DATA CLEANING
. import delimited "data_for_replication/FullTS.csv", encoding(UTF-8) clear
(10 vars, 18,798 obs)
. gen newdate = date(date,"YMD")
. keep if inrange(newdate,21999,22020)
(16,774 observations deleted)
. format newdate %td
. collapse (sum) three_weeks_newcases = newcases, by(country prov_region)
. rename prov_region region
. replace country="UK" if country=="United Kingdom"
(12 real changes made)
. replace region ="Friuli-Venezia Giulia" if region== "Friuli V. G."
(1 real change made)
. replace region ="Emilia-Romagna" if region== "Emilia Romagna"
(1 real change made)
. replace region ="Greater London" if region== "London"
(1 real change made)
. replace region = "Daegu Metropolitan City" if region== "Daegu"
(1 real change made)
. replace region ="Ulsan Metropolitan City" if region== "Ulsan"
(1 real change made)
. replace region ="Incheon Metropolitan City" if region== "Incheon"
variable region was str24 now str25
(1 real change made)
. replace region ="Jeju Island" if region== "Jeju-do"
(1 real change made)
. replace region ="Jeollabuk do" if region== "Jeollabuk-do"
(1 real change made)
. replace region ="Chung-cheong bukdo" if region== "Chungcheongbuk-do"
(1 real change made)
. replace region ="Sejong Special Self-governing City" if region=="Sejong"
variable region was str25 now str34
(1 real change made)
. replace region ="Yorkshire and the Humber" if region== "Yorkshire and The Humber"
(1 real change made)
. replace region ="Kansai" if region== "Kansai "
(1 real change made)
. merge 1:1 country region using "data_for_replication/geo_distribution.dta"
Result # of obs.
-----------------------------------------
not matched 10
from master 10 (_merge==1)
from using 0 (_merge==2)
matched 82 (_merge==3)
-----------------------------------------
. drop if _merge!=3
(10 observations deleted)
. drop _merge
. gen recent_cases_percapita = three_weeks_newcases/(population*1000000)
. save "data_produced/recent_cases.dta", replace
file data_produced/recent_cases.dta saved
. use "data_for_replication/public_dataset.dta", clear
. sdecode region, replace
. replace region = "Fujian" if region == "Fujian "
(48 real changes made)
. replace region = "Gansu" if region == "Gansu "
(8 real changes made)
. replace region ="Kansai" if region== "Kansai "
(170 real changes made)
. merge m:m region using "data_produced/recent_cases.dta", nogen force
(note: variable country was str5, now str14 to accommodate using data's values)
Result # of obs.
-----------------------------------------
not matched 0
matched 6,089
-----------------------------------------
. gen high_prevalence=1
. replace high_prevalence = 0 if inlist(region,"Hainan","Qinghai","Hunan","Anhui","Henan","Jiangxi") // China
(170 real changes made)
. replace high_prevalence = 0 if inlist(region,"Guizhou","Hubei","Gansu","Hebei","Shandong") // China
(222 real changes made)
. replace high_prevalence = 0 if inlist(region,"Calabria","Sicilia","Basilicata","Sardegna","Campania") // Italy
(244 real changes made)
. replace high_prevalence = 0 if inlist(region,"Puglia","Lazio","Molise","Umbria","Abruzzo") // Italy
(235 real changes made)
. replace high_prevalence = 0 if inlist(region,"Chūgoku","Tōhoku","Hokkaidō","Shikoku") // Japan
(189 real changes made)
. replace high_prevalence = 0 if inlist(region,"Jeollabuk do","Chung-cheong bukdo","Busan","Jeollanam-do","Ulsan Metropolitan City") // Korea
(179 real changes made)
. replace high_prevalence = 0 if inlist(region,"Gyeongsangnam-do","Chungcheongnam-do","Daejeon") // Korea
(101 real changes made)
. replace high_prevalence = 0 if inlist(region,"South West","East Midlands","East of England","Northern Ireland","Yorkshire and the Humber","South East") //UK
(497 real changes made)
. replace high_prevalence = 0 if inlist(region,"Texas","California") //US
(478 real changes made)
. sencode region, replace
. replace country = "1" if country == "china"
(996 real changes made)
. replace country = "2" if country == "japan"
(1,015 real changes made)
. replace country = "3" if country == "korea"
(963 real changes made)
. replace country = "4" if country == "italy"
(1,044 real changes made)
. replace country = "5" if country == "uk"
(1,016 real changes made)
. replace country = "6" if country == "us"
(1,055 real changes made)
. destring country, replace
country: all characters numeric; replaced as byte
. lab def country 1 "China" 2 "Japan" 3 "Korea" 4 "Italy" 5 "UK" 6 "US"
. lab val country country
. replace work_change_due_pandemic=3 if work_change_due_pandemic ==-99 // basically saying that there was no change in work arrangement for folks that aren't working
(938 real changes made)
. generate age_large=.
(6,089 missing values generated)
. replace age_large=1 if age_group==1
(736 real changes made)
. replace age_large=2 if age_group==2 | age_group==3
(2,233 real changes made)
. replace age_large=3 if age_group==4 | age_group==5
(2,080 real changes made)
. replace age_large=4 if age_group==6 | age_group==7
(1,035 real changes made)
. label define age_large_lab 1 "18 to 25" 2 "26 to 45" 3 "46 to 65" 4 "Above 66"
. label values age_large age_large_l
. gen id=_n
. bys id: egen pos_non_fin=sum(pos_nonfin_freetime+pos_nonfin_lesspolution+pos_nonfin_lessnoise+pos_nonfin_other)
. bys id: egen neg_non_fin=sum(neg_nonfin_boredom+neg_nonfin_loneliness+neg_nonfin_troublesleep+neg_nonfin_anxiety+neg_nonfin_conflicts+neg_nonfin_other)
. gen started_teleworking=(work_change_due_pandemic==2)
. gen employment_status_pre_pandemic = 1 if labor_status!=4 | inlist(lost_job,1,2)
(1,693 missing values generated)
. replace employment_status_pre_pandemic= 0 if labor_status==4 & lost_job!=1 & lost_job!=2
(1,693 real changes made)
. la def employment_status_pre_pandemic 0 "Not employed before pandemic" 1 "Employed before pandemic"
. la val employment_status_pre_pandemic employment_status_pre_pandemic
. gen drop_savings=(inlist(change_savings,1,2))
. gen lost_job_atleast_temporarily=(inlist(lost_job,1,2))
. replace work_change_due_pandemic = 5 if work_change_due_pandemic ==4
(331 real changes made)
. replace work_change_due_pandemic =4 if work_change_due_pandemic==3 & labor_status==4
(1,245 real changes made)
. la def work_change_due_pandemic_new 1 "I do not work anymore" 2 "I started teleworking" 3 "No change, employed" 4 "No change, not employed" 5 "Other"
. la val work_change_due_pandemic work_change_due_pandemic_new
. gen female = (gender==1)
. pca bothered_meeting_folks bothered_leisure_activities bothered_shop_nonessentials, comp(1)
Principal components/correlation Number of obs = 6,089
Number of comp. = 1
Trace = 3
Rotation: (unrotated = principal) Rho = 0.7634
--------------------------------------------------------------------------
Component | Eigenvalue Difference Proportion Cumulative
-------------+------------------------------------------------------------
Comp1 | 2.29014 1.87374 0.7634 0.7634
Comp2 | .416399 .122942 0.1388 0.9022
Comp3 | .293457 . 0.0978 1.0000
--------------------------------------------------------------------------
Principal components (eigenvectors)
--------------------------------------
Variable | Comp1 | Unexplained
-------------+----------+-------------
bothered_m~s | 0.5792 | .2317
bothered_l~s | 0.5922 | .1969
bothered_s~s | 0.5602 | .2812
--------------------------------------
. predict bothered_social_distance
(score assumed)
Scoring coefficients
sum of squares(column-loading) = 1
------------------------
Variable | Comp1
-------------+----------
bothered_m~s | 0.5792
bothered_l~s | 0.5922
bothered_s~s | 0.5602
------------------------
. gen fall_consumption = (inlist(change_weekly_expenses,1,2))
. save "data_produced/clean_data", replace
file data_produced/clean_data.dta saved
REPLICATE FIGURE 1
. use "data_produced/clean_data", clear
. table country, c(mean lost_job_atleast_temporarily) f(%9.3f)
--------------------------
country | mean(lost_j~y)
----------+---------------
China | 0.432
Japan | 0.145
Korea | 0.250
Italy | 0.369
UK | 0.286
US | 0.323
--------------------------
. qui reg lost_job_atleast_temporarily i.age_large i.income i.female ///
i.current_living_area i.region i.employment_status_pre_pandemic if country==1
. regsave using "data_produced/results", addlabel(country, China) replace
file data_produced/results.dta saved
. forval x=2(1)6 {
. local country_name : label country `x'
. qui reg lost_job_atleast_temporarily i.age_large i.income i.female ///
i.current_living_area i.region i.employment_status_pre_pandemic if country==`x'
. regsave using "data_produced/results", addlabel(country, `country_name') append
. }
file data_produced/results.dta saved
file data_produced/results.dta saved
file data_produced/results.dta saved
(note: variable country was str2, now str5 to accommodate using data's values)
