responses) or W-2 wages

Mean W-2 Wages, 

Observations with 

no CE Wages

Proportion 

with W-2 

but no CE 

Wages

Mean CE Wages, 

Observations with 

no W-2 Wages

Proportion 

with CE but 

no W-2 

Wages

All individuals $16,897 0.05 $37,199 0.19

Sex

Female 13,254 0.06 34,474 0.17

Male 21,171 0.05 39,578 0.21

Race

White Alone 18,228 0.05 39,034 0.19

Black Alone 10,214 0.06 24,100 0.2

AIAN Alone 22,276 0.06 34,112 0.19

Asian Alone 19,278 0.05 47,177 0.18

Pacific Islander 

Alone 7,175 0.04 27,676 0.22

Two or more races 6,878 0.06 30,441 0.35

Age

14-24 11,487 0.09 8,651 0.13

25-44 16,061 0.05 30,464 0.1

45-64 27,682 0.06 42,925 0.1

65+ 15,281 0.08 28,118 0.07

Hispanic Origin

Hispanic 12,636 0.05 25,504 0.26

Not Hispanic 17,736 0.05 40,637 0.18

Income

Above Median CU 

Wages 22,958 0.03 51,336 0.21

Below Median CU 

Wages 14,417 0.08 16,010 0.16

Second Interview Month

1 11,487 0.04 40,345 0.19

2 19,025 0.05 35,327 0.21

3 14,565 0.05 36,494 0.19

4 14,272 0.05 34,701 0.2

5 12,991 0.05 34,737 0.18

6 16,043 0.05 39,948 0.2

7 30,075 0.05 33,976 0.19

8 17,971 0.05 44,531 0.21

9 11,134 0.05 42,615 0.21

10 16,300 0.06 36,554 0.17

11 19,304 0.06 34,906 0.18

12 16,884 0.07 34,888 0.17

Source: CE 2013-2014, IRS W-2s 2012-2013
23

There are two notable features of this set of averages. First, the proportion of individuals with W2 but no CE wages is substantially smaller when including imputed CE wages (Column 2). 

About 18 percent of individuals had W-2 wages but did not report CE wages (See Table 7), 

while about five percent of individuals have W-2 wages but neither report CE wages nor receive 

an imputed wage amount (see Table 9). However, there are many more individuals with CE 

wages but no W-2 wages when including imputations—about 19 percent of individuals either 

report CE wages or receive an imputed value but do not have W-2 wages (see Table 9), 

compared to about 4 percent who report CE wages but do not have W-2 wages (see Table 7). 

Individuals who have W-2 but not CE wages when including imputations are substantially 

poorer than the set of individuals who report no CE wages but W-2 wages (not including 

imputations) ($16,897 vs. $37,975); individuals who have CE wages but not W-2 wages are 

slightly richer on average when including imputations, however ($37,199 vs. $30,304). Among 

age groups in Table 9, the youngest cohort is both most likely to have W-2 but not CE wages 

(and vice versa), and the poorest. Including imputations results in a higher average wage for this 

age group for the sample who have W-2 but not CE wages, and a higher average wage for 

individuals who have CE wages but not W-2 wages.

Looking beyond the mean, even when including imputations, there is still a consistent pattern of 

substantial differences between the two wage distributions at the bottom of the wage distribution, 

and some evidence of under-reporting at the very top of the wage distribution. Figures 3 and 4

produce two visualizations to illustrate this, analogous to Figures 1 and 2 above. Figure 3

presents a kernel density plot of the CE and W-2 wage distributions when including imputations, 

which shows excess mass for the W-2 wage distribution at very low wages. In Figure 4, a plot of 

the average difference between CE and W-2 wages by percentile of the W-2 wage distribution 

shows substantial over-reporting of wages on the CE at the bottom of the distribution, and very 

large under-reporting for the top one percent of the wage distribution. Note that the discrepancies 

between survey and administrative records continue to be present when including imputed 

survey wage responses. This suggests that while the imputation process may reduce 

discrepancies in the middle of the wage distribution, it is not doing do at the tails. However, 

insofar as discrepancies in the tails are due to misreporting, the imputation process would not 

necessarily be expected to reduce these discrepancies.
24

Figure 3: Kernel Density Plot of CE vs. W-2 Wages, Including Imputed and Non-imputed CE 

wages

Source: CE 2013-2014, IRS W-2s 2012-2013
25

Figure 4: Average Difference between CE and W-2 Wages by Percentile of the W-2 Wage 

Distribution, Including Imputed and Non-imputed CE wages

Source: CE 2013-2014, IRS W-2s 2012-2013

4.4 Bracketed Wage Amounts

The CE wage questions consist of a three-part process. Respondents are first asked whether they 

receive any wages. Those who indicate that they do receive wages are then asked how much they 

earned in the past 12 months. Finally, respondents who do not know the exact amount are asked 

to select one of eleven brackets in which their wages fall. We have analyzed the first two of these 
26

wage questions in the previous two subsections.13 We complete our wage analysis by comparing 

these bracketed CE wage responses to the W-2 records.

We assign individuals to CE wage brackets as follows. If an individual reported a wage bracket 

(this is reported in the CE variable SALARYB), we assign that wage bracket. If an individual did 

not report a wage bracket, but did report a (non-imputed) wage amount in SALARYX, we assign

the bracket in which this wage amount falls. To compare this hybrid bracketed series to the W-2 

wage data, we calculate in which CE bracket an individual’s W-2 wages would fall for all 

individuals who have both a CE bracketed wage amount and W-2 wages. Table 10 reports 

agreement between these two bracketed wage series.

Table 10: Agreement Between CE and IRS Wage Income By CE Bracket

W-2 Brackets ($)

[0-

5K)

[5K

-

10K

)

[10K

-

15K)

[15K

-

20K)

[20K

-

30K)

[30K

-

40K)

[40K

-

50K)

[50K

-

70K)

[70K

-

90K)

[90K

-

120K

)

120K

+

CE 

Brackets

($)

[0-

5K) 543 173 79 46 66 38 27 15 * * *

[5K10K) 232 257 132 64 60 11 10 14 * * *

[10K15K) 134 169 246 116 80 44 19 16 * * *

[15K20K) 82 126 166 237 181 58 36 24 13 6 *

[20K30K) 97 108 163 261 763 237 87 80 32 11 12

[30K40K) 71 51 89 99 403 644 183 109 28 29 15

[40K50K) 31 24 34 42 141 328 445 204 48 26 22

[50K70K) 24 24 29 36 80 154 351 892 157 79 56

[70K90K) 13 * 13 * 17 23 41 273 455 98 42

[90K120K)

* * * * 11 11 11 42 143 323 114

120K

+

* * * * * * * 20 30 100 505

Source: CE 2013-2014, IRS W-2s 2012-2013

 

13 Note that respondents who report a bracketed wage amount are assigned an imputation within this bracket, so the 

results in section 4.3 indirectly analyzed these bracketed responses. 
27

For all CE wage brackets, the modal case is one of agreement, in which the CE wage brackets 

and W-2 wage brackets align. For most interior brackets (i.e., not including the top and bottom 

brackets) there are slightly fewer underestimates than over-estimates–the number of individuals 

with CE wages in a lower bracket is smaller than the number of individuals with CE wages in a 

higher bracket, for any given W-2 wage bracket. This is consistent with the previous results, 

which suggests over-reporting of CE wages in the bottom half of the wage distribution. 

