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

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THANKS Throughout the process of writing this thesis I have received assistance from many sides. Foremost I would like to thank my advisor Associate Professor Michael Svarer for his guidance and patience along the way. Great thanks to Assistant Professor Marianne Simonsen for granting access to the data and to PhD student, stud.oecon Niels Skipper for helping with data related problems. Furthermore I would like to thank stud.med. Jacob <strong>An</strong>dersen for his assistance regarding the medicinal content. <strong>An</strong>d last but not least thanks to my fellow students BSc <strong>An</strong>ette Fuglsang and MSc <strong>An</strong>ne Keller for advice and encouragement.
Abstract This thesis aims at analysing how the treatment of depression with antidepressants affects the patients’ employment. Depression is a very prevalent psychic disorder, which involves large costs for the patient and her closest relatives, both personally and economically. Investigating how labour market outcomes are affected by treatment of depression is of importance, since depression can affect the ability to work. This has economic consequences for the individual patient, and for society as a whole seen from an economic perspective, since it causes more social benefits to be paid. Using the article <strong>An</strong> Instrumental Variable Evaluation of <strong>An</strong>tidepressant Use on Employment among HIV-infected Women using Highly-Active <strong>An</strong>tiretroviral Therapy in the United States: 1996- 2004 by Galarraga et al. (2006) as a point of departure, this thesis analyses of the effect of antidepressant use on the probability of being employed for a sample population of female methotrexate users. A short introduction is given to depression and the drug methotrexate. The reader is introduced to a simple static model of labour supply and a model for the demand of health. After a brief overview of the literature in the field, the analysis seeks to elaborate upon the article by Galarraga et al. (2006). The dataset applied consists of a medicine dataset and register data. The effect on employment is estimated with different binary choice models in order to perform a sensitivity analysis of the results. First the estimations are performed analogous to Galarraga et al. (2006), using a dummy variable indicating antidepressant use. Thereafter the estimations are extended, compared to Galarraga et al. (2006), focusing only on the individuals participating in the labour market and using a binary variable indicating employment and unemployment. The analysis is also taken in another direction, by inclusion of the dosages of antidepressants taken, how many years taken out of last five years, and the dosages taken the last five years. The results show that the estimated effect of antidepressants is small and insignificant with an ambiguous direction. A number of drawbacks to the analysis are discussed, among others a problem of omitted variables. Conclusively, a preliminary attempt is made to estimate the effect of antidepressants on a continuous dependent variable, which results in a larger, positive effect, which nonetheless is insignificant.
Page 1: Estimating the Impact of An
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 and 46: Figure 7 The distribution of the us
Page 47 and 48: Figure 9 depicts the defined daily
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
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antidepressant use are displayed in
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As established in chapter 4, the pa
Page 59 and 60:
probit estimation, respectively. Ha
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estimation. And ag
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As can be seen from table 7 the ext
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mtx 0.0002 0.0003 0.0001 0.0002 wag
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probit estimation produces more sig
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health. As a consequence, further a
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8 Conclusion The aim of this thesis
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References Agerbo, E., T. Eriksson,
Page 75 and 76:
Zimmerman, F. and W. Katon. 2005. S
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wage2 Dummy=1 if wage income up to
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data medold; set b.lmdb_pct10_2003;
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if Last.aar then output; /*output o
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KEEP AAR ALDER ANC02 ANC36 ANC79 AN
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set b.register; yr=0+aar; DROP aar;
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HIGHER='Middle-range and higher edu
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dayemp4='Between 8 months and 364 d
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data collect; merge test1 (in=a) te
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data test1; set b.finaldata1; proc
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antidep_lastyr=lag(AD_dummy); if (u
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Estimation results Probit yr 2003 -
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Probit estimates Number of obs = 58
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single | -.3439072 .0893125 -3.85 0
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(Assumption: . nested in A) Prob >
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. /*with proxy, generalised residua
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. . /*Panel probit same as Galarrag
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Iteration 7: log pseudolikelihood =
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-----------------------------------
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. . probit emp antidep mtx wageinc
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ho = 0.2 log likelihood = -681.4270
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ab79 | -.2830833 .3452789 -0.82 0.4
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-----------------------------------
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. predict ohat, resid . . probit em
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y99 | -.1676129 .4540083 -0.37 0.71
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ab02*| -.0132912 .01201 -1.11 0.269
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. gen numerator=normdenxb*[ad_dummy
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Iteration 2: log likelihood = -306.
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. . probit emp antidep_last5yrs mtx
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note: 0 failures and 14 successes c
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y = Pr(emp) (predict) = .64904538 -
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Probit yr 2003 - Table 9: . use /ak
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higher | .0160771 .2154333 0.07 0.9
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ieland1*| .2476048 .11071 2.24 0.02
Page 143 and 144:
Random-effects tobit regression Num
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