This paper compares different methods of imputation for employment data given the wage data from a new establishment. The methods include regression modeling and distribution modeling with maximum likelihood estimators for the parameters, as well as standard procedures such as ratio adjustment and hot deck. Multiple imputation procedures are also developed and examined. The employment and wage data used for this study were obtained from the Bureau of Labor Statistics census of establishments. Nonresponse patterns are simulated using the pattern observed on the universe data base. The effectiveness of the various imputation methods is measured both at the micro and macro level.