Estimation Processes Used in the Occupational 

Requirements Survey

Bradley D. Rhein1, Chester H. Ponikowski1

1U.S.Bureau of Labor Statistics, 2 Massachusetts Ave., NE, Room 3160, 

Washington, DC 20212

Abstract

The Occupational Requirements Survey (ORS) is an establishment survey conducted by 

the Bureau of Labor Statistics (BLS) for the Social Security Administration (SSA). The 

survey collects information on the vocational preparation and the cognitive and physical 

requirements of occupations in the U.S. economy, as well as the environmental conditions 

in which those occupations are performed. The survey estimation process consists of 

multiple steps including calculation of sample weights that reflect a 2-stage stratified 

sampling design, adjustments for non-response at the establishment level and occupational 

level, adjustment for collection of data for a unit other than the assigned unit, adjustment 

for sample rotation, and benchmarking (post-stratification) of weights. Formulas for 

generating various types of ORS estimates and corresponding estimates of variances also 

appear in the paper.

Key Words: estimation, establishment survey, sample weights, adjustment factors, 

benchmarking

1. Introduction

In the summer of 2012, the Social Security Administration (SSA) and the Bureau of Labor 

Statistics (BLS) signed an interagency agreement, which has been updated annually, to 

begin the process of testing the collection of data on occupations. As a result, the 

Occupational Requirements Survey [1] (ORS) was established as a test survey in late 2012. 

The goal of ORS is to collect and publish occupational information that will replace the 

outdated data currently used by SSA. More information on the background of ORS can be 

found in the next section. All ORS products will be made public for use by non-profits, 

employment agencies, state or federal agencies, the disability community, and other 

stakeholders.

An ORS interviewer attempts to collect close to 70 data elements related to the 

occupational requirements of a job. The following four groups of information will be 

collected:

• Physical demand characteristics/factors of occupations (e.g., strength, hearing, or 

stooping)

• Specific vocational preparation requirements, which include educational 

requirements, experience, licensing and certification and post-employment 

training

• Mental and cognitive demands of work
• Environmental conditions in which the work is completed

The survey plans to publish all estimates that meet the reliability and confidentiality 

criteria. Somewhere between three and thirteen estimates will be calculated for each of the 

70 ORS data elements. Around 700 total estimates could be calculated for a single 

occupation or occupational group. Estimate types include the percentage of workers in a 

given category, mean, percentiles (10%, 25%, 50%, 75%, and 90%), and the mode.

This paper explores the ORS estimation processes. Section 2 provides background 

information on the Occupational Requirements Survey. Section 3 summarizes the ORS 

sample design. Section 4 summarizes the ORS data elements and types of estimates that 

will be calculated for each. Section 5 details the process for calculating final sample 

weights, including the use of non-response adjustment at the unit and quote level, 

adjustment for collection of data for a unit other than the assigned unit, adjustment for 

sample rotation, and benchmarking (post-stratification) of weights. Section 6 explores the 

calculation of standard errors. The paper ends with a conclusion and description of further 

research to be completed.

2. Background Information on ORS

In addition to providing Social Security benefits to retirees and survivors, the Social 

Security Administration (SSA) administers two large disability programs, which provide 

benefit payments to millions of beneficiaries each year. Determinations for adult disability 

applicants are based on a five-step process that evaluates the capabilities of workers, the 

requirements of their past work, and their ability to perform other work in the U.S. 

economy. In some cases, if an applicant is denied disability benefits, SSA policy requires 

adjudicators to document the decision by citing examples of jobs the claimant can still 

perform despite restrictions (such as limited ability to balance, stand, or carry objects) [2]. 

For over 50 years, the Social Security Administration has turned to the Department of 

Labor's Dictionary of Occupational Titles (DOT) [3] as its primary source of occupational 

information to process the disability claims [4]. SSA has incorporated many DOT 

conventions into their disability regulations. However, the DOT was last updated in its 

entirety in the late 1970’s, although a partial update was completed in 1991. Consequently, 

the SSA adjudicators who make the disability decisions must continue to refer to an 

increasingly outdated resource because it remains the most compatible with their statutory 

mandate and is the best source of data at this time.

