1 

Introducing Parametric Models and Administrative Records 

into 2014 SIPP Imputations 

Gary Benedetto 

Joanna Motro 

Martha Stinson 

U.S. Census Bureau

4600 Silver Hill Road 

Suitland, MD 20746

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

Abstract 

As part of the redesign of the Survey of Income and Program Participation (SIPP), we have developed a new

imputation process for handling respondents who are missing entire sections of data. This new process creates 

indicator variables called topic flags that determine whether each section of questions was relevant for a respondent 

(e.g. receipt of Food Stamps). We model the joint distribution of these variables and covariates from the survey and 

from independent data sources using a parametric method called Sequential Regression Multiple Imputation 

(SRMI). The resulting, approximate distribution is used to impute missing values for the topic flags. Modeling 

topic flags in this manner is an alternative imputation method to hot-deck imputation or whole-record donation for 

cases where respondents did not complete the majority of the survey. As opposed to hot-deck imputation that can 

only control for a limited number of characteristics, the SRMI approach is able to control for many more variables, 

including household, parent, and spouse characteristics. Moreover, our process incorporates administrative records 

for the first time into SIPP production. These data offer a valuable set of covariates whose availability is unrelated 

to survey non-response and, as a result, helps to mitigate problems caused when survey data are not “missing at 

random”. This paper describes our modeling process, its advantages over more traditional imputation methods like 

hot-deck imputation, and demonstrates the usefulness of linking administrative data into the models. Lastly, we 

show preliminary results for the SIPP 2014 panel topic flags. 

Disclaimer: This report is released to inform interested parties of ongoing research and to encourage discussion. 

The proportions and other statistics in the text, figures, and tables of this report are presented here for the purpose of 

evaluating new imputation methodology. The weighting and design effects necessary to interpret the reported 

statistics as estimates of underlying population parameters are not available at this time. Apparent differences may 

not be statistically significant, but all comparative statements in this report have undergone statistical testing and are 

significant at the 95% confidence level. All data are subject to error arising from a variety of sources, including 

sampling error, nonsampling error, model error and any other sources of error. Any views expressed on statistical, 

methodological, technical, or operational issues are those of the authors and not necessarily those of the U.S. Census 

Bureau. 
2 

Introduction 

In 2006, the Census Bureau undertook a major redesign of the Survey of Income and Program Participation (SIPP). 

The redesign included changing the length of the survey reference period, introducing an event history calendar 

instrument, completely overhauling the data processing system that produces public use data, and using linked 

administrative records to improve the quality of the data. As part of this redesign effort, new imputation methods 

were explored that utilized both the linked data and more modern techniques to improve the handling of missing 

data. This paper explains how the model-based imputation methods that grew out of this research were adopted to 

impute high-level topic screener indicators in the 2014 SIPP, the first production panel to utilize the redesigned 

system. We present the results of a select number of imputed topic indicators to show how the new imputation 

method is an improvement over past methods. Administrative data was also used to correct responses where people 

confused receipt of Social Security Insurance (SSI) and Old-Age, Survivors, and Disability Insurance (OASDI). We 

present the basic results of this correction for SSI. A detailed report of the correction can be found in Giefer, 

Williams, Bendetto, and Motro 2015. 

Background and Improvements of New Imputation Method 

Previously, SIPP processing handled missing data by using a hot deck to choose a donor. The donor’s answers were 

then used to fill in the data for the respondent with missing values. For respondents missing answers to only a few 

questions, the hot deck imputation was done on a variable by variable basis. In cases where the respondent failed to 

answer enough questions for a sufficient partial interview, one donor was selected to provide a full-record donation. 

A major disadvantage of this hot deck method was only being able to control for limited characteristics when 

creating donor cells, due to the need to make the cells sufficiently large. This heightened the perennial concern 

about data not missing at random conditional on the hot deck stratifying variables. In the case of whole record 

donation, any information about a topic that was provided by the respondent was over-written and the resulting 

record was sometimes inconsistent with data reported by other household members. 