file data_produced/results.dta saved
(note: variable country was str2, now str5 to accommodate using data's values)
file data_produced/results.dta saved
. use "data_produced/results", clear
. drop if var=="6.income_group"
(6 observations deleted)
. keep if strpos(var , "female")!=0 | strpos(var , "age_large")!=0 | strpos(var , "income_group")!=0
(118 observations deleted)
. gen ci_l = coef -1.98*stderr
. gen ci_r = coef +1.98*stderr
. replace var = "Male" if var=="0b.female"
(6 real changes made)
. replace var = "Female" if var=="1.female"
(6 real changes made)
. replace var = "18-25" if var== "1b.age_large"
(6 real changes made)
. replace var = "26-45" if var== "2.age_large"
(6 real changes made)
. replace var = "46-65" if var== "3.age_large"
(6 real changes made)
. replace var = "Above 65" if var== "4.age_large"
(6 real changes made)
. replace var = "Income Q1" if var== "1b.income_group"
(6 real changes made)
. replace var = "Income Q2" if var== "2.income_group"
(6 real changes made)
. replace var = "Income Q3" if var== "3.income_group"
(6 real changes made)
. replace var = "Income Q4" if var== "4.income_group"
(6 real changes made)
. replace var = "Income Q5" if var== "5.income_group"
(6 real changes made)
. sencode var, replace
. gen asia=1 if inlist(country,"China","Korea","Japan")
(33 missing values generated)
. sort asia country
. sencode country, replace
. twoway ///
(scatter var coef, ///
msym(square) mcolor(gs2) by(country, scale(1.8) row(1) ///
legend(off) graphregion(color(white) margin(0 0 0 0)) note("")) ///
ylabel(1(1)11, glcolor(gs15) val angle(0) labsize(medsmall))) ///
(rcap ci_l ci_r var, ///
color(gs2) horizontal by(country)) ///
, ytitle("", size(small)) xline(0, lcolor(orange)) ///
xlabel(, grid glcolor(gs15)) xtitle("") ///
subtitle(,lcolor(white) fcolor(white)) xsize(9) ysize(3)
. graph export "figures/age_income_gradients_jobloss_wcontrols_all.png", replace width(850)
(file figures/age_income_gradients_jobloss_wcontrols_all.png written in PNG format)
Figure 1: Age and income gradients on losing job at least temporarily
REPLICATE FIGURE 2
. use "data_produced/clean_data", clear
. table country, c(mean started_teleworking) f(%9.3f)
--------------------------
country | mean(starte~g)
----------+---------------
China | 0.517
Japan | 0.188
Korea | 0.147
Italy | 0.261
UK | 0.182
US | 0.231
--------------------------
. qui reg started_teleworking i.age_large i.income i.female ///
i.current_living_area i.region i.employment_status_pre_pandemic if country==1
. regsave using "data_produced/results", addlabel(country, China) replace
file data_produced/results.dta saved
. forval x=2(1)6 {
. local country_name : label country `x'
. qui reg started_teleworking i.age_large i.income i.female ///
i.current_living_area i.region i.employment_status_pre_pandemic if country==`x'
. regsave using "data_produced/results", addlabel(country, `country_name') append
. }
file data_produced/results.dta saved
file data_produced/results.dta saved
file data_produced/results.dta saved
(note: variable country was str2, now str5 to accommodate using data's values)
file data_produced/results.dta saved
(note: variable country was str2, now str5 to accommodate using data's values)
file data_produced/results.dta saved
. use "data_produced/results", clear
. drop if var=="6.income_group"
(6 observations deleted)
. keep if strpos(var , "female")!=0 | strpos(var , "age_large")!=0 | strpos(var , "income_group")!=0
(118 observations deleted)
. gen ci_l = coef -1.98*stderr
. gen ci_r = coef +1.98*stderr
. replace var = "Male" if var=="0b.female"
(6 real changes made)
. replace var = "Female" if var=="1.female"
(6 real changes made)
. replace var = "18-25" if var== "1b.age_large"
(6 real changes made)
. replace var = "26-45" if var== "2.age_large"
(6 real changes made)
. replace var = "46-65" if var== "3.age_large"
(6 real changes made)
. replace var = "Above 65" if var== "4.age_large"
(6 real changes made)
. replace var = "Income Q1" if var== "1b.income_group"
(6 real changes made)
. replace var = "Income Q2" if var== "2.income_group"
(6 real changes made)
. replace var = "Income Q3" if var== "3.income_group"
(6 real changes made)
. replace var = "Income Q4" if var== "4.income_group"
(6 real changes made)
. replace var = "Income Q5" if var== "5.income_group"
(6 real changes made)
. sencode var, replace
. gen asia=1 if inlist(country,"China","Korea","Japan")
(33 missing values generated)
. sort asia country
. sencode country, replace
. twoway ///
(scatter var coef, ///
msym(square) mcolor(gs2) by(country, scale(1.8) row(1) legend(off) ///
graphregion(color(white) margin(0 0 0 0)) note("")) ///
ylabel(1(1)11, glcolor(gs15) val angle(0) labsize(medsmall))) ///
(rcap ci_l ci_r var, color(gs2) horizontal by(country)) ///
, ytitle("", size(small)) xline(0, lcolor(orange)) ///
xlabel(-0.8(0.3)0.4, grid glcolor(gs15)) xtitle("") ///
subtitle(,lcolor(white) fcolor(white)) xsize(9) ysize(3)
. graph export "figures/age_income_gradients_teleworking_wcontrols_all.png", replace width(850)
(file figures/age_income_gradients_teleworking_wcontrols_all.png written in PNG format)
Figure 2: Age and income gradients on teleworking
REPLICATE FIGURE 3
. use "data_produced/clean_data", clear
. table country, c(mean fall_consumption) f(%9.3f)
--------------------------
country | mean(fall_c~n)
----------+---------------
China | 0.563
Japan | 0.308
Korea | 0.490
Italy | 0.566
UK | 0.396
US | 0.431
--------------------------
. qui reg fall_consumption i.age_large i.income i.female ///
i.current_living_area i.region i.employment_status_pre_pandemic if country==1
. regsave using "data_produced/results", addlabel(country, China) replace
file data_produced/results.dta saved
. forval x=2(1)6 {
. local country_name : label country `x'
. qui reg fall_consumption i.age_large i.income i.female ///
i.current_living_area i.region i.employment_status_pre_pandemic if country==`x'
. regsave using "data_produced/results", addlabel(country, `country_name') append
. }
file data_produced/results.dta saved
file data_produced/results.dta saved
file data_produced/results.dta saved
(note: variable country was str2, now str5 to accommodate using data's values)
file data_produced/results.dta saved
(note: variable country was str2, now str5 to accommodate using data's values)
file data_produced/results.dta saved
. use "data_produced/results", clear
. drop if var=="6.income_group"
(6 observations deleted)
. keep if strpos(var , "female")!=0 | strpos(var , "age_large")!=0 | strpos(var , "income_group")!=0
(118 observations deleted)
. gen ci_l = coef -1.98*stderr
. gen ci_r = coef +1.98*stderr
. replace var = "Male" if var=="0b.female"
(6 real changes made)
. replace var = "Female" if var=="1.female"
(6 real changes made)
. replace var = "18-25" if var== "1b.age_large"
(6 real changes made)
. replace var = "26-45" if var== "2.age_large"
(6 real changes made)
. replace var = "46-65" if var== "3.age_large"
(6 real changes made)
. replace var = "Above 65" if var== "4.age_large"
(6 real changes made)
. replace var = "Income Q1" if var== "1b.income_group"
(6 real changes made)
. replace var = "Income Q2" if var== "2.income_group"
(6 real changes made)
. replace var = "Income Q3" if var== "3.income_group"
(6 real changes made)
. replace var = "Income Q4" if var== "4.income_group"
(6 real changes made)
. replace var = "Income Q5" if var== "5.income_group"
(6 real changes made)
. sencode var, replace
. gen asia=1 if inlist(country,"China","Korea","Japan")
(33 missing values generated)
. sort asia country
. sencode country, replace
. twoway ///
(scatter var coef, ///
msym(square) mcolor(gs2) by(country, scale(1.8) row(1) legend(off) ///
graphregion(color(white) margin(0 0 0 0)) note("")) ///
ylabel(1(1)11, glcolor(gs15) val angle(0) labsize(medsmall))) ///
(rcap ci_l ci_r var, ///
color(gs2) horizontal by(country)) ///
, ytitle("", size(small)) xline(0, lcolor(orange)) ///
xlabel(, grid glcolor(gs15)) xtitle("") ///
subtitle(,lcolor(white) fcolor(white)) xsize(9) ysize(3)
. graph export "figures/age_income_gradients_consumptiondrop_wcontrols_all.png", replace width(850)
(file figures/age_income_gradients_consumptiondrop_wcontrols_all.png written in PNG format)
Figure 3: Age and income gradients on drop in household spending
REPLICATE FIGURE 4
. use "data_produced/clean_data", clear
. egen avg_interactions_1=rowmean(Q291_8 Q291_9 Q291_10 /*Q291_11 Q291_14*/ Q291_12 )
. egen avg_interactions_2=rowmean(Q292_8 Q292_9 Q292_10 /*Q292_11 Q292_14*/ Q292_12 )
. egen avg_interactions_3=rowmean(Q293_8 Q293_9 Q293_10 /*Q293_11 Q293_14*/ Q293_12 )
. preserve
. collapse (mean) avg_interactions_1 avg_interactions_2 avg_interactions_3, by(country age_large)
. keep if age_large!=.
