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# st: Question: Ordered logit- suspicious odds ratio

 From Stefanie Kneer To statalist@hsphsun2.harvard.edu 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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