Comparing Job Characteristics from the 2010 SIPP-EHC Field Test

to the Census Bureau Business Register

Martha Stinson, Graton Gathright, and Jeremy Skog

U.S. Census Bureau

March 20, 2012

1 Introduction

The Census Bureau collects information on households and businesses through mostly separate processes. 

With the redesign of the Survey of Income and Program Participation (SIPP), an eort has begun to 

evaluate household survey data on jobs by comparing them to corresponding reports made by employers. 

The purpose of this comparison is two-fold: frst, to determine what improvements can be made to the 

survey questionnaire to more accurately capture the desired income and employer characteristics; second, 

to document dierences between the household and business data and begin to consider the impact of these 

dierences for the various uses of the SIPP. 

In this paper, we present initial results from comparing data from an early (2010) feld test of the new 

SIPP survey instrument, the SIPP Event History Calendar (SIPP-EHC), to data from IRS Form W-2 and 

to the Census Bureau Business Register (BR). In order to perform such a comparison, we frst use Social 

Security Numbers (SSNs) to match SIPP respondents to all their W-2 records for employment in calendar 

year 2009, the reference period of the survey. Employers fle W-2 forms annually with the Internal Revenue 

Service (IRS) for each employee and so, theoretically, each W-2 record corresponds to a job held by the 

respondent. In addition to SSNs, W-2 records contain Employer Identifcation Numbers (EINs) which link 

businesses to the BR, which contains the name, address, and other characteristics of employers. The survey 

also attempts to collect information about individual jobs held, including the name of the employer and 

the physical address. In order to compare specifc frm characterisitcs reported in the SIPP and the BR, 

we follow our person-level match with a job-level match using the employer name and address. Thus for 

all SIPP respondents with SSNs and W-2s, we attempt to match each reported job in the SIPP to the 

employing establishment in the BR. After accomplishing this link, we are able to compare frm type (i.e. 

single- or multi-unit), frm size (i.e. number of employees), annual earnings, industry coding, and class of 

worker classifcation between the survey and the BR. 

We would like to thank Shawn Klimek, Javier Miranda, Paul Hanczaryk, Gary Benedetto, Judy Eargle, Jason Fields, 

Becky Chenevert, Marina Vornovytskyy, and Dave Hedengren for helpful comments and suggestions. We are grateful to Bill 

Winkler for the use of the BigMatch software for probabilistic linking. This report is released to inform interested parties of 

ongoing research and to encourage discussion of work in progress. 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. All of the data used 

in this paper are confdential data. The U.S. Census Bureau supports external researchers’ use of some of these data items 

through the Research Data Center network (www.census.gov/ces). All comparative statements in this report have undergone 

statistical testing, and, unless otherwise noted, all comparisons are statistically signifcant at the 5 percent signifcance level. 

For more information on the SIPP including sources of sampling and nonsampling error and statistical uncertainty, please 

see the SIPP main website http://www.census.gov/sipp/ and the Source and Accuracy Statement for the 2008 SIPP panel at 

http://www.census.gov/sipp/source.html. 

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Our initial results show that there are a smaller number of jobs reported to the SIPP-EHC than to the IRS 

via the W-2 form. The W-2 count may not necessarily represent the same concept as the survey-reported 

job count, yet, the dierences indicate the possibility that some jobs are not being reported in the survey. 

We fnd higher earnings on average in the SIPP-EHC than on the W-2 records. We believe that this result is 

mostly the case because of extreme outliers in the survey earnings. We fnd substantial disagreement about 

frm type and size between SIPP-EHC and BR, some of which might possibly be explained by dierences in 

timing of the frm reports on number of employees and dierences in how some government agencies such 

as school districts are classifed. For industry, when we compare at the NAICS 2-digit level, the SIPP-EHC 

and the BR agree about 75% of the time. 

Our paper proceeds as follows. We begin with a detailed description of data from 2010 SIPP-EHC, IRS 

Form W-2, and the BR and of the linking process used for combining these data. We then present results 

from comparing person level employment measures and from job-level employer characteristics. We conclude 

with thoughts on future work. 

2 Data

In our analysis, we evaluate data on employment from the 2010 feld test of SIPP-EHC and the 2008 panel 

of SIPP. We link sample persons in the surveys to data from IRS Form W-2. We also link survey-reported 

jobs in 2010 SIPP-EHC to data about employers from the Census Bureau’s Business Register. 

2.1 Data from 2010 SIPP-EHC

From January to March of 2010, the Census Bureau felded a test of the SIPP-EHC instrument. This was 

the frst computer-aided personal interview (CAPI) test and the frst test to start with a fresh survey sample. 

The new instrument is scheduled to be used for the frst time with a full national SIPP sample in 2014. 

The sample for the 2010 SIPP-EHC included 7982 units. The sample was not designed to be nationally 

representative, but was restricted to units from the unit frame, self-representing primary sampling units 

(PSU), the high-povery within-PSU stratum, in one of ten particular states1, and serviced by one of six 

particular feld oÿces2. 

The unit response rate for the feld test was 82% and these responses yielded data on 14,738 sample persons. 

Of these sample persons, 5,686 reported at least one job during the reference year. 

The instrument was designed to collect a one year history for up to fve jobs including up to two employment 

spells per employer, but the instrument did not constrain job details to match across spells within the same 

employer, so, in some cases the second spell may actually correspond to a dierent employer. We treated 

second spells as dierent jobs unless the employer names matched identically. Theoretically, then, up to ten 

dierent employers could be reported for a given sample person in the 2010 SIPP-EHC instrument. A clerical 

review of the spells found that there were only 42 second spells that we erroneously treated as dierent jobs. 

From the unedited data from this 2010 SIPP-EHC feld test, we used or created job-level and person-level 

measures for our analysis. At the job level, we created a measure of annual earnings and we used the 

following reported employer characteristics: industry, number of employees at the place-of-work, number of 

employees frm-wide, whether the employer operated at more than one location, and whether the employer 

was a government entity, and if so what kind. At the person-level, we created a measure of annual earnings 

and a count of jobs held during the one-year reference period. 

Our job-level annual earnings measure was created from data on the timing of employment spells and the 

12010 SIPP-EHC states were California, Connecticut, Illinois, Louisiana, Massachusetts, Maryland, Rhode Island, Texas, 

Wisconsin, and New York. 22010 SIPP-EHC feld oÿces were Boston, Chicago, Dallas, Los Angeles, Philadelphia, and New York. 

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dynamics of hours and pay rate for each job. In the data, start and ending months were reported for all job 

spells; where start day and ending day of jobs spells were missing we replaced them with the frst and last 

days of the month, respectively. We dropped reported within-spell changes in pay rate or hours if there was 

insuÿcient information about when the change occurred. We also dropped spells and changes for which pay 

rate was reported as hourly but no information about hours worked was available. 

