I am attempting to study the gender gap in the life-course evolution of political interest using growth curve modelling (Stata 16.0). The problem is that I cannot reconcile the results of the regression (melogit) and the marginals (margins). In detail, the coefficient of sex##age is an odds ratio > 1, which suggests a steeper growth over time for women, whilst the marginals (margins) show that men have a steeper growth. I suspect this has to do with the fact that sex##age is computed at specific values of other control variables. Any ideas to explain this apparent contradiction? Thank you, Nic
Code:
melogit interest i.sex##c.age c.isced_parents##i.sex##c.age c.gender_attitudes##i.sex##c.age ib3.casmin##i.sex##c.age i.year_1 || pidp: age, diff or cov(unstr)
melogit results
Code:
interest | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------------+----------------------------------------------------------------
sex |
Female | .0797479 .1236744 -1.63 0.103 .0038165 1.666392
age | .9183841 .1291689 -0.61 0.545 .6971152 1.209885
|
sex#c.age |
Female | 1.17762 .2371548 0.81 0.417 .7935718 1.747529
|
isced_parents | 1.10222 .0494202 2.17 0.030 1.009493 1.203466
|
sex#c.isced_parents |
Female | 1.169129 .074481 2.45 0.014 1.031895 1.324614
|
c.isced_parents#c.age | 1.006333 .0058582 1.08 0.278 .994916 1.01788
|
sex#c.isced_parents#c.age |
Female | .9871253 .0081886 -1.56 0.118 .9712057 1.003306
|
gender_attitudes | .7252891 .1985224 -1.17 0.241 .424155 1.240217
|
sex#c.gender_attitudes |
Female | .7648504 .2973847 -0.69 0.491 .3569582 1.638837
|
c.gender_attitudes#c.age | 1.031944 .0343485 0.94 0.345 .9667716 1.101511
|
sex#c.gender_attitudes#c.age |
Female | .9962939 .0485382 -0.08 0.939 .9055616 1.096117
|
casmin |
Less than level 2 | .0421242 .0207223 -6.44 0.000 .0160619 .1104753
Level 2 | .1315831 .046803 -5.70 0.000 .0655292 .2642196
Level 3 (vocational) | .4611131 .2711349 -1.32 0.188 .1456465 1.459872
Higher education (vocational) | .2908482 .174043 -2.06 0.039 .0900138 .9397745
Higher education (general) | .7632587 .3877851 -0.53 0.595 .281972 2.066034
Studying level 2 | .7163233 .2019592 -1.18 0.237 .4122138 1.244789
|
casmin#sex |
Less than level 2#Female | 7.644757 5.476532 2.84 0.005 1.877509 31.12757
Level 2#Female | 3.890349 1.922813 2.75 0.006 1.47666 10.24936
Level 3 (vocational)#Female | 5.605182 4.573374 2.11 0.035 1.132605 27.73966
Higher education (vocational)#Female | 4.526671 3.648537 1.87 0.061 .9326163 21.97126
Higher education (general)#Female | 1.093604 .7399108 0.13 0.895 .2903715 4.118757
Studying level 2#Female | .903155 .3519893 -0.26 0.794 .420747 1.938669
|
casmin#c.age |
Less than level 2 | 1.077859 .0765448 1.06 0.291 .9378067 1.238827
Level 2 | 1.133095 .0662414 2.14 0.033 1.010426 1.270656
Level 3 (vocational) | 1.041306 .0986981 0.43 0.669 .8647666 1.253885
Higher education (vocational) | 1.021982 .0774086 0.29 0.774 .8809886 1.18554
Higher education (general) | 1.087357 .0731461 1.24 0.213 .9530424 1.240601
Studying level 2 | 1.287 .343928 0.94 0.345 .7622719 2.172937
|
casmin#sex#c.age |
Less than level 2#Female | .8207434 .085831 -1.89 0.059 .6686382 1.00745
Level 2#Female | .7887889 .0659503 -2.84 0.005 .6695641 .9292432
Level 3 (vocational)#Female | .7503382 .1027247 -2.10 0.036 .5737513 .9812743
Higher education (vocational)#Female | .8901057 .0908325 -1.14 0.254 .7287509 1.087186
Higher education (general)#Female | .9380943 .0843286 -0.71 0.477 .7865548 1.11883
Studying level 2#Female | 1.175099 .4388004 0.43 0.666 .5652268 2.443017
|
year_1 |
1995 | .9087041 .2070425 -0.42 0.674 .5814087 1.420245
1998 | 1.209628 .2963029 0.78 0.437 .7484225 1.955046
2001 | .9578809 .252838 -0.16 0.870 .5709948 1.606908
2004 | 1.029336 .2907509 0.10 0.918 .5917289 1.79057
2007 | .7295872 .2233028 -1.03 0.303 .4004537 1.329236
2010 | 1.015031 .3713182 0.04 0.967 .4955568 2.079053
2013 | .8386817 .3510156 -0.42 0.674 .3692679 1.904815
2016 | .9891315 .5252397 -0.02 0.984 .3493467 2.800602
2019 | 1.528287 1.167477 0.56 0.579 .3419492 6.830434
|
_cons | .388696 .433558 -0.85 0.397 .0436682 3.459828
--------------------------------------+----------------------------------------------------------------
pidp |
var(age)| .0437968 .0064901 .0327571 .0585571
var(_cons)| 8.008383 .7122384 6.727316 9.5334
--------------------------------------+----------------------------------------------------------------
pidp |
cov(age,_cons)| -.1669749 .0457278 -3.65 0.000 -.2565997 -.0773501
-------------------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline odds (conditional on zero random effects).
