Addresser -- A Persistent Favorite of Vocational Witnesses

Assume a person of the same age, education, and work experience of the claimant and assume that the person is limited to sedentary work, sitting six hours in an eight-hour day, frequent handling, frequent fingering, and limited to simple work with no more than occasional interactions with coworkers, supervisors, and the public. Any work?

That person could work as an addresser representing 30,000 jobs in the national economy. 

Is that occupation performed as described in the DOT.

Sometimes workers use typewriters and hand-address labels and envelopes. Sometimes the worker will simply apply labels to envelopes, packages, and cards.

We have heard the mantra. It is nonsense. It is also the recommended explanation that SSA gives to VW in their training. Let's also beat them at their own game.

SkillTRAN estimates that addresser works in eight industries and is also self-employed for a total of 1,952 jobs. Self-employed work is not unskilled work -- it is running a business. Most of the jobs exist in local government, 1,253 jobs. SkillTRAN does not support the existence of 30,000 addresser jobs. 

The OEWS estimates 36,030 jobs for word processors and typists (SOC 43-9022). The EP, OOH, and O*NET reporting the same data set estimate 40,000 jobs. It is curious that a word processor is applying labels. There might be people applying pre-printed labels but they are not word processors and typists, they are general office clerks or mail clerks. The crosswalk places addresser in word processors and typists. The OEWS and OOH defines word processors and typists:

Use word processor, computer, or typewriter to type letters, reports, forms, or other material from rough draft, corrected copy, or voice recording. May perform other clerical duties as assigned. Excludes “Court Reporters and Simultaneous Captioners” (27-3092), “Medical Transcriptionists” (31-9094), “Secretaries and Administrative Assistants” (43-6010), and “Data Entry Keyers” (43-9021).

The O*NET omits the "excludes" portion of the description but is otherwise identical. None of the descriptions leave room for application of labels. 

The crosswalk tells us that word processors and typists contains eight DOT codes, one of them is unskilled, all are sedentary. We should doubt that all word processors and typists are unskilled addressers. 

The 2023 ORS confirms that word processors and typists represent sedentary work in greater than 99.5% of jobs. Call it 100% and move on. The ORS estimates that 26.3% of jobs have up to one month of training. Semi-skilled and skilled work represent 70.6% of jobs. The "less than" estimate of 10% of jobs with a short demonstration (up to four hours) contains all the standard error. If the five state estimates are accurate, the residual is 3.1% of jobs. Let's round up and call it 30% of jobs are unskilled. That means that 10,800 jobs are sedentary and unskilled. Call that progress. 

The ORS describes greater than 50% of jobs have a choice of sitting or standing, less than 50% do not have a choice. Word processors and typists sit 75% of the day at the 10th percentile and more than 75% of the day at all other reported percentiles. Because the "choice" of sitting or standing is "when" and not "how much," the conclusion would leave a person limited to six hours of sitting in a day to 1,080 jobs. 

The O*NET confirms the obvious -- clerical employees work together with other employees. Word processors and typists have constant contact with other in 69% of jobs and most of the time in 31% of jobs. Any limitation on contact or interaction with others eliminates all jobs. The ORS describes all jobs as requiring at least basic people skills. The ORS states that word processors and typists have verbal interactions less than hourly in 23.8% of jobs. The ORS might lead to 2,400 jobs. 

Finally, my favorite source of job numbers. The OEQ assumes 258,841 word processor and typist jobs with one-eighth of them sedentary and unskilled, to wit 32,385 jobs. The SOEUQ suggests 22,695 jobs. Both sources claim reliance on the OES which is the OEWS. We started with the OEWS -- 36,030 jobs total. 

The existence of 30,000 addresser jobs is not sustainable. The only source consistent with that estimate is the OEQ. The SOEUQ contradicts that estimate and comes from the same publisher. Both sources state reliance on the OES, which does not exist. The OEWS and the EP/OOH/O*NET are wholly inconsistent with the OEQ/SOEUQ. And we end where we started, word processors and typists do not affix labels to envelopes, packages, and cards. That is not their job.

