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File99_UKDS_Derivation&Analyses.do
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*This file is used to derive all the analytic files and carry out analyses using the publically available versions of the files available from the UK Data Service. (https://ukdataservice.ac.uk/)
*Thus should be considered a separate file from the others.
*** Note this Stata file has been complied by Richard Shaw ([email protected]) Other contributors to work include
* Jingmin Zhu (ELSA) & Rebecca Rhead (NCDS, BCS70 Next steps) and undoubtedly been informed by the work of many others.
* Note Richard Shaw has dyslexia, and there may be typos.
clear all
set maxvar 30000
*Ntte reassembling files in their original form.
global source "D:\Data\ARQ7P4\Original"
global destination "D:\Data\ARQ7P4\Derived"
*******************************************************************************8
*** ELSA *** Covid wave 1
*
use "$source\elsa_covid_w1_eulv2.dta"
*Coding all variables
rename _all, lower
foreach var of varlist _all {
rename `var' `var'_w1
}
gen inCVWave1 = 1
rename idauniq_w1 idauniq
save "$destination\tempCV_w1.dta" , replace
* Covid wave 2
use "$source\elsa_covid_w2_eulv2.dta", clear
rename _all, lower
foreach var of varlist _all {
rename `var' `var'_w2
}
generate inCVWave2 = 1
rename idauniq_w2 idauniq
save "$destination\tempCV_w2.dta", replace
*** Assembling ELSA wave 9 data which has been broken into many components.
*Import the first section
use "$source\wave_9_elsa_data_eul_v1.dta" , clear
*Adding the other wave 9 variables needed
merge 1:1 idauniq using "$source\wave_9_financial_derived_variables.dta", keepusing(eqtotinc_bu_s nettotnhw_bu_s tnhwq5_bu_s)
drop _merge
merge 1:1 idauniq using "$source\wave_9_ifs_derived_variables.dta", keepusing(smoker* edqual difjobm)
drop _merge
gen inWave9 = 1
save "$destination\temp_w9.dta", replace
*Importing NS-sec from wave 8, 6 and 5 wave 7 does not appear to have the variable
use "$source\wave_8_elsa_data_eul_v2.dta" , clear
keep idauniq w8nssec8
save "$destination\temp_w8.dta", replace
use "$source\wave_6_elsa_data_v2.dta" , clear
keep idauniq w6nssec8
save "$destination\temp_w6.dta", replace
use "$source\wave_5_elsa_data_v4.dta" , clear
keep idauniq w5nssec8
save "$destination\temp_w5.dta", replace
global source "D:\Data\ARQ7P4\Original"
global destination "D:\Data\ARQ7P4\Derived"
********************************************************************************
*Assembling files
use "$destination\tempCV_w1", clear
merge 1:1 idauniq using "$destination\tempCV_w2"
drop _merge
merge 1:1 idauniq using "$destination\temp_w9"
*drop if _merge==2
drop _merge
egen ELSA_inwave = concat(inWave9 inCVWave1 inCVWave2)
tab ELSA_inwave
merge 1:1 idauniq using "$destination\temp_w8"
drop _merge
merge 1:1 idauniq using "$destination\temp_w6"
drop _merge
merge 1:1 idauniq using "$destination\temp_w5"
drop _merge
*******************************************************************************8
gen study_selection = 1 if corepartner_w1==1 | corepartner_w2==1
replace study_selection = . if inWave9 != 1 | inCVWave1 != 1 | inCVWave2 != 1
gen age_selection = 1
replace age_selection = . if age_arch_w2 >= 66
replace age_selection = . if age_arch_w1 >= 66 & age_arch_w2==.
gen employment_selection = 1 if cvpred_w1 == 2 | cvpred_w1== 3
******************************************************************************
***** outcome variable
*** employment status
* Following discussions with Daniel Kopasker (Economist)
* The self-employed but not currently working have been classified as economically active
* but unemployed.
gen employ = cvpstd_w2
recode employ (1=2)(2=3)(3=1)(4 =3)(5/8=2)(-9/-8=.)
label define employ 1"1:furloygh" 2"2:unemployed" 3"3:employed/self-employed",replace
label values employ employ
*** Economic activity
label define econ_act 0 "Active" 1 "In active"
gen econ_act = .
recode econ_act . = 0 if inlist(cvpstd_w2, 2, 3, 4, 5)
recode econ_act . = 1 if inlist(cvpstd_w2, 1, 7 , 8)
recode econ_act . = 0 if cvpstd_w2 == 6 & cvsearch_w2 == 1
recode econ_act . = 1 if cvpstd_w2 == 6 & cvsearch_w2 == 2
* financial difficulty
gen subfin=cvpostfn_w2
label define subfin 1"1:much worse" 2"2:little worse" 3"3:same" 4"4:little better" 5"5:much better",replace
label val subfin subfin
gen subfin_bi=(subfin==3 | subfin==4 | subfin==5)
*** changing in working time
* number of hours worked
recode cvpsth_w2 cvpreh_w1 (-9/-1=.)
