and Labour Force Participation Disincentives.
In the models discussed in Section 1.1.3 it is assumed that the technology awarding benefits has a propensity to make classification errors of both Type I (false rejection) and Type II (false award). These error propensities are either exogenously given or endogenously determined by the resources dedicated to the monitoring technology (and the effort exerted by employees). A natural question to ask is therefore; are there
estimates of error propensities in real-world welfare programmes? Further, how is the scope for these errors related to the design of welfare programmes?
Both the Social Security Disability Insurance (DI) and Supplemental Security Income (SSI) programmes in the U.S. provide rich case studies to answer these questions. Both benefits are administered by the U.S. Social Security Administration (SSA) and the disability criterion for awards is common to both programmes. Under the Social Security Act, an individual is considered disabled if their medically verifiable physical or mental condition prevents them from engaging in ‘substantial gainful activity’ (see SSA, 2012, p.2)25. Further, this condition should be expected to be terminal or last
for no less than one year.
1.3.1
Estimating Classification Error Propensities
A number of studies have attempted to estimate the propensity of the SSA to make classification errors of both Type I and Type II in awarding both DI and SSI. The first is Nagi (1969), who used external audit data on the independent health assessment of DI applicants by professionals (such as doctors and psychologists) and compared these with the ultimate award decision of the SSA. Based on these comparisons, the author inferred a Type I error propensity of approximately 48% and a Type II error propensity of approximately 19%.
More recently, Benitez-Silva et al. (2004) treat (i) the self-declared disability status of DI/SSI applicants in the Health and Retirement Study; and (ii) the ultimate SSA awards decision (post any appeals), as noisy but unbiased indicators of true disability status. They estimate the Type I error propensity to be approximately 60%, whilst the Type II error propensity is estimated at approximately 22%. Their results are thus quantitatively similar to those in Nagi (1969).
1.3.2
Labour Force Participation Disincentives
A growing empirical literature focuses on the labour force participation (LFP) disin- centives generated by both Social Security Disability Insurance (DI) and Supplemental
25Substantial gainful activity was in 2011 a monthly income of $1,000 for a non-blind individual
Security Income (SSI) programmes in the United States. There are a number of moti- vating factors behind the research in this literature. First, legislation changes in 1984 required the SSA to place greater emphasis on an applicant’s ability to function in the workplace and any pain experienced in doing so, as opposed to basing assessments solely on strict medical criteria. With regard to difficult to verify conditions such as mental illness and musculoskeletal disease (i.e. back pain), these changes are argued to correspond to a reduction in the screening intensity of applicants (Autor and Duggan, 2003, 2006; Von Wachter et al., 2011). Second, the earnings replacement rate26 has
also risen markedly since 1984. The concern is that both of these changes account for (i) the large increase in DI recipients following the 1984 legislation change; (ii) mental illness and musculoskeletal disease being the two most prevalent claimant categories (SSA, 2012); and (iii) the increased propensity of younger individuals to exit the labour force and claim benefits.
The impact of the DI programme on LFP is far from clear in the literature. The basis for this discussion stems from the method developed by Bound (1989), who uses the labour force participation of rejected DI applicants as a control, or upper bound, for that which could be expected from awarded applicants. The assumption is therefore made that the tests run by the benefit authority have some discriminatory power, and thus that rejected applicants are more capable of work than awarded applicants. Using data on the labour market performance of rejected applicants between 1972 and 1978, Bound shows that less than half of rejected prime age (45-64) male applicants provide labour for a sustained period.27 He thus argues that the majority of DI recipients are
in fact disabled.
Parsons (1991), however, argues that Bounds’ analysis is flawed because it does not factor in the persistent role that the DI programme plays in the lives of applicants beyond an initial rejection. Owing in part to the difficulties in precisely defining the eligibility criterion, the programme features an extensive appeals system whereby a number of successive appeals through different bodies can be made prior to an ultimate rejection. Given the lags between an applicant filing an appeal and the response of the relevant authority, coupled with the numerous appeals that can be made, the appeals process is a lengthy one. Parsons notes three reasons - that are acknowledged,
26I.e. Disability income relative to earnings.
27Bound (1989) does not consider those below 45 because less than 20% of recipients in the studied
but given little weight in Bounds’ analysis - as to why a rejected applicant may not return to work. First, rejected applicants may be filing an appeal. Second, they may be enduring a period of unemployment to strengthen a future reapplication. Third and finally, rejected applicants may face difficulty in finding employment due to their sustained period out of work.28
More recent studies provide little consensus. On the one had, the analysis of Chen and van der Klaauw (2008) points to relatively low LFP disincentives induced by the DI programme and thus supports Bound (1989). These authors adopt Bound’s com- parator group approach on a data-set which features a number of important differences from that used in Bound (1989). First, merged survey and administrative data from the 1990s is used, whilst Bound only used survey data which can be unreliable given potential misreporting. Second, data is not restricted to the DI programme, but also includes the SSI programme. Finally, both male and female applicants are included in the data set. The analysis suggests that, in absence of any disability benefit provision, the LFP of recipients would be at most twenty percent higher.
On the other hand, however, the most recent study of Von Wachter et al. (2011) finds substantial LFP for rejected younger applicants and rejected applicants who claimed for musculoskeletal diseases and/or mental health conditions. The authors use a database containing (i) DI administrative information on applications and awards (from 1981- 1999); in addition to (ii) information on earnings pre- and post-applications (from 1978-2006). The results depend on the age group of applicants. Whilst the conclusions of Bound (1989) continue to hold for those in the older age category of 45-64; it is shown that younger rejected applicants aged 30-44 exhibit substantial post-rejection employment. As discussed in the opening of this section, younger applicants form a much larger proportion of total applicants in the data used by Von Wachter et al. (2011) than in that from the 70’s for Bound (1989).
Finally, Autor and Duggan (2003) estimate that the combined effects of (i) the 1984
28In a reply, Bound (1991) readdresses each of these three issues. Concerning the appeals process,
he notes that most rejected applicants in his data-set had applied at least 18 months prior to the survey, and very few appeals processes take this period of time. Turning to reapplications, it is not clear that enduring a sustained period of unemployment can enhance an application. In fact, it may have the opposite effect. Whilst some rejected applicants may behave this way, it is unlikely to play a large role. Finally, concerning the impact of processing lags lowering the employment prospects of rejected applicants, Bound notes that, on average, the applicants studied had been unemployed for seven months prior to making an application.
changes to disability assessment; (ii) increasing replacement rates; and (iii) a reduc- tion in the demand for low-skilled labour, have served to double the propensity of high-school dropouts to exit the labour force over the period 1984-2001. The authors identify instrumental variables to capture exogenous variation in both the ‘supply’ and ‘demand’ for DI. On the supply side, it is noted that the benefit formula is a func- tion of the average wage in the U.S. - which has risen relative to low-skilled wages - but does not account for state level differences in wages. As a consequence, the re- placement rate is higher in some states than others. The supply effect of programme expansions and contractions may thus have different effects in different states. Next, to capture exogenous variation in the demand for DI, the authors project national industry employment changes onto state level industry composition. In summary, the analysis suggests that state level contractions in DI supply generate large increases in LFP among high-school dropouts; whilst DI applications are much more responsive to state level demand shocks following the 1984 legislation changes to eligibility.