Academic journal article Social Security Bulletin

E Xits from the Disability Insurance Rolls: Estimates from a Competing-Risks Model

Academic journal article Social Security Bulletin

E Xits from the Disability Insurance Rolls: Estimates from a Competing-Risks Model

Article excerpt

(ProQuest: ... denotes formulae omitted.)

Introduction

For the Social Security Disability Insurance (DI) program, the numbers of new enrollments and of beneficiaries on the rolls rose rapidly from 1980 through 2010. Clearly, growth in the DI rolls can result when the number of enrollments increases; but it may also result when beneficiaries stay longer in the program. Possible contributing factors include (1) demographic shifts, such as growing shares of younger and female workers entering the rolls; (2) changes in DI policies and in economic conditions (such as high unemployment) influencing workers to enter the program and stay longer in it; and (3) changing health trends, with certain disabling impairments (such as musculoskeletal impairments and mental disorders) becoming more prevalent among various population subgroups. A beneficiary's stay on the DI rolls also depends on individual characteristics such as the type of disability, age at entitlement, sex, employment opportunities, and past health conditions. Available administrative data do not have information on many of these individual characteristics. In this article, I focus on exit-rate patterns by age and sex, by type of disability, and over time, to examine if workforce shifts toward relatively younger workers, more female workers, or more aging workers prone to certain types of disabilities might explain the observed growth in the DI rolls.

A DI beneficiary exits the program for one of three reasons-death, recovery, or conversion to retirement benefits at full retirement age (FRA). A recovery-leaving the program before death or old-age conversion-can be due either to a worker's return to employment that provides a substantial level of earnings or to a Disability Determination Service finding that a beneficiary is no longer disabled. This article does not distinguish between the two.

The probability of exit because of a given cause depends on the probabilities of exit resulting from the competing causes. For instance, the probability of exiting DI because of recovery within a certain time depends on the likelihood that the person did not exit the program earlier because of either death or conversion. Thus, it is important to estimate the exit probabilities of any specific cause jointly with the exit probabilities of the two competing causes. Otherwise, we will have biased estimates (see, for instance, Pintilie 2006). I use a competing-risks statistical method that estimates the exit probabilities for all three competing risks simultaneously. Using these estimates, I present the emerging patterns of DI program exits by age at entitlement, sex, type of disability, and time on the rolls.

A parametric or semiparametric competing-risks hazard model is more appropriate than a cell-frequency method to estimate exit probabilities for two reasons. First, as cells are divided more finely to enhance granularity, some of them may end up containing zero or very few observations. A semiparametric hazard model can handle the small-sample cell problem because it uses information from all cells to estimate parameters that are common to all cells, while the cell-frequency method generally uses a case-by-case graduation method that combines the nearby cell frequencies. The second reason is that a semiparametric duration model can better handle censored observations, which arise because some individuals in the dataset have not yet exited the program at the time of data collection. The censored observations carry useful information about the exit probabilities, and estimates that discard the censored observations are inefficient.

This article is arranged in eight sections, including this introduction. In the second section, I briefly describe the trend of increasing DI entitlements and the data and methodology used in this analysis. In the third section, I first calculate the cumulative incidence of exit because of death, recovery, or conversion for the whole pool of disabled beneficiaries. …

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