In some cases, the reported pay rate seemed implausibly high. These were often hourly pay rates that would 

be more plausibly interpreted as a monthly salary. To address the possibility that questions regarding pay 

rate had been misunderstood or the answers mis-keyed, “soft checks” for extreme values in hourly and annual 

pay rates were included in the next feld test, 2011 SIPP-EHC. For 2012 SIPP-EHC, additional soft checks 

for weekly, bi-weekly, semi-monthly, and monthly pay rates will be introduced. In our 2010 SIPP-EHC 

earnings measure, we dropped spells for which the reported pay rate was beyond the threshold for triggering 

these soft checks. Similarly, we ignored changes to pay rates where the new pay rate exceeded the soft-check 

threshold. 

We calculated our person-level annual earnings measure by summing annual earnings across all jobs for each 

sample person. In our person-level counts of all jobs held annually, we have treated pairs of spells that were 

clerically determined to be for the same employer as single jobs. 

From SIPP-EHC, we also used industry codes for each employer. Respondents’ descriptions of what work 

they do for each job they report were coded to a Census Industry Code (CIC) using a process that involves 

some automation and some hand coding. The process is the same one used for current production SIPP, 

the Current Population Survey, and the American Community Survey. A crosswalk between CIC and North 

American Industry Classifcation System (NAICS) is maintained so that CIC codes can be converted to 

NAICS codes. CIC codes are four digit where as NAICS codes can be up to six digits. Some CIC codes 

only correspond to a three digit NAICS code, meaning that for these industry groups, the Census coding is 

not at the same level of detail as is possible in NAICS. Sometimes a CIC code will correspond to a subset 

of larger digit NAICS codes within a smaller digit NAICS group. For example, CIC 2390 (rubber products 

manufacturing, except tires) corresponds to NAICS 32622 and 32629. Thus any survey job coded with CIC 

2390 is given two NAICS codes. 

2.2 Data from SIPP

In our person-level analysis we also use data from 2008 SIPP in order to compare the extent of agreement 

between administrative records and survey data across SIPP and SIPP-EHC. Accordingly, we restrict our 

analysis of 2008 SIPP to the units that match the 2010 SIPP-EHC sample design. 

We created person-level measures of annual earnings and of the number of employers in the year from edited 

2008 SIPP data. For the annual earnings measure, we sum non-imputed monthly earnings from employment. 

In order to match our annual earnings measure from SIPP-EHC, we code missing earnings as zero in months 

for which the sample person was interviewed. However, we code monthly earnings as missing that are missing 

due to wave non-response or due to a sample person joining the sample after wave 2. In our annual earnings 

measure, we deal with these missing earnings by weighting up the observed person-months for an individual 

to represent all 12 months of the year. 

We create an annual job count from SIPP using the edited employer numbers that are designed to link a 

sample person’s employers across waves. 

2.3 Data from IRS Form W2

From IRS Form W-2 data for Calendar Year 2009, we create job- and person-level annual earnings and 

person-level measures of annual number of employers. Workers in these data are uniquely identifed by 

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a Census Bureau Protected Identity Key (PIK), a confdentiality-protected version of the Social Security 

Number. Employers in these data are identifed by an Employer Identifcation Number (EIN). 

We create our EIN-level measure of annual earnings from these data by summing the value of the “Wages, 

Salary, and Tips” feld with the value of the “Deferred Compensation” feld. The person-level version was 

created by summing the EIN-level measure across EINs for each PIK. We count the number of distinct EINs 

for each PIK to create our person-level measure of annual number of jobs. 

2.4 Data from the Census Bureau Business Register

We link job reports in 2010 SIPP-EHC to data about the employing establishments from the Census Bureau Business Register (BR). Establishments in the BR are operating units of an enterprise that typically 

correspond to a particular location of business. The BR contains data on these establishments which comes 

from the tax flings of these businesses and from their responses to various business surveys. 

Some enterprises in the BR, called single-unit enterprises, operate only one establishment. Enterprises that 

operate more than one establishment are called multi-unit enterprises. Multi-unit enterprises often use more 

than one EIN, the employer identifer on each W2 record, to report economic activity to the IRS about 

sub-groups of establishments. 

The BR records a (potentially two-part) name and two addresses (physical and mailing) for each establishment. A (potentially two-part) name is also sometimes recorded at the EIN level and is called the 

“submaster” name. 

Each establishment in the BR, then, is classifed as belonging to a single- or multi-unit enterprise. The BR 

also records a fve digit NAICS code describing the industry of each establishment. 

For each multi-unit enterprise, the BR records the number of employees at each establishment and in the 

entire enterprise as of March 12 of each year, derived from business survey data and IRS tax reports. We use 

these counts as our measures of size for multi-unit establishments and enterprises. For single-unit enterprises, 

the number of employees at the enterprise/establishment is recorded in the BR based on quarterly tax flings. 

We use these data as our measure of frm size for single-unit enterprises. 

Enterprises in the BR are also classifed as government or non-government. 

2.5 Data Linking

We perform two types of data-linking for our analysis. First, we link respondents in 2010 SIPP-EHC and in 

2008 SIPP to IRS Form W-2 data using a unique person identifer that was added to both datasets using 

standard Census Bureau processes that are documented elsewhere. This identifer is a protected version of 

the Social Security Number and is called the Protected Identity Key (PIK). 

For some sample persons there is no PIK available. The PIK rates in 2010 SIPP-EHC and the geo-matched 

2008 SIPP for sample persons aged at least 15 years at time-of-interview are 81.9% and 74.4%, respectively. 

In 2010 SIPP-EHC, there were 5,686 sample persons reporting at least one job. Of these, 4,379 had a PIK 

available for linking and 3,740 were linked by PIK to at least one W-2 record. 

There were 6,540 jobs reported in 2010 SIPP-EHC, with 5,083 jobs held by sample persons for which a PIK 

was available. For 4,377 of these, there was at least one potentially matching W-2 record. 

Matching reported jobs to specifc W-2’s and to the employing establishments was the second type of linking 

that we performed. Using probabilistic matching on name and address of employer, we linked SIPP-EHC 

reported jobs to establishments in the BR. The establishments to which a given job could be matched were 

all of the establishments belonging to an EIN that had issued a W-2 for the sample person reporting the job. 

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We performed the probabilistic matching using BigMatch, software developed at the U.S. Census Bureau. 

We employed multiple passes using dierent linking variables, parameter values, and score cutos. The 

parameter values and score cutos were chosen by clerical review of sets of matched and non-matched pairs. 

The passes were performed in descending order of stringency of criteria. After excluding any matching results 

that did not meet pass-specifc score cutos, we chose, as the linked establishment for each job, the paired 

establishment with the highest match score in the earliest pass. Prior to the probabilistic matching passes, 

we cleaned and standardized the data using the SAS Data Quality Server and additional custom routines. 

documentation on what this processing does to the data. 

There were 468 jobs that did not appear in any pair with a score suÿciently high to exceed the cutto in 

any pass. For these, we clerically reviewed the possible matches; by this method we were able to match an 

additional 79 jobs to establishments. 

