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* train.do
sysuse auto
replace foreign = 3 if _n < 10
replace foreign = 4 if _n > 10 & _n < 20
svmachines foreign price-gear_ratio if !missing(rep78)
do tests/helpers/inspect_model.do
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* train.do
sysuse auto
replace foreign = 3 if _n < 10
replace foreign = 4 if _n > 10 & _n < 20
svmachines price-gear_ratio if !missing(rep78), type(one_class)
do tests/helpers/inspect_model.do
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* train_svr.do
sysuse auto
svmachines price mpg-gear_ratio if !missing(rep78), type(nu_svr) kernel(sigmoid)
do tests/helpers/inspect_model.do
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* predict_svr.do
sysuse auto
capture svmachines price mpg-gear_ratio if !missing(rep78), type(nu_svr) kernel(sigmoid) probability
if(_rc == 0) {
di as error "svmachines should disallow regression with probability turned on"
exit 1
}
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// there's an off-by-one-ish bug in the new exporting SVs code,
// this code triggers it
sysuse auto
drop make
order gear_ratio
svmachines * if !missing(rep78), sv(Is_SV) type(SVR)
// if the bug is there, you will see --> _model2stata phase 3: warning: overflowed sv_indices before all rows filled. i=74, s=49, l=49
list Is_SV
tab gear_ratio Is_SV
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* inspect_model.do
* Inspect fitted model state, as known to Stata
* NOTE: this is autoincluded to all tests via makefile magic; hence,
* it is wrapped to silence errors on tests which don't make a struct svm_model
* AND *each line* is wrapped so that their errors are independent of the others, just as a defensive measure
* BUT as an output optimization, we assume that if nSV is not defined, we can skip trying the rest
* These two should usually be empty, except for preload which of course has to use scalars to communicate
capture noisily scalar list
capture noisily matrix dir /* unlike 'scalar list', 'matrix list' shows a *single* matrix and 'dir' shows all */
capture matrix list e(rho) /*I can't figure out a better to test if a variable is defined than to try to access it and see if it crashes */
if(_rc==0) {
capture noisily ereturn list /* because of Stata's single-global-return-list design, the only time we can access this data is immediately after a svm_train or svm_import */
capture noisily matrix list e(sv_coef)
capture noisily matrix list e(rho)
}
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* settings.do
set linesize 119
* make all platforms consistent in their batch output;
* NOTE: this is autoincluded to all tests via makefile magic
* tip buried in http://web.stanford.edu/group/ssds/cgi-bin/drupal/files/Guides/Stata_Unix__2011.pdf
* ALSO these comments are *after* the set, because it affects how stata prints out comments.
local TRACE : env TRACE /* you can't use env by itself, for some reason */
// BEWARE: stata resets the trace setting when a do file quits
// do running this as "do
if("`TRACE'"!="") {
set trace on
set more off
}
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M02*
|
/*
Copyright (c) 2018 Uber Technologies, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
*Create Chart
clear all
set obs 12
generate time = _n + 2016
*Scenario number (in terms of speed of adoption) from Matlab Code
*Scenario 1 = 70% deployment
*Scenario 2 = 50% deployment
*Scenario 3 = 20% deployment
local scen_number = 2
*Intensity is the number of driver equivalents provided by an autonomous truck (2, 3, 5)
local inten_number = 2
*Growth rates as provided by ATA Freight Transportation Forecast
local growth_early = 0.027
local growth_late = 0.020
*Checks to make sure the chosen scenario number and intensity multiplier are valid
assert inlist(`scen_number',1,2,3)
assert inlist(`inten_number',2,3,5)
local file_name = "p1_with_growth.csv"
*Extract Jobs Predictions
preserve
clear all
import delimited "output/`file_name'"
rename (v1 v2 v3 v4 v5 v6 v7)(scenario intensity time lwage swage lemp semp)
keep if scenario == `scen_number' & intensity == `inten_number'
keep time lemp semp
save "intermediate/employment_projections.dta"
restore
*Extract AV Numbers from Deployment Projections
preserve
clear all
import delimited "data/deployment.csv"
xpose, clear
generate time = 2017 + _n
if `scen_number' == 3 {
keep time v3
rename v3 deployment
}
if `scen_number' == 2 {
keep time v2
rename v2 deployment
}
if `scen_number' == 1 {
keep time v1
rename v1 deployment
}
set obs `=_N + 1'
replace time = 2017 if time == .
replace deployment = 0 if time == 2017
save "intermediate/deployment_projections.dta"
restore
*Extract Retirements from Retirement Projections
preserve
clear all
use "intermediate/retirement_employment_by_year.dta"
keep year number_retired_
rename year time
save "intermediate/retirement_projections.dta"
restore
*Merge All files together
merge 1:1 time using "intermediate/employment_projections.dta", nogenerate
merge 1:1 time using "intermediate/deployment_projections.dta", nogenerate
merge 1:1 time using "intermediate/retirement_projections.dta", nogenerate
*Erase Temporary Files
capture erase "intermediate/employment_projections.dta"
capture erase "intermediate/deployment_projections.dta"
capture erase "intermediate/retirement_projections.dta"
*Generate the Area Chart
generate bottom = lemp / 10^6 /*long-haul employment (in millions) */
generate top = (lemp + semp) / 10^6 /*short-haul employment (in millions) */
generate full = (lemp + semp + `inten_number' * deployment) / 10^6 /* total driver equivalents (in millions) */
twoway (rarea full top time, xtitle("Year") xlabel(2018(2)2028) ylabel(#6,gstyle(dot)) legend(order(3 2 1) label(1 "Total Truck Equivalents")) fcolor(navy%80) lcolor(bg%1)) ///
(rarea top bottom time, legend(label(2 "Short-Haul Jobs")) fcolor(maroon%80) lcolor(bg%1) yscale(range(0))) ///
(area bottom time, xscale(range(2017 2028)) graphregion(fcolor(white)) fcolor(dkgreen%80) legend(label(3 "Long-Haul Jobs")) lcolor(bg%1) ytitle("Millions"))
graph export "output/pictures/area_chart_s`scen_number'_i`inten_number'.svg", replace
graph export "output/pictures/area_chart_s`scen_number'_i`inten_number'.pdf", replace
export delimited time bottom top full using "output/pictures/area_chart_s`scen_number'_i`inten_number'", replace
*Generate the Bar Chart Figure
keep time lemp semp
foreach x in lemp semp{
generate `x'_status_quo = .
*Impose the status quo growth rates from ATA
replace `x'_status_quo = `x' if time == 2017
replace `x'_status_quo = `x'_status_quo[_n-1] * ///
((inrange(time,2018,2023)) * (1 + `growth_early') + (inrange(time,2024,2028) * (1 + `growth_late'))) if time > 2017
}
rename (lemp semp)(lemp1 semp1)
rename (lemp_status_quo semp_status_quo)(lemp0 semp0)
reshape long lemp semp, i(time) j(sq)
generate lemp_ = lemp / 10^6
generate semp_ = semp / 10^6
graph bar lemp_ semp_, legend(label(1 "Long-Haul") label(2 "Short-Haul")) ///
ytitle("Millions of Jobs") ylabel(,gstyle(dot)) graphregion(fcolor(white)) ///
over(sq, label(labsize(tiny) angle(45)) relabel(1 "Baseline" 2 "With AVs")) ///
over(time, label(angle(22.5))) stack bar(1, color(maroon%80)) bar(2, color(dkgreen%80))
graph export "output/pictures/bar_chart_s`scen_number'_i`inten_number'.svg", replace
graph export "output/pictures/bar_chart_s`scen_number'_i`inten_number'.pdf", replace
export delimited time lemp_ semp_ using "output/pictures/bar_chart_s`scen_number'_i`inten_number'", replace
|
/*
Copyright (c) 2018 Uber Technologies, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
*Master File
*Run this file to replicate all results
clear all
do "code/replication_retirement_curve.do"
clear all
do "code/replication_union.do"
clear all
do "code/replication_demand.do"
clear all
do "code/replication_labor_supply.do"
clear all
|
/*
Copyright (c) 2018 Uber Technologies, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
clear all
set more off
global data_dir = "../data"
global output_dir = "../data"
filelist, directory("$data_dir") pattern("*state*.dta")
drop if regexm(filename,"november")
drop if regexm(filename,"state_by_year")
drop if regexm(filename,"temp")
drop if regexm(filename,"wage_state_year")
levelsof filename, local(filelistnames)
foreach x of local filelistnames{
use "$data_dir/`x'", clear
rename *, lower
destring tot_emp, replace ignore("**")
replace tot_emp = 0 if tot_emp == .
bysort area: egen total = sum(tot_emp)
generate share_in_occ = tot_emp / total
capture rename occ_titl occ_title
keep area st share_in_occ a_mean h_mean occ_code occ_title tot_emp
destring h_mean a_mean, replace ignore("*#")
label variable occ_title "Occupation Name"
label variable area "State FIPS"
label variable st "State Abbreviation"
label variable occ_code "6-Digit Occupation Code"
label variable h_mean "Mean Hourly Wage"
label variable a_mean "Mean Annual Wage"
label variable share_in_occ "Share of Total State Population in Occupation"
label variable tot_emp "Total Employment in Occupation for State"
save "$data_dir/temp_`x'", replace
}
forvalues t = 2000/2002{
use "$data_dir/temp_state_`t'_dl.dta", clear
generate year = `t'
save "$data_dir/temp_state_`t'_dl.dta", replace
}
forvalues t = 2009/2016{
use "$data_dir/temp_state_M`t'_dl.dta", clear
generate year = `t'
save "$data_dir/temp_state_M`t'_dl.dta", replace
}
forvalues t = 2003/2006{
use "$data_dir/temp_state_may`t'_dl.dta", clear
generate year = `t'
save "$data_dir/temp_state_may`t'_dl.dta", replace
}
use "$data_dir/temp_state_May2007_dl.dta", clear
generate year = 2007
save "$data_dir/temp_state_May2007_dl.dta", replace
use "$data_dir/temp_state__M2008_dl.dta", clear
generate year = 2008
save "$data_dir/temp_state__M2008_dl.dta", replace
filelist, directory("$data_dir") pattern("temp*state*.dta")
levelsof filename, local(flistname)
clear
foreach x of local flistname{
append using "$data_dir/`x'"
erase "$data_dir/`x'"
}
order year, after(st)
label variable year "Year (BLS)"
save "$data_dir/wage_state_year_occ.dta", replace
|
/*
Copyright (c) 2018 Uber Technologies, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
clear all
set more off
*Long-Haul Elasticity
use "data/wage_state_year_occ.dta", clear
drop if substr(occ_code,4,4) == "0000"
*Generate total employment in each state-year
bysort st year: egen total_employment_state = sum(tot_emp)
*Keep desired long-haul trucking occupation and states
keep if occ_code == "53-3032"
drop if inlist(st,"PR","GU","VI","HI")
rename tot_emp total_employment_sector
keep st year total_employment_state a_mean total_employment_sector
*Generate "market shares" / insert state numbers for merging with other dataset
generate share_total_employment = total_employment_sector / total_employment_state
generate state = .
replace state = 1 if st == "AL"
replace state = 2 if st == "AK"
replace state = 4 if st == "AZ"
replace state = 5 if st == "AR"
replace state = 6 if st == "CA"
replace state = 8 if st == "CO"
replace state = 9 if st == "CT"
replace state = 10 if st == "DE"
replace state = 11 if st == "DC"
replace state = 12 if st == "FL"
replace state = 13 if st == "GA"
replace state = 15 if st == "HI"
replace state = 16 if st == "ID"
replace state = 17 if st == "IL"
replace state = 18 if st == "IN"
replace state = 19 if st == "IA"
replace state = 20 if st == "KS"
replace state = 21 if st == "KY"
replace state = 22 if st == "LA"
replace state = 23 if st == "ME"
replace state = 24 if st == "MD"
replace state = 25 if st == "MA"
replace state = 26 if st == "MI"
replace state = 27 if st == "MN"
replace state = 28 if st == "MS"
replace state = 29 if st == "MO"
replace state = 30 if st == "MT"
replace state = 31 if st == "NE"
replace state = 32 if st == "NV"
replace state = 33 if st == "NH"
replace state = 34 if st == "NJ"
replace state = 35 if st == "NM"
replace state = 36 if st == "NY"
replace state = 37 if st == "NC"
replace state = 38 if st == "ND"
replace state = 39 if st == "OH"
replace state = 40 if st == "OK"
replace state = 41 if st == "OR"
replace state = 42 if st == "PA"
replace state = 44 if st == "RI"
replace state = 45 if st == "SC"
replace state = 46 if st == "SD"
replace state = 47 if st == "TN"
replace state = 48 if st == "TX"
replace state = 49 if st == "UT"
replace state = 50 if st == "VT"
replace state = 51 if st == "VA"
replace state = 53 if st == "WA"
replace state = 54 if st == "WV"
replace state = 55 if st == "WI"
replace state = 56 if st == "WY"
drop if state == .
rename state state_id
*drop more recent years/incomplete years
drop if year == 2016 | year == 2000
*Merge in median incomes
merge 1:1 state_id year using "data/state_incomes.dta", keep(3) assert(2 3) nogenerate keepusing(median)
*Generate log shares and log wages for elasticities
generate log_share = log(share)
generate log_wage_foruse = log(a_mean)
rename state_id statefip
drop if year == 2016
*Merge in the union instrument
merge m:1 statefip year using "intermediate/cps_state_union_year.dta", nogenerate assert(2 3) keep(3)
*Need CPI to deflate incomes so they are all on the same monetary level
generate cpi = .
replace cpi = 1.37 if year == 2000
replace cpi = 1.34 if year == 2001
replace cpi = 1.32 if year == 2002
replace cpi = 1.29 if year == 2003
replace cpi = 1.25 if year == 2004
replace cpi = 1.21 if year == 2005
replace cpi = 1.17 if year == 2006
replace cpi = 1.14 if year == 2007
replace cpi = 1.10 if year == 2008
replace cpi = 1.10 if year == 2009
replace cpi = 1.09 if year == 2010
replace cpi = 1.05 if year == 2011
replace cpi = 1.03 if year == 2012
replace cpi = 1.02 if year == 2013
replace cpi = 1.00 if year == 2014
replace cpi = 1.00 if year == 2015
replace cpi = 0.99 if year == 2016
generate mediandeflated = median / cpi
ivregress 2sls log_share mediandeflated i.year (log_wage_foruse = union), robust
regress log_wage_foruse union median i.year, robust
*Need to include labor in trucking from outside the state as a regressor/control
*for short-haul -- produces temp dataset with this info
use "data/wage_state_year_occ.dta", clear
keep if occ_code == "53-3032"
drop if inlist(st,"PR","GU","VI","HI")
keep tot_emp area year
destring area, generate(state)
drop area
bysort year: egen total_in_year = sum(tot_emp)
count if year == 2012
local numm = `r(N)'
generate leave_one_out_mean = (total_in_year - tot_emp)/`=`numm'-1'
keep state year leave_one_out_mean
save "intermediate/temp.dta", replace
********************************************************************************
********************************************************************************
********************************************************************************
********************************************************************************
********************************************************************************
*Short-Haul Elasticity
use "data/wage_state_year_occ.dta", clear
drop if substr(occ_code,4,4) == "0000"
*Generate total employment in each state-year
bysort st year: egen total_employment_state = sum(tot_emp)
*Keep desired short-haul trucking occupation and states
keep if occ_code == "53-3031"
drop if inlist(st,"PR","GU","VI","HI")
rename tot_emp total_employment_sector
keep st year total_employment_state a_mean total_employment_sector
*Generate "market shares" / insert state numbers for merging with other dataset
generate share_total_employment = total_employment_sector / total_employment_state
generate state = .
