Do Imputed Earnings Earn Their Keep? Evaluating SIPP 

Earnings and Nonresponse with Administrative Records∗

Rebecca L. Chenevert

Mark A. Klee

Kelly R. Wilkin†

November 2015 

Abstract

Recent evidence suggests that labor earnings reported in household surveys 

compare favorably with labor earnings in administrative records. On the other hand, 

imputed labor earnings in household surveys seem to match labor earnings in administrative records less closely. This finding has led many researchers to question 

the reliability of imputed labor earnings and to exclude these observations from 

wage analyses. However, this strategy might result in sample selection bias if labor 

earnings are not missing at random. In this paper, we compare reported and imputed 

labor earnings from the 2008 panel of the Survey of Income and Program Participation to labor earnings from the Social Security Administration’s Detailed Earnings 

Record. We examine how the relationship between survey data and administrative 

records varies across demographic groups. We also characterize survey nonrespondents in order to improve our understanding of whether and how individuals select 

out of response on observable dimensions. Finally, we consider implications for 

estimates of the earnings structure. 

∗We thank Adam Bee, Gary Benedetto, Molly Heller, and Jonathan Rothbaum for helpful comments. This paper 

was prepared for the 2015 Federal Committee on Statistical Methodology Research Conference, Washington, DC, 

December 1-3, 2015. This paper is released to inform interested parties of ongoing research and to encourage 

discussion of work in progress. The views expressed in this paper are those of the authors and not necessarily those 

of the U.S. Census Bureau. Any errors are our own. 

†Affiliation: Social, Economic, and Housing Statistics Division; U.S. Census Bureau. Address: Social, Economic, and Housing Statistics Division; U.S. Census Bureau; 4600 Silver Hill Road; Washington, DC 20233. Chenevert: rebecca.l.chenevert@census.gov, (301) 763-8538. Klee: mark.a.klee@census.gov, (301) 763-4730. Wilkin: 

kelly.r.wilkin@census.gov, (301) 763-7728. 
1 Introduction

Historically, survey data have been the main source of information about social and economic 

characteristics of households in the United States. Labor economists in particular have used 

survey data to study a variety of topics relating personal and job characteristics to wages and 

earnings. The Survey of Income and Program Participation (SIPP) is a longitudinal survey with 

a rich variety of content that provides researchers the opportunity to study a plethora of topics. 

The focus of the survey is measuring income and participation in government programs, and as 

such SIPP is of particular interest to researchers studying poverty and public policy among other 

topics. However, as with many household surveys, SIPP response rates are declining. And, as is 

also the case with many other surveys, nonresponse rates for earnings and wages are generally 

higher than would be desired. These questions are regarded as quite sensitive for respondents. 

Therefore, in this work we aim to study the earnings data of those who respond and those who 

do not in SIPP. We are able to do this by linking SIPP data to administrative earnings data, the 

Detailed Earnings Record (DER) from the Social Security Administration. 

Several others have looked at similar questions before. Most closely related is the work of 

Bollinger et al. (2015b), who link the Current Population Survey Annual Social and Economic 

Supplement (CPS ASEC) to the DER. They find that nonresponse is most common in the tails 

of the earnings distribution, and that nonresponse patterns differ for men and women. Also 

very closely related is the work of Cristia and Schwabish (2009), who compare earnings from 

the 1996 SIPP panel to the DER and find earnings under-reported on average, and that factors 

positively associated with earnings are negatively correlated with measurement error. This study 

will look at more recent data, and in the future will also compare the redesigned SIPP data as 

well. 

Recent evidence suggests that labor earnings reported in household surveys compare favorably with labor earnings in administrative records.1 However, imputed labor earnings in 

household surveys match labor earnings in administrative records less closely. This finding has 

led many researchers to question the reliability of imputed labor earnings and to exclude these 

observations from wage analyses. However, this strategy might result in sample selection bias 

if labor earnings are not conditionally missing at random. In this paper, we compare reported 

and imputed labor earnings from the 2008 panel of SIPP to labor earnings from the DER. We 

1For an example, see Abowd and Stinson (2013). 

1
examine how the relationship between survey data and administrative records varies across demographic groups. Finally, we characterize survey nonrespondents in order to improve our 

understanding of whether and how individuals select out of response on observable dimensions. 

Overall, the correlation between the survey reported earnings and administrative reported 

earnings is encouraging. More than 90 percent of the sample had earnings in neither or both 

sources, and the mean difference for the matched sample is $969. However, consistent with 

other studies, we find that the administrative reports of earnings tend to be a little higher on 

average than the survey reported data. We find that the difference in imputed survey earnings 

and administrative exhibits a wider variance than the analogous relationship for reported survey 

data, as expected.2 We find that nonrespondents are 5.3 percentage points more likely to have 

positive administrative earnings data than respondents, along with other characteristics that differ between the groups. Therefore, it seems that we may not have values that are missing at 

random. To test the impact, we look at some basic Mincer earnings regressions and unconditional earnings gaps. Preliminary evidence supports that excluding imputed earnings could 

introduce bias in estimated coefficients of interest. 

The rest of this paper is organized as follows: Section 2 describes the existing literature 

that this work complements. Section 3 describes the data used. Sections 4 and 5 describe the 

results of the comparison of the survey and administrative data and the analysis of nonresponse, 

respectively. Section 6 discusses implications of using imputed data for basic regressions of 

broad interest to labor economists. Section 7 concludes. 

2 Literature Review

This paper builds on a well-developed literature that evaluates the quality of earnings data based 

on surveys via comparison to some alternate measure of survey respondents’ earnings. One 

common validation technique measures the degree to which an employee’s self-reported earnings match an employer’s report of that employee’s earnings. Mellow and Sider (1983) utilize 

this strategy in Current Population Survey (CPS) data, while Duncan and Hill (1985) use this 

strategy in data from a Panel Study of Income Dynamics (PSID) survey instrument for a sam2All comparisons are statistically significant at the 90 percent level. The estimates in this paper are based 

on responses from a sample of the population and may differ from actual values because of sampling variability or other factors. As a result, apparent differences between the estimates for two or more groups may not be 

statistically significant. For more information on the source of the data and the accuracy of the estimates, see 

http://www.census.gov/programs-surveys/sipp/tech-documentation/source-accuracy-statements.html. 

2
ple of workers at a large manufacturing company. These studies hypothesize that firms report

employees’ earnings accurately, and they therefore treat any deviation of employee self-reports 

from employer reports as measurement error. They conclude that measurement error in earnings levels appears low on average, although this obscures larger average absolute differences 

between earnings reports. In addition, there is mixed evidence about whether these two sources 

of earnings data yield similar estimates of some aspects of the wage structure. 

A second common validation technique measures the degree to which an employee’s selfreported earnings match administrative records of that employee’s earnings. Both Bound and 

Krueger (1991) and Bollinger (1998) consider survey earnings from the CPS Annual Demographic File and earnings based on payroll tax records from the Social Security Administration (SSA).3 These studies hypothesize that firms report employees’ earnings accurately for 

tax purposes, and they therefore treat any deviation of survey data from administrative data as 

measurement error. They also present evidence that measurement error explains a substantial 

portion of the overall variance in survey earnings. Moreover, measurement error appears to 

be negatively correlated with administrative earnings. If administrative earnings represent the 

truth, then this finding invalidates the common assumption that any measurement error in earnings is “classical”. Primarily, respondents with low administrative earnings disproportionately 

overstate earnings in the CPS Annual Demographic File. 

This second validation technique has also been applied using SIPP survey data and Social Security administrative data. Pedace and Bates (2000) explore how well earnings in the 

1992 SIPP panel match SSA earnings in the Summary Earnings Record (SER). While they find 

that SIPP accurately estimates the number of earnings recipients, they join Bollinger (1998) in 

concluding that respondents at the bottom of the administrative earnings distribution tend to 

overstate their earnings. They also show evidence that respondents at the top of the administrative earnings distribution tend to understate their earnings, suggesting that earnings data is 

mean-reverting. Cristia and Schwabish (2009) provide more definitive evidence by comparing 

the 1996 SIPP panel and the DER. While the SER contains payroll tax records on earnings 

capped at the taxable maximum, the DER contains uncapped earnings data from payroll tax 

records. They corroborate the evidence in Pedace and Bates (2000), and also conclude that demographic characteristics that are positively correlated with earnings are negatively correlated 

3The CPS Annual Demographic File is often referred to as the March CPS, or more recently as the CPS Annual 

Social and Economic Supplement (CPS ASEC). 

3
with measurement error. Roemer (2002) also uses the DER to report that SIPP represents a respondent’s percentile in the wage distribution better than it represents that respondent’s wage in 

dollars. Gottschalk and Huynh (2010) illustrate that this finding bears important implications for 

estimates of inequality. They show that mean-reverting measurement error yields considerably 

lower estimates of inequality in SIPP data than in DER data. By contrast, they also document 

that the relatively strong serial correlation in measurement error yields similar estimates of mobility in SIPP and DER data. 

This paper strongly resembles Pedace and Bates (2000) and Cristia and Schwabish (2009); 

we compare earnings data from the 2008 SIPP panel and the DER, and we consider the correlates of the deviation between these measures. However, these papers placed relatively little 

emphasis on the role of imputed data in explaining the difference between survey earnings and 

administrative earnings. Recent evidence suggests that labor earnings reported in household 

surveys compare favorably with labor earnings in administrative records. Abowd and Stinson 

(2013) argue that reported survey data and administrative data are quite similar in reliability. 

By contrast, they show that imputed survey earnings appear less reliable than administrative 

data. Based on this finding, we highlight the role of imputed data in explaining how well labor 

earnings in household surveys match labor earnings in administrative records. 

A related well-developed literature evaluates the quality of imputed earnings data. Like 

other Census Bureau surveys, SIPP imputes missing data using a “hot-deck” procedure which 

assigns to the nonrespondent data reported by a “donor” with similar demographic characteristics. This imputation method assumes that earnings data are missing at random, conditional 

on the characteristics used to match nonrespondents to donors. One important disadvantage of 

this procedure is that the curse of dimensionality limits the set of characteristics or the values of 

these characteristics that may be used to match nonrespondents to donors. If some determinant 

of nonresponse is omitted from the match criteria, earnings estimates will be biased. Moreover, 

earnings estimates might be biased even if earnings are conditionally missing at random. Hirsch 

and Schumacher (2004) demonstrate that if some observable characteristic such as union status is not used to match earnings nonrespondents to donors, then coefficient estimates on this 

characteristic in a wage equation will be attenuated. Relatedly, Bollinger and Hirsch (2006) 

illustrate that if nonrespondents are matched to donors according to grouped categories of some 

characteristic, such as education, then coefficient estimates on more detailed measures of this 

4
characteristic such as years of education in a wage equation will be attenuated.4 These two 

forms of “match bias” often motivate proposals for more parametric, model-based imputation 

methods. However, Andridge and Little (2010) argue that hot-deck imputation methods perform 

relatively well along various dimensions compared to model-based imputation methods. 

Empirical researchers have pursued various strategies to remove or mitigate match bias and 

response bias. One common approach is to exclude imputed earnings values from analyses. 

Bollinger and Hirsch (2013) examine the validity of this technique, and they conclude that 

omitting imputed earners from OLS wage equations is generally sufficient to avoid major bias 

in slope estimates. Bollinger et al. (2015b) revisit the question of whether response bias is ignorable by investigating the pattern of nonresponse over the earnings distribution conditional 

on covariates. They report a U-shaped pattern of nonresponse, implying that response bias is 

ignorable over most of the distribution, with the exception of the tails. Bollinger et al. (2015a) 

establish that this nonignorable response bias causes CPS ASEC to understate inequality measures relative to DER data. Hokayem et al. (2015) utilize a second strategy by exploiting administrative data to evaluate the impact of earnings nonresponse on official poverty estimates. They 

derive a “full response” measure of poverty by assigning nonrespondents’ earnings from DER 

data, accounting for both the likely deviation of survey from administrative earnings and the 

likely earnings differences among those who can and those who cannot be matched to administrative data. They find evidence that nonresponse leads CPS ASEC to understate the poverty 

rate by about one percentage point. 

3 Data

Next we describe the data sources that we utilize. We begin by detailing in isolation the Survey 

of Income and Program Participation in section 3.1 and the Detailed Earnings Record in 3.2. 

We then discuss the linked dataset. 

3.1 Survey of Income and Program Participation

The SIPP is a panel study program that began in 1984. SIPP collects data and measures change 

for many topics, including economic well-being, family dynamics, education, assets, health 

4While more recent research tends to argue that imputed earnings are unreliable, David et al. (1986) conclude that 

hot-deck imputed earnings perform favorably relative to both model-imputed earnings and administrative earnings 

in IRS data. 

5
insurance, childcare, and food security, following respondents for a panel of roughly three to

four years. While there have been many changes to the survey over time, the core principle of 

measuring the dynamics of these topics has remained the same. SIPP has undergone two major 

redesigns in its time. The first redesign was with the 1996 panel. This changed the structure of 

the program from having overlapping panels that began different years to having one panel at 

a time. The 1996 Panel also marked the change from a paper survey instrument to a Computer 

Assisted Personal Interview (CAPI) survey. There was a somewhat minor redesign with the 

2004 panel, which further leveraged the CAPI functionality and increased the use of dependent 

data. 

Since the 1996 redesign, there have been four panels beginning in 1996, 2001, 2004, and 

2008. Respondents were surveyed every four months, called waves, and these surveys consisted 

of two parts. The first part of the survey is the “core,” which asks about the same topics every 

wave. This includes questions on income from all sources for each month, as well as information 

about changes in household composition, employment, and other topics. The second piece is a 

“topical module.” Topical modules can be either periodic or once per panel. Topical modules 

can range from asking questions about lifetime fertility or employment history to questions 

about assets and liabilities, commuting and work schedule, or retirement savings and pensions.5 

In this paper, we use the data from the core relating to earnings from a job or self-employed 

business. SIPP collects detailed data on up to two jobs and two businesses per wave. We also 

include severance pay and earnings from “moonlighting,” which are collected separately. We 

aggregate earnings for the calendar year, which are collected in three or four different waves 

(depending on rotation group).6 

The SIPP reports an “allocation” status flag associated with almost all variables in the survey.7 In the 2008 panel, these allocation flags indicate one of several statuses: a reported value, 

a value imputed with a hot-deck method, logical imputation, or an imputation using a previous 

wave’s data.8 These flags allow users to identify unit nonrespondents. Among item nonrespondents, the imputation method depends on the availability of earnings data from the previous 

5The complete list of topical modules in the 2008 panel is available at www.census.gov/programssurveys/sipp/tech-documentation/topical-modules/topical-modules-2008.html 

6In the 2008 panel, SIPP divided the sample into four groups which are interviewed on a rotating basis called 

“rotation groups.” For example, in 2009, the rotation group 1 wave 3 interview was in May about the preceding 

4 months (January through April 2009). Rotation group 2 was then interviewed in June about the preceding four 

months, so wave 3 refers to February through May 2009 for this group. 

7Exceptions are recodes, which are transformations of other variables, and those that have no editing. 

8Cold-deck imputation substitutes a value selected by the data editor, not reported data. Cold-deck imputation is 

not a method that is commonly used, and is not used in the variables of interest for this analysis. 

6
month and the likely veracity of any reported earnings data. A hot-deck imputation method

substitutes the data of a responder to fill in for an item with nonresponse, where the donor is 

matched on several observable characteristics. If earnings on a job or business from the prior 

month are available, those earnings are included among the match criteria. If the data-generating 

process for earnings were known, and if all aspects of this process could be incorporated appropriately into the algorithm matching donors to recipients, then this imputation technique would 

predict earnings exactly. However, the data generating process for earnings is largely unknown, 

and the curse of dimensionality limits the observable characteristics that feasibly may be incorporated into the matching algorithm. Finally, if earnings from a job are unusually high or if 

reported earnings are $0 but there is strong reason to believe that the respondent actually earned 

a positive amount, new earnings data are imputed logically. This logical imputation process 

assigns earnings to be implied by either the hourly pay rate and hours worked during the month, 

a reported annual pay rate, or weeks spent away from a job without pay. Logical imputation 

only occurs if that job has no earnings data available from a previous month.9 

While the output file is in a person-month format, it is common for all four monthly values 

for a single wave have the same allocation flag for a respondent. As we are aggregating the data 

up to the annual level, waves can cross over a calendar year. For the analyses in section 4 of 

this paper, we classified a person-year observation as imputed in a particular way if any month 

in that year is imputed. While similar analyses were conducted using the number of imputed 

months as the independent variable of interest, these results are not reported as they did not 

show substantive differences.10 

A commonly overlooked subtlety of SIPP data is that the allocation flags do not identify 

all imputed data. Each respondent also has a person-level interview-status flag. As this is a 

person-level variable, it is constant within each wave.11 This flag indicates whether the survey 

information was obtained from the respondent himself, from a proxy, or whether the person was 

a noninterviewed person in a responding household known as “Type Z.” A Type-Z individual 

has all of their data imputed. Similarly, nonrespondents can have all labor force data imputed if 

a respondent opts out of the entire employment section. We consider both types of individuals 

to be unit nonrespondents. Among unit nonrespondents, the imputation method depends on the 

9Refer to U.S. Census Bureau (2001) for an in-depth discussion of these imputation methods. 

10Estimates are available from the authors upon request. 

11Note that this will be more transparent in the 2014 redesign, as the allocation flags will identify every imputed 

value. 

