CMGPDLN Methodological Lecture Day 5 Marriage Marriage Event

CMGPD-LN Methodological Lecture Day 5 Marriage

Marriage Event history • • Outcome: marriage (or remarriage) in the next three years Logistic regression Restrict to people not currently married Useful for considering the effects of time-varying characteristics – Current characteristics of kin, etc. • Can be carried out for daughters, subject to caveats related to omission of records • Discrete-time event-history via logit or complementary loglog • AT_RISK_MARRY and NEXT_MARRY • AT_RISK_REMARRY and NEXT_REMARRY

Marriage Current marital status • Alternatively, can look at current marital status as an outcome • Fine if right-hand side of variables of interest are fixed – Childhood characteristics etc. • Can construct flag variables for outcomes from MARITAL_STATUS – Ever married (MARITAL_STATUS != 2 & MARITAL_STATUS >= 1) – Currently married (MARITAL_STATUS == 1) – Currently remarried (MARITAL_STATUS==4) • Would want to restrict to observations of people who were MARITAL_STATUS == 3 | MARITAL_STATUS == 4

Marriage Variables for parents’ characteristics • For the study of marriage, and social mobility, parental characteristics are important • In STATA, it is easy to extract information from records of parents, and add it as a variable to an individual’s observation • Basic principle is to merge based on FATHER_ID, MOTHER_ID and so forth • Can also extract information from other kin

Preparing a file with father’s information to be merged in use "C: UsersCameron CampbeDocumentsBaqiCMGPD-LN from ICPSRICPSR_27063DS 000127063 -0001 Data. dta", clear keep PERSON_ID YEAR HAS_POSITION rename PERSON_ID FATHER_ID rename HAS_POSITION FATHER_HAS_POSITION bysort FATHER_ID YEAR: keep if _n == 1 save father_information, replace

Merging the file and carrying out a tabulation use "C: UsersCameron CampbeDocumentsBaqiCMGPD-LN from ICPSRICPSR_27063DS 000127063 -0001 -Data. dta", clear merge m: 1 FATHER_ID YEAR using father_information drop if _merge == 2 label variable FATHER_HAS_POSITION "Father has position" tab HAS_POSITION FATHER_HAS_POSITION if SEX == 2 & AGE_IN_SUI > 16 & AGE_IN_SUI <= 55 & PRESENT & FATHER_HAS_POSITION >= 0, col generate byte IS_MARRIED = MARITAL_STATUS != 2 tab IS_MARRIED FATHER_HAS_POSITION if SEX == 2 & AGE_IN_SUI > 16 & AGE_IN_SUI <= 35 & PRESENT & FATHER_HAS_POSITION >= 0 & MARITAL_STATUS >= 1, col

tab HAS_POSITION FATHER_HAS_POSITION if SEX == 2 & AGE_IN_SUI > 16 & AGE_IN_SUI <= 55 & PRESENT & FATHER_HAS_POSITION >= 0, col +----------+ | Key | |----------| | frequency | | column percentage | +----------+ Has Official | Father has position Position | No Yes | Total ---------------+---------No | 218, 254 9, 256 | 227, 510 | 98. 84 86. 88 | 98. 29 ---------------+---------Yes | 2, 561 1, 398 | 3, 959 | 1. 16 13. 12 | 1. 71 ---------------+---------Total | 220, 815 10, 654 | 231, 469 | 100. 00

. tab IS_MARRIED FATHER_HAS_POSITION if SEX == 2 & AGE_IN_SUI > 16 & AGE_IN_SUI <= 35 & PRESENT & FATHER_HAS_POSITION >= 0 & MARITAL > _STATUS >= 1, col +----------+ | Key | |----------| | frequency | | column percentage | +----------+ | Father has position IS_MARRIED | No Yes | Total ------+-----------+-----0 | 68, 210 2, 452 | 70, 662 | 40. 20 30. 27 | 39. 75 ------+-----------+-----1 | 101, 447 5, 648 | 107, 095 | 59. 80 69. 73 | 60. 25 ------+-----------+-----Total | 169, 657 8, 100 | 177, 757 | 100. 00

Creating one record per father and using it as the basis for the merge use "C: UsersCameron CampbeDocumentsBaqiCMGPD-LN from ICPSRICPSR_27063DS 000127063 -0001 -Data. dta", clear bysort PERSON_ID (HAS_POSITION): generate father_ever_position = HAS_POSITION[_N] bysort PERSON_ID: keep if _n == 1 keep PERSON_ID father_ever_position rename PERSON_ID FATHER_ID save father_ever_position use "C: UsersCameron CampbeDocumentsBaqiCMGPD-LN from ICPSRICPSR_27063DS 000127063 -0001 -Data. dta", clear keep if SEX == 2 merge m: 1 FATHER_ID using father_ever_position, keep(match master) label variable father_ever_position "Father ever held position" tab HAS_POSITION father_ever_position if PRESENT & HAS_POSITION >= 0 & father_ever_position >= 0 & AGE_IN_SUI >= 16 & AGE_IN_SUI <= 55, column

Results +----------+ | Key | |----------| | frequency | | column percentage | +----------+ | Father ever held Has Official | position Position | 0 1 | Total ---------------+---------No | 429, 455 22, 841 | 452, 296 | 98. 76 86. 02 | 98. 03 ---------------+---------Yes | 5, 390 3, 711 | 9, 101 | 1. 24 13. 98 | 1. 97 ---------------+---------Total | 434, 845 26, 552 | 461, 397 | 100. 00

Moving information about a husband into a wife’s record use "C: UsersCameron CampbeDocumentsBaqiCMGPD-LN from ICPSRICPSR_27063DS 000127063 -0001 -Data. dta", clear keep if SEX == 2 keep PERSON_ID YEAR AGE_IN_SUI rename PERSON_ID HUSBAND_ID rename AGE_IN_SUI HUSBAND_AGE bysort HUSBAND_ID YEAR: keep if _n == 1 save husband_information use "C: UsersCameron CampbeDocumentsBaqiCMGPD-LN from ICPSRICPSR_27063DS 000127063 -0001 -Data. dta", clear keep if SEX == 1 merge m: 1 HUSBAND_ID YEAR using husband_information, keep(match master) generate AGE_DIFFERENCE = HUSBAND_AGE - AGE_IN_SUI if HUSBAND_AGE > 0 & AGE_IN_SUI > 0 recode AGE_DIFFERENCE min/-10=-10 10/max=10 bysort PERSON_ID: keep if _n == 1& MARITAL_STATUS > 0 & MARITAL_STATUS != 2 histogram AGE_DIFFERENCE, width(1) discrete scheme(s 1 mono) xtitle("Husband's age - wife's age") fraction

Age differences in newly married couples