gen workhour_w2 = cvpsth_w2
* pre_pandemic working hours
gen workhour_pre=cvpreh_w1
* work hour change
gen workhour_low = workhour_pre*0.9
gen workhour_high = workhour_pre*1.1
gen workhour_change=1 if workhour_w2 > workhour_high
replace workhour_change=2 if workhour_w2 < workhour_low
replace workhour_change=3 if workhour_low <= workhour_w2 <=workhour_high
replace workhour_change=4 if employ==1
replace workhour_change=5 if employ==2 | cvpsth_w2==5
label define change 1"1:increase" 2"2:decrease" 3"3:same" 4"4:non-working furlough" 5"5:non-working non-furlough",replace
label values workhour_change change
*worktime_change
gen worktime_change = 1 if (workhour_w2 > workhour_high ) & workhour_w2 !=.
recode worktime_change . = 2 if (workhour_w2 < workhour_low) & workhour_low !=.
recode worktime_change . = 3 if (workhour_w2 >= workhour_low) & (workhour_w2 <= workhour_high) & workhour_w2 ! =. & workhour_w2 !=.
replace worktime_change =4 if employ==1
replace worktime_change =5 if employ==2 | cvpsth_w2==5
label values worktime_change change
*** exposure
* self-reported covid
** definition 1: positive covid test/ hospitalisation/ one of three core symptoms
** cw1
egen srcovid_w1= rowtotal (cvsymp01_w1 cvsymp02_w1 cvsymp05_w1)
tab srcovid_w1, missing
gen srcovid1_w1=(srcovid_w1>=1) if srcovid_w1 !=.
tab srcovid1_w1
tab cvtestb_w1
tab cvhosp_w1, nolab
recode cvhosp_w1 (-9/-8=.)
gen covcase_w1 =(cvtestb_w1 == 1 | cvhosp_w1 == 1 | srcovid1_w1 == 1) if cvtestb_w1 !=. | cvhosp_w1 !=. | srcovid1_w1 !=.
tab covcase_w1 , missing
*Note all those who had positive tests had been hospitalized.
*Note using the coreset of symptoms as the question for ELSA slightly different and
* may prompt people to report fatigue irrespective of whether they thought it was due to covid.
gen covid_confirmed_w1 = .
recode covid_confirmed_w1 . = 2 if cvtestb_w1 == 1
recode covid_confirmed_w1 . = 0 if cvtestb_w1 == 2
recode covid_confirmed_w1 . = 1 if srcovid_w1 > 0
recode covid_confirmed_w1 . = 0 if srcovid_w1 == 0
label define covid_confirmed_w1 0 "No" 1 "Suspected" 2 "Confirmed"
label values covid_confirmed_w1 covid_confirmed_w1
** cw2
gen covcase_w2=0
replace covcase_w2=1 if (cvtestb_w2 == 1 | cvhosp_w2 == 1)
replace covcase_w2=1 if cvtestwhy_final001_w2==1 & (cvtestb_w2 == 3 | cvtestb_w2 == 4 )
tab covcase_w2
*Test because they had covid symptoms
tab cvtestwhy_final001_w2
*Note no hospital stays in analytic sample
gen covid_confirmed_w2 = .
*Positive tests
recode covid_confirmed_w2 . = 2 if cvtestb_w2 ==1
*Negative tests
recode covid_confirmed_w2 . = 0 if cvtestb_w2 ==2
*Those who had a test due to symptoms
recode covid_confirmed_w2 . = 1 if cvtestwhy_final001_w2 ==1
* remainder not suspected
recode covid_confirmed_w2 . = 0
* People whose test results are not being reported.
replace covid_confirmed_w2 = . if cvtestb_w2 == -9
label define covid_confirmed_w2 0 "No" 1 "Suspected" 2 "Confirmed"
label values covid_confirmed_w2 covid_confirmed_w2
*coding the remaining cases
gen covid_sr = .
recode covid_sr . = 2 if covid_confirmed_w1 ==2 | covid_confirmed_w2 == 2
recode covid_sr . = 1 if covid_confirmed_w1 ==1 | covid_confirmed_w2 == 1
recode covid_sr . = 0
label define covid_sr 0 "No" 1 "Suspected" 2 "Confirmed"
label values covid_sr covid_sr
*What was the result of the covid test
tab cvtestb_w2
* -9 prefer not to answer
*-1 not applicable
* 1 positive
* 2 negative
* 3 inconclusive
* 4 waiting
* timing of covid infection
gen covtime=0 if covcase_w1==0 & covcase_w2==0
replace covtime=1 if covcase_w1==1 & covcase_w2==0
replace covtime=2 if covcase_w1==0 & covcase_w2==1
replace covtime=3 if covcase_w1==1 & covcase_w2==1
*** MAIN COVARIATES
* Age
gen age_gr_w2=age_arch_w2
recode age_gr_w2 (52/54=1) (55/66=2)
label define age 1"1:45-54" 2"2:55-66",replace
label values age_gr_w2 age
* Ethnicity
gen ethnicity =.
gen ethnicity_temp = ethnicity_arch_w1
replace ethnicity_temp = ethnicity_arch_w2 if ethnicity_temp == .
replace ethnicity_temp = fqethnm if ethnicity_temp == .
recode ethnicity_temp (-9/-1 = . ) (1 = 0 "White") (2 = 1 "Non White") , gen(ethnicity_bin)
label variable ethnicity_bin "Ethnicity Binary"
gen ethnicity1 = .
