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

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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 0.0001 -0.0002 0.0001 -0.0008 0.0008 mtx 0.0002 0.0003 0.0001 0.0001 -0.0005 0.0007 wageinc 0.0000*** 0.0000 0.0000*** 0.0000 0.0000*** 0.0000 age -0.0143 0.0151 -0.0010 0.0073 0.0390 0.0586 ab02† 0.1385* 0.0748 -0.0657 0.0854 -0.2856 0.4621 ab36† -0.0303 0.1119 -0.1270** 0.0600 -0.6165* 0.3589 ab79† 0.0698 0.0754 -0.0263 0.0512 -0.2731 0.3456 ab1014† 0.0316 0.0737 -0.0983** 0.0425 -0.5890** 0.2515 single† -0.0185 0.0651 -0.1267*** 0.0325 -0.7602*** 0.2074 ieland1† 0.1257 0.1661 0.2355*** 0.0709 0.8201 0.8636 ieland2† 0.1795** 0.0868 0.2726*** 0.0682 1.4466 0.9496 short† 0.0544 0.0622 0.0759*** 0.0282 0.7106*** 0.2087 higher† 0.0047 0.0646 -0.0154 0.0504 -0.0657 0.2854 agesq 0.0001 0.0002 -0.0001 0.0001 -0.0013* 0.0007 useofmedicin 0.0000 0.0000 -0.0001*** 0.0000 -0.0005*** 0.0001 u -0.0320 0.0209 -0.0203** 0.0095 -0.0605 0.0370 No. of obs. Pseudo R 2 580 0.7211 3508 0.6497 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 % - ***. 3508 0.5876 As can be seen from table 6 the variable of interest, antidep, has very small marginal effects in all estimation methods. None of these marginal effects are significant, however it should be mentioned that when estimated with a pooled probit, the marginal effect on the dosage of antidepressants is significant at 11 %. As in section 6.1, a number of the control variables become significant in the pooled probit estimation and the random effects probit estimation compared to the probit estimation on a single year. Also these control variables have a larger marginal effect compared to the dosage of antidepressants. Some variables are very significant both in column 2 and 3. For example the effect of having children in the age group 3-6 and 10-14, which has a negative effect on the probability of employment. Also being single has a negative effect for the sample population of female MTX users that gets as large as 76.02 percentage points in the random effects probit 57
estimation. <strong>An</strong>d again having a short education compared to the reference category of a basic education has a positive effect on employment, that amounts to 71.06 percentage points in the random effects probit estimation. The pseudo R 2 in table 6 is the measure of goodness of fit that was reported with the probit estimations and is displayed here for informative reasons. These are virtually the same as the ones reported in table 3 for the estimations with a binary explanatory variable. This is not really surprising, since the included explanatory variable in table 6 is a different measure of the same variable that is applied in table 3. Furthermore the same control variables are applied and the models are estimated on unchanged sample sizes. Likewise to section 6.1 a test of endogeneity is performed. Because the explanatory variable in this case is continuous, the test is carried out 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 the coefficient on the saved residuals cannot be rejected to be equal to zero, which means that exogeneity cannot be rejected. In the random effects probit model, it is not possible to save the residuals from the auxiliary regression and therefore the generalised residual is calculated as described in chapter 5 and included in the equation of interest. A likelihood-ratio test is performed and with a χ 2 (1)-value of 0.92[0.3367] the null hypothesis of exogeneity can once again not be rejected. The test is included in the appendices. The random effects probit estimation is tested for sample selection as done in section 6.1, and with a coefficient of the lag of the selection indicator of -0.0949 and a standard error of 0.1627 the null hypothesis of no sample selection bias cannot be rejected. 71 That is, the sample selection does not cause the results to be biased. The estimate of ρ, the magnitude of the unobserved effect, for the random effects estimation displayed in column 3 in table 6 is equal to 0.8080 with a standard error of 0.0246. A likelihoodratio test testing whether ρ is equal to 0 is rejected at a significance level of 1 %. It can therefore once again not be rejected that there is an unobserved effect present in the data. 6.3 Labour force participants All the previous estimations do not distinguish between labour market statuses. That is, the probability of being employed is estimated for all women, regardless of whether they are in the labour force or not. It can be argued that it is not that relevant to try and estimate the effect of 71 The p-value is 0.560 58
Page 1 and 2:
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
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: probit estimation, respectively. Ha
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:
-----------------------------------
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. . 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
Page 143 and 144:
Random-effects tobit regression Num
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