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

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Table 1 Sample size – by number of individuals and observations Individuals Observations Women Men Women Men Total sample 1913 1202 711 5398 3508 1890 <strong>An</strong>tidepressant users 251 185 66 468 350 118 When looking at the number of individuals, almost 63 % of the total sample is women, and when confined to the antidepressant users this figure is close to 74 %. Taking a look at the number of observations, the percentages are even larger. 65 % of the total sample is women and almost 75 % of the antidepressant users are women. The separation between individuals and observations is made here just to give an impression of how many individuals the sample population is comprised of. In the analysis the sample population is narrowed down to women due to a number of reasons. First of all, women behave different in the labour market than do men, since they have a lower rate of labour force participation and work fewer hours than men. 56 Because women have more freedom of choice regarding employment, according to Ruhm (1992) this causes their labour supply decision to be more heavily influenced by bad health compared to men. If both men and women were to be part of the sample population, the analysis would best be performed separately due to these dissimilarities between the sexes. Second, as seen from table1 65 % of the sample population are women, so not much data is lost by confining to women. <strong>An</strong>d finally women in general are also more exposed to depression than men, which make the results more relevant for policy-making. Hereafter, whenever the sample population is mentioned, only the observations on women are meant. The baseline sample population consists of 3508 observations, however in certain estimations the number of observations used will be smaller, but this will be explicitly mentioned. 4.2 Construction of variables Once the sample population is selected from the medicine dataset as described above, additional information on these individuals is gathered, by finding the selected individuals in the register dataset and extracting the socio-economic data on them. Table A in appendix 2 gives a description of all the variables. Some of the variables that were not in the dataset originally have been 56 Ruhm (1992) 33
constructed, and will be explained in sections 4.2.1-4.2.3. These sections will also describe the dependent variable and some of the independent variables in more detail. 4.2.1 The employment indicator The dependent variable in the present thesis is a variable that measures the individual’s participation in the labour market. The original register dataset contained a variable indicating the individual’s primary working position. The variable is used to construct three dummies indicating whether the person is employed, unemployed or outside the labour market. The individuals that are employed, (emp) include (among others) wage-earners, managers and people who are self-employed. The dummy of unemployed (unemp) is defined by people who are out of a job and either receiving unemployment benefits, taking part in an active labour market programme or receiving cash benefits from the state. The final dummy, which is dubbed ‘outside the labour market’ (olm), comprises all the remaining categories. This dummy includes very diverse categories, such as students, individuals on leave and people who are granted early retirement due to their illness. Since the dependent variable is a binary employment indicator, it determines that the analysis is best performed with discrete response models. <strong>An</strong>other measure of employment is the variable daysemp that reports the days in employment per year. Compared to the dummies created from the variable primary working position that are discrete variables, this measure approximates a continuous variable. The problem is that it is missing for about half of the observations. It turns out though, that it is missing mainly for the individuals who are outside the labour force, and therefore it is still interesting to perform the analysis for all the individuals who are in the labour force. 4.2.2 The indicator of antidepressant use The variable of main interest in this analysis is the use of antidepressants. The whole purpose is to find the effect of antidepressant use on employment. As mentioned in section 4.1 the individual’s yearly amount of antidepressants bought is summarized in the variable antidep. N06A defines the subgroup of psychoactive drugs that is used for treating depression. Consequently the part of the sample population taking antidepressants are defined by all the individuals that buy at least one of the drugs contained in the subgroup N06A. For the purpose of the analysis a dummy variable is constructed. The variable ad_dummy, indicating use of antidepressants, takes on the value of 1 when antidep is positive and 0 otherwise. 34
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: Consequently I am dealing with a se
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 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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