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

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might cause the explanatory variable to be endogenous and therefore bias the results. As mentioned by Galarraga et al. (2006) this can be taken into account by applying IV methods, but unfortunately the current dataset does not provide an evident instrument for the use of antidepressants. Keeping this in mind, the following results should be interpreted cautiously. Second, as discussed in chapter 4, I am dealing with an unbalanced panel caused by sample selection, which under certain circumstances can cause problems. The different methods all have weaknesses when applied to the current dataset, but performing them serves as a sensitivity analysis of the results. The methods applied in the following are those presented in chapter 5. The probit estimator on a single year does not exploit all the observations available. The pooled probit estimator exploits all observations, but does not take into account the recurrence of the individuals. Furthermore, since I am dealing with an unbalanced panel, the individuals that are observed every year dominate the analysis compared to the individuals that do not appear every year. Finally, a random effects probit estimator exploits the panel nature of the data, but these results might be biased due to the fact that the panel is unbalanced. In the empirical analysis demand side constraints are incorporated by including the local unemployment rate. As mentioned in chapter 2 the probability of labour force participation and the probability of employment are not equal when demand side constraints and individual characteristics are incorporated into the model, since this introduces the notion of involuntary unemployment. In the empirical estimations, however, the focus is first on the probability of being employed as opposed to not being employed, not distinguishing between voluntary and involuntary unemployment. The estimations are then extended to labour force participants only, that is, the focus is on the probability of being employed as opposed to being involuntary unemployed. When this is the case, it is explicitly mentioned. 6.1 Binary indicator of antidepressant use The first approach taken is to estimate the effect of antidepressants on employment, where the use of antidepressants is indicated by a dummy variable. This resembles the estimations done by Galarraga et al. (2006) although a number of the explanatory variables differ. Galarraga et.al (2006) include some indicators of the disease development that are not existent in the present dataset. Furthermore, some of the included explanatory variables cannot be immediately compared to those used by Galarraga et al. (2006). The results for the estimations using a binary indicator of 51
antidepressant use are displayed in table 3. The variable of main interest, which is the binary indicator of antidepressant use, is in bold. Table 3 Probit results - a binary antidepressant indicator Probit yr 2003 (1) Pooled probit (2) Random effects probit (3) emp Coefficient robust s.e. coefficient robust s.e. coefficient s.e. ad_dummy -0.0208 0.2951 -0.1743 0.1234 -0.3334 0.2447 mtx 0.0006 0.0009 0.0001 0.0004 -0.0005 0.0007 wageinc 0.0000*** 0.0000 0.0000*** 0.0000 0.0000*** 0.0000 age -0.0473 0.0506 -0.0020 0.0186 0.0400 0.0584 ab02 0.5849 0.4139 -0.1684 0.2142 -0.2722 0.4634 ab36 -0.0996 0.3509 -0.3193** 0.1504 -0.6195* 0.3603 ab79 0.2558 0.3007 -0.0745 0.1292 -0.2887 0.3452 ab1014 0.1063 0.2600 -0.2505** 0.1069 -0.5838** 0.2515 single -0.0598 0.2110 -0.3204*** 0.0821 -0.7539*** 0.2070 ieland1 0.3737 0.4497 0.6006*** 0.1921 0.8327 0.8544 ieland2 0.8866 0.7041 0.8195*** 0.2738 1.4750 0.9423 short 0.1817 0.2080 0.1943*** 0.0729 0.6950*** 0.2112 higher 0.0132 0.2149 -0.0402 0.1278 -0.0772 0.2841 agesq 0.0003 0.0006 -0.0003 0.0002 -0.0013** 0.0006 useofmedicin -0.0001 0.0001 -0.0003*** 0.0001 -0.0005*** 0.0001 u -0.1068 0.0669 -0.0510** 0.0241 -0.0606 0.0371 cons 0.3191 1.2735 -0.4027 0.5491 -1.4813 1.5732 No. of obs. Pseudo R 2 580 0.7211 3508 0.6497 Note: Time dummies are included in the pooled regression. The reference categories are less developed county of origin, and basic level of education. The significance levels are indicated as follows: 10 % - *, 5 % - **, 1 % - ***. 3508 0.5878 Column 1 shows the results from a probit estimation on the observations from year 2003. The coefficient on the dummy indicating antidepressant use is negative, but not significant. Column 2 shows the pooled probit estimation for the full sample. Time dummies are not displayed, but are included in the estimation, because this can catch aggregate time effects and it is a good idea to do this when dealing with a relatively large sample size and a small number of time periods. 68 Also the 68 Wooldridge (2002) 52
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 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: The log of equation [5.7] gives the
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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