Table 11 provides an alternate way of comparing the two bracketed wage amounts. As in Table 

10, individuals are ordered by their CE wages. Within these brackets, we report the average CE 

wages, and the average W-2 wages, both unweighted (first two columns) and weighted by CE 

final weights multiplied by the inverse probability of linkage (final two columns), as above. Note 

that this shows a different gap than in Figures 1 and 2 because this shows averages within a 

bracket defined by survey wages. When fixing CE wage brackets, W-2 wages are higher, on 

average, than CE wages for the first six brackets (and marginally higher in the next-to-highest 

bracket). In fact, the average W-2 wages are above the upper bracket bound for the first four 

brackets. This reflects differences between the two data sources within each CE bracket in the 

upper tails: Table A4, in the Appendix, reports similar results for median wages within each 

bracket, and shows no evidence of median matched W-2 wages outside of the CE brackets.

Table 11: Mean Income by Data Source, by CE Income Bracket

Unweighted Weighted

Mean CE 

Wages

Mean W-2 

Wage

Mean CE 

Wages

Mean W-2 

Wage

Net 

Difference

CE Brackets

[0-5K) $1,992 $10,511 $1,989 $10,085 -8096

[5K-10K) 7,162 11,548 7,128 10,948

-3820

[10K15K) 12,179 15,262 12,154 15,053

-2899

[15K20K) 17,101 20,531 17,082 20,352

-3270

[20K30K) 24,439 26,337 24,457 25,969

-1512

[30K40K) 34,175 35,436 34,188 34,841

-653

[40K50K) 43,956 43,711 43,950 43,522 428

[50K70K) 57,974 57,907 57,962 57,689 273

[70K90K) 77,891 75,760 77,909 75,308 2601

[90K120K) 101,219 101,456 101,113 100,451 662

120K+ 197,471 190,140 196,537 185,603 10934

Source: CE 2013-2014, IRS W-2s 2012-2013
28

4.5 Social Security Deductions

We now turn to several other CE questions which can be directly compared to IRS 

administrative records. The first of these concerns Social Security (and Medicare) payroll 

deductions. The exact CE question is “Was there any money deducted from your pay for Social 

Security, including Medicare?”

Individuals who earn wages must pay Federal Insurance Contribution Act (FICA) taxes unless 

they are one of several exempted categories, including some full time students, and some types 

of employees who are covered under an employer-provided retirement plan (and hence are 

excluded from the Social Security (SS) system). Most non-exempt wage earners have their FICA 

taxes deducted from their paychecks. Form W-2, which is filed for almost all wage earners,

reports the amount of FICA-eligible wages, as well as the amount of FICA taxes deducted from 

an individual’s wages. The W-2 extracts which CARRA receives from the IRS contain only the 

amount of FICA-eligible wages reported on a W-2 form. Thus, we will define an individual as 

having W-2 SS deductions if they can be linked to a W-2 form with nonzero FICA eligible 

wages.14

Table 12 compares the proportion of individuals who report having any SS or Medicare payroll 

deductions in the CE to the proportion who have SS payroll deductions in the W-2s, broken out 

by various sociodemographic categories. As with previous tables, the first two columns report 

unweighted proportions, while the final two columns report proportions weighted by CE final 

weights and inverse probability weights. The denominator in both CE and W-2 proportions is the 

total number of CE respondents.

Overall, a substantially larger proportion of individuals have SS payroll deductions in the W-2 

data than report having SS payroll deductions on the CE. The difference in proportions between 

the two data sources is about 10 percentage points for the total CE sample, but some 

demographic groups have substantially larger or smaller differences between CE and W-2 

payroll deduction proportions. Black and AIAN respondents both have larger gaps (about 14 and 

14.4 percentage points, respectively) than do other racial groups, and respondents from below 

median income CUs have larger gaps than do higher income individuals.

 

14 Note that the CE question asks about both SS and Medicare deductions, but some individuals in public pension 

plans may be exempt from SS payroll taxes (hence have no FICA wages), but still will be required to pay Medicare 

payroll taxes.
29

Table 12: Any SS Payroll Deductions Reported by Data Source

Unweighted Weighted

SS 

Deductions, 

CE

SS Deductions, W2

SS 

Deductions, 

CE

SS 

Deductions, 

W-2

Net 

Difference

All individuals 27,914 0.382 0.483 0.378 0.478 -0.100

Sex

Female 14,278 0.359 0.463 0.357 0.462 -0.105

Male 13,636 0.407 0.504 0.400 0.494 -0.094

Race

White Alone 22,477 0.394 0.49 0.392 0.486 -0.094

Black Alone 3,264 0.31 0.451 0.307 0.447 -0.140

AIAN Alone 136 0.301 0.449 0.288 0.432 -0.144

Asian Alone 1,382 0.412 0.502 0.399 0.485 -0.086

Pacific Islander 

Alone 157 0.433 0.529 0.416 0.505 -0.089

Two or more 

races

498 0.251 0.331 0.225 0.301 -0.076

Age

14-24 3,923 0.335 0.485 0.333 0.481 -0.148

25-44 7,078 0.623 0.756 0.624 0.759 -0.135

45-64 7,911 0.549 0.66 0.551 0.661 -0.110

65+ 4,434 0.135 0.219 0.134 0.218 -0.084

Hispanic Origin

Hispanic 4,561 0.323 0.408 0.311 0.396 -0.085

Not Hispanic 23,353 0.394 0.498 0.391 0.494 -0.103

Income

Above Median 

CU Wages 14,760 0.507 0.59 0.495 0.577 -0.082

Below Median 

CU Wages 13,154 0.242 0.363 0.247 0.366 -0.119

Second Interview Month

1 1,917 0.386 0.476 0.387 0.473 -0.086

2 2,120 0.395 0.48 0.393 0.474 -0.081

3 2,087 0.387 0.493 0.384 0.488 -0.104

4 2,124 0.381 0.483 0.364 0.474 -0.110

5 2,072 0.36 0.477 0.352 0.473 -0.121

6 2,145 0.379 0.478 0.373 0.469 -0.096

7 2,152 0.376 0.49 0.371 0.487 -0.116

8 2,147 0.375 0.484 0.373 0.480 -0.107

9 2,090 0.388 0.483 0.385 0.483 -0.098

10 3,094 0.399 0.49 0.398 0.487 -0.089

11 3,099 0.373 0.476 0.364 0.458 -0.094

12 2,867 0.385 0.488 0.384 0.487 -0.103

Source: CE 2013-2014, IRS W-2s 2012-2013
30

4.6 Self-Employment Income Receipt

We next examine how self-employment income receipt varies between CE responses and another 

