****************************** ****************************** ********* DATASETS ********* ****************************** ****************************** *** COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation. * Persistent identifier (DOI): https://doi.org/10.3886/E119964V1 * Accessing the data: Data can be downloaded after registration at - https://www.openicpsr.org/openicpsr/project/119964/version/V1/view;jsessionid=D31451D907E95CEA77C96C1B9230F6FD *** Recode * Language clonevar language = LANGUAGE * Neighbour trust clonevar neitrust = SOC1 recode neitrust (77/99=.) revrs neitrust drop neitrust rename revneitrust neitrust * Neighbour talk (NOW) clonevar neitalk_now = SOC2A recode neitalk_now (77/99=.) revrs neitalk_now drop neitalk_now rename revneitalk_now neitalk_now * Neighbour talk (PAST) clonevar neitalk_past = SOC2B recode neitalk_past (77/99=.) revrs neitalk_past drop neitalk_past rename revneitalk_past neitalk_past * Change in neighbourhood talk pre-/post-Corona gen neitalk_c = neitalk_now - neitalk_past * Categorical version recode neitalk_c (-4/-1=0)(0=1)(1/4=2), into(neitalk_c2) label define neitalk_c2 0 "Neighbourliness declined" 1 "No change" 2 "Neighbourliness increased" label values neitalk_c2 neitalk_c2 * Friends and family at distance (NOW) clonevar frfamtalk_now = SOC3A recode frfamtalk_now (77/99=.) revrs frfamtalk_now drop frfamtalk_now rename revfrfamtalk_now frfamtalk_now * Friends and family at distance (PAST) clonevar frfamtalk_past = SOC3B recode frfamtalk_past (77/99=.) revrs frfamtalk_past drop frfamtalk_past rename revfrfamtalk_past frfamtalk_past * Change in friend/family interaction gen frfamtalk_c = frfamtalk_now - frfamtalk_past * Volunteering clonevar vol_now = SOC4A clonevar vol_past = SOC4B recode vol_past vol_now (77/99=.) revrs vol_now vol_past drop vol_now vol_past rename revvol_now vol_now rename revvol_past vol_past recode vol_now vol_past (1=0)(2=1) label define vol 0 "No" 1 "Yes" label values vol_past vol_now vol * Change in volunteering gen vol_c = vol_now - vol_past * Categorical version recode vol_c (-1=0)(0=1)(1=2), into(vol_c2) label define vol_c2 0 "volunteering declined" 1 "No change" 2 "volunteering increased" label values vol_c2 vol_c2 * Total number of restrictions experienced clonevar ld_school = ECON8A clonevar ld_pschool = ECON8B clonevar ld_college = ECON8C clonevar ld_gath250 = ECON8D clonevar ld_gath50 = ECON8E clonevar ld_gath10 = ECON8F clonevar ld_worship = ECON8G clonevar ld_ptrans = ECON8H clonevar ld_pserv = ECON8I clonevar ld_bars = ECON8J clonevar ld_rest = ECON8K clonevar ld_gym = ECON8L clonevar ld_othb = ECON8M clonevar ld_sprtevnt = ECON8N clonevar ld_work = ECON8O clonevar ld_homoff = ECON8P clonevar ld_qrntn = ECON8Q clonevar ld_inttrvl = ECON8R clonevar ld_domtrvl = ECON8S * Where not sure coded as missing preserve keep SU_ID ld_school-ld_domtrvl renvars ld_school-ld_domtrvl, postf(_1) recode ld_school_1-ld_domtrvl_1 (1=1)(2=0)(77/max=.) label define ld_1 0 "No" 1 "Yes" egen ldact_n_1 = rowtotal(ld_school_1-ld_domtrvl_1) recode ldact_n_1 (1/max=1), into(ldact_1) label variable ldact_n_1 "Total number of lockdown experiences (not sure = missing)" label variable ldact_1 "Experienced lockdown or not (not sure = missing)" label values ld_school_1-ld_domtrvl_1 ldact_1 ld_1 save "C:\WORK\Dropbox\COVID research\Harris Kim collaboration\Covid Impact Study\Data\temp1.dta", replace restore * Where not sure is left in the coding preserve keep SU_ID ld_school-ld_domtrvl renvars ld_school-ld_domtrvl, postf(_2) recode ld_school_2-ld_domtrvl_2 (2=0)(77=1)(1=2)(98/max=.) label define ld_2 0 "No" 1 "Not sure" 2 "Yes" egen ldact_n_2 = rowtotal(ld_school_2-ld_domtrvl_2) recode ldact_n_2 (1/max=1), into(ldact_2) label variable ldact_n_2 "Total number of lockdown experiences (not sure as middle values)" label values ld_school_2-ld_domtrvl_2 ldact_2 ld_2 save "C:\WORK\Dropbox\COVID research\Harris Kim collaboration\Covid Impact Study\Data\temp2.dta", replace restore * Where not sure is coded as NO preserve keep SU_ID ld_school-ld_domtrvl renvars ld_school-ld_domtrvl, postf(_3) recode ld_school_3-ld_domtrvl_3 (2=0)(77=1)(1=1)(98/max=.) label define ld_3 0 "No/Not sure" 1 "Yes" egen ldact_n_3 = rowtotal(ld_school_3-ld_domtrvl_3) recode ldact_n_3 (1/max=1), into(ldact_3) label variable ldact_n_3 "Total number of lockdown experiences (not sure as NO)" label values ld_school_3-ld_domtrvl_3 ldact_3 ld_3 save "C:\WORK\Dropbox\COVID research\Harris Kim collaboration\Covid Impact Study\Data\temp3.dta", replace restore * Depression clonevar depress1 = SOC5A clonevar depress2 = SOC5B clonevar depress3 = SOC5C clonevar depress4 = SOC5D clonevar depress5 = SOC5E recode depress1-depress5 (77/99=.) factor depress1-depress5 rotate, varimax predict depressf factor depress1-depress4 predict depressf2 gen depressm = (depress1+depress2+depress3+depress4+depress5)/5 * Days frequenc7 clonevar depress_1f = depress1 clonevar depress_2f = depress2 clonevar depress_3f = depress3 clonevar depress_4f = depress4 clonevar depress_5f = depress5 recode depress_1f-depress_5f (1=0.5)(2=1.5)(3=3.5)(4=6) gen depress_mf = (depress_1f+depress_2f+depress_3f+depress_4f+depress_5f)/5 * How to pay for emergency food clonevar pay_cardn = ECON7_1 clonevar pay_cardt = ECON7_2 clonevar pay_save = ECON7_3 clonevar pay_bloan = ECON7_4 clonevar pay_frndfam = ECON7_5 clonevar pay_pdloan = ECON7_6 clonevar pay_sell = ECON7_7 clonevar pay_cant = ECON7_8 * Employment status clonevar empstat = ECON1 recode empstat (77/99=.) recode empstat (3=4) if ECON4==1 recode empstat (3=5) if ECON4==2 recode empstat (3=6) if ECON4==3 recode empstat (3=7) if ECON4==8 recode empstat (3=7) if ECON4==9 label define empstat 1 "Yes, I worked for someone else" 2 "Yes, self-employed" 3 "Caring for someone" 4 "Do not want to be employed" 5 "Retired" 6 "Laid-off/Furloughed" 7 "Unemployed before/after Covid-19", modify label values empstat empstat * Employment likelihood in the future - 30 DAYS clonevar empfuture1 = ECON4A recode empfuture1 (77/max=.) * Employment likelihood in the future - 3 months clonevar empfuture2 = ECON4B recode empfuture2 (77/max=.) * COVID experiences clonevar hadcov = PHYS4 clonevar livecov = PHYS5 clonevar deathcov = PHYS6 recode hadcov livecov deathcov (77/99=.) recode hadcov livecov deathcov (1=1)(2=0) label values hadcov livecov deathcov vol * Gender clonevar female = GENDER recode female (77/99=.) recode female (1=0)(2=1) label define female 0 "Male" 1 "Female" label values female female * Race clonevar nonwhite = RACE_R2 recode nonwhite (77/99=.) recode nonwhite (1=0)(2=1) label define nonwhite 0 "White" 1 "Nonwhite" label values nonwhite nonwhite * HH income clonevar hhinc = HHINCOME recode hhinc (77/99=.) * Education clonevar educ = EDUCATION recode educ (77/99=.) * Marital status clonevar marstat = MARITAL * Household size clonevar hhsize = HHSIZE1 recode hhsize (99/99=.) * Region clonevar reg4 = REGION4 clonevar reg9 = REGION9 * Urban/rural clonevar urbrur = P_DENSE * Mode of survey clonevar mode = MODE * Age clonevar age7 = AGE7 recode age7 (88=.) * State clonevar state = P_GEO clonevar shealth = PHYS8 recode shealth (77/max=.) * food worry clonevar foodworry1 = ECON5A_A clonevar foodworry2 = ECON5A_B recode foodworry1 - foodworry2 (77/max=.) revrs foodworry1 revrs foodworry2 drop foodworry1 foodworry2 rename revfoodworry1 foodworry1 rename revfoodworry2 foodworry2 * Prior health clonevar phealth1 = PHYS3A clonevar phealth2 = PHYS3B clonevar phealth3 = PHYS3C clonevar phealth4 = PHYS3D clonevar phealth5 = PHYS3E clonevar phealth6 = PHYS3F clonevar phealth7 = PHYS3G clonevar phealth8 = PHYS3H clonevar phealth9 = PHYS3I clonevar phealth10 = PHYS3J clonevar phealth11 = PHYS3K clonevar phealth12 = PHYS3L clonevar phealth13 = PHYS3M recode phealth1-phealth13 (2=0)(1=1)(77/max=.) label define phealth 0 "No" 1 "Yes" label values phealth1-phealth13 phealth egen phealth_n = rowtotal(phealth1-phealth13) recode phealth_n (1/max=1), into(phealth_b)