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* GÉNERO
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/* Se procede a eliminar 32 observaciones cuyo genero es "inconsistent"
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Se procede también a eliminar las observaciones que vienen de xvdat pero que corresponden a gente que no se entrevisto para UKHLS*/
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gen female=sex_dv
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recode female 2=1 1=0
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lab def fem 1 "Female", add
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lab def fem 0 "Male", add
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lab val female fem
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tab sex_dv female
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recode female -9=. -20=.
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tab female
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* MARITAL STATUS
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/// Esta variable es importante porque debe ayudar en la selección de la muestra de análisis. La primera categoría es de menores de 16 años.
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/// Se recomendó usar mastat_dv, en vez de marstat_dv (ver mensaje del equipo de apoyo de Und.Soc.)
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tab marstat_dv
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gen maritalst=marstat_dv
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/// Simplificación de la variables
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recode maritalst (3/6=0), gen(couple)
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lab def pareja 0 "Not in couple", add
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lab def pareja 1 "Married", add
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lab def pareja 2 "Cohabitation", add
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lab val couple pareja
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recode couple (0=0 "single") (1/2 = 1 "couple"), gen (relationship)
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* EDUCACIÓN
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/* La única variable realmente hábil en esta sección es 'hiqual_dv'. Es una variable que permite la reproducción del método de computación de la sobreeducación (método ORU) seguido por Witeveen (2022). De todos modos, el cruce con otra variable más detallada pero menos hábil (qfhigh_dv) refleja inconsistencias que deberían ser consultadas en el foro de ayuda */
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/* Finalmente, siguiendo a Witeveen (2022), se podrían extraer seis niveles educativos (1. GCSE o menos / 2. A-LEVEL / 3.Teaching qualification with no PGCE / 4.Diploma in HE / 5. First degree + nursing qualification 6.Higher degree). A esos niveles habría que asignarlas un número de años, de acuerdo con Witeveen (2022) */
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tab qfhigh_dv hiqual_dv
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tab nhiqual_dv hiqual_dv
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gen highest_educ=hiqual_dv
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recode highest_educ 4/9=1 3=2 2=3 1=5
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/// Variable con tres niveles
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recode highest_educ 1=1 2/4=2 5/6=3, gen(highest_educ3)
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lab def HE3 1 "Basic", add
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lab def HE3 2 "Middle", add
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lab def HE3 3 "High", add
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lab val highest_educ3 HE3
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tab highest_educ3
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* PARTNER'S EDUCATION
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gen _sp_highest_educ=_sp_hiqual_dv
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recode _sp_highest_educ (4/9=1) (3=2) (2=3) (1=5)
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replace _sp_highest_educ = 0 if relationship==0
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tab _sp_highest_educ
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label define educ_label 0 "nopartner", add
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label define educ_label 1 "Degree", add
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label define educ_label 2 "Other higher", add
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label define educ_label 3 "A level etc", add
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label define educ_label 4 "GCSE etc", add
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label define educ_label 5 "Other qual", add
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label define educ_label 9 "No qual", add
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label values _sp_highest_educ educ_label
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tab _sp_highest_educ
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/// Variable resumen (university graduation vs.less)
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recode _sp_highest_educ 0=0 1/4=1 5/6=2, gen(_sp_highest_educ3)
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lab def _sp_higher_edu 0 "No partner", add
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lab def _sp_higher_edu 1 "Less than HE", add
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lab def _sp_higher_edu 2 "Higher educ", add
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lab val _sp_highest_educ3 _sp_higher_edu
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*-------------------------------------*
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* Step 1: Create 4-group variable
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*-------------------------------------*
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gen gndr_educ_group = .
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replace gndr_educ_group = 1 if female == 0 & highest_educ3 == 2 // Men (Mid)
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replace gndr_educ_group = 2 if female == 0 & highest_educ3 == 3 // Men (High)
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replace gndr_educ_group = 3 if female == 1 & highest_educ3 == 2 // Women (Mid)
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replace gndr_educ_group = 4 if female == 1 & highest_educ3 == 3 // Women (High)
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label define group_lbl 1 "Men (Mid)" 2 "Men (High)" 3 "Women (Mid)" 4 "Women (High)"
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label values gndr_educ_group group_lbl
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table gndr_educ_group _sp_highest_educ3 if highest_educ3 > 1, statistic(percent gndr_educ_group) nformat(%5.1f)
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