The new imputation process seeks to address these concerns by adopting a model-based approach that is able to 

control for many more variables, including household, parent, and spouse characteristics and information from 

administrative records. Topic flags are now modeled the same way for everyone, whether a respondent is missing 

one topic or all of them. Imputation of downstream variables still relies on hot decks but the universe for these 

variables is built off the topic flag with model-based imputations. 

Topic Flags 

A topic is a higher-order level question in a block of SIPP questions about a specific content area. Most often, these 

questions screen respondents to determine whether they should answer subsequent questions about a particular 

content area. For example, a screener question might ask if a person received OASDI benefits. If the respondent 

answers ‘yes,’ they are asked subsequent questions about their OASDI benefits, including when they started and 

how much they received. If the respondent answers ‘no’, they bypass the additional questions and move on to the 

next content area. If the respondent fails to answer the higher-order/screener question, then a response is imputed 

through the model-based process. For example, for those respondents who did not answer the labor force section of 

the survey, we imputed whether they held any jobs at any point during the reference year. Similarly, for those who 

did not answer the social welfare benefits questions, we imputed section screener questions such as Temporary 

Assistance for Needy Families (TANF), Supplemental Nutrition Assistance Program (SNAP), and Supplemental 

Security Income (SSI) receipt. Table 1 shows a full list of topics and percent of data imputed per topic. 

Topic flags are created prior to imputation and contain valid ‘yes’ and ‘no’ responses or flagged as either ‘needs 

imputing’ or ‘missing and not in universe’. After the imputation is complete, topic flags have no missing data where 

a respondent is in universe for a topic. Each topic flag has an accompanied allocation flag indicating if the response 

comes from a valid response, a model-based imputation, a logical imputation1

, or is not in universe. 

 

1

 Logically imputed responses are those where information was provided in other SIPP questions that inform a ‘yes’ 

or ‘no’ response to a topic flag. 
3 

Topic flags serve as an alternative imputation method to hot-deck imputation or whole-record donation for 

respondents who missed entire sections of the SIPP. The process of imputing topic flags also incorporates 

administrative data to mitigate problems caused when survey data are not “missing at random.” These topic flags 

then facilitate downstream question edits, determining who is in universe to answer more detailed questions about 

the specific content area. For people who are in universe for downstream questions but fail to answer those 

questions, those responses are imputed using a hot-deck method. 

Data 

SIPP 

The SIPP is a nationally representative household-based survey focused on sources of income, jobs, and 

government-based assistance programs. The model-based imputation was first implemented for households that 

were interviewed in 2014 using the re-designed SIPP survey instrument to ask about their economic well-being in 

2013. This paper focuses on the first wave of the 2014 SIPP panel. 

In order to reduce respondent burden, some topics were asked to only one person in a household or family member 

within a household on behalf of other household/family members. In this paper, for these topics, we discuss only 

the imputation for people who were supposed to answer the question on behalf of the household or family clump2

within a household, not necessarily the equivalent of people who were covered by certain programs. 

Administrative Data 

The model based imputation makes use of six different sources of data shared with the Census Bureau by the Social 

Security Administration (SSA). First, we use two types of earnings records derived from W-2 forms filed with SSA 

by employers. The Detailed Earnings Record (DER) extract reports uncapped income-taxable earnings for each 

employer that filed a W-2 record from 1978-2012. It also contains a report of earnings that were not income taxable 

and were deferred into accounts like 401(k) plans. We utilize the DER to create a measure of total earnings in a 

given year and to count the number of jobs an individual held. From the DER we also create measures of selfemployed earnings and an indicator of any deferred earnings. We also utilize the Summary Earnings Record (SER) 

extract which contains total earnings capped at the FICA taxable maximum from 1951-2012 to create a count of 

how many years an individual has worked over his or her lifetime. 

Next, we make use of the Master Beneficiary Record (MBR) and Payment History Update System (PHUS) extracts 

to create indicators for whether an individual was eligible for and received OASDI payments due to retirement, 

disability, spouse retirement or death, parent retirement or death, or some combination of reasons. These extracts 

contain both present benefit receipt and historical information so we are able to tell what year an individual started 

receiving benefits and whether they ever stopped. The Supplemental Security Record (SSR) provides the same 

information about SSI benefits. These three files combined together give us a very accurate picture of who was 

receiving OASDI benefits, SSI benefits, or both. This information in turn is very helpful in predicting reports of 

OASDI and SSI receipt. 

