Dear all,
I am using longitudinal data from the SHARE survey (Survey of Health, Ageing and Retirement in Europe) to run a model on the covariates of transition from work into retirement (for a sample aged 50yo and over). Covariates include: one's risk of poverty in wave t-1, education, sex, age groups, equivalised household size, self-reported health status, work type, marital status, and extra working household member.
My co-authors and I thought it best to estimate a binary choice model like Logit model instead of a linear probability model. We start with a simple reg, estat hettest (heteroskedasticity confirmed), followed by logit ..., vce(cl mergeid).
Next, we estimate conditional xtlogit ..., fe and xtlogit ..., re, followed by hausman fe re. As p-value is zero, the panel Logit with FE would be more appropriate. Note that all models include wave- and country-fixed effects. Results and dataex follow after the questions.
My questions are:
I am using longitudinal data from the SHARE survey (Survey of Health, Ageing and Retirement in Europe) to run a model on the covariates of transition from work into retirement (for a sample aged 50yo and over). Covariates include: one's risk of poverty in wave t-1, education, sex, age groups, equivalised household size, self-reported health status, work type, marital status, and extra working household member.
My co-authors and I thought it best to estimate a binary choice model like Logit model instead of a linear probability model. We start with a simple reg, estat hettest (heteroskedasticity confirmed), followed by logit ..., vce(cl mergeid).
Next, we estimate conditional xtlogit ..., fe and xtlogit ..., re, followed by hausman fe re. As p-value is zero, the panel Logit with FE would be more appropriate. Note that all models include wave- and country-fixed effects. Results and dataex follow after the questions.
My questions are:
- As hausman fe re does not allow for robust nor clustered S.E., I am afraid that the results might not be precise in my case. Being this an unbalanced panel, I cannot easily implement the "robust Hausman test" proposed by Cameron & Trivedi. What would you recommend? For instance, coefficients of pov_risk_t_1, a dependent var of interest, change significantly from one model to another.
- In the xtlogit ..., fe results, educ and male variables are omitted, along with all country-FE. In principle, I understand why this happens, but I am puzzled about not being able to include country-FE with individual FE. Also, if I stick to xtlogit ..., fe, I would end up with 13,000 obs (versus 43,000 with xtlogit ..., re). How can I take this constraint into consideration?
Code:
global cov_pov_risk2 pov_risk_t_1 educ male i.age_grp hhsize_eqh_sr sphus_poor i.work_type i.marital_status hhmemb_work *** OLS reg trans $cov_pov_risk2 i.wave i.country if trans==0 | trans==1 rvfplot estat hettest //p-value=0, can reject null of homoskedasticity reg trans $cov_pov_risk2 i.wave i.country if trans==0 | trans==1, vce(cl mergeid) eststo ols_trans_all capture drop insample gen insample=1 if e(sample)==1 *** POOLED LOGIT logit trans $cov_pov_risk2 i.wave i.country if insample==1, vce(cl mergeid) margins, dydx($cov_pov_risk2) post outreg2 using "$result/transition_25-04-24.doc", replace ctitle(Pooled OLS) keep($cov_pov_risk2) label dec(3) pdec(3) addtext(Wave FE, YES, Country FE, YES) *** HAUSMAN TEST ** PANEL LOGIT, FE xtlogit trans $cov_pov_risk2 i.wave i.country if insample==1, fe est sto fe ** PANEL LOGIT, RE xtlogit trans $cov_pov_risk2 i.wave i.country if insample==1, re est sto re hausman fe re //sigmamore option not allowed (?)
Code:
---- Coefficients ----
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fe re Difference Std. err.