source of administrative records - the universe of Form 1040 tax returns. Although CE questions 

ask both about receipt of self-employment income and the amount of self-employment income 

received, the Form 1040 data sent to CARRA by the IRS does not contain the amount of selfemployment income received by a tax unit. CARRA’s Form 1040 data extract does contain flags 

for whether schedule C (reporting wages from self-employment or a sole proprietorship) was 

filed by a tax unit. We will therefore focus our analysis of the proportion of CE respondents who

are in CUs which report any self-employment income compared to the proportion of individuals 

who can be linked to a Form 1040 that included a Schedule C filing.15

Table 13 shows that the differences in the proportions of individuals with some self-employment 

income are quite small. Overall, about 16 percent of individuals are in CUs which have CE selfemployment income, compared to about 15 percent who appear on Form 1040 with selfemployment income. There are a few substantial differences when stratifying by race (blacks are 

more likely to have 1040 self-employment income than CE self-employment income, unlike 

other race groups) and by age (the youngest cohort is substantially less likely to be on a Form 

1040 with self-employment income than to be in a CU with self-employment income). The stark 

results for the youngest cohort may be a result of the differing income receiving units in the way 

we define self-employment receipt. College students who live away from home for most of the 

year may be considered part of their parents’ CU for CE purposes but may file a tax return 

independently of their parents for 1040 purposes. Since college students are less likely to file a 

Schedule C than their parents, family-level self-employment income would be present in the CE 

but not in the college student’s 1040.

 

15 Note that while Schedule C represents the most common type of self-employment income declared on Form 1040, 

there are other types of self-employment income; the IRS 1040 proportions are thus likely under-estimates of all 

forms of self-employment.
31

Table 13: Any Self-employment or Sole Proprietorship Income Reported by Data 

Source

Unweighted Weighted

Obs. Any SE 

Income, CE

Any SE 

Income, 

IRS 1040

Any SE 

Income, CE

Any SE 

Income, IRS 

1040

Net 

Difference

Full Sample 27,914 0.157 0.154 0.156 0.150 0.006

Sex

Female 14,278 0.147 0.153 0.146 0.151 -0.005

Male 13,636 0.168 0.155 0.166 0.149 0.017

Race

White Alone 22,477 0.167 0.158 0.166 0.155 0.011

Black Alone 3,264 0.094 0.135 0.097 0.131 -0.034

Asian Alone 1,382 0.167 0.156 0.156 0.152 0.004

AIAN Alone 136 0.154 0.14 0.191 0.122 0.069

Pacific Islander Alone 157 0.089 0.102 0.099 0.117 -0.018

Two or more races 498 0.131 0.09 0.141 0.085 0.056

Age

14-24 3,923 0.166 0.026 0.170 0.054 0.116

25-44 7,078 0.149 0.233 0.148 0.232 -0.084

45-64 7,911 0.19 0.23 0.189 0.233 -0.044

65+ 4,434 0.093 0.138 0.093 0.138 -0.045

Hispanic Origin

Hispanic 4,561 0.135 0.134 0.132 0.128 0.004

Not Hispanic 23,353 0.161 0.158 0.161 0.154 0.007

Income

Above Median CU 

Wages 14,760 0.178 0.168 0.175 0.164 0.011

Below Median CU 

Wages 13,154 0.134 0.138 0.134 0.134 0.000

Second Interview Month

1 1,917 0.169 0.142 0.163 0.135 0.028

2 2,120 0.151 0.145 0.151 0.142 0.009

3 2,087 0.132 0.149 0.128 0.149 -0.021

4 2,124 0.172 0.174 0.179 0.163 0.016

5 2,072 0.178 0.156 0.179 0.153 0.026

6 2,145 0.176 0.14 0.183 0.136 0.047

7 2,152 0.155 0.162 0.161 0.160 0.001

8 2,147 0.148 0.142 0.144 0.134 0.010

9 2,090 0.185 0.162 0.176 0.157 0.019

10 3,094 0.164 0.165 0.160 0.162 -0.002

11 3,099 0.138 0.148 0.135 0.148 -0.013

12 2,867 0.131 0.157 0.133 0.151 -0.018

Source: CE 2013-2014, IRS 1040s 2012-2013
32

4.7 Retirement Income

Income from employer-sponsored retirement plans (both defined benefit and defined 

contribution) and Individual Retirement Accounts (IRAs) are an important income source for 

older Americans. Income from these sources is accurately captured in administrative tax records 

(due to regulations on pension funds and the tax-favored status of IRAs), but recent research has 

suggested that surveys may not accurately capture either the prevalence or amount of retirement 

income received by older Americans (Bee and Mitchell, 2016, 2017).

We add to this recent literature by comparing CE responses to questions about retirement income 

with administrative records on retirement fund distributions reported on IRS Form 1099-R. The 

CE contains two questions about retirement income, asked for the CU as a whole, first asking 

“did you or any member of your household receive any retirement, survivor, or disability 

pensions?”, and then a follow up asking for the amount of this income for CU’s who indicate 

retirement income receipt.16 IRS Form 1099-R is sent to all recipients of retirement income and 

to the IRS by all retirement plans which made distributions in a given tax year. The extracts 

received by CARRA from the IRS contain fields detailing the amount of the distribution, and 

whether the distribution was from an employer-sponsored retirement plan or an individual 

retirement account. Since the CE questions are asked at the CU level, but the 1099-R are issued 

to individuals, we conduct our analysis at the household level and categorize households by the 

demographic characteristics of the respondent. 

