  The U.S. Bureau of Labor Statistics allows public access to much of the data
acquired through its Occupational Requirements Survey (ORS). This data can be
used to draw inferences about the requirements of various jobs and job classes
within the United States workforce. However, the dataset contains a multitude
of missing observations and estimates, which somewhat limits its utility. Here,
we propose a method by which to impute these missing values that leverages many
of the inherent features present in the survey data, such as known population
limit and correlations between occupations and tasks. An iterative regression
fit, implemented with a recent version of XGBoost and executed across a set of
simulated values drawn from the distribution described by the known values and
their standard deviations reported in the survey, is the approach used to
arrive at a distribution of predicted values for each missing estimate. This
allows us to calculate a mean prediction and bound said estimate with a 95%
confidence interval. We discuss the use of our method and how the resulting
imputations can be utilized to inform and pursue future areas of study stemming
from the data collected in the ORS. Finally, we conclude with an outline of
WIGEM, a generalized version of our weighted, iterative imputation algorithm
that could be applied to other contexts.
