A Comparison of Person Reported Industry to Employer-Reported 

Industry in Survey and Administrative Data1

Emily Isenberg

Liana Christin Landivar

Esther Mezey

U.S. Census Bureau

4600 Silver Hill Rd.

Suitland, MD 20746

Axioma, Inc.

17 State St. Suite 2700 

New York, NY 10004

Proceedings of the 2013 Federal Committee on Statistical Methodology (FCSM) Research Conference

Abstract

The Census Bureau collectsindustry information through surveys and administrative data and creates associated

public-use statistics. In this paper, we compare person-reportedindustry in the American Community Survey (ACS) 

to employer-reported industry from the Quarterly Census of Employment and Wages (QCEW) that is part of the 

Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) program.This research provides necessary 

information on the use of administrative data as a supplement to survey data industry information, and the findings 

will be useful for anyone using industry information from either source. Our project is part of a larger effort to 

compare information on jobs from household survey data to employer-reported information. This research is the first 

to compare ACS job data to firm-based administrative data.We find an overall industry sector match rate of 75 

percent, and a 61 percent match rate at the 4-digit Census Industry Code (CIC) level. Industry match rates vary by 

sector and by whether industry sector is classified using ACS or LEHD industry information. The educational 

services and health care and social assistance sectors have among the highest match rates. The management of 

companies and enterprises sector has the lowest match rate, using either ACS-reported or LEHD-reported sector. For 

individuals with imputed industrydata, the industry sector match rate is only 14 percent.Our findings suggest that 

the industry distribution and the sample in a particular industry sector will differ depending on whether ACS or 

LEHD data are used.

1. Introduction

The Census Bureau collects industry information through surveys and administrative data and creates associated

public-use statistics. In this paper, we compare person-reported industry in the American Community Survey (ACS) 

to employer-reported industry from the Quarterly Census of Employment and Wages (QCEW)that is part of the 

Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) program. This research provides necessary 

information for investigating the use of administrative data as a supplement to survey data industry information, and 

the findings will be useful for anyone using industry information from either source. Our project is part of a larger 

1 We are grateful for the many helpful suggestions from Melissa Chiu, Mark Kutzbach, and Erika McEntarfer. This 

paper has not undergone the review accorded Census Bureau publications and no endorsement should be inferred. 

Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views 

of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed.

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effort to compare information on jobs from household survey data to employer-reported information. This research 

is the first to compare ACS job information to firm-based administrative data.

The ACS, administered by the Census Bureau, is the largest household survey in the United States. About 3.2 

million addresses are sampled each year.The ACS collects household, demographic, and economic data fromall 

residents of a sampled household. Industry data are collected on a person’s current, primary job or the most recent 

job held within the past 5 years if not currently working. The LEHD data covers most ofthe universe of firms for the 

United States. The QCEW data includes all firms that are required to report employment for Unemployment 

Insurance, and the LEHD program has QCEW data for all states.2

The QCEW employer-level data are linked to 

quarterly data on individual workers, primarily from Unemployment Insurance (UI) wage records. We link 

individual work histories from the LEHD program to ACS data using Protected IdentificationKeys (PIKs) created 

by the Census Bureau.Because LEHD collects data on all UI covered jobs, individuals may havemore than one 

eligible job around the time of the ACS interview. When there is not a single job match between LEHD-reported job 

and ACS-reportedjob, we use ACS interview date and earnings to select a main LEHD job to use for industry 

comparisons.

We examine the distribution of industry sectors in ACS and LEHD data, compare industry match rates by ACSreported and LEHD-reported industrysectors, and assess where mismatches occur.We find an industry sector match 

rate of 75 percent, anda match rate at the 4-digit Census Industry Code (CIC)level of 61 percent.3

The educational 

services and health care and social assistance sectors have among the highest match rates. The management of 

companies and enterprises sector has the lowest match rate, using either ACS-reported or LEHD-reported sector. 

The wholesale trade sector also has relatively low industry sector match rates. For individuals with imputed industry

data, the industry sector match rate is only 14 percent.Those with missing industry are more likely to be young, 

lower educated, lower income, Black or Hispanic, unemployed or not in the labor force, less likely to have worked 

in the past year, and worked fewer hours or no hours in the past year.

The only other research we are aware of that compares industry reporting between survey and administrative data is 

Stinson, Gathright, and Skog (2012), henceforth SGS.

4

They compare industry responses in the Survey of Income 

and Program Participation (SIPP)to the Census Bureau’s Business Register.SGS match individuals in the SIPP to 

W-2 forms, which contain a firm identifier, and then use the firm identifier to link to businesses in the Business 

Register.5

 Jobs are matched using employer name and address. Although SGSuse different survey and 

administrative data and a different job-matching algorithm, they find a similar match rate at the 2-digit North 

American Industry Classification System (NAICS) level of about 75 percent.6

They also find similar match rates at 

the 4-digit NAICS level of 63 percent for firms with one establishment and 56 percent for firms with more than one 

establishment.7Wholesale trade and administration and support and waste management sectors also have relatively 

2 Massachusetts participates in the LEHD program, but Massachusetts data have not yet been processed.

3

The Census Bureau has developed and maintained its own industry code list since it started collecting data on 

industry in 1820. The Census Bureau industry code list has followed the structure of the North American Industry 

Classification System (NAICS) since its implementation in 1997, but aggregates smaller categories for 

confidentiality and statistical precision.

4

SGS also examine firm type (single- or multi-unit), firm size, annual earnings, and class of worker classification. 

Other research has compared earnings between survey and administrative data, for example, Bound and Krueger 

(1991), Bound, Brown, Duncan, and Rodgers (1994), Roemer (2002), and Abowd and Stinson (forthcoming). 

Abraham, Haltiwanger, Sandusky, and Spletzer (forthcoming) compare employment status between survey and 

administrative data. None of these studies use ACS data.

5

The Business Register is the Census Bureau’s main business list. The Internal Revenue Service (IRS) is the 

primary source of information on businesses in the Business Register.

6 Becker et al. (2005), Elvery et al. (2006), and Fairman et al. (2008) discuss differences between the QCEWbased Bureau of Labor Statistics (BLS) business list and the Census Bureau Business Register. The two business 

lists differ in scope, with the BLS list containing firms required to report earnings for unemployment insurance and 

the Business Register containing firms reporting to the IRS.Even for businesses in both lists, there can be 

differences in industry. For example, Elvery et al. (2006) note that of matched single-unit firms, the employmentweighted industry sectormatch rate is 87 percent.

7 NAICS sector does not exactly correspond to 2-digit NAICS; some sectors include multiple 2-digit NAICS codes. 

The 4-digit NAICS codes also do not correspond exactly with 4-digit CIC. Census industry codes follow the 

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poor match ratesin the SGS analyses.To the best of our knowledge, there is no other research examining industry 

nonresponse and the quality of imputed industry in surveys.

Our findings suggest that the industry distribution and the sample in a particular industry sector will differ 

depending on whether ACSor LEHD data are used. This variability is relevant for anyone using ACS or LEHD 

public-use statistics and anyone using industry variables in ACS or LEHD microdata. For example, certain industry 

sectors, such as construction and manufacturing, tend to be more affected by recessions, and research that focuses on 

those sectors may have different results depending on whether survey or administrative data are used.Researchers 

with access to matched data may want to test the sensitivity of their results to different industry variables. 

Researchers may also want to investigate howindustry sector samples differ,for example, how the manufacturing 

sector differs between ACS and LEHD data.The low match rate for imputed industry suggests that ACS microdata 

users should use imputed industry values with caution. 

2. Data

2.1. American Community Survey Data

The American Community Surveyis a mandatory household survey conducted by the U.S. Census Bureau. It was 

created to replace the decennial census long form and to provide information on the U.S. population on an annual 

basis instead of every ten years. The questions in ACS are based on the former Census long form. The ACS was 

fully implemented in 2005 and in 2006, the ACS was expanded to include group quarters, so that geographic areas 

could be fully described.8

The survey is an ongoing survey based on monthly samples, with microdata and public 

use statistics consolidated for each year. 

We use the 2009 ACS population file for our analyses.9 Our analyses require the use of internal Census ACS data. 

To meet requirements for confidentiality protection, the Census Bureau implements a variety of strategiesincluding 

“data swapping.” Data swapping is a method of disclosure avoidance done by editing the source of the data or 

exchanging the records of a sample of cases.10 The marginal totals for an area are not affected, but the responsesfor 

any given individual on the file may have been swapped with the responses of another similar individual.The file 

we use for analyses contains pre-swapped, edited data, i.e. variable values have not been swapped for confidentiality

protection, but variables have been edited and imputed.In some tables, as noted, we also use unedited variables 

from this file. We require the use of pre-swapped files to be able to match the response date for the survey and the 

reported industry to the appropriate LEHD reporting quarter. 

The ACS contains a series of questions about household relationships, demographics, income and benefits, and 

where individuals live and work. The survey questions of most relevance to this project are questions 29, 35 to 38, 

and 41 to 44, which ask about employment status, type of employer, and industry.

11 See Figure 1 for questions 29, 

35 to 38, and 41 to 44 of the survey. We use information on employment status and type of employer to select 

individuals likely to be in LEHD data, described in more detailin Section 3. We use the Employment Status Recode 

(ESR) variable to select those employedandat workin the past week. The ESR variable has 6 categories: 1) 

employed, at work; 2) employed, not at work during the reference week; 3) unemployed; 4) military, at work; 5) 

structure of NAICS but differ in level of detail. CIC level of detail varies for two primary reasons: Census Bureau 

coders must be able to obtain enough detail from respondents to code a response to an industry code and an industry 

must be large enough to meet disclosure restrictions. Industries are aggregated based on the NAICS hierarchy when 

there is insufficient detail or it is too small to be reported separately. 

8

The Design and Methodology Report: American Community Survey (2009) contains information on ACS program 

history in Chapter 2. The report also contains much information on survey design and methodology (as the title 

suggests). http://www.census.gov/acs/www/Downloads/survey_methodology/acs_design_methodology.pdf 

9 At the time we started this project, 2009 ACS data was the most recent available. Since then, 2010 and 2011 ACS 

data have become available. However, the 2010 ACS had a drop in response rate, presumably due to the 2010 

decennial census, so we focus on 2009.

10 Data swapping is discussed in section 13.6 of The Design and Methodology Report: American Community Survey

(2009).

11 PDF copies of the ACS questionnaire can be found on the ACS website: 

http://www.census.gov/acs/www/methodology/questionnaire_archive/. 

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military, not at work during the reference week; and 6) not in the labor force.12 For our analyses, we select

individuals who are employed, at work.

The ACS class of worker (COW) variable is based on question 41which asks abouttype of employer: private (for 

profit/not-for-profit), government (local/state/Federal), self-employed (not incorporated/incorporated), or working 

without pay in a family business or farm. Individuals respond to the questions about type of employer and industry 

if they worked in the last 5 years.13 The survey requests information for the primary job if currently working.