replace state = 1 if st == "AL"
replace state = 2 if st == "AK"
replace state = 4 if st == "AZ"
replace state = 5 if st == "AR"
replace state = 6 if st == "CA"
replace state = 8 if st == "CO"
replace state = 9 if st == "CT"
replace state = 10 if st == "DE"
replace state = 11 if st == "DC"
replace state = 12 if st == "FL"
replace state = 13 if st == "GA"
replace state = 15 if st == "HI"
replace state = 16 if st == "ID"
replace state = 17 if st == "IL"
replace state = 18 if st == "IN"
replace state = 19 if st == "IA"
replace state = 20 if st == "KS"
replace state = 21 if st == "KY"
replace state = 22 if st == "LA"
replace state = 23 if st == "ME"
replace state = 24 if st == "MD"
replace state = 25 if st == "MA"
replace state = 26 if st == "MI"
replace state = 27 if st == "MN"
replace state = 28 if st == "MS"
replace state = 29 if st == "MO"
replace state = 30 if st == "MT"
replace state = 31 if st == "NE"
replace state = 32 if st == "NV"
replace state = 33 if st == "NH"
replace state = 34 if st == "NJ"
replace state = 35 if st == "NM"
replace state = 36 if st == "NY"
replace state = 37 if st == "NC"
replace state = 38 if st == "ND"
replace state = 39 if st == "OH"
replace state = 40 if st == "OK"
replace state = 41 if st == "OR"
replace state = 42 if st == "PA"
replace state = 44 if st == "RI"
replace state = 45 if st == "SC"
replace state = 46 if st == "SD"
replace state = 47 if st == "TN"
replace state = 48 if st == "TX"
replace state = 49 if st == "UT"
replace state = 50 if st == "VT"
replace state = 51 if st == "VA"
replace state = 53 if st == "WA"
replace state = 54 if st == "WV"
replace state = 55 if st == "WI"
replace state = 56 if st == "WY"
drop if state == .
*Merge in the aforemntioned temp dataset
merge 1:1 state year using "intermediate/temp.dta", assert(3) nogenerate
erase "intermediate/temp.dta"
label variable leave_one_out_mean "Average Employment in Other States"
rename state state_id
drop if year == 2016
*Merge in median incomes
merge 1:1 state_id year using "data/state_incomes.dta", keep(3) assert(2 3) nogenerate keepusing(median)
*Generate log shares and log wages for elasticities
generate log_share = log(share)
generate log_wage_foruse = log(a_mean)
*Merge in the union instrument
rename state_id statefip
drop if year == 2016 | year == 2000
merge m:1 statefip year using "intermediate/cps_state_union_year.dta", nogenerate assert(2 3) keep(3)
*Need CPI to deflate incomes so they are all on the same monetary level
generate cpi = .
replace cpi = 1.37 if year == 2000
replace cpi = 1.34 if year == 2001
replace cpi = 1.32 if year == 2002
replace cpi = 1.29 if year == 2003
replace cpi = 1.25 if year == 2004
replace cpi = 1.21 if year == 2005
replace cpi = 1.17 if year == 2006
replace cpi = 1.14 if year == 2007
replace cpi = 1.10 if year == 2008
replace cpi = 1.10 if year == 2009
replace cpi = 1.09 if year == 2010
replace cpi = 1.05 if year == 2011
replace cpi = 1.03 if year == 2012
replace cpi = 1.02 if year == 2013
replace cpi = 1.00 if year == 2014
replace cpi = 1.00 if year == 2015
replace cpi = 0.99 if year == 2016
generate mediandeflated = median / cpi
ivregress 2sls log_share mediandeflated i.year leave_one_out_mean (log_wage_foruse = union), robust
erase "intermediate/cps_state_union_year.dta"
|
/*
Copyright (c) 2018 Uber Technologies, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
clear all
set more off
use "data/wage_state_year_occ.dta", clear
* Keep driving occupations only
keep if strpos(occ_code, "53-30")
drop if strpos(occ_code, "0000")
sort st year
by st year: egen tot = sum(tot_emp)
* Labor Force in each state is highest past employment level
by st: egen lforce = max(tot)
* Get job-level shares
gen share = tot_emp/(lforce + 1)
egen oc = group(occ_code)
gen wage = a_mean*tot_emp
gen lh = oc == 5
gen sh = oc == 6
* Collapse jobs to long haul, short haul, and other driving
collapse (sum) wage share tot_emp (mean) lforce tot, by(year st lh sh)
replace wage = wage/tot_emp
* Log share minus log outside job share
gen rel_share = log(share) - log((lforce-tot+1)/(lforce+1))
replace wage = log(wage)
egen sty = group(st year)
egen state = group(st)
* Drop islands
drop if st == "GU" | st == "PR" | st == "VI" | st == "HI"
preserve
* Get Instruments
use "data/wage_state_year_occ.dta", clear
keep if strpos(occ_code, "51-")
collapse (sum) tot_emp, by(area year)
destring area, replace
rename tot_emp mfct_emp
save "intermediate/temp", replace
restore
rename state state_id
mmerge state_id year using "data/state_incomes.dta", type(n:1)
rename state_id area
mmerge area year using "intermediate/temp", type(n:1)
replace mfct_emp = mfct_emp/ lforce
gen other = !lh &!sh
* MAIN REGRESSION *
cap drop d a
reghdfe rel_share sh lh (wage = lh#c.median sh#c.median other#c.median lh#c.mfct_emp sh#c.mfct_emp other#c.mfct_emp), absorb(a=sty) estimator(2sls)
predict d, d
* mean of d is fe_other in projection code
sum d
erase "intermediate/temp.dta"
|
/*
Copyright (c) 2018 Uber Technologies, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
clear all
set more off
*Graphs
*CHOOSE A FILE - SEE readme.txt and equilibrium.m for more information
*Needs to be one of "p1","p1_with_growth"
local use_estimates = "p1_with_growth"
assert inlist("`use_estimates'","p1","p1_with_growth")
insheet using "output/`use_estimates'.csv", comma clear
rename v1 projection
rename v2 multiplier
rename v3 year
rename v4 lh_wage
rename v5 sh_wage
rename v6 lh_jobs
rename v7 sh_jobs
replace sh_wage = sh_wage/1000
replace lh_wage = lh_wage/1000
replace sh_jobs = sh_jobs/1000000
replace lh_jobs = lh_jobs/1000000
sort projection multiplier
by projection multiplier: egen maxl = max(lh_jobs)
by projection multiplier: egen mins = min(sh_jobs)
gen unemployed_lh = maxl - lh_jobs
gen employed_sh = sh_jobs - mins
gen unemployed = unemployed_lh - employed_sh
gen id = 10*projection + multiplier
drop projection multiplier maxl mins
reshape wide lh_wage sh_wage lh_jobs sh_jobs unemployed unemployed_lh employed_sh, i(year) j(id)
forvalues proj = 1/3{
*Overall Employment
twoway (connected lh_jobs`proj'2 year, msymbol(circle) mcolor(red) lcolor(red) msize(vsmall) legend(label(1 "LH 2x Multiplier"))) ( connected lh_jobs`proj'3 year, msymbol(triangle) mcolor(red) lcolor(red) msize(vsmall) legend(label(2 "LH 3x Multiplier"))) ( connected lh_jobs`proj'5 year, msymbol(square) mcolor(red) lcolor(red) msize(vsmall) legend(label(3 "LH 5x Multiplier"))) ///
(connected sh_jobs`proj'2 year, msymbol(circle) mcolor(blue) lcolor(blue) msize(vsmall) legend(label(4 "SH 2x Multiplier"))) ( connected sh_jobs`proj'3 year, msymbol(triangle) mcolor(blue) lcolor(blue) msize(vsmall) legend(label(5 "SH 3x Multiplier"))) ( connected sh_jobs`proj'5 year, msymbol(square) mcolor(blue) lcolor(blue) msize(vsmall) legend(label(6 "LH 5x Multiplier")) ///
legend(rows(2) size(small)) xtitle("Year") ytitle("Employment (Millions)") graphregion(fcolor(white)) ylabel(,nogrid) xlabel(2017(3)2029) xscale(range(2017 2028)))
graph export "output/pictures/`use_estimates'_employment_s`proj'.pdf", replace
*Unemployment Change
twoway connected unemployed`proj'2 year, msize(tiny) xlabel(2017(3)2029) xscale(range(2017 2028))|| connected unemployed`proj'3 year, msize(tiny) || connected unemployed`proj'5 year, xtitle("Year") ytitle("Unemployment Change (Millions)") legend(rows(1) order(1 "2x Multiplier" 2 "3x Multiplier" 3 "5x Multiplier")) graphregion(fcolor(white)) ylabel(,nogrid) msize(tiny)
graph export "output/pictures/`use_estimates'_unemployment_s`proj'.pdf", replace
*Long-Haul Wages
twoway connected lh_wage`proj'2 year, msize(tiny) xlabel(2017(3)2029) xscale(range(2017 2028)) || connected lh_wage`proj'3 year, msize(tiny) || connected lh_wage`proj'5 year , xtitle("Year") ytitle("Long-Haul Wage (Thousands)") legend(rows(1) order(1 "2x Multiplier" 2 "3x Multiplier" 3 "5x Multiplier")) graphregion(fcolor(white)) ylabel(,nogrid) msize(tiny)
graph export "output/pictures/`use_estimates'_lhwage_s`proj'.pdf", replace
*Short-Haul Wages
twoway connected sh_wage`proj'2 year, msize(tiny) xlabel(2017(3)2029) xscale(range(2017 2028)) || connected sh_wage`proj'3 year, msize(tiny) || connected sh_wage`proj'5 year , xtitle("Year") ytitle("Short-Haul Wage (Thousands)") legend(rows(1) order(1 "2x Multiplier" 2 "3x Multiplier" 3 "5x Multiplier")) graphregion(fcolor(white)) ylabel(,nogrid) msize(tiny)
graph export "output/pictures/`use_estimates'_shwage_`proj'.pdf", replace
}
|
/*
Copyright (c) 2018 Uber Technologies, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
clear all
set more off
*Load data
use "data/cps_all.dta", clear
*Define two time periods (early and late)
*Can't use more granular time periods because of data limitations
generate time_dim = 1 * inrange(year, 2000, 2008) + 2 * inrange(year, 2009, 2016)
label variable time_dim "Time Period"
label define tdd 1 "2000-2008" 2 "2009-2016"
label values time_dim tdd
*Need to generate this...makes it easier to add things up later
generate one = 1
*Restrict to adult population
keep if age >= 16
*Create trucker and "not working" dummies
*Create a labor force status variable
generate trucker = (occ2010 == 9600)
generate notworking = (occ2010 == 9920)
generate labor_status = trucker + 2 * notworking
label define labstat 0 "Not Trucking" 1 "Trucking" 2 "Not Working"
label values labor_status labstat
*Generate count and fraction by age, labor status, and early/late designation
collapse (sum) one, by(age labor_status time_dim)
bysort age: egen total1 = sum(one)
generate fraction_truck = one/total1
rename (one fraction_truck)(one_ fraction_truck_)
drop total1
*Reshape the data for computational ease
reshape wide one_ fraction_truck_ , i(age time_dim) j(labor_status)
keep age fraction_truck_1 time_dim
label variable fraction_truck_1 "Fraction in Trucking"
replace fraction_truck_1 = 0 if fraction_truck_1 == .
xtset time_dim age
generate retirement = S.fraction_truck_1
label variable retirement "Retirement Rate (Percentage Points)"
*Generate a graph of percent in a cohort in trucking over early/late
twoway (connected fraction_truck_1 age if time_dim == 1 & inrange(age,61,70), ///
graphregion(fcolor(white)) ylabel(,nogrid) msize(tiny) xlabel(61(3)70) ytitle("Fraction in Trucking") xtitle("Age") legend(label(1 "2000-2008"))) ///
(connected fraction_truck_1 age if time_dim == 2 & inrange(age,61,70), ///
msize(tiny) legend(label(2 "2009-2016")))
*Create new variable that scales up all fractions by a common factor so that the data works for a histogram
keep age time_dim fraction_truck_1
replace fraction_truck_1 = round(fraction_truck_1 * 10^8,1)
replace fraction_truck_1 = 1 if age > 70
*Generate a histogram of age of drivers over early or late
preserve
drop if age > 75
twoway (histogram age if time_dim == 1 [fweight = fraction_truck_1], width(1) start(16) ///
graphregion(fcolor(white)) ylabel(,nogrid) xlabel(15(15)75) ytitle("Fraction in Trucking") xtitle("Age") legend(label(1 "2000-2008")) fcolor(navy%40) lcolor(navy%40)) ///
(histogram age if time_dim == 2 [fweight = fraction_truck_1], width(1) start(16) ///
legend(label(2 "2009-2016")) fcolor(red%40) lcolor(red%40) )
restore
save "intermediate/retirement_scenario.dta", replace
*This section produces projections of age patterns over time
use "intermediate/retirement_scenario.dta", clear
keep age fraction time_dim
*This is to clean up the time series and generate a new era
xtset age time_dim
count
set obs `=`r(N)'+1'
replace age = 90 if _n == _N
replace time_dim = 2 if _n == _N
replace fraction = 0 if age == 90
tsfill, full
replace fraction = 0 if fraction == .
count
set obs `= `r(N)' * 1.5'
local min = `=`r(N)' + 1'
count
local max = `r(N)'
forvalues t = `min'/`max'{
replace age = mod(`t',75) + 15 if _n == `t'
}
replace time_dim = 3 if _n >= `min'
replace age = 90 if age == 15
label define tdd 3 "Future", add
xtset age time_dim
*Set number of drivers to zero for ages > 71
replace fraction_truck_1 = 0 if inrange(age,71,.)