-------------+----------------------------------------------------------------
pov_risk_t_1 | -.6016172 -.1795393 -.4220778 .2444998
age_grp |
2 | -.7621384 1.532987 -2.295125 .5458784
3 | .7511475 3.644596 -2.893449 .7114418
4 | 2.048137 5.536591 -3.488453 .850413
5 | .2238864 5.504902 -5.281015 .9802493
6 | -2.020909 5.611854 -7.632763 1.162086
hhsize_eqh~r | -1.527002 -.6933263 -.8336755 .7235039
sphus_poor | -.0086558 .2115868 -.2202427 .2288058
work_type |
2 | -.7272602 -.0266944 -.7005657 .5069954
3 | -.1223119 -.4053891 .2830772 .6502483
marital_st~s |
4 | .8187897 .5042493 .3145404 2.837579
5 | 1.590961 .6787202 .9122412 .7105594
hhmemb_work | 7.846142 3.672941 4.173201 .4753171
wave |
4 | 4.198946 .3118012 3.887145 .5569936
5 | 7.219535 -.5716207 7.791155 .6539124
6 | 9.880225 -.7593481 10.63957 .7250568
7 | 12.43963 -.7975258 13.23716 .7884817
8 | 15.73511 -.6655215 16.40063 .8880205
------------------------------------------------------------------------------
b = Consistent under H0 and Ha; obtained from xtlogit.
B = Inconsistent under Ha, efficient under H0; obtained from xtlogit.
Test of H0: Difference in coefficients not systematic
chi2(18) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 633.49
Prob > chi2 = 0.0000
Code:
. xtlogit trans $cov_pov_risk2 i.wave i.country if insample==1, fe
note: multiple positive outcomes within groups encountered.
note: 17,818 groups (30,342 obs) omitted because of all positive or
all negative outcomes.
note: educ omitted because of no within-group variance.
note: male omitted because of no within-group variance.
note: 12.country omitted because of no within-group variance.
note: 13.country omitted because of no within-group variance.
note: 14.country omitted because of no within-group variance.
note: 15.country omitted because of no within-group variance.
note: 16.country omitted because of no within-group variance.
note: 17.country omitted because of no within-group variance.
note: 18.country omitted because of no within-group variance.
note: 19.country omitted because of no within-group variance.
note: 23.country omitted because of no within-group variance.
note: 28.country omitted because of no within-group variance.
note: 29.country omitted because of no within-group variance.
note: 31.country omitted because of no within-group variance.
note: 32.country omitted because of no within-group variance.
note: 33.country omitted because of no within-group variance.
note: 34.country omitted because of no within-group variance.
note: 35.country omitted because of no within-group variance.
note: 47.country omitted because of no within-group variance.
note: 48.country omitted because of no within-group variance.
note: 51.country omitted because of no within-group variance.
note: 53.country omitted because of no within-group variance.
note: 55.country omitted because of no within-group variance.
note: 57.country omitted because of no within-group variance.
note: 59.country omitted because of no within-group variance.
note: 61.country omitted because of no within-group variance.
note: 63.country omitted because of no within-group variance.