7
availability of employment data from the previous interview. For new sample members and

for individuals whose previous wave data do not imply that the respondent was working at the 

beginning of the reference period, all labor force data including earnings were imputed from a 

single donor with similar observable characteristics. For individuals whose previous wave data 

imply that the respondent was working at the beginning of the reference period, labor force data 

from the previous wave were imputed longitudinally by projecting through the current interview. 

The second major redesign takes effect with the 2014 panel. In this paper we focus on 

the 2008 panel, but in future work we plan to compare the earnings data in a similar fashion 

as reported here for the 2014 panel. The 2014 redesign increased the recall period from four 

months to slightly over a calendar year. It also involves changing the structure of the survey 

instrument and using an Event History Calendar (EHC) to help aid memory. There will no 

longer be separate core or topical modules to the survey; all questions are in each wave of the 

panel. 

3.2 Detailed Earnings Records

The Detailed Earnings Records (DER) are provided to the Census Bureau by the Social Security Administration. The DER include wage and salary earnings (Box 1), both deferred and 

nondeferred earnings, and self-employment earnings reported to the IRS.12 These earnings are 

not capped at the taxable maximum. The data are provided at the level of one observation per 

person, year, and job (W-2) or business (1040-SE). We aggregate both deferred and nondeferred 

earnings from all jobs and businesses to create a total for the person for each year. If someone had self-employment earnings in either the DER or the SIPP, we classify that person as 

self-employed in the person-year analysis. 

The DER data are processed by the Census Bureau and linked to surveys using a Personal 

Identification Key (PIK). When using survey data linked to administrative data, there are several 

caveats of which one must be aware. For example, errors in amounts from the administrative 

data are likely not from the same sources that we think are typical for survey responses. For 

example, regression to the mean, or the tendency to report closer to the mean than one’s actual 

earnings, is commonly cited as an error prevelant in survey literature. However, there are still 

likely to be systematic differences between those for whom data are available and those for 

12The wage and salary earnings recorded in DER stem from both regular sources and irregular sources such as 

tips, to the extent that these irregular earnings are reported on the W-2. 

8
whom they are not. We expect that those with missing PIK information are different than those

without, in ways that are observable to the researcher as well as ways that may not be. There 

is also the possibility of other types of errors such as clerical or matching errors, which may be 

difficult to detect. 

While many respondents are matched to administrative records, there are differences between those that match and those that do not. Bond et al. (2014) find mobility, lower education, 

poor English-speaking ability, nonemployed, noncitizens, nonparticipants in programs and minorities are all predictive of those that are not able to match to administrative records. All of the 

results presented below should be viewed with the caveat that these groups are under-represented 

in the sample we study. We treat those who have a valid PIK but no record in the DER as having 

zero earnings in the administrative data. 

3.3 SIPP and DER Linked Data

From the SIPP, we aggregate reported earnings data from those who report that they were employed from all earnings sources by year. SIPP respondents can report earnings data in a number 

of ways. Those with a job for an employer are encouraged to report in the way that is easiest for 

them to report gross earnings, and these earnings are tied to each job (up to two per wave). Those 

who are self-employed report their earnings as well as their share of profits for the previous four 

months, which are spread equally across the weeks that the business was held. However, if 

someone has only self-employment income of less than $400, we recode that to $0 because 

self-employment earnings under $400 are not required to be reported for tax filing.13 

For those who report moonlighting earnings, we do not know with certainty whether these 

earnings should be classified as wage/salary or self-employment, so we do not treat those 

with moonlighting earnings as self-employed unless they also report a business or have selfemployment earnings in the administrative data. 

Table 1 shows the matched SIPP and DER sample. Person-year observations with either 

zero earnings in both sources or positive earnings in both sources make up 91.9 percent of 

our sample. Table 2 shows the average unconditional differences in SIPP and DER earnings. 

The first column shows the mean of DER-SIPP earnings for the whole sample and the sample 

restricted to those with positive amounts. The second column shows the same information for 

13Losses are included on the 1040, which is outside the scope of this project. Therefore, losses below any earnings 

from work for an employer are also recoded to zero earnings. 

9
the absolute value of the difference. The magnitude of the differences and the characteristics

correlated with larger differences are explored in the next section. 

4 Benchmarking

We begin benchmarking SIPP earnings to DER earnings by describing the data graphically in 

section 4.1. This allows us to compare data from these two sources overall, by proxy response 

status, and by imputation status. We then proceed to regression analysis in section 4.2, which 

produces estimates of average deviations by proxy response status and by imputation status, 

holding observable characteristics constant. We also address the question of which types of 

individuals have survey earnings that deviate more from administrative earnings on average. 

4.1 Graphical Analysis

Before we analyze the relationship between SIPP earnings and DER earnings conditional on 

observables, it is important to begin by describing the unconditional version of this relationship. 

Figure 1 offers a first glance at how well these data sources compare by depicting a scatterplot 

of SIPP earnings by DER earnings. The sample for this figure is all person-years for individuals 

aged 15 and older, who were assigned a PIK, and who were present in the survey for all 12 

months of the year. For ease of visualization, we plot only a random 15 percent of this sample 

and we additionally narrow our focus to the set of individuals with both SIPP earnings and DER 

earnings between $20,000 and $85,000.14 The primary takeaway from this figure is that the 

bulk of the joint distribution lies within a band around the 45-degree line, where DER earnings 

equal SIPP earnings.15 A secondary, yet still salient, inference to draw from this figure is that 

data points outside of this band are more likely to have DER earnings in excess of SIPP earnings 

than vice versa. A final important lesson to take from this figure is that a nontrivial minority of 

data points lie relatively far from the 45-degree line. 

Figure 2 presents the same unconditional relationship between SIPP earnings and DER earnings in a different format. This figure depicts a histogram for the sample of all person-years for 

individuals aged 15 and older, who were assigned a PIK, and who were present in the survey for 

14Note that for this and all other scatterplots we have perturbed each data point by adding spherical random noise 

in order to avoid disclosing federal tax information. We have also examined the uncensored version of each of these 

scatterplots. No systematic difference between these sets of figures was apparent. 

15Note that all comments on scatterplots in this section represent untested observations about our sample. Consequently, the apparent trends that we highlight might not be statistically significant. 

10
all 12 months of the year. Figure 2 places person-years into bins according to the integer portion of the difference between DER earnings and SIPP earnings, in thousands of dollars. Thus, 

person-years in the “-1” bin have SIPP earnings greater than DER earnings by between $1,000 

and $1,999. The person-years in the “0” bin have either SIPP earnings greater than DER earnings by no more than $999 or DER earnings greater than SIPP earnings by no more than $999. 

The person-years in the “1” bin have DER earnings greater than SIPP earnings by between 

$1,000 and $1,999. Person-years with SIPP earnings in excess of DER earnings by $10,000 or 

more are located in the leftmost bin, while person-years with DER earnings in excess of SIPP 

earnings by $10,000 or more are located in the rightmost bin. The same three inferences that 

Figure 1 makes apparent also materialize in Figure 2. First, 69.3 percent of the sample has DER 

earnings within $5,000 of SIPP earnings. Second, of the remaining 30.7 percent of the joint 

distribution, 25.1 percent is characterized by DER earnings in excess of SIPP earnings. Finally, 

18.8 percent of the sample has DER earnings outside of a $10,000 band around SIPP earnings. 

One might wonder how the basic relationship illustrated in Figures 1 and 2 depends on the 

source of the survey earnings data. For example, analysts often hypothesize that proxy response 

affects data quality. If a household member is absent at the interview, SIPP allows another 

household member who is present at the interview to answer on the absentee’s behalf. This 

form of data collection is known as a proxy interview. Proxy respondents might have relatively 

poor knowledge of other household members’ earnings.16 To gauge the degree to which proxy 

interviews influence facets of the unconditional relationship between administrative earnings 

and survey earnings, Figure 3 plots this relationship by proxy interview status. The panel on 

the left displays SIPP earnings and DER earnings for individuals who experienced no months 

of proxy response during the year, while the panel on the right displays the corresponding relationship for individuals who experienced at least one month of proxy response during the 

year. The two portions of Figure 3 generally appear surprisingly comparable given the degree 

of concern that is often expressed about the quality of proxy-reported data. Nevertheless, two 

characteristics of this figure suggest that proxy-reported survey data correspond less well to administrative data than self-reported survey data do. First, while both panels illustrate that the 

bulk of the distribution lies within a band around the 45-degree line, the associated bandwidth 

appears slightly larger for individuals with at least one month of proxy interview during the 

year. Second, DER earnings outside of this band appear to exceed SIPP earnings with higher 

16See Bollinger and Hirsch (2009). 

11
frequency for individuals with at least one month of proxy interview during the year.

Imputation is another common source of survey earnings data which analysts often hypothesize affects data quality. If a survey member declines to provide any data in general or earnings 

data in particular, Census Bureau surveys fill in the missing data in order to salvage that observation for use in analysis. SIPP uses a nonparametric matching algorithm to identify a survey 

member with similar observables who did report earnings, and then assigns the nonrespondent’s 

earnings to be the amount reported by this “donor.” These limitations have given rise to a natural concern that imputed earnings might deviate considerably from true earnings. Consequently, 

analysts commonly exclude imputed earnings from their analyses. 

To gauge the validity of this concern, Figure 4 plots the unconditional relationship between 

administrative earnings and survey earnings by imputation status.17 The panel on the left displays SIPP earnings and DER earnings for individuals who experienced no months of imputed 

earnings during the year, while the panel on the right displays the corresponding relationship for 

individuals who experienced at least one month of imputed earnings during the year. The two 

portions of Figure 4 generally appear surprisingly comparable at relatively low earnings levels 

given the degree of concern that is often expressed about the quality of imputed data. Nevertheless, expanding our focus to higher earnings levels reveals a relatively high frequency of data 

points for which imputed SIPP earnings deviate substantially from DER earnings. 

Figure 5 presents the same unconditional relationship between administrative earnings and 

survey earnings by imputation status in a different format. In particular, this figure separately 

plots the univariate kernel density estimates of the difference between DER earnings and SIPP 

earnings by imputation status. Our sample for this estimation is all person-years for individuals 

aged 15 and older, who were assigned a PIK, who were present in the survey for all 12 months, 

and whose survey earnings differed from administrative earnings by no more than $100,000 in 

absolute value. Points to the right of the “0” mark indicate that DER earnings exceed SIPP 

earnings, while points to the left of the “0” mark indicate that SIPP earnings exceed DER earnings. The red, dashed line in this figure plots the kernel density for individuals who had no 

months of imputed data during the year, while the blue, solid line plots the kernel density for 

individuals who had at least one month of imputed data during the year. Two salient points 

17Like the other scatterplots described in this section, Figure 4 displays data for the sample of all person-years 

aged 15 and older, who were assigned a PIK, and who were present in the survey for all 12 months. This figure 

differs from the other scatterplots in this section, as it includes data points for a random 25 percent rather than a 

random 15 percent of this sample. 

12
emerge from this figure. First, the distribution of DER-SIPP differences for individuals with

nonimputed survey earnings has more mass located around “0” than does the analogous distribution for individuals with imputed survey earnings. Second, the distribution of differences 

for individuals with imputed survey earnings has more mass at relatively large amounts than 

the analogous distribution for individuals with nonimputed survey earnings. This difference is 

especially visible for negative amounts, which suggests that imputed survey earnings are more 

likely to overstate administrative earnings by a relatively large amount than reported survey 

earnings are. These apparent differences are statistically significant, as a Kolmogorov-Smirnov 

test rejects the null hypothesis that the distribution of differences for imputed survey earnings 

equals the corresponding distribution for nonimputed survey earnings.18 

4.2 Regression Analysis

Given the unconditional patterns of DER-SIPP earnings differences established in Section 4.1, 

we now investigate the effect of non-response on annual earnings estimates in greater detail. In 

general, our econometric specification takes the form 

dit = α +βZit +γNRit +uit, (1) 

where dit is the difference in annual earnings reported in SIPP and the DER for respondent i in 

reference year t; Zit is a vector of person-level characteristics in year t, including demographics, 

education, region, English-speaking ability, citizenship status, an indicator for children in the 

family, metropolitan area size, and an indicator of receipt of any means-tested transfer income; 

NRit is an indicator of nonresponse; uit is a normal error term; and α, β, and γ are parameters 

to be estimated. 

In what follows, we define dit as both the raw difference between SIPP and DER annual 

earnings (DERit −SIPPit) and the absolute value of that difference (|DERit −SIPPit|). Positive 

(negative) cofficient estimates in the raw-difference specifications measure the degree to which 

regressors on average are associated with DER earnings in excess of (less than) SIPP earnings, 

whereas the absolute-deviation specifications estimate the overall magnitudes by which the two 

earnings measures differ. Further, we use several proxies for NRit to include various types of 

item and unit nonresponse. 

18The p-value of this test is 0.000. 

13
Many studies, such as Cristia and Schwabish (2009) and Pedace and Bates (2000), frame

this type of analysis as an investigation of measurement error in earnings data, defining administrative earnings as truth. More recent investigations such as Abowd and Stinson (2013) and 

Hokayem et al. (2015) maintain a more agnostic stance on whether reported survey earnings or 

administrative earnings better reflect truth. We adhere to the latter interpretation when reviewing 

the estimates of β from equation 1. As such, our results speak to the implications of increasingly 

prevalent proposals to replace self-reported survey earnings with administrative earnings in an 

attempt to reduce respondent burden. Abowd and Stinson (2013) also argue that imputed survey 

earnings are less reliable than administrative earnings. Based on their evidence, we interpret our 

estimates of γ from equation 1 as being correlated with the average noise in imputed earnings, 

but not as indicating the average noise in imputed earnings exactly. 

Table 3 presents estimates of equation 1 where NRit is measured as having any Census imputation as part of total SIPP annual earnings.19 Nonresponse is estimated to have two very 

different effects depending on how one defines the deviation in earnings. Column 1 lists the 

results when the dependent variable is the raw difference. The estimate of γ shows SIPP earnings in excess of DER earnings by about $1,900 more on average for individuals with at least 

one month of imputed earnings than for those with no earnings imputation. Column 2 lists the 

results when the dependent variable is the absolute difference. The estimate of γ in this column, 

however, indicates that SIPP and DER earnings differ by about $6,900 more on average for 

individuals with at least one month of imputed earnings than for those with no earnings imputation. While DER earnings differ from SIPP earnings significantly on average when we examine 

the absolute difference, these deviations counterbalance to a large extent. Consequently, DER 

earnings are relatively close to SIPP earnings on average when we examine the raw difference. 

Table 3 also describes individuals whose survey earnings differ more from administrative 

earnings, holding nonresponse constant. Like Cristia and Schwabish (2009), we conclude that 

individuals who are male and more educated tend to have administrative earnings in greater 

excess of survey earnings. Column 1 also establishes the correspondence of SIPP and DER 

earnings by race, ethnicity, and nativity. Foreign-born citizens tend to have administrative earn19The incidence of nonresponse and the correspondence between survey and administrative earnings that we document likely depend upon the particulars of SIPP survey data and DER administrative data to a great extent. Consequently, we question how generalizable our results would be to the nation at large. Instead, our population of interest 

is participants in the 2008 SIPP panel who have been linked to DER data. Accordingly, we do not apply sample 

weights to draw inferences about the nation as a whole. Neither do we account for the complex sample design of 

SIPP in estimating standard errors. 

14
ings in greater excess of survey earnings relative to native-born individuals on average. For

populations of particular interest to many SIPP users, individuals who speak a language other 

than English in the home and individuals who receive means-tested transfer income tend to 

have survey earnings in greater excess of administrative earnings on average. Despite these correlations, the relatively low R2 reveals that very little of the variability in the difference between 

SIPP earnings and DER earnings is explained by variability in our observables. Column 2 shows 

that individuals who are men, married, more educated, and parents of children under 18 exhibit 

greater average absolute deviations of survey data from administrative data than other individuals do. On the other hand, individuals who receive means-tested transfer income display smaller 

average absolute deviations of survey data from administrative data than other individuals do. 

Tables 4 and 5 investigate the deviation of imputed survey earnings from administrative 

earnings in greater detail. Specifically, the model estimated for Table 3 imposes the assumption that unit nonrespondents and item nonrespondents experience the same average deviations 

of survey earnings from administrative earnings. Table 4 relaxes this assumption. Column 1 

shows that survey earnings are not statistically different from administrative earnings for unit 

nonrespondents on average relative to individuals who reported earnings. By contrast, SIPP 

earnings exceed DER earnings by about $2,900 more on average for item nonrespondents than 

for individuals who report earnings. Column 2 illustrates that the imputation process serves to 

increase the absolute difference between survey earnings and administrative earnings for both 

unit nonrespondents and item nonrespondents. 

Table 5 further relaxes the assumption that all unit nonrespondents and all item nonrespondents experience the same deviations of survey earnings from administrative earnings. Indeed, 

SIPP pursues different methods of imputing missing earnings data for different types of unit 

and item nonrespondents. Type-Z individuals have all of their labor force data assigned from 

a single, contemporaneous donor record. Labor force data are imputed longitudinally for other 

unit nonrespondents by projecting data from the individual’s previous interview through the 

current interview. Missing earnings items are imputed via a hot deck that assigns earnings to 

be the amount reported by a donor with similar observable characteristics. If earnings on a 

job or business from the prior month are available, earnings for the current month are assigned 

from a donor who had similar earnings last month. However, the item nonrespondent’s earnings 

from the prior month may also have been imputed based on the prior month’s earnings. We 

expect the quality of data imputed according to this technique to depend on whether this string 

15
of imputed data was based on reported data, hot-deck imputed data, or logically imputed data

initially. Finally, logical imputation uses data reported elsewhere in the survey to enforce logical 

consistency and fill in items that are missing. 

