Question: What are the causes of benchmark revisions of payroll survey estimates?

Answer: In general, differences between universe counts and sample-based estimates result from both sampling and non-sampling error. Although sampling error is present in the payroll survey, as it is in all surveys, the Current Employment Statistics (CES) sample is so large (almost 400,000 reports covering about one-third of universe employment) that sampling error is not usually an important factor in explaining the differences.

Nonsampling error arises in the survey estimates, and in the universe counts, from both the Unemployment Insurance (UI) data and the alternative sources used to establish the noncovered population benchmarks. Nonsampling error is a more significant cause of benchmark revisions. Sources of nonsampling error include coverage, response, and processing errors in both data series. Additionally, the survey is potentially subject to sample design and estimator biases.