3 Results

We present comparisons of information about jobs between the 2010 SIPP-EHC data and administrative 

records, frst at the person-level and then at the job-level. We frst compare fact-of-employment between 

the survey and the W-2 records. Also at the person-level, we compare counts of the number of jobs held 

and annual earnings. To benchmark the performance of the SIPP-EHC, we perform these comparisons for 

the geo-matched 2008 SIPP as well. At the job-level, we compare survey reports with BR records on frm 

organization, public versus private employment, establishment and enterprise size, and industry. In many 

instances, we are cautious about declaring the administrative data to be truth, therefore we simply highlight 

the dierences between the two data sources. 

3.1 Employment, Earnings, Job Counts

3.1.1 SIPP-EHC and W-2 Comparison

We begin our comparison of survey job reports and W-2 data by looking at the percentage of people who 

have job records in both sources. In Table 1, we group individuals by whether they reported a job in the 

SIPP-EHC and whether they had any W-2 records for 2009, the survey reference year. Of those with no 

reported jobs in the SIPP-EHC, 13% had at least one W-2 record. Of those who reported a job in the 

SIPP-EHC, 85% had at least one W-2 record, while approximately 15% had no W-2 records. This last group 

represents at least two types of jobs: self-employment and informal jobs where payments were not reported 

to the IRS by the employer. Next, considering the universe of individuals without W-2 records, it appears 

that about 10% have some type of earnings. For individuals with at least one W-2 record, 81% report 

some job activity in the survey. These results lead us to suspect that the SIPP-EHC is failing to capture 

information about a non-trivial number of jobs. However this table also highlights the value of survey data 

collection for insights into jobs that are not refected in tax records. 

In Table 2, we present average earnings for each cell in Table 1 in order to begin to paint a picture of the types 

of jobs we are missing. For those with no job report in the SIPP-EHC, we report average W-2 earnings when 

there are W-2 reports. This amount ($13,684) is the total amount earned and reported on all W-2 records 

for an individual in 2009, averaged across individuals in the SIPP-EHC and reported in the top right cell of 

Table 23. This amount leads us to believe that we are missing some jobs with signifcant enough earnings 

that total earnings for these households will be artifcally low. For individuals with SIPP-EHC jobs but no 

W-2 records, we report average survey earnings as $36,127. This amount is the total amount earned and 

reported in the SIPP-EHC for an individual in 2009, again averaged across all individuals. Here we conclude 

3We remind the reader that our sample is not nationally representative and hence these average amounts will not compare 

to published national averages. We have not adjusted these amounts for infation. All amounts are in 2009 dollars, as they were 

reported. 

5 
that jobs which do not produce W-2 forms nonetheless report positive amounts of income. Capturing this 

income is one of the strengths of the survey relative to the administrative records. 

In the bottom-right cell of Table 2, we show both average W-2 and survey earnings for individuals who had 

jobs reported in both sources. Contrary to our expectations from past studies of SIPP survey earnings, we 

found that SIPP-EHC earnings were higher on average than W-2 earnings for individuals with job reports 

in both the survey and the tax forms. The dierence of approximately $8000 was signifcant at the .1% 

level4. Investigation into the cause of this has led us to conclude that some respondents gave unreasonable 

answers when asked about their rate of pay. In particular, respondents who said they were paid by the hour 

sometimes reported hourly wages greatly in excess of an amount that would produce a reasonable amount of 

annual earnings. In spite of our attempt to discard the most ergregious reports, enough outliers still remain 

to skew average earnings. 

In order to further investigate our hypothesis that outliers are producing this result, we present median 

earnings for these same three cells in Table 3. The median is lower than the mean for each cell for both types 

of earnings, with skewness coeÿcients of 5.3 for both types of SIPP-EHC earners and 4.1 and 2.5 respectively 

for W-2 earners with and without survey earnings. Thus we conclude that survey non-reporters are a group 

with some high earners who raise the average W-2 earnings to about twice as much as the median. For 

those who do report earnings in the survey, median SIPP-EHC earnings are approximately $14,000 lower 

than average SIPP-EHC earnings for those with W-2 reports and $20,000 lower for those without W-2 

reports. This lends credence to our theory that outliers are the main cause of disagreement between average 

W-2 earnings and average SIPP-EHC earnings. Median SIPP-EHC earnings are only approximately $2,000 

higher than median W-2 earnings for sample persons with jobs reported in both sources. Data from the 2011 

and 2012 SIPP-EHDC feld tests will permit an evaluation of the improvement to earnings measurement 

from implemented instrument changes such as real-time checks on the level of monthly earnings implied by 

answers to pay rate and hours worked questions. 

In Table 4, we compare the number of jobs reported in the SIPP-EHC to the number of jobs producing W-2 

records for those with job reports in both sources. Before examining these percentages, it is worth considering 

what the W-2 job count actually represents. Employers fle W-2s with the IRS using employer identifcation 

numbers (EINs) and it is possible for a multi-unit company to have multiple EINs and to classify dierent 

parts of the total amount earned by a given employee under dierent EINs. It is also possible for a company 

to change ownership during a year and acquire a new EIN, which would then produce a second W-2 for 

every employee. We have done some preliminary work on both these fronts to try to determine the extent to 

which such arrangements are present in our data and we do not believe that these types of occurrences are 

widespread. However, this work is still preliminary and we have not been able, at this point, to produce a 

W-2 job count that combines records which a survey respondent would most likely view as coming from one 

employer. Hence the percentages in this table should be viewed as noisy measures. Nonetheless, there are 

some general patterns of interest. Of individuals who report only one job in the SIPP-EHC, approximately 

20% have two or more W-2 records (sum of row percentages for categories above one job). Of individuals 

with two W-2 records, 65% (column percentage) report only one job in the survey and more than half of 

individuals with three, four, and fve or more W-2 records report only one job in the survey. As in Table 1, 

we conclude that it is likely that SIPP-EHC respondents are failing to report some jobs. 

3.1.2 2008 SIPP, SIPP-EHC, and W-2 Comparison

In order to place these comparisons between W-2 and SIPP-EHC earnings and job counts into context, 

we also compare the geo-matched5 2008 SIPP panel to W-2 records. Unlike the SIPP-EHC, in the 2008 

panel, employer jobs and self-employment were collected in separate parts of the instrument. Hence for 

this comparison between the two surveys, we restrict attention to jobs reported as being for an employer, 

4The t-stat for this comparison was -8.5294 with degrees of freedom equal to 2684. 

5See Section 2. 

6 
as “other arrangement”, or as “contingent work.” We begin by reproducing Table 1, but not counting selfemployment jobs. As shown in Table 5, we see that this has almost no eect on the percentage of people with 

no reported employment in the SIPP-EHC who nevertheless match to W-2 records. However the percentage 

with SIPP-EHC employment and no W-2 records drops by fve percentage points, from 14.5% to 9.3%. Selfemployment appears to explain about a third of the cases where a person reported working but had no W-2. 

In Table 6, we show the same results for the 2008 SIPP and see that the overall picture of survey employment 

and W-2 reports is comparable between the two survey sources. Approximately 10% of respondents with 

reported jobs have no W-2 records while 11% have no survey employment but do have W-2 records. This 

later group is about three percentage points smaller than in the SIPP-EHC. 