*Generate retirement rates by projecting forward average trend
generate change_rate = (fraction_truck_1 - L.fraction_truck_1)/L.fraction_truck_1
generate age_group = 1 * (inrange(age,60,70) != 1) + 2 * (inrange(age,60,70) == 1)
bysort age_group: egen meannn = mean(change_rate)
replace meannn = . if age_group == 1
xtset age time_dim
generate future = .
replace future = L.fraction_truck_1 * (1 + meannn) if time_dim == 3 & age_group == 2
replace future = L.fraction_truck_1 * (1 + L.change_rate) if time_dim == 3 & age_group == 1
replace future = 0 if time_dim == 3 & inrange(age,71,.)
replace fraction = 0 if fraction == .
rename future future_3
keep age future_3 time_dim
keep if time_dim == 3
drop time_dim
tsset age
*Smooth the retirement where necessary
forvalues t= 61/70{
replace future_3 = (L.future_3 + F.future_3)/2 ///
if age == `t' & future_3 > L.future_3
}
replace future_3 = 0 if inrange(age,16,70) == 0
keep if inrange(age,16,90)
save "intermediate/retirement_scenario_projections.dta", replace
*Import retirement rates and generate current age distribution
use "intermediate/retirement_scenario_projections.dta", clear
merge 1:m age using "intermediate/retirement_scenario.dta", nogenerate
keep if time_dim == 2
drop time_dim
sort age
*use this to get at number in age group
keep if inrange(age,16,70)
egen total_fraction = sum(fraction_truck_1)
generate number_here = fraction_truck_1 / total_fraction * 1704520
*According to the BLS there are 1,704,520 "Heavy and Tractor-Trailer Truck Drivers" (as of May 2016)
*https://www.bls.gov/oes/current/oes_nat.htm#53-0000
tsset age
generate retirement_rate = S.future_ / L.future_
*Exercise: Fix the number of drivers in each trucking cohort
*Keep this number fixed until they hit age 60
*Then for each year from 60 --> 70 "retire" them at the rate specified
*Once they hit 71 they are retired with probability one
keep age number_here retirement_rate
save "intermediate/temporary.dta", replace
keep if age>= 61
drop number_here
generate one = 1
reshape wide retirement_rate, i(one) j(age)
save "intermediate/retirement_rates.dta", replace
use "intermediate/temporary.dta", clear
drop retirement_rate
erase "intermediate/temporary.dta"
generate one = 1
merge m:1 one using "intermediate/retirement_rates.dta", nogenerate
drop one
*Generate retirement rate by year-cohort
forvalues s = 2017/2028{
generate retirement_`s' = .
}
forvalues t = 0/9{
replace retirement_2017 = retirement_rate`=`t' + 61' if age == `=`t' + 60'
}
forvalues t = 0/9{
replace retirement_2018 = retirement_rate`=`t' + 61' if age == `=`t' + 59'
}
forvalues t = 0/9{
replace retirement_2019 = retirement_rate`=`t' + 61' if age == `=`t' + 58'
}
forvalues t = 0/9{
replace retirement_2020 = retirement_rate`=`t' + 61' if age == `=`t' + 57'
}
forvalues t = 0/9{
replace retirement_2021 = retirement_rate`=`t' + 61' if age == `=`t' + 56'
}
forvalues t = 0/9{
replace retirement_2022 = retirement_rate`=`t' + 61' if age == `=`t' + 55'
}
forvalues t = 0/9{
replace retirement_2023 = retirement_rate`=`t' + 61' if age == `=`t' + 54'
}
forvalues t = 0/9{
replace retirement_2024 = retirement_rate`=`t' + 61' if age == `=`t' + 53'
}
forvalues t = 0/9{
replace retirement_2025 = retirement_rate`=`t' + 61' if age == `=`t' + 52'
}
forvalues t = 0/9{
replace retirement_2026 = retirement_rate`=`t' + 61' if age == `=`t' + 51'
}
forvalues t = 0/9{
replace retirement_2027 = retirement_rate`=`t' + 61' if age == `=`t' + 50'
}
forvalues t = 0/9{
replace retirement_2028 = retirement_rate`=`t' + 61' if age == `=`t' + 49'
}
*Assign the correct retirement rate to a cohort
tsset age
forvalues s = 2017/2028{
replace retirement_`s' = -1 if age == `=2087-`s'' & retirement_`s' == .
}
forvalues s = 2017/2028{
replace retirement_`s' = -1 if L.retirement_`s' == -1
replace retirement_`s' = 0 if retirement_`s' == .
}
drop retirement_rate*
*Generate number employed/retired in a cohort
forvalues s = 2017/2028{
generate number_retired_`s' = .
generate number_employed_`s' = .
}
replace number_employed_2017 = (1 + retirement_2017) * number_here
forvalues s = 2018/2028{
replace number_employed_`s' = (1 + retirement_`s') * number_employed_`=`s'-1'
}
forvalues s = 2017/2028{
replace number_retired_`s' = number_here - number_employed_`s'
}
collapse (sum) number_employed_* number_retired_* number_here
sum number_here, d
local total = `r(mean)'
display `total'
generate one = 1
reshape long number_retired_ number_employed_, i(one) j(year)
drop one
assert abs(number_retired_ + number_employed_ - `total') < 0.01
*Create per-thousand variable
generate number_retired_rounded = round(number_retired_/1000,.001)
*Generate bar graph of cumulative retirees per year
graph bar number_retired_rounded, over(year) ylabel(,nogrid) graphregion(fcolor(white)) ytitle("Cumulative Projected Number of Retirees (in Thousands)")
save "intermediate/retirement_employment_by_year.dta", replace
*Generate retirement survival curve
use "intermediate/retirement_rates.dta", clear
reshape long retirement_rate, i(one) j(age)
set obs 11
replace age = 60 if _n == _N
replace one = 1 if age == 60
replace retirement_rate = 0 if age == 60
tsset age
replace one = L.one * (1 + retirement_rate) if age > 60
replace one = 100 * one
label variable one "Percentage Not Retired"
set obs 12
replace age = 71 if _n == _N
replace one = 0 if age == 71
set obs 13
replace age = 72 if _n == _N
replace one = 0 if age == 72
twoway connected one age, ///
graphregion(fcolor(white)) ylabel(,nogrid) xtitle("Age") msize(tiny) xlabel(60(2)72)
*Generate number of retirees per year graph
use "intermediate/retirement_employment_by_year.dta", clear
tsset year
count
set obs `=`r(N)'+1'
replace year = 2016 if year == .
replace number_retired_ = 0 if year == 2016
sort year
generate new_retirees = S.number_retired_
drop if year == 2016
generate new_retirees_rounded = round(new_retirees/1000,0.001)
graph bar new_retirees_rounded, over(year) ylabel(,nogrid) graphregion(fcolor(white)) ///
ytitle("Projected Number of Retirees (in Thousands)") bar(1,color(maroon)) ///
yscale(range(0 40)) ylabel(0(10)40)
erase "intermediate/retirement_scenario.dta"
erase "intermediate/retirement_scenario_projections.dta"
erase "intermediate/retirement_rates.dta"
|
/*
Copyright (c) 2018 Uber Technologies, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
clear all
set more off
*Open CPS data
use "data/cps_all.dta"
tabulate union, generate(union_status_)
*Label union status variables
label variable union_status_1 "Not in Universe"
label variable union_status_2 "No union coverage"
label variable union_status_3 "Member of labor union"
label variable union_status_4 "Covered by union but not a member"
*Use the earnings weights (recommended for union variable) from CPS
*Weights should help the CPS population better approximate actual population
generate earn_weight_recip = 1/earnwt
*Calculate number unionized (in union or covered by union in a state-year)
*Note we ignore union_status_1 since these people aren't in the universe asked this question
collapse union_status_* [pweight = earn_weight_recip], by(year state)
generate union = (union_status_3 + union_status_4) / (union_status_2 + union_status_3 + union_status_4)
keep year statefip union
save "intermediate/cps_state_union_year.dta", replace
|
/*
Copyright (c) 2018 Uber Technologies, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
clear all
set more off
global data_dir = "../data/"
* Keep the 2015 dollars figures
import excel "$data_dir\h08.xls", sheet("h08") cellrange(A63) clear
drop if _n > 51
rename B median2015
rename C std2015
rename D median2014
rename E std2014
rename F median2013
rename G std2013
rename H median2012
rename I std2012
rename J median2011
rename K std2011
rename L median2010
rename M std2010
rename N median2009
rename O std2009
rename P median2008
rename Q std2008
rename R median2007
rename S std2007
rename T median2006
rename U std2006
rename V median2005
rename W std2005
rename X median2004
rename Y std2004
rename Z median2003
rename AA std2003
rename AB median2002
rename AC std2002
rename AD median2001
rename AE std2001
rename AF median2000
rename AG std2000
rename A state
keep state median* std*
reshape long median std, i(state) j(year)
generate state_id = .
replace state_id = 1 if state == "Alabama"
replace state_id = 2 if state == "Alaska"
replace state_id = 4 if state == "Arizona"
replace state_id = 5 if state == "Arkansas"
replace state_id = 6 if state == "California"
replace state_id = 8 if state == "Colorado"
replace state_id = 9 if state == "Connecticut"
replace state_id = 10 if state == "Delaware"
replace state_id = 11 if state == "D.C."
replace state_id = 12 if state == "Florida"
replace state_id = 13 if state == "Georgia"
replace state_id = 15 if state == "Hawaii"
replace state_id = 16 if state == "Idaho"
replace state_id = 17 if state == "Illinois"
replace state_id = 18 if state == "Indiana"
replace state_id = 19 if state == "Iowa"
replace state_id = 20 if state == "Kansas"
replace state_id = 21 if state == "Kentucky"
replace state_id = 22 if state == "Louisiana"
replace state_id = 23 if state == "Maine"
replace state_id = 24 if state == "Maryland"
replace state_id = 25 if state == "Massachusetts"
replace state_id = 26 if state == "Michigan"
replace state_id = 27 if state == "Minnesota"
replace state_id = 28 if state == "Mississippi"
replace state_id = 29 if state == "Missouri"
replace state_id = 30 if state == "Montana"
replace state_id = 31 if state == "Nebraska"
replace state_id = 32 if state == "Nevada"
replace state_id = 33 if state == "New Hampshire"
replace state_id = 34 if state == "New Jersey"
replace state_id = 35 if state == "New Mexico"
replace state_id = 36 if state == "New York"
replace state_id = 37 if state == "North Carolina"
replace state_id = 38 if state == "North Dakota"
replace state_id = 39 if state == "Ohio"
replace state_id = 40 if state == "Oklahoma"
replace state_id = 41 if state == "Oregon"
replace state_id = 42 if state == "Pennsylvania"
replace state_id = 44 if state == "Rhode Island"
replace state_id = 45 if state == "South Carolina"
replace state_id = 46 if state == "South Dakota"
replace state_id = 47 if state == "Tennessee"
replace state_id = 48 if state == "Texas"
replace state_id = 49 if state == "Utah"
replace state_id = 50 if state == "Vermont"
replace state_id = 51 if state == "Virginia"
replace state_id = 53 if state == "Washington"
replace state_id = 54 if state == "West Virginia"
replace state_id = 55 if state == "Wisconsin"
replace state_id = 56 if state == "Wyoming"
save "$data_dir/state_incomes.dta", replace
|
*CCR# Female Form Cleaning
**Make sure you are using the Female Database with a date
*Should automatically use the FRQ but if not, use the directory below
*use "$datadir/CCR#_FRQ_$date.dta"
**Examples
*drop duplicates, drop forms that are incorrectly created
*Drop duplicate RE Celine Dion
*Two forms for same persion in EA1/structure/1household1, first one is only partially complete, second is complete
*drop if metainstanceID=="uuid:5734bec2-e9a7-4dd5-8c80-5475b13f04bd"
***drops duplicate Female**
*RE Jennifer Lopez
*created forms without linking household for EA1/structure/1household1
*drop if metainstanceID=="somemetainstanceID"
/*
*Christine-Nafula 66 Alice & Leah[The data is not completely the same and though the name is the same kept the one done on the 3rd visit
drop if metainstanceID=="uuid:7919e3b6-3e59-45d3-91d7-14bb2aaada3b"
drop if metainstanceID=="uuid:b7a9d82e-679c-45bb-8afb-408b2a750532"
replace structure=162 if structure==192 & metainstanceID=="uuid:68437931-e4d2-45f3-8d66-3a412244f19d"
*/
save, replace
|
*CCR# Household Form Cleaning
**Make sure you are using the Household Database with a date
**Should not need to update the dataset directory but if you do, use the command below
*use "$datadir/CCR#_Combined_$date"
***RE/EA specific cleaning
*IF there are exact duplicates, drop
*If there are households that were entered more than once and hh_duplicate_check is yes for one and no for the other, drop no
*If there are households with same number, but different people listed, add 1000 to one of the household numbers to indicate that the number
*was changed from the original but dont have the correct number
**Examples
*drop if metainstanceID=="uuid:3dd03052-4073-454b-9bee-26179f35047a"
*replace household=household+1000 if metainstanceID=="uuid:f10aad48-7ca4-4da3-9ddf-54718bba9fb4"
*replace FRS_form_name="" if metainstanceID=="uuid:a6a4e656-ecff-4fbd-b239-cf3a9ebf41eb" & FRS_form_name=="FR:new_town-5-2-Alberta-23"
*RE Rachel McAdams entered same person twice in EA#/strucutre#/household#
*drop if member_number=="uuid:6b13a668-503e-4ec6-813f-497840b65b37/HH_member[7]"
*replace num_HH_members=6 if metainstanceID=="uuid:6b13a668-503e-4ec6-813f-497840b65b37"
**Changing structure numbers**
save, replace
|
// ========= Drop duplicate Households =========
*=========== Clean Structure and Household number errors ==========================
|
****PMA 2020 Data Quality Checks****
** Original Version Written in Bootcamp July 21-23, 2014****
set more off
local CCRX $CCRX
******************************
use `CCRX'_Combined_$date.dta, clear
*Check if there are any remaining duplicates
duplicates report member_number
duplicates report FQmetainstanceID
capture drop dupFQmeta
duplicates tag FQmetainstanceID, gen(dupFQmeta)
duplicates drop FQmetainstanceID if FQmetainstanceID!="", force
save, replace
********************************************************************************************************************
******************************All country specific variables need to be encoded here********************
/*Section 1 is questions/variables that are in either household/female in all countries
Section 2 is questions/variables only in one country
***Household and Female
*Household
*Update corrected date of interview if phone had incorrect settings. Update to year/month of data collection
**Assets
**Livestock
**Floor
**Roof
**Walls
*Female
*Update corrected date of interview if phone had incorrect settings. Update to year/month of data collection
**School
**FP Provider
*/
local level1 state
local level2 lga
local level3 locality
*local level4 location
*Household DOI
capture drop doi*
gen doi=system_date
replace doi=manual_date if manual_date!="." & manual_date!=""
split doi, gen(doisplit_)
capture drop wrongdate
gen wrongdate=1 if doisplit_3!="2018"
replace wrongdate=1 if doisplit_1!="Apr" & doisplit_1!="May" & doisplit_1!="Jun" & doisplit_1!=""
*If survey spans across 2 years
/*replace wrongdate=1 if doisplit_3!="2018"
replace wrongdate=1 if doisplit_1!="Jan" & doisplit_1!=""
*/
gen doi_corrected=doi
replace doi_corrected=SubmissionDate if wrongdate==1 & SubmissionDate!=""
drop doisplit*
*Assets
split assets, gen(assets_)
local x=r(nvars)
foreach var in electricity radio tv mobile landline refrigerator cable_tv ///
electric_gen ac computer elec_iron fan watch bicycle motorcycle animalcart ///
car canoe boatmotor {
gen `var'=0 if assets!="" & assets!="-99"
forval y=1/`x' {
replace `var'=1 if assets_`y'=="`var'"
}
}
drop assets_*
*Livestock
foreach x in cows_bulls horses goats sheep chickens pigs {
capture rename owned_`x'* `x'_owned
capture label var `x'_owned "Total number of `x' owned"
destring `x'_owned, replace
}
*Roof/Wall/Floor
**Numeric codes come from country specific DHS questionnaire
label define floor_list 11 earth 12 dung 21 planks 22 palm_bamboo 31 parquet 32 vinyl_asphalt 33 ceramic_tiles 34 cement ///
35 carpet 96 other -99 "-99"
encode floor, gen(floorv2) lab(floor_list)
label define roof_list 11 no_roof 12 thatched 21 rustic_mat 22 palm_bamboo 23 wood_planks 24 cardboard ///
31 metal 32 wood 34 ceramic_tiles 35 cement 36 shingles 37 asbestos 96 other -99 "-99"
encode roof, gen(roofv2) lab(roof_list)
label define walls_list 11 no_walls 12 cane_palm 13 dirt 21 bamboo_mud 22 stone_mud 24 plywood 25 cardboard ///
26 reused_wood 31 cement 32 stone_lime 33 bricks 34 cement_blocks 36 wood_planks_shingles 96 other -99 "-99"
encode walls, gen(wallsv2) lab(walls_list)
*Language
capture label define language_list 1 english 2 hausa 3 igbo 4 yoruba 5 pidgin 96 other
encode survey_language, gen(survey_languagev2) lab(language_list)
label var survey_languagev2 "Language of household interview"
****************************************************************
*************************** Female ************************
**Country specific female questionnaire changes
*Year and month of data collection.
gen FQwrongdate=1 if thisyear!=2018 & thisyear!=.
replace FQwrongdate=1 if thismonth!=4 & thismonth!=5 & thismonth!=6 & thismonth!=.
