Survey Methods and Reliability Statement for the May 2021 Occupational 

Employment and Wage Statistics Survey

Introduction

The Occupational Employment and Wage Statistics (OEWS) survey measures occupational employment 

and wage rates for wage and salary workers in nonfarm establishments nationally, and in the 50 states and 

the District of Columbia, Guam, Puerto Rico, and the Virgin Islands. 

About 7.9 million in-scope establishments are stratified within their respective states by substate area, 

industry, size, and ownership. Substate areas include all officially defined metropolitan areas and one or 

more nonmetropolitan areas. The North American Industry Classification System (NAICS) is used to 

stratify establishments by industry. 

Probability sample panels of about 179,000 to 187,000 establishments are selected semiannually. 

Responses are obtained by Internet or other electronic means, mail, email, fax, telephone, or personal 

visit. Respondents report their employees’ job titles and descriptions, which are used to classify workers 

into occupations in the Standard Occupational Classification (SOC) system. OEWS receives individual 

wage rate data for the federal government; the U.S. Postal Service; most state governments; and, as of the 

May 2020 panel, some private sector and local government establishments. For the remaining 

establishments, the OEWS survey data are placed into 12 wage intervals.

Estimates of occupational employment and wage rates are based on six panels of survey data collected 

over a 3-year cycle. The final in-scope sample size when six panels are combined is approximately 1.1 

million establishments. Total 6-panel unweighted employment covers approximately 82 million out of the 

total employment of over 130 million. 

The estimates described here are produced using a new system for model-based estimation using 3 years 

of OEWS data (MB3) to estimate current year occupational employment and wages. The sampling 

methods described here are the same as past years. MB3 estimation has advantages over the past OEWS 

methodology, as described in Model-Based Estimates for the Occupational Employment Statistics 

program. 
2

Occupational and industrial classification systems

The occupational classification system 

The U.S. Office of Management and Budget’s Standard Occupational Classification (SOC) system is used 

to define occupations. All panels in the 2021 estimates were collected using the 2018 SOC system. More 

information about the 2018 SOC system can be found at www.bls.gov/soc/.

The industrial classification system 

The OEWS survey classifies establishments into industries based on the North American Industry 

Classification System (NAICS). All panels used in the 2021 estimates were collected using the 2017 

NAICS. More information about the NAICS can be found at www.bls.gov/bls/naics.htm or at

www.census.gov/naics/. 

Each establishment in the survey is assigned a 6-digit NAICS code based on its primary economic 

activity. 

Industrial scope and stratification 

The survey covers the following NAICS industry sectors:

11 Logging (1133), support activities for crop production (1151), 

 and support activities for animal production (1152) only

21 Mining

22 Utilities

23 Construction

31-33 Manufacturing

42 Wholesale trade

44-45 Retail trade

48-49 Transportation and warehousing

51 Information
3

52 Finance and insurance

53 Real estate and rental and leasing

54 Professional, scientific, and technical services

55 Management of companies and enterprises

56 Administrative and support and waste management and

 remediation services

61 Educational services

62 Healthcare and social assistance

71 Arts, entertainment, and recreation

72 Accommodation and food services

81 Other services, except public administration [private

 households (814) are excluded]

Federal government executive branch (assigned industry code 999100)*

State government (assigned industry code 999200)*

Local government (assigned industry code 999300)*

* These are OEWS-defined industry codes and not a part of the NAICS industry classification.

These sectors are stratified into about 300 industry groups at the 3-, 4-, 5-, or 6-digit NAICS level of 

detail. 

Concepts

An establishment is generally a single physical location at which economic activity occurs (e.g., store, 

factory, restaurant, etc.). Each establishment is assigned a 6-digit NAICS code. When a single physical 

location encompasses two or more distinct economic activities, it is treated as two or more separate 

establishments if separate payroll records are available and certain other criteria are met.
4

Employment refers to the number of workers who can be classified as full- or part-time employees, 

including workers on paid vacations or other types of paid leave; salaried officers, executives, and staff 

members of incorporated firms; employees temporarily assigned to other units; and noncontract 

employees for whom the reporting unit is their permanent duty station, regardless of whether that unit 

prepares their paychecks. 

The OEWS survey includes all full- and part-time wage and salary workers in nonfarm industries. Selfemployed workers, owners and partners in unincorporated firms, household workers, and unpaid family 

workers are excluded.

Occupations are classified based on work performed and on required skills. Employees are assigned to an 

occupation based on the work they perform and not on their education or training. For example, an 

employee trained as an engineer but working as a drafter is reported as a drafter. Employees who perform 

the duties of two or more occupations are reported in the occupation that requires the highest level of skill 

or in the occupation where the most time is spent if there is no measurable difference in skill 

requirements. Working supervisors (those spending 20 percent or more of their time doing work similar 

to that of the workers they supervise) are classified with the workers they supervise. Workers receiving 

on-the-job training, apprentices, and trainees are classified in the occupations for which they are being 

trained.

A wage is money that is paid or received for work or services performed in a specified period. Base rate 

pay, cost-of-living allowances, guaranteed pay, hazardous-duty pay, incentive pay such as commissions 

and production bonuses, and tips are included in a wage. Back pay, jury duty pay, overtime pay, 

severance pay, shift differentials, nonproduction bonuses, employer costs for supplementary benefits, and 

tuition reimbursements are excluded. The federal executive branch, the U.S. Postal Service (USPS), and 

most state governments report individual wage rates as dollars per hour or per year for workers. For the 

November 2018 through November 2019 panels, most wage data were collected using the wage intervals 

below in Tables 1 or 2. Some data for those panels were collected as wage rates and were then placed into 

one of the 12 wage intervals shown in Table 1 and Table 2. Starting in May 2020, individual wage rates 

were collected from all employers, when available. Employers who did not report individual wage rates 

could still report wage interval data as done in past panels. 
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Table 1: Wage Intervals for the November 2019 – May 2021 Survey Panels

-------------------------------------------------------

 | 

 | Wages

 Interval |-------------------------------------------

 | Hourly | Annual

------------|----------------- -|-----------------------

Range A | Under $9.25 | Under $19,240

Range B | $9.25 to $11.99 | $19,240 to $24,959

Range C | $12.00 to $15.49 | $24,960 to $32,239

Range D | $15.50 to $19.74 | $32,240 to $41,079

Range E | $19.75 to $25.49 | $41,080 to $53,039

Range F | $25.50 to $32.74 | $53,040 to $68,119

Range G | $32.75 to $41.99 | $68,120 to $87,359

Range H | $42.00 to $53.99 | $87,360 to $112,319

Range I | $54.00 to $69.49 | $112,320 to $144,559

Range J | $69.50 to $89.49 | $144,560 to $186,159

Range K | $89.50 to $114.99 | $186,160 to $239,199

Range L | $115.00 and over | $239,200 and over

-------------------------------------------------------
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Table 2: Wage Intervals for the November 2018 and May 2019 Survey Panels