14

 If 

the person is not currently working, he or she reports the most recent job held within the past 5 years. From the ACS 

questionnaire for questions 41-46:

41-46 Current or most recent job activity. Describe clearly this person’s chief job activity or business last 

week. If this person had more than one job, describe the one at which this person worked the most hours. If 

this person had no job or business last week, give information on his/her last job or business. 

Question 42 asks employer name to assist with industry classification. In question 43, the person is asked to describe 

the industry activity at the location where employed; the industry question is open-ended.Question 44 asks whether 

this activity was mainly manufacturing, wholesale trade, retail trade, or something else.15 The Census Bureauuses

questions 42 through 44 to code the responses into 269 4-digit CensusIndustry Code (CIC) categories.

16 The CIC 

categories are based on the 6-digit 2007 North American Industry Classification System (NAICS). The Census 

Industry Codes are generally less detailed than the NAICS codes because of potential disclosure risk, but are 

designed so that 6-digit NAICS codes can be mapped to 4-digit CIC codes. 

ACS coding takes place in a centralized location with standardized procedures and a dedicated staff of fully-trained 

coders.Clerical staff at the Census Bureau’s National Processing Center convert the written questionnaire responses 

to Census Industry Codes using the Alphabetical Index of Industries and Occupations.Clerical coders receive 

extensive training and must maintain a 94 percent coding accuracy rate to remain qualified for coding. Industry 

coding is independently verified by additional qualified coders as part of the quality assurance process. Following 

industry coding, the Census Bureau checks for consistency between industry and other related variables, such as 

occupationand type of employer. If industry is missing after editing, industry is imputed using information from a 

“similar” person.17We use the edited and imputed industry variable, IND,for most of our analyses.

ACSinternal-use data includes an interview or reference date variable, RDATE. Interview date is critical for linking 

to the appropriate quarter of LEHD data. The ACS questionnaire is mailed to sample addresses. If the household 

does not mail back the survey, the Census Bureau follows up, first with an attempted telephone interview, and, if 

12 ESR is based primarily on questions 26, 29, and 35 to 37 of the survey. Question 26 asks whether the person has 

ever served in the military and if he or she is now on active duty. Question 29 asks whether the person worked last 

week. If the person did not work last week, he or she is requested to respond to questions 35 to 37. Those questions 

ask if the person was on layoff or temporarily absent from a job, if the person was actively looking for work (if not 

temporarily absent from a job), and if the person could have started a job if offered one (if not temporarily absent 

from a job). Question 38 asks when the person last worked: within the past 12 months, 1 to 5 years ago, or over 5 

years ago or never worked.

13 For questions 41-46, the questionnaire states: “Answer questions 41-46 if this person worked in the last 5 years. 

Otherwise, skip to question 47.”

14 If the respondent has more than one job and cannot select a primary job, he or she is prompted to select the job at 

which he or she worked the most hours in the last week.

15 Other surveys that ask about industry of place of employment, such as the Current Population Survey (CPS) and 

the Survey of Income and Program Participation (SIPP), also ask open-ended questions about industry. The ACS, 

CPS, and SIPP all have follow-up questions about whether the employer is mainly manufacturing, wholesale trade, 

retail trade, or something else. The SIPP also includes service in the follow-up question.

16 Through 2011, CIC was 100 percent coded by clerical staff at the National Processing Center in Jeffersonville, 

Indiana. In 2012, ACS developed an industry auto-coder that used model-based coding for some industry values.

17 American Community Survey, Puerto Rico Community Survey, 2009 Subject Definitions(2009), pp. 84-86, 

contains information on how industry is coded, edited, and imputed. 

http://www.census.gov/acs/www/Downloads/data_documentation/SubjectDefinitions/2009_ACSSubjectDefinitions.

pdf 

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that does not succeed, with an attempted in-person interview. In completed mail returns, RDATE is the date the 

respondent prints on the cover of the questionnaire as “today’s date.” In computer-assisted telephone interviews 

(CATI) or computer-assisted personal interviews (CAPI), RDATE is the last date on which data were collected.An 

ACS CATI or CAPI interview is intended to be completed during a particular calendar month. If the ACS 

questionnaire is completed over multiple interviews,RDATE could be at most 30 days after the initial response to 

interview questions.18We expect most mail surveys or interviews are completed on the same day they were started.

2.2. LEHD Data

We use employer-reported industry collected as part of the Census Bureau’s Longitudinal Employer-Household 

Dynamics (LEHD) program.

19 LEHD data are based on worker-level and employer-level administrative data. 

Individual-level earnings data are derived from state administrative records of the Unemployment Insurance (UI) 

system. They contain quarterly earnings, by job, for individuals employed in a particular state. Anindividual may 

have earnings from more than one employerin a particular quarter, and a job is a particular individual-employer

combination. Quarterly earnings are based on when individuals were paid, not when the work was performed. For 

example, if an individual was paid in quarter t for two weeks of work, where one week of work was in quarter t and 

the other week of work was in the previous quarter, we would only observe earnings in quarter t. 

For all states except Minnesota, earnings are reported at the employerlevel, by State Employer Identification 

Numbers (SEIN).20 Employers may have multiple establishments. Establishments are operating units of the 

employer and each establishment usually corresponds to a particular locationof business. One example of multiple 

establishments would be several branches of a bank. In Minnesota, we know in which establishment an individual 

worked. For all other states, we know the employer but we are not able to link an individual to a particular 

establishment if the employer has more than one establishment. 

In addition to the individual-level data, the LEHD program has employer-level administrative data derived from the 

Quarterly Census of Employment and Wages (QCEW).21 The QCEW contains information on employer industry

and location – state, county, and street address. QCEW data are derived from the quarterly tax reports submitted by 

employers subject to state Unemployment Insurance laws. Each state has an office or departmentthat compiles and 

processes the data. For each state, a new business fills out an initial form that determines liability for state 

Unemployment Insurance (UI) and asks for address and industry information. This form is called the Status 

Determination Form (SDF). After determining UI liability, the firm reports monthly employment and earnings each 

quarter. Every three, four, or five years, depending on funding availability, firms file an Annual Refiling Survey, 

which verifies location of the establishment and industry. 

The specific forms used to determine initial UI liability vary by state. In Virginia, for example, firms are asked an 

open-ended question about industry: “Describe the kind of business in Virginia, giving specific details of items, 

customers, etc., such as retail-women’s clothes; wholesale-office equipment; construction-single family home, 

etc.”

22 In the QCEW, industry is reported using 6-digit NAICS.State QCEW offices use information from the Status 

Determination Form, state and local knowledge of the establishment, and/or contact the company directly to assign a 

6-digit NAICS code. If needed, a state will mail a Non-Classified Account (NCA) form to the establishment. The 

NCA asks for more information about the establishment, requesting a description of the activities and to provide an 

approximate percentage of sales or revenues from each activity. Individuals in the state QCEW offices receive inperson NAICS coding training from the Bureau of Labor Statistics (BLS), which oversees the QCEW program. The 

18 Based on correspondence with Todd Hughes, Assistant Division Chief of Data Collection at the Census Bureau.

19 Abowd et al (2005) provide detailed information about LEHD data.

20 The employer, or SEIN, is equivalent to a firm if all parts of the firm operate in one state. For firms that have 

operations in multiple states, the SEIN would not be equivalent to a firm identifier.

21 The Quarterly Census of Employment and Wages (QCEW) program at the Bureau of Labor Statistics publishes a 

quarterly count of employment and wages reported by employers covering 98 percent of U.S. jobs, available at the 

county,metro area, state, and national levels by industry.

22 The Virginia Report to Determine Liability for State Unemployment Tax (VEC FC-27) is available online: 

http://www.vec.virginia.gov/vecportal/employer/pdf/fc_27new.pdf. Instructions are here: 

http://www.vec.virginia.gov/pdf/fc_27ins.pdf. Information about the form can be found at: 

http://www.vec.virginia.gov/employers/tax-registration. 

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industry coders use a software application, AutoNAICS, to assist in coding. AutoNAICS contains descriptions of 

NAICS codes from the NAICS codebook and enables key word searches. Regional BLS offices also review a 

sample of NAICS codes assigned by state staff.23 A firm can request a NAICS code change if the firm thinks the

assignedNAICS code is inaccurate.24

In some states, NAICS codes are used to determine Unemployment Insurance tax rates, at least for the first several 

years a firm is in operation, after which the firm’s UI claim history is used to determine rates. In those states, QCEW 

offices are required to obtain NAICS codesfor all firms. In addition, the Annual Refiling Survey (ARS), where 

firms verify industry and location information, is mandatory in some states. In other states, completing the ARS is 

not mandatory, but states must obtain certain response rates, such as 75 percent for Washington State, in order to 

comply with states’ QCEW contracts with BLS.

The QCEW data received by the LEHD program may have missing NAICS codes. The LEHD program uses 

longitudinal information from establishments and employers to fill in missing industry information. For employers s 

with no longitudinal information, industry is imputed. Our analysis data has completed LEHD industry information. 

It is difficult to calculate an overall industry missing rate for the QCEW data used as an input for our analysis data. 

However, for a selection of three states and two different quarters of data, establishment industry was missing 

between less than one percent and 14 percent of the time. Between less than 1 percent and 16 percent of employers s 

were missing NAICS information for all establishments; many employers have only one establishment. As noted, in 

states where QCEW NAICS codes are used to determine UI taxrates, we would expect essentially zero missing 

rates.

The individual-level UI data is linked to the employer-level QCEW data by SEIN. For employers with more than 

one establishment in states other than Minnesota, the LEHD program multiply imputes establishments for 

individuals. Location will likely vary across establishments and industry may vary across establishments. UI and 

QCEW data are collected by each state and then shared with the LEHD program as part of the Local Employment 

Dynamics (LED) Partnership. The LEHD program currently has data from all states, including the District of 

Columbia. Massachusetts recently joined the LEHD program and Massachusetts data have not yet been processed.

In these analyses, we use LEHD data from all states except Massachusetts. 

A limitation of the LEHD data is that not all employment is covered by the state UI systems. The areas not covered 

include some agricultural employment, independent contracting, self-employment, military employment, federal 

civilian employment, railroad employment, some elected state and local government officials, and postsecondary 

work-study students.25

2.3 Sample Selectionand Link to Census Protected Identification Keys (PIKs)

From the 2009 ACS data, we select a sample of individuals who are most likely to match to a job in the LEHD data.

We keep individuals who are employed, at work, and not in the Armed Forces (ESR=1). We focus on individuals

who are currently employed so that we can use ACS interview date (RDATE) to link to quarter of employment in 

the LEHD data. We exclude federal government employees (COW=5), self-employed not incorporated (COW=6), 

self-employed incorporated (COW=7), and unpaid family workers (COW=8).Since LEHD data does not currently 

include Massachusetts, we exclude individuals who report working in Massachusetts (POWS=025).