*If survey spans across 2 years
/*replace FQwrongdate=1 if thisyear!=2018 & thisyear!=.
replace FQwrongdate=1 if thismonth!=1 & thismonth!=.
*/
gen FQdoi=FQsystem_date
replace FQdoi = FQmanual_date if FQmanual_date!="." & FQmanual_date!=""
gen FQdoi_corrected=FQdoi
replace FQdoi_corrected=FQSubmissionDate if FQwrongdate==1 & FQSubmissionDate!=""
*Education Categories
label def school_list 0 never 1 primary 2 secondary 3 higher -99 "-99"
encode school, gen(schoolv2) lab(school_list)
*Methods
**The only part that needs to be updated is 5. In countries with only one injectables option it should be injectables instead of injectables_3mo
label define methods_list 1 female_sterilization 2 male_sterilization 3 implants 4 IUD 5 injectables ///
6 injectables_1mo 7 pill 8 emergency 9 male_condoms 10 female_condoms 11 diaphragm ///
12 foam 13 beads 14 LAM 15 N_tablet 16 injectables_sc 30 rhythm 31 withdrawal ///
39 other_traditional -99 "-99"
encode first_method, gen(first_methodnum) lab(methods_list)
order first_methodnum, after(first_method)
encode current_recent_method, gen(current_recent_methodnum) lab(methods_list)
order current_recent_methodnum, after(current_recent_method)
encode recent_method, gen(recent_methodnum) lab(methods_list)
order recent_methodnum, after(recent_method)
encode pp_method, gen(pp_methodnum) lab(methods_list)
order pp_methodnum, after(pp_method)
capture encode penultimate_method, gen(penultimate_methodnum) lab(methods_list)
*Drop variables not included in country
*In variable list on the foreach line, include any variables NOT asked about in country
foreach var of varlist injectables3 injectables1 N_tablet {
sum `var'
if r(min)==0 & r(max)==0 {
drop `var'
}
}
capture confirm var sayana_press
if _rc==0 {
replace sayana_press=1 if regexm(current_method, "sayana_press") & FRS_result==1
}
*Source of contraceptive supplies
label define providers_list 11 govt_hosp 12 govt_health_center 13 FP_clinic 14 mobile_clinic_public 15 fieldworker_public ///
21 private_hospital 22 pharmacy 23 chemist 24 private_doctor 25 mobile_clinic_private 26 fieldworker_private ///
31 shop 32 church 33 friend_relative 34 NGO 35 market ///
96 other -88 "-88" -99 "-99"
encode fp_provider_rw, gen(fp_provider_rwv2) lab(providers_list)
capture encode fp_provider_rw, gen(fp_provider_rwv2) lab(providers_list)
*FQ Language
capture label define language_list 1 english 2 hausa 3 igbo 4 yoruba 5 pidgin 96 other
capture encode FQsurvey_language, gen(FQsurvey_languagev2) lab(language_list)
capture label var FQsurvey_language "Language of Female interview"
***************************************************************************************************
***SECTION 2: COUNTRY SPECIFIC QUESTIONS
capture confirm var religion
if _rc==0 {
label define religion_list 1 catholic 2 other_christian 3 islam 4 traditionalist 96 other -77 "-77" -99 "-99"
encode religion, gen(religionv2) lab(religion_list)
sort metainstanceID religionv2
bysort metainstanceID: replace religionv2 =religionv2[_n-1] if religionv2==.
label var religionv2 "Religion of household head"
}
capture confirm var ethnicity
if _rc==0 {
label define ethnicity_list 1 afo_gwandara 2 alago 3 eggon 4 fufulde 5 hausa 6 igbo 7 izon_ijaw 8 katab_tyap ///
9 mada 10 mambila 11 mumuye 12 ogoni 13 rundawa 14 wurkum 15 yoruba 96 other -99 "-99"
encode ethnicity, gen(ethnicityv2) lab(ethnicity_list)
sort metainstanceID ethnicityv2
bysort metainstanceID: replace ethnicityv2=ethnicityv2[_n-1] if ethnicityv2==.
label var ethnicityv2 "Ethnicity of household head"
}
//REVISION: BL 01Nov2017 follow-up consent
capture confirm var flw_*
if _rc==0 {
label var flw_willing "Willing to participate in another survey"
encode flw_willing, gen(flw_willingv2) lab(yes_no_dnk_nr_list)
label var flw_number_yn "Owns a phone"
encode flw_number_yn, gen(flw_number_ynv2) lab(yes_no_dnk_nr_list)
label var flw_number_typed "Phone number"
}
unab vars: *v2
local stubs: subinstr local vars "v2" "", all
foreach var in `stubs'{
rename `var' `var'QZ
order `var'v2, after(`var'QZ)
}
rename *v2 *
drop *QZ
//Kenya R6
capture label var hh_location_ladder "Location of house on wealth ladder: 1 = poorest, 10 = wealthiest"
***************************************************************************************************
********************************* COUNTRY SPECIFIC WEIGHT GENERATION *********************************
***************************************************************************************************
**Import sampling fraction probabilities and urban/rural
**NEED TO UPDATE PER COUNTRY
/*
merge m:1 EA using "C:/Users/Shulin/Dropbox (Gates Institute)/PMADataManagement_Uganda/Round5/WeightGeneration/UGR5_EASelectionProbabilities_20170717_lz.dta", gen(weightmerge)
drop region subcounty district
tab weightmerge
**Need to double check the weight merge accuracy
capture drop if weightmerge!=3
label define urbanrural 1 "URBAN" 2 "RURAL"
label val URCODE urbanrural
rename URCODE ur
capture rename EASelectionProbabiltiy EASelectionProbability
gen HHProbabilityofselection=EASelectionProbability * ($EAtake/HHTotalListed)
replace HHProbabilityofselection=EASelectionProbability if HHTotalListed<$EAtake
generate completedhh=1 if (HHQ_result==1) & metatag==1
*Denominator is any household that was found (NOT dwelling destroyed, vacant, entire household absent, or not found)
generate hhden=1 if HHQ_result<6 & metatag==1
*Count completed and total households in EA
bysort ur: egen HHnumtotal=total(completedhh)
bysort ur: egen HHdentotal=total(hhden)
*HHweight is1/ HHprobability * Missing weight
gen HHweight=(1/HHProbability)*(1/(HHnumtotal/HHdentotal)) if HHQ_result==1
**Generate Female weight based off of Household Weight
**total eligible women in the EA
gen eligible1=1 if eligible==1 & (last_night==1)
bysort ur: egen Wtotal=total(eligible1)
**Count FQforms up and replace denominator of eligible women with forms uploaded
*if there are more female forms than estimated eligible women
gen FQup=1 if FQmetainstanceID!=""
gen FQup1=1 if FQup==1 & (last_night==1)
bysort ur: egen totalFQup=total(FQup1)
drop FQup1
replace Wtotal=totalFQup if totalFQup>Wtotal & Wtotal!=. & totalFQup!=.
**Count the number of completed or partly completed forms (numerator)
gen completedw=1 if (FRS_result==1 ) & (last_night==1) //completed, or partly completed
bysort ur: egen Wcompleted=total(completedw)
*Gen FQweight as HHweight * missing weight
gen FQweight=HHweight*(1/(Wcompleted/Wtotal)) if eligible1==1 & FRS_result==1 & last_night==1
gen HHweightorig=HHweight
gen FQweightorig=FQweight
**Normalize the HHweight by dividing the HHweight by the mean HHweight (at the household leve, not the member level)
preserve
keep if metatag==1
su HHweight
replace HHweight=HHweight/r(mean)
sum HHweight
tempfile temp
keep metainstanceID HHweight
save `temp', replace
restore
drop HHweight
merge m:1 metainstanceID using `temp', nogen
**Normalize the FQweight
sum FQweight
replace FQweight=FQweight/r(mean)
sum FQweight
drop weightmerge HHProbabilityofselection completedhh-HHdentotal eligible1-Wcompleted
rename REGIONCODEUR strata
*/
***************************************************************************************************
********************************* GENERIC DONT NEED TO UPDATE *********************************
********************************************************************************************************************
*1. Drop unneccessary variables
rename consent_obtained HQconsent_obtained
drop consent* FQconsent FQconsent_start *warning* ///
respondent_in_roster roster_complete ///
deviceid simserial phonenumber *transfer *label* ///
witness_manual *prompt* witness_manual *check* *warn* FQKEY ///
unlinked* error_*heads metalogging eligibility_screen* ///
more_hh_members* *GeoID* dupFRSform deleteTest dupFQ FQresp error *note* ///
HHmemberdup waitchild
capture drop why_not_using_c
capture drop last_time_sex_lab menstrual_period_lab *unlinked close_exit
capture drop begin_using_lab
capture drop anychildren
capture drop yeschildren
capture drop childmerge
capture drop dupFQmeta
capture drop *Section*
rename HQconsent_obtained consent_obtained
capture drop if EA=="9999" | EA==9999
sort metainstanceID member_number
/***************** RECODE CURRENT METHOD **********************************
1. Recent EC users recoded to current users
2. LAM Users who are not using LAM recoded
3. Female sterilization users who do not report using sterilization are recoded
4. SP users recoded to SP
********************************************************************/
**Recode recent EC users to current users
gen current_methodnum=current_recent_methodnum if current_user==1
label val current_methodnum methods_list
gen current_methodnumEC=current_recent_methodnum if current_user==1
replace current_methodnumEC=8 if current_recent_methodnum==8 & current_user!=1
label val current_methodnumEC methods_list
gen current_userEC=current_user
replace current_userEC=. if current_methodnumEC==-99
replace current_userEC=1 if current_recent_methodnum==8 & current_user!=1
gen recent_userEC=recent_user
replace recent_userEC=. if current_recent_methodnum==8
gen recent_methodEC=recent_method
replace recent_methodEC="" if recent_method=="emergency"
gen recent_methodnumEC=recent_methodnum
replace recent_methodnumEC=. if recent_methodnum==8
label val recent_methodnumEC methods_list
gen fp_ever_usedEC=fp_ever_used
replace fp_ever_usedEC=1 if current_recent_methodnum==8 & fp_ever_used!=1
gen stop_usingEC=stop_using
gen stop_usingSIFEC=stop_usingSIF
replace stop_using_why_cc=subinstr(stop_using_why_cc, "difficult_to_conceive", "diff_conceive", .)
replace stop_using_why_cc=subinstr(stop_using_why_cc, "interferes_with_body", "interf_w_body", .)
foreach reason in infrequent pregnant wanted_pregnant husband more_effective no_method_available health_concerns ///
side_effects no_access cost inconvenient fatalistic diff_conceive interf_w_body other {
gen stop_usingEC_`reason'=stop_using_`reason'
replace stop_usingEC_`reason'=. if current_recent_methodnum==8
}
replace stop_usingEC="" if current_recent_methodnum==8
replace stop_usingSIFEC=. if current_recent_methodnum==8
gen future_user_not_currentEC=future_user_not_current
replace future_user_not_currentEC=. if current_recent_methodnum==8
gen future_user_pregnantEC=future_user_pregnant
replace future_user_pregnantEC=. if current_recent_methodnum==8
gen ECrecode=0
replace ECrecode=1 if (regexm(current_recent_method, "emergency"))
*******************************************************************************
* RECODE LAM
*******************************************************************************
tab LAM
* CRITERIA 1. Birth in last six months
* Calculate time between last birth and date of interview
* FQdoi_corrected is the corrected date of interview
gen double FQdoi_correctedSIF=clock(FQdoi_corrected, "MDYhms")
format FQdoi_correctedSIF %tc
* Number of months since birth=number of hours between date of interview and date
* of most recent birth divided by number of hours in the month
gen tsincebh=hours(FQdoi_correctedSIF-recent_birthSIF)/730.484
gen tsinceb6=tsincebh<6
replace tsinceb6=. if tsincebh==.
* If tsinceb6=1 then had birth in last six months
* CRITERIA 2. Currently ammenhoeric
gen ammen=0
* Ammenhoeric if last period before last birth
replace ammen=1 if menstrual_period==6
* Ammenhoerric if months since last period is greater than months since last birth
g tsincep = menstrual_period_value if menstrual_period==3 // months
replace tsincep = int(menstrual_period_value/30) if menstrual_period==1 // days
replace tsincep = int(menstrual_period_value/4.3) if menstrual_period==2 // weeks
replace tsincep = menstrual_period_value*12 if menstrual_period==4 // years
replace ammen=1 if tsincep>tsincebh & tsincep!=.