Finally, we make use of the Numident, a register of all Social Security Numbers (SSNs) ever issued in the United 

States, along with the MBR and SSR, as an administrative source of birth date information. If a person is receiving 

benefits we utilize the birth date from the benefits files in order to create an age for the individual during the survey 

reference period. If the person is not receiving benefits, we use the birth date from the Numident. While this does 

not replace the survey reported age on the final public use data, we do use this age derived from administrative data 

as an explanatory variable in our models. 

Methodology 

Sequential Regression Multivariate Imputation 

 

2

 Age and immediate family members of the household respondent define a clump within a household. For 

example, if a father is the household respondent, his spouse, children under 22, and him are in a clump. For some 

SIPP questions, the father (household respondent) answers on behalf of his spouse and children under 22. In this 

scenario, grandparents and any children 22+ years old living in the household are not in the clump and respond to 

questions themselves. 

5 

3) The same process is employed in the reverse for OASDI benefits. Individuals who reported 

OASDI benefit receipt but had no administrative record of receipt are compared to SSI 

administrative data. If they in fact received SSI benefits, the OASDI topic flag is set to ‘no’ and 

the SSI topic flag is set to ‘yes.’ 

Results 

Table 1 shows basic summary statistics of the percent of missing data needing imputation, by topic imputed through 

SRMI, and the person-level match rate to administrative records. For most topics, about 3% to 7% of respondents in 

universe for a topic are missing and are flagged for imputation. For each topic, about 65% to 70% of respondents 

flagged to be imputed match to administrative records. This high match rate suggests that administrative records are 

likely a good source of independent data to use in the model-based imputation. The final column of Table 1 shows 

the percent of people in universe for each topic who did respond per topic and matched to administrative records. 

Here we see that about 90% of in-universe respondents for each topic matched to administrative records. It is not 

surprising that people who responded to the survey were also more likely to be matched to administrative records 

because the probabilistic matching is likely higher for responders (e.g., better quality matching variables, people 

who do not move often, etc.). 

After the five iterations of SRMI and the post-imputation fix to address SSI-OASDI program confusion, we 

performed some data review to test the quality of the imputations and analyze the value of the administrative data in 

our models. Table 2a shows demographic characteristics for Job Line 1. In the re-designed SIPP, respondents are 

asked about jobs they held in the previous year. In this paper, we focus on the first job reported on Job Line 1. Table 

2b shows demographic characteristics for receiving SNAP benefits. SNAP benefits are only asked of clump 

respondents (defined by family relationships and age) and people 15 years and older who were not in clumps. The 

percentages in these tables are column percentages. The first two columns of each table show the percent of people 

in universe for the topic who responded to the topic and those who were missing and flagged to be imputed. The 

last two columns are people who responded ‘yes’ to having a Job in 2013 (Table 2a) and ‘yes’ to receiving SNAP 

benefits in 2013 (Table 2b). 

One of the most obvious problems with past imputations in the SIPP has been the inconsistency between earnings 

and income-qualified program participation. This has largely been due to hot-deck procedures not being able to 

condition on a very large set of covariates. While the new imputation methods have only been implemented for 

high-level topic flags, we have reason to believe that these models, which allow many more covariates to influence 

the imputations, will help address this problem. 

The SNAP topic flag is a good example of maintaining consistency across earnings and income-qualified program 

participation with the new imputation method. In Table 2c, we see SNAP participation broken down by category of 

total 2012 household administrative earnings. The first two columns show the percent of people who reported ‘yes’ 

received SNAP benefits and those imputed to have received SNAP benefits in 2013. For households that have 

administrative record earnings of $25,000 or more, there is seemingly a big difference in 38% of imputed

households receiving SNAP while only 22% of non-imputed households reported receiving SNAP. Upon further 

inspection, this large percentage point difference is possibly due to differences in the earnings distribution between 

responders and non-responders for everyone in-universe for the SNAP question (the second two columns). In these 

second two columns, a larger percentage of non-responders were in the $25,000 or more category compared to the 

percentage of responders (68% and 55%, respectively). In the final two columns of Table 2c that conditions on 

being in-universe for the SNAP topic, 5.3% reported ‘yes’ received SNAP benefits and 6.7% were imputed to ‘yes’ 

received SNAP benefits. Therefore, the differences in the distribution of household earnings between imputed 

SNAP participants and reported SNAP participants (first two columns) are due mainly to observable differences in 

the distribution of household earnings for non-responders and responders. 