In the LEHD data, individuals are identified by Protected Identification Keys (PIKS), which are assigned by the 

Census Bureau based on personal identifying information in the UI data. In order to match ACS and LEHD data, the 

ACS data also needs PIKs. After selecting a subsample of the 2009 ACS data, we link the ACS data to PIKs using a 

crosswalk. Like with the LEHD data, the Census Bureau uses personal identifying information on the ACS survey to 

assignPIKs. The ACS crosswalk file links ACS identifiers to the PIKs.In a small number of cases, less than 0.2 

percent, more than one ACS individual links to the same PIK. In those cases, we select the ACS individual that 

23 Information on assignment of 6-digit NAICS codes provided by Amanda Chadwick at the Bureau of Labor 

Statistics and Molly Webster at the Washington State Employment Security Department.

24 Request for NAICS code change in California: http://www.labormarketinfo.edd.ca.gov/Content.asp?pageid=176 

25 The LEHD program is in the process of adding federal workers and the self-employed. For detailed information 

on UI covered employment see Stevens (2007).

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looks most similar to the LEHD individual, based on where in the Census PIK assignment process an individual gets 

a PIK and PIK match scores.26 About 92 percent of individuals in our ACS subsample match to PIKs. We use this 

group for our subsequent analyses.

3. Job Match Algorithm

3.1. LEHD Main Job Selection

We link the PIKed ACS subsample to LEHD earnings history dataand then, using a set of rules described below, 

select the LEHD job that is most likely to correspond to the job referenced in the ACS survey. For the LEHD 

earnings history data, we use a custom national person history file (PHF_B) and include earnings through 2010 

quarter one. We refer to the LEHD job selected as the LEHD main job and it is an SEIN-year-quarter observation. 

We use ACS interview date (RDATE) and LEHD quarterly earnings to select the LEHD main job.

There are several challenges associated with selecting an LEHD job (PIK-SEIN in a particular quarter) to 

correspond with the ACS job. First, two timing issues complicate selecting the appropriate quarter for the LEHD 

main job. First, the ACS survey asks to describe job characteristics for last week’s job, which means that 

employment status and job characteristics match a job approximately one week prior to RDATE. Second, forACS 

CATI and CAPI interviews that took place overmultiple days, RDATE may not be the same as the day that the 

person responded to interview questions on job characteristics.27 (For mail responses, RDATE is respondent 

reported “today’s date” and any follow-up CATI or CAPI interviews use RDATE to form the reference period for 

interview questions.A respondent may also start filling out a mail questionnaire on one day and complete the 

questionnaire on a different day. In that case, we would not know whether they used RDATE as the reference date 

for all questions, as directed.) As noted above, RDATE is at most 30 days within the same calendar month beforean 

initial CATI or CAPI interview. Both of these timing issues mean that in some cases we should select an LEHD 

main job from the same quarter as RDATE, while in other cases the LEHD main job may be from the quarter before 

the RDATE quarter.28 For our job match algorithm, if RDATE is in the last two months of the quarter, we select as 

potential LEHD main jobs all SEINs in the RDATE quarter. If RDATE is in the first month of the quarter, we select 

as potential LEHD main jobs all SEINs in the RDATE quarter and all SEINs in the previous quarter.Figure 2has a 

flow chart of the job match process. 

A second job-match challenge is that after selecting one or two quartersthat could correspond to the ACS job, a 

person can match tomore than one job (SEIN). The person may have multiple SEINs in the same quarter or multiple 

SEINs across the two possible quarters. Multiple SEINs exist in the same quarter because an individual changed 

jobs in that quarter (worked at different jobs at different times) or an individual worked at multiple jobs at the same 

time. For ACS survey respondents with more than one job last week, the survey asks the respondent to reference the

primary job or the job with the most hours worked. For most states, LEHD data does not contain information on 

hours worked.29 For our job match algorithm, we have two approachesfor selecting one LEHD main job (SEIN), 

depending on whetherRDATE is in the first month of the quarter. If RDATE is in the last two months of the 

quarter, we selected potential LEHD main jobs from LEHD jobs in the RDATE quarter. If there is one potential job, 

26 In a few cases, it is not possible to tell which ACS individual is more similar to the LEHD individual based on 

information from the PIK process. In those cases, we arbitrarily select an ACS individual. 

27 An ACS interview could also take place over multiple days if the respondent did not have time to complete a 

CATI or CAPI interview in one sitting. For a 2009 ACS subsample based on our sample restrictions, 69 percent of 

respondents responded by mail, 12 percent by CATI, and 18 percent by CAPI. The subsample included individuals 

who were employed, at work (ESR=1), worked for private, state, or local government employers (COW not equal to 

5, 6, 7, or 8) and who worked in a state other than Massachusetts (POWS not equal to 025). We do not know what 

share of CATI/CAPI interviews took place over multiple days.

28 It is also possible that someone becomes employed in the last week or two of a quarter but does not get paid until 

the following quarter. In this case, they may report a job in the ACS that corresponds to a job in the LEHD data from 

the following quarter. We are not including LEHD jobs in the quarter following RDATE.

29 Minnesota and Washington State provided LEHD with data on hours worked.

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we keep that job (SEIN) as the LEHD main job. If there is more than one potential job, we select the job (SEIN) 

with the highest earnings.30We expect this to correspond with the job with the most hours worked in most cases. 

If RDATE is in the first month of the quarter, plausible LEHD main jobs may be in the RDATE quarter or in the 

previous quarter. If there is one plausible LEHD main job, we select that job as the LEHD main job. If there is more 

than one plausible LEHD main job and all plausible jobs are in the same quarter, we select the job with the highest 

earnings. If there is more than one plausible LEHD main job and there are plausible jobs in both the RDATE quarter

and the previous quarter, we use more detailedRDATEinformation. We assume that all survey information was 

collected on RDATE, ignoring any potential multi-day surveys. If RDATE is in the first seven days of the quarter, 

we select jobs from the quarter before RDATE and, if there is more than one job, we select the job with the highest 

earnings. If RDATE is after the first seven days of the quarter, we select jobs fromthe RDATE quarter, and if there 

is more than one job, we select the job with the highest earnings.31 After selecting LEHD main jobs, each individual 

has one LEHD main job and we use this sample for our industry comparisons.32Recallthat an illustration of the job 

matching processis found in Figure 2.

The third challenge with selecting an LEHD main job for an industry comparison is that a SEIN may have multiple 

establishments (SEINUNITs), and the establishmentsmay have different industries.For most of our analyses we use 

employment modal industry for each SEIN and do not use SEINUNIT level industry.In our analysis sample, 42 

percent of LEHD employers (SEINs) are multi unit and 58 percent of employers are single unit. Of multi units, 61 

percent have 4-digit CIC variation across units, which means that of all employers, 26percent are multi units with 

industry variation across units.Of multi units with industry variation, an average of 76 percent of employment for 

the employer is at units with the employment modal CIC. Therefore,we would expect approximately 6percent 

individuals in our analysis sample to have a unit level 4-digit CIC that differs from the employment modal 4-digit 

CIC.

33 Some units that have a different 4-digit CIC from the employment modal CIC may still be in the same 

industry sector. Multi unit employers are discussed further in the Industry Comparison section.

For our analysis sample, we convert the LEHD SEIN employment mode 6-digit NAICS code to the corresponding 

4-digit CIC code. Based on the 4-digit CIC codes for the ACS and LEHD industry variables, we create ACS and 

LEHD NAICS sector variables (with 20 NAICS sectors). For a full list of the 20 sectors, see Table 1. Some of our 

comparisons and industry distributions use sector-levelindustry information. We do not use ACS weights for our 

analyses. The pre-swapped edited file we use does not contain a person weight.The Appendix discusses how 

weights may affect the ACSindustry distribution.

Table 2 lists observation counts for sample selection and job matching. The 2009 ACS population file has 4.5 

million observations, where each observation is an individual. The largest group excluded from our analyses is 

individuals not employed, at work (ESR not equal to 1), 2.5 million observations.This group includes individuals 

age 15 and underwho are not eligible for the employed, at work universe. Class of worker exclusions and working 

in Massachusetts affect a much smaller number of individuals. (Excluded groups are not mutually exclusive.)After 

ACS sample restrictions, we have 1.7 million observations, and after merging to the ACS-PIK crosswalk, we have 

1.6 million observations with PIKs.About 99 percent of the PIKed ACS sample merges to the LEHD earnings 

history file (PHF_B). The earnings history file includes earnings from 1990 quarter one through 2011 quarter four, 

as available for each state. However, when we limit the earnings history file to jobs with positive earnings in the 

ACS interview quarter or the previous quarter, 92 percent of the PIKed ACS merge to the LEHD earnings history 

30 In a small number of cases, 0.04 percent, there were multiple SEINs with identical quarterly earnings for the 

individual. This appeared to be a reporting issue at least some of the time; employers would have identical or very 

similar earnings histories for the individual. We drop individuals with non-unique maximum earnings across 

employers.

31 As when RDATE is in the last two months of the quarter, we drop individuals with non-unique maximum 

earnings across employers after selecting employers from the acceptable quarters.

32 It is possible that some LEHD main jobs do not in fact correspond the to ACS job. This could happen due to the 

complexities of matching noted in the text or if an individual reported an ACS job that is not included in UI earnings 

records. An alternative job matching approach could include employer name and potentially address matching. 

33 In our analysis sample, each individual is associated with one employer. An average of 24 percent of employment 

is not at the employment modal 4-digit CIC. 26 percent of employers that are multi unit with CIC variation across 

units. (24 percent of 26 percent = 6 percent.) 

Β 

 
file; we now have a sample with 1.4 million observations.We further select quarters that have plausible LEHD main 

jobs, based on ACS interview date (RDATE) as described above. Of individuals with at least one plausible LEHD 

main job, 87 percent have one plausible main job and 13 percent have multiple plausible main jobs.

 

3.2. Job Match Assessment

Table 3 compares place of work state, county, and tract information between the LEHD data and ACS data. As a 

robustness check, we are interested in whether state/county/tract match better in the jobs that we have selected as 

main jobs compared to alternative plausible jobs.We expect some differences in geography, especially at the county 

and tract level, since the ACS asks where someone reports to work, while LEHD data hasthe address for the 

employer. This may be problematic for contractors, who work at one employer but are paid by another employer, 

individuals who work at temporary help agencies, and construction workers, to name a few examples. LEHD data 

also has a known address-reporting issue for school districts. Many school districts report only one UI address for all 

workers, usually the school district headquarters, and do not report employment by individual school. However, we 

expect main jobs to have better geographic match rates than alternative plausible jobs. This provides some evidence 

of whether we are selecting the correct main job from our job-match algorithm.Main jobs do appear to have 

substantially higher geography match rates than alternative plausible jobs.34

Our data has very high state match rates – 98 percent for main jobs, 89 percent for other plausible jobs. In LEHD 

data, state is at the SEIN level, while county and tract place of work information is at the SEINUNIT level. For 

single units, we know the SEINUNIT and associated geography information. SEINUNIT is imputed for multi units 

and we use geography information from the first implicate of the SEINUNIT impute.35 Match rates are lower for 

county and tract. For main jobs, 73 percent have matching county information, and only 47 percent have matching 

tract information. For alternative plausible jobs, 51 percent have matching county information and only 13 percent 

have matching tract information.