(3 observations deleted)
. label define age_large_lab 1 "18-25" 2 "26-45" 3 "46-65" 4 "Above 66"
. label values age_large age_large_lab
. rename age_large group
. mkmat * ,mat(A)
. restore
. preserve
. collapse (mean) avg_interactions_1 avg_interactions_2 avg_interactions_3, by(country income_group)
. keep if income_group<6
(6 observations deleted)
. rename income_group group
. replace group = group+4
(30 real changes made)
. mkmat * ,mat(B)
. restore
. mat C=A\B
. clear
. svmat C
number of observations will be reset to 54
Press any key to continue, or Break to abort
number of observations (_N) was 0, now 54
. rename C1 country
. rename C2 group
. la def country 1 "China" 2 "Japan" 3 "Korea" 4 "Italy" 5 "UK" 6 "US"
. la val country country
. label define group 1 "18-25" 2 "26-45" 3 "46-65" 4 "Above 66" 5 "Income Q1" 6 "Income Q2" 7 "Income Q3" 8 "Income Q4" 9 "Income Q5"
. la val group group
. twoway ///
(scatter group C3, ///
by(country, row(1) scale(1.8) note("") graphregion(color(white) ///
margin(0 0 0 0))) msym(X) msize(medlarge) ///
ylabel(1(1)9, glcolor(gs15) val angle(0) )) ///
(scatter group C4, ///
by(country) msym(X) msize(medlarge)) ///
(scatter group C5, ///
by(country) msym(X) msize(medlarge)) ///+
, subtitle(,lcolor(white) fcolor(white)) ///
legend(order(1 "Normal times" 2 "Start pandemic" 3 "Time of survey") row(1) ///
size(small) bmargin(zero) symxsize(5) region(lcolor(white))) ytitle("") ///
xscale(range(1 5)) xlabel(1 "Never" 2 "Rarely" 3 "Sometimes" ///
4 "Very often" 5 "Always" , grid glcolor(gs15) angle(35) labsize(small)) ///
xsize(9) ysize(3) name(gr2, replace)
. graph export "figures/age_income_means_social_interactions.png", replace width(850)
(file figures/age_income_means_social_interactions.png written in PNG format)
Figure 4: Social interactions over time, by age and income groups
REPLICATE FIGURE 5
. use "data_produced/clean_data", clear
. table country, c(mean neg_non_fin) f(%9.3f)
--------------------------
country | mean(neg_no~n)
----------+---------------
China | 1.673
Japan | 1.229
Korea | 1.410
Italy | 1.640
UK | 1.659
US | 1.693
--------------------------
. qui reg neg_non_fin i.age_large i.income i.female i.current_living_area i.region i.employment_status_pre_pandemic if country==1
. regsave using "data_produced/results", addlabel(country, China) replace
file data_produced/results.dta saved
. forval x=2(1)6 {
. local country_name : label country `x'
. qui reg neg_non_fin i.age_large i.income i.female i.current_living_area i.region i.employment_status_pre_pandemic if country==`x'
. regsave using "data_produced/results", addlabel(country, `country_name') append
. }
file data_produced/results.dta saved
file data_produced/results.dta saved
file data_produced/results.dta saved
(note: variable country was str2, now str5 to accommodate using data's values)
file data_produced/results.dta saved
(note: variable country was str2, now str5 to accommodate using data's values)
file data_produced/results.dta saved
. use "data_produced/results", clear
. drop if var=="6.income_group"
(6 observations deleted)
. keep if strpos(var , "female")!=0 | strpos(var , "age_large")!=0 | strpos(var , "income_group")!=0
(118 observations deleted)
. gen ci_l = coef -1.98*stderr
. gen ci_r = coef +1.98*stderr
. replace var = "Male" if var=="0b.female"
(6 real changes made)
. replace var = "Female" if var=="1.female"
(6 real changes made)
. replace var = "18-25" if var== "1b.age_large"
(6 real changes made)
. replace var = "26-45" if var== "2.age_large"
(6 real changes made)
. replace var = "46-65" if var== "3.age_large"
(6 real changes made)
. replace var = "Above 65" if var== "4.age_large"
(6 real changes made)
. replace var = "Income Q1" if var== "1b.income_group"
(6 real changes made)
. replace var = "Income Q2" if var== "2.income_group"
(6 real changes made)
. replace var = "Income Q3" if var== "3.income_group"
(6 real changes made)
. replace var = "Income Q4" if var== "4.income_group"
(6 real changes made)
. replace var = "Income Q5" if var== "5.income_group"
(6 real changes made)
. sencode var, replace
. gen asia=1 if inlist(country,"China","Korea","Japan")
(33 missing values generated)
. sort asia country
. sencode country, replace
. twoway ///
(scatter var coef, ///
msym(square) mcolor(gs2) by(country, scale(1.8) row(1) legend(off) ///
graphregion(color(white) margin(0 0 0 0)) note("")) ///
ylabel(1(1)11, glcolor(gs15) val angle(0) labsize(medsmall))) ///
(rcap ci_l ci_r var, ///
color(gs2) horizontal by(country)) ///
, ytitle("", size(small)) xline(0, lcolor(orange)) ///
xlabel(, grid glcolor(gs15)) xtitle("") ///
subtitle(,lcolor(white) fcolor(white)) xsize(9) ysize(3)
. graph export "figures/age_income_gradients_neg_non_fin_wcontrols_all.png", replace width(850)
(file figures/age_income_gradients_neg_non_fin_wcontrols_all.png written in PNG format)
Figure 5: Age and income gradients on negative well-being
REPLICATE FIGURE 6
. use "data_produced/clean_data", clear
. collapse (mean) neg_nonfin_anxiety neg_nonfin_boredom neg_nonfin_conflicts neg_nonfin_loneliness neg_nonfin_troublesleep, by(country age_large)
. drop if age_large==.
