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Survey Statistics: BLS Jobs Report

Our post about the struggles with surveying nonvoters and young voters focused on the Cooperative Election Study (CES). This week, the headlines focus on the Current Employment Statistics (also CES). I have not worked with this CES data, whose importance is highlighted in very beginning of the textbook by Groves et al.:

At 8:30AM on the day before the first Friday of each month, a group of economists and statisticians enter a soundproof and windowless room …

Those in the room are professional staff of the U.S. Bureau of Labor Statistics (BLS)…

A household survey [CPS] produces the unemployment rate; an employer survey [CES], the jobs count…

However, only when decision makers believe the numbers do they gain value. This is a book about the process of generating such numbers through statistical surveys and how survey design can affect the quality of survey statistics.

Preview

On Friday (August 1, 2025), the Bureau of Labor Statistics (BLS) reported revisions to the jobs count from the CES:

Revisions for May and June were larger than normal. The change in total nonfarm payroll employment for May was revised down by 125,000, from +144,000 to +19,000, and the change for June was revised down by 133,000, from +147,000 to +14,000. With these revisions, employment in May and June combined is 258,000 lower than previously reported. (Monthly revisions result from additional reports received from businesses and government agencies since the last published estimates and from the recalculation of seasonal factors.)

A search for “nonresponse Current Employment Statistics” yields this BLS paper from 2003, when the CES moved from a quota sample to a probability sample. The focus is nonresponse, which includes both nonreporting and late reporting. They write:

assume nonresponse is ignorable within defined estimation cells…estimation cell (defined by industry and, for selected industries, region)

 

One useful path for further research would appear to be refinement of estimation cell definitions, in an attempt to define a more appropriate ignorable nonresponse model… Factors for consideration beyond industry, based upon research to date, would include size of establishment and geography. These factors are utilized in the sample design, but not (with a few exceptions) in the definition of estimation cells…

 

A second path would be to explore other ignorable nonresponse models…

This mirrors our post from last week, where we discussed both approaches.

The current BLS Handbook of Methods: Calculation says:

Cells are defined primarily by industry. Geographic stratification is also used for some construction and government industries.

The current BLS Handbook of Methods: Design says:

The sample strata, or subpopulations, are defined by state, industry, and employment size, yielding a state-based design. Sampling rates for each stratum are determined through a method known as optimum allocation

Beyond skimming these helpful documents, I have not yet learned the details of their methods. My naive understanding is that establishment size is used in the stratification and not the estimation. My naive question is: why ? I am sure the folks at BLS have thought about this carefully and I am curious.

p.s. Andrew tells me his mom used to work for the BLS, maybe he can share some stories ?

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