Column 1 of Table 5 demonstrates the considerable heterogeneity across types of imputation in the degree of concordance between survey and administrative earnings. Among unit 

nonrespondents, individuals who had no prior wave labor force data available display administrative earnings about $2,240 in greater excess of survey earnings on average than individuals 

who reported earnings data. By contrast, unit nonrespondents who had prior wave labor force 

data available display survey earnings about $3,050 in greater excess of administrative earnings 

on average than individuals who report earnings data. Among item nonrespondents, standard 

cross-sectional hot-deck imputation is associated with the largest estimated magnitude of the 

average DER-SIPP gap. Individuals with at least one month of hot-deck imputation display 

survey earnings about $6,625 in greater excess of administrative earnings on average than individuals who report earnings data. Relative to individuals with no months of imputed earnings 

data during the year, administrative earnings are about $3,515 greater than survey earnings on 

average for individuals who experienced at least one month of hot-deck imputation based on 

prior month data and initially based on earnings that were imputed from a cross-sectional hot 

deck. Some methods of imputation do produce survey earnings that correspond better to administrative earnings. For example, person-years with at least one month of imputation based 

on prior month data and initially based on reported earnings exhibit a similar DER-SIPP gap to 

person-years with no months of imputed earnings. 

Column 2 shows that there is also considerable heterogeneity across types of imputation in 

the degree to which imputation increases the average absolute deviation of survey earnings from 

administrative earnings. Individuals who experienced at least one month of any type of imputation, with the exception of Type-Z imputation, exhibit greater average absolute differences 

than individuals who experience no months of imputation during the year. At one extreme, 

individuals who experience at least one month of logical imputation have survey earnings that 

differ from administrative earnings by about $388 more in absolute value than do individuals 

who experienced no months of imputation. At the opposite extreme, individuals who experienced at least one month of hot-deck imputation based on prior month data and initially based 

on data that were hot-deck imputed cross-sectionally have an average DER-SIPP earnings gap 

that is about $7,322 larger in absolute value than individuals who experienced no months of 

16
imputation.

The specifications presented in Tables 3, 4, and 5 include individuals with no annual earnings in either SIPP or DER in order to describe comprehensively how these data sources compare. Often, analysts consider only the population of workers when studying the determinants 

of earnings. To characterize how SIPP earnings compare to DER earnings for this population, 

Table 6 additionally restricts the estimation sample to individuals who have both positive SIPP 

earnings and positive DER earnings. A comparison of column 1 in Tables 5 and 6 reveals that 

restricting our attention to the sample of workers does affect the qualitative inferences that we 

can draw about how well SIPP and DER compare on average. First, we now fail to reject the 

null hypothesis that the DER-SIPP gap is the same for individuals who experienced at least one 

month of logical imputation and individuals who experienced no months of imputation. Second, 

the coefficient estimate on imputation based on prior-month data and initially based on reported 

data is now statistically signficant. In addition, the coefficient estimate on imputation based on 

prior-month data and initially based on logically imputed data is now statistically significant and 

positive. 

A comparison of column 2 in Tables 5 and 6 reveals that restricting our attention to the 

sample of workers also affects the qualitative inferences that we can draw when the dependent 

variable is the absolute difference between SIPP and DER earnings. We now fail to reject the 

null hypothesis that the average absolute difference between DER and SIPP is different for 

individuals with no months of imputed data and individuals with either any month of logically 

imputed data or any month when all labor force data were imputed longitudinally. On the other 

hand, individuals with any month of Type-Z imputed data now exhibit an absolute difference of 

SIPP and DER earnings which is about $1,313 larger on average relative to individuals with no 

months of imputed data. Finally, individuals who experience any month when earnings are hotdeck imputed cross-sectionally now exhibit an average DER-SIPP gap which is about $1,861 

smaller in absolute than individuals who experience no months of imputed earnings. 

Selecting our sample to include only workers allows us to introduce details about individuals’ labor market situation to the set of explanatory variables Zit. Consequently, we learn that 

self-employed individuals’ earnings in SIPP overstate DER earnings by about $11,713 more 

on average relative to individuals who are not self-employed. This result is consistent with either of two hypotheses. First, surveys might measure self-employed earnings relatively poorly 

as Pedace and Bates (2000) and others argue. Alternatively, tax records might measure self17
employed earnings relatively poorly. Although the average raw difference between SIPP and

DER earnings is quite large for self-employed individuals, column 2 reveals that this estimate 

does not differ statistically in magnitude from the average absolute difference. This suggests that 

SIPP earnings tend to overstate DER earnings for self-employed individuals to a much greater 

extent than DER earnings tend to overstate SIPP earnings. By contrast, the average raw difference is considerably smaller in magnitude than the average absolute difference for many highly 

educated individuals. Indeed, survey earnings and administrative earnings on average disagree 

more in absolute for individuals with a professional degree than they do for self-employed individuals. 

Finally, we explore the extent to which imputed earnings influence the average difference 

between survey earnings and administrative earnings by demographic groups. To that end, the 

specifications presented in Tables 6 and 7 differ only because the specifications in Table 7 also 

restrict the estimation sample to include only individuals who displayed no months of imputed 

survey data during the year. Many of the key observations from Table 6 remain in Table 7, which 

suggests that these patterns do not exist solely in imputed data. Column 1 shows that on average 

individuals who are male; more educated; parents of children under 18; and proxy respondents 

have reported DER earnings in greater excess of SIPP earnings than other individuals do. We 

also find that self-employed individuals have SIPP earnings in greater excess of DER earnings 

on average than workers for an employer do. Column 2 shows that individuals who are male; 

more educated; married; self-employed; parents of children under 18; and proxy respondents 

have greater average absolute differences between SIPP earnings and DER earnings than other 

individuals do. 

5 Predictors of Earnings Nonresponse

In the previous section, we illustrated that imputed labor earnings in survey data resemble labor 

earnings in administrative data worse than reported labor earnings in survey data do on average. 

If this deviation of imputed survey data from administrative data reflects measurement error, one 

natural strategy for mitigating bias is to exclude imputed earnings data from analyses. However, 

this strategy is poorly suited to some analyses, such as when statistical power is an especially 

acute concern. Moreover, Bollinger et al. (2015a,b) argue that nonrespondents disproportionately fall in the tails of the administrative earnings distribution, which suggests that excluding 

18
imputed earnings data might bias estimates to some degree. In this section, we explore the pattern of earnings nonresponse with the aim of understanding the implications of the decision to 

include imputed earnings data. 

To begin, Table 8 summarizes the likelihood of nonresponse, both overall and within demographic groups. For people aged 15 and older, 14.9 percent of person-months exhibit any 

nonresponse. Table 8 also decomposes this overall nonresponse into the unit nonresponse rate 

and the item nonresponse rate. Unit nonresponse occurs for 5.8 percent of person-months for 

people aged 15 and older. For our purposes, unit nonrespondents either do not answer any question in the survey or do not answer any question in the survey about their employment situation. 

All employment data are imputed for these individuals. Workers provided some information 

about their employment situation but did not answer questions about earnings for 15.4 percent 

of person-months according to Table 8. Note that this definition of the item nonresponse rate excludes individuals who did not work and therefore received no earnings questions. Alternatively, 

the item nonresponse rate for earnings is 9.6 percent if we classify nonemployed individuals as 

reporting earnings of $0. 

The nonresponse rates by demographic group listed in Table 8 suggest characteristics of 

individuals who are more likely to lack earnings data on average. To investigate further who 

does not respond, Table 9 presents the results of regressions with the following form: 

NRim = ζ +δXim +ηim (2) 

In equation 2, NRim indicates earnings nonresponse for individual i in month m, Xim is a set of 

observable characteristics, ζ is a constant, and ηim is a normally distributed error term.20 We 

estimate the model via ordinary least squares. 

If earnings data are indeed noisier for imputed values than for reported values conditional 

on observables as the previous section suggests, the β coefficients shed light on who is likely 

to exhibit more mismeasured earnings on average. When considering a regression of a mismeasured dependent variable on a set of explanatory covariates, analysts typically assume that the 

measurement error in the dependent variable is uncorrelated with the explanatory covariates. In 

20Recall that at each interview, individuals provide details about each of the preceding four months. Consequently, 

a natural unit of observation for the regression given by equation 2 is the person-month. However, 97.2% of personwave observations exhibit earnings data that are missing for either no month in that wave or every month in that wave. 

Rather than collapse our regressions to the person-wave level, we use the person-month as the unit of observation 

and account for correlations at the person-wave level by clustering standard errors. 

19
this case, the coefficient estimates on the explanatory variables would be unbiased. If instead the

measurement error in the dependent variable is correlated with the explanatory covariates, the 

coefficient estimates on these explanatory variables would be biased. Thus, the β coefficients 

are our parameters of interest, as they point to coefficient estimates which might be biased in 

models that include observations with imputed earnings.21 

Column 1 of Table 9 contains the estimation results of the model given by equation 2, where 

the dependent variable indicates any earnings nonresponse. Our sample for this regression is 

all individuals aged 15 and older. Many characteristics appear to have a statistically significant relationship to earnings nonresponse owing to our very precisely estimated coefficients. 

Among those characteristics with larger point estimates, we find that individuals who are better 

educated and male are more likely to lack earnings data on average. Household structure also 

appears related to nonresponse, as individuals who reside in larger households or who have no 

children under age 18 are also more likely to lack earnings data. Perhaps because our sample 

treats individuals who report no employment as reporting $0 in earnings, individuals who report any receipt of means-tested transfer programs are 6.4 percentage points less likely to lack 

earnings data. Finally, incorporating information about interview status seems to offer insight 

into the likelihood of earnings nonresponse. The use of proxy interviews appears to be especially effective at inducing responses to earnings questions, as nonresponse is 8.1 percentage 

points less likely for person-months characterized by proxy response. Individuals who leave the 

survey but eventually return are 4.5 percentage points more likely to lack earnings data while 

in the survey. Similarly, individuals who leave the survey and never return are 7.4 percentage 

points more likely to lack earnings data before they leave the survey. These findings suggest that 

efforts to interview households that are marginally attached to the survey might be ineffective at 

reclaiming earnings data, even if they are effective at reclaiming other data. Despite the correlations discussed above, the variation in these observables appears to explain relatively little of 

the variation in nonresponse, as R2 is relatively low for all four columns of Table 9. 

Columns 2 through 4 of Table 9 probe the results presented in column 1 by examining the 

likelihood of unit nonresponse and item nonresponse separately. The dependent variable in 

column 2 indicates unit nonresponse. Our sample for this regression is all individuals aged 15 

and older. The pattern of unit nonresponse in this sample strongly resembles the pattern of any 

21For this purpose, the correlations that we document in Table 9 need not bear a causal interpretation. We make 

no claims about which mechanism mediates the correlations between these observable characteristics and earnings 

nonresponse. 

20
earnings nonresponse documented in column 1.22 For example, unit nonrespondents in SIPP 

data are more likely to be male and residing in larger households. However, the pattern of unit 

nonresponse does deviate from the pattern of any nonresponse in several notable ways. First, 

unit nonresponse appears to be nonmonotonic rather than generally increasing in education. 

Unit nonrespondents in SIPP data are more likely to have a high school degree only or an 

Associate’s degree only. Second, individuals who received any means-tested transfer payments 

are only 0.7 percentage points less likely to be unit nonrespondents. 

Column 3 of Table 9 lists the results when the dependent variable indicates item nonresponse. Our sample for this regression is all individuals aged 15 and older who provided at 

least some labor force data. The pattern of item nonresponse in this sample strongly resembles 

the pattern of any earnings nonresponse along some dimensions. For example, individuals who 

are male, better educated, and non-recipients of means-tested programs are more likely to respond to some labor force questions but not to earnings questions. However, the pattern of item 

nonresponse does deviate from the pattern of any nonresponse in several notable ways. First, 

household structure appears to contribute little to the pattern of any nonresponse established in 

column 1. Individuals residing in larger households do not appear more likely to suffer from 

item nonresponse, while individuals with no children are only 1.7 percentage points more likely 

to suffer from item nonresponse. Additionally, the details of survey participation appear to 

predict item nonresponse differently than they predict earnings nonresponse overall. While column 1 of this table showed that proxy responses are effective at reducing earnings nonresponse 

overall, column 3 shows that proxy respondents are 3.7 percentage points more likely than own 

responses to provide some labor force data but no earnings data. Similarly, eventual attritors are 

only 2.0 percentage points more likely to be item nonrespondents while in the survey. 

Analysts often restrict their estimation samples to include only employed individuals. To 

22We recommend caution when interpreting estimates of models that feature unit nonresponse as the dependent 

variable. Bollinger et al. (2015b) explicitly exclude unit nonrespondents from their analysis because neither earnings 

nor many basic observable characteristics are observed for a unit nonrespondent in CPS ASEC data. Instead, the 

missing data are replaced with data provided by a single donor with observable characteristics similar to those characteristics that we can observe for the unit nonrespondent. Several facets of SIPP mitigate the impact of this problem 

for our analysis. First, some individuals whom we treat as unit nonrespondents did provide some demographic data 

despite declining to provide any labor force data. Second, for the remaining unit nonrespondents, U.S. Census Bureau (2001) details how the longitudinal nature of SIPP enables the imputation of some characteristics such as sex, 

race, and age by inferring these characteristics from previous responses. For time-varying variables, we conservatively do not interpret the coefficient estimates in column 2 of Table 9 as indicating characteristics of individuals who 

are more likely to decline to provide all labor force data. We also advise caution in interpreting the results presented 

in column 1 of Table 9, as the indicator for any nonresponse takes value 1 for unit nonrespondents. Nevertheless, 

these analyses remain insightful for an investigation of the (potentially imputed) characteristics of individuals whose 

survey earnings resemble administrative earnings less closely on average. 

21
study the estimated patterns of nonresponse in this population, the specification in column 4

of Table 9 builds on the one in column 3 by excluding individuals who report no employment 

in SIPP data. This sample selection criterion enables the inclusion of covariates that describe 

respondents’ jobs and businesses. Redefining the sample changes the inferences from column 3 

in three notable ways. First, better educated individuals are no longer more likely to be earnings 

nonrespondents. Second, Black, non-Hispanics are now the most likely racial and ethnic group 

to be item nonrespondents. Third, program recipients are no longer less likely to be earnings 

nonrespondents, which suggests that they are relatively more willing to report no employment 

than they are to report earnings conditional on employment. Including characteristics of jobs 

and businesses among the covariates allows us to draw several new inferences about who is 

more likely to provide some labor force data but still lack earnings data. The most stark finding 

is that self-employed individuals are 19.8 percentage points more likely than workers for an employer to be item nonrespondents. Another marked effect is that “contingent” workers are 12.9 

percentage points more likely to be item nonrespondents than individuals who have a regular 

work arrangement. Third, more weeks worked per month or more worked hours per week are 

both positively associated with item nonresponse. Finally, individuals who stopped work during 

a particular wave are more likely to be item nonrespondents. 

Item nonresponse as referenced in columns 3 and 4 of Table 9 may come from various 

sources. We consider total earnings to be imputed if any of its components is missing. Table 

10 investigates the pattern of item nonresponse by considering how the relationship between 

observable covariates and item nonresponse varies across earnings sources.23 To that end, Table 

10 presents the results of regressions with the following form: 

NRi jm = ζ +βXi jm +ηi jm (3) 

In the equation above, NRi jm indicates earnings nonresponse for individual i working at job or 

business j in month m, Xi jm is a set of observable characteristics that includes some details of 

the job or business, and ηi jm is a normally distributed error term.24 We estimate the model via 

23Note that Table 10 does not report results when the dependent variable indicates item nonresponse for questions 

about earnings from moonlighting or severance pay. We also estimated models with these dependent variables. 

However, we did not find these results to be insightful, perhaps due to the relatively low rates at which sample 

members receive these types of pay. We therefore exclude them from our discussion. Estimates are available upon 

request. 

24Recall that at each interview individuals provide earnings on up to two jobs for an employer and up to two selfemployed businesses during each of the preceding four months. Consequently, a natural unit of observation for the 

regression given by equation 3 is the person-job-month or person-business-month. However, 83.2% of person-month 

22
ordinary least squares.

One central motivation for running person-job-month and person-business-month regressions is improving the estimated coefficients on job characteristics. We considered employment 

across all jobs when defining these covariates at the person-month level for Table 9. For example, the class of worker variables in the person-month level regression indicate status on any job 

or business, while weeks worked and hours worked in the person-month level regression measure time worked on all jobs and businesses combined. However, to the extent that individuals 

provide earnings data on one job or business and decline to provide earnings data on another 

job or business in the same month, we expect this strategy to yield coefficient estimates that are 

difficult to interpret. In particular, the mechanism that links hours worked on a job to earnings 

nonresponse on that job might differ from the mechanism that links earnings nonresponse on 

that job to hours worked on other jobs or businesses. By running regressions at the person-jobmonth level and person-business-month level, we can separate out any effect that characteristics 

of other jobs or businesses might have on earnings nonresponse. 