(3 observations deleted)
. twoway ///
(scatter neg_nonfin_anxiety age_large, ///
by(country, graphregion(color(white) margin(0 0 0 0)) note("")) ///
mcolor(emerald) msym(o)) ///
(scatter neg_nonfin_boredom age_large, ///
by(country) mcolor(gold) msym(d)) ///
(scatter neg_nonfin_conflicts age_large, ///
by(country) mcolor(cranberry) msym(t)) ///
(scatter neg_nonfin_loneliness age_large, ///
by(country) mcolor(forest_green) msym(s)) ///
(scatter neg_nonfin_troublesleep age_large, ///
by(country) mcolor(lavender) msize(large) msym(smplus)) ///
(lfit neg_nonfin_anxiety age_large, ///
by(country) lcolor(emerald) lp(.-.)) ///
(lfit neg_nonfin_boredom age_large, ///
by(country) lcolor(gold) lp(.-.)) ///
(lfit neg_nonfin_conflicts age_large, ///
by(country) lcolor(cranberry) lp(.-.)) ///
(lfit neg_nonfin_loneliness age_large, ///
by(country) lcolor(forest_green) lp(.-.)) ///
(lfit neg_nonfin_troublesleep age_large, ///
by(country) lcolor(lavender) lp(.-.)) ///
, xtitle("Age group") ///
xlabel(1 "18-25" 2 "26-45" 3 "46-65" 4 "66+", grid glcolor(gs15)) ///
ytitle("Percentage reporting non-financial distress") ///
legend(order(1 "Anxiety" 2 "Boredom" 3 "Conflicts" 4 "Loneliness" 5 "Trouble sleeping") ///
row(1) size(small) bmargin(zero) symxsize(5) region(lcolor(white))) ///
xsize(9) ysize(6) scale(1.8) subtitle(,lcolor(white) fcolor(white))
. graph export "figures/age_gradients_nonfin.png", replace width(850)
(file figures/age_gradients_nonfin.png written in PNG format)
Figure 6: Age gradients on negative well-being
. use "data_produced/clean_data", clear
. lab def age_large 1 "Age below 26" 2 "Age 26-45" 3 "Age 46-65" 4 "Age above 66"
. lab val age_large age_large
. lab var female "Female"
. lab var recent_cases_percapita "Recent cases per capita"
REPLICATE TABLE A1
. qui eststo country1: reg lost_job_atleast_temporarily i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==1
. qui eststo country2: reg lost_job_atleast_temporarily i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==2
. qui eststo country3: reg lost_job_atleast_temporarily i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==3
. qui eststo country4: reg lost_job_atleast_temporarily i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==4
. qui eststo country5: reg lost_job_atleast_temporarily i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==5
. qui eststo country6: reg lost_job_atleast_temporarily i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==6
. qui eststo all: reg lost_job_atleast_temporarily i.age_large i.income female i.country i.region i.current_living_area i.employment_status_pre_pandemic
. qui eststo all2: reg lost_job_atleast_temporarily i.age_large i.income female i.country i.current_living_area i.employment_status_pre_pandemic c.recent_cases
Table A1: Linear probability model for having lost the job at least temporarily
. estout country1 country2 country3 country4 country5 country6 all all2, ///
ce(b(star fmt(3) label("Coef")) se(par fmt(3))) nobaselevels label ///
varwidth(35) stats(N r2_a, fmt(0 3)) title("TABLE A1") ///
starl(* 0.10 ** 0.05 *** 0.01) ///
keep(*.income_group *.age_large female *.current_living_area *.employment_status_pre_pandemic recent_cases_percapita _cons) ///
drop(6.income_group) mlabels("China" "Japan" "Korea" "Italy" "UK" "US") ///
indicate("Regional fixed effects = *region" "Country fixed effects = *country") ///
order(female *age_large *income_group *current_living_area *employment_status_pre_pandemic recent_cases_percapita)
TABLE A1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
China Japan Korea Italy UK US all all2
Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
Female -0.029 0.055** 0.079*** 0.033 -0.010 0.037 0.026** 0.027***
(0.030) (0.022) (0.026) (0.026) (0.026) (0.026) (0.011) (0.011)
Age 26-45 -0.000 -0.135*** -0.045 -0.043 -0.068 -0.052 -0.052*** -0.052***
(0.046) (0.039) (0.042) (0.045) (0.044) (0.043) (0.018) (0.017)
Age 46-65 0.074 -0.130*** -0.047 -0.100** -0.130*** -0.112*** -0.069*** -0.065***
(0.051) (0.038) (0.042) (0.045) (0.044) (0.043) (0.018) (0.017)
Age above 66 0.183*** -0.061 -0.005 -0.014 -0.104* -0.080 -0.011 -0.006
(0.066) (0.045) (0.049) (0.054) (0.054) (0.049) (0.021) (0.021)
Second quintile -0.032 -0.054 -0.137*** -0.064 -0.025 -0.024 -0.078*** -0.078***
(0.049) (0.035) (0.042) (0.044) (0.043) (0.041) (0.017) (0.017)
Third quintile -0.094* -0.105*** -0.181*** -0.167*** 0.019 -0.096** -0.131*** -0.130***
(0.050) (0.035) (0.040) (0.043) (0.042) (0.040) (0.017) (0.017)
Fourth quintile -0.189*** -0.104*** -0.237*** -0.182*** -0.169*** -0.095** -0.191*** -0.191***
(0.050) (0.037) (0.040) (0.042) (0.041) (0.040) (0.017) (0.017)
Fifth quintile -0.260*** -0.110*** -0.264*** -0.274*** -0.182*** -0.163*** -0.236*** -0.237***
(0.054) (0.039) (0.043) (0.047) (0.043) (0.043) (0.018) (0.018)
Semi-urban / residential 0.121*** -0.073*** 0.011 -0.014 0.001 -0.031 0.005 -0.001
(0.039) (0.026) (0.037) (0.031) (0.029) (0.028) (0.013) (0.012)
Countryside 0.133** -0.050* -0.103 -0.028 0.075* -0.086** -0.013 -0.022
(0.060) (0.030) (0.064) (0.038) (0.042) (0.044) (0.017) (0.017)
Employed before pandemic 0.642*** 0.255*** 0.437*** 0.578*** 0.442*** 0.518*** 0.477*** 0.479***
(0.066) (0.030) (0.030) (0.034) (0.033) (0.032) (0.014) (0.014)
Recent cases per capita 3.852
(2.959)
_cons -0.169* 0.159*** 0.172*** 0.118 0.073 0.117** 0.107* 0.145***
(0.102) (0.057) (0.061) (0.104) (0.070) (0.059) (0.059) (0.025)
Regional fixed effects Yes Yes Yes Yes Yes Yes Yes No
Country fixed effects No No No No No No Yes Yes
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
N 996 1013 962 1042 1016 1055 6084 6084
r2_a 0.141 0.095 0.210 0.292 0.232 0.292 0.229 0.228
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
REPLICATE TABLE A2
. qui eststo country1: reg started_teleworking i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==1
. qui eststo country2: reg started_teleworking i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==2
. qui eststo country3: reg started_teleworking i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==3
. qui eststo country4: reg started_teleworking i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==4
. qui eststo country5: reg started_teleworking i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==5
. qui eststo country6: reg started_teleworking i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==6
. qui eststo all: reg started_teleworking i.age_large i.income female i.country i.region i.current_living_area i.employment_status_pre_pandemic
. qui eststo all2: reg started_teleworking i.age_large i.income female i.country i.current_living_area i.employment_status_pre_pandemic c.recent_cases
Table A2: Linear probability model for having started teleworking
. estout country1 country2 country3 country4 country5 country6 all all2, ///
ce(b(star fmt(3) label("Coef")) se(par fmt(3))) nobaselevels label ///
varwidth(35) stats(N r2_a, fmt(0 3)) title("TABLE A2") ///
starl(* 0.10 ** 0.05 *** 0.01) ///
keep(*.income_group *.age_large female *.current_living_area *.employment_status_pre_pandemic recent_cases_percapita _cons) ///
drop(6.income_group) mlabels("China" "Japan" "Korea" "Italy" "UK" "US") ///
indicate("Regional fixed effects = *region" "Country fixed effects = *country") ///
order(female *age_large *income_group *current_living_area *employment_status_pre_pandemic recent_cases_percapita)
TABLE A2
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
China Japan Korea Italy UK US all all2
Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
Female -0.004 -0.030 -0.027 -0.008 -0.086*** -0.065*** -0.041*** -0.041***
(0.029) (0.023) (0.023) (0.025) (0.023) (0.024) (0.010) (0.010)
Age 26-45 -0.117*** -0.046 -0.097*** -0.020 0.006 0.148*** -0.018 -0.023
(0.044) (0.041) (0.037) (0.044) (0.039) (0.039) (0.017) (0.017)
Age 46-65 -0.252*** -0.090** -0.156*** -0.030 -0.069* 0.031 -0.093*** -0.101***
(0.048) (0.041) (0.038) (0.044) (0.040) (0.039) (0.017) (0.017)
Age above 66 -0.523*** -0.148*** -0.127*** -0.083 -0.084* -0.024 -0.163*** -0.167***
(0.063) (0.048) (0.044) (0.053) (0.049) (0.044) (0.020) (0.020)
Second quintile 0.213*** 0.030 0.007 0.056 -0.012 0.061 0.069*** 0.076***
(0.047) (0.038) (0.037) (0.043) (0.039) (0.037) (0.016) (0.016)
Third quintile 0.067 0.097*** 0.125*** 0.129*** 0.049 0.125*** 0.114*** 0.111***
(0.048) (0.037) (0.036) (0.042) (0.038) (0.036) (0.016) (0.016)
Fourth quintile 0.113** 0.148*** 0.107*** 0.185*** 0.100*** 0.223*** 0.164*** 0.170***
(0.048) (0.040) (0.036) (0.041) (0.037) (0.036) (0.016) (0.016)