The Emergency Message tells the adjudicator to get a further explanation for addresser. When the adjudicators suggest and explanation without looking at the occupational description, we end with a conspiracy to commit idiocy. SSA should go back to the promise made almost 50 years ago -- take administrative notice of jobs, requirements, and job numbers. 

Disgusted.

___________________________


Suggested Citation:

Lawrence Rohlfing, Addresser -- A Persistent Favorite of Vocational Witnesses, California Social Security Attorney (September 12, 2025) https://californiasocialsecurityattorney.blogspot.com


The author has been AV-rated since 2000 and listed in Super Lawyers since 2008.

What's Wrong with the OEQ -- Attacking Its Foundation and Methodology

Ah, the Occupational Employment Quarterly (OEQ), it used to be the only game in town. Ridiculed by David Traver so many years ago, superseded by Job Browser Pro (JBP), and obsoleted by the Occupational Requirements Survey (ORS) joined by the Occupational Employment and Wage Statistics (OEWS). I have labeled the OEQ statistical trash, mostly because it is. But the OEQ does have a cadre of devotees that cling to it like a plank of balsa wood adrift in a sea storm. What's wrong with the OEQ? I am so glad that you asked. 

The OEQ is well-known for its use of the equal distribution method of calculating job numbers. The Seventh Circuit labels that methodology as preposterous. Alaura v. Colvin. As Kevin Liebkemann points out in Job Incidence Numbers in Social Security Disability Claims: ACase Study and Analysis, even SkillTRAN publicly derides the OEQ as using a preposterous equal distribution methodology in its SkillTRAN Process for Estimating Employment Numbers (citing the later decision in Hill v. Colvin). 

In Woods v. Bisignano, the Ninth Circuit affirmed the vocational witness's patent use of the OEQ using the equal distribution method. Judge Nelson concurring states that a categorical rule excluding testimony based on the equal distribution method runs afoul of Biestek v. Berryhill. Woods is wrong and so is Judge Nelson. Job numbers in Standard Occupational Classification (SOC) groups with very few DOT codes leads to results that are absurd. Consider telemarketers -- one sedentary semi-skilled DOT code. The Occupational Outlook Handbook disagrees. Telemarketers typically have short-term on-the-job training. The 2018 ORS dataset describes telemarketers as having up to 1 month of training in 50.3% of jobs. Equal distribution should require an explanation -- every single time it is used. The Seventh Circuit is right.

But let us assume that Judge Nelson is right, the equal distribution method is not so inherently flawed that there do exist some circumstances where it might be reasonable to use it. Let's play along. Ask the witness this question:

What is the data source that US Publishing uses to estimate job numbers stated in the OEQ?

The first page of the OEQ II  3.2 states that column 4 sets out the "current employment for this occupation." The last page of the OEQ 3.2 states that  

- All data are estimates from government sources including the U.S. Department of Labor, Division of Occupational Employment Statistics and of the Local Area Unemployment Statistics.

The US Publishing web site invokes the 2010 decennial census. Clearly US Publishing has not updated its page or claim to use the 2020 decennial census. Nowhere does US Publishing claim to use the Current Population Survey or any other data source. Nor the US Publishing recognize that the OES is now the OEWS. A rose by any other name is still a rose and the OEWS and the OEWS data is found at www.bls.gov/oes/. 