When an applicant is denied SSA benefits, SSA must sometimes document the decision by 

citing examples of jobs that the claimant can still perform, despite their functional 

limitations. However, since the DOT has not been updated for so long, there are some jobs 

in the American economy that are not even represented in the DOT, and other jobs, in fact 

many often-cited jobs, no longer exist in large numbers in the American economy. 

SSA has investigated numerous alternative data sources for the DOT, such as adapting the 

Employment and Training Administration’s Occupational Information Network (O*NET) 

[5], using the BLS Occupational Employment Statistics program (OES) [6], and 

developing their own survey. SSA was not successful with any of these potential data 

sources and turned to the National Compensation Survey [9] program at the Bureau of 

Labor Statistics.
3. ORS Sample Design Summary

The ORS sample design is a 2-stage stratified sample of establishments from the entire 

nation where units are selected by systematic probability proportional to employment size 

sampling (PPS) at each stage. Stratification is by industry and ownership, directly, and also 

implicitly by region. Jobs (quotes) are then sampled from the selected establishments by 

PPS. The frame will be developed from the BLS Quarterly Census of Employment and 

Wages (QCEW) database [10] with railroads added. Allocation will be proportional to 

employment size. ORS samples will follow a three-year rotation, though this rotation cycle 

may change depending on research concerning how often the requirements of work change.

For more details on this design, see “Occupational Requirements Survey Sample Design” 

by Ferguson, et al [7].

4. ORS Data Elements and Possible Estimates Summary

ORS is designed to capture occupational information on educational requirements, 

cognitive and physical demands, and exposures to environmental conditions. An extensive 

description of ORS data elements and how estimates for each element will be calculated 

can be found in the paper “Estimation Considerations for the Occupational Requirements 

Survey” [8].

Many of the ORS data elements will have the percentage of workers, mean, percentiles, 

and modes estimates for each occupational definition. For example, one ORS data element 

measures the amount of time during a typical day that a worker, such as a nurse, spends 

stooping. Occupational definitions are derived from the Standard Occupational 

Classification Manual (SOC) [11]. Physical demands, such as stooping, are captured in 

hours, and so mean and percentile estimates (10%, 25%, 50%, 75%, and 90%) will be 

calculated. Also, the hours of time spent stooping will fall within an SSA-established 

category and so a percentage of workers estimate will be calculated for each category. SSA 

defines five categories by a range of hours spent performing an activity – never, seldomly, 

occasionally, frequently, and constantly. Finally, the mode of the categories will be 

identified, marking the twelfth estimate related to stooping.

Estimator type will depend on the type of data element. For any categorical data element, 

a percentage of workers that fall into a given category will be calculated using Formula 1.

The percentage of nurses that are not required to reach overhead or the percentage of 

security guards that are required to have a bachelor’s degree are two examples of 

categories that will be estimated in ORS. A mode of these percentages will also be 

identified.

Formula 1 – Calculating Percent of workers

100

OccFW

OccFW

ig

I

i 1

G

g 1

ig

ig ig

I

i 1

G

g 1

ig

i

i





























 





 

 

X

X Z

where:
i = Establishment

I = Total number of establishments

g = Occupational quote within establishment i

Gi = Total number of occupational quotes in establishment i

OccFWig = Final occupational quote weight (a weight denoting the 

number of employees in the frame that are represented by 

occupation g in establishment i) for occupation g in 

establishment i

Xig = 1 if occupational quote ig is in the occupational definition

= 0 otherwise

Zig = 1 if the data element is present for occupational quote ig 

= 0 otherwise

For continuous data elements, mean (Formula 2) and percentile (Formula 3) estimates will

be calculated. The number of hours that a teacher is required to stand in a typical day or 

the number of days of previous experience required for a dental hygienist are examples of 

continuous data elements estimated in ORS. Some continuous data are placed within prespecified ranges, they would become categorical values, and percentages will be 

calculated.