Column 1 of Table 10 reports the results when the dependent variable indicates nonresponse 

to earnings questions about jobs for an employer. The estimation sample is the set of all individuals aged 15 and older, who worked on a noncontingent basis at a job for an employer, and who 

provided some information about their labor market situation. The results in column 1 of Table 

10 strongly resemble the results in column 4 of Table 9, as a heavy majority of earnings comes 

from jobs for an employer. Nevertheless, analyzing earnings nonresponse at the person-jobmonth level rather than the person-month level does yield some different inferences, primarily 

for the coefficients on job characteristics. For example, while column 4 of Table 9 reports that 

individuals who work more weeks per month or more hours per week are more likely to be 

item nonrespondents, column 1 of Table 10 suggests that individuals who work more weeks per 

month or more hours per week are less likely to be item nonrespondents. A final difference 

between column 4 of Table 9 and column 1 of Table 10 is that by restricting attention to jobs 

for an employer we can investigate whether workers who are paid by the hour are differentially 

likely to be item nonrespondents.25 We find that these workers are 1.9 percentage points less 

observations on which individuals work two jobs for an employer exhibit earnings data that are missing for either no 

job in that month or both jobs in that month, and 74.4% of person-month observations on which individuals work 

two self-employed businesses exhibit corresponding behavior. Rather than collapse our regressions to the personmonth level, we use person-job-month or person-business-month as the unit of observation and account for arbitrary 

correlations at the person-wave level when constructing standard errors. 

25SIPP does not ask respondents if they are paid by the hour on self-employed jobs. 

23
likely to be item nonrespondents, which is consistent with the hypothesis that gross hourly pay

rates are more salient for survey respondents than other types of pay. 

While column 4 of Table 9 indicates that contingent workers are considerably more likely to 

be item nonrespondents, column 1 of Table 10 omits this covariate. Due to the irregular nature 

of contingent work, SIPP attempts to reduce these respondents’ burden by skipping questions 

on dates worked and class of worker status. To understand whether contingent workers are 

more likely to lack data on earnings for jobs at an employer, column 2 of Table 10 presents 

the results of a model that omits the class of worker indicators, the weeks worked variable, and 

the stopped work indicator and includes a contingent worker indicator. Thus, the estimation 

sample is the set of all individuals aged 15 and older, who worked at a job for an employer, and 

who provided some information about their labor market situation. We conclude that contingent 

workers are 5.8 percentage points more likely than workers with a regular arrangement to lack 

data on earnings for jobs at an employer. 

Column 3 reports the results when the dependent variable indicates nonresponse to earnings 

items about self-employed businesses. The estimation sample is the set of all individuals aged 15 

and older, who worked at a self-employed business, and who provided some information about 

their labor market situation. In general, the coefficient estimates for this model are noisier than 

the corresponding estimates in columns 1 and 2 due to the lower prevalence of self-employed 

businesses. Restricting our attention to earnings from businesses also reveals some inferences 

that were not apparent when we considered earnings from jobs for an employer. For example, 

individuals who work more weeks per month or more hours per week are more likely to lack 

earnings data on self-employed businesses, while columns 1 and 2 show that they are no more 

likely to lack earnings data on jobs for an employer. Finally, by restricting attention to selfemployed businesses we can analyze whether workers who receive different types of business 

earnings are differentially likely to be item nonrespondents. Workers who draw a regular salary 

from their business are 2.5 percentage points less likely and workers who receive some nonsalary 

income out of the money that the business brings in are 21.8 percentage points more likely to 

lack data on earnings from a self-employed business. These findings suggest that regular salary 

income is more salient for business owners. By contrast, irregular nonsalary income appears to 

be less salient for business owners. 

So far, this section has discussed the correlates of nonresponse, thereby pointing to coefficient estimates that are more likely on average to be biased in earnings regressions as a result 

24
of mismeasured earnings. One natural strategy to mitigate this bias would be to omit observations with imputed earnings. However, this strategy assumes that earnings nonresponse is 

ignorable. In an attempt to test this assumption, we include administrative earnings information 

in the model given by equation 2. If we reject the null hypothesis that earnings nonresponse 

is unrelated to true earnings conditional on covariates, then earnings nonresponse is not ignorable. While we do not view administrative earnings as truth per se, we argue that administrative 

earnings are closely correlated with true earnings. 

Column 1 of Table 11 contains the estimation results of this model, where the dependent 

variable indicates any earnings nonresponse. Our sample for this regression is all individuals 

aged 15 and older who were assigned a PIK. We also exclude all person-month observations 

during a year when that individual leaves the sample either temporarily or permanently. These 

additional regressors and sample selection criteria leave many results unchanged qualitatively 

from column 1 of Table 9. For example, the relatively low R2 suggests that much of the variation in earnings nonresponse remains unexplained, even though many of the same observables 

appear to be correlated with nonresponse. Nevertheless, some inferences do appear different 

in column 1 of Table 11 relative to column 1 of Table 9. First, some covariates no longer appear correlated with earnings nonresponse, including: number of family members; the Asian, 

non-Hispanic indicator; the White, non-Hispanic indicator; and the indicator that the respondent does not speak English in the home. Additionally, some covariate estimates appear to have 

reversed sign, including the indicator for a change in family composition and the associate’s 

degree indicator. As a result of these differences, the generally increasing relationship between 

earnings nonresponse and education from Table 9 is no longer apparent. Finally, many coefficient estimates are attenuated in column 1 of Table 11 relative to column 1 of Table 9, including: 

number of household members; the female indicator; the proxy response indicator; the attritor 

indicator; the indicator for any children under 18; and the indicator for any means-tested transfer 

income. 

The coefficient estimates based on administrative earnings data that are reported in column 

1 of Table 11 reveal several interesting patterns about the ignorability of earnings nonresponse. 

First, individuals who work for more employers are more likely to lack earnings data. Second, 

individuals with positive administrative earnings are 5.3 percentage points more likely to suffer 

from missing earnings data than individuals with no administrative earnings records. Among 

respondents with positive administrative earnings, those in the bottom and fourth quintiles are 

25
0.6 and 1.0 percentage points less likely to exhibit earnings nonresponse than individuals in the

middle quintile, respectively. Those in the second quintile are 1.5 percentage points more likely 

to display earnings nonresponse than individuals in the middle quintile. This pattern poses a 

stark contrast to the U-shaped pattern of earnings nonresponse documented for workers who 

are item nonrespondents by Bollinger et al. (2015b). To explore the sources of this difference, 

we analyze how administrative earnings are related to unit nonresponse and item nonresponse 

separately. 

Column 2 of Table 11 contains the estimation results of this model, where the dependent 

variable indicates unit earnings nonresponse. Our sample for this regression is all individuals 

aged 15 and older who were assigned a PIK. We also exclude all person-month observations 

during a year when that individual leaves the sample either temporarily or permanently. There 

are two salient differences between the findings presented in column 2 and the corresponding 

figures in column 1. First, while individuals with positive administrative earnings are more 

likely to decline to provide any labor force data, the differential of 1.0 percentage points is 

smaller than the corresponding differential in the likelihood of any nonresponse.26 Second, 

the likelihood of unit nonresponse displays a relatively weak pattern over the administrative 

earnings distribution. Individuals in the bottom two quintiles are 0.4 percentage points and 0.5 

percentage points less likely to lack all labor force data than those in the middle quintile. By 

contrast, individuals in the top two quintiles are no more or less likely to lack all labor force data 

than those in the middle quintile.27 The relatively weak relationship between unit nonresponse 

and administrative earnings conditional on covariates suggests that unit earnings nonresponse 

might be roughly close to ignorable.28 

Column 3 of Table 11 lists the results when the dependent variable indicates item nonresponse. Our sample for this regression is all individuals aged 15 and older, who provided at 

least some labor force data, and who were assigned a PIK. We also exclude all person-month 

26Note that we observe unit nonrespondents’ administrative earnings rather than their donors’ administrative earnings. Nevertheless, we suggest caution in interpreting the estimates reported in column 2. As stated previously, some 

of the explanatory covariates in the model do not represent the characteristics of unit nonrespondents but rather the 

characteristics of their donors. 

27While Bollinger et al. (2015b) explicitly exclude unit nonrespondents from their primary analysis, they do study 

the pattern of unit nonresponse over the administrative earnings distribution for men and women. They find that unit 

nonrespondents have lower and more dispersed administrative earnings than those who provide at least some CPS 

ASEC data. By contrast, we find that individuals with low administrative earnings are less likely to suffer from unit 

nonresponse. 

28This conclusion corroborates the findings of Bee et al. (2015). They exploit 1040 records matched to CPS ASEC 

unit nonrespondents to show that the income distribution is quite similar for those who participate in the supplement 

and those who do not participate. 

26
observations during a year when that individual leaves the sample either temporarily or permanently. Column 3 of Table 11 shows that individuals with no administrative earnings are 

4.4 percentage points less likely to decline to provide any earnings data. Among individuals 

with positive administrative earnings, the pattern of item nonresponse resembles the U-shaped 

pattern documented by Bollinger et al. (2015b). Sample members in the second administrative 

earnings quintile are 2.0 percentage points more likely to be item nonrespondents than those in 

the middle quintile, while sample members in the fourth administrative earnings quintile are 0.9 

percentage points more likely to be item nonrespondents than those in the middle quintile. However, we find no significant difference between the average tendency to lack earnings data for 

individuals in the bottom or top quintiles and those in the middle quintile. By contrast, Bollinger 

et al. (2015b) showed that individuals in the tails of the administrative earnings distribution are 

more likely to lack earnings data than those in the middle of this distribution. 

One potential explanation for these different patterns of nonresponse at the bottom and top 

of the administrative earnings distribution is that our estimation sample in column 3 included 

individuals with no survey earnings, while Bollinger et al. (2015b) considered only those with 

positive survey earnings. To evaluate the influence of SIPP respondents who report no employment, the estimation sample in column 4 further restricts the sample to only SIPP employed 

individuals. This additional sample selection criterion impacts the inferences from column 3 in 

two important ways. First, the sign of the coefficient on the indicator of positive administrative 

earnings surprisingly reverses. Individuals with any administrative earnings record are now 7.8 

percentage points less likely to lack earnings data. Second, the U-shaped pattern of item nonresponse over the administrative earnings distribution that Bollinger et al. (2015b) illustrated for 

CPS ASEC now becomes apparent in SIPP data. Respondents in the bottom, second, and top 

administrative income quintiles are 4.5, 1.8, and 0.4 percentage points more likely to be item 

nonrespondents than those in the middle quintile, respectively. On the other hand, individuals in 

the fourth quintile display no significantly different tendency to respond to earnings items than 

those in the middle quintile. 

6 Implications for Estimates of the Earnings Structure

Now that we have characterized how the relationship between survey earnings and administrative earnings varies by whether survey earnings were imputed and who is more likely to have 

27
imputed data, one might wonder what implications these relationships have for estimates that

are of broad interest to labor economists. We approach this question by estimating various aspects of the earnings structure, including the gender earnings gap, the Black-White earnings 

gap, and the return to education which we estimate via a Mincer regression. We estimate each 

regression for four different dependent variables: SIPP earnings, DER earnings, only reported 

SIPP earnings, and a hybrid of SIPP and DER earnings. This DER-SIPP hybrid is defined as 

SIPP earnings for individuals who experienced no months of imputed survey earnings during the 

year and DER earnings for individuals who experienced at least one month of imputed survey 

earnings during the year. 

Table 12 lists the estimates of the gender earnings gap, Table 13 lists the estimates of the 

Black-White earnings gap, and Table 14 lists the estimates of the Mincer regression. In each 

table, the dependent variable is SIPP earnings, DER earnings, only reported SIPP earnings, and 

the DER-SIPP hybrid in columns 1, 2, 3, and 4, respectively. The estimation sample in columns 

1, 2, and 4 includes all person-years for individuals aged 15 and older, who were assigned a PIK, 

who were present in the survey for all 12 months of the year, and who displayed both positive 

SIPP earnings and positive DER earnings. The estimation sample in column 3 also excludes 

person-year observations that experienced at least one month of imputed survey earnings during 

the year. Aside from the covariates listed in Tables 12, 13, and 14, the set of regressors includes 

only a constant. 

The results in Tables 12, 13, and 14 offer insight into four issues that are relevant for data 

users. First, comparing the estimates in columns 1 and 4 allows for a test of the impact of any 

additional noise in imputed survey earnings relative to administrative earnings. If this noise 

does indeed bias regression coefficients, we will reject the null hypothesis that SIPP earnings 

and the DER-SIPP hybid yield equal coefficient estimates as dependent variables. Second, 

comparing the estimates in columns 1 and 3 allows us to test the efficacy of the strategy of 

excluding observations with imputed earnings. If any additional noise in imputed earnings does 

indeed bias regression coefficients, we will reject the null hypothesis that all SIPP earnings and 

only reported SIPP earnings yield equal coefficient estimates. The strategy of restricting the 

estimation sample to only individuals with reported earnings assumes that earnings response 

bias is ignorable. Comparing the estimates in columns 3 and 4 allows for a test of the validity of 

this assumption. If earnings response bias is indeed ignorable, we will be unable to reject the null 

hypothesis that the DER-SIPP hybrid and only reported SIPP earnings yield equal coefficient 

28
estimates. Finally, comparing the estimates in columns 2 and 4 allows for a test of the impact of

any additional noise in reported survey earnings relative to administrative earnings. If this noise 

does indeed bias regression coefficients, we will reject the null hypothesis that DER earnings 

and the DER-SIPP hybrid yield equal coefficient estimates. 

Table 12 details how estimates of the gender earnings gap depend on the source of earnings 

data. When the dependent variable is both reported and imputed SIPP earnings, we see that 

women earn approximately $12,035 less than men on average. A comparison of the estimates 

in columns 1 and 4 reveals that any additional noise that is present in imputed survey earnings 

and not in these individuals’ administrative earnings has no statistically significant impact on 

the estimated gender earnings gap. However, a comparison of the estimates in columns 1 and 

3 reveals that the strategy of dropping imputed earnings observations yields a larger estimated 

gender earnings gap. This result suggests that any additional noise in imputed survey earnings 

relative to reported survey earnings attenuates the estimated gender earnings gap. Nevertheless, 

a comparison of the estimates in columns 3 and 4 shows evidence that including administrative 

earnings for survey earnings nonrespondents does not significantly impact our estimate of the 

gender earnings gap. Thus, we join Bollinger and Hirsch (2013) and Bollinger et al. (2015b) 

in concluding that earnings response bias is ignorable for an estimate of average earnings differences even though nonresponse does appear to depend on administrative earnings in the tails 

of the distribution. Finally, comparing the estimates in columns 2 and 4 suggests that replacing reported survey earnings with administrative earnings yields a larger estimate of the gender 

earnings gap. This finding does not support the claim of Abowd and Stinson (2013) that reported 

survey earnings are similar to administrative earnings in reliability. 

Table 13 describes how estimates of the Black-White earnings gap depend on the source of 

earnings data. When the dependent variable is both reported and imputed SIPP earnings, we see 

in column 1 that White workers earn approximately $8,173 more on average than the omitted 

race group.29 The implied average Black-White earnings gap is about $9,278. A comparison 

of the estimates in columns 1 and 4 reveals that any additional noise that is present in imputed 

survey earnings and not in these individuals’ administrative earnings has no statistically significant impact on the estimated Black-White earnings gap. The implied Black-White earnings 

29SIPP gives respondents the option of reporting more than one race. The indicators in this table define racial 

groups to include individuals who reported only one race. Individuals who reported multiple races are included in the 

omitted group. Note that the race indicators in this model are not interacted with ethnicity indicators. Consequently, 

the set of workers who report Black alone may include both Hispanic and non-Hispanic individuals. 

29
gap grows by about $325, so we fail to reject the null hypothesis of an equal Black-White earnings gap across models that use all SIPP earnings and the DER-SIPP earnings hybrid as the 

dependent variable. A comparison of the estimates in columns 1 and 3 reveals that the strategy 

of dropping imputed earnings observations has no statistically significant impact on the estimated Black-White earnings gap. While the point estimate on Black alone grows in magnitude, 

the implied point estimate of the Black-White earnings gap grows by a statistically insignificant $1,281. A comparison of the estimates in columns 3 and 4 shows evidence that including 

administrative earnings for survey earnings nonrespondents does not significantly impact our 

estimate of the Black-White earnings gap. Again, we conclude that earnings response bias is 

ignorable for an estimate of average earnings differences by race even though nonresponse does 

appear to depend on administrative earnings in the tails of the distribution. Finally, comparing 

the estimates in columns 2 and 4 suggests that replacing reported survey earnings with administrative earnings does not significantly impact our estimate of the Black-White earnings gap. 

This strategy reduces our point estimate of the gap by only about $177. 

Finally, Table 14 reports how estimates of the returns to education and potential experience 

depend on the source of earnings data. Column 1 lists the coefficient estimates of a basic Mincer 

regression when the dependent variable is both reported and imputed SIPP earnings. Each additional year of education delivers about $4,953 in additional earnings on average. The first year 

of potential experience bring about $1,899 in additional earnings on average, although the average earnings gain that comes from each subsequent year of potential experience declines over 

time. A comparison of the estimates in columns 1 and 4 reveals mixed evidence about the impact 

of any additional noise that is present in imputed survey earnings and not in these individuals’ 

administrative earnings. On one hand, we fail to reject the null hypothesis of no difference in 

the estimated return to education across models that use all SIPP earnings and the DER-SIPP 

earnings hybrid as the dependent variable. On the other hand, we do reject the null hypothesis of 

no difference in the estimated return to potential experience across these models. A comparison 

of the estimates in columns 1 and 3 suggests that the strategy of dropping observations with 

imputed earnings does indeed increase the magnitude of the estimated returns to education and 

potential experience. A comparison of the estimates in columns 3 and 4 shows that including administrative earnings for survey earnings nonrespondents does significantly impact our estimate 

of the return to education, yet not our estimate of the return to potential experience. This finding suggests that although earnings nonresponse does appear related to administrative earnings 

30
in the tails of the administrative earnings distribution, earnings response bias is ignorable for 

an estimate of the average return to potential experience in a Mincer regression. Nevertheless, 

this pattern of earnings nonresponse does imply non-ignorable response bias for an estimate of 

the average return to education. Finally, comparing the estimates in columns 2 and 4 suggests 

that replacing reported survey earnings with administrative earnings does not significantly impact our estimates of the return to education, while this strategy does significantly affect our 

estimates of the return to potential experience. 