--------------------------------------------------------

 | 

 | Wages

 Interval |-------------------------------------------

 | Hourly | Annual

------------|----------------- -|-----------------------

Range A | Under $9.25 | Under $19,240

Range B | $9.25 to $11.74 | $19,240 to $24,439

Range C | $11.75 to $14.74 | $24,440 to $30,679

Range D | $14.75 to $18.74 | $30,680 to $38,999

Range E | $18.75 to $23.99 | $39,000 to $49,919

Range F | $24.00 to $30.24 | $49,920 to $62,919

Range G | $30.25 to $38.49 | $62,920 to $80,079

Range H | $38.50 to $48.99 | $80,080 to $101,919

Range I | $49.00 to $61.99 | $101,920 to $128,959

Range J | $62.00 to $78.74 | $128,960 to $163,799

Range K | $78.75 to $99.99 | $163,800 to $207,999

Range L | $100.00 and over | $208,000 and over

--------------------------------------------------------

Three-year survey cycle of data collection

The survey is based on a probability sample drawn from the universe of in-scope establishments stratified 

by geography, industry, size, and ownership. The sample is designed to represent all nonfarm 

establishments in the United States. 
7

The OEWS survey allocates and selects a sample of approximately 179,000 to 187,000 establishments 

semiannually. Semiannual samples are referred to as panels. To the extent possible, private sector units 

selected in any one panel are not sampled again in the next five panels.

The survey is conducted over a rolling 6-panel (or 3-year) cycle. This is done in order to provide adequate 

geographic, industrial, and occupational coverage. Over the course of a 6-panel (or 3-year) cycle, 

approximately 1.1 million establishments are sampled. In the May 2021 estimates, data collected for the

May 2021 panel were combined with data collected for the November 2020, May 2020, November 2019, 

May 2019, and November 2018 panels. 

For a given panel, most sampled establishments initially receive a letter or email with instructions for 

reporting their data electronically. At approximately 4-week intervals, nonrespondents receive up to three 

additional mailings of a survey questionnaire or letter with instructions for reporting electronically. 

Nonrespondents may also be contacted by phone or email.

Censuses of federal and state government are collected annually:

• A census of the executive branch of the federal government and the U.S. Postal Service (USPS) is 

collected annually in June from the U.S. Office of Personnel Management (OPM), the Tennessee 

Valley Authority, and the U.S. Postal Service. Data from only the most recent year are retained for 

use in OEWS estimates.

• In each area, a census of state government establishments, except for schools and hospitals, is 

collected annually every November. Data from only the most recent year are retained for use in the 

estimates.

A probability sample is taken of local government establishments, private sector establishments, and state 

government schools. 
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Sampling procedures

Frame construction 

The sampling frame, or universe, is a list of all in-scope nonfarm establishments that file unemployment 

insurance (UI) reports to the state workforce agencies. Employers are required by law to file these reports 

to the state where each establishment is located. Every quarter, BLS creates a national sampling frame by 

combining the administrative lists of unemployment insurance reports from all the states into a single 

database called the Quarterly Census of Employment and Wages (QCEW). Every six months, OEWS 

extracts the administrative data for establishments that are in scope for the OEWS survey from the most 

current QCEW. QCEW files were supplemented with frame files covering rail transportation (NAICS 

4821) and Guam for establishments not covered by the UI program.

Construction of the sampling frame includes a process in which establishments that are linked together 

into multiunit companies are assigned to either the May or November sample. This prevents BLS from 

contacting multiunit companies more than once per year for this survey. Furthermore, the frame is 

matched to the 5 prior sample panels, and units that have been selected in the 5 prior panels are marked as 

ineligible for sampling for the current panel.

Stratification 

Establishments on the frame are stratified by geographic area and industry group:

• The 2021 estimates include about 600 specified areas made up of Metropolitan Statistical Areas 

(MSAs) and nonmetropolitan or Balance-of-State (BOS) areas. MSAs are defined and mandated 

by the U.S. Office of Management and Budget (OMB). The May 2021 estimates use the August 

2017 MSA definitions delineated in OMB Bulletin 17-01. Each officially defined metropolitan 

area within a state is specified as a substate area. Cross-state MSAs have a separate portion for

each state contributing to that MSA. In addition, states may have up to six residual 

nonmetropolitan areas that together cover the remaining non-MSA portion of their state.

• Industry—The May 2021 estimates have about 300 industry groups defined at the NAICS 3-, 4-, 

5-, or 6-digit level.
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• Ownership—Schools are also stratified by state government, local government, or private 

ownership. Also, local government casinos and gambling establishments are sampled separately 

from the rest of local government.

• Size—Establishments are divided into certainty and noncertainty size classes.

Sample is taken from nonempty frame cells1

. Frame cells are defined by State/MSA-BOS/NAICS 3-, 4-, 

5-, 6-digit/ownership strata. When comparing nonempty strata between frames, there may be substantial 

frame-to-frame differences. The differences are due primarily to normal establishment birth and death 

processes and normal establishment growth and shrinkage. Other differences are due to establishment 

NAICS reclassification and changes in geographic location. 

A small number of establishments indicate the state in which their employees are located, but do not 

indicate the specific county in which they are located. These establishments are also sampled and used in 

the calculation of the statewide and national estimates. They are not included in the estimates of any 

substate area. Therefore, the sum of the employment in the MSAs and nonmetropolitan areas within a 

state may be less than the statewide employment. 

Allocation of the sample to strata 

Each time a sample is selected, a 6-panel allocation of the 1.1 million sample units among these strata is 

performed. The largest establishments are removed from the allocation because they will be selected with 

certainty once during the 6-panel cycle. For the remaining noncertainty strata, a set of minimum sample 

size requirements based on the number of establishments in each cell is used to ensure coverage for 

industries and MSAs. For each nonempty frame cell, a sample allocation is calculated using a power 

Neyman allocation. The actual 6-panel sample allocation is the larger of the minimum sample allocation 

and the power allocation. To determine the current single panel allocation, the 6-panel allocation is 

divided by 6 and the resulting quotient is randomly rounded. 

1 About 145,000 nonempty cells in May 2021 frame.
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Two factors influence the power Neyman allocation. The first factor is the square root of the employment 

size of each stratum. With a Neyman allocation, strata with higher levels of employment generally are 

allocated more sample than strata with lower levels of employment. Using the square root within the 

Neyman allocation softens this effect. The second factor is a measure of the occupational variability of 

the industry based on prior OEWS survey data. The occupational variability of an industry is measured by 

computing the coefficient of variation (CV) for each occupation within the 90th percentile of occupational 

employment in a given industry, averaging those CVs, and then calculating the standard error from that 

average CV. Using this measure, industries that tend to have greater occupational variability will get more 

sample than industries that are more occupationally homogeneous. 

