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

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therefore she will change her composition of goods purchased. Similarly in labour supply theory, a wage increase (or decrease) can influence the labour supply both through the substitution effect and the income effect. The substitution effect has so far included the good’s own substitution effect, which takes into account the effect on the demand curve of the good, when the price changes. But the substitution terms can be expanded to the cross-substitution effect. The cross-substitution effect takes into account the effect on the demand curve of the good, when the price of another good changes. Applied to labour supply theory this means that a wage change affects the supply of labour, but also the demand of goods. Although simplistic and seemingly far from reality the static model of labour supply gives a good impression of the choice an individual faces when deciding upon participation in the labour market. Correspondingly to consumer demand theory, the individual maximizes his utility subject to some restraints. The supply of labour can also be modelled by a dynamic model. I will not go deeper into dynamic models here, but it can shortly be mentioned that dynamic models look at the labour supply decision over several time periods. The influence of decisions taken in this period on the labour supply in the next period is taken into account, and also it is possible to accumulate wealth over the time periods. 28 In this setting it is assumed that there are no demand side constraints and the individuals choose their hours of labour supply according to their utility maximization, and therefore it is supposed that all unemployment is voluntary. This means that there is no distinction between voluntary nonparticipation and involuntary unemployment. 29 As long as this assumption is kept, the probability of labour force participation and the probability of being employed are the same thing since all unemployment is assumed to be voluntary. As Haan and Uhlendorff (2007) mention this assumption seems far from reality. It is possible to extend the model and distinguish between voluntary and involuntary unemployment. Other factors are recognized to play a part in the probability of employment, or for that matter in the probability of involuntary unemployment, which occurs when the individual would have a higher utility of he was working. These factors comprise of demand side constraints and individual characteristics. 30 With this expansion of the model, the individual is faced with the choice among employment, non-participation and job seeking. Demand side restriction consist of for example employment protection laws that make 28 Killingsworth and Heckman (1986) 29 Haan and Uhlendorff (2007) 30 Haan and Uhlendorff (2007) 13
employers more reluctant to hire new employees, since they know it is difficult to dismiss them again in an economic downturn. 31 <strong>An</strong>other example of a demand side restriction is the unemployment rate. If the unemployment rate is very high, this reduces the probability of being employed. Characteristics specific to each individual consist of for instance his age, number of children, education and health. How exactly health relates to the probability of being employed is explained in greater detail in the next section. 2.2 The influence of health problems on labour force participation This thesis looks at a marginal group in the labour market, namely people who are physically and presumably mentally ill. As to be expected, health problems have an influence on their rate of participation on the labour market. In what follows, these effects are reflected upon. According to Ruhm (1992) problems of bad health are probable to increase the disutility of working, and at the same time decrease the marginal productivity. He analyzes the effects of physical and mental health on female labour supply and his cross-tabular analysis insinuates that the state of health has at least some significance in the probability of employment and participation in the labour force. His analyses are based on the use of medicine taken to treat particular diseases, and it turns out that there is a negative relationship between bad health and participation in the labour force. Comparing physical and mental illness, the negative relationship is even more noticeable in the case of medicine used for mental illness. In addition, Ruhm (1992) looks at the economywide effects by comparing demographic and health effects regarding aggregate participation and employment rates. He finds that demographic and economic factors rather than health factors play a role in the decision of labour supply. The analysis is taken a step further by comparing the effects of physical and mental health on labour force participation and employment. Ruhm (1992) finds that mental health explains a considerable part of the decline in labour supply caused by health-related problems compared to the attention paid to mental illness in the analysis. With regard to the effect on employment, the importance of mental health is even larger relatively than physical health. Restricting his analysis to women of the age 50 and above, Ruhm (1992) finds that health variables account for even more of the decline in both labour force participation and employment. With ageing the role of physical health becomes relatively more important though. To conclude, Ruhm (1992) observes that health problems play an important part in the labour supply decision at the individual level, but at the economywide level health problems explain less than half 31 Cahuc and Zylberberg (2004) 14
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: H > 0 or a corner solution, H = 0 .
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 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
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wage2 Dummy=1 if wage income up to
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data medold; set b.lmdb_pct10_2003;
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if Last.aar then output; /*output o
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KEEP AAR ALDER ANC02 ANC36 ANC79 AN
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set b.register; yr=0+aar; DROP aar;
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HIGHER='Middle-range and higher edu
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dayemp4='Between 8 months and 364 d
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data collect; merge test1 (in=a) te
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data test1; set b.finaldata1; proc
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antidep_lastyr=lag(AD_dummy); if (u
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Estimation results Probit yr 2003 -
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Probit estimates Number of obs = 58
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single | -.3439072 .0893125 -3.85 0
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(Assumption: . nested in A) Prob >
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. /*with proxy, generalised residua
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. . /*Panel probit same as Galarrag
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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
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. gen numerator=normdenxb*[ad_dummy
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Iteration 2: log likelihood = -306.
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. . 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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