Estimation of multi-state life table functions and their variability from complex survey data using the SPACE Program

The multistate life table (MSLT) model is an important demographic method to document life cycle processes. In this study, we present the SPACE (Stochastic Population Analysis for Complex Events) program to estimate MSLT functions and their sampling variability. It has several advantages over other programs, including the use of microsimulation and the bootstrap method to estimate the sampling variability. Simulation enables researchers to analyze a broader array of statistics than the deterministic approach, and may be especially advantageous in investigating distributions of MSLT functions. The bootstrap method takes sample design into account to correct the potential bias in variance estimates.bootstrap, health expectancy, multi-state life table, population aging

The estimation of a preference-based measure of health from the SF-36

This paper reports on the findings of a study to derive a preference-based measure of health from the SF-36 for use in economic evaluation. The SF-36 was revised into a six-dimensional health state classification called the SF-6D. A sample of 249 states defined by the SF-6D have been valued by a representative sample of 611 members of the UK general population, using standard gamble. Models are estimated for predicting health state valuations for all 18,000 states defined by the SF-6D. The econometric modelling had to cope with the hierarchical nature of the data and its skewed distribution. The recommended models have produced significant coefficients for levels of the SF-6D, which are robust across model specification. However, there are concerns with some inconsistent estimates and over prediction of the value of the poorest health states. These problems must be weighed against the rich descriptive ability of the SF-6D, and the potential application of these models to existing and future SF-36 data set

Value of Sample Separation Information in a Sequential Probit Model: Another Look at SSA's Disability Determination Process

We have estimated a 4-step sequential probit model with and without sample separation information to characterize SSA's disability determination process. Under the program provisions, different criteria dictate the outcomes at different steps of the process. We used data on health, activity limitations, demographic traits, and work from 1990 SIPP exact matched to SSA administrative records on disability determinations. Using GHK Monte Carlo simulation technique, our estimation results suggest that the correlations in errors across equations that may arise due to unobserved individual heterogeneity are not statistically significant. In addition, we examined the value of administrative data on the basis for allow/deny determinations at each stage of the process. Following the marginal likelihood approach adopted by Benitez-Silva, Buchinsky, Chan, Rust, and Sheidvasser (1999), we also estimated the above sequential probit model without the sample separation information for the purpose of direct comparison. We found that without the detailed administrative information on outcomes at each stage of the screening process, we could not properly evaluate the importance of a large number of program-relevant survey-based explanatory variables. In terms of both in-sample and jackknife-type out-of-sample predictive analysis, the value of modeling the sequential structure of the determination process in generating correct eligibility probabilities is confirmed.Disability, Method of Simulated Movements, Multivariate Probit, Social Security

Value of Sample Separation Information in a Sequential Probit Model: Another Look at SSA's Disability Determination Process

We have estimated a 4-step sequential probit model with and without sample separation information to characterize SSA's disability determination process. Under the program provisions, different criteria dictate the outcomes at different steps o f the process. We used data on health, activity limitations, demographic traits, and work from 1990 SIPP exact matched to SSA administrative records on disability determinations. Using GHK Monte Carlo simulation technique, our estimation results suggest that the correlations in errors across equations that may arise due to unobserved individual heterogeneity are not statistically significant. In addition, we examined the value of administrative data on the basis for allow/deny determinations at each sta ge of the process. Following the marginal likelihood approach adopted by Benitez-Silva, Buchinsky, Chan, Rust, and Sheidvasser (1999), we also estimated the above sequential probit model without the sample separation information for the purpose of direct comparison. We found that without the detailed administrative information on outcomes at each stage of the screening process, we could not properly evaluate the importance of a large number of program-relevant survey-based explanatory v ariables. In terms of both in-sample and jackknife-type out-of-sample predictive analysis, the value of modeling the sequential structure of the determination process in generating correct eligibility probabilities is confirmed.

Value of Sample Separation Information in a Sequential Probit Model: Another Look at SSA's Disability Determination Process

Disability, Method of Simulated Movements, Multivariate Probit, Social Security.