In Table 7, we present comparisons of dierences between survey and W-2 measures for annual earnings and 

for annual job counts across 2008 geo-matched SIPP and 2010 SIPP-EHC. We regress dierences between the 

administrative and survey versions of each measure on a constant and an indicator for inclusion in the SIPPEHC (SIPPEHC=1) or SIPP (SIPPEHC=0) survey. In the frst column, we calculate the dierence between 

total 2009 survey earnings and total 2009 W-2 earnings at the person level and then for all individuals 

with positive W-2 earnings. The constant represents the average dierence between tax records and survey 

reports for the geo-matched 2008 SIPP sample is approximately -$4000. Since the coeÿcient is negative, 

the W-2 earnings are higher and the dierence is signifcantly dierent from zero. The coeÿcient on the 

SIPP-EHC indicator is not signifcantly dierent from zero and we conclude that for this broad group, there 

is a signifcant dierence between survey and tax earnings but this dierence does not vary across the two 

surveys in a statistically signifcant way. 

In the second column of Table 7, we restrict ourselves to comparing only respondents with positive earnings 

in both survey and tax records. The average dierence drops for 2008 SIPP respondents to -$2900. Given 

that we dropped individuals who had zero earnings in the survey, this change is not surprising. The dierence 

between zero earnings and positive earnings is larger on average than the dierence between two positive 

earnings reports. However, the coeÿcient on the SIPP-EHC indicator is now positive and quite large, 

approximately $11000. This refects our earlier fndings that average SIPP-EHC earnings were substantially 

higher than average W-2 earnings, a feature not present in the SIPP 2008 data. 

In the third column of Table 7, we look at average dierences in job counts. Relative to their W-2 job counts, 

respondents in the 2008 SIPP report approximately a third of a job less. Respondents in the SIPP-EHC 

report almost a half a job less (sum of constant and SIPP-EHC indicator coeÿcients). The coeÿcient on 

the SIPP-EHC indicator is statistically signifcant, meaning that this dierence between job counts in the 

2008 SIPP and the SIPP-EHC is signifcantly dierent from zero. Since the 2008 SIPP panel interviewed 

respondents every four months, the job count is dependent on the correct tracking of jobs across waves. If 

job reports from multiple interviews are not properly connected with a common employer identifer, the job 

count will be artifcally infated. Historically there have been many diÿculties in linking employer reports 

across waves in the SIPP and while many problems have been resolved, we believe that some still remain. 

Hence it is unclear if the SIPP-EHC is really worse than the 2008 SIPP in collecting job counts or whether 

there are simply fewer seam issues. In any case, both surveys collect fewer jobs than are found in the W-2 

records. 

3.2 Job Characteristics

We now turn to job-level comparisons between the survey and administrative data. Because the SIPPEHC collected the identifying information for employing establishment including place-of-work address, we 

were able to compare establishment level job characteristics in a way not heretofore possible for SIPP6. 

These comparisons take advantage of the link between survey-reported jobs and specifc establishments in 

the BR that is discussed in Section 2. As reported in Table 8, we were able to match 69.6% of the 4,377 

6The American Community Survey collects both name and address of the employer but no SIPP panel prior to the SIPP-EHC 

has collected this information. 

7 
survey-reported jobs for sample persons having a PIK and at least one W-2 record. 

We had not expected to match jobs that were reported as self-employment for an incorporated business paying 

a salary to the worker. However, of these 105 unlikely-to-match jobs, 22 did match to an establishment in the 

BR. This suggests the possibility of some mis-reporting about the type of job. For jobs that were expected 

to match to the BR, the match rate was 70.8%. Jobs may have failed to match because of insuÿcient or 

poor quality information for identifying the employing establishment, or because the employment did not 

generate a W-2 record. Some reported jobs have no W-2 record because the employer failed to report the 

employment to the IRS. Other reported jobs have no W-2 record because the reported job was actually the 

type of self-employment that generates no W-2 record. 

3.2.1 Company Organization: Number of Locations and Private versus Government Sector

Both the survey and BR data furnish measures of whether an employer operates at only one or at more 

than one location (see Section 2). In Table 9, we present a job-level comparison between the survey report 

of whether the employer operates at more than one location and the BR classifcation of the enterprise as 

single-unit or multi-unit. Of the jobs reported to be for employers operating in multiple location, 62.51% 

matched to establishments of multi-unit enterprises; the remaining 37.49% matched to single-unit enterprises. 

Agreement between survey and administrative data was stronger for jobs reported to be for single-location 

employers: 75.54% of these jobs matched to single-unit enterprises. 

The discrepancies between survey-reported and BR-recorded number of employer locations may indicate 

mis-reporting by survey respondents. Some of the disagreement may also arise from dierences between 

the concepts of location in the two measures. For instance, it appears that in public sector employment, 

business entities likely to be operating in multiple locations are sometimes recorded in the business register 

as single-unit enterprises. In particular, school districts may operate multiple schools but be recorded as 

single-unit enterprises in the administrative data. 

In Table 10, we present a comparison between the survey and BR on whether the employer was in the 

private or government sectors. Of jobs reported in the survey as being for the federal government, 72.5% 

matched to a government enterprise in the BR. Some of tis disagreement about employment for the federal 

government may arise from mis-reporting about type of employer by government contractors responding 

to the survey. Agreement between the survey and BR about the employer being a government entity was 

strong for jobs reported as state government (86.4%), local government (90.5%) and armed forces (87.5%).7

Nearly all (98.6%) of the jobs reported to be for for-proft private-sector employers matched to private-sector 

enterprises in the BR. Agreement between survey and BR about government status was 88.7% for jobs 

reported to be in the non-proft private sector. 

3.2.2 Establishment and Enterprise Size

We compare survey-reported information about the number of employees at employing establishments and 

enterprises8 with counts of employees at establishments and enterprises in the BR. As described in Section 

2, the survey measure of establishment and enterprise size (number of employees) is collected as one of 5 mutually exclusive collectively exhaustive size ranges in which the employee count would fall. As a convenience 

in comparing these data to the BR, we similarly discretize the employee counts recorded in the BR. 

For dierent types of enterprises in the BR, we use dierent BR measures of establishment and enterprise 

size. For single-unit establishments/enterprises, we use the quarterly employment measures in the BR that 

come from quarterly tax flings and represent number of employees present at the end of a tax quarter. 

In our comparisons, we use the size measures for the last quarter when the respondent was employed by 

7Only one job reported as being for the armed forces was matched to a non-government enterprise in the BR. 

8We use the term enterprise to mean the employing enterprise. 

8 
the enterprise. For multi-unit enterprises and the establishments of multi-unit enterprises, we use the BR 

employee counts from business surveys that are intended to measure establishment and enterprise sizes on 

March 12th. 

