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st: Question: Ordered logit- suspicious odds ratio
From
Stefanie Kneer <[email protected]>
To
[email protected]
Subject
st: Question: Ordered logit- suspicious odds ratio
Date
Mon, 16 Apr 2012 12:12:59 +0100
Dear Statalist
I am encountering running an ordered logit regression with the
following panel data ranging from 1992-2010.
describe p11101 regionunempl unempl_empl interaction
storage display value
variable name type format label variable label
p11101 byte %8.0g life satisfaction -discrete from 0-10
regionunempl float %9.0g Region unempl. %
unempl_empl float %9.0g dummy for being 1=unemployed and 0=employed
interaction float %9.0g regionalunemployment rate*unemployment dummy
I am trying to identify the link between unemployment and happiness
and in particular whether there exists a social norm effect. P11101 is
thus my dependent variable and as it is discrete I was planning to run
an ordered logit but I am encountering difficulties when trying to
calculate the odds ratio. As can be seen the social norm effect, and
thus whether you feel more comfortable when other people around you
are not working, too is massively big (27). That looks kind of
suspicious to me. Could you give me an advice on why this is the case
and what I should do about it?
. ologit p11101 regionunempl unempl interaction
Iteration 0: log likelihood = -107509.12
Iteration 1: log likelihood = -105541.28
Iteration 2: log likelihood = -105528.36
Iteration 3: log likelihood = -105528.35
Ordered logistic regression Number of obs = 56383
LR chi2(3) = 3961.52
Prob > chi2 = 0.0000
Log likelihood = -105528.35 Pseudo R2 = 0.0184
p11101 Coef. Std. Err. z P>z [95% Conf. Interval]
regionunempl -2.432835 .2575799 -9.44 0.000 -2.937682
-1.927988
unempl_empl -1.553812 .0582462 -26.68 0.000 -1.667972
-1.439651
interaction 3.328277 .5334905 6.24 0.000 2.282655 4.3739
/cut1 -5.818645 .0631414 -5.9424 -5.69489
/cut2 -5.117788 .0485202 -5.212886 -5.02269
/cut3 -4.1215 .0365478 -4.193132 -4.049867
/cut4 -3.197854 .03115 -3.258907 -3.136801
/cut5 -2.555403 .029175 -2.612585 -2.498221
/cut6 -1.412781 .0273739 -1.466432 -1.359129
/cut7 -.7113589 .0268356 -.7639557 -.658762
/cut8 .3526261 .0266994 .3002962 .4049559
/cut9 2.181154 .0297466 2.122851 2.239456
/cut10 3.81373 .0426953 3.730049 3.897411
ologit p11101 regionunempl unempl interaction,or
Iteration 0: log likelihood = -107509.12
Iteration 1: log likelihood = -105541.28
Iteration 2: log likelihood = -105528.36
Iteration 3: log likelihood = -105528.35
Ordered logistic regression Number of obs = 56383
LR chi2(3) = 3961.52
Prob > chi2 = 0.0000
Log likelihood = -105528.35 Pseudo R2 = 0.0184
p11101 Odds Ratio Std. Err. z P>z [95% Conf. Interval]
regionunempl .0877876 .0226123 -9.44 0.000 .0529884 .1454406
unempl_empl .2114405 .0123156 -26.68 0.000 .1886292 .2370104
interaction 27.89025 14.87919 6.24 0.000 9.802672 79.35247
/cut1 -5.818645 .0631414 -5.9424 -5.69489
/cut2 -5.117788 .0485202 -5.212886 -5.02269
/cut3 -4.1215 .0365478 -4.193132 -4.049867
/cut4 -3.197854 .03115 -3.258907 -3.136801
/cut5 -2.555403 .029175 -2.612585 -2.498221
/cut6 -1.412781 .0273739 -1.466432 -1.359129
/cut7 -.7113589 .0268356 -.7639557 -.658762
/cut8 .3526261 .0266994 .3002962 .4049559
/cut9 2.181154 .0297466 2.122851 2.239456
/cut10 3.81373 .0426953 3.730049 3.897411
Many thanks,
Stefanie
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