Hi there. I am attempting to use the CPS basic monthly survey to examine employment trends by occupation. When calculating standard errors for monthly employment by occupation, I tried using the BLS’s Generalized Variance Formulas and borrowed parameters from the broad SOC categories they provide parameters for. Unfortunately, the calculated standard errors were much larger than is reasonable (in many cases 30-50 times larger than the estimate), presumably because the GVF factors I borrowed were are supposed to be used on broader occupational categories with many more observations.
Is there a standard practice for calculating these standard errors or a clear way I should proceed?
Following CPS Technical Paper 77 (chapter 2-4), we typically recommend using replicate weights to estimate variances with CPS microdata (see our detailed CPS replicate weight user guide). However, replicate weights are only available for CPS supplements and are not provided for monthly BMS data. Since you reference borrowing GVF factors for variance estimation, I assume that you have reviewed the BLS instructions for calculating standard errors and confidence intervals. This guide states that “when considering multiple series to borrow from, using the 𝛼 and 𝛽 parameters that generate the highest standard error is generally advised”, though I understand that having standard errors that are 30-50 times larger than the estimates may not be particularly helpful.
While the PSU and strata sample design parameters are not released publicly, Davern et al. (2007) showed that specifying the lowest level of identifiable geography (sequentially as INDIVIDCC, COUNTY, METFIPS, and STATEFIP) as the strata, and household SERIAL (only unique to each household in a given survey month and year) as the cluster, performed reasonably well at estimating standard errors when compared to using the internal sample design data.
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