In Table 11, we present a comparison of the survey and BR establishment size measures for jobs identifed 

in the BR as single-unit enterprises. Of jobs matched to single-unit enterprises with less than 10 employees 

according to the BR, 76.0% reported in the survey in the same size range. Another 16.7% were reported to 

have between 10 and 24 employees, inclusive. Some of the jobs for which the survey and BR measures dier 

may refect the dierences in the count dates across the measures rather than respondent mis-reporting. 

For jobs matched to single-unit enterprises with 10 to 24 employees, the percentage with the survey report 

of enterprise size in the same range as the BR measure was 52.7%. Another 28.6% of these jobs fell in the 

next lower size range and 15.8% in the next higher size range. A very similar pattern was found for the jobs 

matched to single-unit enterprises with 25 to 99 employees. For the jobs matched to single-unit establishments 

in the 100 to 499 range, the jobs were spread more evenly across the matching survey reported size range 

and then one below. The two highest BR-recorded size ranges have the lowest level of correspondence to the 

survey reported size range. For frms in the 1000+ category in the BR, only 17.8% are reported to be in that 

category in the SIPP-EHC. The largest number of 1000+ employee frms are reported to be in the 25-99 

and 100-499 categories. The same pattern is true for the 500-999 BR size category which agrees with the 

SIPP-EHC report in only 12.5% of cases and has approximately 67% of survey reports in the two categories 

below the BR size. 

Across all of the BR enterprise size categories, substantial numbers of jobs which matched to BR single-unit 

enterprises reported a lower enterprise size category than recorded in the BR. In future work, we would 

like to investigate to what extent these were jobs for which the company organization (number of locations) 

was reported as multi-unit. If the respondent viewed her place of work as a multi-unit enterprise, then it 

is not unreasonable that her establishment size report would only account for part of the enterprise’s total 

employment. As mentioned above, we found that school districts likely to be operating at multiple locations 

were often recorded in the BR as single-unit establishments. 

In Table 12, we begin to look at size comparisons for multi-unit frms, beginning with a cross-tabulation of 

survey and BR measures of establishment size (i.e. work location). Of jobs matched to multi-unit enterprises 

with less than 10 employees at the establishement according to the BR, only 39.3% have survey-reported size 

in that same size range. Another 22.1% were reported to have between 25 and 99 employees, inclusive. Nearly 

30% is split evenly between the 10 to 24 and 100-499 categories. Perhaps the partitioning of enterprises into 

units for reporting purposes is fner than respondents perceive based on the notion of “location.” Some of 

this disagreement may also refect the dierences in the dates to which the counts correspond across rather 

than mis-reporting by respondents. 

For jobs matched to establishments belonging to multi-unit enterprises and having establishment-level employment in the 10 to 24 employees range, 35.6% had a survey report in that same establishment size range. 

Another 28.8% were reported to be in the 25 to 99 employee range. For the jobs matching to establishments 

of multi-unit enterprises having establishment size in the ranges 25 to 99 and 100 to 499, approximately 51% 

of matching jobs had survey reported establishment size that agreed with the BR counts, and approximately 

20% reported establishment size in the next lower category. 

Of the jobs matched to establishments belonging to multi-unit enterprises and having establishment-level 

employment in the 500-999 range, approximately 60% are roughly split between the survey-reported establishment size ranges of 100 to 499 and 500 to 999. Another 24.7% were reported in the survey to have 

establishment-level employment in excess of 1000 employees. Nearly 50% of the jobs matched to establishments belonging to multi-unit enterprises and having establishment-level employment in excess of 1000 

employees had survey-reported establishment size that agreed. Another approximately 30% was roughly split 

between the next two lower size ranges. Greater agreement for these size ranges may refect that intra-year 

fuctuation in establishment size may be less likely to move an establishment out of these larger size ranges. 

9 
It may also be easier for survey respondents to correctly place employers in these large ranges. 

In Table 13, we move to size comparisons at the enterprise level which utilizes the survey question asking 

about the number of employees at all locations of the employer. We present a cross-tabulation of enterpriselevel employment as reported in the survey and recorded in the BR using the BR employment counts as 

of March 12. Of the survey-reported jobs that match to multi-unit enterprises, 75.1% match to enterprises 

with over 1000 employees, 82.3% of which reported as much in the survey. For the jobs matched to multiunit enterprises in next three lower enterprise size ranges, there appears to be substantial over-estimation of 

enterprise-wide employment by survey respondents. Nearly half of the jobs matched to enterprises in the 500 

to 999 range were reported as being for enterprises with 1000 or more employees. For the 25 to 99 and 100 to 

499 ranges, the corresponding percentages were 24.2 and 27.7, respectively. Only two multi-unit enterprises 

had less than 25 employees, both of these had more than 10 employees. 

3.2.3 Industry

We next compare the survey-reported industry of the employing establishments with the industry of establishments as recorded in the BR. We perform multiple comparisons at increasing level of detailed coding, 

specifcally, at NAICS digit-levels 2 to 5. Also, as discussed in Section 2 above, survey-reported industry is 

sometimes coded as corresponding to a set of higher-digit NAICS codes within a smaller-digit NAICS code. 

In our comparisons, we consider the survey-reported industry to agree with the BR if any of the NAICS 

codes corresponding to the survey-reported industry matches the NAICS code recorded in the BR for the 

establishment to which the job is matched. 

In Table 14 we present rates of agreement between survey report and BR on industry coding at digit levels 2 

to 5 for jobs that matched to single-unit enterprises. At the least-detailed, two-digit level, survey-responsebased coding agreed with the BR for 74.1% of the jobs. Each successive higher-digit-level comparison excludes 

more jobs since survey-reported industry is coded only to the detail level that the CIC to NAICS crosswalk 

allows. The agreement rates at the 3-, 4- and 5- digit levels are 67.0%, 62.9%, and 60.4%, respectively. 

In Table 15, we present the corresponding results for jobs that matched to establishments of multi-unit 

enterprises. At the two-digit level, the survey-report-based NAICS coding agreed with the BR for 72.70% 

of these jobs. The agreement rates at the three-, four- and fve-digit levels are 66.6%, 56.0%, and 47.3%, 

respectively. Since industry classifcation can dier across establishments within an enterprise, it is possible 

that some of the agreement about industry classifcation for jobs matched to establishments of multi-unit 

enterprises is due to matching jobs to the incorrect establishment with an enterprise. 

It is also important to note that when asked about what an employing frm does, the respondent is asked to 

classify the frm into a broad category (manufacturing, wholesale trade, retail trade, service, or something 

else) and then to give the main activity of the frm. If the employer is a multi-unit enterprise, then the 

respondent is asked to report the main activity of the enterprise at the location where the sample person 

is employed by the enterprise. If the frm is engaged in multiple activities at the work location, then the 

respondent is asked to choose the main one. Since people are best informed about what they personally do, 

some respondents may have trouble reporting a main activity and instead simply report their own activity. 