7 Conclusion

In this paper, we explore the impact of the decision to include observations with imputed earnings in regressions. Our findings corroborate many conclusions of the existing literature. We 

join Cristia and Schwabish (2009) in concluding that men and better educated individuals have a 

greater difference in survey and administrative earnings. We echo Abowd and Stinson (2013) in 

showing that earnings for individuals with at least one month imputed compare less favorably to 

administrative data than for individuals with no months imputed. After controlling for observables, individuals with any nonresponse display a DER-SIPP gap that is about $1,920 smaller on 

average than respondents, while the absolute value of this gap is about $6,898 larger on average 

for individuals with any nonresponse. When attempting to understand the implications of this 

result for regression estimates, our evidence supports Hokayem et al. (2014) who find that respondents who are self-employed or Black, non-Hispanic are less likely to respond to earnings 

questions. We document a U-shaped pattern of earnings nonresponse over the administrative 

earnings distribution among survey members who worked, similar to Bollinger et al. (2015b). 

Finally, our result that earnings unit nonresponse is roughly close to ignorable is consistent with 

results of Bee et al. (2015). 

We also contribute to the literature by documenting relationships that have not yet been 

established to our knowledge. First, there appears to be considerable heterogeneity in the proximity of survey earnings to administrative earnings across different imputation methodologies. 

For example, survey earnings that are imputed based on last month’s survey earnings deviate 

from administrative earnings by substantially more than other methods if this imputation was 

initially based on an imputed earnings value. Second, individuals who receive means-tested 

transfer income have survey earnings that deviate from administrative earnings by less than in31
dividuals who do not receive transfer income. Additionally, we report that individuals who ever

leave the survey either temporarily or permanently are more likely to be earnings nonrespondents while in the survey. Next, the details of how sample members are paid appear to matter for 

whether they respond to earnings questions. Individuals who receive regular hourly or salaried 

pay appear more likely to respond to these questions. Similarly, those who receive more erratic 

pay, such as contingent workers and business owners who receive nonsalary income, appear less 

likely to respond to earnings questions. Next, we show evidence of a novel pattern of nonresponse over the administrative earnings distribution. Bollinger et al. (2015b) show a U-shaped 

pattern of nonresponse over this distribution, excluding from their analysis unit nonrespondents 

and individuals with either no survey earnings or no administrative earnings. By contrast, we 

find that individuals with no administrative earnings are 5.3 percentage points less likely to lack 

reported earnings data. Similarly, when we include individuals who have no survey earnings in 

our nonresponse regressions, individuals whose administrative earnings lie in the bottom quintile of the distribution appear no more likely to lack survey earnings data than individuals in 

the middle quintile of this distribution. When we include unit nonrespondents, the U-shaped 

pattern of nonresponse disappears as the likelihood of nonresponse is smaller for individuals in 

the bottom quintile of the earnings distribution than for individuals in the middle quintile of this 

distribution. Finally, we show that earnings response bias and any additional measurement error 

that might exist in survey earnings relative to administrative earnings do affect some estimates 

of key aspects of the earnings structure. 

Our paper offers practical insight for analysts studying earnings. Often analysts include 

observations with imputed earnings in their analyses, for example when sample size is an especially acute concern. Given the conclusion that imputed survey earnings are less reliable than 

administrative earnings, estimated regression coefficients on observable characteristics that are 

correlated with earnings nonresponse are susceptible to bias. Nevertheless, observable characteristics explain relatively little of the variation in nonresponse, so the risk of bias may be small. 

In an attempt to mitigate bias, many researchers exclude observations with imputed earnings 

from analyses. This tactic will yield unbiased estimates if the likelihood of earnings nonresponse is unrelated to earnings itself. However, our investigation reveals a complex relationship 

between earnings nonresponse and administrative earnings. Among a sample of workers, we 

report that earnings response bias due to item nonresponse is nonignorable in the tails of the 

distribution. Bollinger et al. (2015a) argue that this condition biases inequality estimates. When 

32
we also include nonemployed individuals and unit nonrespondents, we find that earnings nonresponse remains correlated with administrative earnings, although the pattern differs. Individuals 

with no administrative earnings are less likely to exhibit any nonresponse. This relationship in 

principle could bias upwards estimates of means-tested program eligibility. Among individuals with positive administrative earnings, those in the bottom quintile are less likely to exhibit 

any nonresponse than those in the middle quintile. Thus, including unit nonrespondents could 

mitigate the earnings response bias in the left tail of the administrative earnings distribution 

documented by Bollinger et al. (2015b). 

Our investigation also points to the potential consequences of the increasingly prevalent proposals to utilize administrative records more extensively in the production of household survey 

data.30 In the extreme, these proposals call for data producers to reduce respondent burden 

by removing earnings questions from surveys and to replace self-reported earnings data with 

transformed administrative earnings data. Our investigation documents that this strategy would 

alter self-reported earnings data considerably on average for individuals who are male; married; 

more educated; self-employed; Black, non-Hispanic; and Asian, non-Hispanic. Abowd and 

Stinson (2013) and Bollinger et al. (2015b) argue that this change would offer a different measure of earnings, though not necessarily an improved measure. We also show that the strategy 

of replacing survey earnings data with administrative earnings data would alter the data considerably on average for individuals who experienced at least one month of imputed earnings 

during the year. The results of Abowd and Stinson (2013) would suggest that this change represents an improvement, underscoring the need for future research to explore how administrative 

earnings records might be incorporated into the process of imputing earnings data for public 

use. Accordingly, we reject the null hypothesis that replacing imputed SIPP earnings with DER 

earnings leaves several key estimates of the earnings structure unchanged. Nevertheless, this 

strategy does not appear to alter other key estimates of the earnings structure significantly. 

The scope for future work remains tremendous given several changes to SIPP earnings data 

collection implemented by the forthcoming 2014 panel. First, in an effort to reduce the cost 

of the survey, SIPP reduced the frequency with which it conducts interviews. Survey participants in the 2008 panel were interviewed three times per year, each time providing information 

about the preceding four months. One commonly cited advantage of the 2008 SIPP panel is 

30See Meyer et al. (2015) for one recent, prominent example that proposes more comprehensive use of administrative data to improve survey data on program participation and income from programs and to mitigate the increasing 

trend of nonresponse. 

33
the relatively high interview frequency, which might serve to reduce recall bias in earnings reports. By contrast, survey participants in the 2014 panel are interviewed once per year, each 

time providing information about the preceding calendar year. Second, earnings questions in 

the 2008 panel primed respondents by reminding them of the amount reported at the last interview and offered respondents the opportunity to report the same amount at the current interview. 

By contrast, earnings questions in the 2014 panel neither “feed back” the amount that was reported at the last interview nor offer the opportunity to report no change in earnings since the 

last interview. Similarly, the 2008 panel imputed some missing earnings data conditional on 

earnings in the previous wave, while this option was not available for the 2014 panel. Third, 

the 2014 SIPP panel requires some individuals to aggregate earnings amounts manually before 

reporting, whereas the 2008 panel allowed these individuals to report each payment received. 

For example, individuals who received tips must report this income as a monthly amount in the 

2014 panel, while the 2008 panel allowed them to report up to five separate payments received 

in each month. Similarly, individuals who receive highly variable pay other than tips, bonuses, 

commisions, and overtime must report this income as a monthly average that pertains to multiple months potentially in the 2014 panel. The 2008 panel allowed these sample members to 

report up to five separate payments received in each month. Fourth, the 2008 SIPP panel attempted to minimize earnings nonresponse by offering some sample members who decline to 

provide earnings data different methods of reporting this same data. On the other hand, the 2014 

SIPP panel pursues a different approach by offering some individuals the opportunity to report 

earnings in a range when they initially decline to provide earnings data. Fifth, in an attempt to 

reduce measurement error during the interview the 2008 SIPP panel converted reported hourly 

and bi-weekly amounts into more salient amounts that respondents verified and had the opportunity to correct.31 In an attempt to reduce respondent burden, the 2014 SIPP panel does not 

convert hourly and bi-weekly reports into more salient amounts for verification.32 Finally, the 

2014 panel offers respondents more flexibility in accounting periods when reporting earnings in 

the hope of inducing individuals to report in the most accurate and least burdensome way. The 

2014 panel will offer a natural experiment to evaluate whether these changes increase earnings 

volatility, the deviation of survey earnings from administrative earnings, or the likelihood of 

31For example, individuals who reported an hourly amount were prompted with a bi-weekly paycheck amount, 

and individuals who reported a bi-weekly amount were prompted with a monthly take home pay amount. 

32The 2014 panel does ask respondents to verify hourly or bi-weekly amounts that imply unusually large pay 

amounts assuming that this pay rate is received by a full-time, full-year worker. 

34
response.

35 
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37
Table 1. The Presence of SIPP Earnings and DER Earnings

No DER earnings Positive DER earnings Total 

No SIPP earnings 

Positive SIPP earnings 

56,410 

4,745 

8,216 

90,394 

64,626 

95,139 

Total 61,155 98,610 159,765 

Source: Authors’ calculation from the 2008 panel of the Survey of Income and Program Participation, Waves 1 through 14 and the Social Security Administration’s Detailed Earnings Record, 

calendar years 2009 through 2012. 

Note: Sample for this table is all person-years for people aged 15 and older, who were assigned a 

PIK, and who were present in the survey for all 12 months of the year. This table counts the number 

of unweighted person-year cases exhibiting positive SIPP earnings and positive DER earnings. 

38
Table 2. The Average Deviation of SIPP Earnings and DER Earnings

(1) (2) 

DER-SIPP |DER-SIPP| Observations 

Including Zero Earners $969 $6,277 158,168 

Excluding Zero Earners $1,928 $10,429 89,418 

Source: Authors’ calculation from the 2008 panel of the Survey of Income and Program Participation, Waves 1 through 14 and the Social Security Administration’s Detailed Earnings Record, calendar years 2009 through 2012. 

Note: Sample for this table is all person-years for people aged 15 and older, who were 

assigned a PIK, who were present in the survey for all 12 months of the year, and 

whose absolute deviation of survey from administrative earnings was in the bottom 99 

percent of this distribution. The sample in the second row additionally exclude personyears that displayed either zero SIPP earnings or zero DER earnings. The estimates in 

column 1 are sample means of the average raw difference between DER earnings and 

SIPP earnings. The estimates in column 2 are sample means of the average absolute 

difference between DER earnings and SIPP earnings. 

39
20 40 60 80 SIPP Annual Earnings

20 40 60 80

DER Annual Earnings

Source: Authors' calculation from the 2008 panel of the Survey of Income and Program

Participation, Waves 1 through 14 and the Social Security Administration's Detailed

Earnings Record, calendar years 2009 through 2012.

(in thousands of dollars)

Survey and Administrative Earnings

Figure 1. The data points pictured represent a random 15 percent sample of all person-years for people aged 15 

and older, who were assigned a PIK, and who were present in the survey for all 12 months of the calendar year. 

We focus on the set of individuals with both SIPP earnings and DER earnings between $20,000 and $85,000 for 

ease of visualization. This figure plots the relationship between administrative earnings (on the horizontal axis) and 

survey earnings (on the vertical axis). We perturb each data point by adding spherical random noise in order to avoid 

disclosing federal tax information. 

40
0 10 20 30 40 50 Frequency (%)

<-10-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 >10

(in thousands of dollars)

Distribution of DER Earnings - SIPP Earnings

Figure 2. The sample is all person-years for people aged 15 and older, who were assigned a PIK, and who were 

present in the survey for all 12 months of the calendar year. This figure plots the difference between administrative 

earnings and survey earnings. Each bin represents the integer portion of the difference between DER earnings and 

SIPP earnings, in thousands of dollars. For example, the bin labeled “-1” includes person-years for which SIPP 

earnings exceed DER earnings by some amount between $1,000 and $1,999, inclusive; the bin labeled “0” includes 

person-years for which either SIPP earnings exceed DER earnings by up to and including $999 or DER earnings 

exceed SIPP earnings by up to and including $999; and the bin labeled “1” includes person-years for which DER 

earnings exceed SIPP earnings by some amount between $1,000 and $1,999, inclusive. The bin labeled “< −10” 

includes person-years for which SIPP earnings exceed DER earnings by $10,000 or more. The bin labeled “> 10” 

includes person-years for which DER earnings exceed SIPP earnings by $10,000 or more. 

Source: Authors’ calculation from the 2008 panel of the Survey of Income and Program Participation, Waves 1 

through 14 and the Social Security Administration’s Detailed Earnings Record, calendar years 2009 through 2012. 

41
20 40 60 80

20 40 60 80 20 40 60 80

No months proxy At least one month proxy SIPP Annual Earnings

DER Annual Earnings

Source: Authors' calculation from the 2008 panel of the Survey of Income and Program

Participation, Waves 1 through 14 and the Social Security Administration's Detailed

Earnings Record, calendar years 2009 through 2012.

(in thousands of dollars)

Non-Proxy and Proxy

Survey and Administrative Earnings

Figure 3. The data points pictured represent a random 15 percent sample of all person-years for people aged 15 

and older, who were assigned a PIK, and who were present in the survey for all 12 months of the calendar year. We 

focus on the set of individuals with both SIPP earnings and DER earnings between $20,000 and $85,000 for ease of 

visualization. This figure plots the relationship between administrative earnings (on the horizontal axis) and survey 

earnings (on the vertical axis) by proxy interview status. We perturb each data point by adding spherical random 

noise in order to avoid disclosing federal tax information. The scatterplot on the left includes only individuals whose 

data were not provided by proxy response for any month of the year. The scatterplot on the right includes only 

individuals whose data were provided by proxy response for at least one month of the year. 

42
20 40 60 80

20 40 60 80 20 40 60 80

No imputation At least one month imputed SIPP Annual Earnings

DER Annual Earnings

Source: Authors' calculation from the 2008 panel of the Survey of Income and Program

Participation, Waves 1 through 14 and the Social Security Administration's Detailed

Earnings Record, calendar years 2009 through 2012.

(in thousands of dollars)

Non-Imputed and Imputed

Survey and Administrative Earnings

Figure 4. The data points pictured represent a random 25 percent sample of all person-years for people aged 15 

and older, who were assigned a PIK, and who were present in the survey for all 12 months of the calendar year. 

We focus on the set of individuals with both SIPP earnings and DER earnings between $20,000 and $85,000 for 

ease of visualization. This figure plots the relationship between administrative earnings (on the horizontal axis) and 

survey earnings (on the vertical axis) by imputed earnings status. We perturb each data point by adding spherical 

random noise in order to avoid disclosing federal tax information. The scatterplot on the left includes only individuals 

whose survey earnings data were not imputed for any month of the year. The scatterplot on the right includes only 

individuals whose survey earnings data were imputed for at least one month of the year. 

43
Frequency

-100 -75 -50 -25 0 25 50 75 100

DER Earnings - SIPP Earnings

 Imputed Non-Imputed

Source: Authors' calculation from the 2008 panel of the Survey of Income and Program

Participation, Waves 1 through 14 and the Social Security Administration's Detailed

Earnings Record, calendar years 2009 through 2012.

(in thousands of dollars)

Distribution of the Gap between

Administrative and Survey Earnings

Figure 5. The data points pictured represent all person-years for people aged 15 and older, who were assigned a 

PIK, who were present in the survey for all 12 months of the calendar year, who had both positive SIPP earnings and 

positive DER earnings, and who exhibited an absolute difference between survey and administrative earnings that 

did not exceed $100,000. This figure plots the univariate kernel density estimates of the difference between DER 

earnings and SIPP earnings by imputed earnings status. Points to the left of the “0” label exhibited SIPP earnings 

larger than DER earnings, while points to the right of the “0” label exhibited DER earnings larger than SIPP earnings. 

The red, dashed line plots estimates only for individuals whose survey earnings data were not imputed for any month 

of the year. The blue, solid line plots estimates only for individuals whose survey earnings data were imputed for at 

least one month of the year. 