Table 14 summarizes a comparison between the CE and administrative records for receipt of any 

retirement income, broken out by select demographic categories. Because of the small implicit 

cell sizes for smaller race groups, we collapse AIAN, Pacific Islanders and two or more races 

into an “Other Race” category, and due to the nature of the income source, we omit the overall 

average, instead focusing on receipt by the full retirement age population aged 65 and older. The 

second column of Table 14 reports the proportion of households who report receiving retirement 

income on the CE. The final three columns report the proportion of households who receive 

retirement income in the universe of Form 1099-Rs – Column 3 reports the proportion with any 

 

16 Note that the instructions to field representatives clarify that retirement income should be defined as “Regular 

income from annuities and IRA or KEOGH retirement plans”, which is a narrower definition of retirement income 

than the definition captured by the 1099-R universe (which is the universe of all disbursements, whether regular or 

not.) It is debatable whether retirement income should only be counted only if it is regular given almost no one who 

has an IRA or 401k uses them in this manner. The CPS ASEC was redesigned in 2014 to add questions about 

retirement account withdrawals reflecting the changing retirement landscape. Further, as shown in Bee and Mitchell 

(2017), the underreporting of retirement income is not limited to IRAs and 401ks, but rather defined benefit income 

is also missing. Even if one wants to stick with the regular income concept the CPS ASEC has about the same 

retirement income rate of receipt for persons 65+ (37 percent) that the CE has for anyone in the household among 

householders 65+. If one looks at household level receipt in the CPS, receipt is closer to 47 percent. In other words, 

relative to the 2013 (pre-redesign) CPS ASEC, the CE is still very much underreporting retirement income receipt. It 

is possible that the respondent, when reporting retirement income, is only reporting his or her own receipt and not 

counting spouses retirement income. See Bee and Mitchell (2017) for further details on comparing 1099-R and CPS 

ASEC retirement income receipt. 
33

1099-R income, Column 4 1099-R pension (employer-sponsored) income and Column 5 1099-R 

IRA income. 

Across all age, sex and racial/ethnic respondent demographic groups, the proportion of 

households reporting retirement income on the CE is dramatically smaller than the proportion 

with retirement income in the administrative records. Among the over 65 population, about 39 

percent of CU householders report retirement income for someone in the household, while 73 

percent received a retirement distribution on the 1099-R (of these, 64 percent receive employersponsored income, while 34 percent receive an IRA distribution). Male and female respondent 

households have similar proportions of CE retirement income reporting, while male 

householders have a slightly higher proportion of 1099-R retirement income. Whites and nonHispanics have higher gaps between CE and 1099-R retirement income receipt than do nonwhites and Hispanics.

Table 14: Any Retirement Income for Householders by Source

Obs. Any CE 

Retirement

Any 

1099-R

Any 1099-R 

Pension

Any 1099-R 

IRA

Age of Respondent

25-44 2207 0.014 0.114 0.085 0.038

45-54 1314 0.046 0.178 0.137 0.061

55-64 1308 0.173 0.370 0.307 0.123

65-74 975 0.369 0.687 0.605 0.268

75-84 511 0.411 0.784 0.701 0.444

85+ 190 0.404 0.763 0.671 0.397

All Respondents 

65+ 1676 0.386 0.725 0.642 0.337

 Sex

Female 910 0.388 0.704 0.630 0.328

Male 766 0.383 0.751 0.656 0.347

 Race

White Alone 1408 0.403 0.746 0.655 0.375

Black Alone 173 0.316 0.657 0.609 0.107

Asian Alone 53 0.210 0.453 0.414 *

Other Race 44 * * 0.476 *

Hispanic Origin

Hispanic 119 0.236 0.483 0.457 *

Not Hispanic 1557 0.396 0.742 0.655 0.355

Note: Other Race includes AIAN, Pacific Islander and Two or More Races

Source: CE 2013-2014, IRS 1099-R 2012-2013
34

Table 15 follows up the analysis of retirement income receipt with an analysis of the degree to 

which retirement income amounts reported on the CE are comparable to retirement income 

distributions from the 1099-R’s. Especially for pension distributions, mean 1099-R retirement 

income amounts are substantially larger than the mean reported CE amounts. IRA distributions 

are smaller on average, than employer-sponsored pension distributions. However, as Table 14 

suggests, few individuals over 65 have IRA, but not employer-sponsored pension income. 

Overall, average retirement income for the over 65 population is about $9,500 in the CE, but the 

average retirement income in the 1099-R is nearly double this, at around $18,500. 

Table 15: Mean Retirement Income Amounts by Data Source

Obs. Any CE 

Retirement Any 1099-R

Any 1099-R 

Pension

Any 1099-R 

IRA

Age of Respondent

25-44 2207 $3137 $3218 $2797 $3471

45-54 1314 7317 9123 8650 7147

55-64 1308 15544 17534 16004 9663

65-74 975 10457 19822 17776 8310

75-84 511 9022 18919 15843 5382

85+ 190 7439 13656 9738 4455

All Respondents 

65+ 1676 9559 18475 16204 6352

Sex

Female 910 6940 15008 12460 4826

Male 766 14146 22467 20011 8998

Race

White Alone 1408 9587 19113 16547 6554

Black Alone 173 8346 14589 14788 2152

Asian Alone 53 16963 8287 9666 *

Other Race 44 * * 5952 *

Hispanic Origin

Hispanic 119 6897 8384 8329 *

Not Hispanic 1557 9719 18960 16492 6400

Note: Other Race includes AIAN, Pacific Islander and Two or More Races

Source: CE 2013-2014, IRS 1040s 2012-2013

5 Survey Non-response Bias Analysis

Thus far, the analysis of CE income-related responses (wages, payroll deductions, selfemployment and retirement income) has proceeded by linking individual respondents to 

administrative records by the use of unique anonymous identifiers (PIKs). This analysis provides 

insight into the measurement error properties of CE responses, and can inform ongoing efforts to 
35

improve the imputation process used to address item non-response. This PIK-based analysis, 

however, is silent about survey non-response bias.

Our final set of analyses in this section will focus on survey non-response bias in two ways.

First, by linking responding and non-responding households to administrative tax records by 

address and comparing the characteristics of responding and non-responding households, we will 

describe the qualities and extent of survey non-response bias. Second, we will use the same 

address matching process to provide a validation of an important component of the weighting 

process used by BLS to address bias due to survey non-response.

5.1 Comparing WSI and AGI Income 

The first analysis of survey non-response bias will compare characteristics of responding and 

non-responding households from linked administrative records. Using survey frame information, 

most responding and non-responding households can be assigned a MAFID (there was about a 

93 percent MAF match rate). We use these assigned MAFIDs to link these sample units to two

sets of administrative records: the universe of Form 1040 tax returns (used above in the selfemployment analysis) and the universe of Form W-2 wage returns (used above in the wage 

analysis).

This address-based linkage presents certain challenges that are less pressing concerns for the 

previous person-based matching. The most pressing of these challenges are the related problems 

of definitional differences between addresses and tax units, and potential duplicate matches for 

individual addresses. Because multiple tax units may reside at the same address, or may file 

taxes from the same address (in case of households moving), there are many more addresses with 

multiple matches than was the case in the previous person based match. Unfortunately, the 

relatively sparse demographic information available for non-responding households and on the

administrative tax data makes separating cases where there are multiple matches difficult. Our 

approach to aggregating these cases is as follows: first, we drop any CE sample units that are 

linked to 10 or more unique Form 1040s, and second, we average the incomes listed on all linked 

1040s for each CE sample unit. In multiple match cases, we are thus comparing average income 

at the tax unit at an address level.