* Only women both ammenhoerric and birth in last six months can be LAM
gen lamonly=1 if current_method=="LAM"
replace lamonly=0 if current_methodnumEC==14 & (regexm(current_method, "rhythm") | regexm(current_method, "withdrawal") | regexm(current_method, "other_traditional"))
gen LAM2=1 if current_methodnumEC==14 & ammen==1 & tsinceb6==1
tab current_methodnumEC LAM2, miss
replace LAM2=0 if current_methodnumEC==14 & LAM2!=1
* Replace women who do not meet criteria as traditional method users
capture rename lam_probe_current lam_probe
capture confirm variable lam_probe
if _rc==0 {
capture noisily encode lam_probe, gen(lam_probev2) lab(yes_no_dnk_nr_list)
drop lam_probe
rename lam_probev2 lam_probe
replace current_methodnumEC=14 if LAM2==1 & lam_probe==1
replace current_methodnumEC=30 if lam_probe==0 & lamonly==0 & regexm(current_method, "rhythm")
replace current_methodnumEC=31 if current_methodnumEC==14 & lam_probe==0 & lamonly==0 & regexm(current_method, "withdrawal") & !regexm(current_method, "rhythm")
replace current_methodnumEC=39 if current_methodnumEC==14 & lam_probe==0 & lamonly==0 & regexm(current_method, "other_traditional") & !regexm(current_method, "withdrawal") & !regexm(current_method, "rhythm")
replace current_methodnumEC=39 if lam_probe==1 & current_methodnumEC==14 & LAM2==0
replace current_methodnumEC=. if current_methodnumEC==14 & lam_probe==0 & lamonly==1
replace current_userEC=0 if current_methodnumEC==. | current_methodnumEC==-99
}
else {
replace current_methodnumEC=39 if LAM2==0
}
drop tsince* ammen
*******************************************************************************
* RECODE First Method Female Sterilization
*******************************************************************************
replace current_methodnumEC=1 if first_methodnum==1
capture replace current_methodnumEC=1 if sterilization_probe==1
*******************************************************************************
* RECODE Injectables_SC
*injectable
*******************************************************************************
capture replace current_methodnumEC=16 if (injectable_probe_current==2 | injectable_probe_current==3) ///
& regexm(current_recent_method,"injectable")
capture replace recent_methodnumEC=16 if (injectable_probe_recent==2 | injectable_probe_recent==3)
gen first_methodnumEC=first_methodnum
capture replace first_methodnumEC=16 if injectable_probe_first==2
capture replace pp_methodnum=16 if (injectable_probe_pp==2 | injectable_probe_pp==3)
*******************************************************************************
* Define CP, MCP, TCP and longacting
*******************************************************************************
gen cp=0 if HHQ_result==1 & FRS_result==1 & (last_night==1)
replace cp=1 if HHQ_result==1 & current_methodnumEC>=1 & current_methodnumEC<=39 & FRS_result==1 & (last_night==1)
label var cp "Current use of any contraceptive method"
gen mcp=0 if HHQ_result==1 & FRS_result==1 & (last_night==1)
replace mcp=1 if HHQ_result==1 & current_methodnumEC>=1 & current_methodnumEC<=19 & FRS_result==1 & (last_night==1)
label var mcp "Current use of any modern contraceptive method"
gen tcp=0 if HHQ_result==1 & FRS_result==1 & (last_night==1)
replace tcp=1 if HHQ_result==1 & current_methodnumEC>=30 & current_methodnumEC<=39 & FRS_result==1 & (last_night==1)
label var tcp "Current user of any traditional contraceptive method"
gen longacting=current_methodnumEC>=1 & current_methodnumEC<=4 & mcp==1
label variable longacting "Current use of long acting contraceptive method"
label val cp mcp tcp longacting yes_no_dnk_nr_list
sort metainstanceID member_number
gen respondent=1 if firstname!="" & (HHQ_result==1 | HHQ_result==5)
replace respondent=0 if (HHQ_result==1 | HHQ_result==5) & respondent!=1
bysort metainstanceID: egen totalresp=total(respondent)
replace respondent=0 if totalresp>1 & totalresp!=. & relationship!=1 & relationship!=2
recast str244 names, force
saveold `CCRX'_Combined_ECRecode_$date.dta, replace version(12)
****************** KEEP GPS ONLY *******************
********************************************************************
preserve
keep if FQmetainstanceID!=""
keep FQLatitude FQLongitude FQAltitude FQAccuracy RE FQmetainstanceID $GeoID household structure EA
export excel using "`CCRX'_FQGPS_$date.csv", firstrow(var) replace
restore
preserve
keep if metatag==1
keep locationLatitude locationLongitude locationAltitude locationAccuracy RE metainstanceID $GeoID household structure EA
rename location* HQ*
export excel using "`CCRX'_HHQGPS_$date.csv", firstrow(var) replace
restore
****************** REMOVE IDENTIFYING INFORMATION *******************
*******************************************************************
capture rename facility_name* facility_nm*
drop *name* *Name*
drop *Latitude *Longitude *Altitude *Accuracy location*
capture drop *GPS*
capture rename facility_nm* facility_name*
capture drop flw_number_type
saveold `CCRX'_NONAME_ECRecode_$date.dta, replace version(12)
|
****PMA 2020 Data Quality Checks****
***Version Jan 17 2017****
**VERSION HISTORY AT END OF DO FILE
**First do file in series
/*Code is based on Stata 12 but some commands for Stata 11 and below are included and commented with *.
To use Stata 11 and below, comment out Stata 12 comments and remove the * before Stata 11 commands
This do file is designed to clean and check data. Information from Briefcase will need to be downloaded and exported as csv.
The do file will then (by country):
Step 1
a. Append all different versions of the Household Questionnaire into one version and destrings variables as appropriate, codes, and labels each questionnaire
b. Append all different versions of the Household Roster into one version and destrings variables as appropriate, codes, and labels each questionnaire
c. Append all different versions of the Female Questionnaire into one version and destrings variables as appropriate, codes, and labels each questionnaire
*All duplicates are tagged and dropped if they are complete duplicates
Step 2
a. Merge the Household Questionnaire, the Household Roster, and the Female Questionnaire into one file
*Also identifies any female questionnaires that exist but do not merge with a household form and all
*female quesitonnaires that are identified by an HHRoster but that do not have a FQ
It then runs the following checks by RE and EA (in some cases, REs may conduct interviews in EAs other
than their own. This catches any potential outside survey)s:
1. Total number of HHQs that are uploaded
2. Total number of HHQs that are marked as complete
3. Total number of HHQs that are marked as refused
4. Total number of HHQs that are marked as No One Home
5. Total number of eligible women identified
6. Total number of FQ forms that are uploaded to the server
7. Total number of FQ forms that were completed
8. Total number of FQ forms that were refused
9. Total number of FQ forms that were not at home
Additional information includes minimum time to complete surveys, number of HHQ and FQ
that do not have GPS or GPS more than 6 m
**********************************************************************************
*/
clear matrix
clear
set more off
set maxvar 30000
*******************************************************************************
* SET MACROS: UPDATE THIS SECTION FOR EACH COUNTRY/ROUND
*******************************************************************************
*BEFORE USE THE FOLLOWING NEED TO BE UPDATED:
*Country/Round/Abbreviations
global Country CD
global Round Round7
global round 7
global country CD
global CCRX CDR7
*Locals (Dont need to Update)
local Country "$Country"
local Round "$Round"
local CCRX "$CCRX"
*Year of the Survey
local SurveyYear 2018
local SYShort 18
******CSV FILE NAMES ****
*HHQ CSV File name
global HHQcsv CDR7_Household_Questionnaire_v6
*FQ CSV File name
global FQcsv CDR7_Female_Questionnaire_v6
***If the REs used a second version of the form, update these
*If they did not use a second version, DONT UPDATE
*global HHQcsv2 GHR5_Household_Questionnaire_v6
*global FQcsv2 GHR5_Female_Questionnaire_v6
**************
**************
*******DO FILE NAMES******
*HHQ_DataChecking File Name
local HHQdofile CCRX_HHQ_Datachecking_v19.0_25Sep2018_AR
*FRQ_DataChecking File Name
local FRQdofile CCRX_FRQ_DataChecking_v27.0_15Jun2018_AR
*HHQmember_DataChecking File Name
local HHQmemberdofile CCRX_HHQmember_DataChecking_v7.0_30Oct2017_BL
*WASH do file
local WASHdofile CCRX_WASH_v19.0_24Apr2018
*CleaningByRE_Female date and initials
local CleanFemaledate 05Oct2015
*GPS check Spatial Data (requires to generate cleaned Listing.dta beforehand)
local hhq_monit CCRX_HHQ_GeoMonitoring
*CleaningByRE_HHQ date and intitials
local CleanHHQdate 05Oct2015
*Country Specific Clean Weight and initials
local CountrySpecificdate v30.1_28Sep2018
*Country/Round specific module
local module1do CCRX_CCP_v1_14Nov2017_SJ
/*local module2do CCRX_InjectablesSC_v8_28Mar2018_BL
local module3do CCRX_Abortion_v06_13Jul2018_sob
local module4do CCRX-AbtModuleDataChecking-v06-13Jul2018-sob
*/
//REVISION: SJ 17AUG2017 add Analytics to parent file
*local analytics RJR3_HHQFQ_Analytics_Dofile_v7_10Aug2017_NS
************
**** GEOGRAPHIC IDENTIFIERS ****
global GeoID "level1 level2 level3 level4 EA"
*Geographic Identifier lower than EA to household
global GeoID_SH "structure household"
*rename level1 variable to the geographic highest level, level2 second level
*done in the final data cleaning before dropping other geographic identifiers
global level1name level1
global level2name level2
global level3name level3
global level4name level4
*Number of households selected per EA
global EAtake=35
**************
**** DIRECTORIES****
**Global directory for the dropbox where the csv files are originally stored
global csvdir "/Users/ealarson/Dropbox (Gates Institute)/7 DRC/PMADataManagement_DRC/Round7/Data/CSV_Files"
**Create a global data directory -
global datadir "/Users/ealarson/Documents/DRC/Data_NotShared/Round7/HHQFQ"
**Create a global do file directory
global dofiledir "/Users/ealarson/Dropbox (Gates Institute)/7 DRC/PMADataManagement_DRC/Round7/Cleaning_DoFiles/Current"
*******************************************************************************************
******* Stop Updating Macros Here *******
*******************************************************************************************
*******************************************************************************************
******* Stop Updating Macros Here *******
*******************************************************************************************
/*Define locals for dates. The current date will automatically update to the day you are running the do
file and save a version with that day's date*/
local today=c(current_date)
local c_today= "`today'"
global date=subinstr("`c_today'", " ", "",.)
cd "$datadir"
**The following commands should be run after the first time you run the data. These commands
*archive all the old versions of the datasets so that data is not deleted and if it somehow is,
*we will have backups of all old datasets. The shell command accesses the terminal in the background
*(outside of Stata) but only works for Mac. It is not necessary to use shell when using Windows but the commands are different
*The command zipfile should work for both Mac and Windows, however shell command only works for Mac.
*The following commands will zip old datasets and then remove them so that only the newest version is available
*Make sure you are in the directory where you will save the data
* Zip all of the old versions of the datasets and the excel spreadsheets.
*Replaces the archive, does not save into it so create a new archive every date you run the file
capture zipfile `CCRX'*, saving (Archived_Data/ArchivedHHQFQData_$date.zip, replace)
capture shell erase `CCRX'*
**Start log
capture log close
log using `CCRX'_DataCleaningQuality_$date.log, replace
*******************************************************************************************
******* Start the cleaning *******
*******************************************************************************************
*******************************************************************************************
*Step 1. Running the following do-file command imports all of the versions of the forms
*tags duplicates, renames variables, and change the format of some of the variables
**Dataset is named `CCRX'_HHQ_$date.dta
run "$dofiledir/`HHQdofile'.do"
duplicates drop metainstanceID, force
save, replace
**This is not fully cleaned. It is just destrung and encoded with variable labels
************************************************************************************
*******************************************************************************************
* Step 2 Household Roster Information - Repeats the same steps for the Household Roster
** Generates data file `CCRX'_HHQmember_$date.dta
run "$dofiledir/`HHQmemberdofile'.do"
**This is not fully cleaned. It is just destrung and encoded with variable labels
************************************************************************************
**Merges the household and the household roster together
use `CCRX'_HHQ_$date.dta
merge 1:m metainstanceID using `CCRX'_HHQmember_$date, gen (HHmemb)
save `CCRX'_HHQCombined, replace
run "$dofiledir/`WASHdofile'.do"
save `CCRX'_Combined_$date, replace
************************************************************************************************************************
******************************HOUSEHOLD FORM CLEANING SECTION*********************************************
*********************************************************************************************************
******After you initially combine the household and household memeber, you will need to correct duplicate submisttions.
* You will correct those errors here and in the section below so that the next time you run the files, the dataset will
* be cleaned and only errors that remain unresolved are generated.
**Write your corrections into a do file named "/Whatever/your/path/name/is/CCR#_CleaningByREHHQ_DateYouWriteFile.do
run "$dofiledir/`CCRX'_CleaningByRE_HHQ_`CleanHHQdate'.do"
capture drop dupHHtag
egen GeoID=concat($GeoID), punc(-)
egen GeoID_SH=concat($GeoID structure household), punc(-)
save, replace
*******************************************************************************************
* Step 3 Female Questionnaire Information - Repeats the same steps for the Female Questionnaire
** Generates data file `CCRX'_FQ_$date.dta
************************************************************************************
run "$dofiledir/`FRQdofile'.do"
egen FQGeoID=concat($GeoID), punc(-)
egen FQGeoID_SH=concat($GeoID structure household), punc(-)
*This exports a list of female forms that are duplicated. Use this to track if any REs seem to be having trouble uploading forms
*dont need to make changes based on this list other than dropping exact duplicates and making sure REs are being patient and not hitting send
*multiple times
preserve
keep if dupFQ!=0
sort metainstanceName
capture noisily export excel metainstanceID RE FQGeoID_SH firstname FQ_age metainstanceName using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(DuplicateFemale) replace
if _rc!=198{
restore
}
else{
set obs 1
gen x="NO DUPLICATE FEMALE FORMS"
export excel x using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(DuplicateFemale) replace
restore
}
duplicates drop metainstanceID, force
save, replace
************************************************************************************************************************
******************************FEMALE FORM CLEANING SECTION*********************************************
*********************************************************************************************************
******After running the dataset each time, the excel file will generate a list of errors. You will correct those errors
*here and in the section below so that the next time you run the files, the dataset will be cleaned and only errors that remain
*unfinished are generated. *This is where you will clean the female forms for duplicates
*If you find multiple female forms submitted for the same person, or if the names do not exactly match,
*you will correct those errors here.