Fifth quintile 0.165*** 0.267*** 0.133*** 0.297*** 0.161*** 0.266*** 0.241*** 0.252***
(0.052) (0.041) (0.038) (0.046) (0.038) (0.039) (0.017) (0.017)
Semi-urban / residential -0.130*** -0.004 -0.032 -0.083*** -0.034 -0.035 -0.069*** -0.067***
(0.037) (0.028) (0.033) (0.030) (0.027) (0.025) (0.012) (0.011)
Countryside -0.241*** -0.023 -0.028 -0.103*** -0.054 -0.105*** -0.100*** -0.103***
(0.058) (0.032) (0.058) (0.037) (0.038) (0.040) (0.017) (0.016)
Employed before pandemic 0.150** 0.151*** 0.129*** 0.269*** 0.177*** 0.187*** 0.165*** 0.169***
(0.063) (0.032) (0.027) (0.033) (0.030) (0.029) (0.013) (0.013)
Recent cases per capita 12.500***
(2.821)
_cons 0.533*** 0.038 0.107* 0.060 0.043 -0.032 0.357*** 0.342***
(0.098) (0.061) (0.055) (0.102) (0.063) (0.053) (0.056) (0.024)
Regional fixed effects Yes Yes Yes Yes Yes Yes Yes No
Country fixed effects No No No No No No Yes Yes
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
N 996 1013 962 1042 1016 1055 6084 6084
r2_a 0.231 0.166 0.058 0.178 0.139 0.286 0.231 0.222
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
REPLICATE TABLE A3
. qui eststo country1: reg fall_consumption i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==1
. qui eststo country2: reg fall_consumption i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==2
. qui eststo country3: reg fall_consumption i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==3
. qui eststo country4: reg fall_consumption i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==4
. qui eststo country5: reg fall_consumption i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==5
. qui eststo country6: reg fall_consumption i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==6
. qui eststo all: reg fall_consumption i.age_large i.income female i.country i.region i.current_living_area i.employment_status_pre_pandemic
. qui eststo all2: reg fall_consumption i.age_large i.income female i.country i.current_living_area i.employment_status_pre_pandemic c.recent_cases
Table A3: Linear probability model for having experienced drop in spending
. estout country1 country2 country3 country4 country5 country6 all all2, ///
ce(b(star fmt(3) label("Coef")) se(par fmt(3))) nobaselevels label ///
varwidth(35) stats(N r2_a, fmt(0 3)) title("TABLE A3") ///
starl(* 0.10 ** 0.05 *** 0.01) ///
keep(*.income_group *.age_large female *.current_living_area *.employment_status_pre_pandemic recent_cases_percapita _cons) ///
drop(6.income_group) mlabels("China" "Japan" "Korea" "Italy" "UK" "US") ///
indicate("Regional fixed effects = *region" "Country fixed effects = *country") ///
order(female *age_large *income_group *current_living_area *employment_status_pre_pandemic recent_cases_percapita)
TABLE A3
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
China Japan Korea Italy UK US all all2
Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
Female 0.035 0.051* 0.050 0.013 -0.078*** -0.045 -0.002 -0.000
(0.032) (0.030) (0.034) (0.031) (0.030) (0.031) (0.013) (0.013)
Age 26-45 -0.053 -0.092* -0.070 0.079 -0.012 0.033 -0.028 -0.029
(0.049) (0.053) (0.054) (0.054) (0.051) (0.052) (0.021) (0.021)
Age 46-65 -0.119** -0.172*** -0.013 0.064 -0.006 -0.028 -0.056*** -0.060***
(0.054) (0.052) (0.054) (0.054) (0.052) (0.051) (0.021) (0.021)
Age above 66 -0.265*** -0.133** -0.033 0.035 0.002 -0.019 -0.070*** -0.072***
(0.070) (0.062) (0.063) (0.065) (0.064) (0.059) (0.025) (0.025)
Second quintile 0.080 -0.027 -0.058 0.029 0.073 0.097** 0.033 0.032
(0.052) (0.048) (0.053) (0.053) (0.051) (0.049) (0.021) (0.020)
Third quintile 0.025 -0.017 -0.111** -0.030 0.192*** 0.098** 0.031 0.030
(0.053) (0.048) (0.051) (0.051) (0.050) (0.048) (0.020) (0.020)
Fourth quintile -0.017 -0.017 -0.034 0.021 0.090* 0.128*** 0.040** 0.038*
(0.054) (0.051) (0.051) (0.051) (0.049) (0.048) (0.020) (0.020)
Fifth quintile -0.092 -0.029 -0.083 -0.052 0.116** 0.189*** 0.026 0.030
(0.058) (0.053) (0.055) (0.057) (0.050) (0.052) (0.021) (0.021)
Semi-urban / residential 0.010 0.008 -0.081* -0.017 -0.037 0.038 -0.017 -0.019
(0.042) (0.036) (0.048) (0.037) (0.034) (0.034) (0.015) (0.014)
Countryside -0.011 -0.028 -0.102 -0.077* 0.020 -0.014 -0.035* -0.042**
(0.065) (0.041) (0.082) (0.045) (0.050) (0.053) (0.021) (0.020)
Employed before pandemic 0.069 0.114*** 0.110*** 0.103** 0.212*** 0.207*** 0.145*** 0.148***
(0.071) (0.042) (0.038) (0.041) (0.039) (0.039) (0.017) (0.017)
Recent cases per capita 5.022
(3.547)
_cons 0.455*** 0.296*** 0.453*** 0.365*** 0.181** 0.253*** 0.323*** 0.446***
(0.109) (0.078) (0.079) (0.126) (0.082) (0.071) (0.070) (0.030)
Regional fixed effects Yes Yes Yes Yes Yes Yes Yes No
Country fixed effects No No No No No No Yes Yes
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
N 996 1013 962 1042 1016 1055 6084 6084
r2_a 0.026 0.019 0.026 0.022 0.094 0.086 0.066 0.059
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
REPLICATE TABLE A4
. qui eststo country1: reg neg_non_fin i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==1
. qui eststo country2: reg neg_non_fin i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==2
. qui eststo country3: reg neg_non_fin i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==3
. qui eststo country4: reg neg_non_fin i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==4
. qui eststo country5: reg neg_non_fin i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==5
. qui eststo country6: reg neg_non_fin i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==6
. qui eststo all: reg neg_non_fin i.age_large i.income female i.country i.region i.current_living_area i.employment_status_pre_pandemic
. qui eststo all2: reg neg_non_fin i.age_large i.income female i.country i.current_living_area i.employment_status_pre_pandemic c.recent_cases
Table A4: Ordinary least square for negative well-being
. estout country1 country2 country3 country4 country5 country6 all all2, ///
ce(b(star fmt(3) label("Coef")) se(par fmt(3))) nobaselevels label ///
varwidth(35) stats(N r2_a, fmt(0 3)) title("TABLE A4") ///
starl(* 0.10 ** 0.05 *** 0.01) ///
keep(*.income_group *.age_large female *.current_living_area *.employment_status_pre_pandemic recent_cases_percapita _cons) ///
drop(6.income_group) mlabels("China" "Japan" "Korea" "Italy" "UK" "US") ///
indicate("Regional fixed effects = *region" "Country fixed effects = *country") ///
order(female *age_large *income_group *current_living_area *employment_status_pre_pandemic recent_cases_percapita)
TABLE A4
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
China Japan Korea Italy UK US all all2
Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
Female 0.028 0.400*** 0.257*** 0.292*** 0.405*** 0.416*** 0.300*** 0.299***
(0.067) (0.068) (0.072) (0.074) (0.085) (0.086) (0.031) (0.030)
Age 26-45 -0.326*** -0.315*** -0.195* -0.403*** -0.328** -0.228 -0.311*** -0.312***
(0.101) (0.120) (0.116) (0.128) (0.145) (0.141) (0.051) (0.050)
Age 46-65 -0.583*** -0.558*** -0.399*** -0.581*** -0.566*** -0.766*** -0.571*** -0.574***
(0.111) (0.119) (0.118) (0.129) (0.147) (0.140) (0.051) (0.051)
Age above 66 -0.430*** -0.588*** -0.472*** -0.810*** -1.084*** -1.034*** -0.773*** -0.782***
(0.145) (0.140) (0.135) (0.154) (0.181) (0.160) (0.060) (0.060)
Second quintile 0.010 0.007 -0.075 0.137 -0.173 0.105 -0.012 -0.012
(0.108) (0.109) (0.115) (0.127) (0.143) (0.134) (0.050) (0.049)
Third quintile 0.120 -0.093 0.066 -0.045 -0.209 0.062 -0.043 -0.057
(0.109) (0.109) (0.110) (0.122) (0.141) (0.132) (0.049) (0.048)
Fourth quintile -0.043 0.042 -0.084 -0.025 -0.390*** 0.104 -0.079 -0.086*
(0.111) (0.115) (0.111) (0.121) (0.138) (0.130) (0.049) (0.049)
Fifth quintile 0.085 0.069 -0.055 -0.089 -0.318** -0.082 -0.080 -0.074
(0.119) (0.120) (0.119) (0.135) (0.141) (0.142) (0.052) (0.051)
Semi-urban / residential 0.287*** -0.034 -0.050 0.037 0.107 0.086 0.072** 0.054
(0.086) (0.082) (0.104) (0.088) (0.098) (0.091) (0.037) (0.034)
Countryside 0.034 -0.025 -0.328* -0.359*** -0.052 -0.041 -0.122** -0.162***
(0.133) (0.093) (0.178) (0.108) (0.141) (0.144) (0.050) (0.048)
Employed before pandemic 0.856*** 0.363*** 0.079 0.144 0.200* 0.160 0.243*** 0.240***
(0.145) (0.095) (0.082) (0.098) (0.110) (0.106) (0.041) (0.040)
Recent cases per capita 15.178*
(8.566)
_cons 0.809*** 1.188*** 1.632*** 1.724*** 1.767*** 1.820*** 1.403*** 1.745***
(0.225) (0.178) (0.170) (0.301) (0.231) (0.193) (0.170) (0.073)
Regional fixed effects Yes Yes Yes Yes Yes Yes Yes No
Country fixed effects No No No No No No Yes Yes
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
N 996 1013 962 1042 1016 1055 6084 6084
r2_a 0.116 0.096 0.032 0.088 0.090 0.106 0.098 0.092