Since we know the US Publishing relied on OES/OEWS data from the OEQ and from the web page, we can compare and contrast the gross job number cited by the OEQ to the OEWS. A sample:

Occupation	OEQ total employment 4th Qtr. 2024	OEWS total employment  2024
Credit Authorizers, Checkers, and Clerks SOC 43-4041	63,662	11,960
Order Clerks  43-4151	216,280	83,420
Couriers and Messengers SOC 43-5021	232,941	71,920
Word Processors and Typists  SOC 43-9022	258,841	36,020
Office Clerks, General SOC 43-9061	2,351,948	2,510,550
Electrical and Electronic Equipment Assemblers SOC 51-2022	179,597	261,140 SOC 51-2028 (includes SOC 51-2022 and SOC 51-2023)
Inspectors, Testers, Sorters, Samplers, and Weigher SOC 51-9061	727,005	591,180
Helpers—Production Workers SOC 51-9198	273,294	167,490
Production Workers, All Other SOC 51-9199	813,370	277,060
Cleaners of Vehicles and Equipment SOC 53-7061	395,474	373,960
Packers and Packagers, Hand SOC 53-7064	676,479	601,440
Stock Clerks and Order Fillers SOC 43-5081	2,009,370	2,779,530 Stockers and Order Fillers SOC 53-7065

 The numbers are not reconcilable. Most of the occupations selected off the top of my head are so far off that they are clearly unreliable. 

That is strike two against the OEQ. US Publishing uses equal distribution based on the number of exertion-skill DOT codes resident in the SOC code. US Publishing's stated source for job numbers does not support the job numbers stated. Of the 867 codes in the 2018 SOC, 485 have at least some change from the 2010 SOC. US Publishing and its OEQ have not kept up nor paid attention to the combination of two SOC detailed groups into a single reported group (51-2028) in the current dataset. 

The OEQ as it is currently constituted needs to die.

___________________________



Suggested Citation:

Lawrence Rohlfing, What's Wrong with the OEQ -- Attacking Its Foundation and Methodology, California Social Security Attorney (August 27, 2025) https://californiasocialsecurityattorney.blogspot.com


The author has been AV-rated since 2000 and listed in Super Lawyers since 2008.

What's Wrong with SSR 24-3p?

 SSR 24-3p introduces a new interpretation of the stable administrative notice regulation, 20 CFR 404.1566(d). The Commissioner has long held as a matter of law to the proposition that when it comes to unskilled sedentary, light, and medium work, the Commissioner will take administrative notice of reliable governmental and other (private) published data for the requirements and numbers of jobs. SSR 00-4p responded to a growing number of cases -- and a split in the circuits -- that the ALJ must address conflicts between vocational testimony and the Dictionary of Occupational Titles (DOT). 20 CFR 404.1566(d)(1). The Commissioner conceded to the fact that the Selected Characteristics of Occupations (SCO) was part of the single data set. SSR 00-4p imposed on the ALJ the duty to investigate the existence of a conflict or apparent conflict and to resolve that conflict based on evidence. 

But the DOT and its dataset never stated job numbers, never. Job numbers are now and in 1978 stated in the Occupational Outlook Handbook (OOH) and County Business Patterns (CBP). 20 CFR 404.1566(d)(2), (5).Now, the Bureau of Labor Statistics publishes online the Employment Projections (EP) and the Occupational Employment and Wage Statistics (OEWS, formerly the OES). 

Labor abandoned the DOT and its data set never updating the DOT fourth edition, revised published in 1991 or the SCO published in 1993. Labor transferred responsibility of the Employment Training Administration from the DOT to the Occupational Information Network (ONET). Recognizing the problem that 10,000 of the 13,000 DOT codes had a date last updated of 1977, the Commissioner was forced to collaborate with Labor to develop a new data set -- the Occupational Requirements Survey (ORS). 

Private sources published data as well. United Stat Publishing published and publishes what is now known as the Occupational Employment Quarterly (OEQ) in various formats for national, state, and local data. That publication uses the equal distribution method of estimating job numbers -- each DOT code within a Standard Occupational Classification (SOC) represents the same number of jobs. The OEQ publishes the job numbers sorted by exertion/skill combinations. United Stat Publishing also publishes the Specific Occupational Employment - Unskilled Quarterly (SOEUQ). The SOEUQ estimates sedentary occupations by industry. The SOEUQ does not state its methodology. 

