14
would make use of Sassam and AM if being offered Sassam and AM early but not if being
offered later on.
Table 2 presents the effects on the status 15 months after the screening. All
estimations control for differences in covariates; however, these results are very similar
to those when no control variables are added to the regressions (see Table C1 in
Appendix C).
12
The table has two panels. The top panel concerns the effect of Sassam and
the bottom the results of AM.
The results from the first-step estimations are presented in column (1) in the first row
of each panel. These results confirm the pattern seen in the raw data. Belonging to the
prioritized groups involves a 16.4 and 4.4 percentage point higher probability of receiving
Sassam and AM respectively.
The reduced form, or intent-to-treat, estimates are presented in the second row of the
two panels. No significant effects are found in the AM experiment. In the Sassam
experiment, the effect on both sickness absence and unemployment is close to zero.
However, the result shows an 0.8 percentage point or an 18 (0.8/4.4) percent i increased
probability of receiving DB.
13
From the two-stage least squares estimation (row 3) we get that this translates into
a 5.1 percentage points increase in disability benefit take-up rates for individuals who
would take use of Sassam if being offered early but not if being offered late.
12
We control for the covariates displayed in Table 1, where we also have added polynomials for the
nonqualitative variables (i.e., age and historic number of days in social insurance).
13
We obtain the same results when estimating the models without control variables. Because of this
there is no need to use the nonparametric LATE estimator suggested by Frölich (2007).
