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

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Figure 8 The average percentage of users and non-users of antidepressants across different incomes From figure 8 it is apparent that users of antidepressants are much more likely to have zero wage income than non-users of antidepressants in the population of MTX users. The correlation coefficient between usage of antidepressants and wage income is -0.0776 at the 1 % significance level. Similar correlations are found in the literature, but again contradicted by others. For instance, Zimmerman & Katon (2005) affirm that income is significantly correlated with depression, but when controlling for other factors this is no longer the case. They do confirm that employment status is causally related to depression, as put forward earlier. Percentage Percentage 80 70 60 50 40 30 20 10 0 25 20 15 10 5 0 Zero wage income Up to 100.000 kr 101.000- 200.000 kr Income 201.000- 300.000 Above 300.000 kr No use Up to 200 201-400 401-600 601-800 801-1000 1001-1500 1501-2000 2001 and above Defined daily dosages of other medicine Non-antidep <strong>An</strong>tidep Non-antidep <strong>An</strong>tidep Figure 9 The average dosage of other medicine taken by users and non-users of antidepressants 43
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
Page 1 and 2: Estimating the Impact of An
Page 3 and 4: Abstract This thesis aims at analys
Page 5 and 6: 1 Introduction According to the Wor
Page 7 and 8: consisting of lack of data on the d
Page 9 and 10: experience symptoms of depression o
Page 11 and 12: (SSRI’s), also called the new ant
Page 13 and 14: heumatologists. But since descripti
Page 15 and 16: H > 0 or a corner solution, H = 0 .
Page 17 and 18: employers more reluctant to hire ne
Page 19 and 20: One of the most cited models when a
Page 21 and 22: One of the factors greatly influenc
Page 23 and 24: Galarraga et al. (2006) have chosen
Page 25 and 26: channel runs through enhanced compl
Page 27 and 28: 3.2 The effect of antidepressants o
Page 29 and 30: of patients receiving appropriate c
Page 31 and 32: Inspired by Galarraga et al. (2006)
Page 33 and 34: are used by other patients groups,
Page 35 and 36: Consequently I am dealing with a se
Page 37 and 38: constructed, and will be explained
Page 39 and 40: As mentioned in chapter 1, one of t
Page 41 and 42: Number of observations 2364 252 211
Page 43 and 44: is perhaps not surprising, since th
Page 45: Figure 7 The distribution of the us
Page 49 and 50: where φ ( z) is the standard norma
Page 51 and 52: 5.3 The binary response model and p
Page 53 and 54: The log of equation [5.7] gives the
Page 55 and 56: antidepressant use are displayed in
Page 57 and 58: As established in chapter 4, the pa
Page 59 and 60: probit estimation, respectively. Ha
Page 61 and 62: estimation. And ag
Page 63 and 64: As can be seen from table 7 the ext
Page 65 and 66: mtx 0.0002 0.0003 0.0001 0.0002 wag
Page 67 and 68: probit estimation produces more sig
Page 69 and 70: health. As a consequence, further a
Page 71 and 72: 8 Conclusion The aim of this thesis
Page 73 and 74: References Agerbo, E., T. Eriksson,
Page 75 and 76: Zimmerman, F. and W. Katon. 2005. S
Page 77 and 78: wage2 Dummy=1 if wage income up to
Page 79 and 80: data medold; set b.lmdb_pct10_2003;
Page 81 and 82: if Last.aar then output; /*output o
Page 83 and 84: KEEP AAR ALDER ANC02 ANC36 ANC79 AN
Page 85 and 86: set b.register; yr=0+aar; DROP aar;
Page 87 and 88: HIGHER='Middle-range and higher edu
Page 89 and 90: dayemp4='Between 8 months and 364 d
Page 91 and 92: data collect; merge test1 (in=a) te
Page 93 and 94: data test1; set b.finaldata1; proc
Page 95 and 96: antidep_lastyr=lag(AD_dummy); if (u
Page 97 and 98:
Estimation results Probit yr 2003 -
Page 99 and 100:
Probit estimates Number of obs = 58
Page 101 and 102:
single | -.3439072 .0893125 -3.85 0
Page 103 and 104:
(Assumption: . nested in A) Prob >
Page 105 and 106:
. /*with proxy, generalised residua
Page 107 and 108:
. . /*Panel probit same as Galarrag
Page 109 and 110:
Iteration 7: log pseudolikelihood =
Page 111 and 112:
-----------------------------------
Page 113 and 114:
. . probit emp antidep mtx wageinc
Page 115 and 116:
ho = 0.2 log likelihood = -681.4270
Page 117 and 118:
ab79 | -.2830833 .3452789 -0.82 0.4
Page 119 and 120:
-----------------------------------
Page 121 and 122:
. predict ohat, resid . . probit em
Page 123 and 124:
y99 | -.1676129 .4540083 -0.37 0.71
Page 125 and 126:
ab02*| -.0132912 .01201 -1.11 0.269
Page 127 and 128:
. gen numerator=normdenxb*[ad_dummy
Page 129 and 130:
Iteration 2: log likelihood = -306.
Page 131 and 132:
. . probit emp antidep_last5yrs mtx
Page 133 and 134:
note: 0 failures and 14 successes c
Page 135 and 136:
y = Pr(emp) (predict) = .64904538 -
Page 137 and 138:
Probit yr 2003 - Table 9: . use /ak
Page 139 and 140:
higher | .0160771 .2154333 0.07 0.9
Page 141 and 142:
ieland1*| .2476048 .11071 2.24 0.02
Page 143 and 144:
Random-effects tobit regression Num
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