**Write your corrections into a do file named "/Whatever/your/path/name/is/`CCRX'_CleaningByRE_FEMALE_DateYouWriteFile.do
run "$dofiledir/`CCRX'_CleaningByRE_FEMALE_`CleanFemaledate'.do"
******************************************************************************************
************************************************************************************
************************************************************************************
*Step Four: Merge the datasets together and save a copy that is NOT cleaned of unneccessary variables
clear
use `CCRX'_FRQ_$date.dta
foreach var of varlist SubmissionDate times_visited system_date manual_date ///
start end today acquainted-firstname marital_status Latitude-Accuracy {
rename `var' FQ`var'
}
duplicates list metainstanceName RE
duplicates drop metainstanceID, force
duplicates report RE metainstanceName
duplicates tag RE metainstanceName, gen(dupFRSform)
sort RE metainstanceName
*rename province-household FQprovince-FQhousehold so that missing values from unlinked forms dont merge over
foreach var in $GeoID structure household {
rename `var' FQ`var'
}
rename FQEA EA
capture replace EA=unlinkedEA if unlinked=="1"
preserve
keep if dupFRSform!=0
capture noisily export excel metainstanceID RE FQGeoID FQstructure FQhousehold ///
metainstanceName FQfirstname FQ_age FQSubmissionDate FRS_result FQstart FQend unlinkedEA using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(FRS_Duplicate_in_Female) sheetreplace
if _rc!=198{
restore
}
if _rc==198 {
clear
set obs 1
gen x="NO DUPLICATE FEMALE FORM NAME IN FRQ Dataset"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(FRS_Duplicate_in_Female) sheetreplace
restore
}
drop duplink
duplicates tag link, gen(duplink)
preserve
keep if duplink!=0
capture noisily export excel metainstanceID RE FQGeoID FQstructure FQhousehold metainstanceName ///
link FQfirstname FQ_age FQSubmissionDate FRS_result FQstart FQend unlinkedEA using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(Duplicate_Link_in_FRQ) sheetreplace
if _rc!=198{
restore
}
if _rc==198 {
clear
set obs 1
gen x="NO DUPLICATE LINK ID IN FRQ DATASET"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(Duplicate_Link_in_FRQ) sheetreplace
restore
}
rename metainstanceName FRS_form_name
rename metainstanceID FQmetainstanceID
rename available FQavailable
capture rename *int FQ*int
rename KEY FQKEY
**This lists remaining duplicate female form names that have not already been cleaned. You cannot merge with duplicate female forms
*Must update the CleaningByRE_FEMALE do file above or drop duplicates
*To merge, must drop all remaining by duplicates
*BUT BEFORE FINAL CLEANING YOU MUST IDENTIFY WHICH OF THE FEMALE FORMS IS THE CORRECT ONE!!!!
gen linktag=1 if link=="" & unlinked=="1"
gen linkn=_n if linktag==1
tostring linkn, replace
replace link=linkn if linktag==1
duplicates drop link, force
save, replace
******************* Merge in Female Questionnaire ********************************
use `CCRX'_Combined_$date
**Above code drops duplicate FRS_form_name from FRQ but also need to make sure that there are no duplicates
*in the household
*Identify any duplicate FRS forms in the household. Make sure the households are also not duplicated
* and drop any remaining duplicated female and household forms before merging
*Write the instances to drop in the CleaningByRE files
*IF there are two women in the household with the same name and age, they will have the same FRS_form_name
*Rename one of the women FRS_form_nameB in the female, find the same woman in the household and rename
duplicates tag FRS_form_name if FRS_form_name!="", gen(dupFRSform)
tab dupFRSform
preserve
keep if dupFRSform!=0 & dupFRSform!=.
sort FRS_form_name
capture noisily export excel metainstanceID member_number RE GeoID_SH names FRS_form_name using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(FRS_Duplicate_in_Household) sheetreplace
if _rc!=198{
restore
}
else {
clear
set obs 1
gen x="NO DUPLICATE FRS_form_name IN HOUSEHOLD"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(FRS_Duplicate_in_Household) sheetreplace
restore
}
save, replace
preserve
keep if eligible==1
rename link_transfer link
merge m:1 link using `CCRX'_FRQ_$date.dta, gen(FQresp)
tempfile FRStemp
save `FRStemp', replace
restore
drop if eligible==1
append using `FRStemp'
sort metainstanceID member_number
egen metatag=tag(metainstanceID)
replace link="" if linktag==1
drop linktag
save, replace
**********************************************************************************
**********************************************************************************
**********************************************************************************
*******************Clean and Check Merged Data********************
**********************************************************************************
**Now you will clean the household file of duplicate households or misnumbered houses. Save these changes in this do file
**Use cleaning file to drop problems that have been cleaned already (update this file as problems are resolved)
capture drop dupHHtag
**Complete duplicates have already been exported out. Those that have been resolved already will be cleaned using the
*previous do file. If the observations have not been cleaned yet, the data will be exported out below
*This information exports out variables that have duplicate structures and households from forms submitted multiple times
**Establish which form is correct (check based on visit number, submission date, start date and end date and work with
*supervisor and RE to identify which form is correct and which should be deleted
preserve
keep if metatag!=0
duplicates tag GeoID_SH, gen(dupHHtag)
keep if dupHHtag!=0
sort GeoID_SH RE hh_duplicate_check
capture noisily export excel metainstanceID RE GeoID_SH names times_visited hh_duplicate_check resubmit_reasons HHQ_result system_date end SubmissionDate using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(DuplicateEAStructureHH) sheetreplace
if _rc!=198{
restore
}
else {
clear
set obs 1
gen x="NO DUPLICATE GeoID"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(DuplicateEAStructureHH) sheetreplace
restore
}
save, replace
**This line of code identifies households and structures that have the same number but in which there are more than one group of people
*Identify if the people are actually one household and the RE created more than one form OR if they are two different households
*and the RE accidentally labeled them with the same number
*Export out one observation per household/name combination for each household that has more than one group of people
preserve
keep if metatag==1
egen HHtag=tag(RE EA GeoID_SH names)
*Checking to see if there are duplicate households and structure that do NOT have the same people listed
*Tags each unique RE EA structure household and name combination
*Totals the number of groups in a household (should only ever be 1)
bysort RE EA GeoID_SH: egen totalHHgroups=total(HHtag)
keep if HHtag==1 & totalHHgroups>1 & metatag==1
capture noisily export excel metainstanceID RE GeoID_SH names hh_duplicate_check using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(DuplicateHH_DifferentNames) sheetreplace
if _rc!=198 {
restore
}
else {
clear
set obs 1
gen x="NO DUPLICATE HOUSEHOLDS WITH DIFFERENT NAMES"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(DuplicateHH_DifferentNames) sheetreplace
restore
}
/*IF THERE ARE ANY FEMALE FORMS THAT DO NOT MERGE or eligible females that do not have
a form merged to them, these will be flagged and exported for followup */
gen error=1 if FQresp==2
replace error=1 if FQresp==1 & eligible==1
save, replace
preserve
keep if error==1
gsort FQresp -unlinked RE
capture noisily export excel RE metainstanceID GeoID_SH link FRS_form_name firstname ///
FQmetainstanceID FQfirstname unlinked SubmissionDate FQSubmissionDate using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(FRQmergeerror) sheetreplace
*Stata 12 or above use export excel
if _rc!=198{
restore
}
else{
clear
set obs 1
gen x="NO FEMALE QUESTIONNAIRE MERGE ERRORS"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(FRQmergeerror) sheetreplace
restore
}
*Stata 11 or below use outsheet
*capture outsheet using RE frsformname FQmetainstanceID- FQfirstname using `CCRX'_HHQFQErrors_FRQmerge_$date.csv, comma replace
/* This line of code will identify if there are duplicate observations in the household. Sometimes the entire
roster duplicates itself. This will check for duplicate name, age, and relationships in the household*/
duplicates tag metainstanceID firstname age relationship if metainstanceID!="", gen(HHmemberdup)
preserve
drop if FQresp==2
keep if HHmemberdup!=0
sort RE
capture noisily export excel RE metainstanceID member_number GeoID_SH firstname age relationship using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(DupHHmember) sheetreplace
*Stata 12 or above use export excel
if _rc!=198{
restore
}
else{
clear
set obs 1
gen x="No duplicated records of household members"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(DupHHmember) sheetreplace
restore
}
save, replace
clear
**********************************************************************************
**********************************************************************************
**********************************************************************************
*Step Three: Run basic checks on the Household Questionnaire
use `CCRX'_Combined_$date.dta, replace
gen totalint=minutes(endSIF-startSIF)
gen FQtotalint=minutes(FQendSIF-FQstartSIF)
save, replace
capture drop HHtag
**Check: Number of files uploaded by RE
/*Count the total number of surveys, the total number of surveys by version uploaded, and the total number
of completions and refusals by RE and EA (since in some cases, REs may go to more than one EA). Also
calculate the mean number of hhmembers per household*/
preserve
keep if metatag==1
forval x = 1/9 {
gen HHQ_result_`x'=1 if HHQ_result==`x'
}
collapse (count) HHQ_result_* HHQ_result, by (RE $GeoID)
rename HHQ_result HQtotalup
rename HHQ_result_1 HQcomplete
rename HHQ_result_2 HQnothome
rename HHQ_result_4 HQrefusal
rename HHQ_result_8 HQnotfound
gen HQresultother=HHQ_result_5 + HHQ_result_6 + HHQ_result_3 + HHQ_result_7 + HHQ_result_9
save `CCRX'_ProgressReport_$date, replace
restore
**********************************************************************************
**********************************************************************************
*** Run basic checks on the Household Member Questionnaire
/*Number of eligible women identified and average number of eligible women per household*/
preserve
*Counting total number of eligible women identified in EA based only on COMPL`CCRX'ED FORMS
collapse (sum) eligible if HHQ_result==1, by(RE $GeoID)
rename eligible totaleligible
label var totaleligible "Total eligible women identified in EA - COMPLETED HH FORMS ONLY"
tempfile collapse
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
restore
**********************************************************************************
**********************************************************************************
*** Run basic checks on the Female Respondent Questionnaire
**Number of female surveys completed
**Female survey response rate
**Non-response of sensitive questions
preserve
**Number of female surveys uploaded, number of female surveys that do not link (error)
collapse (count) FQresp if FQresp!=1, by (RE $GeoID)
rename FQresp FQtotalup
label var FQtotalup "Total Female Questionnaires Uploaded (including errors)"
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
*Number of female questionnaires that are in the FQ database but do not link to a household
restore
preserve
capture collapse (count) FQresp if FQresp==2, by (RE $GeoID)
if _rc!=2000{
rename FQresp FQerror
label var FQerror "Female Questionnaires that do not match Household"
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
}
*Number of eligible women who are missing a female questionnaire (this should always be zero!)
restore
preserve
capture collapse (count) FQresp if eligible==1 & FQresp==1, by (RE $GeoID)
if _rc!=2000{
rename FQresp FQmiss
label var FQmiss "Eligible women missing female questionnaires"
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
}
**Completion and refusal rates for female questionnaire
restore
preserve
forval x = 1/6 {
gen FRS_result_`x'=1 if FRS_result==`x'
}
collapse (count) FRS_result_* FRS_result if FRS_result!=., by (RE $GeoID)
*Count the number of surveyes with each completion code
rename FRS_result_1 FQcomplete
rename FRS_result_4 FQrefusal
rename FRS_result_2 FRS_resultothome
gen FQresultother = FRS_result_3 + FRS_result_5 + FRS_result_6
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
restore
preserve
keep if metatag==1
keep if HHQ_result==1
drop if totalint<0
keep if totalint<=10
sort RE
capture noisily export excel RE metainstanceID GeoID_SH names totalint assets num_HH_members water_sources_all sanitation_all using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(HQInterview10min)
*Stata 12 or above use export excel
if _rc!=198{
restore
}
else{
clear
set obs 1
gen x="NO COMPLETE HOUSEHOLD INTERVIEWS LESS THAN 10 MINUTES"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(HQInterview10min)
restore
}
preserve
keep if metatag==1
keep if HHQ_result==1
drop if totalint<0
keep if totalint<10
capture collapse (count) totalint , by(RE $GeoID)
if _rc!=2000{
rename totalint HHQintless10
tempfile collapse
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
}
else {
use `CCRX'_ProgressReport_$date , clear
gen HHQintless10=0
save, replace
}
restore
preserve
**Minimum time to COMPLETED FQ form
keep if FRS_result==1 & HHQ_result==1
drop if FQtotalint<0
keep if FQtotalint<=10
sort RE
capture noisily export excel RE FQmetainstanceID GeoID_SH FRS_form_name FQtotalint FQ_age FQmarital_status current_user using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(FQInterview10min)
*Stata 12 or above use export excel
if _rc!=198{
restore
}
else{
clear
set obs 1
gen x="NO COMPLETE FEMALE INTERVIEWS LESS THAN 10 MINUTES"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(FQInterview10min)
restore
}
preserve
keep if FRS_result==1 & HHQ_result==1
drop if FQtotalint<0
keep if FQtotalint<10
capture collapse (count) FQtotalint , by(RE $GeoID)
if _rc!=2000{
rename FQtotalint FQintless10
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
}
else {
use `CCRX'_ProgressReport_$date, clear
gen FQintless10=0
save, replace
}
restore
**Add GPS checks for anything over 6m (or missing)
destring locationAccuracy, replace
gen GPSmore6=1 if locationAccuracy>6 | locationAccuracy==.
egen tag=tag(RE $GeoID structure household)
preserve
keep if GPSmore6==1 & metatag==1
sort RE
capture noisily export excel RE metainstanceID GeoID_SH names locationAccuracy using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(HHGPSmore6m)
if _rc!=198 {
restore
}
else {
clear
set obs 1
gen x="NO HH GPS MISSING OR MORE THAN 6M"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(HHGPSmore6m)
restore
}
preserve
keep if metatag==1
sort RE
capture collapse (count) metatag if locationAccuracy>6 | locationAccuracy==., by(RE $GeoID)
if _rc!=2000{
rename metatag HHQGPSAccuracymore6
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
}
else {
clear
use `CCRX'_ProgressReport_$date
gen HHQGPSAccuracymore6=0
save, replace
}
restore
**GPS Spatial data error-checks - By RE & Full list
preserve
do "$dofiledir/`hhq_monit'.do"
restore
**Repeat for Female Accuracy
drop GPSmore6
capture destring FQAccuracy, replace
gen GPSmore6=1 if (FQAccuracy>6 | FQAccuracy==.) & FRS_result!=.
preserve
keep if GPSmore6==1 & FRS_result!=.
