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.

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:

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

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:

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:

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:

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.