Sample selection 

Sample selection within strata is approximately proportional to size. To provide the most occupational 

coverage, establishments with higher employment are more likely to be selected than those with lower 

employment; some of the largest establishments are selected with certainty. The unweighted employment 

of sampled establishments made up 62 percent of total employment in 2021.

Permanent random numbers (PRNs) are used in the sample selection process. To minimize sample 

overlap between the OEWS survey and other large surveys conducted by the U.S. Bureau of Labor 

Statistics, each establishment is assigned a PRN. For each stratum, a specific PRN value is designated as 

the “starting” point to select a sample. From this “starting” point, we sequentially select the first ‘n’

eligible establishments in the frame into the sample, where ‘n’ denotes the number of establishments to be 

sampled.

Single panel weights (sampling weights) 

Sampling weights are computed so that each panel will roughly represent the entire universe of 

establishments. Under the new MB3 estimation method, sample weights are used to fit wage interval 

models, but do not otherwise play a role in estimation because data are predicted for every establishment 

in the universe.
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Federal government, USPS, and state government units are assigned a panel weight of 1. Other sampled 

establishments are assigned a design-based panel weight, which reflects the inverse of the probability of 

selection.

National sample counts 

The combined sample for each set of estimates is the equivalent of six panels. The sample allocations, 

excluding federal government and U.S. Postal Service (USPS), for the panels in the 2021 estimates cycle 

are:

187,410 establishments for May 2021

179,303 establishments for November 2020

179,824 establishments for May 2020

179,391 establishments for November 2019

182,809 establishments for May 2019

186,679 establishments for November 2018

Each set of estimates includes a census of about 8,000 federal and USPS units. The combined sample size 

for each set of estimates is approximately 1.1 million establishments, which includes only the most recent 

data for federal and state government. Federal and state government units from older panels are deleted to 

avoid double counting.

Sample response rates

Table 3, below, contains information on unit and employment response rates and the number of viable 

and responding units for the 2021 estimates. The initial combined sample had approximately 1.1 million 

establishments.
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Table 3: Response Rates for 2021 Sample

Year

Viable 

Units

Responding 

Units

Unit Response 

Rate

Employment 

Response Rate

2021 1,022,287 687,213 67.2% 64.5%

MB3 estimation methodology

Modeled occupational employment and wage estimates are made directly from a predicted population. 

The OEWS population is defined as the set of in-scope establishments present in the universe during the 

survey reference period. Each population establishment is defined by information including industry, size, 

ownership, and location; these are known to be strong predictors of occupational employment and wages. 

OEWS survey response data provide occupational employment distributions, known as staffing patterns, 

and wage information for a portion of the population. Models based on OEWS response data from the 

current panel and five previous panels predict staffing patterns and wages for the majority of the 

population.

Matching population units to respondent data

The prediction framework splits the population into the categories of “observed units” and “unobserved 

units.” Observed units are defined as stable establishments with response data from the previous three 

years. Unit stability is defined by comparing QCEW and reported values for a number of variables, as 

described in the following section. For any given observed unit, occupational employment and wage data 

come from that unit’s survey data.

Unobserved units may be nonsampled units, nonresponding units, or responding units that do not meet 

stability criteria. For any given unobserved unit, occupational employment and wages are predicted using 

data from similar responding establishments. Responding units that do not satisfy stability criteria are still 

usable for prediction purposes.
13

Exact matching—observed units

Response data from observed units are used directly in the estimates, provided a given unit is stable. The 

stability criteria for observed units require that reported values exactly match QCEW for the May 

reference panel values for 6-digit NAICS, ownership, and MSA, and be similar to population 

employment. Population employment for a given unit is taken as the average of the most recent May and 

November QCEW employment for a given reference period. The criteria for stable employment, for 

population employment EEPP and respondent employment EERR, fulfill either condition:

|EERR − EEPP |/EEPP < 00. 55 or |EERR − EEPP | < 55.

During data collection, states can correct the QCEW NAICS, MSA, and previous employment values if 

they are incorrect. These corrections are applied to the population file prior to the stability calculations so 

that incorrect population data do not automatically prevent a unit from being classified as stable.

Observed unit wage data from previous years are adjusted using the wage adjustment model to reflect 

current wage levels, and employment data are scaled to population employment levels. Partial respondent 

data contain information on occupational employment, but not wages. Missing wage data for partial 

respondents are imputed through hot deck imputation, and the units are then used as respondents. For 

each occupation in the nonrespondent’s staffing pattern that does not have complete wage data, a wage 

distribution is imputed from a pool of similar respondents reporting that occupation. The donor search is 

limited to the most recent survey panel and is initially defined by MSA/BOS, NAICS 4, size class, and, 

for some industries, ownership. If there are not enough donors to provide wages distributions for the 

partial respondents that need them, then the search criteria are loosened and the search repeated. Once a 

sufficiently large donor pool is found, the donor pool’s wage distribution is used to prorate the recipient’s 

reported employment in the occupation across the 12 wage intervals outlined in the “Concepts” section of 

this survey methods statement.

Complete nonrespondents that are missing both employment and wage data are also imputed using hot 

deck imputation. A single donor is used to impute the entire employment staffing pattern, followed by hot 

deck wage imputation with multiple donors. These units are used in the wage modeling process, but they 

are later treated the same as unobserved units during the MB3 prediction process.
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Prediction—unobserved units

The staffing patterns and wages of unobserved units in the population are predicted using data from 

nearest neighbor respondents. Responding units that do not pass stability criteria are no longer 

representative of the population cell for which they were sampled, but are usable to predict units in the 

cell to which they currently belong. A combination of unit matching and model-based adjustments 

produces these predictions for each unobserved unit. A pool of 10 nearest neighbor responding units is 

used to predict each unobserved unit. Matching is deterministic, so the predicted staffing pattern and 

wages of any unit of a given size, location, ownership group, and industry will be identical.

Prediction of unobserved unit staffing patterns and wages derives from a weighted sum of response data

from similar units. Data from responding units that closely match the unobserved units are given more 

match weight relative to data from dissimilar units. For a given unobserved unit, a set of (typically) 10 

responding units with the highest relative weights are used for the prediction.2 Industry and establishment 

size as measured by total employment are the strongest predictors of staffing patterns; matches that are 

close in those dimensions are preferred, and a difference in either of these dimensions results in a large

penalty to matching weight. Time, location, and ownership are also important predictors that play a part 

in finding matches; differences in any of these dimensions result in relatively smaller weight penalties

than are given for industry and size differences. Matching unit weights are determined as the product of 

scoring functions based on each of these factors.