This would cause disagreement between the survey job report and the BR industry code in a way that is 

dierent from standard mis-reporting models. For instance an individual who is employed by a restaurant 

but works in the gift shop may be classifed by the Census industry coding process as belonging to the retail 

sector when, in fact, his employer is classifed as belonging to the restaurant service industry. We may be 

hesitant to declare the worker’s response to be “wrong” in this case. However, the output from this employer, 

i.e. the product of the worker, is classifed in the national product accounts under the service industry. More 

research is needed to determine the eects on analyses from classifying workers according to their frms’ 

industry designations or their own. 

In Table 16, we present the distribution across two-digit BR NAICS code of jobs matched to single-unit 

10 
enterprises for which the industry coding does not agree between survey-report and BR. The highest rates 

of disagreement were in “Wholesale Trade” (12.57%), “Admin. Support, Waste Management” (13.90%), 

and “Public Admin” (16.84%). Wholesale trade is probably diÿcult for survey respondents to describe in a 

way that does not sound like retail trade or manufacturing. The Admin. Support and Waste Management 

category includes temporary employment agencies for which some respondents may be describing the work 

that they perform at their job in the frm where they are placed, but this would not match to the temporary 

employment industry coding. Public Administration had the highest rate of disagreement suggesting that 

the jobs are hard to code correctly or the industry covers many dierent functions - schools are sometime 

coded as Public Administration instead of education in the BR, for example. 

Table 17 presents the corresponding distribution for jobs matched to establishments of multi-unit enterprises. 

The highest rates of disagreement were in “Management of Companies” (14.29%), “Admin. Support, Waste 

Management” (12.24%), and “Health Care, Social Assistance” (10.46%). It may be that employees working 

at a company’s headquarters but whose activity is something quite dierent from management are generating 

the high error rate in the Management of Companies category. For both single and multi-unit frms, it appears 

that industry disagreement is not entirely random and there are some categories that are either harder for 

respondents to describe or harder for Census to code. 

Finally, we present the distribution of jobs over two-digit level survey-report-based industry classifcations 

for jobs for which we found no matching establishment in the BR. We split this group into two categories: 

1) jobs for sample persons with no W-2 record and 2) jobs for sample persons with at least one W-2 record. 

Results for the frst group are presented in Table 18. Clusters of non-matching jobs for persons with no W-2s 

are in “Construction” (15.13%), “Health Care, Social Assistance” (10.96%), and “Other Service” (12.17%) 

which includes dry cleaning, beauty salons, and car washes. Other industry categories with a substantial 

number of these non-matching jobs are “Accomodations, Food Service” (7.48%), which includes hotels, and 

“Admin. Support,Waste Management” (8.69%), which includes landscaping companies. These are industry 

categories where it would not be surprising to discover employment that is not generating W-2 records. 

Collecting information on such employment is one of the strengths of household survey data. 

Table 19 presents similar results for unmatched jobs for sample persons who had a least one W-2 record. The 

industry category with the largest clustering of such jobs is “Health Care, Social Assistance” (15.23%). Other 

industries with clusters are “Educational Services” (9.36%) and “Admin. Support, Waste Management” 

(7.80%). Some of these non-matches arise from jobs that are not closely tied to a specifc location and are 

therefore more diÿcult to match to a specifc establishment. However others are likely from jobs that are 

not generating W-2 records. We conclude that the survey provides a useful picture of the informal labor 

market and captures some earnings that would be missed by using administrative data alone. 

4 Conclusion

We have documented instances where the SIPP-EHC reports on jobs and W-2 records disagree. In general, 

W-2 records produce higher job counts and lower earnings. Reports about frm organization often does not 

agree with the BR for those reporting work at multi-unit frms. Firm size is diÿcult to measure and to 

report in the small size categories, with many reports being just above or below the BR category. In larger 

sizes for multi-units, there is more agreement while in larger sizes for single-units there is less agreement and 

less of a clear pattern. When comparing industry, we fnd that three quarters of the jobs have the same two 

digit NAICS from the BR and the SIPP-EHC but this agreement decreases as we compare at greater levels 

of detail. Many of the industry disagreements are concentrated in Wholesale trade, Public Administration, 

Firm Management, and the sector which includes temporary help frms. 

Not all of this disagreement is necessarily the result of reporting error by SIPP-EHC respondents. Work 

remains to be done to investigate the structure of frms in the BR (especially in the government sector) 

and to create a more accurate job count from the W-2 records that refects parent ownership and succes11 
sor/predecessor relationships. There is also the need for more detailed industry comparisons and investigation 

into how dierences in household and frm industry reports aect analyses using industry classifcations. 

In spite of these qualifcations on the need for more careful investigation on data issues, we feel that this work 

sheds light on areas of further improvement for the SIPP-EHC. We are hopeful that the 2011 SIPP-EHC feld 

test will show substantial improvement in the reported earnings. We also feel that it is likely that the survey 

is missing jobs and so we continue to consider ways to collect a more complete picture of each respondent’s 

employment situation. We anticipate performing this evaluation for both the 2011 and 2012 SIPP-EHC feld 

tests and continuing to learn about dierences between the survey and the Business Register and the causes 

of these dierences. 

12 
Table 1: Percentage of SIPP-EHC Respondents with PIKs reporting jobs versus linking to W-2 Records 

Jobs Reported in SIPP-EHC 

No 

Matched to W-2 Records 

No Yes Total 

row%/col% row%/col% row%/col% 

86.64 13.36 100.00 

89.76 18.77 59.62 

Yes 14.59 

10.24 

85.41 

81.23 

100.00 

40.38 

Total 

N

57.55 

100.00 

10845 

42.45 

100.00 

100.00 

100.00 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

Table 2: Average Annual Survey and W-2 Earnings for SIPP-EHC Respondents with PIKs by reporting and 

linking status 

No SIPPEHC Jobs 

No W2 Jobs 

W2 Earn/SIPPEHC EARN 

W2 Jobs 

W2 Earn/SIPPEHC EARN 

13684.37 

. 

SIPPEHC Jobs 

Observations 

. 

36127.85 

324 

29515.53 

38967.43 

4604 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

13 
Table 3: Median Annual Survey and W-2 Earnings for SIPP-EHC Respondents with PIKs by reporting and 

linking status 

No SIPPEHC Jobs 

No W2 Jobs 

W2 Earn/SIPPEHC EARN 

W2 Jobs 

W2 Earn/SIPPEHC EARN 

5934.00 

. 

SIPPEHC Jobs 

Observations 

. 

