Estimates of labor statistics are susceptible to being heavily influenced by certain businesses responding to surveys of employment, hours worked, and earnings. Winsorization can be used to identify and treat influential microdata, and is a method that the Current Employment Statistics State and Area program uses to improve efficiency in employment estimates. However, for the average weekly hours and hourly earnings estimates, there is not currently a robust method to identify outliers. This is mainly due to differences in estimation techniques. While employment estimates target a population total, average weekly hours and hourly earnings estimates use ratios of two totals. Subsequently, measuring the influence of a report on the latter estimates is not as straightforward as it is for employment because of the differing complexity in estimation techniques. This paper presents four different influence functions that were developed for catching outliers in the hours and earnings data. Each one is evaluated via simulations to determine the most efficient estimator. While some influence functions performed better than others, all proved better than having no robust method at all.