36 We may find a lower match rate for main jobs for individuals with more than one 

plausible job, since there could be timing errors in identifying the job worked at the time of the ACS interview and, 

in the case of multiple jobs held at the same time, ACS respondents describe the job with the most hours worked last 

week and we use earnings to select the main job. The match rates for main jobs for PIKs with more than one 

plausible job are lower than the match rate for all main jobs, which includes many cases with only one plausible job, 

but are still higher than the match rate for alternative plausible jobs. The relatively low tract match rates suggest that 

there could be some challenges in using address to select main jobs, since county and tract information disagree in a 

substantial number of cases and there is a high ACS missing rate for tract information.

In Minnesota, SEINUNITs are reported for workers, and we calculate similar county and tract match rates using 

only Minnesota. The county and tract match rates are higher for all main jobs and main jobs for PIKs with more than 

one plausible job. This suggests that the lack of SEINUNIT information for workers in states other than Minnesota 

is contributing to the lower county and tract match rates.37

In other analyses not shown, we find that place of work geography match rates vary across industry sectors. We 

would expect lower geography match rates in sectors where physical place of work differs from address of 

employer, such as the sector with temporary help agencies – administrative and support and waste management. The 

sector does indeed have lower tract and county match rates than most other sectors.Mining, quarrying, and oil and 

gas extraction has the lowest tract match rates; the physical place of work may often differ from address of 

employer.

34 We do not distinguish by LEHD geography data quality for these analyses.

35 We have SEINUNIT matched to individual workers for Minnesota.

36 The ACS place of work information may be edited, imputed, or missing. We would expect worse geography 

matches when ACS place of work is edited or imputed. One percent of jobs in each group are missingACS county 

information; 21 percent of main jobs are missing ACS tract information; 23 percent of main jobs with more than one 

plausible job and 23 percent of alternative plausible jobs are missing tract information.

37 The Census Bureau has an ongoing project that will examine place of work geography matching between the ACS 

and the QCEW in more depth than this paper.

Γ 

 
4. Industry Comparison

There are a number of possible reasons for ACS and LEHD industry data mismatches. The ACS survey respondent, 

responding for him- or herself, may misunderstand the work done by his or her employer, or the ACS survey 

respondent responding for another householdmember may not be sufficiently familiar with the work of the 

employer of the other household member. Both the ACS and LEHD data ask open-ended industry questions, and the

text is then coded to 4-digit CIC or 6-digit NAICS codes. The industry questions for ACS and LEHD are worded 

differently and people may respond with different information. The coding proceduresfor the ACS and LEHD data 

may also be different, resulting in different numeric codes for similar text.38 To the authors’ knowledge, there has 

been no systematic study comparing the industry coding and editing procedures of the ACS to the industry coding 

and editing procedures of the LEHD, or other employer-reported data. Finally, for most of our analyses we use SEIN 

employment mode industry from the LEHD data. If the employer has multiple establishments with industry 

variation across establishments, the individual could actually be working at an establishment with a different 

industry.We do not expect this to be a large issue, given the multi unit discussion in the Job Match Algorithm 

section (Section 3). 

Table 4 presents industry match rates for main jobs at the sector level and the 4-digit CIC level. Using the edited 

ACS industry variable (IND) and the LEHD SEIN employment mode industry, there is an industry sector match rate 

of 75 percent, and a match rate at the 4-digit CIC level of 61 percent. We separately compare the industry match for 

when industry is as reported, assigned, or imputed in the ACS.

39 The match rate is substantially better for the asreported cases. ACS industry is imputed in three percent of our analysis sample and assigned in 0.1 percent of our 

analysis sample. The industry sector match rate drops from 77 percent for as-reported cases, to 65 percent for 

assigned cases, down to 14 percent for imputed cases.Match rates similarly decline for 4-digit CIC. 

We also assess whether industry match rate may differ for LEHD single unit and multi unit employers. For multi 

unit employers, some workers may be working at a SEINUNIT with a different industry from the SEIN employment 

mode industry. Of main job employers, 58 percent are single unit; 42 percent are multi unit. Industry match rate is 

similar for single unit and multi unit SEIN; 74 percent for single units and 77 percent for multi units. Surprisingly, 

the match rate is slightly higher for multi units. Match rates between single and multi units are also similar at the 4-

digit CIC level, with multi unit SEIN having a slightly higher match rate. In the second panel, we calculated industry 

match rates using SEINUNIT industry. For multi units, we use the SEINUNIT from the first implicate of the 

SEINUNIT impute. The match rates using SEINUNIT industry are slightly lower than industry match rates using 

SEIN employment mode industry; 73 percent match for industry sector and 58 percent match for 4-digit CIC. Match 

rates are similar for single units and multi units, with slightly higher match rates for single units.

Figure 3 compares the industry sector distribution for the ACS and LEHD industry variables (IND and SEIN 

employment mode industry). In figures, ACS sector refers to the ACS IND sector and LEHD sector refers to the 

LEHD SEIN employment modalsector. (The distributions are unweighted, since we do not have person weights in

our internal ACS file.) The industry sector distributionprovides information on which sectors have the most 

employment, based on our ACS subsample, and which sectors have relatively more employment in ACS compared 

to LEHD data and vice versa. To the extent that a sector has more employment in ACS than LEHD data, for 

example, the “extra” ACS jobs would be in a different LEHD sector and would be mismatched. (Of course, there 

would likely be some mismatches even if the percent employment in the ACS and LEHD sectors were the same.) 

Overall, the industry distributions from ACS and LEHD data are similar. The manufacturing and retail trade sectors 

have relatively more employment in the ACS than LEHD data. Wholesale trade, management of companies and 

enterprises, administrative and support and waste management and remediation services, and public administration 

have relatively more employment in LEHD than ACS data. In the LEHD data, some multi unit employers have a 

headquarters unit assigned a management of companies and enterprises industry code. At those establishments, all 

individuals would get the management industry code, even though individuals at headquarters may understand that 

38 ACS data is coded to 4-digit CIC while QCEW data is coded to 6-digit NAICS, so the codes would not be 

identical for the same text, but we would expect the 6-digit NAICS to correspond with the 4-digit CIC after taking 

into account appropriate mapping of the codes.

39 Assignment is an imputation method in which values for a missing or inconsistent item can be derived from the 

person’s other responses to the survey. Items that cannot be derived from other responses may be imputed based on 

answers from other members of the household or from people believed to have similar characteristics. 

ヱヰ

 
they work in manufacturing, for example. We would therefore expect LEHD data to have more individuals in the 

management sector than ACS data. Tables 6 and 7 display the percent of main jobs in each LEHD (ACS) sectorfor 

each ACS (LEHD) sector, which provide further information on where industry mismatches occur. These tables are 

discussed in more detail below.

Figure 4 charts the percent of jobs in each ACS (LEHD) sector that are in the same sector in the LEHD (ACS) data. 

From Table 3, recall there is an overall sector match rate of 75 percent. The match rate varies across sectors and by 

whether sector is defined using ACS industry information or LEHD industry information. The management of 

companies and enterprises sector has the lowest match rate, which is not surprising, given that few ACS respondents 

report working in this industry or they provide insufficient information for coders to classify them appropriately 

(e.g., no mention of headquarters)(see Figure 3). For jobs in this industry in the LEHD data, the match rate is 2 

percent. Wholesale trade has low match rates, especially using the LEHD-defined sector. The agriculture, other 

services, and arts, entertainment, and recreation sectors also have relatively low match rates. Finance and insurance, 

educational services, health care and social assistance, and accommodation and food services all have relatively high

match rates, greater than 80 percent basedon both ACS and LEHD sectors.Table 8 presents a summary of results 

by industry sector. It includes results from Figures 2 and 3 and includes a selection of results from the other industry 

analyses.

As discussed above, there are several potential sources of mismatch between the ACS and LEHD industry data. To 

examine the extent to which multi units may contribute to mismatch through our use of the SEIN employment mode 

industry, Table 5 presents the percent of SEINs that are multiunit by ACS and LEHD industry sector and industry 

sector match rates by single/multi unit and ACS and LEHD sector. Recall, 58 percent of employers in our analysis 

sample are single unit; 42 percent are multiunit. Of multi units, 61 percent have 4-digit CIC industry variation 

across units, which means that of all employers, 25 percent are multi units with industry variation across units. Of 

multi units with CIC variation across units, an average of 76 percent of employment is at the modal NAICS. 

Overall, the sector match rate is very similar between single units and multi units, and the match rate is actually 

slightly higher for multi units. From Table 3, single units have an overallmatch rate of 74 percent and multi units 

have a match rate of 77 percent. From Table 4, the utilities, retail trade, finance and insurance, management of 

companies and enterprises, and public administration sectors allhave more than half of employers as multiunits.40

A high percentage of multi-unit employers does not necessarily correspond with a low match rate. For ACS sectors, 

the correlation between percent multiunit and percent sector match is 0.20; for LEHD sectors, the correlation is 

0.02. In some ACS sectors, such as construction, single units are more likely to match the LEHD industry sector, 

while in other ACS sectors, such as agriculture, forestry, fishing, and hunting, multi units are more likely to match 

the LEHD industry sector.

Table 6 shows the percent of jobs in the LEHD sectors for each ACS sector.41 The diagonal, highlighted in yellow, 

is the industry sector match rate for the row sector. For example, for ACS sector 42 (wholesale trade), the table

shows that 57 percent of jobs in sector 42are also in sector 42in LEHD data. Eleven percent of jobs are in LEHD 

sector 31-33 (manufacturing) and 12 percent of jobs are in LEHD sector 44-45 (retail trade). This suggests that it 

may be difficult for employees or industry coders to know whether the job is manufacturing, wholesale trade, or 

retail trade. It may also be difficult to code responses to these sectors in the QCEW data. Each row sums to 100.

Table 7 is similar to Table 6 but shows the percent of jobs in the ACS sectors for each LEHD sector. In Table 7, the 

LEHD wholesale trade sector has 23 percent of jobs in the ACS manufacturing sector and 17 percent of jobs in the 

ACS retail trade sector, similar to Table 6. 

Appendix Table 1shows the percent of jobs in the LEHD sectors for each ACS 4-digit CIC. The first column lists 

the 4-digit CIC. The second column is a description of the 4-digit CIC. The third column is the percent of PIKs 

(equivalent to the percent of main jobs) that have that ACS 4-digit CIC. The remaining columns show the 

percentageof jobs in each LEHD sector; each row sums to 100.The yellow and orange cells are the match rates: the 

percent of each 4-digit CIC that is in the same sector in the LEHD data. Orange cells have match rates below 50 

40 For ACS or LEHD sectors.

41 Noted earlier, Table 1 lists the twenty 2007 NAICS sectors: the numeric codes and a description of each sector. 

This table is useful for Tables 6, 7, and 8, which use numeric codes for the sectors, since there is insufficient space 

to list descriptions of each sector. 