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
REPLICATE TABLE A5
. qui eststo country1: reg pos_non_fin i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==1
. qui eststo country2: reg pos_non_fin i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==2
. qui eststo country3: reg pos_non_fin i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==3
. qui eststo country4: reg pos_non_fin i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==4
. qui eststo country5: reg pos_non_fin i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==5
. qui eststo country6: reg pos_non_fin i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==6
. qui eststo all: reg pos_non_fin i.age_large i.income female i.country i.region i.current_living_area i.employment_status_pre_pandemic
. qui eststo all2: reg pos_non_fin i.age_large i.income female i.country i.current_living_area i.employment_status_pre_pandemic c.recent_cases
Table A5: Ordinary least square for positive well-being
. estout country1 country2 country3 country4 country5 country6 all all2, ///
ce(b(star fmt(3) label("Coef")) se(par fmt(3))) nobaselevels label ///
varwidth(35) stats(N r2_a, fmt(0 3)) title("TABLE A5") ///
starl(* 0.10 ** 0.05 *** 0.01) ///
keep(*.income_group *.age_large female *.current_living_area *.employment_status_pre_pandemic recent_cases_percapita _cons) ///
drop(6.income_group) mlabels("China" "Japan" "Korea" "Italy" "UK" "US") ///
indicate("Regional fixed effects = *region" "Country fixed effects = *country") ///
order(female *age_large *income_group *current_living_area *employment_status_pre_pandemic recent_cases_percapita)
TABLE A5
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
China Japan Korea Italy UK US all all2
Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
Female 0.010 0.002 0.093 -0.009 0.103 0.095 0.037 0.037
(0.062) (0.051) (0.060) (0.063) (0.069) (0.069) (0.025) (0.025)
Age 26-45 -0.096 -0.147* -0.174* 0.003 -0.242** 0.055 -0.118*** -0.121***
(0.095) (0.089) (0.095) (0.109) (0.118) (0.114) (0.042) (0.042)
Age 46-65 -0.453*** -0.124 -0.269*** 0.093 0.053 -0.073 -0.132*** -0.137***
(0.103) (0.089) (0.097) (0.110) (0.119) (0.113) (0.042) (0.042)
Age above 66 -0.447*** 0.069 -0.367*** 0.122 0.292** -0.140 -0.112** -0.109**
(0.135) (0.104) (0.111) (0.131) (0.146) (0.129) (0.050) (0.050)
Second quintile 0.025 0.067 0.224** 0.132 -0.201* 0.134 0.085** 0.087**
(0.100) (0.081) (0.095) (0.108) (0.116) (0.108) (0.041) (0.041)
Third quintile 0.063 0.096 0.152* 0.227** -0.192* 0.224** 0.127*** 0.120***
(0.102) (0.081) (0.091) (0.104) (0.114) (0.106) (0.040) (0.040)
Fourth quintile -0.082 0.077 0.219** 0.145 -0.060 0.109 0.112*** 0.115***
(0.103) (0.086) (0.092) (0.103) (0.112) (0.105) (0.041) (0.040)
Fifth quintile 0.079 0.127 0.040 0.256** -0.084 0.060 0.135*** 0.150***
(0.111) (0.089) (0.098) (0.115) (0.114) (0.114) (0.043) (0.042)
Semi-urban / residential 0.264*** -0.159*** 0.028 0.150** 0.092 0.074 0.056* 0.044
(0.080) (0.061) (0.085) (0.075) (0.079) (0.073) (0.030) (0.028)
Countryside 0.167 -0.146** 0.244* -0.082 0.058 0.037 0.022 0.004
(0.124) (0.069) (0.147) (0.092) (0.114) (0.116) (0.042) (0.040)
Employed before pandemic 0.341** 0.263*** -0.068 0.015 0.305*** 0.166* 0.109*** 0.119***
(0.135) (0.070) (0.068) (0.083) (0.089) (0.085) (0.034) (0.033)
Recent cases per capita -6.841
(7.091)
_cons 1.435*** 0.338** 0.853*** 1.532*** 1.121*** 1.096*** 1.431*** 1.318***
(0.210) (0.133) (0.140) (0.256) (0.187) (0.155) (0.141) (0.061)
Regional fixed effects Yes Yes Yes Yes Yes Yes Yes No
Country fixed effects No No No No No No Yes Yes
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
N 996 1013 962 1042 1016 1055 6084 6084
r2_a 0.065 0.038 0.019 0.015 0.016 0.021 0.116 0.111
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
REPLICATE TABLE A6
. qui eststo country1: reg bothered_social_distance i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==1
. qui eststo country2: reg bothered_social_distance i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==2
. qui eststo country3: reg bothered_social_distance i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==3
. qui eststo country4: reg bothered_social_distance i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==4
. qui eststo country5: reg bothered_social_distance i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==5
. qui eststo country6: reg bothered_social_distance i.age_large i.income female i.region i.current_living_area i.employment_status_pre_pandemic if country==6
. qui eststo all: reg bothered_social_distance i.age_large i.income female i.country i.region i.current_living_area i.employment_status_pre_pandemic
. qui eststo all2: reg bothered_social_distance i.age_large i.income female i.country i.current_living_area i.employment_status_pre_pandemic c.recent_cases
Table A6: Ordinary least squares for index of dissatisfaction with social distance
. estout country1 country2 country3 country4 country5 country6 all all2, ///
ce(b(star fmt(3) label("Coef")) se(par fmt(3))) nobaselevels label ///
varwidth(35) stats(N r2_a, fmt(0 3)) title("TABLE A6") ///
starl(* 0.10 ** 0.05 *** 0.01) ///
keep(*.income_group *.age_large female *.current_living_area *.employment_status_pre_pandemic recent_cases_percapita _cons) ///
drop(6.income_group) mlabels("China" "Japan" "Korea" "Italy" "UK" "US") ///
indicate("Regional fixed effects = *region" "Country fixed effects = *country") ///
order(female *age_large *income_group *current_living_area *employment_status_pre_pandemic recent_cases_percapita)
TABLE A6
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
China Japan Korea Italy UK US all all2
Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se Coef/se
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
Female -0.253*** 0.403*** 0.161 0.042 0.098 0.107 0.097** 0.096**
(0.076) (0.107) (0.110) (0.084) (0.099) (0.099) (0.039) (0.039)
Age 26-45 -0.214* -0.150 -0.290 0.140 -0.276 0.058 -0.147** -0.131**
(0.116) (0.189) (0.176) (0.145) (0.170) (0.164) (0.065) (0.064)
Age 46-65 -0.279** -0.275 -0.279 -0.009 -0.208 -0.006 -0.185*** -0.180***
(0.126) (0.188) (0.179) (0.146) (0.172) (0.162) (0.065) (0.064)
Age above 66 -0.047 -0.636*** -0.706*** -0.100 -0.427** 0.092 -0.347*** -0.334***
(0.165) (0.221) (0.206) (0.174) (0.211) (0.186) (0.077) (0.076)
Second quintile 0.341*** 0.073 0.369** -0.016 0.392** 0.270* 0.228*** 0.235***
(0.123) (0.173) (0.175) (0.144) (0.167) (0.155) (0.063) (0.063)
Third quintile 0.322*** 0.064 0.585*** 0.142 0.389** 0.227 0.263*** 0.271***
(0.125) (0.173) (0.168) (0.138) (0.164) (0.153) (0.062) (0.061)
Fourth quintile 0.290** 0.191 0.800*** 0.327** 0.530*** 0.319** 0.414*** 0.420***
(0.126) (0.182) (0.169) (0.137) (0.161) (0.151) (0.062) (0.062)
Fifth quintile 0.356*** 0.367* 0.701*** 0.396*** 0.499*** 0.266 0.424*** 0.425***
(0.136) (0.189) (0.181) (0.153) (0.165) (0.165) (0.066) (0.065)
Semi-urban / residential 0.135 0.039 -0.352** 0.142 -0.028 0.012 0.020 0.026
(0.098) (0.130) (0.158) (0.099) (0.114) (0.106) (0.047) (0.044)
Countryside -0.014 0.066 -0.664** -0.145 0.014 0.215 -0.008 0.011
(0.151) (0.147) (0.271) (0.122) (0.164) (0.167) (0.064) (0.061)
Employed before pandemic 0.913*** 0.525*** 0.289** 0.245** 0.156 0.579*** 0.368*** 0.376***
(0.165) (0.149) (0.125) (0.111) (0.129) (0.123) (0.052) (0.051)
Recent cases per capita -2.817
(10.877)
_cons -1.332*** -0.495* -0.467* -0.115 -0.371 -0.563** -0.977*** -0.395***
(0.256) (0.281) (0.259) (0.340) (0.270) (0.224) (0.216) (0.093)
Regional fixed effects Yes Yes Yes Yes Yes Yes Yes No
Country fixed effects No No No No No No Yes Yes
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
N 996 1013 962 1042 1016 1055 6084 6084
r2_a 0.103 0.059 0.054 0.043 0.043 0.036 0.045 0.042
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
REPLICATE TABLE A7
. import excel "data_for_replication/geo_distribution.xlsx", sheet("Sheet1") firstrow case(lower) clear
(7 vars, 131 obs)
. keep if area_survey !=""
(9 observations deleted)
. rename area_survey region
. drop g
. collapse (sum) population, by(region country)
. save "data_for_replication/geo_distribution.dta", replace
file data_for_replication/geo_distribution.dta saved
. use "data_produced/clean_data", clear
. sdecode country, replace
. sdecode region, replace
. replace region ="Kansai" if region=="Kansai "
(0 real changes made)
. replace region ="Fujian" if region=="Fujian "
(0 real changes made)
. replace region ="Gansu" if region=="Gansu "
(0 real changes made)
. merge m:m country region using "data_for_replication/geo_distribution.dta"
Result # of obs.