Iteration 0: Log likelihood = -884.53166
Iteration 1: Log likelihood = -457.83119
Iteration 2: Log likelihood = -368.83669
Iteration 3: Log likelihood = -365.37058
Iteration 4: Log likelihood = -365.34699
Iteration 5: Log likelihood = -365.34698
Conditional fixed-effects logistic regression Number of obs = 12,985
Group variable: panel Number of groups = 4,637
Obs per group:
min = 2
avg = 2.8
max = 6
LR chi2(18) = 8344.49
Log likelihood = -365.34698 Prob > chi2 = 0.0000
--------------------------------------------------------------------------------------------
trans | Coefficient Std. err. z P>|z| [95% conf. interval]
---------------------------+----------------------------------------------------------------
pov_risk_t_1 | -.6016172 .2503352 -2.40 0.016 -1.092265 -.1109691
educ | 0 (omitted)
male | 0 (omitted)
|
age_grp |
55-59yo | -.7621384 .5653121 -1.35 0.178 -1.87013 .3458531
60-64yo | .7511475 .7270053 1.03 0.302 -.6737567 2.176052
65-69yo | 2.048137 .8667529 2.36 0.018 .3493329 3.746942
70-74yo | .2238864 .9984175 0.22 0.823 -1.732976 2.180749
75+yo | -2.020909 1.181374 -1.71 0.087 -4.33636 .2945416
|
hhsize_eqh_sr | -1.527002 .7280251 -2.10 0.036 -2.953905 -.1000989
sphus_poor | -.0086558 .233234 -0.04 0.970 -.4657862 .4484745
|
work_type |
2. Public sector employee | -.7272602 .5089185 -1.43 0.153 -1.724722 .2702018
3. Self-employed | -.1223119 .652457 -0.19 0.851 -1.401104 1.15648
|
marital_status |
2. Never married | .8187897 2.838588 0.29 0.773 -4.744741 6.38232
3. Divorced/widowed | 1.590961 .712656 2.23 0.026 .1941812 2.987742
|
hhmemb_work | 7.846142 .4802585 16.34 0.000 6.904853 8.787431
|
wave |
Wave 4 (2011/12) | 4.198946 .5628121 7.46 0.000 3.095854 5.302037
Wave 5 (2013) | 7.219535 .6587072 10.96 0.000 5.928492 8.510577
Wave 6 (2015) | 9.880225 .7291923 13.55 0.000 8.451035 11.30942
Wave 7 (2017/18) | 12.43963 .7921748 15.70 0.000 10.887 13.99226
Wave 8 (2019/20) | 15.73511 .891713 17.65 0.000 13.98739 17.48284
|
country |
Germany | 0 (omitted)
Sweden | 0 (omitted)
Netherlands | 0 (omitted)
Spain | 0 (omitted)
Italy | 0 (omitted)
France | 0 (omitted)
Denmark | 0 (omitted)
Greece | 0 (omitted)
Belgium | 0 (omitted)
Czech Republic | 0 (omitted)
Poland | 0 (omitted)
Luxembourg | 0 (omitted)
Hungary | 0 (empty)
Portugal | 0 (empty)
Slovenia | 0 (omitted)
Estonia | 0 (omitted)
Croatia | 0 (omitted)
Lithuania | 0 (empty)
Bulgaria | 0 (empty)
Cyprus | 0 (empty)
Finland | 0 (empty)
Latvia | 0 (empty)
Malta | 0 (empty)
Romania | 0 (empty)
Slovakia | 0 (empty)
--------------------------------------------------------------------------------------------
Code:
. xtlogit trans $cov_pov_risk2 i.wave i.country if insample==1, re //LR test of rho=0: chibar2(01
> ) = 2.27
note: 59.country != 0 predicts failure perfectly;
59.country omitted and 18 obs not used.
Fitting comparison model:
Iteration 0: Log likelihood = -21005.955
Iteration 1: Log likelihood = -12053.908
Iteration 2: Log likelihood = -10779.721
Iteration 3: Log likelihood = -10584.603
Iteration 4: Log likelihood = -10579.394
Iteration 5: Log likelihood = -10579.358
Iteration 6: Log likelihood = -10579.358
Fitting full model:
tau = 0.0 Log likelihood = -10579.358
tau = 0.1 Log likelihood = -10580.232
Iteration 0: Log likelihood = -10580.232
Iteration 1: Log likelihood = -10578.224
Iteration 2: Log likelihood = -10578.222
Iteration 3: Log likelihood = -10578.222
Random-effects logistic regression Number of obs = 43,309
Group variable: panel Number of groups = 22,437
Random effects u_i ~ Gaussian Obs per group:
min = 1
avg = 1.9
max = 6
Integration method: mvaghermite Integration pts. = 12
Wald chi2(44) = 3530.49
Log likelihood = -10578.222 Prob > chi2 = 0.0000