ヱヱ

 
percent.Green cells show(mismatched)LEHD industry sectors that have more than 10 percent of jobs for that 4-

digit CIC. Cells based on very small numbers of PIKs may be less reliable than cells based on more observations.

5. Missing ACS Industry Analyses

We do some additional analyses on characteristics and industry distributions for individuals missing values for the 

unedited industry variable (UIND). Table 9 compares the characteristics of individuals with missing industry 

information (UIND is blank) to those with non-missing industry information. Industry is not missing at random. 

Those with missing industry are more likely to be young, lower educated, lower income, Black or Hispanic, 

unemployed or not in the labor force, less likely to have worked in the past year, and worked fewer hours or no 

hours in the past year.42 Many of the demographic characteristics listed in the table are used in the ACS industry 

imputation model to account for differences between those with missing industry and those with non-missing 

industry. This table is based on an ACS sample of individuals with industry in universe, where industry is edited and 

imputed if missing. In the sample, age is greater than or equal to 16 and the individual worked in the last five years. 

The ACS sample is not linked to the ACS PIK crosswalk or to LEHD data sets, and the calculations are unweighted.

Figure 5 compares the assigned or imputed industry distribution for individuals missing values forthe unedited 

industry variable (UIND) to the industry distribution for those with non-missing values for UIND. (Industry may 

still be edited or imputed if UIND is missing.)Figure 5 includes both the ACS and LEHD industry sector 

distributions.From Figure 5, the assigned/imputed ACS industry sector distribution for those missing UINDdiffers 

from the ACS industry distribution for those with reported UIND. This is not surprising, given their different 

demographic characteristics.43 The LEHD distribution for individuals missing UIND is similar to the 

assigned/imputed ACS distribution for those missing UIND. This suggests that the ACS imputation model is 

assigning a similar industry distribution. However, for any given individual, the industry sector match rate for those 

with imputed IND is very low, 14 percent (Table 5). Therefore, while aggregate industry analyses with imputed 

ACS data may reflect a similar distribution to LEHD data, multivariate analyses (e.g., industry by sex or earnings) 

may differ. The assigned/imputed ACS and LEHD sector shares are more similar in some sectors than others. For 

example, in the construction sector, the ACS imputation models assigns/imputes more individuals to that sector than 

appear in the LEHD data. In wholesale trade, the ACS assigned/imputed industry sector and the LEHD sector have 

different shares of the distribution, but the ACS and LEHD data have low match ratesfor wholesale trade. 

6. Conclusionand Discussion

We compare person-reported industry in the ACS to employer-reported industry in the QCEW data. ACS 

individuals are linked to LEHD individuals using Protected Identification Keys (PIKs). The date of the ACS 

interview and LEHD earnings information are usedto select a job in the LEHD data that likely corresponds to the 

job referenced in the ACS.We find an overall industry sector match rate of 75 percent and a 4-digit CIC match rate

of 61 percent. Industry match rates vary by sector and by whether industry sector is defined using ACS or LEHD 

industry information. 

The educational services and health care and social assistance sectors have some of the highest industry sector match 

rates. Wholesale trade has relatively low match rates, especially for the LEHD sector. Jobs in this sector in the 

LEHD data are often categorized in the manufacturing or retail trade sectors in the ACS data, and vice versa. 

Descriptions of industries in wholesale trade may be difficult to distinguish from manufacturing or retail trade. The 

ACS survey directly asks respondents whether their employer is manufacturing, wholesale trade, retail trade, or 

something else, but in the QCEW firms can review assigned NAICS codes and they verify NAICS codes in the 

Annual Refiling Survey. The management of companies and enterprises sector is a relatively small sector, but it has 

very low industry sector match rates. Analysts whowant everyone employed at headquarters included in the 

management of companies sector, may want to use LEHD public-use statistics or microdata. Analysts who are 

42 Bollinger and Hirsch (2013) conclude that high-earning men are more likely to have earnings nonresponse in the 

CPS and that earnings nonresponse is not ignorable. Bollinger and Hirsch (2006) also show that the inclusion of 

imputed earnings in regression models leads to match bias. Earnings imputation rates in the CPS are much higher 

(around 20 percent in the March Annual Social and Economic Supplement) than ACS industry imputation rates.

43 UIND is missing for three percent of the sample.

ヱヲ

 
interested in the employer’s primary activity, as reflected in the ACS, may want to use ACS public-use statistics or 

microdata. 

Industry matching provides some information on the quality of ACS imputed industry data. For individuals with 

assigned or imputed ACS industry, the ACS industry sector distribution is similar to the LEHD industry sector 

distribution. This suggests the ACS imputation model is approximating the LEHD industry distribution. However, 

for individuals with imputed industry, the industry sector match rate is only 14 percent, even with an overall 

industry sector match rate of 75 percent (Table 5). This suggests that ACS microdata users should cautiously use 

imputed industry values. Public-use statistics that tabulate other variables by industry, for example, earnings by 

industry, may be slightly inaccurate to the extent that industry is imputed and the imputation is incorrect. Industry 

was imputed for only three percent of respondentsin 2009, but missing response has increased since then due to a 

scaling back of Census Bureau survey follow-up operations for budgetary reasons. In addition, administrative data 

may be useful for providing information on industry when industry is not reported, with the acknowledgement that 

the administrative data industry distribution differs from an underlying survey industry distribution. Nonetheless, 

incorporating administrative data regularly into ACS data would require a substantial investment of staff time, and 

the project may be too costly relative to the three percent of respondents with imputed industry.

It is difficult to assess the overall accuracy of the ACS data compared with the LEHD data. While we would expect 

a firm to provide better information about industry than a worker at the firm(or a family member of the worker), the 

open-ended nature of the industry questions in the QCEW and the ACS mean that the employer or the worker may 

provide insufficient information to accurately code industry.In addition, the ACS and QCEW may use different 

procedures to assign CIC or NAICS codes from the text. To the authors’ knowledge,there has been no systematic 

study comparing the industry coding and editing procedures of the ACS (respondent-reported) to the industry coding 

and editing procedures of the QCEW, or other employer-reported data.Future studies on survey-based and firmbased industry data should consider a detailed examination of data collection, coding, and editing procedures to 

provide additional information on potential sources of error and data mismatching, and potential advantages for 

using each type of data.

ヱン
Appendix: ACS Survey Weight and Class of Worker Restriction Analyses

In Appendix Figure 1, we examine how using ACS survey weights (person weight - PWGT) would change our 

industry sector distribution. As noted earlier, the pre-swappedACS data we use does not contain person weights and 

we do not use them in our analyses. Figure 6 is based on data where variable values have been swapped for 

confidentiality protection and person weights have been calculated. We select a sample based on the same ACS 

variable restrictions used for our analyses (ESR=1, etc.), but it is not limited to individuals with non-missing PIKs 

and those that link to LEHD data. Figure 4 shows industry sector distributions using the edited and imputed IND 

variable. The UIND variable, without edits or imputations, produces very similar results. Using person weights 

increases the number of jobs in construction, retail, administrative, and support and waste management, and 

accommodation and food services. The use of person weights decreases the number of jobs in manufacturing, 

education, and health care and social assistance. Even with these small differences, the weighted and unweighted 

distributions are similar, and the fact that our analyses are unweighted does not appearto be substantially 

influencing our results. Appendix Table 2 presents the industry distributions found in Figure 5 (weighted and 

unweighted) and separately for the unedited and edited industry variables (UIND and IND).

The ACS publishes data on all class of worker categories. For our industry comparison, we limit the sample to 

private wage and salary workers and state and local government workers, excluding federal government workers, the 

self-employed, and unpaid family workers. In Appendix Table 3, we compare the characteristics of an ACS sample 

using our sample restrictions to an ACS sample of federal government, self-employed, and unpaid family workers. 

This sheds light on how our sample restrictions affect characteristics of the group. Both samples in Appendix Table 

3 are based on swapped 2009 ACS data that are not linked to PIKs or LEHD data. Both samples are also limited to 

the employed, at work population (ESR=1) and individuals working in states other than Massachusetts (POWS not 

equal to 025). The workers in the analysis sample are younger, less educated, and less likely to have served in the 

military than the excluded workers. The analysis sample workers are less likely to be in management or professional 

occupations and construction occupations and more likely to be in production, transportation, and material moving 

occupations and sales and office occupations. Not surprisingly, the analysis sample workers are less likely to report

self-employment income than the excluded workers, which include the self-employed.

ヱヴ
References

Abowd, John and Martha Stinson. Forthcoming. “Estimating Measurement Error in Annual Job Earnings: A 

Comparison of Survey and Administrative Data.” Review of Economics and Statistics. 

Abowd, John M., Bryce E. Stephens, Lars Vilhuber, Fredrik Andersson, Kevin L. McKinney, Marc Roemer, and 

Simon Woodcock. 2005. “The LEHD Infrastructure Files and the Creation of the Quarterly Workforce 

Indicators.” LEHD Technical Paper TP-2006-01.

Abraham, Katharine G., John C. Haltiwanger, Kristin Sandusky, James Spletzer. Forthcoming. “Exploring 

Differences in Employment between Household and Establishment Data.” Journal of Labor Economics. 

Becker, Randy, Joel Elvery, Lucia Foster, C.J. Krizan, Sang Nguyen, and David Talan. “A Comparison of the 

Business Registers Used by the Bureau of Labor Statistics and the Bureau of the Census.” Presented at the 

2005 American Statistical Association Joint Statistical Meetings. http://www.bls.gov/osmr/pdf/st050270.pdf

Bollinger, Christopher R. and Barry T. Hirsch. 2006. “Match Bias from Earnings Imputation in the Current 

Population Survey: The Case of Imperfect Matching.” Journal of Labor Economics, 24: 483-519.

Bollinger, Christopher R. and Barry T. Hirsch. 2013. “Is Earnings Response Ignorable?” The Review of Economics 

and Statistics, 95(1): 407-416.

Bound, John and Alan B. Krueger. 1991. “The Extent of Measurement Error in Longitudinal Earnings Data: Do 

Two Wrongs Make a Right?” Journal of Labor Economics, 9(1): 1-24.

Bound, John, Charles Brown, Greg J. Duncan, and Willard L. Rodgers. 1994. “Evidence on the Validity of Crosssectional and Longitudinal Labor Market Data.” Journal of Labor Economics, 11(3): 345-368.

Elvery, Joel, Lucia Foster, C.J. Krizan, and David Talan. “Preliminary Micro Data Results from the Business List 

Comparison Project.” Presented at the 2006 American Statistical Association Joint Statistical Meetings. 

Fairman, Kristin, Lucia Foster, C.J. Krizan, and Ian Rucker. “An Analysis of Key Differences in Micro Data: 

Results from the Business List Comparison Project.” Presented at the 2008 American Statistical Association 

Joint Statistical Meetings.

Polivka, Anne E. and Jennifer M. Rothgeb. 1993. “Overhauling the Current Population Survey: Redesigning the 

Questionnaire.” Monthly Labor Review, 116(9) (September), pp. 10-28.