We will compare the distribution of two income concepts between responding and nonresponding households: total wage and salary income (WSI), and adjusted gross income (AGI). 

WSI is the total amount of wage and salary income reported by a tax unit on a form 1040. AGI is 

a broader definition of income, including all gross income (wages, interest, dividends, taxable 

gains, etc.) but excluding “above-the-line” deductions. Table 16 summarizes a comparison of 

responding and non-responding households in terms of their total WSI. The second and third 

columns compare medians (respondents in Column 1, non-respondents in Column 2), while the 

final two columns compare means (respondents in Column 4, non-respondents in Column 5).

Using either the median or mean as a measure of central tendency, the typical non-responding 

sample unit is richer than the typical responding sample unit. The gap between responding and 
36

non-responding sample units is larger for means than for medians, which indicates larger 

differences in the right tail of the wage distribution. The largest gaps between responding and 

non-responding sample units are for Whites (the average White non-responder’s WSI was nearly 

$55,000, compared to about $46,000 for White responders) and for Married filing jointly tax 

units (non-responders’ average WSI was $78,000, compared to $66,000 for responders). 

Appendix Table A5 provides a similar set of analyses comparing aggregated address linked W-2 

wages between responding and non-responding sample units, with largely similar results.

Table 16: Household Wage and Salary Income by CE Response Status

Universe: Responding and Non-responding SUs Linked to 1040s

Median WSI Mean WSI

Obs. CE 

Respondent 

Household

CE NonRespondent 

Household

CE 

Respondent 

Household

CE Non-Respondent 

Household

All 

households 21,169 $28,964 $32,102 $45,035 $52,383

Race of Householder

White Alone 10,340 30,110 34,565 45,662 54,909

Black Alone 1,470 21,601 23,230 30,395 34,551

AIAN Alone 35 36,817 36,544 52,072 28,041

Asian Alone 555 39,822 37,712 59,724 62,571

Pacific 

Islander Alone 220 23,597 26,229 35,868 45,604

Two or more 

races

235 26,643 28,270 39,765 39,880

Unknown 8,314 29,110 31,946 45,816 53,588

IRS 1040 Filing Status

Head of 

Household 2,962 21,824 23,616 28,239 30,200

Married Filing 

Jointly 8,984 47,595 54,874 65,553 78,437

Married Filing 

Separately 293 27,952 31,910 32,616 38,210

Single 8,930 22,509 24,830 30,705 34,527

Source: CE 2013-2014 and IRS 1040s, 2012-2013
37

Table 17 turns to a comparison of the average AGI between responding and non-responding 

sample units. The table structure mirrors Table 16, with a comparison of medians in the first two 

columns, and comparison of means in the second two columns. Consistent with the previous 

WSI results, non-responding sample units have substantially higher AGI than responding 

households, both when looking at medians and means. The differences between responding and 

non-responding sample unit median AGI are much smaller than the differences between mean 

AGI. This again suggests that there is substantial differences in the tail of the distribution. The 

differences between responding and non-responding sample unit mean AGI are much larger than 

are the difference between mean WSI across these groups. Whites and sample units with a 

householder of unknown race have large gaps between respondents and non-respondents, as do 

Pacific Islanders, and Married Filing Jointly tax units.

Table 17: Household Adjusted Gross Income (AGI) by CE Response Status

Obs.

Median AGI 

CE 

Respondents

Median AGI 

CE NonRespondents

Mean AGI 

CE 

Respondents

Mean AGI 

CE NonRespondents

All households 21,169 $40,088 $43,918 $63,344 $89,263

Race of Householder

White Alone 10,340 43,476 48,427 66,707 91,014

Black Alone 1,470 24,137 26,706 34,932 40,084

AIAN Alone 35 47,360 38,394 59,179 38,310

Asian Alone 555 48,830 43,459 71,507 84,439

Pacific Islander Alone 220 28,026 30,592 49,341 113,689

Two or more races 235 33,375 36,108 48,530 49,432

Unknown 8,314 39,478 44,713 63,918 107,091

IRS 1040 Filing Status

Head of Household 2,962 24,126 26,672 32,104 36,046

Married Filing Jointly 8,984 68,277 74,559 96,189 131,873

Married Filing 

Separately 293 32,933 37,258 47,723 43,182

Single 8,930 30,855 32,720 41,712 66,175

Source: CE 2013-2014 and IRS 1040s, 2012-2013

These results together paint a picture of differential survey non-response by more advantaged 

households. Non-responding households have higher incomes on average, measured either in 

terms of wages and salary or in terms of broader AGI. 
38

5.2 Analysis of Zip Code AGI Classification

Linking respondents and non-respondents to the universe of tax returns provides direct evidence 

for potential survey non-response bias along income lines. This confirms existing conventional 

wisdom that higher income households are less likely to respond to surveys. BLS employs a 

number of weighting processes to address this nonresponse bias. One major part of the weighting 

process involves categorizing responding and non-responding sample units into “low,” “middle” 

and “high” income categories on the basis of zip-code level information on average AGI 

provided by the IRS. Since we have information on the actual individual incomes of sample 

units, we can provide a validation of this categorization, and assess the degree to which 

categorizing sample units by zip code AGI is properly categorizing sample units by individual 

AGI.

For this part of the weighting process, BLS matches both respondents and Type A noninterviews to the most recently available annual average zip code AGI data available. For each 

interview month, BLS sorts sample units by zip code AGI and categorizes all CUs and Type A 

non-interview sample units into “low”, “middle” and “high” AGI categories. These categories 

are assigned so that the bottom 10 percent of sample units in a Census region in a given month 

are in the “low” category, the middle 80 percent are in the “middle” category, and the top 10 

percent are in the “high” category.

BLS provided these categorizations (in the variable WTIRSINC) for respondents only. We use 

the address matching process used above to assign an individual AGI value to these responding 

sample units (thus using a process which would be feasible for both respondents and nonrespondents). Using these address-matched sample unit AGI values, we construct a set of 

categorizations analogous to the zip code categories in WTIRSINC, assigning the bottom 10 

percent of sample units to a “low TU AGI” category, the middle 80 percent of sample units to a 

“middle TU AGI” category, and the “top 10 percent” to a high TU AGI category. Table 18 

summarizes the agreement between the WTIRSINC categories and the TU AGI categories for 

the full address linked sample, and broken out by Census region, with the columns representing 

IRS-derived grouping and the rows reflecting the CE-derived groups.