LR test vs. logistic model: chi2(3) = 3362.19 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.Code:
margins sex, at(age=(0(1)14))
Code:
Predictive margins Number of obs = 12,159
Model VCE : OIM
Expression : Marginal predicted mean, predict()
1._at : age = 0
2._at : age = 1
3._at : age = 2
4._at : age = 3
5._at : age = 4
6._at : age = 5
7._at : age = 6
8._at : age = 7
9._at : age = 8
10._at : age = 9
11._at : age = 10
12._at : age = 11
13._at : age = 12
14._at : age = 13
15._at : age = 14
Delta-method
Margin Std. Err. z P>z [95% Conf. Interval]
_at#sex
1#Male .2834477 .0173198 16.37 0.000 .2495015 .3173939
1#Female .2224946 .0145547 15.29 0.000 .1939679 .2510212
2#Male .2983585 .0161681 18.45 0.000 .2666696 .3300474
2#Female .2256212 .0132417 17.04 0.000 .199668 .2515744
3#Male .3147989 .0157898 19.94 0.000 .2838516 .3457463
3#Female .2300119 .0126628 18.16 0.000 .2051932 .2548305
4#Male .3327152 .0163698 20.32 0.000 .300631 .3647994
4#Female .2357405 .013263 17.77 0.000 .2097455 .2617354
5#Male .3518121 .0176267 19.96 0.000 .3172644 .3863598
5#Female .2428226 .0150916 16.09 0.000 .2132437 .2724016
6#Male .3716529 .0191098 19.45 0.000 .3341983 .4091075
6#Female .251041 .0174754 14.37 0.000 .2167899 .2852922
7#Male .3918779 .0206967 18.93 0.000 .3513131 .4324426
7#Female .2600038 .0196706 13.22 0.000 .2214501 .2985575
8#Male .4122473 .022445 18.37 0.000 .3682559 .4562387
8#Female .2693942 .0214995 12.53 0.000 .2272559 .3115325
9#Male .4325343 .0242777 17.82 0.000 .3849508 .4801178
9#Female .2790412 .0230641 12.10 0.000 .2338364 .324246
10#Male .4524836 .026015 17.39 0.000 .4014951 .5034721
10#Female .288811 .0243535 11.86 0.000 .2410789 .336543
11#Male .4718567 .0275408 17.13 0.000 .4178777 .5258358
11#Female .298551 .0252958 11.80 0.000 .2489722 .3481299
12#Male .4904753 .0288469 17.00 0.000 .4339363 .5470143
12#Female .3081192 .0259088 11.89 0.000 .2573389 .3588995
13#Male .5082308 .0299683 16.96 0.000 .449494 .5669676
13#Female .3174089 .0262743 12.08 0.000 .2659122 .3689056
14#Male .5250719 .030921 16.98 0.000 .4644678 .585676
14#Female .3263432 .0264707 12.33 0.000 .2744617 .3782248
15#Male .5409722 .0316926 17.07 0.000 .4788559 .6030885
15#Female .3348735 .0265998 12.59 0.000 .2827388 .3870082Dataset
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float interest double sex byte age float(isced_parents gender_attitudes casmin year_1) . 2 0 15 3.0714285 7 1998 . 2 1 15 3.0714285 7 1998 . 2 2 15 3.0714285 3 1998 . 2 3 15 3.0714285 3 2001 0 2 4 15 3.0714285 3 2001 . 2 5 15 3.0714285 3 2001 0 2 6 15 3.0714285 . 2004 . 2 7 15 3.0714285 . 2004 0 2 8 15 3.0714285 . 2004 . 2 9 15 3.0714285 . 2007 . 2 10 15 3.0714285 . 2007 . 2 11 15 3.0714285 . 2007 . 2 12 15 3.0714285 . 2010 . 2 13 15 3.0714285 . 2010 . 2 14 15 3.0714285 . 2010 . 2 0 15 3.7142856 7 2001 0 2 1 15 3.7142856 7 2001 0 2 2 15 3.7142856 3 2001 0 2 3 15 3.7142856 3 2004 . 2 4 15 3.7142856 3 2004 end label values sex bm_sex label def bm_sex 2 "Female", modify label values isced_parents isced_parents label values casmin casmin label def casmin 3 "Level 3 (general)", modify label def casmin 7 "Studying level 2", modify
0 Response to Inconsistency between odds ratios and marginals in growth curve model
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