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

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Comparing the results from the estimations on the labour force participants only with the earlier results on the whole sample population from section 6.2 the overall findings are similar. The effect of antidepressants is minimal in all estimations and insignificant. Exogeneity cannot be rejected in all estimations and sample selection bias is rejected in both sections. The relative importance of the unobserved effect is somewhat bigger when performing the estimations on the labour force participants only. In contrast, as already mentioned above, the pseudo R 2 is noticeably smaller and also the marginal effects of the probit and the pooled probit estimation are overall smaller and less significant when refining the analysis to the labour force participants only. 6.4 Duration of antidepressant use the last five years As mentioned by Galarraga et.al (2006) one of the limitations to their analysis consists of the fact that, they do not take the length of time antidepressants are taken into account. The current data allows me to do just that. A new variable is created, summarizing the number of years that antidepressants are taken within the last five years. The estimations are performed on a reduced dataset covering the years 1999- 2003, since the inclusion of the new variable only makes sense if data is actually available for the last five years. Due to the reduced dataset and the results stated in the end of section 6.3, namely a reduced R 2 and smaller marginal effects, the present estimations are not performed on the labour force participants only, but on the full sample, as was done in sections 6.1 and 6.2. Also this allows for, in the pooled probit estimations, for individuals to move over time from not participating in the labour to participating in the labour force. Table 8 shows the marginal effects of these estimations. As it appears, the model where the duration measure is included is only estimated with a cross-section probit and a pooled probit. This is due to the fact that a random effects probit estimation already takes into account the development over time, which is one of the advantages of panel data methods. The pooled probit does not do this, since it assumes that all observations are independent. By including of the duration measure, the development over time is taken into account. Table 8 Marginal effects – duration of antidepressant use the last five years as an explanatory variable Probit yr 2003 (1) Pooled probit (2) variable dy/dx s.e. dy/dx s.e. antidep_last5yrs 0.0016 0.0245 -0.0178 0.0191 61
mtx 0.0002 0.0003 0.0001 0.0002 wageinc 0.0000*** 0.0000 0.0000*** 0.0000 age -0.0143 0.0152 -0.0045 0.0090 ab02† 0.1384* 0.0748 0.0028 0.0811 ab36† -0.0301 0.1121 -0.0759 0.0697 ab79† 0.0701 0.0759 -0.0642 0.0597 ab1014† 0.0316 0.0737 -0.0708 0.0515 single† -0.0186 0.0653 -0.1131*** 0.0392 ieland1† 0.1257 0.1661 0.1697* 0.0900 ieland2† 0.1797** 0.0866 0.2234*** 0.0742 short† 0.0545 0.0617 0.0605* 0.0325 higher† 0.0047 0.0645 -0.1147* 0.0667 agesq 0.0001 0.0002 0.0000 0.0001 useofmedicin 0.0000 0.0000 -0.0001*** 0.0000 u -0.0319 0.0207 -0.0163 0.0132 No. of obs. Pseudo R 2 580 0.7211 Note: † For dummy variables dy/dx is for discrete change from 0 to 1 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 % - ***. 2364 0.6668 The column 1 shows the marginal effects of a probit estimation on the observations from year 2003. The variable of main interest is antidep_last5yrs, which measures the number of years antidepressants are taken out of the last 5 years. The marginal effect of this variable is 0.16 percentage points, but the result is not significant. Column 2 shows the marginal effects of a pooled probit estimation for the full sample. The marginal effect of antidep_last5yrs is -1.78 percentage points, which is contradictory to the marginal effect from column 1. As found earlier, the pooled probit estimation produces more significant marginal effects on the control variables than the probit on year 2003. The pseudo R 2 is high, but this is likely to be caused by the relatively large number of observations, when comparing to the previous estimations in section 6.3. A test of endogeneity was performed using the Rivers-Vuong method. Including the saved residuals from the auxiliary regression into the equation of interest was not possible due to collinearity. For that reason the test was performed with the generalised residuals, and when applying this method, the null hypothesis of exogeneity could not be rejected under both estimation methods. 62
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Estimating the Impact of An
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Abstract This thesis aims at analys
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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
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
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: As can be seen from table 7 the ext
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
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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
Page 127 and 128:
. gen numerator=normdenxb*[ad_dummy
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Iteration 2: log likelihood = -306.
Page 131 and 132:
. . 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
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Random-effects tobit regression Num
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