SkillTRAN publishes OccuBrowse, Job Browser Pro, and OASYS. The latter two estimate job numbers by DOT code. SkillTRAN uses a SOC/OEWS code intersection with selected industries (NAICS codes) and uses equal distribution to estimate the number of jobs per DOT code at those SOC-NAICS intersections. SkillTRAN uses a proprietary and unpublished methodology and does not use the data from the EP or OEWS that publish SOC-NAICS data. SkillTRAN uses the CBP to modify the data. The SkillTRAN SOC-NAICS data resembles but does not duplicate either the EP or OEWS SOC-NAICS data. 

That is the basic lay of the data. Job requirements are still found in the DOT and the Commissioner clings to that data set. Job requirements are found with current data in the ONET and the ORS. Job numbers are still found in the OOH and CBP -- they are up to date -- as well as the EP and OEWS. Three data sets for requirements and four data sets for job numbers. No one should use the OEQ for any purpose. JBP and OASYS continue to have utility for stating the SOC-NAICS intersection job numbers but does not parse that data based on occupational classifications nor erode for any impairment. JPB and OASYS are starting points. 

Here is the problem. The vocational witness claims to have considered the broad range of data along with their vast (local and anecdotal) experience to derive a job numbers based on no discernible methodology. Some will default to JBP/OASYS. That is at least a defensible starting point. Some will claim that the OEQ remains in the mix. That is bogus. 

And the ALJ corps blindly accepts testimony that is incoherent and meaningless. The witnesses are not consistent across time. They are not consistent with each other according to the cases. Because the claimants have privacy of their medical data, we never get to see the testimony that the witnesses give in different cases or to compare different witnesses in same and similar cases. The system lacks accountability and reliability. The system invoked by SSR 24-3p creates vocational witness lottery. That is not a system of administrative justice; it is legalized gambling with people's lives and the social safety net. 

But I never get passionate about these issues. 

___________________________

Suggested Citation:

Lawrence Rohlfing, What's Wrong with SSR 24-3p?, California Social Security Attorney (February 26, 2025)  https://californiasocialsecurityattorney.blogspot.com

The author has been AV-rated since 2000 and listed in Super Lawyers since 2008.

Proof of Use of the Equal Distribution Method of Calculating Job Numbers by the OEQ

Several vocational experts have testified that the Occupational Employment Quarterly uses a very complicated occupational density model to estimate job numbers based on exertion and skill level.  The testimony that the vocational experts give is demonstrably false.

I broke down and order the 4th Quarter 2017 OEQ and will likely buy the 2018 OEQ next year.  Representatives should have a copy of the OEQ to use in cross-examination.  The point is to prove that the OEQ uses an equal distribution method of stating job numbers.  I converted the rows used in the OEQ into columns.  I divided the total number of jobs by the total number of occupations.  That is the average number of jobs per DOT code.  I took the number of jobs reported in each of the 12 columns (rows on my chart) and divided that reported number by the average I previously computed.  The last column of my calculations is rounded to the nearest hundredth.  I then totaled my raw calculation and the rounded calculation just for fun.  Here is what I got:

SOC-OES Code		51-9199	Calculations
Census Code		8965
SOC - OES CODE TITLES		Production Workers, All Other	Average
Current # Employed		771,069	485.254248
# DOT Titles		1589	Quotient	Rounded
UNSKILLED EMPLOYMENT (SVP=1 OR SVP=2)	Sed.	25,233	51.99954479	52.00
	Light	196,528	405.000061	405.00
	Med.	89,772	184.9999261	185.00
	Heavy +	20,866	43.00013877	43.00
SEMI-SKILLED EMPLOYMENT (SVP=3 OR SVP=4)	Sed.	17,954	36.99916091	37.00
	Light	162,560	334.9996434	335.00
	Med.	119,858	247.0004137	247.00
	Heavy +	36,879	75.99933469	76.00
SKILLED EMPLOYMENT (=SVP >4)	Sed.	3,397	7.000453915	7.00
	Light	43,673	90.00024252	90.00
	Med.	47,555	98.00017249	98.00
	Heavy +	6,794	14.00090783	14.00
TOTALS:			1589.0000	1589

And there we have it.  Mathematical proof of equal distribution of the job numbers based on the number of DOT codes within each exertion-skill level intersection.  The same method works for every SOC-OES/Census code reported in the OEQ.  I know; I checked.  