Formula 2 – Calculating the Mean













 













  





 

 

ig ig

I

i 1

G

g 1

ig

ig ig ig

I

i 1

G

g 1

ig

i

i

OccFW

OccFW

X Z

X Z Q

where:

i = Establishment

I = Total number of establishments in the survey

g = Occupational quote within establishment i

Gi = Total number of occupational quotes in establishment i

OccFWig = Final occupational quote weight for occupation g in establishment i

Xig = 1 if occupational quote ig is in the occupational definition

= 0 otherwise

Zig = 1 if the data element is present for occupational quote ig

= 0 otherwise

Qig = Value of the continuous data element for occupational quote ig

Formula 3 – Calculating Percentiles (10%, 25%, 50%, 75%, 90%)

The p-th percentile is the value Qig such that 

 the sum of final quote weights (OccFWig) across quotes with a value less than Qigj is 

less than p percent of all final quote weights, and 

 the sum of final quote weights (OccFWig) across quotes with a value more than Qigj is 

less than (100 – p) percent of all final quote weights.
It is possible that there are no specific quotes ig for which both of these properties hold. 

This occurs when there exists a plan for which the OccFWig of records whose value is less 

than Qig equals p percent of all final quote weights. In this situation, the p-th percentile is 

the average of Qig and the value on the record with the next lowest value (note: the SAS 

proc means procedure handles this special case automatically). The Qig values must be 

sorted in ascending order.

Include only quotes that meet the occupational definition and the ORS data element criteria

– i.e., where:

1 Xig  Zig  .

where:

i = Establishment

g = Occupation within establishment i

Xig = 1 if quote ig meets the condition set in the occupational definition

= 0 otherwise

Zig = 1 if quote ig meets the condition set in the ORS data element criteria

= 0 otherwise

5. Derivation of Sample Weights

The calculation of sample weights involves several steps. First, the initial weights are 

calculated, reflecting the two stages of sampling. Then, the initial weights are adjusted for 

non-response at the establishment level and occupational level. Next, factors are calculated 

for the collection of a unit other than the assigned unit, sample rotation, and benchmarking 

(post-stratification). These factors are applied to the weight that was adjusted for nonresponse.

Both unit and item non-response are expected to occur in the collection of the Occupational 

Requirements Survey. Unit non-response occurs when a sampled establishment or 

occupation refuses to provide any ORS data, while item non-response occurs when we 

obtain incomplete information for an occupation within an establishment.

ORS plans to adjust for unit non-response by re-weighting establishments and occupations 

within a pre-specified non-response adjustment cell. Once a weighting adjustment has been 

made to the establishments and occupations within a cell where a unit refused, the weights 

of all viable units will be further adjusted by benchmarking the weights to the current 

national employment to obtain the Final Occupational Quote Weight. Imputation will be

used to account for item non-response, though details on this topic are being left for another 

paper.

Establishment Non-response Adjustment Factor

Non-response adjustment among the establishments involves adjusting the weights of the 

usable units, those with viable data, to account for the unusable units. The process begins 

by grouping the establishments into non-response cells, identifying the usable and unusable 

units, calculating the non-response factor, and then either accepting the factor or collapsing 

the cell in cases where the factor exceeds a maximum value.
Establishment non-response cells are defined by industry, size class, and area. There are 

23 detailed industries, four size classes, and twenty-four areas (see Appendix A). Cells may 

collapse within industry, size class, and area – that is, a single industry may collapse to a 

group of industries – before pieces of the cell itself are collapsed. If the cell collapses, area 

drops out first, then size class. Should the industry collapse pattern be exhausted, any units 

not adjusted for will remain unadjusted. Collapse patterns can be found in Appendix B.

Non-response cells must collapse under the following circumstances:

 There are zero usable units in the non-response cell

 The non-response adjustment factor exceeds a maximum value

The formula for the non-response adjustment factor follows.

