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

channel runs through enhanced compliance with the HAART medicine caused by the antidepressant
use and therefore also gives rise to improved employment possibilities.
3.1.3 The methods
The authors apply a linear probability model, which models the response probability for the
outcome variable being equal to one. In this case the outcome variable is equal to one if the patient
is currently employed. The binary explanatory variable is equal to one if the patient has used
antidepressants since their last visit at the clinic. The other explanatory variables consist of among
others the patient’s age, the depression symptoms score, a quality of life index, a measure of the
HIV viral load and dummy variables for among others their level of schooling, their race and the
number of children. The authors analyse the results through fixed effects and random effects
regressions, which are two different approaches to allow for unobserved individual-specific
components.
The experimental nature of the data gives rise to an endogeneity problem, since selection into
treatment is not random. The selection bias operates through several channels, since patients with
more indications of depression are more likely to use antidepressants and simultaneously the
patients in the WIHS study that are more depressed are less likely to be employed due to
demographic, socio-economic and other factors. Galarraga et al. (2006) try to solve this problem by
employing instruments in their estimations that do not affect the probability of employment, but are
highly correlated with the probability of receiving treatment.
The authors use Medicaid data containing drug reimbursement information for the appropriate
states to construct six instruments, which were tested for relevance. The idea behind these
instruments is that the level of diffusion of certain antidepressant drugs in a particular state
influences how likely the patient is to take antidepressants, but it should not influence the likelihood
of whether the patient is employed or not. The difference in antidepressant diffusion in different
states is exploited in order to distinguish the treatment effect. Even though the instrumental variable
(IV) method takes self-selection into account and the data contains a lot of information about the
patients, there might still be some unobservables influencing the outcome such as the patient’s
capability to cope with the disease and their degree of optimism. Galarraga et al. (2006) perform
IV- and OLS regressions in order to compare the results. The authors use two- stage least squares
(2SLS) methods when applying the instrumental variables. They decide that the two instruments
with the highest t-statistic in the first stage will be used in the second stage estimation, where they
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