capture noisily export excel RE metainstanceID GeoID_SH FRS_form_name FQAccuracy using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(FQGPSmore6m)
if _rc!=198 {
restore
}
else {
clear
set obs 1
gen x="NO FQ GPS MISSING OR MORE THAN 6M"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(FQGPSmore6m)
restore
}
preserve
keep if FRS_result==1
capture collapse (count) GPSmore6 if FRS_result!=., by(RE $GeoID)
if _rc!=2000{
rename GPSmore6 FQGPSAccuracymore6
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
}
restore
***** Creating 14/15 and 49/50 Age ratios for Females by RE/EA
preserve
foreach y in 14 15 49 50{
gen age`y'=1 if age==`y' & gender==2
}
capture collapse (sum) age14 age15 age49 age50, by(RE $GeoID)
if _rc!=2000 {
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
}
restore
** Exporting out forms that identify Bottled water and Refill water as only source of water AND/OR that identify bottled water/refill water for cooking/washing
**tag forms
gen bottletag=1 if (water_sources_all=="bottled" | water_sources_all=="sachet" | water_sources_all=="refill" | water_sources_all=="bottled sachet" | water_sources_all=="bottled refill")
replace bottletag=1 if (water_main_drinking=="bottled"| water_main_drinking=="sachet" | water_main_drinking=="refill") & (water_uses_cooking==1 | water_uses_washing==1)
preserve
keep if bottletag==1 & metatag==1
tab bottletag
sort RE
capture noisily export excel metainstanceID RE GeoID_SH water_sources_all water_main_drinking water_uses_cooking water_uses_washing using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(Bottledwater) sheetreplace
di _rc
if _rc!=198{
restore
}
else{
clear
set obs 1
gen x="NO PROBLEM WITH BOTTLED/REFILL WATER"
capture noisily export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(Bottledwater) sheetreplace
restore
}
preserve
collapse (sum) bottletag if metatag==1, by(RE $GeoID)
if _rc!=2000{
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID EA using `collapse', nogen
save, replace
}
else{
use `CCRX'_ProgressReport_$date
gen bottletag=.
save, replace
}
restore
***Checking data quality for HH integer variables
*Identify if there are any HH integer variables with a value of 77, 88, or 99 indicating a potential mistype on the part of the RE or in the Cleaning file
preserve
keep if metatag==1
keep country-link
sort level1-household RE
**Checking if numeric variables have the values
gen mistype=0
gen mistype_var=""
foreach var of varlist _all{
capture destring *_ow*, replace
capture confirm numeric var `var'
if _rc==0 {
replace mistype=mistype+1 if (`var'==77 | `var'==88 | `var'==99)
replace mistype_var=mistype_var+" "+"`var'" if `var'==77 | `var'==88 | `var'==99
}
}
*Exclude entries for structure and household
recode mistype 0=.
replace mistype_var=strtrim(mistype_var)
replace mistype=. if mistype_var=="structure" | mistype_var=="household" | mistype_var=="structure household"
replace mistype_var="" if mistype_var=="structure" | mistype_var=="household" | mistype_var=="structure household"
*Keep all variables that have been mistyped
levelsof mistype_var, local(typo) clean
keep if mistype!=.
keep metainstanceID RE GeoID_SH `typo'
capture drop structure
capture drop household
capture drop minAge
order metainstanceID RE GeoID_SH, first
capture noisily export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(HH_Potential_Typos) sheetreplace
if _rc!=198 {
restore
}
else {
clear
set obs 1
gen x="NO NUMERIC VARIABLES WITH A VALUE OF 77, 88, OR 99"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(HH_Potential_Typos) sheetreplace
restore
}
***Checking data quality for FQ integer variables
*Identify if there are any FQ integer variables with a value of 77, 88, or 99 indicating a potential mistype on the part of the RE or in the Cleaning file
preserve
keep FQSubmissionDate-FQresp GeoID_SH RE
sort GeoID_SH RE
**Checking if numeric variables have the values
gen mistype=0
gen mistype_var=""
foreach var of varlist _all{
capture confirm numeric var `var'
if _rc==0 {
replace mistype=mistype+1 if (`var'==77 | `var'==88 | `var'==99)
replace mistype_var=mistype_var+" "+"`var'" if `var'==77 | `var'==88 | `var'==99
}
}
*Exclude entries for structure and household
recode mistype 0=.
replace mistype_var=strtrim(mistype_var)
replace mistype=. if mistype_var=="FQstructure" | mistype_var=="FQhousehold" | mistype_var=="FQstructure FQhousehold"
replace mistype_var="" if mistype_var=="FQstructure" | mistype_var=="FQhousehold" | mistype_var=="FQstructure FQhousehold"
*Keep all variables that have been mistyped
levelsof mistype_var, local(typo) clean
keep if mistype!=.
keep FQmetainstanceID RE GeoID_SH `typo'
capture drop FQstructure
capture drop FQhousehold
order FQmetainstanceID RE GeoID_SH, first
capture noisily export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(FQ_Potential_Typos) sheetreplace
if _rc!=198 {
restore
}
else {
clear
set obs 1
gen x="NO NUMERIC VARIABLES WITH A VALUE OF 77, 88, OR 99"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(FQ_Potential_Typos) sheetreplace
restore
}
**Flag forms where the number of household members listed in the dataset is not equal to the number calculated by ODK
gen numberpeopletag=1 if KEY!=""
bysort metainstanceID: egen numberpeoplelisted=total(numberpeopletag)
drop numberpeopletag
gen numberpeopletag =1 if numberpeoplelisted!=num_HH_members
preserve
keep if numberpeopletag==1 & metatag==1 & (HHQ_result==1 | HHQ_result==5)
sort RE
capture noisily export excel metainstanceID RE GeoID_SH names numberpeoplelisted num_HH_members ///
using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(Number_HH_member)
if _rc!=198{
restore
}
if _rc==198 {
clear
set obs 1
gen x="NUMBER OF HOUSEHOLD MEMBERS IN ODK AND IN DATASET IS CONSISTENT IN ALL FORMS"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(Number_HH_member) sheetreplace
restore
}
preserve
collapse (sum) numberpeopletag if metatag==1 & (HHQ_result==1 | HHQ_result==5), by (RE $GeoID)
if _rc!=2000{
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
}
else {
use `CCRX'_ProgressReport_$date
gen numberpeopletag==.
save, replace
}
restore
**Export out forms and total the number of forms where the date is entered incorrectly
split system_date, gen (system_date_)
capture confirm var system_date_3
if _rc!=0{
drop system_date_*
split system_date, gen(system_date_) parse(/ " ")
}
gen datetag=1 if system_date_3!="`SurveyYear'" & system_date_3!="`SYShort'"
drop system_date_*
split start, gen(start_)
capture confirm var start_3
if _rc!=0{
drop start_*
split start, gen(start_) parse(/ " ")
}
replace datetag=1 if start_3!="`SurveyYear'" & start_3!="`SYShort'"
drop start_*
split end, gen(end_)
capture confirm var end_3
if _rc!=0{
drop end_*
split end, gen(end_) parse(/ " ")
}
replace datetag=1 if end_3!="`SurveyYear'" & end_3!="`SYShort'"
drop end_*
preserve
keep if datetag==1 & metatag==1
sort RE
capture noisily export excel metainstanceID RE GeoID_SH names system_date start end datetag ///
using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(PhoneDateFlag)
if _rc!=198{
restore
}
if _rc==198 {
clear
set obs 1
gen x="NO FORMS WITH AN INCORRECT DATE"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(PhoneDateFlag) sheetreplace
restore
}
preserve
collapse (sum) datetag if metatag==1, by (RE $GeoID)
if _rc!=2000{
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
}
else {
use `CCRX'_ProgressReport_$date
gen datetag==.
save, replace
}
restore
**Flag any forms where at least one observation for household member info is missing
egen missingroster=rowmiss(gender age relationship usually last_night) if HHQ_result==1
replace missingroster=missingroster+1 if marital_status==. & age>=10
egen noresponseroster=anycount(gender age relationship usually last_night) if HHQ_result==1, values(-99 -88)
replace noresponseroster=noresponseroster+1 if marital_status==-99 & age>=10 & HHQ_result==1
gen missinginfo_roster=missingroster+noresponseroster
preserve
keep if missinginfo_roster>0 & missinginfo_roster!=.
sort RE
capture noisily export excel metainstanceID RE GeoID_SH firstname-last_night missinginfo_roster ///
using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(MissingRosterInfo)
if _rc!=198{
restore
}
if _rc==198 {
clear
set obs 1
gen x="NO OBSERVATIONS HAVE MISSING/NONRESPONSE INFORMATION IN THE ROSTER"
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(MissingRosterInfo) sheetreplace
restore
}
preserve
gen missinginfotag=1 if missinginfo_roster!=0 & missinginfo_roster!=.
collapse (sum) missinginfotag if metatag==1, by (RE $GeoID)
if _rc!=2000{
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
}
else {
use `CCRX'_ProgressReport_$date
gen missinginfotag==.
save, replace
}
restore
**Total the number of DNK for first marriage year, recent marriage year, age at first birth, age at first sex by RE
gen DNKfirstmarriage=1 if firstmarriageyear==2020
gen DNKcurrentmarriage=1 if recentmarriageyear==2020
gen DNKfirstbirth=1 if regexm(first_birth, "2020")
gen DNKrecentbirth=1 if regexm(recent_birth, "2020")
capture gen DNKpenultbirth=1 if regexm(penultimate_birth, "2020")
gen DNKNRfirstsex=1 if age_at_first_sex==-88 | age_at_first_sex==-99
gen DNKNRlastsex=1 if last_time_sex==-88 | last_time_sex==-99
preserve
keep if FQmetainstanceID!=""
collapse (sum) DNK* , by (RE $GeoID)
if _rc!=2000{
egen DNKNRtotal=rowtotal(DNK*)
save `collapse', replace
use `CCRX'_ProgressReport_$date
merge 1:1 RE $GeoID using `collapse', nogen
save, replace
}
else {
use `CCRX'_ProgressReport_$date
gen DNK==.
save, replace
}
restore
use `CCRX'_ProgressReport_$date, clear
drop FRS_result_*
save, replace
gen date="$date"
order date, before(RE)
preserve
order EA HQtotalup HQcomplete HQrefusal HQnothome HQnotfound HQresultother totaleligible FQtotalup FQcomplete FQrefusal FRS_resultothome FQresultother, last
collapse (sum) HQtotalup-FQresultother (min) HHQGPS* FQGPS* HHQintless10 FQintless10, by(date RE $GeoID)
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(SupervisorChecks) sheetreplace
restore
preserve
collapse (min) age14 age15 age49 age50 (sum) bottletag numberpeopletag datetag missinginfotag, by(date RE $GeoID)
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(AdditionalCleaning) sheetreplace
restore
export excel RE $GeoID DNK* using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(DNK_NR_Count) sheetreplace
***Overall counts
preserve
collapse (sum) HQtotalup HQcomplete-HHQ_result_5 totaleligible FQtotalup FQcomplete
label var HQtotalup "Total HH uploaded"
label var HQcomplete "Total HH complete"
gen HHresponse=HQcomplete/(HQcomplete + HQnothome + HHQ_result_3+ HQrefusal + HHQ_result_5)
label var HHresponse "Household response rate"
label var FQtotalup "Total FQ uploaded"
label var FQcomplete "Total FQ completed"
gen FQresponse=FQcomplete/FQtotalup
label var FQresponse "Female response rate"
tempfile temp
save `temp', replace
restore
clear
use `CCRX'_Combined_$date.dta
preserve
gen male=1 if gender==1
gen female=1 if gender==2
egen EAtag=tag($GeoID)
bysort $GeoID: egen EAtotal=total(metatag)
gen EAcomplete=1 if EAtotal==$EAtake & EAtag==1
collapse (sum) male female EAtag EAcomplete
gen sexratio=male/female
label var sexratio "Sex ratio - male:female"
label var EAtag "Number of EAs with any data submitted"
label var EAcomplete "Number of EAs with $EAtake HH forms submitted"
tempfile temp2
save `temp2'
use `temp'
append using `temp2'
keep HQtotalup HQcomplete HHresponse FQtotalup FQcomplete FQresponse sexratio EAtag EAcomplete
export excel using `CCRX'_HHQFQErrors_$date.xls, firstrow(varlabels) sh(OverallTotals) sheetreplace
restore
clear
**After the data is merged, use cleaning program and analysis program for basic checks
*****************************************************************************
********************************* Country and Round Specific Cleaning ***********************************
use `CCRX'_Combined_$date.dta, clear
capture noisily do "$dofiledir/`module1do'"
capture noisily do "$dofiledir/`module2do'"
capture noisily do "$dofiledir/`module3do'"
capture noisily do "$dofiledir/`module4do'"
save `CCRX'_Combined_$date.dta, replace
do "$dofiledir/`CCRX'_CountrySpecific_CleanWeight_`CountrySpecificdate'.do"
************************************************************************************
translate `CCRX'_DataCleaningQuality_$date.log `CCRX'_DataCleaningQuality_$date.pdf, replace
|
/****PMA 2020 Indonesia Data Quality Checks****
***Version Created in Bootcamp July 21-24, 2014
**Fourth do file in series***
This do file labels each variable in the Female Respondent questionnaire */
set more off
**If you want to run this file separately from the parent file, change the working directory below
cd "$datadir"
*all of the data is imported automatically as string
clear
clear matrix
local CCRX $CCRX
local FQcsv $FQcsv
local FQcsv2 $FQcsv2
clear
capture insheet using "$csvdir/`FQcsv'.csv", comma case
tostring *, replace force
save `CCRX'_FRQ.dta, replace
/*If you need to add an extra version of the forms, this will check if that
version number exists and add it. If the version does not, it will continue*/
clear
capture insheet using "$csvdir/`FQcsv2'.csv", comma case
if _rc==0 {
tostring *, replace force
append using `CCRX'_FRQ.dta, force
save, replace
}
use `CCRX'_FRQ.dta
save, replace
***REVISION HISTORY OF LARGE SCALE CHANGES
rename name_grp* *
rename date_group* *
rename location_information* *
rename location_* Zlocation_*
rename location* *
rename Zlocation_* location_*
rename *_grpfirst* **
rename *_grpcurrent* **
rename *_grprecent* **
rename *_method_method* *_method*
rename *_grpfp_provider* **
rename *_grpwhy_not_using* **
rename *grpfp_ad_* **
rename geographic_info_* *
rename unlinked*_* unlinked*
capture drop birthdate_grpbday_note birthdate_grpbday_note_unlinked
capture confirm var EA
if _rc!=0{
capture rename quartier EA
}
**Dropping variables from form re-programming April 2016
capture drop why_not_using_grp*
capture drop FQ_age
capture rename FQAage FQ_age
capture drop FQA*
capture rename AFSage_at_first_sex age_at_first_sex
capture drop AFS*
capture drop rec_birth_date
capture rename MOPmonths_pregnant months_pregnant
capture drop MOP*
capture drop births_live*
capture confirm var more_children_some
if _rc==0 {
replace more_children_none=more_children_some if more_children_some!=""
drop more_children_some
rename more_children_none more_children
replace wait_birth_none=wait_birth_some if wait_birth_some!=""
rename wait_birth_none wait_birth
drop wait_birth_some
gen pregnancy_last_desired=PDEpregnancy_desired if pregnant=="no"
gen pregnancy_current_desired=PDEpregnancy_desired if pregnant=="yes"
drop PDE*
replace visited_fac_none=visited_fac_some if visited_fac_some!=""
drop visited_fac_some
rename visited_fac_none visited_a_facility
}
capture drop rec_husband_date
capture rename BUSbegin_using begin_using
replace begin_using=SUSante_start_using if SUSante_start_using!=""
capture rename sussus_m ante_begin_using_month
capture rename susante_start_using ante_begin_using
capture rename busbus_m begin_using_month
capture drop BUS*
capture drop SUS*
capture drop age_begin_using
capture drop fp_provider_grp*
capture rename LTSlast_time_sex last_time_sex
capture drop LTS*
capture drop re_name calc_space deleteTest metalogging
capture rename mhm_grp* *
capture rename birthdate_grp* *
capture rename HCF* *
capture rename HCS* *
capture rename FB* *
capture rename RB* *
capture rename PB* *
capture rename CD* *
drop SPUstop_using_full_lab
rename SPU* *
gen day=substr(begin_using,-2,.)