Formula 4 – Non-response Adjustment Factor



  



A

A B

NRAF

where,

NRAF = Non-response Adjustment Factor

A = Weighted Employment for Viable units in the cell

B = Weighted Employment for Non-usable units in the cell

Occupational Non-response Adjustment Factor

Non-response adjustment among occupations follows the same methods as establishment 

non-response adjustment with one difference – the definition of the non-response cell. For 

occupations, the non-response cell also includes an occupational group definition (see 

Appendix C for group definitions and collapse pattern). The occupational group is the last 

piece of the non-response cell to collapse.

Other Adjustment Factors

Another adjustment factor that may be calculated is the Different than Assigned Factor 

(DTA factor). In some cases, the collected unit’s employment differs greatly from the 

originally sampled unit. There are two such instances – the collected unit is something 

more than the originally sampled unit or the collected unit is only a piece of the originally 

sampled unit. These circumstances are noted by the field and, as a result, a DTA factor will

be calculated and applied (the DTA factor is set to 1 otherwise). The formula is as follows.

Formula 5 – DTA Factor

DTA Factor =

sampled employment

reported employment

ORS has a three-year rotation, and each year a fully representative sample will be collected. 

Since up to three years of data will be used in estimation, a sample group factor must be 

applied so that each sample group contributes an appropriate amount of the total weighted 

employment. Given that three equally sized samples are used in estimation, the Sample 

Rotation Factor for each sample will be 1/3.
There are times when a weight adjustment to an establishment or occupation does not fit 

into the standard non-response adjustment. Instead, a manual weight adjustment is needed. 

For example, there may be an ambiguous increase in employment at an establishment 

where a business fluctuation was noted at the same time as a merger with another company. 

A manual establishment (or occupational) adjustment factor will allow for such an 

adjustment to the weight, if necessary. If there are no adjustments, this factor is set to 1.

Benchmarking (Post-stratification) Factor

Benchmarking is the process of adjusting the weights so that the final weighted 

employment matches a targeted population control number. By matching to a population 

control number, the estimates of weighted employment will better match the employment 

levels in the current economy.

The process begins with defining benchmark cells, then a population control number is 

calculated from the product of a growth factor and the current employment levels before 

the benchmarking factor is calculated. The original benchmark cells are formed from 

sampled North American Industry Classification System (NAICS) [12] industry definitions 

(see Appendix A).

The population control numbers are derived from the QCEW Longitudinal Database 

(LDB), Railroad file, and the Current Employment Survey (CES). The LDB and Railroad 

file provide the employment data. Since these sources do not have current employment 

data, the CES is used to make an adjustment to the employment. 

To calculate the employment population control numbers, first a CES Growth Factor is 

calculated. The CES employment is summed within each benchmark cell for the current 

quarter and for the reference period of the LDB data. After both sets of CES data are 

summed by benchmark cell, the following CES Growth Factor for each benchmark cell is 

calculated.

Formula 6 – CES Growth Factor

CES Growth Factor = Total Current Quarter CES Employment / Total CES Employment 

for the older reference period

After the CES Growth Factor has been calculated for each cell, the employment from all 

units in the LDB and OES Railroad file is summed for each benchmark cell. The final 

employment population control numbers for each cell are calculated by multiplying the 

total LDB (with railroad file) employment by the CES Growth Factor for the cell.

Formula 7 – Employment Control Number

Employment control number = LDB employment * CES Growth Factor

Application of the CES Growth Factor differs slightly for state and local government cells.

The CES does not provide data for government at the level of detail of the benchmark cells.

The government CES Growth Factors are calculated as described above but are applied to 

more broadly defined industries. See Appendix A for the details. 

During the benchmark process, it may be necessary to collapse benchmark cells and 

combine the observations into a larger cell. Cell collapsing is the process of combining the 
contents of multiple benchmark cells to form a new and larger cell. It is necessary to 

collapse cells when the number of establishments does not reach a minimum threshold or 

if the benchmarking factor reaches a maximum value. Collapse patterns define how the 

cells should be collapsed. See Appendix B for collapse patterns.

The original benchmark cells are collapsed as necessary to achieve the minimum number 

of establishments per cell. Then, for each collapsed benchmark cell, calculate the total 

weighted employment.

