***************************** ANALYSIS ******************************************************
use plos.dta, clear

**** TABLE 1
sum age male urban ses impair barrier
tab education
bysort country: sum age male urban ses impair barrier
bysort country: tab education
bysort country: tab 
* sig tests: 
tab country, gen(x)
reg age x1 x2 x3 x4, nocons
logit male x1 x2 x3 x4, nocons
logit urban x1 x2 x3 x4, nocons
reg education x1 x2 x3 x4, nocons
reg ses x1 x2 x3 x4, nocons
reg impair x1 x2 x3 x4, nocons
reg barrier x1 x2 x3 x4, nocons


*** GOT HEALTH CARE - as described in article ***
gen didnt = .
label var didnt "Did not get health care last time needed"
replace didnt = 0 if gothealthcare ==1
replace didnt = 1 if gothealthcare==2

* FIG 1
logistic didnt impair
predict lhat
predict lr_index, xb
predict se_index, stdp
gen lb = lr_index - invnormal(0.975)*se_index
gen ub = lr_index + invnormal(0.975)*se_index
gen plb = exp(lb)/(1+exp(lb))
gen pub = exp(ub)/(1+exp(ub))
tw(scatter lhat impair, c(connect))(line pub impair)(line plb impair)


*TABLE 2
recode impair (1=1 "No activity limitation")(1.1/1.9=2 "Some activity limitation") (2/4=3 "Severe activity limitation"), gen(impairgroup)
tab healthcareservices
tab healthcareservices if impairgroup==1
tab healthcareservices if impairgroup==2
tab healthcareservices if impairgroup==3




* TABLE 3
sem (SES -> radio refridgerator hifi microvawe electricity dvd solarenergy cellphone electricalgenerator telephonehouse pc iron bicycle fan mc heater dishwasher ac beds stovegas livestock ///
	stoveparafin washingmachine sofa sattelitedish tv car) ///
	(WG6 -> seeingx hearingx walkingx (rememberingx, init(8)) (communicatingx, init(8)) (selfcarex, init(8))) ///
	(Barriers -> lackoftransportx noservicesx physicalaccessx faithx negativeattitudeshealthwx noaccommodationx communicationx standardhealthfacilityx journeydangerousx wheretogox costx ///
	documentsx notsickenoughx deniedx inadequatedrugsx tomeoffx badlytreatedx transportcostx) ///
	(Barriers -> lackoftransportx noservicesx physicalaccessx faithx negativeattitudeshealthwx noaccommodationx communicationx standardhealthfacilityx journeydangerousx wheretogox costx ///
	documentsx notsickenoughx deniedx inadequatedrugsx tomeoffx badlytreatedx transportcostx) ///
	(Barriers <- age_std age_std2 urban education SES WG6 namibia malawi sudan), method(ml)
estat eqgof
sem (SES -> radio refridgerator hifi microvawe electricity dvd solarenergy cellphone electricalgenerator telephonehouse pc iron bicycle fan mc heater dishwasher ac beds stovegas livestock ///
	stoveparafin washingmachine sofa sattelitedish tv car) ///
	(WG6 -> (seeingx, init(10)) (hearingx, init(10)) (walkingx, init(10)) (rememberingx, init(10)) (communicatingx, init(10)) (selfcarex, init(10))) ///
	(Barriers -> lackoftransportx noservicesx physicalaccessx faithx negativeattitudeshealthwx noaccommodationx communicationx standardhealthfacilityx journeydangerousx wheretogox costx ///
	documentsx notsickenoughx deniedx inadequatedrugsx tomeoffx badlytreatedx transportcostx) ///
	(Barriers -> lackoftransportx noservicesx physicalaccessx faithx negativeattitudeshealthwx noaccommodationx communicationx standardhealthfacilityx journeydangerousx wheretogox costx ///
	documentsx notsickenoughx deniedx inadequatedrugsx tomeoffx badlytreatedx transportcostx) ///
	(Barriers <- age_std age_std2 urban education SES WG6), method(ml) group(country)
estat eqgof