* Education
label define education 0 "NVQ 4 or 5" 1 "NVQ 3" 2 "NVQ 2 & 1" 3 "None"
recode w9edqual (-9/-1=.) (1/2 = 0 ) (3 = 1) (4/5 = 2) (6/7 = 3), gen(education)
label variable education "Education NVQ eqvs"
label values education education
recode education (0 = 0 "NVQ 4 or 5") (1/3 = 1 "NVQ3 or less") , gen(education_bin)
*Soc 2000
/*Commenting out Soc2000 as data is not available.
* Soc2000 - for this variable, we need to gather info from previous waves (issues with w7!)
* Note1 -- I created in ELSAw1 a soc2000 var, and for w7 I derived it from nssec
* Note2 -- we also gather number of room for 'overcrowding'
* In ELSA w6, for the loop to work, you first need to rename HoRoom as horoom
cd "/Users/jm/OneDrive - University College London/ELSA_data/loop"
foreach h in 1 2 3 4 5 6 7 8 {
merge 1:1 idauniq using "wave_`h'_elsa_data", keepusing(w`h'soc2000 horoom)
rename horoom horoom_w`h'
drop if _merge==2
drop _merge
}
recode w*soc2000* (-9/-1=.)
generate occupation_1d=floor(w9soc2000r/10)
foreach h in 8 6 5 4 3 2 1 7 {
replace occupation_1d=floor(w`h'soc2000/10) if occupation_1d==.
}
gen occupation_2d=w9soc2000r_w9
foreach h in 8 6 5 4 3 2 1 7 {
replace occupation_2d=w`h'soc2000 if occupation_2d==.
}
* industry - SIC2003-2 digits -for this variable, we need to gather info from previous waves
foreach h in 4 5 6 7 8 {
merge 1:1 idauniq using "wave_`h'_elsa_data", keepusing(w`h'sic2003)
drop if _merge==2
drop _merge
}
recode w9sic2003r_w9 w8sic2003 w7sic2003 w6sic2003 w5sic2003 w4sic2003 (-3/-1=.)(-8=.)
rename w9sic2003r_w9 w9sic2003
gen sic2003_2d=.
foreach h in 9 8 7 6 5 4 {
replace sic2003_2d=w`h'sic2003 if sic2003_2d==.
}
merge m:m sic2003_2d using /Users/jasmine/Documents/UCL/NCS_7HS_P4_employment/sic2003_2007.dta
drop if _merge==2
drop _merge
*/
* household composition
*Note that cvnump_w`k' indicate number in household
* Target variable will have the following groups Alone, partner, partner & children, lone parent, other person
* Note to be consistent with CLS and USoc this has been amended to include grandchildren as offspring, not just children
egen offspring_w1=anymatch(demographics_*_cvrelp_w1), values(2 3 4)
egen offspring_w2=anymatch(demographics_*_cvrelp_w2), values(2 3 4)
egen partinhh_w1=anymatch(demographics_*_cvrelp_w1), values(1)
egen partinhh_w2=anymatch(demographics_*_cvrelp_w2), values(1)
recode partinhh_w1 -1=0
forvalues k=1/2 {
generate cvhhcomp_w`k'=.
replace cvhhcomp_w`k'=1 if cvnump_w`k'==1
replace cvhhcomp_w`k'=2 if cvnump_w`k'==2 & partinhh_w`k'==1
replace cvhhcomp_w`k'=3 if cvnump_w`k'>=2 & partinhh_w`k'==1 & offspring_w`k'==1
replace cvhhcomp_w`k'=4 if cvnump_w`k'>=2 & partinhh_w`k'==0 & offspring_w`k'==1
replace cvhhcomp_w`k'=5 if cvnump_w`k'==-1 | (cvnump_w`k'>=2 & partinhh_w`k'==0 & offspring_w`k'==0) | (cvnump_w`k'>=3 & partinhh_w`k'==1 & offspring_w`k'==0)
}
forvalues k=1/2 {
recode cvhhcomp_w`k' (2=1)(3=2)(4=3)(1=4)(5=4)
label define cvhhcomp 1"1:only partner" 2"2:partner+kids" 3"3:single parent" 4"4:alone or other", replace
label values cvhhcomp_w* cvhhcomp
}
*New variable hh_comp with categories as used for the CLS studies.
label define hh_comp 0 "alone" 1 "Partner" 2 "Partner & children" 3 "lone parent" 4 "other person"
gen hh_comp =.
replace hh_comp = 0 if cvnump_w1 == 1
replace hh_comp = 1 if partinhh_w1 == 1
recode hh_comp 1 = 2 if offspring_w1 == 1
replace hh_comp = 3 if offspring_w1 == 1 & partinhh_w1 == 0
recode hh_comp . = 4 if cvnump_w1 >1 & cvnump_w1 !=.
label values hh_comp hh_comp
* Self-rated health
tab hehelf
clonevar sr_health = hehelf
recode sr_health -1 = .