44
Table 3. Deviation of SIPP Earnings from DER Earnings: Any Nonresponse 

45

(1) (2) 

VARIABLES DER-SIPP |DER-SIPP|

Any nonresponse -1,919.228*** 6,898.190*** 

(150.799) (115.027) 

Midwest -287.687* -576.744*** 

(152.410) (124.611) 

South -111.066 -162.447 

(147.172) (119.463) 

West -288.231* -213.553 

(168.233) (137.777) 

Number of household members 7.261 -173.747*** 

(81.387) (65.229) 

Number of family members 62.347 56.399 

(82.686) (66.248) 

Age -488.859*** -113.168 

(111.729) (93.510) 

Age squared 21.680*** 24.635*** 

(3.595) (3.024) 

Female -681.496*** -3,068.404*** 

(98.211) (80.129) 

Black, non-Hispanic 633.243** 512.375** 

(266.341) (221.789) 

Asian, non-Hispanic 1,278.955*** 791.004** 

(423.778) (341.302) 

White, non-Hispanic 367.802 626.719*** 

(234.691) (196.445) 

Hispanic 1,344.658*** 347.540 

(293.877) (241.735) 

Married, spouse absent 324.423 -83.473 

(486.621) (413.664) 

Never married -405.975** -1,326.117*** 

(159.209) (130.404) 

Previously married -104.780 -502.648*** 

(121.522) (100.496) 

Elementary school -275.560 -114.830 

(173.785) (151.381) 

Some high school 23.273 -483.810*** 

(118.404) (102.572) 

Some college 327.244** 651.344*** 

(138.261) (113.620) 

Associate’s degree 291.111** 532.843*** 

(130.985) (106.704) 

Bachelor’s degree 975.960*** 2,909.959*** 

(172.324) (139.714) 

Master’s degree 1,239.306*** 4,055.376*** 

(272.902) (218.525) 

(continued...) 
Table 3 (continued). Deviation of SIPP Earnings from DER 

Earnings: Any Nonresponse 

(1) (2) 

VARIABLES 

(...continued) 

DER-SIPP |DER-SIPP|

Professional degree 2,851.460*** 10,487.695*** 

(869.993) (693.289) 

Doctorate degree 2,214.646*** 6,993.012*** 

(781.239) (630.579) 

Foreign-born, citizen 740.761*** 431.731** 

(252.788) (202.519) 

Foreign-born, non-citizen 186.290 260.202 

(328.652) (261.065) 

Any children under 18 -97.282 583.070*** 

(139.758) (111.768) 

Non-English speaker -463.043** 343.148** 

(218.351) (173.367) 

Any transfer income -730.051*** -3,050.304*** 

(92.119) (94.792) 

Observations 158,168 158,168 

R2 0.010 0.182 

Source: Authors’ calculation from the 2008 panel of the Survey of Income 

and Program Participation, Waves 1 through 14 and the Social Security Administration’s Detailed Earnings Record, calendar years 2009 through 2012. 

Note: Sample includes all person-years for people aged 15 and older, who 

were assigned a PIK, who were present in the survey for all 12 months of the 

calendar year, and whose absolute deviation of survey from administrative 

earnings was in the bottom 99 percent of this distribution. The estimates in 

this table result from OLS regressions. The dependent variable in column 1 

is the difference between DER earnings and SIPP earnings. The dependent 

variable in column 2 is the absolute difference between DER earnings and 

SIPP earnings. Any nonresponse indicates whether earnings data were imputed for any reason, which may reflect either unit nonresponse or item nonresponse. Unit nonresponse occurs in two scenarios: when non-interviewed 

individuals reside with interviewed individuals and when interviewed individuals decline to provide any information about their labor market situations. Item nonresponse occurs when interviewed individuals provide some 

information about their labor market situations but decline to provide information about either earnings from a job for an employer, earnings from 

a self-employed business, earnings from moonlighting, or severance pay. 

Other controls include CBSA size indicators, cubic age, and quartic age. 

Non-English speaker indicates individuals who speak a language other than 

English in the home. All time-varying explanatory variables are defined as 

of December for each year. Standard errors are clustered at the person level 

and are listed in parentheses. *** denotes p < 0.01, ** denotes p < 0.05, 

and * p < 0.1. 

46
Table 4. Deviation of SIPP Earnings from DER Earnings: Unit and 

Item Nonresponse 

47

(1) (2) 

VARIABLES DER-SIPP |DER-SIPP|

Any unit nonresponse 4.553 5,128.445*** 

(204.553) (162.857) 

Any item nonresponse -2,896.065*** 7,796.814*** 

(192.934) (143.902) 

Midwest -307.679** -558.353*** 

(152.074) (124.354) 

South -121.716 -152.649 

(146.967) (119.158) 

West -332.794** -172.558 

(167.870) (137.501) 

Number of household members -71.120 -101.642 

(81.027) (65.307) 

Number of family members 80.495 39.704 

(82.527) (66.375) 

Age -454.320*** -144.942 

(111.784) (93.193) 

Age squared 21.064*** 25.202*** 

(3.594) (3.013) 

Female -690.149*** -3,060.444*** 

(98.056) (79.910) 

Black, non-Hispanic 614.426** 529.686** 

(265.456) (221.091) 

Asian, non-Hispanic 1,263.419*** 805.296** 

(423.408) (340.345) 

White, non-Hispanic 403.357* 594.011*** 

(233.854) (195.877) 

Hispanic 1,365.633*** 328.245 

(293.092) (241.055) 

Married, spouse absent 273.262 -36.408 

(486.750) (412.292) 

Never married -469.064*** -1,268.079*** 

(158.815) (130.012) 

Previously married -114.835 -493.398*** 

(121.341) (100.143) 

Elementary school -242.832 -144.937 

(172.991) (150.334) 

Some high school 23.979 -484.459*** 

(118.468) (101.825) 

Some college 360.295*** 620.939*** 

(137.994) (113.361) 

Associate’s degree 286.993** 536.631*** 

(130.886) (106.372) 

Bachelor’s degree 1,014.509*** 2,874.496*** 

(172.137) (139.419) 

Master’s degree 1,289.614*** 4,009.096*** 

(272.617) (218.389) 

(continued...) 
Table 4 (continued). Deviation of SIPP Earnings from DER 

Earnings: Unit and Item Nonresponse 

(1) (2) 

VARIABLES 

(...continued) 

DER-SIPP |DER-SIPP|

Professional degree 2,911.722*** 10,432.258*** 

(867.134) (691.560) 

Doctorate degree 2,260.214*** 6,951.093*** 

(780.115) (628.231) 

Foreign-born, citizen 729.819*** 441.797** 

(252.630) (201.830) 

Foreign-born, non-citizen 167.903 277.116 

(328.162) (260.785) 

Any children under 18 -16.553 508.805*** 

(139.482) (111.468) 

Non-English speaker -462.908** 343.024** 

(217.922) (172.861) 

Any transfer income -783.509*** -3,001.126*** 

(91.796) (93.965) 

Observations 158,168 158,168 

R2 0.012 0.184 

Source: Authors’ calculation from the 2008 panel of the Survey of Income 

and Program Participation, Waves 1 through 14 and the Social Security Administration’s Detailed Earnings Record, calendar years 2009 through 2012. 

Note: Sample includes all person-years for people aged 15 and older, who 

were assigned a PIK, who were present in the survey for all 12 months of the 

calendar year, and whose absolute deviation of survey from administrative 

earnings was in the bottom 99 percent of this distribution. The estimates in 

this table result from OLS regressions. The dependent variable in column 

1 is the difference between DER earnings and SIPP earnings. The dependent variable in column 2 is the absolute difference between DER earnings 

and SIPP earnings. Unit nonresponse occurs in two scenarios: when noninterviewed individuals reside with interviewed individuals and when interviewed individuals decline to provide any information about their labor market situations. Item nonresponse occurs when interviewed individuals provide some information about their labor market situations but decline to provide information about either earnings from a job for an employer, earnings 

from a self-employed business, earnings from moonlighting, or severance 

pay. Other controls include CBSA size indicators, cubic age, and quartic age. 

Non-English speaker indicates individuals who speak a language other than 

English in the home. All time-varying explanatory variables are defined as 

of December for each year. Standard errors are clustered at the person level 

and are listed in parentheses. *** denotes p < 0.01, ** denotes p < 0.05, 

and * p < 0.1. 

48
Table 5. Deviation of SIPP Earnings from DER Earnings: Detailed Nonresponse Type

49

(1) (2) 

VARIABLES DER-SIPP |DER-SIPP|

Any hot-deck imputation -6,625.835*** 3,681.865*** 

(317.637) (243.895) 

Any Type-Z imputation 2,239.581*** 162.567 

(193.760) (170.977) 

Any longitudinal labor force imputation -3,051.713*** 2,686.463*** 

(448.855) (343.988) 

Any imputation based on last month — Reported 408.605 2,269.766*** 

(309.375) (232.990) 

Any imputation based on last month — Imputed 3,515.126*** 7,322.420*** 

(431.305) (313.817) 

Any imputation based on last month — Logical 1,264.158 5,519.321*** 

(828.437) (624.038) 

Any proxy response 663.439*** 192.747** 

(105.395) (87.480) 

Any logical imputation 861.216*** 387.869** 

(210.012) (163.952) 

Midwest -242.947 -527.380*** 

(151.169) (125.598) 

South -137.319 -113.122 

(145.999) (120.468) 

West -246.388 -104.167 

(166.834) (138.628) 

Number of household members -110.245 -99.125 

(81.124) (65.655) 

Number of family members 49.995 19.150 

(82.354) (66.543) 

Age -353.291*** -199.345** 

(111.733) (94.431) 

Age squared 18.471*** 27.193*** 

(3.584) (3.047) 

Female -657.707*** -3,155.184*** 

(98.411) (81.507) 

Black, non-Hispanic 589.069** 422.143* 

(264.124) (222.568) 

Asian, non-Hispanic 1,227.504*** 700.237** 

(419.560) (340.854) 

White, non-Hispanic 408.323* 569.125*** 

(231.986) (197.436) 

Hispanic 1,329.741*** 252.460 

(290.758) (242.839) 

Married, spouse absent 329.051 -6.163 

(483.246) (413.319) 

Never married -392.356** -1,263.864*** 

(159.063) (131.828) 

(continued...) 
Table 5 (continued). Deviation of SIPP Earnings from DER 

Earnings: Detailed Nonresponse Type 

50

(1) (2) 

VARIABLES 

(...continued) 

DER-SIPP |DER-SIPP|

Previously married 75.194 -453.692*** 

(123.523) (103.957) 

Elementary school -150.105 -157.089 

(171.942) (151.558) 

Some high school 127.941 -464.543*** 

(117.814) (103.425) 

Some college 396.103*** 663.960*** 

(137.428) (115.034) 

Associate’s degree 336.891*** 616.686*** 

(130.146) (108.013) 

Bachelor’s degree 1,061.617*** 2,947.809*** 

(171.100) (141.157) 

Master’s degree 1,342.442*** 4,088.196*** 

(270.686) (219.819) 

Professional degree 3,036.228*** 10,754.686*** 

(863.685) (701.219) 

Doctorate degree 2,365.209*** 7,087.293*** 

(777.970) (639.216) 

Foreign-born, citizen 663.093*** 517.547** 

(251.161) (203.632) 

Foreign-born, non-citizen 150.369 371.979 

(325.031) (261.043) 

Any children under 18 157.235 542.436*** 

(139.970) (113.302) 

Non-English speaker -382.178* 357.823** 

(216.416) (174.164) 

(continued...) 
Table 5 (continued). Deviation of SIPP Earnings from 

DER Earnings: Detailed Nonresponse Type 

VARIABLES 

(...continued) 

Any transfer income 

(1) 

DER-SIPP 

-641.890*** 

(92.211) 

(2) 

|DER-SIPP|

-3,163.082*** 

(94.931) 

Observations 

R2 

158,168 

0.019 

158,168 

0.177 

Source: Authors’ calculation from the 2008 panel of the Survey of 

Income and Program Participation, Waves 1 through 14 and the Social Security Administration’s Detailed Earnings Record, calendar 

years 2009 through 2012. 

Note: Sample includes all person-years for people aged 15 and 

older, who were assigned a PIK, who were present in the survey for 

all 12 months of the calendar year, and whose absolute deviation of 

survey from administrative earnings was in the bottom 99 percent of 

this distribution. The estimates in this table result from OLS regressions. The dependent variable in column 1 is the difference between 

DER earnings and SIPP earnings. The dependent variable in column 

2 is the absolute difference between DER earnings and SIPP earnings. Type-Z imputation was performed for non-interviewed individuals who reside with interviewed individuals. Longitudinal labor 

force imputation was performed for interviewed individuals who decline to provide any information about their labor market situations 

when information about this situation was available last wave. Hot 

deck imputation was performed when some component of earnings 

is missing and no information about this income is available from 

a previous month. Imputation based on last month was performed 

when some component of earnings is missing and information about 

this earnings is available from a previous month. This previous earnings may also have been imputed based on last month’s data. The 

initial month’s earnings that is used to impute subsequent months’ 

earnings was either reported, hot-deck imputed, or logically imputed. 

Other controls include CBSA size indicators, cubic age, and quartic 

age. Non-English speaker indicates individuals who speak a language other than English in the home. All time-varying explanatory 

variables are defined as of December for each year. Standard errors 

are clustered at the person level and are listed in parentheses. *** 

denotes p < 0.01, ** denotes p < 0.05, and * p < 0.1. 

51
Table 6. Deviation of Positive SIPP Earnings from Positive DER Earnings

52

(1) (2) 

VARIABLES DER-SIPP |DER-SIPP|

Any hot-deck imputation -2,373.737*** -1,860.838*** 

(454.256) (335.743) 

Any Type-Z imputation 3,218.760*** 1,312.942*** 

(655.354) (504.035) 

Any longitudinal labor force imputation -2,671.681*** -493.946 

(776.459) (589.965) 

Any imputation based on last month — Reported -603.211* 2,826.048*** 

(361.068) (272.946) 

Any imputation based on last month — Imputed 1,844.083*** 11,223.464*** 

(569.619) (410.897) 

Any imputation based on last month — Logical 2,918.492*** 6,352.998*** 

(972.768) (750.235) 

Any proxy response 847.808*** 657.701*** 

(187.429) (146.315) 

Any logical imputation 267.916 247.797 

(239.249) (185.570) 

Midwest -615.439** -1,349.044*** 

(279.660) (218.516) 

South -443.940 -417.339* 

(279.527) (216.850) 

West -235.521 20.172 

(318.280) (246.939) 

Number of household members -106.104 -213.291** 

(133.669) (102.711) 

Number of family members 45.234 171.977* 

(134.928) (103.311) 

Female -1,024.383*** -3,073.664*** 

(208.986) (164.882) 

Black, non-Hispanic 195.201 728.434* 

(533.986) (435.294) 

Asian, non-Hispanic 1,475.102* 1,398.454** 

(759.755) (602.822) 

White, non-Hispanic 471.783 450.169 

(473.633) (392.899) 

Hispanic 1,147.854** 373.014 

(551.438) (452.080) 

Married, spouse absent 1,169.394 1,228.840 

(995.899) (783.567) 

Never married -223.674 -939.063*** 

(256.934) (202.702) 

Previously married -41.703 -1,036.111*** 

(257.049) (200.273) 

Elementary school 436.021 -953.262** 

(529.826) (435.697) 

Some high school 619.300** -407.339* 

(302.389) (241.937) 

(continued...) 
Table 6 (continued). Deviation of Positive SIPP Earnings from 

Positive DER Earnings 

53

(1) (2) 

VARIABLES 

(...continued) 

DER-SIPP |DER-SIPP|

Some college 653.364*** 776.717*** 

(241.446) (189.188) 

Associate’s degree 370.267 444.024** 

(232.189) (181.709) 

Bachelor’s degree 1,848.996*** 3,291.526*** 

(302.260) (237.072) 

Master’s degree 2,829.393*** 5,192.329*** 

(470.571) (363.572) 

Professional degree 7,759.132*** 14,005.270*** 

(1,438.533) (1,063.294) 

Doctorate degree 5,447.593*** 7,402.478*** 

(1,093.960) (856.368) 

Private 89.980 -364.557 

(624.663) (474.257) 

Federal government -498.744 293.206 

(737.221) (550.200) 

State government -2,538.030*** -2,741.091*** 

(658.282) (499.733) 

Local government -1,472.692** -2,336.468*** 

(623.949) (473.924) 

Self-employed -11,713.350*** 11,776.952*** 

(503.736) (375.654) 

Hours worked 47.637*** 142.884*** 

(7.779) (6.035) 

Foreign-born, citizen 854.224* -28.569 

(443.535) (338.278) 

Foreign-born, non-citizen 708.273 -99.991 

(530.578) (413.882) 

Any children under 18 578.220** 737.791*** 

(225.810) (177.320) 

Non-English speaker -271.529 198.085 

(394.104) (305.255) 

(continued...) 
Table 6 (continued). Deviation of Positive SIPP 

Earnings from Positive DER Earnings 

VARIABLES 

(...continued) 

Any transfer income 

(1) 

DER-SIPP 

-665.609 

(450.033) 

(2) 

|DER-SIPP|

-900.633** 

(360.909) 

Observations 

R2 

82,936 

0.057 

82,936 

0.184 

Source: Authors’ calculation from the 2008 panel of the Survey of Income and Program Participation, Waves 1 through 

14 and the Social Security Administration’s Detailed Earnings 

Record, calendar years 2009 through 2012. 

Note: Sample includes all person-years for people aged 15 and 

older, who were assigned a PIK, who were present in the survey for all 12 months of the calendar year, whose absolute deviation of survey from administrative earnings was in the bottom 99 percent of this distribution, and who had both positive 

SIPP earnings and positive DER earnings. The estimates in 

this table result from OLS regressions. The dependent variable 

in column 1 is the difference between DER earnings and SIPP 

earnings. The dependent variable in column 2 is the absolute 

difference between DER earnings and SIPP earnings. Type-Z 

imputation was performed for non-interviewed individuals who 

reside with interviewed individuals. Longitudinal labor force 

imputation was performed for interviewed individuals who decline to provide any information about their labor market situations when information about this situation was available last 

wave. Hot deck imputation was performed when some component of earnings is missing and no information about this income is available from a previous month. Imputation based on 

last month was performed when some component of earnings 

is missing and information about this earnings is available from 

a previous month. This previous earnings may also have been 

imputed based on last month’s data. The initial month’s earnings that is used to impute subsequent months’ earnings was 

either reported, hot-deck imputed, or logically imputed. Other 

controls include CBSA size indicators, a quartic in age, and 2­

digit occupational affiliation according to the 2000 Census occupation classification system. Class of worker (i.e. private, 

federal, state, local, self-employed) and occupation indicate 

characteristics of employment on any job or business. Hours 

worked measures time worked on all jobs and businesses combined. Non-English speaker indicates individuals who speak a 

language other than English in the home. Standard errors are 

clustered at the person level and are listed in parentheses. *** 

denotes p < 0.01, ** denotes p < 0.05, and * p < 0.1. 