The match scores can be thought of as a set of penalties for deviation from a perfect match, which would 

have a score of 1. Matches are penalized (given lower weight) using these scores, each with a value 

between 0 and 1. The scoring function for each predictive factor aims to assign a score value based on the 

relative importance of each factor. The specific score values used in the MB3 system were evaluated 

using simulation studies. Various proposed scoring functions were used to generate estimates and the best 

performing of these were used. 

2 There is not technically an upper bound on the number of donors. If several donors have the same weight, which 

puts the total number greater than or equal to 10 donors, they will all be used. For example, if there are 15 donors 

with the 10th highest weight, all will be used, resulting in a total of 24 donors. For the sake of explanation, we will 

be assuming 10 donors, which is typically the case.
15

Where establishment a is an unobserved unit and establishment b is a potential match, each component of 

the score function accounts for differences between a and b. The score of the match is:

S(aa, bb) = SE(aa, bb) ⋅ ST(aa, bb) ⋅ SI(aa, bb) ⋅ SO(aa, bb) ⋅ SA(aa, bb) wwheeeeee:

S(aa, bb) ≤ 1

SE(aa, bb) – Score for difference in total employment between a and b

ST(aa, bb) – Score for difference in time between b and the most recent panel 

SI(aa, bb) – Score for difference in six-digit industry between a and b

SO(aa, bb) – Score for difference in ownership between a and b

SA(aa, bb) – Score for difference in detailed area between a and b 

A potential donor that differs in size from the unit to be predicted will be penalized for this difference. 

The employment component is SE(aa, bb) = �1 − |EEaa−EEbb|

EEaa+EEbb

�, where EEaa and EEbb are the employment totals for 

the respective units. For a potential donor with 20 employees and a unit to be predicted with 15 

employees, this works out to SE(aa, bb) = �1 − |15−20|

15+20 � = 0.857.

Recently collected data are favored over data collected in previous panels. The time score component that 

reflects this is ST(aa, bb) = 1 − ppbb

6 , where ppbb is the number of panels between the collection of data for b

and the reference period. A unit b observed in the current panel would have ST(aa, bb) = 1 and a unit 

sampled 5 panels previously would have ST(aa, bb) = 1 − 5

6 = 16

.

Donors would ideally be in the same industry or ownership group as the unit to be predicted, but may be 

in a similar industry or ownership. 

The score component reflecting differences in industry is SI(aa, bb) = � 1 iiii iiiiiiiiiiii ii mm mmhee

0.25 iiii iiiiiiiiiiii ii mm mm mmhee

While any difference in industry is given the same penalty, donors are chosen according to the hierarchy 

and therefore more similar industry matches will be used before more different matches.
16

The score component reflecting differences in ownership is SOO(aa, bb) = �

1 iiii oo hii mm mmhee

0.5 iiii oo hii mm mm mmhee

For example, if an unobserved unit is a private school, a private school donor would have an ownership 

score of 1, while a public school would have an ownership score of 0.5.

Donors in the same geographical area are preferred and are treated at 4 different matching levels. Units in 

the same state and MSA or nonmetropolitan area have an area match score of 1, whereas units in the same 

state and same urban/rural status but differing MSAs receive an area match score of 0.75. Units from the 

same state but with differing MSAs and urban/rural status receive a score of 0.5. Units from different 

states receive a score of 0.25. The score component reflecting differences in area is:

SA(aa, bb) = �

1 iiii MMMMMM aa

0.75 aa aa aa

0.5 iiii

0.25 oo heeee ee

For a potential donor with 20 employees that would predict a unit of 15 employees (SEE(aa, bb) = 0.857)

from 2 panels previous to the reference period (STT(aa, bb) = 0.667), with a mismatching industry 

(SII(aa, bb) = 0.25), where both are privately owned (SOO(aa, bb) = 1), and a matching state but differing 

MSA/nonmetropolitan area and urban/rural status (SAA(aa, bb) = 0.5), the match score is:

S(aa, bb) = 0.857 ⋅ 0.667 ⋅ 0.25 ⋅ 1 ⋅ 0.5 = 0.0714

Depending on the match scores of other potential donors, the unit may or may not be used in prediction.

The relative matching weight WWbbii of any match bbii among 10 matches, bb1, bb2, ... , bb10, is:

WWbbii = (aa, bbii)

∑jj=1

10 S�aa, bbjj�

Current panel data from the same MSA, detailed industry, ownership, and employment level as an 

unobserved unit will receive a weight of 1, the maximum possible weight. Potential matches are found by 

a hierarchical nearest neighbor search detailed in Table 4 below. All establishments with the same 

employment, NAICS, ownership category, state, and MSA will be predicted using the same set of donors.
17

An employment criterion is defined for each level such that the donor’s employment must be within a 

certain percentage of the unobserved unit’s. For example, in the first hierarchical level, the donor must be 

in the same state, NAICS, ownership category, and MSA of the unit to be predicted while having 

employment within ± 10 percent of the unit to be predicted’s employment. Broader industry cells use the 

industry groups of published OEWS estimates, which are defined at the 4-digit NAICS level in most 

industries, but at the 3-, 5-, or 6-digit NAICS level for a minority of industries. The search for matches 

proceeds through hierarchical levels and stops when the 10 highest scoring matching donors are found. If 

fewer than 10 donor units are available at hierarchy level 10, prediction will still proceed if at least 5 

donor units are found when the search reaches the highest level. The matches with the highest scores are 

used for prediction.

Table 4: Hierarchical Levels of Donor Matches

Hierarchy 

Level

Characteristics that must match 

between the Prediction Cell and

Responder (Donor)

Employment

Criterion

1 State – NAICS – Ownership – MSA 10%

2 State – NAICS – Ownership – MSA 20%

3 State – NAICS – Ownership 10%

4 State –NAICS group– Ownership 20%

5 State – NAICS – Ownership None

6 State – NAICS group None

7 NAICS group– Ownership 10%

8 NAICS group– Ownership 20%

9 NAICS None

10 NAICS group None

Table note: NAICS is 6-digit NAICS. NAICS group is the most detailed NAICS level for which 

OEWS publishes estimates. 

Employment, staffing pattern, and wage data for the 10 closest matches are used to predict the staffing 

pattern and wages of each unobserved unit. If the closest matches include several donors with the same 

weight, they will all be used, which may result in more than 10 donors. If a match differs from the 

unobserved unit’s employment, occupational employment numbers will be scaled to the appropriate level. 