*lim_ill
gen lim_ill = .
replace lim_ill = 0 if heill == 2 | helim == 2
replace lim_ill = 1 if helim == 1
label define lim_ill 0 "No" 1 "Yes"
label values lim_ill lim_ill
* keyworker
recode cvkey_w* (-9/-1=.)
gen keyworker = cvkey_w1
replace keyworker = cvkey_w2 if keyworker == .
recode keyworker 2 = 0 . = 0
label define keyworker 0 "No" 1 "Yes"
label values keyworker keyworker
* pre-pandemic mental health - ces-d in wave9
recode pscedd pscedf (2=1) (1=0) (-9/-1=.)
recode psceda pscedb pscedc pscede pscedg pscedh (2=0) (-9/-1=.)
egen depression_w9=rowtotal(psced*), missing
generate depressed_w9=(depression_w9>=4)
replace depressed_w9=. if depression_w9==.
* lli
*generate lli=(helim_w9==1)
*label define lli 0"no long-standing limiting illness" 1"lli", replace
*label values lli lli
* shielding at first pandemic wave
recode cvvuln_w1 (-9/-1=.)
gen shielding=(cvvuln_w1==1) if cvvuln_w1!=.
*** Five health conditions: pre-pandemic wave combined with first pandemic wave
**Note other pre-covid waves are added the suffix _w9 will need to be added to some vars or other ways of selecting variables found
* cancer
gen cancer=hedbsca
recode cancer (-8=.)(-1=0)(2=0)
replace cancer=1 if hedibca ==1
replace cancer=1 if cvhecond09_w1==1
* heart disease and high blood pressure
recode hedas95 hedasar hedashf hedashm heyra hediahf hediahm hedim85 hedim86 (-9/-1=.)
gen heartd=0
foreach k in hedas95 hedasar hedashf hedashm heyra hediahf hediahm hedim85 hedim86 {
replace heartd=1 if `k'==1
}
replace heartd=1 if cvhecond02_w1==1
replace heartd=1 if cvhecond03_w1==1
//high blood pressure part//
replace heartd=1 if hedasbp==1
replace heartd=1 if hediabp==1
replace heartd=1 if cvhecond01_w1==1
* obesity at first pandemic wave
recode wtimperial_wtstones_w1 wtimperial_wtpounds_w1 wtkilos_w1 dvheight_w2 (-9/-1=.)
gen cvweight_w1=6.35*wtimperial_wtstones_w1
replace cvweight_w1=cvweight_w1+0.45*wtimperial_wtpounds_w1 if wtimperial_wtpounds_w1!=.
gen cvestwt_w1=wtkilos_w1
replace cvestwt_w1=cvweight_w1 if cvestwt_w1==. & cvweight_w1!=.
gen cvbmi_w1 = round(cvestwt_w1/[(dvheight_w2/100)^2], .1)
gen obesity=(cvbmi_w1>=30) if cvbmi_w1!=.
* respiratory disorders
recode hedblu hediblu hedbsas hedibas (-9/-1=.)
gen respiratory=0
foreach k in hedblu hediblu hedbsas hedibas {
replace respiratory=1 if `k'==1
}
replace respiratory=1 if cvhecond06_w1==1
replace respiratory=1 if cvhecond07_w1==1
********************************************************************************
*****Variables for analysis
*** Study management
* Person id
*rename llc_0010_stud_id LLC_0010_stud_id
* Cohort id
gen cohort_id = "ELSA"
*** Outcomes
*Employment status: employ -> employment_status
tab employ
label define employment_status 0 "Employed" 1 "Furloughed" 2 "Not in employment"
recode employ 3 = 0 , gen(employment_status)
label values employment_status employment_status
*Financial difficulties: subfin subfin_bi -> (finance_change finance_change_bi)
clonevar finance_change = subfin
recode subfin_bi 0 = 1 1 = 0, gen(finance_change_bin)
label define finance_change_bi 0 "Same - better" 1 "Much worse - little worse"
*Change in working hours: worktime_change -> worktime_change
tab worktime_change
label define worktime_change 0 "Stayed the same" 1 "Increased" 2 "Decreased" 3 "Furloughed" 4 "Not in employed"
recode worktime_change 3 = 0 4 = 3 5 =4
label values worktime_change worktime_change
***Exposures
*Any covid symptoms: covtime -> covid_ever
recode covtime 0 = 0 1/3 = 1 , gen(covid_ever)
label define covid_ever 0 "No" 1 "Yes"
label values covid_ever covid_ever
*Timing of Covid: covtime -> covid_timing
clonevar covid_timing = covtime
label define covid_timing 0 "No Covid" 1 "First Wave" 2 "Second Wave" 3 "Both Waves"
label values covid_timing covid_timing
*LongCovid: long_covid_out -> long_covid
*Going to leave now as there are problems
gen long_covid = .
