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An establishment's average wage, computed from administrative wage data, hasbeen found to be related to occupational wages. These occupational wages are aprimary outcome variable for the Bureau of Labor Statistics OccupationalEmployment Statistics survey. Motivated by the fact that nonresponse in thissurvey is associated with average wage even after accounting for otherestablishment characteristics, we propose a method that uses the administrativedata for imputing missing occupational wage values due to nonresponse. Thisimputation is complicated by the structure of the data. Since occupational wagedata is collected in the form of counts of employees in predefined wage rangesfor each occupation, weighting approaches to deal with nonresponse do notadequately adjust the estimates for certain domains of estimation. To preservethe current data structure, we propose a method to impute each missingestablishment's wage interval count data as an ordered multinomial randomvariable using a separate survival model for each occupation. Each modelincorporates known auxiliary information for each establishment associated withthe distribution of the occupational wage data, including geographic andindustry characteristics. This flexible model allows the baseline hazard tovary by occupation while allowing predictors to adjust the probabilities of anemployee's salary falling within the specified ranges. An empirical study andsimulation results suggest that the method imputes missing OES wages that areassociated with the average wage of the establishment in a way that moreclosely resembles the observed association.

Adjusting models of ordered multinomial outcomes for nonignorable nonresponse in the occupational employment statistics survey