Counting the Disabled: Using Survey Self-Reports to Estimate Medical Eligibility for Social Security’s Disability Programs

by Debra Dwyer, Jianting Hu, Denton R. Vaughan, and Bernard Wixon
ORES Working Paper No. 90 (released January 2001)

Text description for Chart 1.
SSA disability determination process

This flow chart details the five-step process used in establishing the medical eligibility of disability applicants. Steps 1–3 are screens: step 1 is an earnings screen, and steps 2 and 3 are medical screens.

An applicant is denied at step 1 if he or she earns more than the maximum SGA amount. In step 2, impairment(s) is assessed to determine severity; if impairment(s) is not severe, the applicant is denied at this stage. A duration test, typically at step 2, is used to determine whether the impairment(s) has lasted or is expected to last 12 months, or whether the impairment(s) is expected to result in death.

At step 3, an applicant is allowed if the impairment(s) satisfies the Listing of Impairments criteria. A severely impaired applicant who is not allowed at step 3 is evaluated at the final two steps (steps 4 and 5) of the determination process, involving an assessment of his or her residual capacity to work.

At step 4, an applicant who is found able to perform his or her past work is denied. After step 4, a remaining applicant is allowed in step 5 if he or she is found unable to do any work; that applicant is otherwise denied at this stage.

Text description for Chart 2.
The sequential disability determination model

This flow chart is a model of steps 2–5 of the disability determination process. The vertical line shows the four decision nodes in the process—k, l, m, and n—which result in five outcomes.

The first outcome, d2 (right arrow extending from the first node, k) denotes a denial at step 2, based on nonseverity of medical impairment(s).

The second outcome, a3 (left arrow extending from the second node, l) denotes allowance at step 3, based on the Listing of Impairments.

The third outcome, d4 (right arrow extending from the third node, m) denotes denial at step 4, based on residual capacity for past work.

The fourth outcome, a5 (left arrow extending from the fourth node, n) denotes allowance at step 5, based on residual incapacity for any work.

The fifth outcome, d5 (right arrow extending from the fourth node, n) denotes denial at step 5, based on residual capacity for any work.

Text description for Chart 3.
Disability Allowance Probabilities, By Work Limitation Status, With Sample Selection, Full Sample

Y-axis = Frequency (0–0.8); X-axis = Allowance probability (0–1).

This line chart plots allowance probabilities for the full sample by health status for the models with sample selection. The chart shows that both distributions, "limited" and "not limited," center on an allowance probability of 0.2, but the distribution is wider for respondents with work limitations.

Text description for Chart 4.
Disability Allowance Probabilities, By Work Limitation Status, Without Sample Selection, Full Sample

Y-axis = Frequency (0–0.8); X-axis = Allowance probability (0–1).

This line chart plots allowance probabilities for the full study sample by health status for the models without sample selection. Compared with Chart 3, which uses sample selection, Chart 4 shows that without sample selection, the probabilities of allowance are centered near 0.4 for both the "limited" and "not limited" groups.

The exaggerated probabilities shown here are expected because this chart gives allowance probabilities without sample-selection.

Text description for Chart 5.
Disability Allowance Probabilities, By Work Limitation Status, With Sample Selection, Restricted Sample

Y-axis = Frequency (0–0.8); X-axis = Allowance probability (0–1).

This line chart illustrates how sample selection alters the distribution of allowance probabilities for the restricted sample. It shows that the model with sample selection does a more accurate job of identifying applicants with and without severe health limitations. The chart also shows that an alternative probability cutoff of 0.4 would distinguish between people with work limitations and high allowance probabilities (those most severely impaired) from those without work limitations.

The contrast in the distributions is not as pronounced for those in the restricted sample (Charts 5 and 6) as it is for those in the full sample (Charts 3 and 4) because there is less variation in health status among members of the restricted sample.

Text description for Chart 6.
Disability Allowance Probabilities, By Work Limitation Status, Without Sample Selection, Restricted Sample

Y-axis = Frequency (0–0.8); X-axis = Allowance probability (0–1).

This line chart illustrates the degree to which sample selection alters the distribution of disability allowance probabilities for the restricted sample. The distribution between those with work limitations and high allowance probabilities and those with no work limitations cannot be drawn as efficiently without sample selection. The contrast in the two distributions is not as pronounced for members of the restricted sample because there is less variation in health status among those members.

In addition, the presence of a work limitation appears to be more correlated with allowance probabilities for the model with sample selection (Chart 5) than for the model without sample-selection controls (Chart 6).

Text description for Chart 7.
Disability Allowance Probabilities, by Application Status, With Sample Selection, Full Sample

Y-axis = Frequency (0–0.8); X-axis = Allowance probability (0–1).

This line chart plots allowance probabilities for nonapplicants and applicants. The chart shows that for nonapplicants, the distribution of disability allowance probabilities is centered at 0.2, with little variation. The distribution for the applicant pool, on the other hand, is much more uniform, suggesting that an alternative probability cutoff at 0.5 might be too restrictive and would miss most allowed applicants.