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

More documents

Recommendations

Info

antidepressant use on employment if the woman is not in the labour force to begin with. Galarraga et.al (2006) also mention this as an important drawback to their analysis. Again the current dataset allows me to make this distinction. This is done in the following. It should be noted though, that this distinction does have consequences for the analysis. More than half of the sample population are outside the labour force, and excluding these women from the analysis reduces the sample substantially, which also may influence the results. Table 7 shows the marginal effects from the estimations for labour participants only. Table 7 Marginal effects – estimations for labour force participants only Probit yr 2003 (1) Pooled probit (2) Random effects probit (3) variable dy/dx s.e. dy/dx s.e. dy/dx s.e. antidep 0.0000 0.0000 0.0000 0.0000 -0.0013 0.0012 mtx 0.0000 0.0000 0.0000 0.0000 0.0003 0.0017 wageinc 0.0000 0.0000 0.0000*** 0.0000 0.0000*** 0.0000 age -0.0017 0.0015 -0.0034*** 0.0013 -0.3615*** 0.1307 ab02† 0.0025 0.0027 -0.0096 0.0102 -0.4999 0.7843 ab36† 0.0016 0.0019 0.0039 0.0035 0.4617 0.6016 ab79† 0.0015 0.0019 0.0015 0.0039 -0.1985 0.6540 ab1014† 0.0021 0.0024 -0.0067 0.0050 -0.1619 0.4460 single† -0.0001 0.0024 -0.0182** 0.0077 -1.8257*** 0.5586 ieland1† 0.0185 0.0255 0.0617* 0.0352 2.5429 1.5767 ieland2† 0.0101*** 0.0038 3.7104** 1.8724 short† 0.0012 0.0026 0.0047 0.0035 0.8885* 0.5084 higher† 0.0044 0.0044 0.0094** 0.0042 2.0621* 1.2108 agesq 0.0000 0.0000 0.0000*** 0.0000 0.0047*** 0.0017 useofmedicin 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 u -0.0008 0.0014 -0.0007 0.0013 0.0459 0.0946 No. of obs. Pseudo R 2 312 0.5114 1666 0.4357 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 % - ***. 1666 0.4257 59
As can be seen from table 7 the extension to looking at labour force participants only does not alter results of the variable of interest dramatically. Once again the marginal effects of the use of antidepressants are miniscule and insignificant. The general picture from section 6.1 and 6.2 emerges once more. A number of the control variables become significant when comparing column 2 and 3 to column 1. Also on the whole the marginal effects become numerically larger in the random effects probit estimation in column 3 compared to the probit and the pooled probit estimations in column 1 and 2. Like I mentioned above, this extension to the estimation does have consequences. Comparing the results from table 7 to table 6, the marginal effects of the probit and the pooled probit estimation are overall smaller when refining the analysis to the labour force participants only. On the other hand, this is not the case for the marginal effects from the random effects probit estimation. Also in columns 2 and 3, having children in the age groups 3-6 and 10-14 is no longer significant, and similarly the use of other medicine is no longer significant. These results might indicate that women with young children and a large consumption of other medicine are mostly not participating in the labour force and therefore the negative effect of these variables disappears when excluding the individuals not participating in the labour force. Furthermore, age and therefore also age squared, since it is a function of age, are significant in contrast to the results from table 6. <strong>An</strong>other thing that catches the eye is the pseudo R 2 from the probit estimations which is displayed in table 7. This measure of goodness of fit is clearly smaller in the current estimations. Exactly the same variables are included in the estimations, so this measure is likely to be influenced by the smaller sample sizes. It is also tested whether the explanatory variable is endogenous. Similarly to section 6.3 this is done by including the saved residuals of the auxiliary regression in the equation of interest in the probit on year 2003 and the pooled probit estimation. <strong>An</strong>d in the random effects probit model the generalised residual is calculated and included in the equation of interest. In all three estimations, the null hypothesis of exogeneity cannot be rejected. The tests are enclosed in the appendices. As before a test of sample selection is performed on the random effects probit estimation. The coefficient of the lag of the selection indicator is equal to -0.0044 with a standard error of 0.2994 and cannot be rejected to equal zero at a p-value of 0.988, which means that the null hypothesis of no sample selection bias cannot be rejected and therefore the sample selection existent in the dataset does not bias the results. Likewise the presence of an unobserved effect cannot be rejected, and ρ is estimated to 0.9139 with a standard error of 0.0365. 60
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 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: estimation. And ag
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
show all

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

Create successful ePaper yourself

Delete template?

Save as template?