-----------------------------------------
not matched 0
matched 6,089 (_merge==3)
-----------------------------------------
. collapse (mean) population (count) observations=population, by(country region)
. bysort country: egen country_pop=total(population)
. bysort country: egen country_obs=total(observations)
. gen pop_share = population/country_pop
. gen sample_share = observations/country_obs
. keep country region pop_share sample_share
. format pop_share %9.3f
. format sample_share %9.3f
. sort country region
Table A7: Geographical Representation
. list
+--------------------------------------------------------------------+
| country region pop_sh~e sample~e |
|--------------------------------------------------------------------|
1. | China Anhui 0.053 0.054 |
2. | China Fujian 0.033 0.048 |
3. | China Gansu 0.022 0.008 |
4. | China Guangdong 0.095 0.153 |
5. | China Guizhou 0.030 0.010 |
|--------------------------------------------------------------------|
6. | China Hainan 0.008 0.006 |
7. | China Hebei 0.063 0.092 |
8. | China Heilongjiang 0.032 0.029 |
9. | China Henan 0.080 0.047 |
10. | China Hubei 0.050 0.038 |
|--------------------------------------------------------------------|
11. | China Hunan 0.058 0.032 |
12. | China Jiangsu 0.067 0.094 |
13. | China Jiangxi 0.039 0.027 |
14. | China Jilin 0.023 0.022 |
15. | China Liaoning 0.036 0.052 |
|--------------------------------------------------------------------|
16. | China Qinghai 0.005 0.004 |
17. | China Shaanxi 0.032 0.027 |
18. | China Shandong 0.084 0.074 |
19. | China Shanxi 0.031 0.047 |
20. | China Sichuan 0.070 0.065 |
|--------------------------------------------------------------------|
21. | China Yunnan 0.040 0.010 |
22. | China Zhejiang 0.048 0.058 |
23. | Italy Abruzzo 0.022 0.019 |
24. | Italy Basilicata 0.009 0.015 |
25. | Italy Calabria 0.032 0.021 |
|--------------------------------------------------------------------|
26. | Italy Campania 0.096 0.090 |
27. | Italy Emilia-Romagna 0.074 0.072 |
28. | Italy Friuli-Venezia Giulia 0.020 0.021 |
29. | Italy Lazio 0.097 0.116 |
30. | Italy Liguria 0.026 0.027 |
|--------------------------------------------------------------------|
31. | Italy Lombardia 0.167 0.174 |
32. | Italy Marche 0.025 0.019 |
33. | Italy Molise 0.005 0.005 |
34. | Italy Piemonte 0.072 0.076 |
35. | Italy Puglia 0.067 0.078 |
|--------------------------------------------------------------------|
36. | Italy Sardegna 0.027 0.044 |
37. | Italy Sicilia 0.083 0.063 |
38. | Italy Toscana 0.062 0.059 |
39. | Italy Trentino-Alto Adige 0.018 0.009 |
40. | Italy Umbria 0.015 0.008 |
|--------------------------------------------------------------------|
41. | Italy Valle d'Aosta 0.002 0.003 |
42. | Italy Veneto 0.081 0.081 |
43. | Japan Chūbu 0.168 0.156 |
44. | Japan Chūgoku 0.058 0.055 |
45. | Japan Hokkaidō 0.042 0.044 |
|--------------------------------------------------------------------|
46. | Japan Kansai 0.177 0.167 |
47. | Japan Kantō 0.343 0.421 |
48. | Japan Kyūshū 0.113 0.070 |
49. | Japan Shikoku 0.030 0.024 |
50. | Japan Tōhoku 0.069 0.063 |
|--------------------------------------------------------------------|
51. | Korea Busan 0.066 0.086 |
52. | Korea Chung-cheong bukdo 0.031 0.025 |
53. | Korea Chungcheongnam-do 0.042 0.024 |
54. | Korea Daegu Metropolitan City 0.047 0.059 |
55. | Korea Daejeon 0.029 0.030 |
|--------------------------------------------------------------------|
56. | Korea Gangwon-do 0.029 0.028 |
57. | Korea Gyeonggi-do 0.283 0.239 |
58. | Korea Gyeongsangbuk-do 0.052 0.045 |
59. | Korea Gyeongsangnam-do 0.065 0.051 |
60. | Korea Incheon Metropolitan City 0.057 0.056 |
|--------------------------------------------------------------------|
61. | Korea Jeju Island 0.013 0.010 |
62. | Korea Jeollabuk do 0.035 0.028 |
63. | Korea Jeollanam-do 0.035 0.035 |
64. | Korea Sejong Special Self-governing City 0.006 0.004 |
65. | Korea Seoul 0.187 0.268 |
|--------------------------------------------------------------------|
66. | Korea Ulsan Metropolitan City 0.022 0.011 |
67. | UK East Midlands 0.072 0.073 |
68. | UK East of England 0.093 0.080 |
69. | UK Greater London 0.134 0.156 |
70. | UK North East 0.040 0.050 |
|--------------------------------------------------------------------|
71. | UK North West 0.110 0.094 |
72. | UK Northern Ireland 0.028 0.023 |
73. | UK Scotland 0.082 0.083 |
74. | UK South East 0.137 0.146 |
75. | UK South West 0.084 0.091 |
|--------------------------------------------------------------------|
76. | UK Wales 0.047 0.045 |
77. | UK West Midlands 0.089 0.083 |
78. | UK Yorkshire and the Humber 0.082 0.078 |
79. | US California 0.361 0.257 |
80. | US Florida 0.196 0.260 |
|--------------------------------------------------------------------|
81. | US New York 0.178 0.287 |
82. | US Texas 0.265 0.196 |
+--------------------------------------------------------------------+
REPLICATE TABLES A9 AND A10
. use "data_produced/clean_data", clear
. qui foreach var of var lost_job_atleast_temporarily started_teleworking fall_consumption neg_non_fin pos_non_fin {
. mat table = lost_job_atleast_temporarily,started_teleworking,fall_consumption,neg_non_fin,pos_non_fin
. clear
. svmat table
number of observations will be reset to 36
Press any key to continue, or Break to abort
number of observations (_N) was 0, now 36
. foreach x of var * {
. qui replace `x'=round(`x',0.001)
. }
. qui tostring *, replace format(%9.3f) force
. forval n=1(1)15{
. qui replace table`n' = "(" + table`n' + ")" if inlist(_n,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36)
. }
. preserve
. keep table1 table4 table7 table10 table13
. gen Country = ""
(36 missing values generated)
. loc j = 1
. foreach state in China Japan Korea Italy UK US{
. qui replace Country = "`state'" in `j'
. loc j = `j' + 6
. }
. gen Prevalence = ""
(36 missing values generated)
. foreach num of numlist 1 7 13 19 25 31{
. loc j = `num'
. foreach name in All Low High{
. qui replace Prevalence = "`name'" in `j'
. loc j = `j' + 2
. }
. }
. rename table1 Lost_job
. rename table4 Teleworking
. rename table7 Spending_drop
. rename table10 Negative_well_being
. rename table13 Positive_well_being
. order Country Prevalence
Table A9: Mean Outcomes by Infection Prevalence Areas (standard errors in parentheses)
. list, separator(6)
+---------------------------------------------------------------------------+
| Country Preval~e Lost_job Telewo~g Spendi~p Negati~g Positi~g |
|---------------------------------------------------------------------------|
1. | China All 0.432 0.517 0.563 1.673 1.445 |
2. | (0.016) (0.016) (0.016) (0.034) (0.031) |
3. | Low 0.467 0.459 0.559 1.602 1.480 |
4. | (0.025) (0.025) (0.025) (0.055) (0.050) |
5. | High 0.409 0.555 0.566 1.719 1.422 |
6. | (0.020) (0.020) (0.020) (0.043) (0.039) |
|---------------------------------------------------------------------------|