It appears that there is not much agreement between these categories, particularly for the “low” 

and “high” zip code AGI categories. Only about 18 percent of sample units categorized as low 

zip code AGI are in the bottom 10 percent of tax unit AGI, while only 32 percent of sample units 

categorized as high zip code AGI are in the top 10 percent of tax unit AGI. There is more 

agreement in the middle category. About 82 percent of sample units categorized as middle zip 

code AGI are in the middle 80 percent of the tax unit AGI distribution. There is regional 

variation in the degree of agreement between these two categorizations. While 21 percent of low 

zip code AGI sample units in the Northeast region are in the bottom 10 percent of the TU AGI 

distribution, only 13 percent of low zip code AGI sample units in the South region are in the 

bottom 10 percent of the TU AGI distribution. The Northeast region also has the highest degree 
39

of agreement for the high zip code AGI category (36 percent), while the West region has the 

lowest level of agreement for the high zip code AGI category (30 percent).

Table 18: Agreement Between CE Zip Code AGI Categories and Form 1040 AGI

Bottom 10% 

TU AGI

Middle 

80% TU 

AGI

Top 10% TU 

AGI

Full Sample

Bottom 10% Zip Code AGI 0.177 0.811 0.012

Middle 80% Zip Code AGI 0.097 0.821 0.082

Top 10% Zip Code AGI 0.059 0.623 0.318

Northeast Region

Bottom 10% Zip Code AGI 0.212 0.765 0.023

Middle 80% Zip Code AGI 0.095 0.827 0.078

Top 10% Zip Code AGI 0.036 0.607 0.357

Midwest Region

Bottom 10% Zip Code AGI 0.2 0.8 0

Middle 80% Zip Code AGI 0.095 0.823 0.082

Top 10% Zip Code AGI 0.063 0.619 0.317

South Region

Bottom 10% Zip Code AGI 0.131 0.842 0.027

Middle 80% Zip Code AGI 0.1 0.819 0.081

Top 10% Zip Code AGI 0.073 0.613 0.313

West Region

Bottom 10% Zip Code AGI 0.161 0.833 0.005

Middle 80% Zip Code AGI 0.099 0.816 0.085

Top 10% Zip Code AGI 0.054 0.642 0.304

Source: CE 2013-2014 and IRS 1040s, 2012-2013
40

Table 19: Summary Statistics of AGI, by CE Zip Code AGI Categories

Average TU 

AGI

Average TU 

AGI, First 

Quartile

Average TU AGI, 

Second Quartile

Average TU AGI, 

Third Quartile

Average TU AGI, 

Fourth Quartile

Full Sample

Bottom 10% 

Zip Code AGI $33,937 $8,648 $17,784 $29,128 $80,122

Middle 80% 

Zip Code AGI 56,207 9,453 29,502 52,901 132,949

Top 10% Zip 

Code AGI 129,492 19,937 59,427 108,002 330,007

Northeast Region

Bottom 10% 

Zip Code AGI 47,789 6,882 17,171 30,459 136,645

Middle 80% 

Zip Code AGI 60,584 11,640 33,137 59,365 138,195

Top 10% Zip 

Code AGI 148,806 29,579 71,259 124,245 370,142

Midwest Region

Bottom 10% 

Zip Code AGI 29,526 7,389 15,803 29,802 64,756

Middle 80% 

Zip Code AGI 54,850 5,874 30,496 52,910 130,119

Top 10% Zip 

Code AGI 121,261 19,211 52,856 101,580 308,967

South Region

Bottom 10% 

Zip Code AGI 31,154.51 9,632 17,839 28,758 68,389

Middle 80% 

Zip Code AGI 50,845.44 11,284 26,252 47,252 118,588

Top 10% Zip 

Code AGI 123,043.70 17,002 53,137 100,718 321,317

West Region

Bottom 10% 

Zip Code AGI 31,435 10,070 19,605 29,008 66,440

Middle 80% 

Zip Code AGI 62,236 8,775 31,605 56,611 151,831

Top 10% Zip 

Code AGI 133,346 21,972 65,075 114,451 331,887

Source: CE 2013-2014 and IRS 1040s, 2012-2013
41

Of course, some degree of disagreement between these two categorizations would be expected, 

as long as the income distributions within zip codes are not perfectly equal. We investigate this 

further by examining the distribution of tax unit AGI within each zip code AGI category. Table 

19 summarizes this distribution of TU AGI for the full linked sample, and also breaks out the 

summary statistics by Census region. The first column reports the mean TU AGI for all tax units 

within a zip code AGI category. Columns 2 through 5 report the average TU AGI within a 

quartile of the AGI distribution within a zip code AGI category. For instance, the first row entry 

of Column 2 represents the average AGI for tax units who are in the bottom 25 percent of the 

AGI distribution within the “Bottom 10% Zip Code AGI” category.

On average, the zip code categories do correspond to increasing tax unit AGI: the overall 

national average tax unit AGI in the low zip code AGI category is about $34,000, about $56,000 

in the middle zip code AGI category and $129,000 in the high AGI category. Nonetheless, there 

is substantial inequality within each zip code AGI category. Indeed, for the full sample, and 

within each Census region, the top quartile of tax unit AGI in the low zip code AGI category 

have higher incomes than the average AGI in the middle zip code AGI category. The bottom 

quartile of tax unit AGI in the high zip code AGI category have lower incomes than the average 

tax unit in the low zip code AGI category.17

This widespread of incomes within the BLS-assigned zip code AGI categories suggests that the 

weighting process may be proceeding under incorrect assumptions about which sample units are 

in fact low or high income. Although the average incomes within each category are consistent 

with the low, middle and high income assignments, there are a substantial number of individuals 

who are categorized as high income by WTIRSINC in fact have quite low tax unit incomes. 

Additionally, many sample units who are categorized as low income by WTIRSINC are in fact at 

the upper end of the middle income category in terms of tax unit AGI. This misclassification 

suggests that, if possible, adopting a weighting process that categorizes respondents and nonrespondents by address-linked tax unit AGI may yield conceptually superior results.

6 Conclusion

In order to investigate the possibility of using administrative records to improve the Consumer 

Expenditure Survey, we link IRS tax records on wages, payroll deductions, self-employment 

income, retirement income and broad measures of tax unit income to responses and nonresponses in the CE survey. These linked data have made possible new insights into the 

measurement error properties of CE income data, have illuminated possible ways that the 

imputation process could be improved, and have allowed for an examination of both survey nonresponse bias and the weighting process used to address it.

 

17 Low income individuals in a high AGI zip code may in fact be high-wealth individuals who are retired. However, 

householders in the bottom quartile of high AGI zip codes are in fact younger on average than individuals in the top 

3 quartiles of these zip codes, so it is more likely that the AGI of these individuals represents their true resources.
42

About 77 percent of CE respondents can be assigned a PIK, the unique person identifier used to 

link survey and administrative records, while about 93 percent of sample units can be assign 

MAFIDs, the unique household identifier used to link survey and administrative records by 

address. We are able to link about 77 percent of individuals to 1040s by MAFID, and about 70 

percent by PIK; we can link 39 percent of individuals to the universe of W-2s by PIK. This 

implies a tax return filing rate of about 90 to 95 percent, and an employment to population ratio 

of about 52 percent, both numbers that align with aggregate statistics.