54
Table 7. Deviation of Positive Reported SIPP Earnings from Positive 

DER Earnings 

55

(1) (2) 

VARIABLES DER-SIPP |DER-SIPP|

Any proxy response 1,038.594*** 987.877*** 

(185.045) (151.796) 

Midwest -718.202*** -1,124.216*** 

(271.745) (228.361) 

South -893.430*** -381.083 

(276.719) (232.126) 

West -216.262 318.130 

(318.415) (263.952) 

Number of household members -152.016 -226.755** 

(136.433) (108.334) 

Number of family members 189.845 240.225** 

(136.546) (108.274) 

Female -1,314.574*** -2,638.019*** 

(207.542) (171.934) 

Black, non-Hispanic 795.904 1,030.500** 

(559.961) (463.345) 

Asian, non-Hispanic 1,449.170* 1,402.569** 

(779.660) (653.376) 

White, non-Hispanic 40.039 121.738 

(502.879) (417.214) 

Hispanic 1,273.960** 581.252 

(574.097) (476.995) 

Married, spouse absent 2,223.700* 1,596.311* 

(1,147.519) (968.613) 

Never married 213.401 -637.329*** 

(246.565) (205.548) 

Previously married -6.247 -946.922*** 

(236.052) (193.536) 

Elementary school -47.112 -459.205 

(534.686) (453.559) 

Some high school -96.796 -234.877 

(292.804) (244.986) 

Some college 536.609** 779.355*** 

(236.675) (193.370) 

Associate’s degree 211.984 437.447** 

(222.384) (181.181) 

Bachelor’s degree 1,695.431*** 2,740.109*** 

(285.425) (236.162) 

Master’s degree 2,250.369*** 4,512.895*** 

(463.938) (377.410) 

Professional degree 6,309.052*** 12,114.769*** 

(1,464.160) (1,182.989) 

Doctorate degree 4,945.980*** 6,820.032*** 

(1,130.617) (946.786) 

Private 214.250 -332.997 

(658.468) (526.334) 

(continued...) 
Table 7 (continued). Deviation of Positive Reported SIPP Earnings from Positive DER Earnings 

VARIABLES 

(...continued) 

DER-SIPP |DER-SIPP|

Federal government -730.979 53.019 

(776.244) (607.078) 

State government -2,441.784*** -2,658.389*** 

(686.309) (551.760) 

Local government -1,633.911** -2,156.390*** 

(649.778) (517.038) 

Self-employed -12,065.154*** 9,625.887*** 

(691.173) (531.082) 

Hours worked 45.289*** 135.401*** 

(8.478) (6.920) 

Foreign-born, citizen 772.930* -203.578 

(433.429) (352.287) 

Foreign-born, non-citizen 593.051 -181.629 

(530.455) (438.146) 

Any children under 18 549.427** 679.849*** 

(222.427) (186.333) 

Non-English speaker -200.662 93.211 

(386.737) (317.399) 

Any transfer income -369.979 -685.672* 

(445.473) (379.950) 

Observations 57,580 57,580 

R2 0.054 0.141 

(1) (2) 

Source: Authors’ calculation from the 2008 panel of the Survey of Income and 

Program Participation, Waves 1 through 14 and the Social Security Administration’s Detailed Earnings Record, calendar years 2009 through 2012. 

Note: Sample includes all person-years for people aged 15 and older, who 

were assigned a PIK, who were present in the survey for all 12 months of the 

calendar year, whose absolute deviation of survey from administrative earnings 

was in the bottom 99 percent of this distribution, who had both positive SIPP 

earnings and positive DER earnings, and who had no months of imputed earnings during the year. The estimates in this table result from OLS regressions. 

The dependent variable in column 1 is the difference between DER earnings 

and SIPP earnings. The dependent variable in column 2 is the absolute difference between DER earnings and SIPP earnings. Other controls include CBSA 

size indicators, a quartic in age, and 2-digit occupational affiliation according to the 2000 Census occupation classification system. Class of worker (i.e. 

private, federal, state, local, self-employed) and occupation indicate characteristics of employment on any job or business. Hours worked measures time 

worked on all jobs and businesses combined. Non-English speaker indicates 

individuals who speak a language other than English in the home. Standard errors are clustered at the person level and are listed in parentheses. *** denotes 

p < 0.01, ** denotes p < 0.05, and * p < 0.1. 

56
Table 8. Characteristics of Nonrespondents

CHARACTERISTICS 

Any 

Nonresponse 

(percent) Obs 

Unit 

Nonresponse 

(percent) Obs 

Item 

Nonresponse 

(percent) Obs 

Overall

Gender

14.9 3,849,934 5.8 3,849,934 15.4 1,915,457 

Female 12.9 2,031,415 5.2 2,031,415 14.6 939,035 

Male 

Education

17.0 1,818,519 6.5 1,818,519 16.2 976,422 

Less than HS 10.5 639,948 6.0 639,948 13.7 163,309 

HS or some college 15.7 2,260,485 6.5 2,260,485 15.6 1,132,641 

Bachelor’s or postgraduate 

Race and ethnicity

15.8 949,501 4.0 949,501 15.6 619,507 

White, non-Hispanic 14.6 2,668,350 5.1 2,668,350 15.7 1,362,327 

Black, non-Hispanic 16.9 450,707 7.9 450,707 18.4 197,373 

Asian, non-Hispanic 16.6 159,323 7.8 159,323 14.7 82,903 

Hispanic 

Marital status

13.8 448,721 7.3 448,721 11.4 218,223 

Married 14.4 1,994,751 4.2 1,994,751 15.3 1,110,916 

Divorced or separated 14.1 479,649 4.7 479,649 14.8 258,318 

Never married or widowed 

Age

15.8 1,375,534 8.6 1,375,534 16.0 546,223 

Under 25 18.0 643,615 11.3 643,615 17.2 224,395 

25 — 34 17.2 576,752 7.2 576,752 13.1 381,774 

35 — 44 16.5 615,278 5.0 615,278 14.2 429,509 

45 — 54 17.2 698,198 4.9 698,198 15.6 467,505 

55 — 64 14.8 612,212 4.1 612,212 17.1 322,871 

65 or older 

Family Structure

6.4 703,879 2.8 703,879 20.4 89,403 

No children under 18 14.8 2,541,950 5.9 2,541,950 16.3 1,176,222 

Any children under 18 

Usual weekly

hours worked

15.0 1,307,984 5.8 1,307,984 14.1 739,235 

Less than 20 hours — — — — 18.5 127,605 

20 — 34 hours — — — — 16.7 271,208 

35 or more hours — — — — 15.0 1,516,644 

Source: Authors’ calculation from the 2008 panel of the Survey of Income and Program Participation, Waves 1 through 14. 

Note: Sample for ”Any Nonresponse” and ”Unit Nonresponse” columns is all person-months for people aged 15 and older. 

Sample for ”Item Nonresponse” column is all person-months for people aged 15 and older who worked on a job or business, 

moonlighted, or earned severance pay. We also restrict this sample to people who were not imputed to work unpaid at a 

family business and people who provided some information about their job, business, moonlighting, or severance pay. Family 

structure includes only children living in the household. Usual hours worked includes work at all jobs and businesses. Usual 

weekly hours worked data are imputed for all unit nonrespondents. 

57
Table 9. Predictors of Earnings Nonresponse

58

(1) (2) (3) (4) 

Any Non- Unit Non- Item Non- Item NonVARIABLES response response response response 

Midwest -0.007*** 0.008*** -0.015*** -0.030*** 

(0.001) (0.001) (0.001) (0.002) 

South 0.005*** 0.007*** -0.002** 0.002 

(0.001) (0.001) (0.001) (0.001) 

West -0.021*** 0.007*** -0.030*** -0.049*** 

(0.001) (0.001) (0.001) (0.002) 

Number of household members 0.039*** 0.046*** -0.003*** -0.005*** 

(0.001) (0.001) (0.001) (0.001) 

Number of family members -0.011*** -0.016*** 0.005*** 0.011*** 

(0.001) (0.001) (0.001) (0.001) 

Age -0.023*** -0.027*** 0.004*** 0.002 

(0.001) (0.001) (0.001) (0.002) 

Age squared 0.001*** 0.001*** 0.000*** -0.000 

(0.000) (0.000) (0.000) (0.000) 

Female -0.040*** -0.020*** -0.023*** -0.005*** 

(0.001) (0.000) (0.001) (0.001) 

Black, non-Hispanic 0.021*** 0.011*** 0.012*** 0.029*** 

(0.002) (0.001) (0.002) (0.003) 

Asian, non-Hispanic 0.008*** 0.012*** -0.004* 0.007* 

(0.003) (0.002) (0.002) (0.004) 

White, non-Hispanic 0.015*** 0.003*** 0.013*** 0.006** 

(0.002) (0.001) (0.002) (0.003) 

Hispanic -0.013*** -0.006*** -0.008*** -0.015*** 

(0.002) (0.002) (0.002) (0.003) 

Married, spouse absent 0.030*** 0.017*** 0.016*** 0.028*** 

(0.004) (0.003) (0.003) (0.005) 

Never married 0.021*** 0.016*** 0.007*** 0.020*** 

(0.001) (0.001) (0.001) (0.002) 

Previously married -0.006*** -0.013*** 0.006*** 0.013*** 

(0.001) (0.001) (0.001) (0.001) 

Elementary school -0.037*** -0.021*** -0.018*** -0.015*** 

(0.002) (0.001) (0.001) (0.003) 

Some high school -0.050*** -0.025*** -0.028*** -0.022*** 

(0.001) (0.001) (0.001) (0.002) 

Some college -0.008*** -0.015*** 0.006*** -0.011*** 

(0.001) (0.001) (0.001) (0.002) 

Associate’s degree 0.019*** 0.016*** 0.005*** -0.010*** 

(0.001) (0.001) (0.001) (0.001) 

Bachelor’s degree 0.010*** -0.010*** 0.019*** -0.004** 

(0.001) (0.001) (0.001) (0.002) 

Master’s degree 0.007*** -0.015*** 0.022*** -0.003 

(0.002) (0.001) (0.001) (0.002) 

Professional degree 0.045*** -0.009*** 0.056*** -0.009** 

(0.004) (0.002) (0.003) (0.004) 

(continued...) 
Table 9 (continued). Predictors of Earnings Nonresponse

59

(1) (2) (3) (4) 

Any Non- Unit Non- Item Non- Item NonVARIABLES response response response response 

(...continued) 

Doctorate degree 0.013*** -0.016*** 0.028*** -0.027*** 

(0.004) (0.002) (0.003) (0.004) 

Private — — — 0.070*** 

(0.003) 

Federal government — — — 0.085*** 

(0.004) 

State government — — — 0.053*** 

(0.004) 

Local government — — — 0.051*** 

(0.003) 

Self-employed — — — 0.198*** 

(0.002) 

Weeks worked — — — 0.018*** 

(0.000) 

Hours worked — — — 0.000*** 

(0.000) 

Foreign-born, citizen 0.029*** 0.017*** 0.014*** 0.009*** 

(0.002) (0.001) (0.001) (0.002) 

Foreign-born, non-citizen 0.024*** 0.028*** -0.002 -0.003 

(0.002) (0.001) (0.001) (0.002) 

Proxy response -0.081*** -0.128*** 0.037*** 0.064*** 

(0.001) (0.001) (0.001) (0.001) 

Number of interviews 0.002*** 0.001*** 0.001*** 0.002*** 

(0.000) (0.000) (0.000) (0.000) 

Any sample gaps 0.045*** 0.019*** 0.030*** 0.052*** 

(0.001) (0.001) (0.001) (0.001) 

Attritor 0.074*** 0.061*** 0.020*** 0.037*** 

(0.001) (0.001) (0.001) (0.001) 

Any children under 18 -0.066*** -0.056*** -0.017*** -0.024*** 

(0.001) (0.001) (0.001) (0.001) 

Change in family composition 0.011*** 0.011*** 0.001 0.004* 

(0.002) (0.001) (0.001) (0.002) 

Non-English speaker -0.013*** -0.010*** -0.004*** -0.010*** 

(0.001) (0.001) (0.001) (0.002) 

Any transfer income -0.064*** -0.007*** -0.061*** -0.007 

(0.001) (0.001) (0.001) (0.004) 

Stopped work — — — 0.035*** 

(0.002) 

(continued...) 
Table 9 (continued). Predictors of Earnings Nonresponse

VARIABLES 

(...continued) 

Contingent worker 

(1) 

Any Nonresponse 

— 

(2) 

Unit Nonresponse 

— 

(3) 

Item Nonresponse 

— 

(4) 

Item Nonresponse 

0.129*** 

(0.007) 

Observations 

R2 

3,849,934 

0.058 

3,849,934 

0.124 

3,625,618 

0.032 

2,160,613 

0.069 

Source: Authors’ calculation from the 2008 panel of the Survey of Income and Program 

Participation, Waves 1 through 14. 

Note: Sample for columns 1 and 2 is all person-months for people aged 15 and older. 

Sample for column 3 is all person-months for people aged 15 and older who provided at 

least some data about their labor market situation. Sample for column 4 is all personmonths for people aged 15 and older who provided at least some data about their labor 

market situation and who worked on a non-contingent job or business. We also restrict the 

sample in columns 3 and 4 to people who were not imputed to work unpaid at a family 

business. The estimates in this table result from OLS regressions. The dependent variable in column 1 indicates any earnings nonresponse. The dependent variable in column 

2 indicates unit earnings nonresponse. Unit nonresponse occurs in two scenarios: when 

non-interviewed individuals reside with interviewed individuals and when interviewed individuals decline to provide any information about their labor market situations. Item 

nonresponse occurs when interviewed individuals provide some information about their 

labor market situations but decline to provide information about either earnings from a job 

for an employer, earnings from a self-employed business, earnings from moonlighting, or 

severance pay. Other controls in columns 1 through 4 include CBSA size indicators, cubic 

age, and quartic age. Other controls in column 4 also include 2-digit occupational affiliation according to the 2000 Census occupation classification system. In column 4, class 

of worker (i.e. private, federal, state, local, self-employed), stopped work, and occupation 

indicate characteristics of employment on any job or business. Hours worked and weeks 

worked measure time worked on all jobs and businesses combined. Any sample gaps indicates individuals who leave the survey and later return to the survey. Non-English speaker 

indicates individuals who speak a language other than English in the home. Standard 

errors are clustered at the person-wave level and are listed in parentheses. *** denotes 

p < 0.01, ** denotes p < 0.05, and * p < 0.1. 

60
Table 10. Predictors of Earnings Item Nonresponse

(1) (2) (3) 

VARIABLES Jobs Jobs Businesses 

Midwest -0.019*** -0.019*** -0.026*** 

(0.002) (0.001) (0.007) 

South 0.004*** 0.003** -0.014** 

(0.001) (0.001) (0.006) 

West -0.036*** -0.035*** -0.090*** 

(0.002) (0.001) (0.007) 

Number of household members -0.003*** -0.003*** -0.010** 

(0.001) (0.001) (0.005) 

Number of family members 0.010*** 0.009*** 0.008 

(0.001) (0.001) (0.005) 

Age 0.001 0.002 0.034*** 

(0.002) (0.002) (0.011) 

Age squared -0.000 -0.000 -0.001*** 

(0.000) (0.000) (0.000) 

Female -0.006*** -0.006*** 0.007 

(0.001) (0.001) (0.005) 

Black, non-Hispanic 0.029*** 0.028*** 0.023 

(0.003) (0.003) (0.017) 

Asian, non-Hispanic 0.007* 0.008** 0.023 

(0.004) (0.004) (0.018) 

White, non-Hispanic 0.006** 0.006** -0.012 

(0.003) (0.003) (0.014) 

Hispanic -0.009*** -0.008*** -0.004 

(0.003) (0.003) (0.016) 

Married, spouse absent 0.030*** 0.030*** 0.019 

(0.005) (0.005) (0.020) 

Never married 0.021*** 0.021*** 0.028*** 

(0.002) (0.001) (0.008) 

Previously married 0.015*** 0.014*** 0.010 

(0.002) (0.001) (0.007) 

Elementary school -0.023*** -0.022*** -0.011 

(0.003) (0.003) (0.013) 

Some high school -0.029*** -0.027*** -0.003 

(0.002) (0.002) (0.011) 

Some college -0.015*** -0.015*** 0.003 

(0.002) (0.002) (0.008) 

Associate’s degree -0.016*** -0.016*** -0.002 

(0.002) (0.002) (0.007) 

Bachelor’s degree -0.006*** -0.007*** -0.006 

(0.002) (0.002) (0.007) 

Master’s degree -0.010*** -0.011*** 0.024*** 

(0.002) (0.002) (0.009) 

Professional degree -0.010** -0.011** 0.001 

(0.004) (0.004) (0.014) 

Doctorate degree -0.027*** -0.027*** -0.005 

(0.004) (0.004) (0.016) 

Private 0.009*** — — 

(0.003) 

(continued...) 61 
Table 10 (continued). Predictors of Earnings Item Nonresponse

(1) (2) (3) 

VARIABLES Jobs Jobs Businesses 

(...continued)

State government -0.002 — —

(0.003) 

Local government -0.006** — — 

(0.003) 

Weeks worked -0.003*** — 0.013*** 

(0.001) (0.004) 

Hours worked -0.001*** -0.001*** 0.001*** 

(0.000) (0.000) (0.000) 

Foreign-born, citizen 0.008*** 0.008*** -0.004 

(0.002) (0.002) (0.009) 

Foreign-born, non-citizen -0.006** -0.005** 0.008 

(0.002) (0.002) (0.011) 

Proxy response 0.055*** 0.054*** 0.105*** 

(0.001) (0.001) (0.005) 

Number of interviews 0.001*** 0.001*** 0.005*** 

(0.000) (0.000) (0.001) 

Any sample gaps 0.053*** 0.052*** 0.040*** 

(0.001) (0.001) (0.005) 

Attritor 0.037*** 0.036*** 0.027*** 

(0.001) (0.001) (0.005) 

Any children under 18 -0.025*** -0.024*** -0.024*** 

(0.001) (0.001) (0.006) 

Change in family composition 0.007*** 0.005** 0.013 

(0.002) (0.002) (0.011) 

Non-English speaker -0.012*** -0.011*** -0.008 

(0.002) (0.002) (0.008) 

Any transfer income -0.009** -0.008* -0.013 

(0.005) (0.004) (0.021) 

Stopped work 0.045*** — 0.003 

(0.002) (0.018) 

Paid hourly -0.019*** -0.018*** — 

(0.001) (0.001) 

Contingent worker — 0.058*** 0.205*** 

(0.007) (0.058) 

Salaried — — -0.025*** 

(0.004) 

(continued...) 