The difference in administrative data between the missing and non-missing populations is a good example of how 

having an independent source of data on the survey respondents helps to address the classic problem of survey data 

not missing at random. Perhaps the clearest example of this is with the employment indicators. Table 3 shows a 

cross-tabulation of the presence of a job in the first job line of the survey with the indicator for positive earnings in 

the 2012 administrative data. We see that 59.6% of job line 1 responders say they have a job in the survey and 58% 

have positive earnings in the 2012 administrative data. For the non-responders, we impute 62.9% to have a job in 

job line 1, which is 3 percentage points higher than the rate for responders. However, the non-responders also have 
6 

positive earnings in the 2012 administrative data at a rate of 61.7% which is also about 3 percentage points higher 

than what we see for the responders. Without this extra source of covariation in our model, it is not clear that the 

imputed rate for the presence of a job would be different from the responders despite the real presence of differences 

(observed through the administrative records, but unobserved in the survey data) between the two populations. 

As mentioned in the Methodology section, we were also able to use the administrative records in a combined editimputation to correct for obvious program confusion. After the SRMI but prior to the program confusion edit, Table 

4 shows that 1559 respondents have positive participation in SSI in the survey but do not have SSI receipt in the 

administrative data implying a false positive rate of 48.2%. The post-imputation edit reduces this cell dramatically to 

329 resulting in a much more acceptable false positive rate of 15.6%. 

Finally, while there is a dearth of diagnostics for the quality of imputations, Raghunathan and Bondarenko (2007) 

suggest a promising statistic that is fairly intuitive. They suggest using the full, completed data to estimate a logistic 

regression for whether a surveyed individual responds to an item or not. If a strong model can be estimated to 

predict response, then one can compare the imputed and non-imputed values of the variable in question for 

respondents with similar propensities to respond. If the imputations are good under the missing-at-random 

assumption (or missing-at-random conditional on a good set of independent covariates such as the administrative 

records we use), then the distributions of imputed and reported values for individuals with similar response 

propensities should also be similar. Table 5 shows the rate of the “Yes” response for 5 topic flags broken down by 

imputed versus reported for each quintile of the predicted propensity of response for that topic. The five topics (Job 

Line 1, SNAP, WIC, Disability, and Education Enrollment) were chosen because of sufficient sample sizes and 

because we were able to estimate response models which did not fail the Hosmer-Lemeshow goodness-of-fit test.5

For most cells, the imputed and reported means are not statistically different. For all but one of the cells where the 

means are statistically different, the magnitude of the difference is small from an analyst perspective. For example, 

for Job Line 1, quintile 2, roughly 72% of the responders have a job and roughly 68% of non-responders are imputed 

to have a job. That difference, while statistically significant, is quite small in magnitude. The one major problem cell 

is the 2nd quintile of the propensity to respond for SNAP. This result indicates that the SNAP imputation model may 

need further refinement in future iterations of modeling. 

Conclusion 

The introduction of parametric models conditioning on survey and administrative data into the missing data 

imputation process is a great step forward for the SIPP. Problems with not missing-at-random have been addressed. 

A much larger set of covariates, from both the survey and the administrative records, can now influence the 

imputations of very important, high-level screeners. Linking to administrative data also allows for corrections in 

survey data when there is strong evidence of program confusion. We hope that the improvements made at this high 

level will also benefit the more traditional edits and imputations for the more detailed questions downstream of the 

topic flags. 

In addition to benefiting the SIPP, these imputation methods have the potential to benefit other Census products as 

well. This work shows that it is possible to obtain and integrate administrative records into survey processing 

systems in a timely manner and that these outside data sources are highly valuable in identifying differences 

between individuals that are not apparent from the survey data. Other surveys could implement similar methods and 

reduce the bias that comes from the violation of the missing-at-random assumption. 