Formula 8 – Total Weighted Employment by Cell

   

USE NMJ

WgtedEmp OccFWgts AsgEmp* AsgEstabWgt *SGF

where,

WgtedEmp = Total Weighted Employment

FinalOccWgt = Final Occupational Quote Weight

AsgEmp = Sampled Establishment Employment

AsgEstabWgt = Original Sample Weight

SGF = Sample Rotation Factor (from Other Factors above)

USE = All usable quotes

NMJ = All quotes that out of scope of the survey (No Matching Job)

Next, the employment benchmark factors are calculated for each cell by dividing the 

employment control number for the cell, calculated in Formula 7, by the total weighted 

employment derived using Formula 8.

Formula 9 – Employment Benchmark Factor

Emp Benchmark Factor =

Employment Control Number for Cell

Total Weighted Employment for Cell

If the employment benchmark factor is less than or equal to the maximum value, then the 

employment benchmark factor calculation is complete. If the population control number 

for a cell is 0, then the benchmark factor for the cell is set equal to 1 (A cell with a 

population control number equal to 0 is not collapsed with other cells.) If the factor is 

greater than the maximum value, the benchmark cells will collapse to the next level in the 

collapse pattern and the benchmark factor will be re-calculated for the new cell.

In circumstances for which the benchmark factor is still greater than the maximum value, 

the process of collapsing cells and calculating benchmark factors continues until all 

benchmark factors are less than or equal to the maximum value or the collapse patterns are 

exhausted.

If the collapse patterns are exhausted, and the factor is still greater than the maximum value 

or if the minimum number of establishments was never met, the value of the benchmark 

factor will be set to the maximum value for all establishments and occupations in the final 

cell.
The final quote weight - the weight that represents each sampled occupation in estimation 

- is the product of the sampling weight and all adjustment factors, shown below in Formula 

10.

Formula 10

Final Occupational Quote Weight (OccFWig) = Sample Weight * Establishment 

Non-response Adjustment Factor * Occupational Non-response Adjustment 

Factor * Sample Rotation Factor * DTA Factor * Manual Adjustment Factors * 

Employment Benchmark Factor

6. Calculation of Variance Estimates

ORS will provide standard errors for all percentages, means, and percentiles using a 

modified version of the Balanced Repeated Replication (BRR) method, known as the Fay’s 

BRR method. Using Fay’s BRR for variance calculation is a process of several steps. First, 

variance strata are defined and half-samples are constructed within each of the variance 

strata definitions. The method requires that the number of replicates be a multiple of four 

and be at least equal to or greater than the number of variance strata. The ORS sample 

design specifies 23 detailed industry strata in the private sector and 10 industry strata in 

the government sector, plus 4 regions, making for 132 variance strata. So, the number of 

replicates to be used in the calculation of variances will be 132.

A Hadamard Matrix helps construct the replicate half-samples by increasing or decreasing 

the occupational replicate weight based on the variance strata selection flag. All usable 

quotes were assigned a selection flag during sampling and will appear in each replicate

half-sample with an appropriate weight adjustment. 

Once the replicate half-samples are established and the occupational replicate weights have 

been adjusted, estimation is run for each of the 132 replicate half-samples. Variances are 

calculated for each estimate, using the sum of the differences between the full sample 

estimate and each of the 132 replicate estimates. The full sample estimates were calculated 

using the original, unadjusted weights. The variance formula is as follows:

Formula 11 – Variance Calculation

 











 

 

 











R

r

c Ycr Yc

R k

V Y

1

2

2

ˆ ˆ

1

ˆ 1

where:











 V Yc

ˆ

ˆ

= Variance estimate for estimate of ORS data element c 

c = ORS data element

R = Number of replicates (132)

k = a constant where 0 



k < 1 (the value of k is 0.5)

cr Y

ˆ

= Estimate for replicate r for ORS data element c 
Yc

ˆ

= Estimate for the full sample for ORS data element c 

Standard errors are simply the square root of the variance. Relative standard errors are 

also calculated using the following formula:

Formula 12 – Relative Standard Error Calculation

 

100 ˆ

ˆ ˆ

c

c



Y

V Y

where,

c = ORS data element

 c 

ˆ

V

ˆ

Y = Variance estimate for estimate of ORS data element c

c

ˆ

Y = Estimate for the full sample for ORS data element c

A significance test will be run to measure the strength of each mode, determining whether 

the stated mode is significantly different from all of the other possible mode values. A flag 

will indicate whether the mode is significantly different and, therefore, considered a 

“strong” mode.

