Chart 1 shows that eligibility for the LIS under Medicare Part D may occur through automatic eligibility (for those who are SSI recipients or Medicaid or Medicare Savings beneficiaries), or occur through direct eligibility (based on meeting income and resource thresholds in table below).
Direct eligibility ($) | Income criterion (% of poverty) | |||
---|---|---|---|---|
Less than 135 | 135–150 | Greater than 150 | ||
Resource criterion for single/couple | Less than 6K/9K | Full | Partial | Not eligible |
6K/9K–10K/20K | Partial | Partial | Not eligible | |
Greater than 10K/20K | Not eligible | Not eligible | Not eligible | |
Return to Chart 1 in main document
Chart 2 provides a schematic representation of the methodological approach to using the SIPP. In particular, we use waves 7 to 10 of the 2004 panel, covering calendar year 2006 as well as several topical modules. We account for potential bias from nonrandom attrition from the baseline wave and nonrandom attrition when matching to administrative data records in 2006. We then apply our algorithm for the LIS eligibility determination rules to the 2006 SIPP survey data and 2006 matched administrative data to estimate the number of LIS-eligibles in 2006.
Return to Chart 2 in main document
Chart 3 provides a schematic representation of the methodological approach to using the HRS. In particular, we use the 2002 and 2004 HRS matched to administrative records for 2002. We account for potential bias because of nonrandom attrition when matching to the administrative data as well as measurement error in Medicaid/Medicare Savings status. Because we do not observe administrative records for 2006, we assume the same conditional relationship observed in the matched survey/administrative data from 2002 to impute equivalent administrative survey data measures. We also account for nonrandom attrition from the baseline HRS wave when analyzing the 2002, 2004, and 2006 waves of HRS data. We then apply our algorithm for the LIS eligibility determination rules to the 2006 HRS survey data and 2006 imputed administrative data to estimate the number of LIS-eligibles in 2006
Return to Chart 3 in main document
Estimate | Estimated LIS-eligible population (millions) |
Standard error | Bottom confidence interval | Top confidence interval |
---|---|---|---|---|
Baseline: SIPP and HRS averaged | 12.2 | 0.425 | 11.4 | 13.1 |
Baseline: SIPP preference | 13.4 | 0.620 | 12.2 | 14.6 |
Baseline: HRS preference | 11.1 | 0.406 | 10.3 | 11.9 |
Return to Chart 4 in main document
Estimate | Estimated LIS-eligible population (millions) | Standard error | Bottom confidence interval | Top confidence interval |
---|---|---|---|---|
Baseline: SIPP and HRS averaged | 12.2 | 0.425 | 11.4 | 13.1 |
Baseline: SIPP preference | 13.4 | 0.620 | 12.2 | 14.6 |
Baseline: HRS preference | 11.1 | 0.406 | 10.3 | 11.9 |
S1, H0: SIPP and HRS averaged | 11.8 | 0.394 | 11.0 | 12.6 |
S0, H1: SIPP and HRS averaged | 12.3 | 0.424 | 11.5 | 13.2 |
S1, H1: SIPP and HRS averaged | 11.9 | 0.393 | 11.1 | 12.6 |
S2, H0: SIPP and HRS averaged | 11.5 | 0.402 | 10.7 | 12.3 |