***Control variables
*Country variable does not appear to be available so using
gen country = rgn_arch_w2
recode country -1 = .
replace country = rgn_arch_w1 if rgn_arch_w2 == .
recode country -1 =. 1/9 = 1 10 = 2 11 = 3
label define country 1 "England" 2 "Scotland" 3 "Wales" 4 "Northern Ireland"
label values country country
*Age: ch_age ch_agecat -? age age_group
clonevar age = age_arch_w2
recode age_arch_w2 25/39 = 0 40/54 = 1 55/66 = 2, gen(age_group)
label define age_group 0 "25 to 39" 1 "40 to 54" 2 "55 +"
label values age_group age_group
*sex: Sex_w2 >- sex
gen temp_sex = sex_w2
replace temp_sex = sex_w1 if temp_sex == .
gen sex = temp_sex - 1
label define sex 0 "Male" 1 "Female"
label values sex sex
*Household composition: ????? -> hh_comp
*Will need recoding when looking at other cohorts
* Note using wave 1 as it won't be consequence of the outcome.
*clonevar hh_comp = cvhhcomp_w1
* NS-SEC "Seven category" to be consistent across the studies.
* Using seven categories as the ELSA 8 categories are different from the other cohorts.
recode w5nssec8 (-9/-1 99 = 9 "Other Unclassifiable") (1 = 1 "Higher management or professional") (2 = 2 "Lower management and professional" ) (3 = 3 "Intermediate") (4 = 4 "Small Employer") (5 = 5 "Lower supervisory") (6 = 6 "Semi-routine") (7 = 7 "Routine") (8 =8 "long-term unemployed" ) , gen(nssec7w5)
label variable nssec7w5 "NS-SEC 7 Categories w6"
recode w6nssec8 (-9/-1 99 = 9 "Other Unclassifiable") (1 = 1 "Higher management or professional") (2 = 2 "Lower management and professional" ) (3 = 3 "Intermediate") (4 = 4 "Small Employer") (5 = 5 "Lower supervisory") (6 = 6 "Semi-routine") (7 = 7 "Routine") (8 =8 "long-term unemployed" ) , gen(nssec7w6)
label variable nssec7w6 "NS-SEC 7 Categories w6"
recode w8nssec8 (-9/-1 99 = 9 "Other Unclassifiable") (1 = 1 "Higher management or professional") (2 = 2 "Lower management and professional" ) (3 = 3 "Intermediate") (4 = 4 "Small Employer") (5 = 5 "Lower supervisory") (6 = 6 "Semi-routine") (7 = 7 "Routine") (8 =8 "long-term unemployed" ) , gen(nssec7w8)
label variable nssec7w8 "NS-SEC 7 Categories w8"
recode w9nssec8 (-9/-1 99 = 9 "Other Unclassifiable") (1 = 1 "Higher management or professional") (2 = 2 "Lower management and professional" ) (3 = 3 "Intermediate") (4 = 4 "Small Employer") (5 = 5 "Lower supervisory") (6 = 6 "Semi-routine") (7 = 7 "Routine") (8 =8 "long-term unemployed" ) , gen(nssec7w9)
label variable nssec7w9 "NS-SEC 7 Categories w9"
* NS-SEC "Five category" to be consistent
clonevar nssec7 = nssec7w9
replace nssec7 = nssec7w8 if nssec7 == 9 | nssec7 == .
replace nssec7 = nssec7w6 if nssec7 == 9 | nssec7 == .
replace nssec7 = nssec7w5 if nssec7 == 9 | nssec7 == .
*SOC 2010/2007 pre-pandemic: soc_2d soc_designation -> soc_2d soc_designation
gen soc_2d = .
gen soc_designation = .
*SIC 2007 : lob_sic07_sec -> sic_1d
gen sic_1d = .
*Key worker status
tab keyworker
*Mental health: depressed_w9 -> mental_health
clonevar mental_health = depressed_w9
*Shielding: ?????? -> shielding
tab shielding
*clonevar shielding = shield
*Cancer: ??????? -> cancer
tab cancer
*Heart disease
gen heart = heartd
*Depression
gen depression = .
* Obesity
gen obese = obesity
* respiratory
tab respiratory
tab1 employment_status econ_act covid_sr age sex education education_bin ethnicity ethnicity_bin ethnicity1 country hh_comp nssec7 sr_health keyworker mental_health shielding
*Creating a String id variable for analysis
tostring idauniq, gen(id_temp)
gen id_string = "E_" + id_temp
*Note removing interview date variables as not needed
keep id_string cohort_id study_selection age_selection employment_selection ///
employment_status econ_act covid_sr age sex education education_bin ethnicity ethnicity_bin ethnicity1 country hh_comp nssec7 sr_health keyworker mental_health shielding
save "$destination\ELSA_analytic_sample_15May23.dta" , replace
************************************************************************************************************************************************
*** USoc Code
clear all
*Recoding all covid waves with single file in same loop.
*NB this is all waves apart from e and f, and there are two versions of f.
*NB there are current two versions of wave cf I am using the single file version that drops a couple of variables at the end.