Occupational wage values are scaled using a wage adjustment factor if a match differs from the 

unobserved unit in industry, ownership, area, employment, or data collection panel. Methods for wage 

adjustment are discussed in the following section. For a given unobserved unit U and set of matches 

bbii iiii (bb1, bb2, ... , bb10), the predicted employment E for occupation O in wage interval M will be:
18

EEUU UU = �WWbbii ⋅ EEUU ∙

EEbbiiOO

EEbbii

10

ii=1

where EEbbiiOO is the employment in wage interval M for the occupation O in establishment bbii, EEbbii is the 

employment for establishment bbii, EEUU is the employment of the unit to be predicted, and WWbbii is the relative 

matching weight of match bbii. The occupational wage w predicted for unit U is derived from a weighted 

composite of the occupational employment of the donor units. Assuming 10 donors, this is given by:

wwUU UU = ∑ WWbbii ⋅ AO(UU, bbii) ⋅ wwbbiiOO ⋅ I�EEbbiiOO ≠ 0� 10

ii=1

∑ WWbbii ⋅ I�EEbbiiOO ≠ 0� 10

ii=1

Here, AO(UU, bbii) is the wage adjustment factor discussed below under Model-based adjustments and

wwbbiiOO is the wage interval M mean for the occupation O in the location associated with establishment bbii. 

The function I�EEbbiiOO ≠ 0� equals 1 when the establishment’s occupational employment is nonzero for a 

wage interval and equals 0 otherwise.

To illustrate the prediction of an unobserved unit, suppose the unobserved unit U is a jeweler in a 

medium-sized town. The staffing pattern is predicted for each wage interval of each occupation in the 

nearest donors. Detailed below is the prediction for sales associates in wage interval C. Suppose that of 

the 10 nearest respondents available, eight had employment in wage interval C. Of those eight, four are 

other jewelers of similar size in the same MSA and most recent panel. Three are other jewelers in the 

same MSA with larger differences in size. Of those three, one is from one panel back. The last unit is in 

the same state and most recent panel, of a similar size, but in a different industry and MSA. To predict the 

employment and wage for sales associates in unobserved unit U, wage interval C, we use the example 

data and calculations in table 5.
19

Table 5: Example Data for Predicting Employment

Matching Unit: i 1 2 3 4 5 6 7 8 9 10

Unit i relative match weight: 

WWbbii 0.116 0.116 0.116 0.113 0.113 0.110 0.107 0.097 0.088 0.022

Unit i employment in interval C, 

occupation O: EEbbiiOOOO 4 3 0 2 3 7 8 0 3 11

Unit i total employment: EEbbii 21 21 21 22 20 19 18 21 25 21

Unobserved unit total employment: EEUU 21 21 21 21 21 21 21 21 21 21

Unit i occupational employment ratio in 

interval C, occupation O: 

EEbbiiOOOO

EEbbii 0.190 0.143 0 0.091 0.150 0.368 0.444 0 0.120 0.524

Wage adjustment factor: AAOO(UU, bbii) 1 1 1 1 1 0.9 1 1.03 1.03 1.1

Unit i wage in interval C, occupation O:

 wwbbiiOOOO

13.25 13.25 13.25 13.25 13.25 13.25 13.25 13.05 13.05 13.25

Computed unit i Employment Share:

WWbbii ⋅ EEUU ⋅

EEbbiiOOOO

EEbbii

0.463 0.348 0 0.216 0.356 0.850 0.998 0 0.222 0.242

Computed unit i Wage Share:

WWbbii ⋅ AAOO(UU, bbii) ⋅ wwbbiiOOOO ⋅ II�EEbbiiOOOO ≠ 00�

∑ WWbbii ⋅ II�EEbbiiOOOO ≠ 00� 11

ii=11

1.958 1.958 0 1.907 1.907 1.671 1.806 0 1.507 0.408

Note: data do not correspond to existing establishments or weights.

Summing the second-to-last line of Table 5 yields predicted employment of sales associates O in wage 

interval C for establishment U: 

EEUU = �WWbbii ⋅

EEUU

EEbbii

⋅ EEbbiiOOOO

11

ii=11
20

= 00. 444444 + 00. 33 + 00 + 00. 22 + 00. + 00. + 00. 9999 + 00 + 00. 222222 + 00. 22 22

= .

Although this does not add up to a whole number, for estimation purposes it is reasonable. Summing the 

last line of table 5 yields the predicted wage of sales associates O in wage interval C for establishment U: 

wwUU = �WWii ⋅ AAOO(UU, bbii) ⋅ wwbbiiOOOO ⋅ II�EEbbiiOOOO ≠ 00�

∑ WWii ⋅ II�EEbbiiOOOO ≠ 00� 11

ii=11

11

ii=11

= 11. 99 + 11. 99 + 00 + 11. 99 99 + 11. 99 99 + 11. + 11. + 00 + 11. 55 + 00. 44

= $11 . 11

When this process is completed for all occupation groups and wage interval levels observed in the match 

units, the predicted wage and employment profile of establishment U can be used for estimation. 

Wage parameters

Wage data processing uses both wage rates and the wage interval groups as shown in Tables 1 and 2

above. Using interval data to compute mean wage estimates requires that a wage value be assigned to 

each employee. MB3 wage estimates use wage interval means that are computed using log-normal 

models fit to each panel of OEWS wage data aggregated by occupation group and area group.

Predicting unobserved units also requires adjusting wages in the donor units to current local dollars for 

the unobserved units. For example, let’s suppose an interior design firm (NAICS 541410) in a large 

metropolitan area contributes to the wage prediction for an industrial design firm (NAICS 541420) in a 

small metropolitan area. Occupational wages will differ between these firms due to both geography and 

industry effects. Thus, wages from the first unit must be adjusted with these factors in mind to give a 

reasonable prediction of the second unit. A fixed effect linear regression model, fit to observed unit data, 

is the basis for these adjustments.
21

The wage interval mean model and wage adjustment model both use weighted least squares regression to 

estimate model parameters. Benchmarked sample weights are used in this process, such that weighted 

employment totals for the current panel will equal QCEW frame values for each industry, state, MSA, 

and size subgroup. 

In MB3, benchmarking factors are used only to adjust data for the purposes of model fitting and are not 

used directly for estimation. Benchmarking for OEWS uses the average of the May 2021 and November 

2020 QCEW employment to adjust the weighted reported occupational employment and improve the 

accuracy of the wage interval mean and wage adjustment models. To balance the states’ need for 

estimates at differing levels of geography and industry, the ratio estimation process is carried out through 

a series of four hierarchical employment ratio adjustments. The ratio adjustments are also known as 

benchmark factors (BMFs). The BMFs are calculated for the cells defined at each of the following 

hierarchy levels:

Level Area Industry Size Ownership

1 MSA/BOS NAICS 3/4/5/6 digits 1-19, 20-49, 50-249, 250+

2 State NAICS 3/4/5/6 digits

3 State NAICS 3 digits For hospitals, schools, 

gambling establishments, and 

casino hotels

4 State NAICS 2 digits

For each establishment, a BMF is generally calculated by finding the ratio of QCEW employment 

(average of May 2021 and November 2020) to weighted cell employment for the hierarchy level. There is 

a universal maximum and minimum BMF value to which the BMF will be set if it is higher or lower than 

the maximum and minimum. The second, third, and fourth BMF hierarchy levels are computed to account 

for inadequate coverage of the universe employment—for example, if an establishment is in a first-level 

hierarchy cell with no other establishments. The BMFs are dependent upon the establishment’s previous 

hierarchy levels BMFs. A final benchmark factor is calculated for each establishment as the product of its 
22

four hierarchical benchmark factors. A benchmark weight value is then calculated as the product of the 

establishment’s six-panel combined sample weight and final benchmark factor.