Our analysis of the individuals who can be assigned PIKs suggests that, overall, average wages 

in the CE and average wages in the universe of W-2’s have only very small differences. 

Nonetheless, looking throughout the wage distribution for individuals with both CE and W-2 

wages, there is evidence of over-reporting of wages (CE wages are higher than W-2 wages) at 

the bottom of the wage distribution, and under-reporting of wages (CE wage are lower than W-2 

wages) at the top of the distribution. There are a relatively small number of individuals who 

report no CE wages, but who nonetheless earned wages in the W-2 universe; the imputation 

process does not always assign an imputed value to these individuals. Under-reporting and 

incorrect wage item non-response seem to be concentrated among individuals in the youngest 

cohort, aged 14-24.

We also compare CE responses to administrative records for questions about payroll deductions, 

self-employment income and retirement income. We find that W-2 wage records suggest a 

slightly higher SS payroll deduction rate than the CE responses (suggesting under-reporting), but 

very similar rates of self-employment income receipt across the two data sources. Retirement 

income appears to be dramatically under-reported on the CE. Approximately twice as many 

individuals receiving retirement income in the universe of 1099-R than report retirement income 

on the CE, although this may in part represent a conceptual difference between the CE and IRS 

definitions of retirement income. 

We find evidence of systematic survey non-response across the income distribution by linking 

responding and non-responding households to administrative records by address. Nonresponding sample units are much more likely to be richer than responding households, 

measured either by wage and salary income or by broader AGI. We additionally use the addresslinked data to analyze one part of the non-response weighting process, and find that the 

categorization of sample units by zip code AGI does not line up cleanly with the individual 

incomes of these sample units. In particular, poor sample units in high-income zip codes and 

richer sample units in low-income zip codes are likely misclassified under this categorization.

These results suggest potential ways forward to improve income statistics produced from the CE. 

In particular, both the PIK and MAFID based analysis suggest areas where current imputation 

and weighting procedures may be producing less than optimal outcomes. Incorporating the 

linkage of administrative records into these production processes has the potential to improve the 

accuracy and quality of statistics produced from the CE.
43

References

Bee, C. Adam, and Joshua Mitchell. 2017. Do Older Americans Have More Income Than We Think? US 

Census Bureau.

Bee, C. Adam, and Joshua Mitchell. 2016. The Hidden Resources of Women Working Longer: Evidence 

from Linked Survey-Administrative Data. NBER Working Paper No. 22970.

Luque, Adela, and Renuka Bhaskar. 2014. 2010 American Community Survey Match Study. U.S. Census 

Bureau.

O’Hara, Amy, Sonya Rastogi, James Noon, Ellen A. Zapata, Cindy Espinoza, Leah B. Marshall, Teresa 

A. Schellhamer, and J. David Brown. 2012. 2010 Census Match Study. US Census Bureau.

Wagner, Deborah, and Mary Layne. 2014. The Person Identification Validation System (PVS): Applying 

the Center for Administrative Records Research and Applications’ (CARRA) Record Linkage 

Software. US Census Bureau.
44

Appendix: Additional Tables and Figures

Table A1: Comparing Median Individual Wages, CE vs. W-2 (Nonimputed CE 

Responses only)

Unweighted Weighted

Obs. Median Wages, 

CE

Median Wages, 

W-2

Median 

Wages, CE

Median 

Wages, W-2

All individuals 27,914 $34,820 $29,796 33,000 28,719

Sex

Female 14,278 27,998 24,646 27,000 23,980

Male 13,636 41,020 36,238 40,000 35,096

Race

White Alone 22,477 34,999 30,870 34,460 29,844

Black Alone 3,264 25,997 22,004 25,000 21,146

AIAN Alone 136 23,950 21,740 37,261 35,319

Asian Alone 1,382 37,985 36,104 23,000 21,446

Pacific Islander 

Alone 157 31,250 28,622 32,000 31,460

Two or more 

races

498 23,628 20,637 20,000 20,206

Age

14-24 3,923 9,000 7,369 8,400 7,274

25-44 7,078 38,001 33,562 36,000 32,446

45-64 7,911 41,998 40,458 41,061 39,788

65+ 4,434 19,978 15,673 20,000 15,490

Hispanic Origin

Hispanic 4,561 25,002 22,997 24,960 22,606

Not Hispanic 23,353 36,001 31,232 35,000 30,000

Income

Above Median 

CU Wages 14,760 50,001 42,499 48,000 40,929

Below Median 

CU Wages 13,154 16,000 15,754 15,900 15,524

Second Interview Month

1 1,917 32,039 29,175 30,000 27,788

2 2,120 33,486 30,832 32,789 30,567

3 2,087 31,267 28,584 30,824 28,447

4 2,124 35,011 29,578 34,857 28,517

5 2,072 31,963 27,900 30,000 26,257

6 2,145 35,000 31,293 35,000 30,841

7 2,152 31,964 28,987 30,000 27,755

8 2,147 35,042 31,123 35,000 30,104

9 2,090 33,614 29,866 32,000 28,666

10 3,094 35,032 30,404 34,987 28,846

11 3,099 35,968 29,941 35,000 28,782

12 2,867 34,978 29,887 32,589 28,376

Source: CE 2013-2014, IRS W-2s 2012-2013
45

Table A2: Comparing Median Individual Wages, CE vs. W-2 (Imputed and 

Nonimputed CE Responses)

Unweighted Weighted

Obs. Median Wages, 

CE

Median Wages, 

W-2

Median Wages, 

CE

Median 

Wages, W-2

All individuals 27,914 $31,490 $29,796 30,000 28,719

Sex

Female 14,278 26,000 24,646 25,212 23,980

Male 13,636 36,000 36,238 35,000 35,096

Race

White Alone 22,477 32,904 30,870 25,212 23,980

Black Alone 3,264 24,063 22,004 35,000 35,096

AIAN Alone 136 23,950 21,740 25,212 23,980

Asian Alone 1,382 37,998 36,104 35,000 35,096

Pacific Islander 

Alone 157 28,046 28,622 25,212 23,980

Two or more 

races

498 23,084 20,637 35,000 35,096

Age

14-24 3,923 8,572 7,369 8,025 7,274

25-44 7,078 35,000 33,562 34,649 32,446

45-64 7,911 40,000 40,458 40,000 39,788

65+ 4,434 22,022 15,673 21,263 15,490

Hispanic Origin

Hispanic 4,561 23,000 22,997 23,000 22,606

Not Hispanic 23,353 34,000 31,232 32,366 30,000

Income

Above Median 

CU Wages 14,760 45,000 42,499 44,806 40,929

Below Median 

CU Wages 13,154 16,420 15,754 16,000 15,524

Second Interview Month

1 1,917 30,003 29,175 28,000 27,788

2 2,120 30,804 30,832 30,000 30,567

3 2,087 29,985 28,584 30,000 28,447

4 2,124 29,998 29,578 28,568 28,517

5 2,072 30,017 27,900 27,984 26,257

6 2,145 31,989 31,293 31,796 30,841

7 2,152 30,001 28,987 28,000 27,755

8 2,147 32,447 31,123 32,000 30,104

9 2,090 31,995 29,866 30,000 28,666

10 3,094 33,620 30,404 32,000 28,846

11 3,099 32,005 29,941 31,538 28,782

12 2,867 31,999 29,887 30,000 28,376

Source: CE 2013-2014, IRS W-2s 2012-2013
46

Table A3: Comparing Median Individual Wages, CE vs. W-2 Nonimputed CE Responses, all 

individuals with both CE and W-2 Wages

Unweighted Weighted

Obs.

