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

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Appendices Appendix 1 <strong>An</strong> example of a detailed classification – taken from www.whocc.no/atcddd/atcsystem.html A Alimentary tract and metabolism (1 st level, anatomical main group) A10 Drugs used in diabetes (2 nd level, therapeutic subgroup) A10B Oral blood glucose lowering drugs (3 rd level, pharmacological subgroup) A10BA Biguanides (4 th level, chemical subgroup) A10BA02 Metformin (5 th level, chemical substance) Appendix 2 Table A Desciption of the employed variables Variable Description mtx Sum of number of daily defined dosages methotrexate bought per year antidep Sum of number of daily defined dosages antidepressants bought per year wageinc The total taxable income stated in the tax information sheet age Age of the observed individual agesq Age squared sex Dummy variable for sex, 1 if male and 0 if female ad_dummy Dummy=1 if the individual uses antidepressants, 0 otherwise ab02 Dummy=1 if there are children in the age of 0-2 in the family, 0 otherwise ab36 Dummy=1 if there are children in the age of 3-6 in the family, 0 otherwise ab79 Dummy=1 if there are children in the age of 7-9 in the family, 0 otherwise ab1014 Dummy=1 if there are children in the age of 10-14 in the family, 0 otherwise ieland1 Dummy=1 if from Denmark, Greenland & the Faroe Islands, 0 otherwise ieland2 Dummy=1 if from more developed countries, 0 otherwise ieland3 Dummy=1 if from less developed countries, 0 otherwise basic Dummy=1 if basic education, upper-secondary or unknown, 0 otherwise short Dummy=1 if voccational or shorter higher education, 0 otherwise higher Dummy=1 if middle-range, BA, higher education or researcher, 0 otherwise emp Dummy=1 if employed, 0 otherwise unemp Dummy=1 if unemployed, 0 otherwise olm Dummy=1 if outside the labour market, 0 otherwise age1 Dummy=1 if age between 16-25, 0 otherwise age2 Dummy=1 if age between 26-35, 0 otherwise age3 Dummy=1 if age between 36-45, 0 otherwise age4 Dummy=1 if age between 46-55, 0 otherwise age5 Dummy=1 if age between 56-64, 0 otherwise wage1 Dummy=1 if zero wage income, 0 otherwise 73
wage2 Dummy=1 if wage income up to 100.000 kr, 0 otherwise wage3 Dummy=1 if wage income between 101.000-200.000 kr, 0 otherwise wage4 Dummy=1 if wage income between 201.000-300.000 kr, 0 otherwise wage5 Dummy=1 if wage income above 300.000 kr, 0 otherwise mtx1 Dummy=1 if dosage of MTX is up to 100 DDD per year, 0 otherwise mtx2 Dummy=1 if dosage of MTX is between 101-200 DDD per year, 0 otherwise mtx3 Dummy=1 if dosage of MTX is between 201-300 DDD per year, 0 otherwise mtx4 Dummy=1 if dosage of MTX is 301DDD and above per year, 0 otherwise antidep1 Dummy=1 if dosage of antidepressants is up to 100 DDD per year, 0 otherwise antidep2 Dummy=1 if dosage of antidepressants is between 101-200 DDD per year, 0 otherwise antidep3 Dummy=1 if dosage of antidepressants is between 201-300 DDD per year , 0 otherwise antidep4 Dummy=1 if dosage of antidepressants is 301 DDD and above per year, 0 otherwise daysemp Variable indicating the number of days worked per year useof medicin Sum of number of daily defined dosages of medicin, excluding MTX and antidepressants, bought per year m0 Dummy=1 if the individual has no use of other medicine apart from MTX and antidepressants per year, 0 otherwise m1 Dummy=1 if the individual uses up to 200 DDD of other medicine apart from MTX and antidepressants per year, 0 otherwise m2 Dummy=1 if the individual uses between 201-400 DDD of other medicine apart from MTX and antidepressants per year, 0 otherwise m3 Dummy=1 if the individual uses between 401-600 DDD of other medicine apart from MTX and antidepressants per year, 0 otherwise m4 Dummy=1 if the individual uses between 601-800 DDD of other medicine apart from MTX and antidepressants per year, 0 otherwise m5 Dummy=1 if the individual uses between 801-1000 DDD of other medicine apart from MTX and antidepressants per year, 0 otherwise m6 Dummy=1 if the individual uses between 1001-1500 DDD of other medicine apart from MTX and antidepressants per year, 0 otherwise m7 Dummy=1 if the individual uses between 1501-2000 DDD of other medicine apart from MTX and antidepressants per year, 0 otherwise m8 Dummy=1 if the individual uses 2001 DDD and above of other medicine apart from MTX and antidepressants per year, 0 otherwise kbh Dummy=1 if the individual lives in Copenhagen municipality, 0 otherwise fre Dummy=1 if the individual lives in Frederiksberg municipality, 0 otherwise kbhamt Dummy=1 if the individual lives in Copenhagen county, 0 otherwise freamt Dummy=1 if the individual lives in Frederiksborg county, 0 otherwise ros Dummy=1 if the individual lives in Roskilde county, 0 otherwise vestsj Dummy=1 if the individual lives in Vestsjællands county, 0 otherwise storstr Dummy=1 if the individual lives in Storstrøms county, 0 otherwise born Dummy=1 if the individual lives on Bornholm, 0 otherwise fyn Dummy=1 if the individual lives in Fyn county, 0 otherwise sonjyl Dummy=1 if the individual lives in Sønderjyllands county, 0 otherwise ribe Dummy=1 if the individual lives in Ribe county, 0 otherwise vejle Dummy=1 if the individual lives in Vejle county, 0 otherwise ringk Dummy=1 if the individual lives in Ringkøbing county, 0 otherwise arhus Dummy=1 if the individual lives in Århus county, 0 otherwise viborg Dummy=1 if the individual lives in Viborg county, 0 otherwise njyl Dummy=1 if the individual lives in Nordjylland county, 0 otherwise u The unemployment rate in the county that the individual lives in antidep_last5yrs Duration of antidepressant use the last five years cons_antidep Dosages of antidepressant use the last five years 74
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
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consisting of lack of data on the d
Page 9 and 10:
experience symptoms of depression o
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(SSRI’s), also called the new ant
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heumatologists. But since descripti
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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
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One of the factors greatly influenc
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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
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: Zimmerman, F. and W. Katon. 2005. S
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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