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Regionalization of National Staffing Patterns

Emsi Burning Glass develops occupation job counts data by taking Bureau of Labor Statistics’ Quarterly Census of Employment and Wages (QCEW) industry job counts and applying staffing patterns to transform industry jobs to occupation jobs. Click here to read more about the occupation jobs process.

We begin with the national staffing patterns and regionalize to the state level using OES state-level occupation job counts and QCEW state-level industry job counts (for information on why Emsi Burning Glass does not use OES state-level staffing patterns, see this article). After regionalizing to states, we repeat the same step one more time, regionalizing from the state level to the metropolitan/non-metropolitan areas inherent in OES data. For this step, OES metropolitan/non-metropolitan occupation job counts are used in conjunction with metropolitan/non-metropolitan industry job counts from QCEW (offered at the county level and easily summed to match OES at the MSA/non-metro level).

Walkthrough

We begin with three elements, the national OES staffing pattern, which shows what percent of each industry (column) is staffed by each occupation (row); the state-level occupation job counts from OES by occupation; and the state-level industry job counts from QCEW by industry.

National OES Staffing Pattern
NAICS 1 NAICS 2 NAICS 3
SOC 1 0.9 0.07 0.03
SOC 2 0.6 0.2 0.2
SOC 3 0.34 0.33 0.33
100% 100% 100%

 

OES State-Level Occupation (SOC) Job Counts
Missouri Job Counts
SOC 1 6500
SOC 2 760
SOC 3 850

 

QCEW State-Level Industry (NAICS) Job Counts
NAICS 1 NAICS 2 NAICS 3
Missouri Job Counts 3000 4320 1090

 

State-level industry jobs from QCEW sum to 8410 (3000 + 4320 + 1090), and state-level occupation jobs from OES sum to 8110 (6500+760+850). We first adjust OES to match the QCEW total, since QCEW job counts are our gold standard. This is done by proportionally adjusting OES job counts to match QCEW. For the above case, QCEW is 4% higher than OES, so each OES SOC is bumped up by 4% (e.g. 6500 * 1.04). After this adjustment, the new OES state-level job counts sum to match QCEW state-level job counts:

OES State-Level Occupation (SOC) Counts Proportionally Adjusted to QCEW Industry Counts
Missouri Job Counts
SOC 1 6740
SOC 2 788
SOC 3 882

 

We can now combine the state-level industry and occupation job counts with the initial national staffing pattern:

OES National Staffing Pattern, OES Adjusted Occupation State Job Counts, QCEW Industry Job Counts
NAICS 1 NAICS 2 NAICS 3
3000 4320 1090
SOC 1 6740 0.9 0.6 0.34
SOC 2 788 0.07 0.2 0.33
SOC 3 882 0.03 0.2 0.33
TOTAL 100% 100% 100%

 

The staffing pattern is regionalized by swapping in regionalized employment counts as seed values in place of the percentages. The goal is to end up with a balanced matrix that shows the distribution of employment across occupations and industries for a given region. Regionalized employment seed values are created by taking regional industry employment and multiplying by the percentages in the national staffing pattern:

NAICS 1 NAICS 2 NAICS 3
3000 4320 1090
SOC 1 6740 2700 2592 370.6
SOC 2 788 210 864 359.7
SOC 3 882 90 864 359.7
TOTAL 3000 4320 1090

 

Like the first staffing pattern shown above, this matrix shows each column summing to the QCEW employment total for the industry, representing the occupation percent breakout for each industry. However, rows do not currently sum to the occupation totals. We apply a three-dimensional, hierarchical proportioning algorithm to the above matrix. The algorithm is a proprietary implementation of the proportional algorithms commonly found in literature. This algorithm adjusts the employment counts across rows and down columns, repeatedly, until both rows and columns sum to the margin totals. The result is an employment matrix that sums to both the regional industry and occupation job counts:

NAICS 1 NAICS 2 NAICS 3 TOTAL
SOC 1 2859.06 3305.38 575.56 6740
SOC 2 93.07 461.13 233.8 788
SOC 3 47.88 553.49 280.63 882
TOTAL 3000 4320 1090

 

At the end of this process, we have regionalized employment matrices for each state in the nation. These employment counts can be converted to percentages along either columns or rows to show either percent of industry staffed by occupations, or percent of an occupation distributed across industries, respectively:

Percent of Industry Staffed by Occupations (“Staffing Pattern”)
NAICS 1 NAICS 2 NAICS 3
3000 4320 1090
SOC 1 6740 0.95 0.77 0.53
SOC 2 788 0.03 0.1 0.2
SOC 3 882 0.02 0.13 0.26
TOTAL 100% 100% 100%

 

Percent of Occupation Distributed Across Industries (“Inverse Staffing Pattern”)
NAICS 1 NAICS 2 NAICS 3
3000 4320 1090 TOTAL
SOC 1 6740 0.42 0.49 0.09 100%
SOC 2 788 0.12 0.59 0.29 100%
SOC 3 882 0.05 0.63 0.32 100%

 

The last step is to further regionalize each state’s staffing pattern to the metropolitan and non-metropolitan areas within it. This is done by repeating the regionalization process above; however, this time QCEW industry job counts and OES occupation job counts for the metro/non-metro areas within the state are used to create the initial matrix. The same hierarchical proportioning algorithm is used to create final staffing patterns for the metro/non-metro areas. County-level QCEW industry job counts are then sent through the corresponding regionalized staffing pattern, and the result is occupation job counts by county.

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Related

Occupation Employment Process

Emsi Burning Glass develops occupation job counts data by taking Bureau of Labor Statistics’ Quarterly Census of Employment…

Why Doesn’t Emsi Burning Glass Use OES State-Level Staffing Patterns?

Emsi Burning Glass develops occupation job counts data by taking Bureau of Labor Statistics’ Quarterly Census of Employment…

Submit a Question

Let us know what specific questions we can help you with (we may even add your question to our knowledge base).

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