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

Figure 9 depicts the defined daily dosages of other medicine taken by users and non-users of
antidepressants, and includes neither antidepressants nor MTX. It is evident that there are great
differences between users and non-users of antidepressants regarding the consumption of other
medicine. There is a positive correlation between the usage of antidepressants and other medicine
(0.2319) at the 1 % significance level. These results confirm the arguments put forward in section
4.2.3 for using the use of other medicine as a proxy-variable in the regressions.
This section looked at the correlation among the use of antidepressants and the outcome variable
and also at the correlation among the use of antidepressants and some of the control variables. Of
particular interest it appears that the use of antidepressants correlates negatively with employment.
This will be looked further into in the analysis. In the following the applied estimation methods are
introduced.
5 Binary response estimation of the employment decision
In chapter 2 the theoretical framework for this thesis was established in the form of a static labour
supply model. The aim of this thesis is to estimate the probability of employment in order to find
out which effect the use of antidepressants has on this. The decision of employment can profitably
be modelled with a discrete response model, since the outcome is binary. The dependent variable is
a variable that takes on the values 1 or 0 and these two outcomes are often called success and
failure, respectively, as a way of distinguishing between the two outcomes. As in the case of
employment, where I am interested in the likelihood of being employed, which is called success. I
am concerned about the probability of success
( Y X ) P(
X )
P = 1 ≡
[5.1]
Following Wooldridge (2002) this chapter will describe the binary response models employed in
the further analysis.
5.1 The probit model
When estimating a model with a binary outcome, the simplest model is the linear probability model
(LPM). Because Y is a Bernoulli variable the following holds
and Y can be modelled as follows
[ Y X ] = 0 ⋅ P(
Y = 0 X ) + 1⋅
P(
Y = 1 X ) = P(
Y = X )
E 1
44