The data set includes three types of variables that can be potentially be used as predictors of productivity that do not themselves (i.e., independently of productivity) affect wages: age, education, and job requirements. The information on job requirements comes from survey questions asking the employer whether specific experience, general experience, and vocational education or formal training are ’’absolutely required, strongly preferred, mildly preferred, or does not matter.” It seems reasonable to suppose that if these are absolutely required of a hire, then that hire must possess these qualifications, and the data are used in this manner. In fact, this supposition could be checked using another question on whether a high school diploma was required and corresponding information on the reported actual education of the worker hired; only 1.4 percent of those hires for which a high school diploma was absolutely required (27 percent of the hires in the sample) did not actually have a high school diploma. so

Finally, because of measurement problems attention is restricted to the bulk of the sample (about 70 percent) paid hourly wages. The most important problem is that the only hours information comes from a question regarding how many hours per week are usually worked, with no distinction between the time periods referring to the starting wage and the current wage. Consequently, there is no way to accurately construct an hourly starting wage and hourly current wage for those paid on a non-hourly basis. This is likely to be further complicated by differences between hourly and non-hourly workers in the value of non-wage compensation.