--------------------------------------------------------------------------------------------
trans | Coefficient Std. err. z P>|z| [95% conf. interval]
---------------------------+----------------------------------------------------------------
pov_risk_t_1 | -.1795393 .0537363 -3.34 0.001 -.2848605 -.0742182
educ | -.045703 .0050548 -9.04 0.000 -.0556102 -.0357958
male | .0069455 .0383919 0.18 0.856 -.0683012 .0821921
|
age_grp |
55-59yo | 1.532987 .1469509 10.43 0.000 1.244968 1.821005
60-64yo | 3.644596 .1496238 24.36 0.000 3.351339 3.937853
65-69yo | 5.536591 .1675063 33.05 0.000 5.208284 5.864897
70-74yo | 5.504902 .1896016 29.03 0.000 5.133289 5.876514
75+yo | 5.611854 .2126046 26.40 0.000 5.195157 6.028551
|
hhsize_eqh_sr | -.6933263 .0810102 -8.56 0.000 -.8521035 -.5345491
sphus_poor | .2115868 .0452328 4.68 0.000 .1229321 .3002416
|
work_type |
2. Public sector employee | -.0266944 .0442009 -0.60 0.546 -.1133267 .0599378
3. Self-employed | -.4053891 .0536401 -7.56 0.000 -.5105218 -.3002564
|
marital_status |
2. Never married | .5042493 .0756994 6.66 0.000 .3558811 .6526175
3. Divorced/widowed | .6787202 .0546258 12.42 0.000 .5716556 .7857848
|
hhmemb_work | 3.672941 .0687164 53.45 0.000 3.538259 3.807623
|
wave |
Wave 4 (2011/12) | .3118012 .0807195 3.86 0.000 .1535939 .4700084
Wave 5 (2013) | -.5716207 .0793329 -7.21 0.000 -.7271103 -.4161311
Wave 6 (2015) | -.7593481 .0775501 -9.79 0.000 -.9113435 -.6073526
Wave 7 (2017/18) | -.7975258 .0764036 -10.44 0.000 -.9472741 -.6477775
Wave 8 (2019/20) | -.6655215 .0810649 -8.21 0.000 -.8244058 -.5066372
|
country |
Germany | -1.168965 .10466 -11.17 0.000 -1.374095 -.963835
Sweden | -1.480859 .1034137 -14.32 0.000 -1.683546 -1.278172
Netherlands | -1.273757 .1367407 -9.32 0.000 -1.541764 -1.00575
Spain | -1.186293 .1140347 -10.40 0.000 -1.409797 -.9627893
Italy | -.9683722 .1125943 -8.60 0.000 -1.189053 -.7476915
France | -.4131417 .1014051 -4.07 0.000 -.611892 -.2143913
Denmark | -1.683413 .1049886 -16.03 0.000 -1.889186 -1.477639
Greece | -1.922236 .1445765 -13.30 0.000 -2.205601 -1.638871
Belgium | -.5099378 .0995423 -5.12 0.000 -.7050371 -.3148385
Czech Republic | .0235277 .1025167 0.23 0.818 -.1774014 .2244568
Poland | -.4236044 .1582189 -2.68 0.007 -.7337077 -.1135011
Luxembourg | .8005673 .1579398 5.07 0.000 .4910109 1.110124
Hungary | -.2384578 .3374907 -0.71 0.480 -.8999274 .4230119
Portugal | -1.744507 .3401498 -5.13 0.000 -2.411188 -1.077825
Slovenia | .2154525 .1305904 1.65 0.099 -.0404999 .4714049
Estonia | -2.091223 .1080082 -19.36 0.000 -2.302915 -1.879531
Croatia | -.9198809 .2152789 -4.27 0.000 -1.34182 -.4979419
Lithuania | -1.531874 .3288001 -4.66 0.000 -2.17631 -.8874374
Bulgaria | -1.750336 .5283161 -3.31 0.001 -2.785816 -.714855
Cyprus | -1.822 .8435301 -2.16 0.031 -3.475289 -.1687116
Finland | -1.361717 .4022767 -3.39 0.001 -2.150165 -.5732691
Latvia | -2.491216 .6616125 -3.77 0.000 -3.787953 -1.19448
Malta | 0 (empty)
Romania | -1.484375 .6202134 -2.39 0.017 -2.699971 -.2687794
Slovakia | -1.40925 .3980663 -3.54 0.000 -2.189446 -.6290545
|
_cons | -2.556813 .2192327 -11.66 0.000 -2.986501 -2.127124
---------------------------+----------------------------------------------------------------
/lnsig2u | -2.16503 .7041196 -3.545079 -.7849811
---------------------------+----------------------------------------------------------------
sigma_u | .3387425 .1192576 .169901 .6753727
rho | .0337032 .0229313 .008698 .1217642
--------------------------------------------------------------------------------------------
LR test of rho=0: chibar2(01) = 2.27 Prob >= chibar2 = 0.066
Code:
. hausman fe re //sigmamore option not allowed
---- Coefficients ----
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fe re Difference Std. err.