Wage interval mean modeling

Wage interval means are modeled for each panel using only weighted data from that panel to represent 

the population. Occupation and locality are the strongest predictors of wages and may cause substantial 

differences in wage levels between establishments. To provide greater homogeneity within the data, 

occupations and areas with similar median wages are aggregated into groups. 

We assign occupation group codes to all occupations with median wages in a given wage interval, and 

likewise assign group codes to all geographic areas with similar median wages. Then, all units of a given 

ownership status that fall in the same occupation and area groups are pooled together for median wage 

estimation. The units within an occupation-area-ownership group are not necessarily related in any way 

other than the wage interval that the median falls into. Starting with the 2021 estimates, we also 

incorporated wage rate data (specific wages of each employee) from private establishments. The wage 

interval mean wages for each group are computed using a log normal model fit using a log-likelihood 

expression that incorporates both wage rate and wage interval data.

The assignment of occupation wage groups uses single panel sample weights and reported employment 

levels within wage intervals to compute the national wage distribution for each detailed occupation, and 

then determine into which interval the median wage for that occupation falls. This determines the wage

occupation group for every six-digit occupation. To be specific, we calculate occupation-specific 

employment in each of the twelve wage intervals in panel p:

EE�oobbpppp = � wwee × EEoobbppee

ee∈RRpp
23

where RRpp represents the set of panel p OEWS responders, wwee is the sample weight for establishment e in 

panel p, and EEoobbppee is the reported level of employment in occupation o at establishment e in wage 

interval bbpp and panel p.3 We then calculate total occupation-specific employment:

EE�oo = � EE�oobbpppp

bbpp

and compute the relative employment shares by wage interval:

̂

bbpp|oo,pp = EE�oobbpppp

EE�oo

�

We then compute cumulative employment shares:

ππbbpp|oo,pp = � ̂

bbpp|oo,pp

bb≤bbpp

The detailed occupation is mapped into the aggregate occupation O in the lowest wage interval that 

contains at least 50 percent of the detailed occupation’s cumulative employment:

ππOO−1|oo,pp < 0.5 ≤ ππoo|oo,pp

so that each aggregate occupation corresponds to a wage interval.

Typically, there are either 11 or 12 aggregate occupations corresponding to the various wage intervals.

4

For example, tax preparers, substitute teachers, and fast food cooks all have median wages in interval C,

so they are grouped together in occupation group C, while architectural and civil drafters, actors, and 

3 The wage interval is indexed by p; wage interval definitions may change across panels.

4 There are 12 wage intervals, but each wage interval is not necessarily assigned an aggregate occupation.
24

construction and building inspectors all have median wages in interval F and are therefore grouped 

together into occupation group F.

Similarly, we compute the wage distribution for each detailed geographic area (across all occupations) 

and then determine in which interval the median wage for that area would fall. This determines the 

aggregate area for every detailed MSA or BOS area. To be specific, we calculate area-specific 

employment in each of the twelve wage intervals bbpp in the current panel:

EE�aabbpppp = � � wwee × EEoobbppee

oo ee∈RRaa

where RRaa represents the set of panel p OEWS responders in area a, wep represents the sampling weight

for establishment e in panel p, and EEoobbppee is the reported level of employment in occupation o at 

establishment e in wage interval bbpp and panel p.

5 We then calculate total area-specific employment:

EE�aa = � EE�aabbpppp

bbpp

and compute the relative employment shares by wage interval:

̂

bbpp|aa,pp = EE�aabbpppp

EE�aa

�

We then compute cumulative employment shares:

ππbbpp|aa,pp = � ̂

bbpp|aa,pp

bb≤bbpp

5 The wage interval is indexed by p; wage interval definitions may change across panels.
25

The detailed area is mapped into the aggregate area A in the lowest wage interval that contains at least 50 

percent of the detailed area’s cumulative employment:

ππ −1|aa,pp < 0.5 ≤ ππ |aa,pp

so that each aggregate area corresponds to a wage interval.

Typically, there are only three or four aggregate areas, corresponding to interval C, D, E, or F. For 

example, the median wages in San Francisco, CA, and Boston, MA, fall into wage interval F and these 

areas are therefore grouped together in area group F, while the median wages in Chicago, IL, and Atlanta, 

GA, fall into wage interval E and these areas are therefore grouped together in area group E.

Now that we have calculated the separate aggregate occupations and aggregate areas, we combine them to 

create aggregated area and occupation groups within a wage interval. These aggregate occupation-areas 

are necessary to correctly adjust the parameters of the log-normal model and subsequently predict local 

wage area means.

For every possible aggregate occupation-area, denoted as OA, we compute the single panel sampleweighted employment levels for each wage interval:

EE�OO bbpppp = � � wwee × EEoo bbpppp

oo∈OO ee∈RR

where RRAA is the panel p sample in aggregate area A, wep represents the sampling weight for 

establishment e in panel p, and EEoobbppee is the reported level of employment in occupation o at 

establishment e in wage interval bbpp and panel p.

6 In general, there will be a limited number of aggregate 

occupation-area groups, typically between 33 and 48.

6 The wage interval is indexed by p; wage interval definitions may change across panels.
26

For example: nurses and paralegals are in occupation group D for a given panel, while doctors and 

lawyers happen to be in occupation group G. Their employers, a hospital and a law firm, are in different 

metropolitan areas, but both areas are in area group C. Both employers are also privately owned with 

ownership code 5. The data for nurses from the hospital and paralegals from the law office are pooled 

with other data from the same occupation-area-ownership combination to estimate wage group DC5, 

while the data for doctors in the hospital and lawyers in the law firm are pooled with other data to 

estimate wage group GC5.

A log-normal model is fit to these aggregated-occupation-by-aggregated-area cells. A maximum 

likelihood estimator and the sample-weighted employment sums from the current sample are used to 

estimate the two parameters of the lognormal model for wage w, occupation O, and area A, which falls 

into a wage interval:

ln(wwAA ) ~ N(μAA , σAA

2 )

Local wage interval means are predicted using these wage parameter estimates. All data from occupationarea-ownership group OA are used to fit a log-normal model. We then integrate over each wage interval 

to calculate an interval mean for each wage interval. For example, say a paralegal’s reported wage falls 

into wage interval E, but their occupation and geographic location fall into occupation-area group DC.