15642.86 

324 

22721.50 

24611.43 

4604 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

14 
15 

Table 4: SIPP-EHC Job Counts by W-2 Job Counts

W-2 Job Count

SIPP-EHC Job Count 1 job 2 jobs 3 jobs 4 jobs 5+ jobs Total 

row%/col% row%/col% row%/col% row%/col% row%/col% row%/col%

1 job 80.13 14.48 3.45 1.19 0.75 100.00 

93.59 65.16 55.84 59.38 63.16 85.32 

2 jobs 33.12 48.10 13.50 3.80 1.48 100.00 

5.75 32.16 32.49 28.12 18.42 12.67 

3+ jobs 24.00 25.33 30.67 10.67 9.33 100.00 

0.66 2.68 11.68 12.50 18.42 2.01 

Total 73.05 18.96 5.27 1.71 1.02 100.00 

100.00 100.00 100.00 100.00 100.00 100.00 

N 3740 

Sources:U.S. Census Bureau:Survey of Income and Program Participation Event History Calendar Field Test 2010Internal Revenue Service:Form W-2 Records Tax Year 2009 
Table 5: SIPP-EHC Respondents with PIKs reporting employer and contingent jobs versus linking to W-2 

Records 

Emp./Cont.Work Jobs Reported in SIPP-EHC 

No 

Matched to W-2 Records 

No Yes Total 

row%/col% row%/col% row%/col% 

86.10 13.90 100.00 

93.99 20.57 62.82 

Yes 9.30 

6.01 

90.70 

79.43 

100.00 

37.18 

Total 

N

57.55 

100.00 

10845 

42.45 

100.00 

100.00 

100.00 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

Table 6: SIPP-2008 Respondents with PIKs reporting employer and contingent jobs versus linking to W-2 

Records 

Emp./Cont. Work Jobs Reported in SIPP 

No 

Matched to W-2 Records 

No Yes Total 

row%/col% row%/col% row%/col% 

88.93 11.07 100.00 

92.63 14.83 58.59 

Yes 10.02 

7.37 

89.98 

85.17 

100.00 

41.41 

Total 

N

56.25 

100.00 

6291 

43.75 

100.00 

100.00 

100.00 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation 2008 Panel 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

16 
Table 7: Comparisons of Average Annual Earnings and Job Counts between SIPP-2008, SIPP-EHC, and 

W-2s 

earn1 earn2 jobs 

sippehc -152.1 

(759.4) 

11153.3

(1060.4) 

-0.168

(0.0246) 

cons -4020.0 -2873.5 -0.302

(382.1) (426.7) (0.0203) 

N 7353 4954 7298 

Standard errors in parentheses 

Sources: 

U.S. Census Bureau: 

SIPP 2008 Panel and Event History Calendar Field Test 2010 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

 p < 0.05,  p < 0.01,  p < 0.001 

17 
Table 8: Job Match rate between SIPP-EHC and W-2 

W-2 Match Expectations 

Not expected to match to W-2 

No match 

rcpt/cpct 

79.05 

6.24 

Match Success 

Yes match Total 

rcpt/cpct rcpt/cpct 

20.95 100.00 

0.72 2.40 

Expected to match to W-2 29.21 

93.76 

70.79 

99.28 

100.00 

97.60 

Total 

N

30.41 

100.00 

4377 

69.59 

100.00 

100.00 

100.00 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

Table 9: Firm Organization Comparison: Single versus Multi Unit 

SIPP-EHC: more than one frm location 

Yes 

Firm Organization type in Business Register 

Multi-unit Single-unit Total 

row%/col% row%/col% row%/col% 

62.51 37.49 100.00 

81.96 46.88 64.00 

No 24.46 

18.04 

75.54 

53.12 

100.00 

36.00 

Total 

N

48.81 

100.00 

2817 

51.19 

100.00 

100.00 

100.00 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Business Register 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

18 
Table 10: Government Status Comparison 

SIPP-EHC Class of Worker 

Federal Govt 

BR Govt. Indicator 

Non-government Government 

row%/col% row%/col% 

27.50 72.50 

0.44 7.77 

Total 

row%/col% 

100.00 

1.39 

State Govt 13.56 

0.64 

86.44 

27.35 

100.00 

4.11 

Local Govt 9.52 

0.72 

90.48 

45.84 

100.00 

6.58 

Armed Forces 12.50 

0.04 

87.50 

1.88 

100.00 

0.28 

Unknown Govt type 71.43 

0.20 

28.57 

0.54 

100.00 

0.24 

Private, for proft 98.61 

88.23 

1.39 

8.31 

100.00 

77.85 

Private, non-proft 88.69 

9.73 

11.31 

8.31 

100.00 

9.54 

Total 

N

87.01 

100.00 

2871 

12.99 

100.00 

100.00 

100.00 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Business Register 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

19 
Table 11: Single Unit Firm Size Comparison: SIPP-EHC employment at location worked versus BR employment at end of last quarter worked at frm 

SIPP-EHC 

1-9 

1-9 

row%/col% 

51.69 

76.02 

10-24 

row%/col% 

17.85 

28.57 

25-99 

row%/col% 

10.15 

11.34 

Business Register 

100-499 

row%/col% 

7.69 

10.50 

500-999 

row%/col% 

1.85 

12.50 

1000+ 

row%/col% 

10.77 

10.74 

Total 

row%/col% 

100.00 

24.49 

10-24 14.07 

16.74 

40.68 

52.71 

28.14 

25.43 

7.60 

8.40 

0.76 

4.17 

8.75 

7.06 

100.00 

19.82 

25-99 2.99 

4.98 

8.70 

15.76 

39.67 

50.17 

19.02 

29.41 

5.16 

39.58 

24.46 

27.61 

100.00 

27.73 

100-499 1.31 

1.36 

1.75 

1.97 

13.10 

10.31 

39.30 

37.82 

5.68 

27.08 

38.86 

27.30 

100.00 

17.26 

500-999 2.99 

0.90 

2.99 

0.99 

4.48 

1.03 

34.33 

9.66 

8.96 

12.50 

46.27 

9.51 

100.00 

5.05 

1000+ 0.00 

0.00 

0.00 

0.00 

6.67 

1.72 

13.33 

4.20 

2.67 

4.17 

77.33 

17.79 

100.00 

5.65 

Total 16.65 

100.00 

15.30 

100.00 

21.93 

100.00 

17.94 

100.00 

3.62 

100.00 

24.57 

100.00 

100.00 

100.00 

N 1327 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Business Register 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

20 
Table 12: Multi Unit Firm Size Comparison: SIPP-EHC employment at location worked versus BR employment at establishment on March 12 

SIPP-EHC 

1-9 

1-9 

row%/col% 

47.83 

39.29 

10-24 

row%/col% 

15.65 

11.25 

25-99 

row%/col% 

13.04 

4.62 

Business Register 

100-499 

row%/col% 

15.65 

4.62 

500-999 

row%/col% 

4.35 

6.17 

1000+ 

row%/col% 

3.48 

2.52 

Total 

row%/col% 

100.00 

9.16 

10-24 11.24 

14.29 

32.02 

35.62 

38.20 

20.92 

11.24 

5.13 

0.56 

1.23 

6.74 

7.55 

100.00 

14.18 

25-99 9.28 

22.14 

13.77 

28.75 

49.70 

51.08 

22.75 

19.49 

1.80 

7.41 

2.69 

5.66 

100.00 

26.61 

100-499 5.73 

14.29 

5.73 

12.50 

15.47 

16.62 

57.31 

51.28 

7.16 

30.86 

8.60 

18.87 

100.00 

27.81 

500-999 6.80 

5.00 

1.94 

1.25 

4.85 

1.54 

35.92 

9.49 

23.30 

29.63 

27.18 

17.61 

100.00 

8.21 

1000+ 3.98 

5.00 

9.66 

10.62 

9.66 

5.23 

22.16 

10.00 

11.36 

24.69 

43.18 

47.80 

100.00 

14.02 

Total 11.16 

100.00 

12.75 

100.00 

25.90 

100.00 

31.08 

100.00 

6.45 

100.00 

12.67 

100.00 

100.00 

100.00 

N 1255 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Business Register 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

