An Analysis on Danish Micro Data - School of Economics and ...

8 Conclusion
The aim of this thesis was to analyse how treatment with antidepressants affects employment. The
approach taken was to estimate a binary choice model with three different probit models, in order to
perform a sensitivity analysis of the results. In chapter 4 it was verified that the data were not
flawless, because the panel was unbalanced and the treatment variable was potentially endogenous
due to unobservables. The sensitivity analysis had the objective to detect whether these issues
regarding the data influenced the robustness of the results. In chapter 6 the effect of antidepressants
on the probability of employment was analysed, using an article by Galarraga et al. (2006) as a
starting point. The estimation methods applied were a probit estimator, a pooled probit estimator
and a random effects probit estimator. The estimations were performed using a binary
antidepressant indicator, using the dosages of antidepressants taken, and looking at the number of
years antidepressants were taken and the dosages taken over the course of the last 5 years.
Furthermore, in section 6.3, the analysis was refined to labour force participants only. Overall the
results were small and insignificant, with an ambiguous sign across the different estimation
methods. The marginal effects are numerically bigger, when a binary or discrete indicator of
antidepressant use is included, compared to the estimations that include a measure of the dosages of
antidepressants. This was explained to be the case because the marginal effects are evaluated at the
mean, and it was suggested that maybe another method of calculating the marginal effects would
have given another picture.
In all estimations the variable of main interest was tested for endogeneity and could not be rejected
to be exogenous. Also the test for sample selection showed that there is no problem with sample
selection bias, although the panel is unbalanced. Consequently, the tests show that the mentioned
issues regarding the data should not influence the results. However, the results should nevertheless
be interpreted with great caution, as also can be concluded from the ambiguous results concerning
the sign of the found marginal effects.
As pointed out in chapter 6, there are several drawbacks to this analysis. First of all, the fact that
important information is unobserved is a limitation that remains throughout the analysis. The
applied methods are not capable of taking this problem into account and the dataset does not contain
any variables suitable as instruments. Second, because the degree of depression is unobserved and
therefore the use of antidepressants is both the indicator of depression and also the treatment, the
small effect of antidepressants might be explained with the fact that the antidepressants counteract
the negative effect on the probability of employment, which depression has. Third, utilizing
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