Then this paralegal will be assigned the wage interval mean E wage rate that was calculated based on the 

model fit from all data in occupation-area group DC.

This process converts all wage data to wage rate rather than interval data. These wage interval means 

directly define wages for all respondents and are adjusted, if needed, to define wages for unobserved 

units. For each interval containing a state or federal minimum wage, the wage interval mean is a weighted 

linear combination of the minimum wage and the portion of the interval above the minimum. 

Wage regression modeling

Using observed units to predict unobserved units relies on similarity between the units. Wage adjustment 

is necessary if the unobserved unit differs from donor units in industry, size, location, or time of data 

collection. Sample response data from the current and previous two years are used to fit fixed effect linear 
27

regression models for wage corrections. Coefficients are determined using maximum likelihood 

estimation over data from the six panels. For a given occupation O, the model is of the form: 

ln(wwOO ) ~βO + βIH + βA + βS ⋅ E + βTM + εε

ww – wage

βO – occupation effect across about 850 detailed occupations

βIH − industry-ownership combined effects across about 1,100 detailed NAICS and 

ownership combinations

βA − area effect across about 480 detailed areas

βS − size effect linear coefficient

E – total establishment employment

βTM − time effect computed independently for each of 22 major occupational groups

Model-based adjustments: wage aging and cell-level adjustments

Aging factors, which provide adjustments for changes to occupational wages over time, and locality 

adjustments are both computed directly using wage regression model parameters. All direct match units 

are separately aged according to a factor based on the combination of year and SOC major group. All 

donors, including unstable units, are independently adjusted to account for the year and SOC major group 

combination, detailed occupation, industry, ownership, and size of the unit to be predicted.

Suppose unit b is selected as a donor for unit a. Unit b is one of typically 10 donors for unit a and might 

come from one or more panels back, in which case unit b’s wage data are adjusted to match local current 

dollars for unit a. The adjusted donor wage for occupation O in unit a based on adjusted unit b data is:

ww�bb = wwbb ⋅ OO(aa, bb)

Where:

OO(aa, bb) = exp �

βO(aa) + βIT(aa) + βA(aa) + βS ⋅ Ea + βMP(aa)

βO(bb) + βIT(bb) + βA(bb) + βS ⋅ EEbb + βMP(bb)

�
28

Estimates

Occupational employment and wage estimates are computed using observed data and predicted data for 

the population of about 8 million7 units. Predicted data are available for each unit of the population, so 

employment estimates are computed by summing employment within an estimation cell and wage 

estimates are computed by dividing summed wages by total employment for an estimation cell.

Occupational employment estimates

Estimates of occupational employment totals are computed by summing all employment counts of a given 

occupation over the modeled population data. Estimates are made over area, industry, and ownership. For 

occupation o, where unit i is any establishment in cell c, the occupational employment estimate is:

XX�oo,cc = � xxii,oo

ii∈oo,cc

Hourly wage rate estimates 

Mean hourly wage is calculated as the total hourly wages for an occupation divided by its total modeled 

population employment. Wage rate information is available for federal executive branch and USPS 

employees, most state government employees, and some private sector and local government employees. 

All other wage data are placed in wage interval ranges and converted to local hourly wages for each 

employee. These local hourly wages are predicted using adjusted estimates of local interval means, and 

thereafter are treated as wage rate data. Mean wage is calculated as a sum of the hourly wage for each 

employee in a cell divided by the total number of employees in a cell. Employees E in a given occupation 

and wage interval at a single establishment will all have the same predicted wage w. For establishment i,

wage range r, and occupation o in cell c, the computation is as follows:

ww�cc,oo = ∑ii∈cc,oo ∑ xxiiii ⋅ wwiiii

∑ii∈cc,oo xxiiii

7 Millions of units per year: 7.92 in May 2021.
29

Percentile wage rate estimates are computed directly from the predicted population using the empirical 

distribution function with averaging, which is available in many statistical packages.

Annual wage rate estimates 

These estimates are calculated by multiplying mean or percentile hourly wage rate estimates by a “yearround, full time” figure of 2,080 hours (52 weeks x 40 hours) per year for most occupations. These 

estimates, however, may not represent mean annual pay should the workers work more or less than 2,080 

hours per year.

Although both annual and hourly wage estimates are produced for most occupations, only annual or only 

hourly wages are produced for some occupations. For example, some workers such as teachers, pilots, 

and flight attendants are paid based on an annual basis but do not work the usual 2,080 hours per year. For 

these workers, survey respondents report annual wages. Since the survey does not collect the actual 

number of hours worked, hourly wage rates cannot be derived from annual wage rates with any 

reasonable degree of confidence. As a result, only annual wage estimates are produced for these

occupations. Other workers, such as some entertainment workers, are paid hourly rates, but generally do 

not work 40 hours per week, year round. For these workers, only hourly wage estimates are produced.

Variance estimation

Variances for both mean wage estimates and occupational employment estimates are computed using the 

“bootstrap” replication technique. Many weights may be associated with a given respondent in MB3 

estimates because that respondent may be used to predict multiple unobserved units. This presents 

problems for many approaches to computing sampling variances. However, bootstrap sample replication 

is amenable to this design because the full MB3 estimation system may be applied to each replicate 

sample. Studies that were performed using simulated data informed decisions on the specifics of the 

bootstrapping approach used here and the number of replicates needed for estimates to converge.

The MB3 variances are computed over 300 bootstrap sample replicates. Each set of replicate estimates is 

based on a subsample of the full sample and includes model fitting as well as population prediction based 

on this subsample. The subsample is drawn from the full sample using a stratified simple random sample 

with replacement design, where the size of the subsample is equal to the size of the full sample. By 
30

sampling with replacement, we are up-weighting some sampled units by including them more than once 

in the subsample and down-weighting others by not including them at all. MB3 selects six independent 

subsamples, one from each of the six semiannual OEWS panel samples. The stratification plan is the 

same used for drawing the full sample, where strata are defined by state, MSAs, aggregate NAICS 

industry, and ownership for schools and hospitals. 

Subsampling only occurs for the noncertainty sample units. All certainty units from the full sample are 

used in every replicate’s bootstrap sample. Some strata may only contain a single noncertainty unit, for 

which a variance cannot be computed. These are referred to as 1-PSU strata. A collapsing algorithm 

combines these 1-PSU strata with other like strata to ensure that two or more noncertainty sample units 

are present in a particular stratum. The collapsing is by the hierarchy detailed in Table 6.