21 
Table 13: Multi Unit Firm Size Comparison: SIPP-EHC employment at all locations versus BR employment 

at enterprise on March 12 

SIPP-EHC 

1-9 

10-24 

row%/col% 

0.00 

0.00 

25-99 

row%/col% 

9.09 

3.03 

Business 

100-499 

row%/col% 

27.27 

2.19 

Register 

500-999 

row%/col% 

9.09 

1.64 

1000+ 

row%/col% 

54.55 

0.85 

Total 

row%/col% 

100.00 

1.18 

10-24 5.88 

50.00 

11.76 

6.06 

17.65 

2.19 

5.88 

1.64 

58.82 

1.42 

100.00 

1.82 

25-99 1.61 

50.00 

20.97 

39.39 

25.81 

11.68 

11.29 

11.48 

40.32 

3.56 

100.00 

6.63 

100-499 0.00 

0.00 

6.77 

27.27 

51.13 

49.64 

7.52 

16.39 

34.59 

6.55 

100.00 

14.22 

500-999 0.00 

0.00 

0.00 

0.00 

15.52 

6.57 

20.69 

19.67 

63.79 

5.27 

100.00 

6.20 

1000+ 0.00 

0.00 

1.22 

24.24 

5.81 

27.74 

4.59 

49.18 

88.38 

82.34 

100.00 

69.95 

Total 0.21 

100.00 

3.53 

100.00 

14.65 

100.00 

6.52 

100.00 

75.08 

100.00 

100.00 

100.00 

N 935 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Business Register 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

Table 14: Industry Comparison: Single-unit 

No 

NAICS agree 2 digit 

pct/obs 

25.89 

NAICS agree 3 digit 

pct/obs 

32.98 

NAICS agree 4 digit 

pct/obs 

37.06 

NAICS agree 5 digit 

pct/obs 

39.64 

Yes 74.11 67.02 62.94 60.36 

Total 100.00 

(1518) 

100.00 

(1407) 

100.00 

(1001) 

100.00 

(111) 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Business Register 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

22 
Table 15: Industry Comparison: Multi-unit 

No 

NAICS agree 2 digit 

pct/obs 

27.30 

NAICS agree 3 digit 

pct/obs 

33.40 

NAICS agree 4 digit 

pct/obs 

43.98 

NAICS agree 5 digit 

pct/obs 

52.69 

Yes 72.70 66.60 56.02 47.31 

Total 100.00 

(1436) 

100.00 

(1410) 

100.00 

(914) 

100.00 

(186) 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Business Register 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

23 
Table 16: Single-unit Industry Comparison: BR NAICS when disagree with SIPP-EHC 

Single Units 

pct 

Ag,Forest,FishHunt 0.53 

Utilities 0.27 

Construction 5.88 

Manufacturing 1 2.67 

Manufactuing 2 1.60 

Manufacturing 3 2.94 

Wholesale Trade 12.57 

Retail Trade 1 3.74 

Retail Trade 2 2.67 

Transportation 1.34 

Postal Service,Couriers,Warehousing 0.80 

Information 2.67 

Finance 1.07 

Real Estate 4.55 

Prof,Scientifc,Technical Services 5.35 

Admin.Support,Waste Management 13.90 

Educational Services 2.94 

Health Care,Social Assistance 2.67 

Arts,Entertain,Rec 4.01 

Accomodations,Food Service 4.28 

Other Services 6.68 

Public Admin 16.84 

Total 100.00 

N 374 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Business Register 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

24 
Table 17: Multi-unit Industry Comparison: BR NAICS when disagree with SIPP-EHC 

Multi Units 

pct 

Mining,Oil,Gas 0.77 

Utilities 0.26 

Construction 1.53 

Manufacturing 1 1.02 

Manufactuing 2 3.06 

Manufacturing 3 3.57 

Wholesale Trade 9.44 

Retail Trade 1 6.12 

Retail Trade 2 4.08 

Transportation 1.53 

Postal Service,Couriers,Warehousing 2.81 

Information 3.57 

Finance 1.53 

Real Estate 2.30 

Prof,Scientifc,Technical Services 4.08 

Management of Companies 14.29 

Admin.Support,Waste Management 12.24 

Educational Services 4.08 

Health Care,Social Assistance 10.46 

Arts,Entertain,Rec 1.53 

Accomodations,Food Service 8.16 

Other Services 3.57 

Total 100.00 

N 392 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Business Register 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

25 
Table 18: NAICS Industries of SIPP-EHC Jobs for Respondents with no W-2s 

No W-2s 

pct 

Ag,Forest,FishHunt 0.87 

Construction 15.13 

Manufacturing 1 2.09 

Manufactuing 2 1.22 

Manufacturing 3 2.26 

Wholesale Trade 2.78 

Retail Trade 1 6.43 

Retail Trade 2 2.26 

Transportation 7.13 

Postal Service,Couriers,Warehousing 0.17 

Information 2.09 

Finance 1.74 

Real Estate 3.30 

Prof,Scientifc,Technical Services 5.57 

Admin.Support,Waste Management 8.70 

Educational Services 3.48 

Health Care,Social Assistance 10.96 

Arts,Entertain,Rec 2.96 

Accomodations,Food Service 7.48 

Other Services 12.17 

Public Admin 1.22 

Total 100.00 

N 575 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Business Register 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

26 
Table 19: NAICS Industries of SIPP-EHC Jobs that do not match specifc W-2 Records 

No W-2 match for SIPP job 

pct 

Ag,Forest,FishHunt 0.73 

Mining,Oil,Gas 0.28 

Utilities 0.09 

Construction 6.06 

Manufacturing 1 1.93 

Manufactuing 2 1.93 

Manufacturing 3 3.58 

Wholesale Trade 2.29 

Retail Trade 1 7.16 

Retail Trade 2 1.83 

Transportation 3.58 

Postal Service,Couriers,Warehousing 1.83 

Information 2.39 

Finance 2.94 

Real Estate 3.03 

Prof,Scientifc,Technical Services 3.30 

Admin.Support,Waste Management 7.80 

Educational Services 9.36 

Health Care,Social Assistance 15.23 

Arts,Entertain,Rec 3.03 

Accomodations,Food Service 7.16 

Other Services 7.06 

Public Admin 7.43 

Total 100.00 

N 1090 

Sources: 

U.S. Census Bureau: 

Survey of Income and Program Participation Event History Calendar Field Test 2010 

Business Register 

Internal Revenue Service: 

Form W-2 Records Tax Year 2009 

27