* The variables are not needed in this analysis and the two file version appears to drop variable labels.
foreach x in a b c d e f g h {
use "$source\c`x'_indresp_w.dta" , clear
rename (racel_dv psu strata) (c`x'_racel_dv c`x'_psu c`x'_strata)
gen in_c`x' = 1
save "$destination\c`x'_indresp_w_racel.dta", replace
}
global source "D:\Data\ARQ7P4\Original"
global destination "D:\Data\ARQ7P4\Derived"
*This is wave g
use "$source\g_indresp.dta" , clear
save "$destination\g_indresp.dta", replace
*This is wave h
use "$source\h_indresp.dta" , clear
save "$destination\h_indresp.dta", replace
*This is wave i
use "$source\i_indresp.dta" , clear
save "$destination\i_indresp.dta", replace
*This is wave j
use "$source\j_indresp.dta" , clear
keep if j_intdaty_dv <= 2019 | (j_intdatm_dv <= 2 & j_intdaty_dv == 2020)
save "$destination\j_indresp.dta", replace
*Adding wave k
use "$source\k_indresp.dta" , clear
drop if k_intdaty_dv == -9
keep if k_intdaty_dv <= 2019 | (k_intdatm_dv <= 2 & k_intdaty_dv == 2020)
save "$destination\k_indresp.dta", replace
*****Importing data
*Data files to be used at this point are the web survey Pandemic waves c`x'_indresp_w ca - ch (April 2020 to March 2021) and pre-pandemic waves `x'_indresp g - j (2015 to 2020)
***Setting File locations
**
clear all
set maxvar 30000
set more off
**File Path
*** Merging data files
clear
**Pre-pandemic waves
*Wave 10
use "$destination\j_indresp.dta"
gen in_w10 = 1
*add wave 11
merge 1:1 pidp using "$destination\k_indresp.dta", gen(w11mrg)
*Add wave 9
merge 1:1 pidp using "$destination\i_indresp.dta", gen(w9mrg)
*Add wave 8
merge 1:1 pidp using "$destination\h_indresp.dta", gen(w8mrg)
*Add wave 7
merge 1:1 pidp using "$destination\g_indresp.dta", gen(w7mrg)
**Covid data
*add covid data
foreach x in a b c d e f g h {
merge 1:1 pidp using "$destination\c`x'_indresp_w_racel.dta", gen(c`x'_mrg)
tab c`x'_mrg
}
********************************************************************************
label define no_yes 0 "No" 1 "Yes"
***** Deriving outcomes
*** Employment status
*** Employment status (latest C19 Sweep)
*This is derived from ch_sempderived and ch_newfurlough
*ch_sempderived is a derived variable. Universe is ALL, but around 1.5% are inapplicable. Not clear why
*ch_newfurloughis is asked of those who are employed or both employed and self-employed from ch_sempderived
* Note if self-employed hours = 0 classifed as not working. Not doing this for both employed and self-employed due to being employed.
gen ch_employment_stat = .
replace ch_employment_stat = 2 if ch_newfurlough == 1 // Recodes those reporting as furloughed
replace ch_employment_stat = 1 if ch_newfurlough == 2 // recodes those not furloughed as employed
recode ch_employment_stat . = 3 if ch_sempderived == 2 & ch_hours == 0 // Selfemployed and 0 hours
recode ch_employment_stat . = 1 if ch_sempderived == 2 & ch_hours != 0 & ch_hours !=. // Selfemployed are coded as in employment.
recode ch_employment_stat . = 3 if ch_sempderived == 4
label define ch_employment_stat 1 "In employment" 2 "Furloughed" 3 "Not Employed"
label values ch_employment_stat ch_employment_stat
* Note that using 0 - working and 2 working as to be consistent with other cohorts.
* Not coding furlough for this paper as that not the relevant data.
gen cg_employment_stat = .
recode cg_employment_stat . = 0 if cg_sempderived == 1 | cg_sempderived == 3
recode cg_employment_stat . = 0 if cg_sempderived == 2 & cg_hours > 0 & cg_hours !=.
recode cg_employment_stat . = 2 if cg_sempderived == 2 & cg_hours == 0
recode cg_employment_stat . = 2 if cg_sempderived == 4
label variable cg_employment_stat "Working or not January"
label define cg_employment_stat 0 "Working " 2 "Not working"
label values cg_employment_stat cg_employment_stat
*** economic activity status form cg
gen econ_act = .
recode econ_act . = 0 if cg_sempderived ==1 | cg_sempderive == 2 | cg_sempderived == 3
recode econ_act . = 0 if cg_sempderive ==4 & cg_julk4wk == 1
recode econ_act . = 1 if cg_sempderive ==4 & cg_julk4wk == 2
label define econ_act 0 "Active" 1 "Inactive"
label values econ_act econ_act
*******************************************************************************
**** Exposures
gen usoc_main = ch_surveystart
gen usoc_alt = cg_surveystart
label define c19_confirmed 0 "No" 1 "Suspected" 2 "Confirmed"
*** Covid confirmed
foreach wave in a b c d e f {
gen c`wave'_c19_confirmed = .
recode c`wave'_c19_confirmed . = 2 if c`wave'_testresult == 1
recode c`wave'_c19_confirmed . = 0 if c`wave'_testresult == 2
recode c`wave'_c19_confirmed . = 1 if c`wave'_hadsymp == 1
recode c`wave'_c19_confirmed . = 0 if c`wave'_hadsymp == 2
label values c`wave'_c19_confirmed c19_confirmed
}
gen cg_c19_confirmed = .