Median Wages, CE Median Wages, W-2

Median 

Wages, 

CE

Median 

Wages, 

W-2

All individuals 9,124 $36,000 $34,620 $36,000 $34,081

Sex

Female 4,504 30,000 29,107 30,000 28,790

Male 4,620 44,329 40,969 42,928 40,402

Race

White Alone 7,647 38,000 35,595 36,690 35,014

Black Alone 833 28,012 26,463 28,012 25,678

AIAN Alone 41 32,333 25,469 32,333 24,338

Asian Alone 442 39,954 38,842 39,954 38,261

Pacific 

Islander Alone 62 32,500 31,320 36,616 31,110

Two or more 

races

99 29,833 27,081 26,667 26,396

Age

14-24 1,070 11,932 8,608 11,125 8,622

25-44 3,841 40,003 36,099 38,002 35,931

45-64 3,711 45,002 43,885 44,001 43,535

65+ 502 22,534 23,799 21,917 23,841

Hispanic Origin

Hispanic 1,259 27,012 26,666 27,012 26,384

Not Hispanic 7,865 38,996 36,272 38,000 35,854

Income

Above Median 

CU Wages 6,359 50,001 46,381 50,001 45,736

Below Median 

CU Wages 2,765 17,999 18,091 17,999 17,932

Second Interview Month

1 667 35,005 33,667 35,005 33,141

2 744 36,947 35,178 37,008 34,606

3 722 34,012 32,166 34,012 31,614

4 704 40,004 36,622 38,463 36,317

5 683 33,903 30,500 32,852 29,882

6 721 38,019 36,112 38,019 35,708

7 682 34,556 33,548 33,170 32,304

8 684 38,027 35,400 38,027 34,954
47

9 660 37,111 34,019 36,064 33,776

10 994 37,992 35,550 36,123 34,886

11 940 39,790 35,531 39,527 34,944

12 923 35,974 35,732 34,972 35,120

Source: CE 2013-2014, IRS W-2s 2012-2013

Table A4: Median CE vs. W-2 Income, by CE Income Bracket

Unweighted Weighted

Mean CE 

Wages

Mean W-2 

Wage

Mean CE 

Wages

Mean W-2 

Wage

CE Brackets ($)

[0-5K) $1,854 $4,385 $1,854 $4,248

[5K-10K) 7,179 7,910 7,094 7,784

[10K-15K) 12,002 12,697 12,000 12,541

[15K-20K) 17,069 16,949 17,046 16,903

[20K-30K) 24,573 23,779 24,573 23,711

[30K-40K) 34,512 32,207 34,537 31,980

[40K-50K) 44,088 41,051 44,099 41,050

[50K-70K) 58,000 54,395 58,000 54,461

[70K-90K) 78,000 73,497 78,000 73,686

[90K120K) 100,000 97,159 100,000 96,488

120K+ 164,915 146,513 165,000 145,692

Source: CE 2013-2014, IRS W-2s 2012-2013
48

Table A5: Comparing Total MAFID W-2 Wages Responding vs. Nonresponding Households

Median W-2 Wages Mean W-2 Wages

CE 

Respondent

Households

CE NonRespondent

Households

CE 

Respondent

Households

CE NonRespondent

Households

All households $50,279 $51,594 $73,991 $96,274

Race of Householder

White Alone 52,912 54,445 76,840 86,486

Black Alone 32,470 38,029 47,925 52,784

AIAN Alone 52,662 32,407 64,635 32,255

Asian Alone 71,114 61,915 104,063 99,944

Pacific Islander 

Alone 32,394 40,314 76,165 70,974

Two or more 

races

37,370 50,095 62,298 66,937

Unknown 49,630 49,726 73,407 145,655

Source: CE 2013-2014, IRS W-2s 2012-2013
49

Table A6: Comparing Mean CE and IRS W-2 Wages, Individuals with Imputed CE Wages Only

Unweighted Weighted

Obs.

Mean 

Wages, 

CE

Mean 

Wages, 

W-2

Mean Wages, 

CE

Mean Wages, 

W-2

All individuals 7,630 $40,016 $46,767 38,709 44,943

Sex

Male 4,055 43,915 58,701 42,256 56,342

Female 3,575 35,594 33,425 34,799 32,671

Race

White Alone 5,937 41,705 48,070 40,388 45,963

Black Alone 1,012 27,768 33,267 27,219 32,202

AIAN Alone 36 36,116 24,270 29,064 28,210

Asian Alone 403 51,822 63,560 52,453 67,780

Pacific Islander Alone 45 26,533 48,034 28,382 46,943

Two or More Races 197 31,701 35,308 30,656 37,564

Age

14-24 777 13,658 11,602 13,065 11,465

25-44 1,724 39,042 46,106 38,275 44,750

45-64 1,713 48,449 62,196 48,047 61,086

65+ 380 32,657 46,836 31,648 45,920

Hispanic Origin

Not Hispanic 6,284 42,696 49,267 41,238 47,273

Hispanic 1,346 27,507 29,306 27,494 29,308

Income

Above Median CU Wages 4,883 52,136 57,469 50,597 55,338

Below Median CU Wages 2,747 18,472 25,619 18,016 24,700

Second Interview Month

1 495 40,887 43,911 39,345 41,910

2 536 38,227 55,327 39,006 53,450

3 547 41,494 52,364 38,159 48,948

4 608 37,810 43,089 35,929 40,091

5 524 43,674 44,247 42,650 42,210

6 607 42,678 42,962 42,383 42,541

7 644 37,618 44,653 35,874 43,905

8 649 39,529 45,453 39,169 44,535

9 610 40,356 51,945 40,021 49,456

10 827 39,451 42,883 37,046 39,882

11 852 39,009 44,748 38,040 44,100

12 731 40,711 52,714 38,736 50,150

Source: CE 2013-2014, IRS W-2s 2012-2013