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

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set b.nyti2001; KEEP AAR ALDER ANC02 ANC36 ANC79 ANC1014 ANC1517 ANSAAR ANSDAGE ARLEDGR CSTATUS CTYPE HDTID2 HFFSP IELAND IETYPE KOEN KOMMK PSTILL2 SLON UDTRNR; proc sort data=aar2001; by udtrnr aar; data aar2002; set b.nyti2002; KEEP AAR ALDER ANC02 ANC36 ANC79 ANC1014 ANC1517 ANSAAR ANSDAGE ARLEDGR CSTATUS CTYPE HDTID2 HFFSP IELAND IETYPE KOEN KOMMK PSTILL2 SLON UDTRNR; proc sort data=aar2002; by udtrnr aar; data aar2003; set b.nyti2003; KEEP AAR ALDER ANC02 ANC36 ANC79 ANC1014 ANC1517 ANSAAR ANSDAGE ARLEDGR CSTATUS CTYPE HDTID2 HFFSP IELAND IETYPE KOEN KOMMK PSTILL2 SLON UDTRNR; proc sort data=aar2003; by udtrnr aar; data samle; merge aar1995 aar1996 aar1997 aar1998 aar1999 aar2000 aar2001 aar2002 aar2003; by udtrnr aar; data ud.register; set samle; /*the medicine database lmdb(MTX antidepressives) are merged with registerdata*/ data medicin; set b.lmdb; yr=0+aar; proc sort data=medicin; by udtrnr yr; data test1; 81
set b.register; yr=0+aar; DROP aar; proc sort data=test1; by udtrnr yr; data collect; merge medicin (in=a) test1; by udtrnr yr; if a; data ud.data1; set collect; by udtrnr yr; rename SLON=wageinc; rename ANSDAGE=daysemp; /*use of antidepressants*/ label antidep='Use of antidepressants'; /*use of methotrexate*/ label MTX='Use of methotrexate'; /*wage income*/ label wageinc='Wage income'; /*employmentdays*/ label daysemp='Days of employment'; data test2; set b.data1; /*List of variables is created*/ Data variable; set b.data1; /*age is transformed to a numeric variable*/ age=0+alder; label age='Age'; /*dummys for sex, 1=male, 0=female*/ if koen=1 then sex=1; if koen=2 then sex=0; /*missings in antidep are put equal to 0*/ if antidep='.' then antidep=0; /*dummys for use of antidepressants*/ if antidep=0 then AD_dummy=0; if antidep NE 0 then AD_dummy=1; label 82
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
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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 .
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employers more reluctant to hire ne
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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
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channel runs through enhanced compl
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3.2 The effect of antidepressants o
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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 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: KEEP AAR ALDER ANC02 ANC36 ANC79 AN
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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