62
Table 10 (continued). Predictors of Earnings Item Nonresponse 

VARIABLES 

(1) 

Jobs 

(2) 

Jobs 

(3) 

Businesses 

(...continued) 

Other income — — 0.218*** 

(0.014) 

Observations 

R2 

1,703,276 

0.027 

1,756,622 

0.026 

201,191 

0.039 

Source: Authors’ calculation from the 2008 panel of the Survey of 

Income and Program Participation, Waves 1 through 14. 

Note: Sample for column 1 is all person-job-months for people 

aged 15 and older, who worked on a non-contingent basis at a job 

for an employer, and who provided some information about their labor market situation. Sample for column 2 is all person-job-months 

for people aged 15 and older, who worked at a job for an employer, 

and who provided some information about their labor market situation. Sample for column 3 is all person-business-months for people 

aged 15 and older, who worked at a self-employed business, and 

who provided some information about their labor market situation. 

We also restrict the sample in columns 1 and 2 to people who were 

not imputed to work unpaid at a family business. The estimates 

in this table result from OLS regressions. The dependent variable 

in columns 1 and 2 indicates item nonresponse to questions about 

earnings at a job for an employer. The dependent variable in column 

3 indicates item nonresponse to questions about earnings at a selfemployed business. Other controls in all columns include CBSA 

size indicators, cubic age, and quartic age, and 2-digit occupational 

affiliation according to the 2000 Census occupation classification 

system. A federal government worker indicator is also among the 

controls in columns 1 and 2. Class of worker indicators (i.e. private, federal, state, local, self-employed), stopped work indicator, 

occupation indicators, usual weekly hours worked, weeks worked, 

hourly pay indicator, salaried indicator, and other income indicator are all defined separately for each person-job-month or personbusiness month observation based on the characteristics of each job 

or business. Any sample gaps indicates individuals who leave the 

survey and later return to the survey. Non-English speaker indicates 

individuals who speak a language other than English in the home. 

Standard errors are clustered at the person-wave level and are listed 

in parentheses. *** denotes p < 0.01, ** denotes p < 0.05, and * 

p < 0.1. 

63
Table 11. Ignorability of Earnings Nonresponse

(1) (2) (3) (4) 

Any Non- Unit Non- Item Non- Item NonVARIABLES response response response response 

Midwest -0.011*** 0.003*** -0.014*** -0.029*** 

(0.001) (0.001) (0.001) (0.002) 

South 0.007*** 0.004*** 0.004*** 0.008*** 

(0.001) (0.001) (0.001) (0.002) 

West -0.021*** 0.003*** -0.025*** -0.045*** 

(0.001) (0.001) (0.001) (0.002) 

Number of household members 0.021*** 0.026*** -0.003*** -0.005*** 

(0.001) (0.001) (0.001) (0.001) 

Number of family members 0.001 -0.004*** 0.006*** 0.011*** 

(0.001) (0.001) (0.001) (0.001) 

Age -0.023*** -0.021*** -0.003*** 0.008** 

(0.001) (0.001) (0.001) (0.003) 

Age squared 0.001*** 0.001*** 0.000*** -0.000*** 

(0.000) (0.000) (0.000) (0.000) 

Female -0.028*** -0.012*** -0.017*** -0.004** 

(0.001) (0.001) (0.001) (0.001) 

Black, non-Hispanic 0.005* 0.003* 0.002 0.019*** 

(0.003) (0.002) (0.002) (0.004) 

Asian, non-Hispanic -0.003 0.003 -0.005* -0.000 

(0.004) (0.002) (0.003) (0.005) 

White, non-Hispanic 0.004 -0.006*** 0.010*** 0.008** 

(0.003) (0.002) (0.002) (0.004) 

Hispanic -0.026*** -0.010*** -0.017*** -0.017*** 

(0.003) (0.002) (0.002) (0.004) 

Married, spouse absent 0.009** 0.001 0.008** 0.021*** 

(0.005) (0.003) (0.004) (0.007) 

Never married 0.017*** 0.011*** 0.008*** 0.018*** 

(0.002) (0.001) (0.001) (0.002) 

Previously married -0.007*** -0.012*** 0.004*** 0.007*** 

(0.001) (0.001) (0.001) (0.002) 

Elementary school -0.012*** -0.008*** -0.004*** -0.012** 

(0.002) (0.001) (0.001) (0.005) 

Some high school -0.018*** -0.009*** -0.010*** -0.016*** 

(0.002) (0.001) (0.001) (0.003) 

Some college -0.008*** -0.008*** -0.000 -0.010*** 

(0.001) (0.001) (0.001) (0.002) 

Associate’s degree -0.002* -0.004*** 0.002 -0.008*** 

(0.001) (0.001) (0.001) (0.002) 

Bachelor’s degree 0.000 -0.011*** 0.011*** -0.002 

(0.001) (0.001) (0.001) (0.002) 

Master’s degree -0.003 -0.013*** 0.009*** -0.002 

(0.002) (0.001) (0.002) (0.003) 

Professional degree 0.031*** -0.008*** 0.040*** -0.010* 

(0.005) (0.002) (0.004) (0.006) 

Doctorate degree -0.005 -0.013*** 0.007 -0.030*** 

(0.005) (0.002) (0.004) (0.006) 

(continued...) 

64
Table 11 (continued). Ignorability of Earnings Nonresponse

(1) (2) (3) (4) 

Any Non- Unit Non- Item Non- Item NonVARIABLES response response response response 

(...continued) 

Private 0.066*** 

(0.004) 

Federal government — — — 0.086*** 

(0.005) 

State government — — — 0.052*** 

(0.005) 

Local government — — — 0.050*** 

(0.005) 

Self-employed — — — 0.197*** 

(0.003) 

Weeks worked — — — 0.018*** 

(0.000) 

Hours worked — — — 0.001*** 

(0.000) 

Foreign-born, citizen 0.015*** 0.008*** 0.008*** 0.008*** 

(0.002) (0.001) (0.002) (0.003) 

Foreign-born, non-citizen 0.008*** 0.009*** -0.001 -0.001 

(0.003) (0.002) (0.002) (0.003) 

Proxy response -0.050*** -0.086*** 0.030*** 0.054*** 

(0.001) (0.001) (0.001) (0.001) 

Number of interviews 0.003*** 0.001*** 0.002*** 0.004*** 

(0.000) (0.000) (0.000) (0.000) 

Any sample gaps 0.035*** 0.019*** 0.019*** 0.034*** 

(0.001) (0.001) (0.001) (0.002) 

Attritor 0.039*** 0.030*** 0.012*** 0.023*** 

(0.001) (0.001) (0.001) (0.002) 

Any children under 18 -0.042*** -0.035*** -0.010*** -0.021*** 

(0.001) (0.001) (0.001) (0.002) 

Change in family composition -0.023*** -0.017*** -0.008*** -0.014*** 

(0.002) (0.001) (0.002) (0.003) 

Non-English speaker -0.003 -0.004*** 0.001 -0.006** 

(0.002) (0.001) (0.002) (0.003) 

Any transfer income -0.027*** -0.006*** -0.022*** -0.008 

(0.002) (0.001) (0.001) (0.006) 

Stopped work — — — 0.028*** 

(0.003) 

Contingent worker — — — 0.107*** 

(0.010) 

Any admin records 0.053*** 0.010*** 0.044*** -0.078*** 

(0.002) (0.001) (0.002) (0.004) 

Number of admin records 0.031*** 0.002*** 0.031*** 0.024*** 

(0.001) (0.001) (0.001) (0.001) 

Bottom admin earnings quintile -0.006*** -0.004*** -0.002 0.045*** 

(0.002) (0.001) (0.002) (0.003) 

Second admin earnings quintile 0.015*** -0.005*** 0.020*** 0.018*** 

(0.002) (0.001) (0.002) (0.002) 

(continued...) 

65
Table 11 (continued). Ignorability of Earnings Nonresponse

(1) (2) (3) (4) 

Any Non- Unit Non- Item Non- Item NonVARIABLES response response response response 

(...continued) 

Fourth admin earnings quintile -0.010*** -0.002 -0.009*** -0.003 

(0.002) (0.001) (0.002) (0.002) 

Top admin earnings quintile -0.002 

(0.002) 

-0.002 

(0.001) 

-0.001 

(0.002) 

0.004** 

(0.002) 

Observations 1,910,102 1,910,102 1,843,692 1,055,629

R2 0.054 0.076 0.048 0.080

Source: Authors’ calculation from the 2008 panel of the Survey of Income and Program Participation, 

Waves 1 through 14 and the Social Security Administration’s Detailed Earnings Record, calendar years 

2009 through 2012. 

Note: Sample for columns 1 and 2 is all person-months for people aged 15 and older, who were assigned 

a PIK, and who were present in the survey for all 12 months of the calendar year. Sample for column 

3 is all person-months for people aged 15 and older, who were assigned a PIK, who were present in 

the survey for all 12 months of the year, and who provided at least some data about their labor market 

situation. Sample for column 4 is all person-months for people aged 15 and older; who were assigned a 

PIK; who were present in the survey for all 12 months of the year; who provided at least some data about 

their labor market situation; and who worked on a non-contingent job or business. We also restrict the 

sample in columns 3 and 4 to people who were not imputed to work unpaid at a family business. The 

estimates in this table result from OLS regressions. The dependent variable in column 1 indicates any 

earnings nonresponse. The dependent variable in column 2 indicates unit earnings nonresponse. Unit 

nonresponse occurs in two scenarios: when non-interviewed individuals reside with interviewed individuals and when interviewed individuals decline to provide any information about their labor market 

situations. Item nonresponse occurs when interviewed individuals provide some information about their 

labor market situations but decline to provide information about either earnings from a job for an employer, earnings from a self-employed business, earnings from moonlighting, or severance pay. Other 

controls in columns 1 through 4 include CBSA size indicators, cubic age, and quartic age. Other controls in column 4 also include 2-digit occupational affiliation according to the 2000 Census occupation 

classification system. In column 4, class of worker (i.e. private, federal, state, local, self-employed), 

stopped work, and occupation indicate characteristics of employment on any job or business. Hours 

worked and weeks worked measure time worked on all jobs and businesses combined. Any sample gaps 

indicates individuals who leave the survey and later return to the survey. Non-English speaker indicates 

individuals who speak a language other than English in the home. We constructed the distribution of 

person-year level, positive administrative earnings without sample weights. Standard errors are clustered 

at the person-wave level and are listed in parentheses. *** denotes p < 0.01, ** denotes p < 0.05, and * 

p < 0.1. 

66
Table 12. Impact of Earnings Nonresponse: Gender Earnings Gap Estimates

VARIABLES 

(1) 

SIPP 

(2) 

DER 

(3) 

Reported SIPP 

(4) 

SIPP-DER Hybrid 

Female -12,035.131*** 

(317.847) 

-13,288.647*** 

(359.504) 

-13,032.824*** 

(380.895) 

-12,242.796*** 

(331.799) 

Observations 

R2 

88,971 

0.035 

88,971 

0.035 

60,994 

0.041 

88,971 

0.034 

Source: Authors’ calculation from the 2008 panel of the Survey of Income and Program Participation, Waves 1 

through 14 and the Social Security Administration’s Detailed Earnings Record, calendar years 2009 through 2012. 

Note: Sample includes all person-years for people aged 15 and older, who were assigned a PIK, who were present 

in the survey for all 12 months of the calendar year, who exhibited both positive SIPP earnings and positive DER 

earnings, and whose SIPP and DER earnings were both in the bottom 99 percent of their respective distributions. 

Column 3 also restricts the sample to individuals who had no imputed earnings data for any month of the year. The 

estimates in this table result from OLS regressions. The dependent variable in columns 1 and 3 is SIPP earnings. 

The dependent variable in column 2 is DER earnings. The dependent variable in column 4 is a hybrid, which is 

defined as SIPP earnings for people who had no month of imputed SIPP earnings data during the year and DER 

earnings for people who had at least one month of imputed SIPP earnings data during the year. Standard errors 

are clustered at the person level and are listed in parentheses. *** denotes p < 0.01, ** denotes p < 0.05, and * 

p < 0.1. 

67
Table 13. Impact of Earnings Nonresponse: Racial Earnings Gap Estimates

VARIABLES 

(1) 

SIPP 

(2) 

DER 

(3) 

Reported SIPP 

(4) 

SIPP-DER Hybrid 

Black alone 

White alone 

Asian alone 

-1,105.722 

(845.454) 

8,172.760*** 

(775.277) 

14,448.261*** 

(1,212.485) 

-717.163 

(955.005) 

8,708.535*** 

(880.799) 

17,454.187*** 

(1,407.620) 

-2,533.923** 

(1,049.950) 

8,024.679*** 

(967.692) 

15,590.213*** 

(1,490.752) 

-1,070.417 

(878.123) 

8,532.522*** 

(808.963) 

15,526.097*** 

(1,270.065) 

Observations 

R2 

88,971 

0.011 

88,971 

0.011 

60,994 

0.014 

88,971 

0.012 

Source: Authors’ calculation from the 2008 panel of the Survey of Income and Program Participation, Waves 

1 through 14 and the Social Security Administration’s Detailed Earnings Record, calendar years 2009 through 

2012. 

Note: Sample includes all person-years for people aged 15 and older, who were assigned a PIK, who were 

present in the survey for all 12 months of the calendar year, who exhibited both positive SIPP earnings and 

positive DER earnings, and whose SIPP and DER earnings were both in the bottom 99 percent of their respective 

distributions. Column 3 also restricts the sample to individuals who had no imputed earnings data for any month 

of the year. The estimates in this table result from OLS regressions. The dependent variable in columns 1 and 

3 is SIPP earnings. The dependent variable in column 2 is DER earnings. The dependent variable in column 

4 is a hybrid, which is defined as SIPP earnings for people who had no month of imputed SIPP earnings data 

during the year and DER earnings for people who had at least one month of imputed SIPP earnings data during 

the year. SIPP gives respondents the option of reporting more than one race. The indicators in this table define 

racial groups to include individuals who reported only one race. Individuals who reported multiple races are 

included in the omitted group. Standard errors are clustered at the person level and are listed in parentheses. 

*** denotes p < 0.01, ** denotes p < 0.05, and * p < 0.1. 

68
Table 14. Impact of Earnings Nonresponse: Mincer Regression Estimates

VARIABLES 

(1) 

SIPP 

(2) 

DER 

(3) 

Reported SIPP 

(4) 

SIPP-DER Hybrid 

Years of education 

Potential experience 

Potential experience squared 

4,952.861*** 

(69.979) 

1,899.405*** 

(29.135) 

-31.146*** 

(0.608) 

5,206.608*** 

(80.359) 

2,182.327*** 

(32.053) 

-35.785*** 

(0.665) 

5,367.872*** 

(83.510) 

1,987.822*** 

(35.487) 

-32.806*** 

(0.723) 

5,082.564*** 

(73.782) 

2,034.514*** 

(29.938) 

-33.304*** 

(0.626) 

Observations 

R2 

88,971 

0.233 

88,971 

0.222 

60,994 

0.252 

88,971 

0.236 

Source: Authors’ calculation from the 2008 panel of the Survey of Income and Program Participation, Waves 1 through 14 

and the Social Security Administration’s Detailed Earnings Record, calendar years 2009 through 2012. 

Note: Sample includes all person-years for people aged 15 and older, who were assigned a PIK, who were present in the 

survey for all 12 months of the calendar year, who exhibited both positive SIPP earnings and positive DER earnings, and 

whose SIPP and DER earnings were both in the bottom 99 percent of their respective distributions. Column 3 also restricts 

the sample to individuals who had no imputed earnings data for any month of the year. The estimates in this table result from 

OLS regressions. The dependent variable in columns 1 and 3 is SIPP earnings. The dependent variable in column 2 is DER 

earnings. The dependent variable in column 4 is a hybrid, which is defined as SIPP earnings for people who had no month 

of imputed SIPP earnings data during the year and DER earnings for people who had at least one month of imputed SIPP 

earnings data during the year. When education was reported in a range, years of education is defined as the midpoint of that 

range. Potential experience is defined as age minus years of education minus 5. Standard errors are clustered at the person 

level and are listed in parentheses. *** denotes p < 0.01, ** denotes p < 0.05, and * p < 0.1. 

69