-------------+----------------------------------------------------------------
pov_risk_t_1 | -.6016172 -.1795393 -.4220778 .2444998
age_grp |
2 | -.7621384 1.532987 -2.295125 .5458784
3 | .7511475 3.644596 -2.893449 .7114418
4 | 2.048137 5.536591 -3.488453 .850413
5 | .2238864 5.504902 -5.281015 .9802493
6 | -2.020909 5.611854 -7.632763 1.162086
hhsize_eqh~r | -1.527002 -.6933263 -.8336755 .7235039
sphus_poor | -.0086558 .2115868 -.2202427 .2288058
work_type |
2 | -.7272602 -.0266944 -.7005657 .5069954
3 | -.1223119 -.4053891 .2830772 .6502483
marital_st~s |
4 | .8187897 .5042493 .3145404 2.837579
5 | 1.590961 .6787202 .9122412 .7105594
hhmemb_work | 7.846142 3.672941 4.173201 .4753171
wave |
4 | 4.198946 .3118012 3.887145 .5569936
5 | 7.219535 -.5716207 7.791155 .6539124
6 | 9.880225 -.7593481 10.63957 .7250568
7 | 12.43963 -.7975258 13.23716 .7884817
8 | 15.73511 -.6655215 16.40063 .8880205
------------------------------------------------------------------------------
b = Consistent under H0 and Ha; obtained from xtlogit.
B = Inconsistent under Ha, efficient under H0; obtained from xtlogit.
Test of H0: Difference in coefficients not systematic
chi2(18) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 633.49
Prob > chi2 = 0.0000
Code:
* Example generated by -dataex-. For more info, type help dataex clear input float(trans pov_risk_t_1 educ) byte male float(age_grp hhsize_eqh_sr sphus_poor work_type) byte marital_status float hhmemb_work str12 mergeid . . . 1 1 1.4142135 0 . 1 0 "AT-000327-01" 1 0 . 1 1 1.4142135 1 2 1 1 "AT-000327-01" 0 . 3 0 1 1.4142135 0 1 1 1 "AT-000327-02" . . 3 0 2 1 0 3 5 0 "AT-000674-01" 1 1 3 0 3 1 0 3 5 0 "AT-000674-01" . 0 3 0 3 1 0 . 5 0 "AT-000674-01" . . 15 0 5 1 0 . 4 0 "AT-001215-01" . 0 15 0 5 1 1 . 4 0 "AT-001215-01" . 0 15 0 6 1 1 . 4 0 "AT-001215-01" . 0 15 0 6 1 1 . 4 0 "AT-001215-01" . 0 15 0 6 1 1 . 4 0 "AT-001215-01" . . 11 0 2 1.4142135 0 . 1 0 "AT-001492-01" . 0 11 0 3 1.4142135 0 . 1 0 "AT-001492-01" . 0 11 0 3 1.4142135 0 . 1 1 "AT-001492-01" . 0 11 0 4 1.4142135 0 . 1 0 "AT-001492-01" . 0 11 0 4 1.4142135 0 . 1 0 "AT-001492-01" . . 13 1 2 1.4142135 0 1 1 0 "AT-001492-02" 0 0 13 1 3 1.4142135 0 1 1 0 "AT-001492-02" 1 0 13 1 3 1.4142135 0 1 1 1 "AT-001492-02" . 0 13 1 4 1.4142135 0 . 