6. Conclusion and Next Steps

ORS will use a few re-weighting methods to account for unit non-response. Weight 

adjustments will occur at both the establishment and occupational level. Four types of 

factors will be calculated: non-response adjustment factors, employment different than 

assigned adjustment factors, manual adjustment factors, and benchmarking (poststratification) factors.

Non-response adjustment factors will be calculated for cells that contain non-responding 

units. The weight of responding units will increase to account for the weight of nonresponding units. Collapsing of the cell will occur if no responding units can be found in 

the cell, or if the non-response adjustment factor exceeds a pre-determined maximum 

value. These factors will be calculated for both establishments and occupations.

A different than assigned (DTA) factor will be calculated for all establishments. This factor

adjusts for differences in the assigned sample unit compared with the collected sample unit.

There are two ways this could happen: an establishment could be collected with 

employment larger than assigned or it could be collected with employment less than 

assigned. In either case, a weight adjustment will be made to account for the difference in 

employment.

Manual adjustment factors are calculated when an adjustment needs to be made but such 

an adjustment does not fit within the non-response adjustment factor or DTA factor. In 

these circumstances, a special factor is calculated manually. Such a factor could be 

calculated for establishments or occupations.
Benchmarking factors ensure that the final weighted employment matches the current 

employment in the economy. For this process, a ratio will be calculated comparing the 

frame employment at the time of sampling to employment currently found in the economy.

Benchmarking factors are applied to all weights that are used in the estimation process.

ORS will provide a measure of accuracy for every estimate produced. For means, 

percentages, and percentiles, a standard error will be calculated using Fay’s BRR method.

For modes, a significance test will be performed to determine if the mode is significantly 

different from the other possible values.

ORS plans to adjust for item non-response by using imputation methods or providing

estimates for missing or unknown responses. Methods are currently being investigated.

Testing began in the spring of 2015 and is ongoing. Results will be presented in a future 

paper.

References/Footnotes

[1] Occupational Requirements Survey, http://www.bls.gov/ors/.

[2] Social Security Administration, Occupational Information System Project, 

http://www.ssa.gov/disabilityresearch/occupational_info_systems.html.

[3] U.S. Department of Labor, Employment and Training Administration (1991), 

“Dictionary of Occupational Titles, Fourth Edition, Revised 1991”

[4] Occupational Information Development Advisory Panel, 2010, 

http://www.socialsecurity.gov/oidap/index.htm

[5] U.S. Department of Labor, O*Net Online, http://www.onetonline.org/

[6] Bureau of Labor Statistics, Occupational Employment Statistics Program, 

http://www.bls.gov/oes/

[7] Ferguson, Gwyn R., McNulty, Erin. 2015. Occupational Requirements Survey 

Sample Design. In JSM proceedings, Economic Data: CPI, PPI, NCS Section. 

Alexandria, VA: American Statistical Association.

[8] Rhein, Brad, Ponikowski, Chester, and McNulty, Erin. 2014. Estimation 

Considerations for the Occupational Requirements Survey. In JSM proceedings, 

Government Statistics Section. Alexandria, VA: American Statistical 

Association. 2134-2146.

[9] National Compensation Survey, http://www.bls.gov/ncs/.

[10] Quarterly Census of Employment and Wages, http://www.bls.gov/cew/.

[11] Standard Occupational Classification System, http://www.bls.gov/soc/.

[12] North American Industry Classification System, 

http://www.census.gov/eos/www/naics/.