 

 

5

 The Hosmer-Lemeshow test is a goodness-of-fit test for logistic regressions. It has some limitations in that 

rejecting the null implies a bad model fit, but not rejecting the null does not necessarily mean it is a good model. 

Moreover, if the sample is too small, we may not reject the null even with a poor model. 
7 

References 

Ambler G, Omar RZ, Royston P. 2007. “A comparison of imputation techniques for handling missing data predictor 

values in a risk model with a binary outcome.” Statistical Methods in Medical Research 16:277–298.

van Buuren S. 2007. “Multiple imputation of discrete and continuous data by fully conditional specification.” 

Statistical Methods in Medical Research 16: 219–242.

Giefer, Katherine, Williams, Abby, Benedetto, Gary, and Motro, Joanna, 2015. “Program Confusion in the 2014 

SIPP: Using Administrative Records to Correct False Positive SSI Reports.” Forthcoming in FCSM 2015 

Proceedings. 

He, Yulei, et al. 2009. “Multiple imputation in a large-scale complex survey: a practical guide.” Statistical Methods 

in Medical Research 19 (6): 653-670.

Raghunathan, Trivellore, Lepkowski, James M., van Hoewyk, John, and Solenberger, Peter. 2001. “A multivariate 

technique for multiply imputing missing values using a sequence of regression models.” Survey 

Methodology 27 (1): 85-96.

Raghunathan, Trivellore and Bondarenko, Irina, 2007. “Diagnostics for Multiple Imputations.” SSRN Working 

Paper Series. Available at SSRN: http://ssrn.com/abstract=1031750 or 

http://dx.doi.org/10.2139/ssrn.1031750 

 
8 

Table 1: Summary of Topic Flag Data 

Topic 

Total in 

Universe 

Percent of inUniverse 

Respondents 

Imputed 

Percent of inUniverse, Imputed

Respondents who 

Linked to AdRecs 

Percent of inUniverse, Reported

Data Respondents 

who Linked to 

AdRecs 

Disability 58090 6.0 68.1 91.7 

Disability Payments 9946 34.2 68.0 94.3 

Disability Limitations 72401 7.5 70.7 91.2 

Energy Assistance 18614 4.5 81.1 92.1 

Fertility 58367 4.3 66.4 91.3 

Lump Sum Payments 58030 6.1 68.1 91.7 

Retired 43857 4.8 69.3 92.9 

Retirement Payments 13048 16.2 68.6 96.0 

Spousal Payments 4607 3.6 76.3 95.3 

OASDI-Self 55148 5.8 67.3 91.7 

OASDI-Kids 20454 8.7 70.5 89.6 

Unemployment 

Compensation 58090 6.0 68.1 91.6 

Veterans Benefits 4999 3.6 73.2 95.9 

Worker's Compensation 58050 6.0 69.0 91.6 

Education Enrollment 70441 6.0 69.3 91.2 

SNAP 41011 5.0 61.8 91.0 

General Assistance 41011 4.9 61.6 91.0 

Job 1 58367 5.6 68.1 91.5 

Job 2 58367 5.8 69.3 91.5 

Private Health Insurance 73215 6.4 65.7 91.0 

Military Health Insurance 73215 7.5 67.1 91.1 

Medicare Health Insurance 73215 7.7 67.4 91.1 

Medicaid Health Insurance 73215 10.2 73.6 91.1 

SSI* (pre-correction) 72065 6.3 69.5 91.2 

Dependent Care 20835 2.1 72.0 92.9 

TANF 41011 4.9 61.7 91.0 

WIC 58367 3.4 70.5 90.9 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2014 Panel (Wave 1) and administrative records from the Master 

Beneficiary Record (MBR), Supplemental Security Record (SSR), Detailed Earnings Record (DER), Payment History (PHUS), Summary 

Earnings Record (SER), and Numident. 
9 

Table2a: Demographic characteristics of respondents in universe for the Job 1 screener questions; 

respondents who answered having a job and imputed to have a job in 2013 

 All Respondents 

Respondents with a 

Job in 2013 

 