1 0 "AT-001492-02" . 0 13 1 4 1.4142135 0 . 1 0 "AT-001492-02" . . . 0 3 1.4142135 0 . 1 0 "AT-001816-01" . . 8 1 2 1.4142135 0 . 1 0 "AT-001816-02" 0 0 8 1 2 1.7320508 0 1 1 0 "AT-001816-02" . . . 0 6 1.4142135 0 1 1 0 "AT-001881-01" . 0 . 0 6 1.4142135 1 . 1 0 "AT-001881-01" . 0 . 0 6 1.4142135 1 . 5 0 "AT-001881-01" . 1 . 0 6 1.4142135 0 . 5 0 "AT-001881-01" . 0 . 0 6 1.4142135 1 . 5 0 "AT-001881-01" . . . 1 6 1.4142135 1 . 1 0 "AT-001881-02" . 0 . 1 6 1.4142135 1 . 1 0 "AT-001881-02" . 0 . 1 6 1.7320508 1 . 1 0 "AT-001881-02" . 1 . 1 6 1.7320508 0 . 1 0 "AT-001881-02" . 0 . 1 6 1.7320508 0 . 1 0 "AT-001881-02" . . 10 0 5 2.44949 1 . 5 0 "AT-002132-01" . 1 10 0 5 1 1 . 5 0 "AT-002132-01" . . 20 1 2 1.7320508 1 . 1 0 "AT-002136-01" . 0 20 1 3 1.7320508 0 . 1 0 "AT-002136-01" . 0 20 1 3 1.7320508 1 . 1 0 "AT-002136-01" . 0 20 1 4 1.4142135 1 . 1 0 "AT-002136-01" . 0 20 1 4 1.4142135 1 . 1 0 "AT-002136-01" . . 14 0 2 1.7320508 0 . 1 0 "AT-002136-03" . 0 14 0 3 1.7320508 0 . 1 0 "AT-002136-03" . 0 14 0 3 1.7320508 0 . 1 0 "AT-002136-03" . 0 14 0 3 1.4142135 0 . 1 0 "AT-002136-03" . 0 14 0 4 1.4142135 0 . 1 0 "AT-002136-03" . . . 1 6 2.44949 1 . 1 0 "AT-002180-02" . 1 . 1 6 2.44949 1 . 1 0 "AT-002180-02" . . 2 0 5 2.44949 1 . 1 0 "AT-002180-03" . 1 2 0 5 2.44949 1 . 1 0 "AT-002180-03" . 1 2 0 6 2.236068 1 . 5 0 "AT-002180-03" . . . 1 5 1.4142135 1 . 1 0 "AT-002355-01" . . . 0 5 1.4142135 1 . 1 0 "AT-002355-02" . . . 0 4 1.4142135 1 . 1 0 "AT-002525-01" . 0 . 0 5 1.4142135 1 . 1 0 "AT-002525-01" . 0 . 0 5 1.4142135 1 . 1 0 "AT-002525-01" . 0 . 0 5 1.4142135 1 . 1 0 "AT-002525-01" . 0 . 0 6 1.4142135 1 . 1 0 "AT-002525-01" . . . 1 4 1.4142135 1 . 1 0 "AT-002525-02" . 0 . 1 5 1.4142135 1 . 1 0 "AT-002525-02" . 0 . 1 5 1.4142135 1 . 1 0 "AT-002525-02" . 0 . 1 6 1.4142135 1 . 1 0 "AT-002525-02" . . 8 0 6 1.4142135 0 . 1 0 "AT-002573-01" . 0 8 0 6 1.4142135 0 . 1 0 "AT-002573-01" . . 8 1 4 1.4142135 0 . 1 0 "AT-002573-02" . 0 8 1 5 1.4142135 0 . 1 0 "AT-002573-02" . . 23 0 3 1 0 1 1 0 "AT-002800-01" 1 0 23 0 3 1 0 1 1 0 "AT-002800-01" . 0 23 0 3 1 0 1 1 0 "AT-002800-01" . 0 23 0 4 1 1 . 1 0 "AT-002800-01" . . 8 0 6 1.4142135 0 . 5 0 "AT-002965-02" . 1 8 0 6 1.4142135 1 . 5 0 "AT-002965-02" . 1 8 0 6 1.4142135 1 . 