7. | Japan All 0.145 0.188 0.308 1.229 0.596 |
8. | (0.011) (0.012) (0.015) (0.034) (0.025) |
9. | Low 0.101 0.063 0.280 1.042 0.545 |
10. | (0.022) (0.018) (0.033) (0.074) (0.058) |
11. | High 0.155 0.217 0.315 1.271 0.608 |
12. | (0.013) (0.014) (0.016) (0.038) (0.027) |
|---------------------------------------------------------------------------|
13. | Korea All 0.250 0.147 0.490 1.410 0.952 |
14. | (0.014) (0.011) (0.016) (0.035) (0.029) |
15. | Low 0.254 0.121 0.461 1.371 0.864 |
16. | (0.026) (0.020) (0.030) (0.065) (0.054) |
17. | High 0.249 0.158 0.502 1.426 0.988 |
18. | (0.017) (0.014) (0.019) (0.041) (0.034) |
|---------------------------------------------------------------------------|
19. | Italy All 0.369 0.261 0.566 1.640 1.606 |
20. | (0.015) (0.014) (0.015) (0.038) (0.031) |
21. | Low 0.378 0.251 0.549 1.720 1.505 |
22. | (0.022) (0.020) (0.023) (0.056) (0.045) |
23. | High 0.361 0.269 0.581 1.572 1.692 |
24. | (0.020) (0.019) (0.021) (0.051) (0.042) |
|---------------------------------------------------------------------------|
25. | UK All 0.286 0.182 0.396 1.659 1.318 |
26. | (0.014) (0.012) (0.015) (0.043) (0.034) |
27. | Low 0.268 0.157 0.396 1.553 1.316 |
28. | (0.020) (0.016) (0.022) (0.061) (0.049) |
29. | High 0.304 0.206 0.395 1.761 1.320 |
30. | (0.020) (0.018) (0.021) (0.061) (0.046) |
|---------------------------------------------------------------------------|
31. | US All 0.323 0.231 0.431 1.693 1.177 |
32. | (0.014) (0.013) (0.015) (0.042) (0.032) |
33. | Low 0.305 0.184 0.439 1.655 1.232 |
34. | (0.021) (0.018) (0.023) (0.063) (0.048) |
35. | High 0.338 0.270 0.425 1.724 1.132 |
36. | (0.020) (0.019) (0.021) (0.056) (0.044) |
+---------------------------------------------------------------------------+
. restore
. preserve
. drop table1 table4 table7 table10 table13
. gen Country = ""
(36 missing values generated)
. loc j = 1
. foreach state in China Japan Korea Italy UK US{
. qui replace Country = "`state'" in `j'
. loc j = `j' + 6
. }
. gen Prevalence = ""
(36 missing values generated)
. foreach num of numlist 1 7 13 19 25 31{
. loc j = `num'
. foreach name in All Low High{
. qui replace Prevalence = "`name'" in `j'
. loc j = `j' + 2
. }
. }
. rename table2 Lost_job_Age
. rename table5 Teleworking_Age
. rename table8 Spending_drop_Age
. rename table11 Negative_well_being_Age
. rename table14 Positive_well_being_Age
. rename table3 Lost_job_Income
. rename table6 Teleworking_Income
. rename table9 Spending_drop_Income
. rename table12 Negative_well_being_Income
. rename table15 Positive_well_being_Income
. order Country Prevalence
Table A10: Linear Age and Income Gradients by Infection Prevalence Areas (p-values in parentheses)
. list, separator(6)
+----------------------------------------------------------------------------------------------------------------------------------+
| Country Preval~e Lost_~ge Lost_~me Telew~ge Telew~me Spend~ge Spend~me Negat~ge Negat~me Posit~ge Posit~me |
|----------------------------------------------------------------------------------------------------------------------------------|
1. | China All 0.056 -0.069 -0.172 0.018 -0.085 -0.031 -0.194 0.016 -0.203 0.015 |
2. | (0.004) (0.000) (0.000) (0.121) (0.000) (0.015) (0.000) (0.538) (0.000) (0.532) |
3. | Low 0.110 -0.052 -0.212 0.042 -0.068 0.005 -0.188 0.106 -0.247 0.099 |
4. | (0.002) (0.006) (0.000) (0.018) (0.065) (0.816) (0.014) (0.012) (0.000) (0.009) |
5. | High 0.027 -0.080 -0.153 -0.002 -0.092 -0.053 -0.195 -0.046 -0.180 -0.045 |
6. | (0.251) (0.000) (0.000) (0.890) (0.000) (0.001) (0.000) (0.164) (0.000) (0.152) |
|----------------------------------------------------------------------------------------------------------------------------------|
7. | Japan All -0.012 -0.032 -0.053 0.065 -0.053 -0.009 -0.203 0.009 0.036 0.019 |
8. | (0.397) (0.000) (0.000) (0.000) (0.004) (0.461) (0.000) (0.747) (0.257) (0.343) |
9. | Low 0.033 -0.027 -0.017 0.036 -0.043 -0.010 -0.236 0.084 -0.157 0.037 |
10. | (0.281) (0.196) (0.498) (0.039) (0.346) (0.757) (0.017) (0.214) (0.045) (0.490) |
11. | High -0.020 -0.034 -0.058 0.069 -0.055 -0.008 -0.196 -0.003 0.073 0.019 |
12. | (0.188) (0.000) (0.001) (0.000) (0.007) (0.531) (0.000) (0.909) (0.034) (0.379) |
|----------------------------------------------------------------------------------------------------------------------------------|
13. | Korea All 0.002 -0.065 -0.042 0.034 0.007 -0.015 -0.165 -0.016 -0.118 0.010 |
14. | (0.877) (0.000) (0.002) (0.000) (0.732) (0.230) (0.000) (0.557) (0.000) (0.659) |
15. | Low 0.042 -0.066 -0.020 0.035 -0.005 -0.008 -0.160 0.067 -0.131 -0.042 |
16. | (0.121) (0.000) (0.373) (0.025) (0.880) (0.739) (0.032) (0.179) (0.043) (0.337) |
17. | High -0.015 -0.065 -0.049 0.034 0.009 -0.020 -0.158 -0.051 -0.114 0.027 |
18. | (0.414) (0.000) (0.003) (0.001) (0.698) (0.177) (0.001) (0.099) (0.004) (0.293) |
|----------------------------------------------------------------------------------------------------------------------------------|
19. | Italy All -0.014 -0.066 -0.023 0.071 0.013 -0.010 -0.262 -0.033 0.047 0.051 |
20. | (0.398) (0.000) (0.152) (0.000) (0.517) (0.434) (0.000) (0.265) (0.239) (0.046) |
21. | Low 0.005 -0.072 -0.024 0.067 0.007 0.007 -0.254 -0.026 0.061 0.058 |
22. | (0.813) (0.000) (0.308) (0.000) (0.800) (0.690) (0.000) (0.561) (0.278) (0.116) |
23. | High -0.037 -0.065 -0.020 0.080 0.017 -0.028 -0.276 -0.036 0.037 0.038 |
24. | (0.109) (0.000) (0.356) (0.000) (0.540) (0.117) (0.000) (0.371) (0.504) (0.295) |
|----------------------------------------------------------------------------------------------------------------------------------|
25. | UK All -0.044 -0.053 -0.040 0.044 0.008 0.021 -0.340 -0.080 0.133 0.001 |
26. | (0.008) (0.000) (0.007) (0.000) (0.665) (0.058) (0.000) (0.011) (0.003) (0.957) |
27. | Low -0.031 -0.040 -0.015 0.051 0.033 0.011 -0.263 -0.049 0.178 0.064 |
28. | (0.188) (0.004) (0.460) (0.000) (0.227) (0.479) (0.001) (0.278) (0.006) (0.092) |
29. | High -0.063 -0.066 -0.062 0.040 -0.022 0.031 -0.412 -0.106 0.088 -0.056 |
30. | (0.008) (0.000) (0.006) (0.002) (0.423) (0.051) (0.000) (0.016) (0.157) (0.113) |
|----------------------------------------------------------------------------------------------------------------------------------|
31. | US All -0.029 -0.039 -0.029 0.075 -0.018 0.043 -0.373 -0.007 -0.060 0.014 |
32. | (0.062) (0.000) (0.041) (0.000) (0.333) (0.000) (0.000) (0.830) (0.140) (0.587) |
33. | Low -0.046 -0.041 -0.023 0.067 -0.018 0.048 -0.384 -0.012 -0.028 -0.041 |
34. | (0.036) (0.005) (0.230) (0.000) (0.513) (0.006) (0.000) (0.807) (0.630) (0.285) |
35. | High -0.015 -0.037 -0.031 0.081 -0.016 0.038 -0.363 -0.001 -0.080 0.056 |
36. | (0.488) (0.005) (0.120) (0.000) (0.533) (0.014) (0.000) (0.985) (0.158) (0.104) |
+----------------------------------------------------------------------------------------------------------------------------------+
. restore