Roemer, Marc. 2002. “Using Administrative Earnings Records to Assess Wage Data Quality in the March Current 

Population Survey and the Survey of Income and Program Participation.” LEHD Technical Paper TP-2002-

22.

Stevens, David W. 2002. “Employment That is Not Covered by State Unemployment Insurance Laws.” LEHD 

Technical Paper TP-2002-16.

Stinson, Martha, Graton Gathright, and Jeremy Skog. 2012. “Comparing Job Characteristics from the 2010 SIPPEHC Field Test to the Census Bureau Business Register.” Working Paper.

U.S. Census Bureau. 2009. American Community Survey, Puerto Rico Community Survey, 2009 Subject Definitions. 

http://www.census.gov/acs/www/Downloads/data_documentation/SubjectDefinitions/2009_ACSSubjectDefi

nitions.pdf

U.S. Census Bureau. 2009. Design and Methodology: American Community Survey. 

http://www.census.gov/acs/www/Downloads/survey_methodology/acs_design_methodology.pdf

ヱヵ
Sector Description

ヱヱ Agriculture, Forestry, Fishing and Hunting

ヲヱ Mining, Quarrying, and Oil and Gas Extraction

ヲヲ Utilities

ヲン Construction

ンヱ-ンン Manufacturing

ヴヲ Wholesale Trade

ヴヴ-ヴヵ Retail Trade

ヴΒ-ヴΓ Transportation and Warehousing

ヵヱ Information

ヵヲ Finance and Insurance

ヵン Real Estate and Rental and Leasing

ヵヴ Professional, Scientific, and Technical Services

ヵヵ Management of Companies and Enterprises

ヵヶ Administrative and Support and Waste Management and Remediation Services

ヶヱ Educational Services

ヶヲ Health Care and Social Assistance

Αヱ Arts, Entertainment, and Recreation

Αヲ Accommodation and Food Services

Βヱ Other Services (except Public Administration)

Γヲ Public Administration

Table 1: 2007 NAICS Sectors

ヱヶ
N

ACS-LEHD 

Match 

Rate

Percent of 

Individuals/ 

Jobs

(ヱ) ヲヰヰΓ ACS Population File ヴ,ヵンヴ,ヶンヲ -- --

 Populations Excluded from Analysis Sample

 NOT employed, at work (ESR!=ヱ) ヲ,ヵンヲ,Βヵヱ -- --

 Federal government employee (COW=ヵ) Βヴ,ヰヶン -- --

 Self-employed not incorporated (COW=ヶ) ヱΓヰ,Αヰヰ -- --

 Self-employed incorporated (COW=Α) Γヴ,ヱΒΒ -- --

 Unpaid family workers (COW=Β) Α,Γンヴ -- --

 Work in Massachusetts (POWS=ヰヲヵ) ヴン,ΓΓヶ -- --

(ヲ) ACS Subsample ヱ,ヶΓヲ,ヶヰヱ -- --

(ン) ACS Subsample with PIKs ヱ,ヵヵヰ,Βヴヵ -- --

(ヴ) Sample (ン) that merged to LEHD PHF_B (all available years)

(A) ヱ,ヵンヱ,ンヵヵ ΓΓ --

(ヵ) Sample (ヴ) with positive LEHD earnings in the ACS interview quarter or 

the previous quarter(B)

ヱ,ヴンヲ,ヰヰΒ Γヲ --

(ヶ) Sample (ヵ) with at least one plausible job ヱ,ヴヱヰ,ヱΒヱ -- --

 ACS Respondents in Sample (6): Percents Below Based on Line (6)

(Β) With one plausible job ヱ,ヲンン,Γヰン -- ΒΑ

(Γ) With multiple plausible jobs ヱΑヶ,ヲΑΒ -- ヱン

Table 2: Sample Selection and Job Match Observation Counts

Note: The ヲヰヰΓ ACS PIK crosswalk contains some ACS observations with identical PIKs; PIKs are unduplicated 

before merging to the ACS population file. Populations excluded from the analysis sample are not mutually 

exclusive. The PIK rate of the ACS subsample is Γヲ percent. Plausible jobs are defined as follows. For ACS 

respondents with RDATE in the last two months of the quarter, LEHD jobs with positive earnings in the ACS 

interview quarter are considered plausible jobs. For ACS respondents with RDATE in the first month of the quarter, 

LEHD jobs with positive earnings in the ACS interview quarter or the previous quarter are considered plausible 

jobs. Therefore, individuals will be considered to have zero plausible jobs if RDATE is in the last two months of the 

quarter and they have LEHD earnings only in the quarter prior to the ACS interview. The main job is the plausible 

job with the highest earnings. See text for additional details. Percents in (Β) and (Γ) sum to ヱヰヰ.

Footnotes: (A) ACS-LEHD Match Rate=ヱヰヰ*((ヵ)/(ヴ)); (B) ACS-LEHD Match Rate=ヱヰヰ*((ヶ)/(ヴ)).

ヱΑ
All Main Jobs

Main Jobs for PIKs 

with >1 Plausible Job Alternative Plausible Jobs

State ΓΒ Γヶ ΒΓ

County Αン ヶΒ ヵヱ

County, LEHD State=Minnesota Βヰ Αン ヵヱ

Tract ヴΑ ンΓ ヱン

Tract, LEHD State=Minnesota ヶヴ ヵヶ ヱΒ

Percent Match

Table 3: Geography Match Rates for Main Jobs and Alternative Plausible Jobs

Note: In the LEHD data, state place of work is at the SEIN level. County and tract comparisons are based on the 

first implicate of the SEINUNIT impute for multi units. In Minnesota, SEINUNIT is reported and not imputed in 

most cases. The number of observations varies by geographic level and job type. For the national sample, one 

percent of jobs in each group are missing ACS county information. ヲヱ of main jobs are missing ACS tract 

information; ヲン percent of main jobs with more than one plausible job and ヲン percent of alternative plausible jobs 

are missing tract information. The LEHD program imputes county and tract for firms missing information.

ヱΒ
ACS Edited Industry Variable (IND)/

 LEHD SEIN Employment Mode Industry Percent Main Jobs Sector 4-digit CIC

All PIKs ヱヰヰ Αヵ ヶヱ

 By IND Reporting Status

 IND As Reported ΓΑ ΑΑ ヶヲ

 IND Assigned <ヱ ヶヵ ンヵ

 IND Imputed ン ヱヴ ヵ

 By LEHD Single/Multi Unit SEIN

 LEHD Single Unit SEIN ヵΒ Αヴ ヶヰ

 LEHD Multi Unit SEIN ヴヲ ΑΑ ヶヱ

ACS Edited Industry Variable (IND)/

 LEHD SEINUNIT Industry Percent Main Jobs Sector 4-digit CIC

All SEIN ヱヰヰ Αン ヵΒ

 By LEHD Single/Multi Unit SEIN

 LEHD Single Unit SEIN ヵΒ Αン ヵΓ

 LEHD Multi Unit SEIN ヴヲ Αン ヵΑ

Percent Match

Note: For main jobs, there is one job (SEIN) per person (PIK). IND is non-missing for our analysis sample. 

SEINUNIT industry is based on the SEINUNIT from the first implicate of the SEINUNIT impute.

Table 4: Industry Match Rates for Main Jobs

Percent Match

ヱΓ
ACS Sector

LEHD 

Sector ACS Sector LEHD Sector All Firms

Single 

Units Multi Units All Firms

Single 

Units Multi Units

Agriculture, Forestry, Fishing, and 

Hunting

ヲヴ ヱヴ Βヲ Βヰ ヵヵ ヶン ヲΑ ヶヵ ヶΑ ヵヴ

Mining, Quarrying, and Oil and Gas 

Extraction

ヴヲ ヴヵ Βヱ Βヲ ヶΑ ヶヶ ヶΒ ヶヵ ヶΓ ヶヱ

Utilities ヵΒ ヶΒ Βヲ ΒΓ ヵΒ ヴヲ ヶΓ ΒΑ Βン Γヰ

Construction ヱΒ ヱン ΑΒ Βン ヶΒ Αヴ ヴヰ Αヶ ΑΒ ヶヱ

Manufacturing ンΑ ンΑ Βヱ Βヲ Αン Αヴ Αヲ Βヴ Βヵ Βン

Wholesale Trade ンヶ ンヰ Βン Βヴ ヵΑ ヶヱ ヴΓ ンΒ ンΒ ヴヰ

Retail Trade ヶヱ ヶヵ ΒΑ ΒΒ ΑΒ ヶン ΒΑ Βン Αヵ ΒΑ

Transportation and Warehousing ヴン ヴヴ Γヱ Γヲ Αヱ ヶΓ Αヵ Αヶ Αヵ ΑΒ

Information ヴΒ ヴΒ ΒΑ Γヱ ヶヵ ヵΑ Αヲ ヶΑ ヶヰ Αヴ

Finance and Insurance ヵΓ ヵΓ ΒΓ Γヱ Βン ΑΒ ΒΑ ΒΓ Βヵ Γヲ

Real Estate and Rental and Leasing ンン ンヱ Βヵ ΒΓ ヶン ヶヲ ヶヴ ヶヲ ヶヰ ヶヶ

Professional, Scientific, and Technical 

Services

ヲヶ ヲン Βヵ Βヴ ヶΒ Αヲ ヵヵ ヶΑ ヶΓ ヶヱ

Management of Companies and 

Enterprises

ヵヲ ヶΓ Αヶ ヶΑ ヲヱ ヲヱ ヲヱ ヲ ン ヱ

Administrative and Support and 

Waste Management and 

Remediation Services

ンヴ ンン ΒΓ Γヱ ヶヲ ヶヲ ヶヱ ヴン ヴン ヴン

Educational Services ヴヵ ヴΒ Γヲ ΒΒ Γヰ ΒΓ Γヲ ΒΑ Γヱ Βヲ

Health Care and Social Assistance ヴヱ ヴヲ Βヴ Βヴ Βヴ Βヵ Βヲ ΒΑ ΒΓ Βヵ

Arts, Entertainment, and Recreation ヲヴ ヲヵ ΑΒ ヶΒ ヵヴ ヵΑ ヴヴ ヶΒ Αン ヵヴ

Accommodation and Food Services ヴヰ ヴヲ Γヵ Γン Βン Βヲ Βヶ Βヲ Βヲ Βヲ

Other Services ヲヶ ヱΒ Βヵ Γヰ ヵヵ ヶヱ ンΓ ヵΒ ヵΑ ヵΓ

Public Administration ヵΑ ヴΓ ヶΑ Αヰ ΑΑ Βヴ Αヱ ヶヵ ヶヲ ヶΓ

Note: The ACS and LEHD sector match rates (all firms) are also displayed in Figure ヴ. The average percent employment at employment mode industry sector 

includes multi units with no variation in industry across units, which would have ヱヰヰ percent of employment at employment mode industry sector.