5 0 "AT-002965-02" . 1 8 0 6 1.4142135 1 . 5 0 "AT-002965-02" . . 2 1 5 1.4142135 0 . 1 0 "AT-003194-01" . 0 2 1 6 1.4142135 0 . 1 0 "AT-003194-01" . 0 2 1 6 1.4142135 0 . 1 0 "AT-003194-01" . 0 2 1 6 1.4142135 0 . 1 0 "AT-003194-01" . 0 2 1 6 1.4142135 0 . 1 0 "AT-003194-01" . . 2 0 5 1.4142135 1 . 1 0 "AT-003194-02" . . 3 0 1 1.4142135 0 1 1 0 "AT-003683-01" . . 3 1 1 1.4142135 0 1 1 0 "AT-003683-02" . . 8 1 1 1.4142135 1 1 1 0 "AT-004234-01" . 0 8 1 2 1.4142135 1 . 1 0 "AT-004234-01" . 0 8 1 3 1.4142135 1 . 1 0 "AT-004234-01" . . 8 0 1 1.4142135 0 1 1 0 "AT-004234-02" 0 0 8 0 2 1.4142135 1 1 1 0 "AT-004234-02" . . 8 0 4 1 0 1 4 0 "AT-004379-01" . 0 8 0 5 1 0 . 4 0 "AT-004379-01" . 1 8 0 6 1 1 . 4 0 "AT-004379-01" . . 8 1 6 1.4142135 1 . 1 0 "AT-004855-01" . 1 8 1 6 1.4142135 1 . 1 0 "AT-004855-01" . . 7 0 5 1.4142135 0 . 1 0 "AT-004855-02" . 1 7 0 5 1.4142135 1 . 1 0 "AT-004855-02" . 0 7 0 6 1.7320508 0 . 5 0 "AT-004855-02" . 1 7 0 6 1.7320508 1 . 5 0 "AT-004855-02" . 1 7 0 6 1.7320508 1 . 5 0 "AT-004855-02" . 1 7 0 6 1.7320508 1 . 5 0 "AT-004855-02" . 1 7 0 6 1.4142135 1 . 5 0 "AT-004855-02" . . 3 1 4 1 0 . 5 0 "AT-004974-01" end label values trans trans label def trans 0 "0. No transition", modify label def trans 1 "1. Retirement", modify label values pov_risk_t_1 pov_risk label def pov_risk 0 "not in risk of poverty", modify label def pov_risk 1 "in risk of poverty", modify label values educ eduyears_mod label values male gender label def gender 0 "Female", modify label def gender 1 "Male", modify label values age_grp age_grp label def age_grp 1 "50-54yo", modify label def age_grp 2 "55-59yo", modify label def age_grp 3 "60-64yo", modify label def age_grp 4 "65-69yo", modify label def age_grp 5 "70-74yo", modify label def age_grp 6 "75+yo", modify label values work_type work_type_val label def work_type_val 1 "1. Private sector employee", modify label def work_type_val 2 "2. Public sector employee", modify label def work_type_val 3 "3. Self-employed", modify label values marital_status marital_status label def marital_status 1 "1. Married/in partnership", modify label def marital_status 4 "2. Never married", modify label def marital_status 5 "3. Divorced/widowed", modify
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