IMPUTING HOURLY WAGES IN THE NATIONAL MEDICAL EXPENDITURE SURVEY

Introduction The Household Component of the National Medical Expenditure Survey (NMES) was established to provide an assessment of health care u t i l i z a t i o n , costs, sources of payment, and insurance coverage of the U.S. c i v i l i an noninst i tu t ional population. The household component is an 18-month panel survey with 1987 as the reference period, col lect ing measures on health status, use of health care services, expenditures and sources of payment, insurance coverage, employment, income and assets, as well as demographic information. Complete nonresponse at the person level for NMES survey data was accounted for by adjusting the sampling weights. However, the proportion of item missing data is a problem for a large number of variables. Logical edits have been implemented and, whenever possible, data has been imputed based on other information provided. The issue being addressed is how to deal with the remaining item nonresponse, given that standard analysis techniques for modeling, or inference, require data sets with complete data pro f i les . In the absence of this c r i t e r ion , the current choices are ei ther to impute for missing data or to analyze only cases with complete data. Imputing for missing data is done to maintain the representativeness of the sample, and to enable users to make national and regional estimates. Imputation also ensures consistency between results of d i f fe ren t analyses, reduces nonresponse bias for item nonresponse, and enhances the a b i l i t y to apply standard analysis techniques to data sets with complete data prof i les without loss of sample size. Imputation does introduce a new component of v a r i a b i l i t y , variance due to imputation. This v a r i a b i l i t y can be quanti f ied by implementing mult ip le imputation for each missing value. In this paper, two methods of mult iple imputation are carried out. The f i r s t uses the "hot deck" procedure, and the second uses the Bayesian approach (Rubin, 1987). The variance due to imputation is computed, and a comparison is conducted to examine whether there is a s ign i f icant dif ference in the v a r i a b i l i t y due to imputation captured by each of these two methods. Analyzing only cases with complete data can be viewed as analyzing the data as reported by persons par t ic ipat ing in the survey. I f the assumption is made that respondents are simi lar in the i r character ist ics to nonrespondents, no bias and no var iat ion due to imputation is introduced in data analysis. Moreover the representativeness of the sample can be maintained by proper weight adjustments. There is some loss of precision depending on the rate of item nonresponse since the sample size gets smaller, but the e f fo r t associated with obtaining a set with complete data is s impl i f ied. The assumption that respondents are s imi lar in their character ist ics to nonrespondents is not a necessary assumption that can be used for estimating parameters of in terest , but i t is made to f a c i l i t a t e imputation of data. Although many studies have shown that there is a d i f f e ren t i a l between respondents and nonrespondents, the assumption is very d i f f i c u l t to prove or disprove. I t can, however, be inferred based on par t ia l responses. Thus, the choice between imputing or analyzing complete data is a tradeo f f between addit ional v a r i a b i l i t y due to imputation versus nonresponse bias and increased variance of the estimators. The focus of this paper is to invest igate and assess the various al ternat ives for dealing with item nonresponse. We have focused only on one variable. "hourly wage". The sample universe for this analysis is al l employed persons in the f i r s t wave of the Household component of the National Medical Expenditure Survey (NMES). The imputation techniques that are being evaluated include. (1) Model-based imputation (2) Nonresponse adjustments to sample weights (3) hot deck imputation, and (4) mult ip le imputation. The s ta t i s t i ca l assessment includes analysis of the ef fect of imputation on the d i s t r i bu t ion and on the v a r i a b i l i t y of survey estimates such as means and proportions, and the i r respective standard errors. There is no assessment of bias. An assessment of bias can be done i f one creates a synthetic subset of nonrespondents, imputes for missing data using each of the methods, and compares the imputed values to the observed. This w i l l be done as a fol low-up to this study. Data Base Used for Imputation The p opulati6h includes al l e l i g ib le persons who reported having a job and are not se l f employed in the Household Component of NMES. This segment of the data base includes 13,605 e l i g ib le persons with posi t ive weights. Of those, 11,614 had reported the i r hourly wage. The remaining 1,991 persons did not report the i r hourly wage. Hourly wages were derived based on a sequence of questions on work related patterns including" i . Is the person employed? I f so does he/she work for someone else? 2. How many hours does the person work a week ? 3. How many days a week ? 4. What is the wage rate, and the uni t , "PER WHAT" (per hour, per day, per month, per year), associated with the wage rate. Over 99% of the non-reporters did not complete the "PER WHAT" question (part 2 of question 4). 88.4% provided neither a do l lar value for wage rate nor the unit of payment (per hour, per year). Al l non-reporters completed the question on the number of hours they worked per week, but only 7.2% answered the question concerning the number of days per week. The pattern for nonresponse to these questions