Why Every Representative Must Own the Resource Used by Vocational Experts

Vocational experts typically state that they use the Bureau of Labor Statistics, Job Browser Pro, and the Occupational Employment Quarterly.  The BLS data is available on the internet.  Job Browser Pro is available from SkillTran.  The Occupational Employment Quarterly is available from US Publishing. 

Every representative should own or have access to these data sources.  Vocational experts rely on them.  Any effective cross-examination on job numbers must start with a comparison of what the data sources say and testimony of the vocational expert.  Take the recent district court decision in Beamesderfer v. Berryhill.  From the court decision:

The ALJ then proposed several hypotheticals to the VE. (AR 51-55). As relevant to this matter, the VE testified that a person with Plaintiff's RFC could perform the following jobs: 

Sweeper/cleaner, DOT code 389.683-10 (sic), a medium, unskilled (SVP 2) occupation, with 96,500 positions in the national economy.

Floor waxer, DOT code 381.687-034, a medium, unskilled (SVP 2) occupation, with 97,000 positions in the national economy.

Laundry worker I, DOT 361.684-014, a medium, unskilled (SVP 2) occupation, with 38,500 positions in the national economy.

Mary Jesko testified that she "relies on a computer program known as SkillTRAN." 

First, don't you mean Job Browser Pro?  

The company that published the program is SkillTran.  The companies other products do not estimate job numbers for a DOT code except in the incidence of a single DOT code comprising the entire know content of the SOC group.   

• Job Browser Pro reports 16,881 jobs as a sweeper-cleaner industrial.  Why is JBP off by a factor of six?
• Job Browser Pro reports 22,784 jobs as a floor waxer.  Why is JBP off by a factor of four?
• Job Browser Pro reports 5,346 jobs as a laundry worker I?  Why is JBP off by a factor of seven? 

The witness should explain why she departs from that on which she relies.  We are still in harmless error territory so we have to keep going.  But the jab sets up the power punch. 

• Do you agree with JBP as to the SOC code assignments?
• Do you agree with JBP as to the industries?
• Do you agree with JBP as to the presence of other DOT codes within that SOC code and the specified industries?

Floor waxer and sweeper cleaner are in the same SOC.  So keep going:  

• Janitors and cleaners, except maids and housekeeping cleaners has 15 DOT codes and five of them are semi-skilled.  Do they have the same incidence in the industries in which more than one DOT code works?
• Laundry and dry-cleaning workers has 23 DOT codes and 15 are skilled or semi-skilled.  Do they have the same incidence in the industries in which more than one DOT code works?

Jesko and others like her think that you are too cheap to invest in the program and look over their shoulders.  If you don't have the program, you can't learn it on the fly and many ALJs will not let you look at their screens (except in the Seventh Circuit). 

As the representatives, we just have access to everything that the vocational experts cite and rely upon.  So go get your copy of Job Browser Pro and buy an annual copy of the OEQ.  OccuCollect collected relevant data from the Dictionary of Occupational Titles; Selected Characteristics of Occupations; O*NET OnLine; Occupational Requirements Survey; and Occupational Outlook Handbook.