recode cg_c19_confirmed . = 2 if cg_testresult_test1 == 1 | cg_testresult_test2 == 1 | cg_testresult_test3 == 1
recode cg_c19_confirmed . = 0 if cg_testresult_test1 == 2 | cg_testresult_test2 == 2 | cg_testresult_test3 ==2
recode cg_c19_confirmed . = 1 if cg_hadsymp == 1
recode cg_c19_confirmed . = 0 if cg_hadsymp == 2
label values cg_c19_confirmed c19_confirmed
gen ch_c19_confirmed = .
recode ch_c19_confirmed . = 2 if ch_testpos == 1
recode ch_c19_confirmed . = 0 if ch_testpos == 2
recode ch_c19_confirmed . = 1 if ch_hadsymp == 1
recode ch_c19_confirmed . = 0 if ch_hadsymp == 2
label variable ch_c19_confirmed "ch C19 SR confirmed"
label values ch_c19_confirmed c19_confirmed
gen covid_sr = .
foreach wave in a b c d e f g {
recode covid_sr . = 2 if c`wave'_c19_confirmed == 2
}
foreach wave in a b c d e f g {
recode covid_sr . = 1 if c`wave'_c19_confirmed == 1
}
recode covid_sr . = 0 if cg_c19_confirmed == 0
replace covid_sr = . if cg_c19_confirmed ==.
*********************************************************************************
*****Control Variables
*** UK Country of residence
gen pan_region = ca_gor_dv
recode pan_region -9 = .
foreach wave in b c d e f {
replace pan_region = c`wave'_gor_dv if pan_region == .
recode pan_region -9 = .
}
recode pan_region 1/9 = 1 10 = 3 11 = 2 12 = 4 , gen(country)
label define country 1 "England" 2 "Scotland" 3 "Wales" 4 "Northern Ireland"
label values country country
*Country for ch_wave
recode ch_gor_dv 1/9 = 1 10 = 3 11 = 2 12 = 4, gen(country_main)
label values country_main pan_country
*Country for cg_wave
recode cg_gor_dv 1/9 = 1 10 = 3 11 = 2 12 = 4, gen(country_alt)
label values country_alt pan_country
***Age - Pandemic outcome
recode cg_age 25/39 = 0 40/54 = 1 55/66 = 2, gen(cg_agecat)
replace cg_agecat = . if ch_age < 25 | cg_age >66
label variable cg_agecat "Age March 2021 in categories"
label define cg_agecat 0 "25 to 39" 1 "40 to 54" 2 "55 +"
label values cg_agecat cg_agecat
gen age_main = ch_age
gen age_alt = cg_age
*** age first covid wave with corrections
*Note that there are some inconsistencies with ca_age which outside the TRE
* I have previously addressed with ca_pidpcorrected which is not available.
* Note solution is I am coding as missing in ca_age and then replacing with next available
* or year of birth.
label define age_entry 1 "16-24" 2 "25-34" 3 "35-44" 4 "45-54" 5 "55-64" 6 "65-74" 7 "75+"
gen lob_birthy = k_birthy
recode lob_birthy (-9/-1 = .)
replace lob_birthy = j_birthy if lob_birthy == .
recode lob_birthy (-9/-1 = .)
replace lob_birthy = i_birthy if lob_birthy == .
recode lob_birthy (-9/-1 = .)
replace lob_birthy = h_birthy if lob_birthy == .
recode lob_birthy (-9/-1 = .)
replace lob_birthy = g_birthy if lob_birthy == .
recode lob_birthy (-9/-1 = .)
gen dob_age = 2020 - lob_birthy
*Note that there are some inconsistencies with ca_age which
* I have previously addressed with ca_pidpcorrected which is not available.
* Note that I am recoding ca_ages
gen ca_age_temp = ca_age
foreach wave in b c d e f g h {
replace ca_age_temp = . if c`wave'_age < ca_age & ca_age != .
replace ca_age_temp =. if ca_age + 1 < c`wave'_age & c`wave'_age !=.
}
gen age_at_start = ca_age_temp
foreach wave in b c d e f g h {
replace age_at_start = c`wave'_age if age_at_start == .
}
replace age_at_start = dob_age if age_at_start ==.
*** Sex - Pandemic
gen temp_sex = cg_sex
foreach wave in h f e d c b a {
replace temp_sex = c`wave'_sex if temp_sex == 3
}
gen sex = temp_sex - 1
label define sex 0 "Male" 1 "Female"
label values sex sex
drop temp_sex
*** Household composition - pandemic
* Note that wave ca is missing relationship data so will be classified on the basis
* of first valid data person has from remaining waves. Designed to be consistent with CLS
label define household_comp 0 "Alone" 1 "Partner" 2 "Partner & children" 3 "Lone parent" 4 "Other person"
foreach wave in cb cc cd ce cf cg ch {
egen `wave'_offspring = anymatch(`wave'_relation*), values(3 6)
egen `wave'_partner = anymatch(`wave'_relation*), values(1 2)
gen `wave'_household_comp = .
recode `wave'_household_comp . = 2 if `wave'_partner == 1 & `wave'_offspring == 1 & `wave'_couple != .
recode `wave'_household_comp . = 1 if `wave'_partner == 1 & `wave'_offspring == 0 & `wave'_couple != .
recode `wave'_household_comp . = 3 if `wave'_partner == 0 & `wave'_offspring == 1 & `wave'_couple != .
recode `wave'_household_comp . = 4 if `wave'_partner == 0 & `wave'_hhnum >=2 & `wave'_hhnum <= 20 & `wave'_couple != .
recode `wave'_household_comp . = 0 if `wave'_partner == 0 & `wave'_couple != .
label values `wave'_household_comp household_comp
}
gen hh_comp = cb_household_comp
foreach wave in cc cd ce cf cg ch {
replace hh_comp = `wave'_household_comp if hh_comp == .
