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.
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