SIPP Job 1 

Reported 

SIPP Job 1 

Missing 

SIPP Job 1 

Reported 

SIPP Job 1 

Imputed 

Col. % Col. % Col. % Col. % 

Sex 

Male 47.3 51.4 51.5 54.0 

Female 52.7 48.6 48.5 46.0 

Age 

15-25 15.9 31.9 12.8 24.7 

26-49 38.5 40.6 51.3 51.6 

50-64 25.6 19.3 29.0 20.9 

65+ 20.0 8.2 7.0 2.8 

Race 

White 65.1 55.3 66.0 57.7 

Black 13.4 15.7 12.4 14.2 

Hispanic 6.8 10.2 6.6 9.5 

Other 14.7 18.8 15.1 18.7 

Marital Status 

Married 48.3 35.4 52.8 39.7 

Not Married 51.7 64.6 47.2 60.3 

Education 

No HS 18.4 22.7 10.4 13.6 

HS 29.4 31.7 29.1 31.9 

SoCo 19.6 18.9 21.0 20.9 

Assoc 8.1 6.4 9.7 8.0 

College 15.6 14.2 19.5 17.9 

Grad 9.0 6.2 11.3 7.7 

Household Income from Admin Records* 

No Earnings 18.2 6.7 3.5 1.8 

Under $25,000 22.7 21.4 21.4 18.7 

$25,000 and Above 59.1 71.9 75.1 79.6 

N 55170 3197 32865 2011

*Administrative earnings lagged one year (2012) because of availability. Imputed where missing. 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2014 Panel (Wave 1) and administrative records from the Master 

Beneficiary Record (MBR), Supplemental Security Record (SSR), Detailed Earnings Record (DER), Payment History (PHUS), Summary 

Earnings Record (SER), and Numident. 

 
10

Table 2b: Demographic characteristics of respondents who answered the SNAP screener questions; 

respondents who answered receiving SNAP benefits and imputed as receiving SNAP benefits in 2013 

 All Respondents 

Respondents Receiving 

SNAP in 2013 

 

SIPP SNAP 

Reported 

SIPP SNAP 

Missing 

SIPP SNAP 

Reported 

SIPP SNAP 

Imputed 

Col. % Col. % Col. % Col. % 

Sex 

Male 46.6 52.9 29.0 38.6 

Female 53.4 47.1 71.0 61.4 

Age 

15-25 12.0 29.3 10.8 29.6 

26-49 40.8 45.7 51.2 43.2 

50-64 25.9 16.9 23.8 20.0 

65+ 21.3 8.1 14.3 7.3 

Race 

White 63.8 51.0 45.4 39.6 

Black 15.0 17.0 27.9 25.9 

Hispanic 6.7 10.9 6.6 7.7 

Other 14.6 21.1 20.0 26.8 

Marital Status 

Married 37.2 17.2 18.5 12.7 

Not Married 62.8 82.8 81.5 87.3 

Education 

No HS 15.3 19.9 30.5 28.6 

HS 30.4 34.5 37.9 43.2 

SoCo 20.9 19.6 20.3 16.4 

Assoc 8.4 6.4 5.7 4.6 

College 16.0 14.6 4.2 X 

Grad 9.0 4.9 1.4 X 

Household Income from Admin Records* 

No Earnings 20.5 6.7 35.3 16.4 

Under $25,000 24.9 25.0 43.0 45.9 

$25,000 and Above 54.6 68.4 21.7 37.7 

N 39003 2008 5300 220

*Administrative earnings lagged one year (2012) because of availability. Imputed where missing. 

