This dissertation is comprised of two papers. Epidemiological studies of the associations between nutrients and health may yield misleading conclusions if relative prices are not taken into account. The first paper applies a household production approach to assess impacts of nutrients and other health inputs on health. Choices of health inputs in the health production technology are assumed to respond to nutrient prices. Moreover, potential measurement error associated with the health inputs biases the estimates of the health production parameters. Thus, prices, along with wages and other exogenous variables, serve as instruments in the demand for health inputs and the resulting reduced-form health equations to correct the problems of endogeneity and measurement error of the health inputs in the health production function. Empirical findings suggest that food prices are important determinants of health. Hence, policy implications concerning food price interventions to improve health are discussed. Moreover, household production and benchmark epidemiological estimates of the impacts of health inputs upon blood pressure are compared to examine the existence of endogeneity and measurement error associated with the health inputs. A comparable worth pay analysis for the State of Iowa Merit Employment Pay System was conducted in 1984 by Arthur Young Consulting Company of Milwaukee. Greig (1987) suspected that Arthur Young\u27s recommended pay plans were biased due to possible measurement error in the job evaluation. Hence Greig explored the sensitivity analysis of pay recommendations to various measurement error corrections. His estimates were confounded by multicollinearity among several of Arthur Young\u27s originally recommended thirteen job evaluation factors. The second paper aims to obtain unbiased estimates for the job factor weights in comparable worth pay analysis by correcting both the problems of measurement error and multicollinearity in the job evaluation factors simultaneously. Potential measurement error correlations between pairwise job evaluation factors are explored to analyze the sensitivity and statistical robustness of the estimates for the job evaluation factor weights to various measurement error correlation specifications.
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