Table 6: Hierarchical Definitions for Collapsing 1-PSU Strata

Hierarchy 

Level Collapse 

1 Panels*: (0,1), (2,3), and (4,5)

2 Panels*: (0,1,2), and (3,4,5) 

3 Panels*: (0,1,2,3,4,5)

4 MSAs

5 Allocation NAICS (A_NAICS)

6 Nationally

* Panels are labeled 0 to 5, where 0 corresponds to the May 2021 panel

The probability of selection for resampling a given unit is proportional to that unit’s share of stratum 

employment. This ensures that bootstrap sample replicates yield unbiased estimates of the full sample’s 

estimates. Sampling variance estimates obtained through these methods do not use the same probabilities 

used in selection of the full sample, which presents a possible source of error. Studies indicate that these 

estimates are approximately unbiased and perform well in estimating sampling variance.
31

The six replicate subsamples are combined for calculating MB3 replicate estimates. Single panel sample 

weights for the most recent panel are retained for computation of wage interval means and the wage 

adjustment factors in each replicate. All matching, wage parameter, and estimation methods described 

previously are used with each 6-panel bootstrap subsample to create occupational employment and wage 

replicate estimates for every estimation cell. This process is repeated to create 300 sets of replicate 

estimates. For every estimation cell in which OEWS calculates an estimate, there are occupational 

employment and mean wage estimates based on the full OEWS sample, as well as 300 occupational 

employment and mean wage replicate estimates each based on a different bootstrap subsample. The 

variance estimates for the occupational estimates based on the full sample are calculated by finding the 

variability across the occupational replicate estimates. The below formula calculates the bootstrap 

variance estimates:

vvBBBB�θθ�

jj,DD� = 1

(300−1)

∑ �θθ� jj,DD

(bb) − θθ�

jj,DD�

2

300

bb=1

where 

θθ�

jj,DD = occupational estimate (employment or mean wage) for occupation j, within estimation 

domain D, based on full sample

θθ�

jj,DD

(bb) = occupational replicate estimates (employment or mean wage) for occupation j, within 

estimation domain D, based on the bootstrap subsample for replicate b

Reliability of the estimates

Estimates developed from a sample will differ from the results of a census. An estimate based on a 

sample survey is subject to two types of error: sampling and nonsampling error. An estimate based on a 

census is subject only to nonsampling error. 

Nonsampling error 

This type of error is attributable to several causes, such as errors in the sampling frame; an inability to 

obtain information for all establishments in the sample; differences in respondents' interpretation of a 

survey question; an inability or unwillingness of the respondents to provide correct information; and 
32

errors made in recording, coding, or processing the data. Explicit measures of the effects of nonsampling 

error are not available. 

Sampling error 

When a sample, rather than an entire population, is surveyed, estimates differ from the true population 

values that they represent. This difference, the sampling error, occurs by chance and depends on the 

particular random sample used in a survey. Sampling error is characterized by the variance of the estimate 

or the standard error of the estimate (square root of the variance). The relative standard error is the ratio of 

the standard error to the estimate itself. 

Estimates of sampling variability for occupational employment and mean wage rates are provided for all 

employment and mean wage estimates to allow data users to determine if those statistics are reliable 

enough for their needs. Sample estimates from a given design are said to be unbiased when an average of 

the estimates from all possible samples yields the true population value. Empirical studies support that 

MB3 methods provide accurate estimates of sampling variability.

Estimated standard errors should be taken to indicate the magnitude of sampling error only. They are not 

intended to measure nonsampling error, including any biases in the data. Particular care should be 

exercised in the interpretation of small estimates or of small differences between estimates when the 

sampling error is relatively large or the magnitude of the bias is unknown. 

Quality control measures

Several edit and quality control procedures are used to reduce nonsampling error. For example, data 

submissions are checked for consistency. Follow-up emails, postal mailings, and phone calls are sent out 

to nonresponding establishments to improve the survey response rate. 

The OEWS survey is a federal-state cooperative effort that enables states to conduct their own surveys. A 

major concern with a cooperative program such as OEWS is to accommodate the needs of BLS and other 

federal agencies, as well as state-specific publication needs, with limited resources while simultaneously 

standardizing survey procedures across all 50 states, the District of Columbia, and the U.S. territories. 
33

Controlling sources of nonsampling error in this decentralized environment can be difficult. One 

important quality control tool used by the OEWS survey is a computerized survey data management 

system. This was developed to provide a consistent and automated framework for survey processing and 

to reduce the workload for state, regional, and national office analysts. 

To ensure standard sampling methods in all areas, the sample is drawn in the national office. 

Standardizing data processing activities, such as validating the sampling frame; allocating and selecting 

the sample; refining mailing addresses; designing and updating letters, emails, and questionnaires;

conducting electronic review; producing management reports; and calculating estimates, have resulted in 

the overall standardization of the OEWS survey methodology. This has reduced the number of errors on 

the data files as well as the time needed to review them.

Other quality control measures used in the OEWS survey include:

• Follow-up email, postal mail, and telephone solicitations of nonrespondents, especially critical 

or large nonrespondents

• Review of data during collection to verify its accuracy and reasonableness 

• Adjustments for atypical reporting units on the data file 

• Validation of unit matching and donor profiles

• Validation by comparison between official MB3 estimates and design-based estimates

Confidentiality

BLS has a strict confidentiality policy that ensures that the survey sample composition, lists of reporters, 

and names of respondents will be kept confidential. Additionally, the policy assures respondents that 

published figures will not reveal the identity of any specific respondent and will not allow the data of any 

specific respondent to be inferred. The most relevant statute which governs BLS confidentiality is the 

Confidential Information Protection and Statistical Efficiency Act (CIPSEA). Each published estimate is 

screened to ensure that it meets these confidentiality requirements. To further protect the confidentiality 

of the data, the specific screening criteria are not listed in this publication. For additional information 

regarding confidentiality, please visit the BLS website at www.bls.gov/bls/confidentiality.htm. 
34

Data presentation

Included are cross-industry data for the United States as a whole, for individual U.S. states, and for 

metropolitan and nonmetropolitan areas, along with U.S. industry-specific estimates by 2-digit, 3-digit, 

most 4-digit, and some 5- and 6-digit NAICS levels. Available data include estimates of employment;

annual mean wages; 10th, 25th, 50th (median), 75th, and 90th percentile wages; and relative standard errors 

(RSEs) for the employment and mean wage estimates. 

Uses

For many years, the OEWS survey has been a major source of detailed occupational employment data for 

the nation, states, and areas, and by industry at the national level. This survey provides information for 

many data users, including individuals and organizations engaged in planning vocational education 

programs, higher education programs, and employment and training programs. OEWS data are used to 

prepare information for career counseling, for job placement activities performed at state workforce 

agencies, and for personnel planning and market research conducted by private enterprises. OEWS data 

also are used by the Department of Labor’s Foreign Labor Certification (FLC) program, which sets the 

rate at which workers on certain work visas in the United States must be paid.