"X" indicates suppressed for disclosure reasons. 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2014 Panel (Wave 1) and administrative records from the Master 

Beneficiary Record (MBR), Supplemental Security Record (SSR), Detailed Earnings Record (DER), Payment History (PHUS), Summary 

Earnings Record (SER), and Numident. 
11

Table 2c: 2013 SNAP Benefit Topic Flag Response and Receipt by 2012 Earnings from Administrative 

Records 

 

 

“Yes” Received 

2013

SNAP in In-universe for SNAP 

Conditional “Yes” 

Percentages on In-universe 

for SNAP 

SIPP SIPP SIPP SIPP SIPP SIPP 

 

Reported Imputed Reported Missing Reported Imputed 

Col % Col % Col % Col % 

Household Income 

from Administrative 

Records 

No Earnings 35.3 16.4 20.5 6.7 23.5 22.4 

<$25,000 43.0 45.9 24.9 25 23.5 19.8 

>=$25,000 21.7 37.7 54.6 68.4 5.3 6.7 

Total 100 100 100 100

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2014 Panel (Wave 1) and administrative records from the Master 

Beneficiary Record (MBR), Supplemental Security Record (SSR), Detailed Earnings Record (DER), Payment History (PHUS), Summary Earnings 

Record (SER), and Numident. 

 
12

Table 3: Job Line 1 Topic Flag compared to 2012 AdRec Employment 

Overall Percentages 

jobs held (94.5% of 

for cases where SIPP respondent answered 

in-universe respondents) 

the first question about 

Percent worked for pay in 2013? Percent with W-2/Schedule C 

earnings in 2012? 

positive 

Yes 59.6 Yes 58.0 

No 40.4 No 42.0 

Overall Percentages for cases where SIPP respondent DID NOT answer 

about jobs held and TF was imputed (5.5% of in-universe respondents) 

the first question 

Percent worked for pay in 2013? Percent with W-2/Schedule C 

earnings in 2012? 

positive 

Yes 62.9 Yes 61.7 

No 37.1 No 38.4 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2014 Panel (Wave 1) and administrative records from the Master 

Beneficiary Record (MBR), Detailed Earnings Record (DER), Summary Earnings Record (SER), and Numident. 

 
13

Table 4a: Distribution of Pre- and Post-Correction SIPP SSI Receipt by Administrative Record SSI Receipt:

2013. (only respondents successfully linked to Administrative Records) 

SIPP SSI Topic Flag 

Administrative Record 

SSI Receipt: "Yes" 

Administrative Record 

SSI Receipt: "No" 

N N 

Before Correction 

Yes 1674 1559

No 535 61603 

After Correction 

Yes 1778 329

No 431 62833 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2014 Panel (Wave 1) and administrative records from the Master 

Beneficiary Record (MBR), Supplemental Security Record (SSR), Detailed Earnings Record (DER), Payment History (PHUS), Summary 

Earnings Record (SER), and Numident. 

Table 4b: Distribution of Pre- and Post-Correction SIPP OASDI Receipt by Administrative Record SSI 

Receipt: 2013. (only respondents successfully linked to Administrative Records) 

SIPP OASDI Topic Flag 

Administrative Record 

OASDI Receipt: "Yes" 

Administrative Record 

OASDI Receipt: "No" 

N N 

Before Correction 

Yes 11329 821

No 2157 51064 

After Correction 

Yes 11668 524

No 1818 51361 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2014 Panel (Wave 1) and administrative records from the Master 

Beneficiary Record (MBR), Supplemental Security Record (SSR), Detailed Earnings Record (DER), Payment History (PHUS), Summary 

Earnings Record (SER), and Numident. 

 
14

Table 5: Rate of “Yes” by Quintile of Predicted Response Propensity. In each pair, left (blue) bars represent 

imputed cases for non-responders, and right (red) bars represent actual responses. 

(a) Job Line 1 Screener (b) SNAP Screener 

(c) WIC Screener (d) Disability Screener 

(e) Education Enrollment Screener 

*indicates the difference in mean between imputed and non-imputed cases is statistically different at 95% confidence level. 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2014 Panel (Wave 1) and administrative records from the Master 

Beneficiary Record (MBR), Supplemental Security Record (SSR), Detailed Earnings Record (DER), Payment History (PHUS), Summary 

Earnings Record (SER), and Numident.

0

0.2

0.4

0.6

0.8

1* 2* 3 4 5

0

0.05

0.1

0.15

0.2

0.25

1* 2* 3 4 5

0

0.01

0.02

0.03

0.04

0.05

0.06

1 2 3 4 5

0

0.05

0.1

0.15

0.2

0.25

0.3

1* 2 3 4* 5

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1* 2 3* 4 5