Production Workers, All Other, and the Occupational Employment Quarterly

Despite the availability of better data, vocational experts continue to rely upon the Occupational Employment Quarterly (OEQ) from United Statistics Publishing (USP)  Confession is good for the soul and here is mine -- for years I defended the OEQ as the only game in town and we needed some source of data as a starting point.  That was more than a decade ago and now I know better.  Today we explore the OEQ and our favorite occupational group, production workers, all other in SOC code 51-9199.

According to the Bureau of Labor Statistics, this occupational group represents 241,910 jobs in the nation.   The Occupational Employment Statistics (OES) relies upon employer's surveys to estimate numbers of jobs in the nation within specific O*NET occupational groups.  The corresponding Census Code, 8965, represents the results of the Current Population Survey.   The Current Population Survey is the result of a survey of households, workers, asking them what they do.  BLS publishes a compilation of the current population survey stating that production workers, all other, represents 944,000 jobs.  That leaves a discrepancy of over 700,000 jobs between the two data sets, a discrepancy beyond the focus of this article.

In the fourth quarter of 2016, USP states that production workers, all other represents 760,983 jobs in the nation.  USP accurately states that the occupational group represents 1589 distinct DOT codes.  In the unskilled range of work, USP estimates the number of sedentary, light, medium, and heavy jobs:

Sed.         Light        Med.       Heavy +
24,903     193,957    88,598    20,593

Let's do the math, just for fun.

760,983 / 1589 = 478.91

The average number of jobs within production workers, all other, assuming the accuracy of the aggregate job numbers reported by USP is thus 478.91.  I rounded up, use your calculator to get a more accurate number.

If we assume 52 sedentary unskilled the DOT codes within the SOC code/OES group/Census code, we get 24,903.  How many sedentary unskilled DOT codes exist within 51-9199/8965?  The answer is 52.

Dividing 193,957 by 478.91 yields 405.  How many light unskilled DOT codes exist within 51/9199 and 51-3099 or census code 8965?  The answer is 405.

Dividing 88,598 by 478.91 yields 184.999 or 185.  How many medium unskilled DOT codes exist within 51/9199 and 51-3099 or census code 8965?  The answer is 185.

Dividing 20,593 by 478.91 yields 42.9997 or 43.  How many heavy unskilled DOT codes exist within 51/9199 and 51-3099 or census code 8965?  The answer is 43.

USP states that the semiskilled and skilled ranges of work aggregate two 432,933 jobs.  Dividing that number by the average number of jobs per DOT code comes out to 904.  Adding together the results of our divide and conquer request from the sedentary, light, medium, and heavy ranges of work brings the total number of occupations to 1,589.   By doing the math, we have ascertained that USP uses an aggregation methodology that starts at the aggregate number of jobs within the Census code.  The methodology assumes that every occupation within the group represents the same number of jobs.  Statisticians call this aggregation error.

If we take a look at USP's other publication, The specific Occupational-Unskilled Quarterly (SOEQ), we can readily ascertain that the number of jobs described in the OEQ as belonging in 51-9199/8965 is wrong.  Adding up the 52 sedentary occupations totals 14,432 jobs.

If USP has used a valid measure for estimating the number of sedentary unskilled jobs, then USP would have the same result in both publications.  USP does not have the same result in both publications because the two publications use a vastly different methodology for estimating the number of jobs.  The OEQ uses a frank aggregation, dividing the number of jobs by the total number of DOT codes and then multiplying by the number of DOT codes within a specific classification, e.g. sedentary and unskilled.

The SOEQ uses the industries to estimate the number of sedentary unskilled jobs.  But the SOEQ reports that 11 different DOT codes have exactly the same number of jobs, 193.  There are several sets of pairs were two occupations have the same number of jobs.

What is clear is that it is unreasonable to rely upon the OEQ to estimate the number of jobs.  USP does not start with the BLS job number reported in the OES.  USP does not start with the number of jobs reported in the Occupational Outlook Handbook.  The starting point for the number of jobs is unreliable; the methodology is invalid; and the other publications from USP demonstrate that the OEQ is not substantial evidence.