Any opinions expressed in this paper are those of the authors and do not constitute 

policy of the Bureau of Labor Statistics or the Social Security Administration.
Appendix A – Industry, Size Class, and Area Definitions

Private Industry

Cell NAICS Codes Industry

21A 21 Mining

23A 23 Construction

31A 31-33 (including 336411) Manufacturing

22A 22 Utilities

42A 42 Wholesale Trade

44A 44-45 Retail Trade

48A 48-49 Transportation and Warehousing

51A 51 Information

52A 52(excl 524) Finance (Rest of)

52B 524 Insurance

53A 53 Real Estate, Renting, Leasing

54A 54 Professional and Scientific Services

55A 55 Management of Companies/Enterprises

56A 56 Admin., Support, Waste Management

61A 61(excl 6111-6113) Educational Services (Rest of)

61B 6111 Elementary and Secondary Schools

61C 6112,6113 Junior Colleges, Colleges & Universities

62A 62(excl 622,623) Health Care, Social Assistance (Rest of)

62B 622 Hospitals

62C 623 Nursing and Residential Care Facilities

71A 71 Arts, Entertainment, Recreation

72A 72 Accommodation and Food Services

81A 81(excl 814) Other Services (except Public Admin.)

Local Government

Cell NAICS Codes Industry

10L 21, 23, 31-33 Goods-Producing

20L 42, 44-45, 48-49, 22 Trade, Transportation, and Utilities

30L 6111 Elementary and Secondary Education

40L 6112, 6113 Colleges and Universities

50L 61 excl. 6111-6113 Rest of Education

60L 622 Hospitals

70L 623 Nursing Homes

80L 62 excl. 622-623 Rest of Health and Social Services

90L 92 excl. 928 Public Administration

99L 51, 52-53, 54-56, 71-72, 81 Other Service-producing

State Government

Cell NAICS Codes Industry

10S 21, 23, 31-33 Goods-Producing

20S 42, 44-45, 48-49, 22 Trade, Transportation, and Utilities

30S 6111 Elementary and Secondary Education

40S 6112, 6113 Colleges and Universities

50S 61 excl. 6111-6113 Rest of Education

60S 622 Hospitals

70S 623 Nursing Homes

80S 62 excl. 622-623 Rest of Health and Social Services
90S 92 excl. 928 Public Administration