Table 5: Industry Sector Match Rates by Single/Multi Unit

Percent Multi Unit ACS Sector LEHD Sector

Multi Units Percent Match

Average Percent Employment at 

Employment Mode Sector

ヲヰ
ACS IND Sector

11 21 22 23 31-33 42 44-45 48-49 51 52 53 54 55 56 61 62 71 72 81 92

11 ヵヵ <ヱ <ヱ ヲ ヱヱ ヱヰ ヴ ヲ <ヱ <ヱ ヱ ヱ ヱ ン ヲ ヱ ヱ ヱ ヲ ン

21 <ヱ ヶΑ ヱ ヵ Γ ン ヱ ン <ヱ <ヱ ヱ ン ヲ ヲ <ヱ ヱ <ヱ <ヱ ヱ <ヱ

22 <ヱ ヱ ヵΒ ヴ ヲ ヲ ヱ ヱ <ヱ <ヱ <ヱ ヲ ヶ ヴ <ヱ <ヱ ヱ <ヱ ヱ ヱヶ

23 <ヱ ヱ <ヱ ヶΒ ヴ ヲ ヲ ヱ <ヱ <ヱ ヱ ン ヱ ヴ ヱ ヱ <ヱ ヱ ヱ Α

31-33 <ヱ ヱ <ヱ ヲ Αン Β ヲ ヱ ヱ <ヱ <ヱ ヴ ヲ ン <ヱ ヱ <ヱ ヱ ヱ <ヱ

42 ヱ ヱ <ヱ ヲ ヱヱ ヵΑ ヱヲ ン ヱ <ヱ ヱ ン ヲ ン <ヱ ヱ <ヱ ヱ ヱ <ヱ

44-45 <ヱ <ヱ <ヱ ヱ ヲ Α ΑΒ ヱ ヱ <ヱ ヱ ヱ ヱ ヲ ヱ ヲ <ヱ ヲ ヱ <ヱ

48-49 <ヱ <ヱ <ヱ ヱ ン ヴ ン Αヱ <ヱ <ヱ ヱ ヲ ヱ ヵ ヲ ヱ <ヱ ヱ ヱ ン

51 <ヱ <ヱ <ヱ ン ン ン ン <ヱ ヶヵ ヱ ヱ Β ヱ ヵ ヱ ヱ ヱ ヱ ヱ ヴ

52 <ヱ <ヱ <ヱ <ヱ <ヱ ヱ ヱ <ヱ ヱ Βン ヱ ン ン ン ヱ ヲ <ヱ <ヱ ヱ ヱ

53 <ヱ <ヱ <ヱ ヴ ヱ ヱ ヲ ヱ ヱ ヲ ヶン ン ヱ ヵ ヱ ヵ ヱ ヲ ン ン

54 <ヱ <ヱ <ヱ ヱ ヵ ヴ ヲ ヱ ヵ ヲ ヱ ヶΒ ヱ ヵ ヲ ヲ <ヱ ヱ ヱ ヱ

55 <ヱ ヲ ヱ ヱ ヱヲ Α ヱヶ ヲ ヲ ヵ ン Α ヲヱ Β <ヱ ン ヱ ヶ ヲ <ヱ

56 <ヱ <ヱ <ヱ ン ン ン ヲ ヲ ヱ ヲ ヱ ヵ ヱ ヶヲ ヱ ヴ ヱ ン ヲ ン

61 <ヱ <ヱ <ヱ <ヱ <ヱ <ヱ ヱ <ヱ <ヱ <ヱ <ヱ ヱ <ヱ ヱ Γヰ ン <ヱ ヱ ヱ ヱ

62 <ヱ <ヱ <ヱ <ヱ <ヱ <ヱ ヱ <ヱ <ヱ ヱ <ヱ ヱ ヱ ヲ ン Βヴ <ヱ ヱ ヲ ン

71 <ヱ <ヱ <ヱ ヱ ヱ ヱ ヲ <ヱ ヱ <ヱ ヱ ヱ <ヱ ヲ ン ヲ ヵヴ ヱヱ ヶ ヱヱ

72 <ヱ <ヱ <ヱ ヱ ヱ ヱ ヴ <ヱ <ヱ <ヱ ヱ ヱ <ヱ ヲ ヱ ヱ ヲ Βン ヱ <ヱ

81 <ヱ <ヱ <ヱ ヲ ン ヴ Γ ヱ ヱ ヱ ヱ ン ヱ ン ヴ Γ ヱ ヲ ヵヵ ヱ

92 <ヱ <ヱ <ヱ ヱ <ヱ <ヱ ヱ ヱ <ヱ ヱ <ヱ ヱ <ヱ ヲ Β ヶ ヱ <ヱ ヱ ΑΑ

Table 6: Industry Mismatches: Percent Jobs in LEHD Sectors, for Each ACS Sector

(Each Row Sums to 100)

LEHD SEIN Employment Mode Sector

Note: The diagonal, highlighted in yellow, is the industry sector match rate for the row sector. 

ヲヱ
LEHD SEIN Employment Mode Sector

11 21 22 23 31-33 42 44-45 48-49 51 52 53 54 55 56 61 62 71 72 81 92

11 ヶヵ <ヱ <ヱ ヲ Α Α ヵ ヲ <ヱ <ヱ ヱ ヲ <ヱ ヲ ヱ ヱ <ヱ ヲ ヱ ヱ

21 <ヱ ヶヵ ヲ ヶ ヱヱ ヵ ヲ ン <ヱ <ヱ <ヱ ヲ <ヱ ヱ <ヱ <ヱ <ヱ <ヱ ヱ <ヱ

22 <ヱ ヱ ΒΑ ン ヲ ヱ ヱ ヱ <ヱ <ヱ <ヱ ヱ <ヱ ヱ <ヱ <ヱ <ヱ <ヱ <ヱ ヲ

23 <ヱ ヱ ヱ Αヶ ヵ ヱ ン ヱ ヲ <ヱ ヱ ヲ <ヱ ヲ <ヱ ヱ <ヱ ヱ ヱ ヱ

31-33 ヱ <ヱ <ヱ ヲ Βヴ ン ヲ ヱ ヱ <ヱ <ヱ ン <ヱ ヱ <ヱ ヱ <ヱ ヱ ヱ <ヱ

42 ヲ <ヱ <ヱ ヲ ヲン ンΒ ヱΑ ン ヱ ヱ <ヱ ヵ <ヱ ヲ ヱ ヱ <ヱ ヱ ヲ <ヱ

44-45 <ヱ <ヱ <ヱ ヱ ヲ ン Βン ヱ ヱ <ヱ <ヱ ヱ <ヱ ヱ ヱ ヱ <ヱ ヲ ヲ <ヱ

48-49 <ヱ ヱ <ヱ ヲ ヴ ン ヴ Αヶ <ヱ <ヱ <ヱ ヲ <ヱ ヲ ヱ ヱ <ヱ ヱ ヱ ヱ

51 <ヱ <ヱ <ヱ ヱ ヴ ヱ ヵ <ヱ ヶΑ ヲ <ヱ ヱヲ <ヱ ヲ ヱ ヱ ヱ <ヱ ヱ <ヱ

52 <ヱ <ヱ <ヱ <ヱ ヱ <ヱ ヱ <ヱ <ヱ ΒΓ ヱ ヲ <ヱ ヱ ヱ ヲ <ヱ <ヱ <ヱ <ヱ

53 <ヱ <ヱ <ヱ ヵ ヲ ヲ ヵ ン ヱ ヲ ヶヲ ヲ <ヱ ン ヱ ヴ ヱ ン ヲ ヱ

54 <ヱ <ヱ <ヱ ヲ Β ヱ ン ヱ ン ン ヱ ヶΑ <ヱ ン ヱ ン <ヱ ヱ ヱ ヱ

55 <ヱ ヱ ヶ ン ヲΒ ヶ ヱヱ ン ン ヱン ヲ ヵ ヲ ヲ ヱ Β ヱ ン ヲ <ヱ

56 <ヱ <ヱ ヱ ヴ Γ ヲ ヵ ヴ ン ヴ ヲ Α <ヱ ヴン ヲ Α ヱ ン ヲ ヲ

61 <ヱ <ヱ <ヱ <ヱ <ヱ <ヱ ヱ ヱ <ヱ <ヱ <ヱ ヱ ヰ <ヱ ΒΑ ヴ ヱ ヱ ヱ ン

62 <ヱ <ヱ <ヱ <ヱ ヱ <ヱ ヱ <ヱ <ヱ ヱ ヱ ヱ <ヱ ヱ ヲ ΒΑ <ヱ ヱ ヲ ヲ

71 <ヱ <ヱ ヱ ヱ ヱ <ヱ ン ヱ ヲ <ヱ ヱ ヱ <ヱ ヲ ン ン ヶΒ Α ヲ ヴ

72 <ヱ <ヱ <ヱ ヱ ヱ <ヱ ン <ヱ <ヱ <ヱ <ヱ ヱ <ヱ ヱ ヲ ヲ ヴ Βヲ ヱ <ヱ

81 <ヱ <ヱ <ヱ ヲ ヴ ヱ ヴ ヱ ヱ ヱ ヲ ヲ <ヱ ヲ ン ヱヰ ヴ ヲ ヵΒ ヲ

92 <ヱ <ヱ ヴ ヶ <ヱ <ヱ ヱ ヲ ヲ ヱ ヱ ヱ <ヱ ヲ ヲ Β ヴ <ヱ ヱ ヶヵ

Table 7: Industry Mismatches: Percent Jobs in ACS Sectors, for Each LEHD Sector

(Each Row Sums to 100)

ACS IND Sector

Note: The diagonal, highlighted in yellow, is the industry sector match rate for the row sector. 

ヲヲ
Industry Sector

ACS, LEHD 

Percent of 

Main Jobs

ACS, LEHD 

Sector Match 

Rates Other Main Results and Notes

Agriculture, Forestry, Fishing and Hunting ヱ, ヱ ヵヵ, ヶヵ One of the smallest sectors. For ACS sector, ヱヱ percent jobs in 

manufacturing sector and ヱヰ percent jobs wholesale trade sector in LEHD 

data. Comprised of six ヴ-digit CIC codes. Several ACS ヴ-digit CICs in this 

sector have a substantial share of jobs in the LEHD manufacturing, 

wholesale trade, or public administration sectors, suggesting there is 

insufficient information in the text descriptions or different approaches 

to coding these industries.

Mining, Quarrying, and Oil and Gas Extraction ヱ, ヱ ヶΑ, ヶヵ One of the smallest sectors. This sector has a relatively low place of 

work tract match rate. Comprised of five ヴ-digit CIC codes.

Utilities ヱ, ヱ ヵΒ, ΒΑ One of the smallest sectors. This sector has substantially different match 

rates depending on whether sector is defined using ACS or LEHD 

information. For ACS sector, ヱヶ percent of jobs are in public 

administration sector in LEHD data.

Construction ヵ, ヴ ヶΒ, Αヶ Consists of one ヴ-digit CIC code.

Manufacturing ヱン, ヱヱ Αン, Βヴ One of the largest sectors. This sector has more jobs in the ACS data than 

LEHD data and relatively high match rates. We would expect a higher 

match rate for the LEHD sector, given the greater reporting of this sector 

in the ACS data. Comprised of numerous ヴ-digit CIC codes. This sector 

has a relatively high place of work tract match rate.

