The effect of three factors on social security old-age and survivors insurance (OASI) benefits: earnings, mortality, and the consumer price index

Earnings are found to be higher for Whites relative to Nonwhites, for males relative to females. OASDI, with its formula based on earnings history, tends to compound the benefit differences, although the structure does provide proportionately larger returns for those at lower earnings levels. But the disparity between the Social Security benefits of Whites and Blacks, and between males and females, remains large. In addition, mortality rates were found to be higher for Nonwhites relative to Whites, for males relative to females, and for the less educated relative to the more educated. Some of researchers pointed out that differential mortality rates may have a significant influence on the distributional character of the Social Security program. Therefore, the redistribution effect of the progressive benefit formula, intended to provide a higher rate of return on the contributions of workers with low earnings than for those with high earnings, may not be as strong as expected.;In addition, if the basis for the CPI is empirical, we must consider whether an escalator intended for a specific demographic group, such as Social Security beneficiaries, should reflect the expenditure patterns of that group. Furthermore, the index number biases themselves could have differing impacts across different demographic groups. Therefore, it is important to discover how measured annual inflation rates and cumulative cost of living differed among specific demographic groups

Characterization of surface profiles using discrete measurement systems

Form error estimation techniques based on discrete point measurements can lead to significant errors in form tolerance evaluation. By modeling surface profiles as random variables, we are able to show how sample size and fitting techniques affect form error estimation. Depending on the surface characteristics, typical sampling techniques can result in estimation errors of as much as 50%;We investigate current available interpolation procedures. Kriging is an optimal interpolation for spatial data when the model of variogram is known a priori. Due to the difficulty in identifying the correct variogram model from the limited sampled data and lack of complete computer software, there is no significant advantage to apply kriging to estimate form error in the inspection process;We apply the Shannon sampling theorem and represent the surface profiles as band-limited signals. We show that the Shannon sampling function is in fact an infinite degree B-spline interpolation function and thus a best approximation for band-limited signals. Both Shannon sampling series and universal kriging (using a priori correlation function) are applied to flatness error estimation for uniform sample points measured from five common machined surfaces. The results show both methods perform similarly. The probability of over-estimating form error increases and the probability of accepting bad parts decreases using interpolation methods versus using the points directly