99S 51, 52-53, 54-56, 71-72, 81 Other Service-producing

Size Class Employment Range

1 0-49

2 50-99

3 100-499

4 500+

Area Area Code

Atlanta-Athens-Clarke-Sandy Springs, GA CSA 00122

Boston-Worcester-Providence, MA-NH-RI-CT CSA 00148

Chicago-Naperville, IL-IN CSA 00176

Dallas-Fort Worth, TX CSA 00206

Detroit-Warren-Ann Arbor, MI CSA 00220

Houston-The Woodlands, TX CSA 00288

Los Angeles-Long Beach, CA CSA 00348

Minneapolis-St. Paul, MN-WI CSA 00378

New York-Newark, NY-NJ-CT-PA CSA 00408

Philadelphia-Camden-Reading, PA-NJ-DE-MD CSA 00428

San Jose-San Francisco-Oakland, CA CSA 00488

Seattle-Tacoma, WA CSA 00500

Washington-Baltimore-Arlington, DC-MD-VA-WV CSA 00548

Miami-Fort Lauderdale-Port St. Lucie, FL 33100

Phoenix-Mesa-Scottsdale, AZ 38060

Rest of New England 99901

Rest of Middle Atlantic 99902

Rest of East South Central 99903

Rest of South Atlantic 99904

Rest of East North Central 99905

Rest of West North Central 99906

Rest of West South Central 99907

Rest of Mountain 99908

Rest of Pacific 99909

Appendix B – Collapse Patterns for Industry, Size Class, and Area

Industry NAICS Codes Collapse Pattern

21A 21 none

22A 22 22A&48A

23A 23 none

31A 31-33 (including 336411) none

42A 42 none

44A 44-45 none

48A 48-49 22A&48A

51A 51 51A-53A

52A 52(excl 524) 51A-53A

52B 524 51A-53A
53A 53 51A-53A

54A 54 54A-56A

55A 55 54A-56A

56A 56 54A-56A

61A 61(excl 6111-6113) none

61B 6111 none

61C 6112,6113 none

62A 62(excl 622,623) none

62B 622 none

62C 623 none

71A 71 71A-81A

72A 72 71A-81A

81A 81(excl 814) 71A-81A

10L 21, 23, 31-33 10L,20L,90L,99L; 10L-99L

20L 42, 44-45, 48-49, 22 10L,20L,90L,99L; 10L-99L

30L 6111 30L,40L,50L; 10L-99L

40L 6112, 6113 30L,40L,50L; 10L-99L

50L 61 excl. 6111-6113 30L,40L,50L; 10L-99L

60L 622 60L,70L,80L; 10L-99L

70L 623 60L,70L,80L; 10L-99L

80L 62 excl. 622-623 60L,70L,80L; 10L-99L

90L 92 excl. 928 10L,20L,90L,99L; 10L-99L

99L 51, 52-53, 54-56, 71-72, 81 10L,20L,90L,99L; 10L-99L

10S 21, 23, 31-33 10S,20S,90S,99S; 10S-99S

20S 42, 44-45, 48-49, 22 10S,20S,90S,99S; 10S-99S

30S 6111 30S,40S,50S; 10S-99S

40S 6112, 6113 30S,40S,50S; 10S-99S

50S 61 excl. 6111-6113 30S,40S,50S; 10S-99S

60S 622 60S,70S,80S; 10S-99S

70S 623 60S,70S,80S; 10S-99S

80S 62 excl. 622-623 60S,70S,80S; 10S-99S

90S 92 excl. 928 10S,20S,90S,99S; 10S-99S

99S 51, 52-53, 54-56, 71-72, 81 10S,20S,90S,99S; 10S-99S

Size Class Employment Range Collapse Pattern

1 0-49 1; 1-2; 1-4

2 50-99 2; 1-2; 1-4

3 100-499 3; 3-4; 1-4

4 500+ 4; 3-4; 1-4

Area # Area Name Area Cell 1st Collapse 2nd Collapse

1 Atlanta 00122 1, 13, 14 1, 13, 14, 19 

2 Boston 00148 2, 16 none

3 Chicago 00176 3, 5 3, 5, 20

4 Dallas 00206 4, 6 4, 6, 22

5 Detroit 00220 3, 5 3, 5, 20

6 Houston 00288 4, 6 4, 6, 22

7 Los Angeles 00348 7, 11, 12 7, 11, 12, 24

8 Minneapolis 00378 8, 21 none
9 New York 00408 9, 10 9, 10, 17

10 Philadelphia 00428 9, 10 9, 10, 17

11 San Francisco 00488 7, 11, 12 7, 11, 12, 24

12 Seattle 00500 7, 11, 12 7, 11, 12, 24

13 Washington 00548 1, 13, 14 1, 13, 14, 19

14 Miami 33100 1, 13, 14 1, 13, 14, 19

15 Phoenix 38060 15, 23 none

16 Rest of New England 99901 2, 16 none

17 Rest of Middle Atlantic 99902 9, 10, 17 none

18 East South Central 99903 none none

19 Rest of South Atlantic 99904 1, 13, 14, 19 none

20 Rest of East North Central 99905 3, 5, 20 none

21 Rest of West North Central 99906 8, 21 none

22 Rest of West South Central 99907 4, 6, 22 none

23 Rest of Mountain 99908 15, 23 none

24 Rest of Pacific 99909 7, 11, 12, 24 none

Appendix C – SOC Aggregate Group (SAG) Definitions

SAG SOC Code SOC

1 11, 13 Management, Business, Financial

2 15, 17, 19, 21, 23, 25, 27, 29 Professional and Related

3 31, 33, 35, 37, 39 Service

4 41 Sales and Related

5 43 Office and Administrative Support

6 45, 47 Construction, Extraction, Farming, Fishing

7 49 Installation, Maintenance, and Repair

8 51 Production

9 53 Transportation and Material Moving