}
label values hh_comp household_comp
tab cb_household_comp, miss
tab cb_couple,miss
*** Self-rated health
label define sr_health 1 "Excellent" 2 "Very Good" 3 "Good" 4 "Fair" 5 "Poor"
label define sr_health_bin 0 "Excellent-Good" 1 "Fair-Poor"
foreach wave in g h i j k cf cg ch {
recode `wave'_scsf1 (-9/0 = . ) , gen(`wave'_sr_health)
label values `wave'_sr_health sr_health
recode `wave'_sr_health (1/3 = 0) (4/5 = 1), gen(`wave'_sr_health_bin)
label values `wave'_sr_health_bin sr_health_bin
}
gen sr_health = .
foreach wave in k j i h g {
replace sr_health = `wave'_sr_health if sr_health == .
}
label values sr_health sr_health
*** Ethnicity - pandemic
recode cg_racel_dv (-9/0=.) (1/4=1) (5/8=4) (9/13=2) (14/16=3) (17 97=5), gen(ethnicity)
label variable ethnicity "Ethnicity broad "
label define ethnicity 1 "White" 2 "Asian" 3 "Black" 4 "Mixed" 5 "Other"
label values ethnicity ethnicity
recode ethnicity (1 = 0 "White") (2/5 = 1 "Non White") , gen(ethnicity_bin)
label variable ethnicity_bin "Ethnicity Binary"
label define ethnicity1 1 "White" 2 "Mixed" 3 "Indian" 4 "Pakistani" 5 "Bangladeshi" 6 "Black Caribbean" 7 "Black African" 8 "Other"
recode ch_racel_dv(-9/-1 = . ) (1/4 = 1) (5/8 = 2 ) (9 = 3) (10 = 4) (11 = 5) (12/13 = 8) (14 = 6) (15 = 7) (16/97 = 8) , gen(ethnicity1)
label values ethnicity1 ethnicity1
**** NSSEC 7
* Note that I am focusing on ELSA first.
*These are a derived variable showing last job NSSEC
foreach x in k j i h g {
gen `x'_nssec8 = `x'_jbnssec8_dv
replace `x'_nssec8 = `x'_jlnssec8_dv if `x'_jbnssec8_dv == -8 & `x'_jlnssec8_dv > 0 & `x'_jlnssec8_dv !=.
}
gen nssec8 = .
foreach x in k j i h g {
replace nssec8 = `x'_nssec8 if nssec8 == .
recode nssec8 -9/-1 = .
}
recode nssec8 . = 9
label define nssec8 1 "Large employers & higher management" 2 "Higher professional" 3 "Lower management & professional" 4 "Intermediate" 5 "Small employers & own account" 6 "Lower supervisory & technical" 7 "Semi-routine" 8 "routine" 9 "Unclassifiable"
label values nssec8 nssec8
*Note some of the unclassifiable might be coded as long-term unemployed at this point to distinguish those in educaiton or training.
recode nssec8 (1/2 = 1 "Higher management or professional") (3 = 2 "Lower management and professional" ) (4 = 3 "Intermediate") (5 = 4 "Small Employer") (6 = 5 "Lower supervisory") (7 = 6 "Semi-routine") (8 = 7 "Routine") (999 = 8 "long-term unemployed" ) ( 9 = 9 "Other Unclassifiable") , gen(nssec7)
label variable nssec7 "NS-SEC 7 Categories"
recode nssec7 (1/2 = 1 "Management & professional ") (3 = 2 "Intermediate") (4 = 3 "Small Employer") (5 = 4 "Lower supervisory & Technical") (6/7 = 5 "Semi-routine & Routine") (8 = 6 "long-term unemployed" ) ( 9 = 9 "Other Unclassifiable") , gen(nssec5)
label variable nssec5 "NS-SEC 5 Categories"
*** SOC - pre-pandemic
*NB not sure this is necessarily the most efficient, and have corrected some of Mike's code.
*Namely d2 needs both `y' and `z' and d3 needs both `y' `z' `k'
*Can probably just divide the three digit version by ten for the 2 digit version then
*SOC codes
foreach job in soc00 soc10 {
gen lob_`job'_d3=.
}
foreach x in k j i h g {
foreach job in jbsoc00 jlsoc00 jbsoc10 jlsoc10{
recode `x'_`job'_cc (-9/-1=.), gen(`x'_`job')
}
foreach code in soc00 soc10{
replace lob_`code'_d3=`x'_jb`code' if lob_`code'==.
replace lob_`code'_d3=`x'_jl`code' if lob_`code'==.
}
}