	
** ROBUSTNESS CHECKS as described (but not reported in article

* SEM models run countrywise 
**** Full model, by country
sem (SES -> radio refridgerator hifi microvawe electricity dvd solarenergy cellphone electricalgenerator telephonehouse pc iron bicycle fan mc heater dishwasher ac beds stovegas livestock ///
	stoveparafin washingmachine sofa sattelitedish tv car) ///
	(WG6 -> (seeingx, init(10)) (hearingx, init(10)) (walkingx, init(10)) (rememberingx, init(10)) (communicatingx, init(10)) (selfcarex, init(10))) ///
	(Barriers -> lackoftransportx noservicesx physicalaccessx faithx negativeattitudeshealthwx noaccommodationx communicationx standardhealthfacilityx journeydangerousx wheretogox costx ///
	documentsx notsickenoughx deniedx inadequatedrugsx tomeoffx badlytreatedx transportcostx) ///
	(Barriers -> lackoftransportx noservicesx physicalaccessx faithx negativeattitudeshealthwx noaccommodationx communicationx standardhealthfacilityx journeydangerousx wheretogox costx ///
	documentsx notsickenoughx deniedx inadequatedrugsx tomeoffx badlytreatedx transportcostx) ///
	(Barriers <- age_std age_std2 urban education SES WG6) if country==3, method(ml)

* Models from table 3 with additional path education -> SES 
sem (SES -> radio refridgerator hifi microvawe electricity dvd solarenergy cellphone electricalgenerator telephonehouse pc iron bicycle fan mc heater dishwasher ac beds stovegas livestock ///
	stoveparafin washingmachine sofa sattelitedish tv car) ///
	(WG6 -> seeingx hearingx walkingx (rememberingx, init(8)) (communicatingx, init(8)) (selfcarex, init(8))) ///
	(Barriers -> lackoftransportx noservicesx physicalaccessx faithx negativeattitudeshealthwx noaccommodationx communicationx standardhealthfacilityx journeydangerousx wheretogox costx ///
	documentsx notsickenoughx deniedx inadequatedrugsx tomeoffx badlytreatedx transportcostx) ///
	(Barriers -> lackoftransportx noservicesx physicalaccessx faithx negativeattitudeshealthwx noaccommodationx communicationx standardhealthfacilityx journeydangerousx wheretogox costx ///
	documentsx notsickenoughx deniedx inadequatedrugsx tomeoffx badlytreatedx transportcostx) ///
	(Barriers <- age_std age_std2 urban education SES WG6 namibia malawi sudan) (education -> SES) , method(ml)
	
sem (SES -> radio refridgerator hifi microvawe electricity dvd solarenergy cellphone electricalgenerator telephonehouse pc iron bicycle fan mc heater dishwasher ac beds stovegas livestock ///
	stoveparafin washingmachine sofa sattelitedish tv car) ///
	(WG6 -> (seeingx, init(10)) (hearingx, init(10)) (walkingx, init(10)) (rememberingx, init(10)) (communicatingx, init(10)) (selfcarex, init(10))) ///
	(Barriers -> lackoftransportx noservicesx physicalaccessx faithx negativeattitudeshealthwx noaccommodationx communicationx standardhealthfacilityx journeydangerousx wheretogox costx ///
	documentsx notsickenoughx deniedx inadequatedrugsx tomeoffx badlytreatedx transportcostx) ///
	(Barriers -> lackoftransportx noservicesx physicalaccessx faithx negativeattitudeshealthwx noaccommodationx communicationx standardhealthfacilityx journeydangerousx wheretogox costx ///
	documentsx notsickenoughx deniedx inadequatedrugsx tomeoffx badlytreatedx transportcostx) ///
	(Barriers <- age_std age_std2 urban education (SES, init(2.5)) (WG6, init(0.5))) (education -> SES), method(ml) group(country) iter(52)

* Models from table 3 run as OLS
reg barrier age_std age_std2 urban education ses impair, cl(country)
reg barrier age_std age_std2 urban education ses impair namibia malawi sudan , vce