The Occupational Employment Statistics (OES) is a joint Federal/State partnership program with a sample size of 1.2 million establishments over a 3-year period (six semi-annual panels each consisting of 200,000 establishments). The OES collects occupational employment and wage data for approximately 800 occupations at the MSA by 4-5 digit industrial (NAICS) level. Because of the burden on respondents, this survey is designed to collect wage data in intervals rather than exact wages for individual employees. In this talk, we will present the previous research work on the construction of lower and upper bounds of the intervals; alternative methods for estimating mean wages—arithmetic, geometric, and NCS mean wages; updating of wages from prior panels; and calculation of mean wages for the upper open-ended Interval L (i.e., employees making $70 or more per hour in the years 2003-2005). This study further examines several methods for approximating mean wages for Interval L for occupations that have significant employment (>5%) in Interval L and validates the OES methodology on independent data sets from the Current Population Survey for years 2003, 2004, and 2005. KEY WORDS: Open-ended interval wage estimation, geometric mean, kernel density

In this paper the results of an empirical investigation of different imputation methods for item non-response from new establishments are presented. The imputation is for employment data given that wage data are known. This

The Occupational Employment Statistics (OES) is a joint Federal/State partnership program with a sample size of 1.2 million establishments over a 3-year period (six semi-annual panels each consisting of 200,000 establishments). The OES collects occupational employment and wage data for approximately 800 occupations at the MSA by 4-5 digit industrial (NAICS) level. Because of the burden on respondents, this survey is designed to collect wage data in intervals rather than exact wages for individual employees. In this paper, we will present the previous research work on the construction of lower and upper bounds of the intervals; alternative methods for estimating mean wages—arithmetic, geometric, and NCS mean wages; updating of wages from prior panels; and calculation of mean wages for the upper open-ended Interval L (i.e., employees making $70 or more per hour in the years 2003-2005). This study further examines several methods for approximating mean wages for Interval L for occupations that have significant employment (>5%) in Interval L and validates the OES methodology on independent data sets from the Current Population Survey for years 2003, 2004, and 2005. Key Words. Open-ended interval wage estimation, geometric mean, kernel density