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Why Doesn’t Emsi Break Out the Postsecondary Teachers SOC?

Emsi data does not contain the detailed Postsecondary Teacher categories that are available in OES data. This is due to highly suppressed data at the metro level for these categories and historically, to follow the convention used by the BLS’s NIOEM dataset, which Emsi uses to create projections. However, NIOEM now uses more granular Postsecondary Teacher SOCs, and Emsi’s unsuppression algorithms have improved over time. Given these factors, in early 2019 we decided to research the possibility of introducing detailed Postsecondary Teacher categories into our data. Unfortunately, the study found that disclosed data is still too sparse to warrant expanding this category. Following is the Emsi Data team’s research and analysis.

Research Reintroducing Postsecondary Teacher Categories


Historically, Emsi has rolled all Postsecondary Teacher jobs into one code (25-1099) due to lack of disclosed data from OES and NIOEM’s method for handling them. However, in 2012 NIOEM began providing data for the more detailed codes. The purpose of this issue is to test if we are now able to produce reliable employment estimates for the detailed Postsecondary Teacher categories.


After examining Postsecondary Teacher categories at the MSA level, we have determined that Postsecondary Teacher job data is not reliable enough to break down into the specific codes available to us. Especially at lower levels of employment (typically < 100), the data is often inexplicably volatile. This is possibly due to the survey nature of data collection.

General Postsecondary Teacher Category

Baton Rouge is home to Louisiana State University. The Emsi-unsuppressed data for the total Postsecondary Teachers code for this Metro is relatively smooth, as shown here:

Rapid City is home to the South Dakota School of Mines, with a total teaching staff of about 200. Again, the Emsi-unsuppressed data for the total Postsecondary Teacher code is relatively smooth:

Particular Postsecondary Teacher SOCs

When considering the Emsi-unsuppressed data for specific, lower-employment categories, we see volatility in the data which is not easily explainable.

Social Sciences Teachers, Postsecondary, All Other (25-1069), Baton Rouge:

Engineering Teachers, Postsecondary (25-1032), Baton Rouge:

Art, Drama, and Music Teachers, Postsecondary (25-1121), Baton Rouge:

Computer Science Teachers, Postsecondary (25-1021), Baton Rouge:

Nursing Instructors and Teachers, Postsecondary (25-1072), Baton Rouge:

Finally, LSU has both a Chemical & Biological Engineering department and a Civil & Environmental Engineering department. However, we show zero employment in Baton Rouge for both Chemistry Teachers, Post-Secondary (25-1052) and Environmental Science Teachers, Postsecondary (25-1053).


Given these findings, we recommend producing data only for the aggregate category Postsecondary Teachers. Because of the paucity of disclosed OES data for more granular categories for local geographies, our unsuppression algorithms simply do not receive enough disclosed data points to be able to make accurate assumptions about suppressed data points.

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