Wholesale Trade ン, ヵ ヵΑ, ンΒ This sector has more jobs in LEHD data than ACS data. Much lower match 

rates than manufacturing or retail trade, especially in LEHD data. More 

than ヱヰ percent of ACS wholesale trade jobs are in the LEHD 

manufacturing and retail trade sectors. For the LEHD wholesale trade 

sector, ヲン percent of jobs are in the ACS manufacturing sector and ヱΑ 

percent of jobs are in the retail trade sector.

Retail Trade ヱヲ, ヱヱ ΑΒ, Βン One of the largest sectors. Relatively low place of work tract and county 

match rates.

Transportation and Warehousing ン, ン Αヱ, Αヶ For ACS and LEHD sectors, mismatched jobs spread over several sectors.

Information ン, ヲ ヶヵ, ヶΑ For LEHD (ACS) sector, ヱヲ (Β) percent of jobs in professional, scientific, 

and technical services sector.

Finance and Insurance ヶ, ヵ Βン, ΒΓ Relatively high sector match rates.

Table 8: Main Results for Each Industry Sector

ヲン
Industry Sector

ACS, LEHD 

Percent of 

Main Jobs

ACS, LEHD 

Sector Match 

Rates Other Main Results and Notes

Real Estate and Rental and Leasing ヱ, ヱ ヶン, ヶヲ For ACS and LEHD sectors, mismatched jobs spread over many sectors.

Professional, Scientific, and Technical Services ヶ, ヶ ヶΒ, ヶΑ Relatively high place of work tract and county match rates.

Management of Companies and Enterprises <ヱ, ヱ ヲヱ, ヲ Lowest ACS and LEHD sector match rate of all sectors. ACS reports very 

few jobs in this sector. (See text for further discussion.) This sector 

consists of only one ヴ-digit CIC and therefore has identical sector and ヴdigit CIC match rates. More than ヱヰ percent of ACS management jobs are 

in LEHD manufacturing and retail trade sectors.

Administrative and Support and Waste 

Management and Remediation Services

ン, ヴ ヶヲ, ヴン This sector has more jobs in LEHD data than ACS data and has a higher 

match rate in the ACS data. For the ACS and LEHD sectors, the nonmatched jobs are spread over many different sectors. This sector 

includes temporary help agencies and has relatively low county and 

tract place of work match rates. 

Educational Services ヱヱ, ヱヲ Γヰ, ΒΑ This sector is one of the largest sectors and has very high sector match 

rates. This sector has four ヴ-digit CIC codes and has very similar sector 

and ヴ-digit CIC match rates. This sector has relatively low place of work 

tract match rates. This is likely due to underreporting of establishments 

(schools) in LEHD data; information is often reported at the firm (SEIN) 

level.

Health Care and Social Assistance ヱヵ, ヱヴ Βヴ, ΒΑ This sector is one of the largest sectors and has very high sector match 

rates. Comprised of numerous ヴ-digit CIC codes. This sector has some of 

the highest place of work tract and county match rates.

Arts, Entertainment, and Recreation ヲ, ヲ ヵヴ, ヶΒ Higher sector match rate based on LEHD sector. More than ヱヰ percent of 

ACS arts, entertainment and recreation jobs are in the LEHD 

accomodation and food services and public administration sectors.

Accommodation and Food Services ヶ, ヶ Βン, Βヲ Relatively high sector match rates.

Other Services ン, ン ヵヵ, ヵΒ Relatively low sector match rates.

Public Administration ヵ, ヵ ΑΑ, ヶヵ This sector has more jobs in LEHD data than ACS data and has a higher 

match rate in the ACS data. The ACS public administration sector has Β 

percent of jobs in the LEHD educational services sector and ヶ percent of 

jobs in the arts, entertainment, and recreation sector.

Note: ACS, LEHD perent of main jobs are the same numbers presented in Figure ン. ACS, LEHD sector match rates are the same numbers presented in 

Figure ヴ.

Table 8 continued: Main Results for Each Industry Sector

ヲヴ
Industry Not Reported 

(UIND='')

Inudstry Reported 

(UIND not equal to '')

Age (AGE)

ヱヶ to ヲヴ ヲヶ ヱヴ

ヲヵ to ンヴ ヱヵ ヱΒ

ンヵ to ヴヴ ヱヵ ヲヰ

ヴヵ to ヵヴ ヱΑ ヲン

ヵヵ to ヶヴ ヱヵ ヱΒ

ヶヵ+ ヱン Α

Educational Attainment (SCHLR)

Nursery school to ヱヲth grade, no diploma ヲン ヱヱ

High school graduate ンン ヲヶ

Some college, but less than ヱ year Β Β

ヱ or more years of college, no degree ヱヵ ヱΑ

Associates degree ヵ Β

Bachelors degree ヱヱ ヱΓ

Masters degree ヴ Β

Professional degree beyond a bachelors degree ヱ ヲ

Doctorate degree ヱ ヱ

Class of Worker, Unedited (UCOW)

Private for-profit ヶヶ ヶヵ

Private not-for-profit ヶ Β

Local government ヵ Β

State government ン ヵ

Federal government ン ン

Self-employed, not inc. ヱヰ Α

Self-employed, inc. ン ヴ

Unpaid family workers ヲ <ヱ

Private (unspecified) ヲ ヱ

Government (unspec.) <ヱ <ヱ

Self-employed (unspec.) <ヱ <ヱ

Sex (SEX)

Male ヵヲ ヵヱ

Female ヴΒ ヴΓ

Employment Status Recode (ESR)

Employed, at work ヴヶ Αヶ

Employed, with a job but not at work ヱ ヲ

Unemployed ヲヱ Α

Armed Forces, at work <ヱ ヱ

Armed Forces, not at work -- ヰ

Not in labor force ンヱ ヱヵ

Table 9: Characteristics by ACS Industry Reported/Not Reported

Percent Respondents

ヲヵ
Inudstry Reported 

(UIND not equal to '')

When Last Worked (WKL)

Within past ヱヲ months ヶヱ Γヰ

ヱ-ヵ years ago ヲヶ ヱヰ

Over ヵ years ago or never worked ヱン <ヱ

Weeks Worked Past 12 Months, Unedited (UWKW)

ヵヰ to ヵヲ weeks ヱΒ ンヵ

ヴΒ to ヴΓ weeks ヵ ヶ

ヴヰ to ヴΑ weeks ヱヱ ヱヵ

ヲΑ to ンΓ weeks ヱヲ ヱヵ

ヱヴ to ヲヶ weeks ヱヲ ヱヲ

ヱン weeks or less ヴン ヱヶ

Hours worked per week, Unedited (UWKH)

ヰ hours ヱヵ ヱ

ヱ-Γ hours Α ン

ヱヰ-ヱΓ hours Β ヶ

ヲヰ-ヲΓ hours ヱヱ Γ

ンヰ-ンΓ hours ヱヱ ヱヴ

ヴヰ to ヴΓ hours ヴヰ ヵヱ

ヵヰ or more hours Β ヱヶ

Service in Armed Forces, Unedited (UMIL)

Yes, on active duty now <ヱ ヱ

Yes, on active duty in past ヱヲ months <ヱ <ヱ

Yes, on active duty more than ヱヲ months ago Β Β

No, training only ヱ ヱ

No, never served Γヰ ΒΓ

Yes, on active duty (unspec.) <ヱ <ヱ

No (unspec.) <ヱ ヰ

Wages/Salary Income, Unedited (UWAG)

ヰ ヱン ヱ

ヱ to Γ,ΓΓΓ ンヵ ヲヰ

ヱヰ,ヰヰヰ to ヲΓ,ΓΓΓ ヲΒ ヲΒ

ンヰ,ヰヰヰ to ヴΓ,ΓΓΓ ヱン ヲン

ヵヰ,ヰヰヰ to Αヴ,ΓΓΓ Α ヱヶ

Αヵ,ヰヰヰ to ΓΓ,ΓΓΓ ヲ ヶ

ヱヰヰ,ヰヰヰ to ヱヴΓ,ΓΓΓ ヲ ヵ

ヱヵヰ,ヰヰヰ to ヱΓΓ,ΓΓΓ <ヱ ヱ

ヲヰヰ,ヰヰヰ to ヲヴΓ,ΓΓΓ <ヱ ヱ

ヲヵヰ,ヰヰヰ+ <ヱ ヱ

Industry Not Reported 

(UIND='')

Table 9 continued: Characteristics by ACS Industry Reported/Not Reported

Percent Respondents

ヲヶ
Inudstry Reported 

(UIND not equal to '')

Self-Employment Income, Unedited (USEM)

Loss or missing ヴΒ ヱΓ

ヱ to Γ,ΓΓΓ ヲΑ ンヲ

ヱヰ,ヰヰヰ to ヲΓ,ΓΓΓ ヱヴ ヲン

ンヰ,ヰヰヰ to ヴΓ,ΓΓΓ ヵ ヱヰ

ヵヰ,ヰヰヰ to Αヴ,ΓΓΓ ヲ ヶ

Αヵ,ヰヰヰ to ΓΓ,ΓΓΓ ヱ ン

ヱヰヰ,ヰヰヰ to ヱヴΓ,ΓΓΓ ヱ ン

ヱヵヰ,ヰヰヰ to ヱΓΓ,ΓΓΓ <ヱ ヱ

ヲヰヰ,ヰヰヰ to ヲヴΓ,ΓΓΓ <ヱ ヱ

ヲヵヰ,ヰヰヰ+ ヱ ヲ

Hispanic Origin Group (HSGP)

Not Hispanic Βヵ ΒΓ

Hispanic ヱヵ ヱヱ

Disability Recode (DIS)

With a disability ヱヵ Β

No disability Βヵ Γヲ

In Poverty (POV)

Not in poverty Βヲ Γヲ

In poverty ヱΒ Β

Census Region (REGION)

Northeast ヲヰ ヱΓ

Midwest ヲヴ ヲΒ

South ンヵ ンン

West ヲヱ ヲヱ

Veteran/Nonveteran Status (VETSTAT)

In armed services <ヱ ヱ

Veteran Γ Γ

Nonveteran Γヱ Γヱ

Race (Based on TOTRACE)

White alone Αヰ Βヱ

Black alone ヱヶ Γ

AIAN alone ヱ ヱ

Asian alone ヶ ヴ

Some other race or two or more races Α ヵ

Industry Not Reported 

(UIND='')

Note: Calculations based on an ACS sample of individuals with industry in universe, where industry is 

edited or imputed if missing. In this sample, age is greater than or equal to ヱヶ and the individual 

worked in the last five years or the individual is unemployed. The ACS sample is not linked to the ACS

PIK crosswalk or to LEHD data sets, and the calculations are unweighted. The number of observations 

varies by descriptive variable and whether industry is reported or not reported.

Table 9 continued: Characteristics by ACS Industry Reported/Not Reported

Percent Respondents

ヲΑ