gen month=substr(begin_using,6,2)
gen year=substr(begin_using,1,4)
gen str begin_usingv2=month + "/" + day + "/" + year if month!="" & day!="" & year!=""
drop begin_using
rename begin_usingv2 begin_using
destring day month year, replace
gen begin_usingSIF=mdy(month,day,year)
format begin_usingSIF %td
foreach date in birthdate hcf hcs fb rb spu {
replace `date'_y=subinstr(`date'_y, "Jan", "Feb", .) if `date'_m=="1"
replace `date'_y=subinstr(`date'_y, "Jan", "Mar", .) if `date'_m=="2"
replace `date'_y=subinstr(`date'_y, "Jan", "Apr", .) if `date'_m=="3"
replace `date'_y=subinstr(`date'_y, "Jan", "May", .) if `date'_m=="4"
replace `date'_y=subinstr(`date'_y, "Jan", "Jun", .) if `date'_m=="5"
replace `date'_y=subinstr(`date'_y, "Jan", "Jul", .) if `date'_m=="6"
replace `date'_y=subinstr(`date'_y, "Jan", "Aug", .) if `date'_m=="7"
replace `date'_y=subinstr(`date'_y, "Jan", "Sep", .) if `date'_m=="8"
replace `date'_y=subinstr(`date'_y, "Jan", "Oct", .) if `date'_m=="9"
replace `date'_y=subinstr(`date'_y, "Jan", "Nov", .) if `date'_m=="10"
replace `date'_y=subinstr(`date'_y, "Jan", "Dec", .) if `date'_m=="11"
}
rename birthdate_y birthdatev2
rename hcf_y husband_cohabit_start_firstv2
rename hcs_y husband_cohabit_start_recentv2
rename fb_y first_birthv2
rename rb_y recent_birthv2
rename spu_y stop_usingv2
replace birthmonth="-88" if birthdate_m=="-88"
rename *_m *_month
rename survey_language FQsurvey_language
****
capture label def yes_no_dnk_nr_list 0 no 1 yes -77 "-77" -88 "-88" -99 "-99"
label var times_visited "Visit number to female respondent"
label var your_name "Resident Enumerator name"
label var your_name_check "To RE: Is this your name?"
label var name_typed "RE Name if not correct in FQ C"
label var system_date "Date and Time"
label var system_date_check "Confirm Correct Date and Time"
label var manual_date "Correct Date and Time if not correct in FQ D1"
label var today "Date of Interview"
label var location_pro "Prompt"
label var EA "EA"
label var structure "Structure number"
label var household "Household number"
label var location_con "Confirmation screen"
label var name_check "Confirmatation interviewing correct woman"
label var aquainted "How well acquainted with the respondent"
label var available "Respondent present and available at least once"
label var consent_start "Consent screen"
label var consent "Consent screen"
label var begin_interview "May I begin the interview now?"
label var consent_obtained "Informed consent obtained"
label var witness_auto "Interviewer name"
label var witness_manual "Name check"
label var firstname "Name of respondent"
label var birthdate "Birth date"
label var birthyear "Year of Birth"
label var birthmonth "Month of Birth"
label var thismonth "Month of Interview - Used for age calculations"
label var thisyear "Year of Interview - Used for age calculations"
label var FQ_age "Age in Female Respondent Questionnaire"
capture label var age_check "Same age in Household Roster?"
capture label var age_label "Label regarding age"
capture label var age "Age in Household Roster"
label var school "Highest level of school attended"
label var marital_status "Marital status"
label var marriage_history "Been married once or more than once"
label var husband_cohabit_start_first "Month and year started living with first partner"
label var firstmarriagemonth "Month started living with first partner"
label var firstmarriageyear "Year started living with first partner"
label var husband_cohabit_start_recent "Year started living with current or most recent partner"
label var young_marriage_recent "Women married only once - less than 10"
label var marriage_warning_recent "Confirm less age 10 current marriage"
label var young_marriage_first "Women married more than once - first marriage less than 10"
label var marriage_warning_first "Confirm less age 10 first marrige"
capture label var other_wives "Partner have other wives"
rename birth_events birth_events_rw
capture label var birth_events_rw "How many times have you given birth"
label var first_birth "Date of FIRST live birth"
label var recent_birth "Most recent birth?"
capture label var days_since_birth "Days since most recent birth"
label var menstrual_period "Last menstrual period"
label var menstrual_period_value "Value if days, weeks, months, or years"
capture label var months_since_last_period
label var pregnant "Pregnancy status"
label var month_calculation "Months since last birth"
capture label var pregnant_hint "Hint if recent pregnancy"
label var months_pregnant "How many months pregnant"
label var more_children "Prefer to have another child or no more children - not pregnant"
label var more_children_pregnant "Prefer to have another child or no more children - currently pregnant"
label var wait_birth "How long would you like to wait until next child - not pregnant"
label var wait_birth_pregnant "How long would you like to wait until next child - pregnant"
label var wait_birth_value "How long to wait - value if months or years"
label var pregnancy_last_desired "Last birth - did you want it then, later, not at all - not pregnant"
label var pregnancy_current_desired "Current birth - did you want it then, later, not at all - currently pregnant"
capture label var fp_ever_user "Done anything to delay or avoid getting pregnant"
label var fp_ever_used "Ever used method of FP"
label var age_at_first_use "Age at first use of FP"
label var age_at_first_use_children "Number of living children at first use of FP"
label var first_method "First FP method used"
label var current_user "Currently using method of FP?"
label var current_method "Current FP method"
capture rename sterlization_permanent_inform sterilization_permanent_inform
capture label var sterilization_permanent_inform "Sterilization - did provider tell you it was permanent"
label var future_user_not_current "Do you think you will use a method in the future - not pregnant"
label var future_user_pregnant "Do you think you will use a method in the future - currently pregnant"
label var recent_user "Used a method last 12 months?"
label var recent_method "FP Method used in last 12 months"
label var current_or_recent_user
label var current_recent_method
label var current_recent_label
label var begin_using "When did you begin using method"
label var stop_using "When did you stop using method"
rename stop_using_why stop_using_why_cc
label var stop_using_why_cc "Why did you stop using method"
capture rename fp_provider fp_provider_rw
label var fp_provider_rw "Where did you obtain method when you started using"
label var fp_provider_check
label var method_fees "How much did you play pay the last time FP was obtained?"
label var fp_side_effects "When obtained method, told about side effects?"
label var fp_side_effects_instructions "Told what to do if experienced side effects?"
label var fp_told_other_methods "Told about FP methods you could use?"
label var fp_obtain_desired "Did you obtain the method you wanted?"
label var fp_obtain_desired_whynot "If not, why not?"
label var fp_final_decision "Who made final decision about the method?"
label var return_to_provider "Would you return to provider?"
label var refer_to_relative "Refer provider to relative or friend?"
label var why_not_using "Why not using a method?"
label var visited_by_health_worker "Visited by health worker about FP last 12 months"
label var visited_a_facility "Visited health facility last 12 months"
label var facility_fp_discussion "Talked to about FP at health facility"
label var partner_know "Partner aware of FP use"
label var penultimate_method_yn "Where you doing something before current method to delay or avoid pregnancy"
label var penultimate_method "Method used before current method"
label var pp_method_units "Specify days, months or years"
label var pp_method_value "Value in days, months or years"
label var pp_method_yn "Used something to avoid/delay pregnancy post most recent birth"
label var pp_method "Method used post most recent birth"
label var fp_ad_radio "Heard about FP on radio"
label var fp_ad_tv "Heard about FP on television"
label var fp_ad_magazine "Read about FP in newspaper/magazine"
label var fp_ad_call "Receive FP in voice or text message"
label var age_at_first_sex "Age at first sex"
label var years_since_first_sex
label var months_since_first_sex
label var last_time_sex "Last time you had sex - days, weeks, months, years"
label var last_time_sex_value "Last time you had sex - value"
label var thankyou
label var Latitude "Latitude"
label var Longitude "Longitude"
label var Altitude "Altitude"
label var Accuracy "Accuracy"
label var FRS_result "Result"
label var start "Start time"
label var end "End time"
label var emergency_12mo_yn "Used EC in the last 12 months"
label var FQsurvey_language "Language in which female survey was conducted"
destring times_visited, replace
destring EA, replace
destring consent_obtained, replace
destring structure, replace
destring household, replace
destring birthmonth, replace
destring birthyear, replace
destring thismonth, replace
destring thisyear, replace
destring FQ_age, replace
capture destring age, replace
destring young_marriage_first, replace
destring young_marriage_recent, replace
destring marriage_warning_first, replace
destring first_method_che, replace
destring recent_method_c, replace
destring current_or_recent_user, replace
destring recentmarriagemonth, replace
destring recentmarriageyear, replace
destring firstmarriagemonth, replace
destring firstmarriageyear, replace
capture destring birth_events_rw, replace
capture destring days_since_birth, replace
destring menstrual_period_value, replace
capture destring months_since_last_period, replace
destring month_calculation, replace
destring months_pregnant, replace
destring wait_birth_value, replace
destring age_at_first_use, replace
destring age_at_first_use_children, replace
destring method_fees, replace
destring age_at_first_sex, replace
destring years_since_first_sex, replace
destring months_since_first_sex, replace
destring last_time_sex_value, replace
destring pp_method_value, replace
destring age_ante_begin_using, replace
destring age_first_reported_use, replace
destring age_first_birth, replace
destring months_last_sex, replace
destring Latitude, replace
destring Longitude, replace
destring Altitude, replace
destring Accuracy, replace
capture label def yes_no_dnk_nr_list 0 no 1 yes -77 "-77" -88 "-88" -99 "-99"
foreach var of varlist your_name_check system_date_check location_con name_check ///
available begin_interview ///
pregnant fp_ever_used current_user ///
future_user_not_current future_user_pregnant recent_user fp_side_effects ///
fp_side_effects_instructions fp_told_other_methods fp_obtain_desired return_to_provider ///
refer_to_relative visited_by_health_worker visited_a_facility facility_fp_discussion ///
fp_ad_radio fp_ad_tv fp_ad_magazine fp_ad_call fp_ever_user penultimate_method_yn pp_method_yn ///
partner_know emergency_12mo_yn {
encode `var', gen(`var'v2) lab(yes_no_dnk_nr_list)
}
capture encode other_wives, gen(other_wivesv2) lab(yes_no_dnk_nr_list)
capture encode sterilization_permanent_inform , gen(sterilization_permanent_inform ) lab(yes_no_dnk_nr_list)
label def acquainted_list 1 very_well_acquainted 2 well_acquainted 3 not_well_acquainted 4 not_acquainted
encode aquainted, gen(aquaintedv2) lab(acquainted_list)
label define FQmarital_status_list 5 never_married 1 currently_married 2 currently_living_with_man 3 divorced 4 widow -99 "-99"
encode marital_status, gen(marital_statusv2) lab(FQmarital_status_list)
label define lived_list 1 once 2 more_than_once -99 "-99"
encode marriage_history, gen(marriage_historyv2) lab(lived_list)
capture label drop dwmy_list
label define dwmy_list 1 "days" 2 "weeks" 3 "months" 4 "years" -99 "-99" -88 "-88"
label define menstrual_list 1 days 2 weeks 3 months 4 years 5 menopausal_hysterectomy 6 before_birth 7 never -99 "-99"
encode menstrual_period, gen(menstrual_periodv2) lab(menstrual_list)
label define more_children_list 1 have_child 2 no_children 3 infertile -88 "-88" -99 "-99"
encode more_children, gen(more_childrenv2) lab(more_children_list)
encode more_children_pregnant, gen(more_children_pregnantv2) lab(more_children_list)
label define wait_child_list 1 months 2 years 3 soon 4 infertile 5 other -88 "-88" -99 "-99"
encode wait_birth, gen(wait_birthv2) lab(wait_child_list)
encode wait_birth_pregnant, gen(wait_birth_pregnantv2) lab(wait_child_list)
label define pregnancy_desired_list 1 then 2 later 3 not_at_all -99 "-99"
encode pregnancy_last_desired, gen(pregnancy_last_desiredv2) lab(pregnancy_desired_list)
encode pregnancy_current_desired, gen(pregnancy_current_desiredv2) lab(pregnancy_desired_list)
replace stop_using_why_cc=subinstr(stop_using_why_cc, "difficult_to_conceive", "diff_conceive", .)
replace stop_using_why_cc=subinstr(stop_using_why_cc, "interferes_with_body", "interf_w_body", .)
foreach reason in infrequent pregnant wanted_pregnant husband more_effective no_method_available health_concerns ///
side_effects no_access cost inconvenient fatalistic diff_conceive interf_w_body other {
gen stop_using_`reason'=0 if stop_using_why_cc!="" & stop_using_why_cc!="-99"
replace stop_using_`reason'=1 if (regexm(stop_using_why_cc, "`reason'"))
}
label define whynot_list 1 not_married 2 infrequent_sex 3 menopausal_hysterectomy 4 infecund 5 not_menstruated ///
6 breastfeeding 7 husband_away 8 fatalistic 9 respondent_opposed 10 partner_opposed 11 others_opposed ///
12 religion 13 no_knowledge 14 no_source_known 15 side_effects 16 health 17 no_access 18 cost ///
19 preferred_unavailable 20 no_method_available 21 inconvenient 22 interferes_with_body 23 other -88 "-88" -99 "-99"
label define decision_list 1 you_alone 2 provider 3 partner 4 you_and_provider 5 you_and_partner 6 other -99 "-99" -88 "-88"
encode fp_final_decision, gen(fp_final_decisionv2) lab(decision_list)
label define whynomethod_list 1 out_of_stock 2 unavailable 3 untrained 4 different 5 ineligible 6 decided_not_to_adopt ///
7 cost 8 other -88 "-88" -99 "-99"
encode fp_obtain_desired_whynot, gen(fp_obtain_desired_whynotv2) lab(whynomethod_list)
encode last_time_sex, gen(last_time_sexv2) lab(dwmy_list)
label define dwmy_future_list 1 days 2 weeks 3 months 4 years -99 "-99"
encode pp_method_units, gen(pp_method_unitsv2) lab(dwmy_future_list)
label define FRS_result_list 1 completed 2 not_at_home 3 postponed 4 refused 5 partly_completed 6 incapacitated
encode FRS_result, gen(FRS_resultv2) lab(FRS_result_list)
*Participated in previous survey
capture label var previous_PMA "Previously participated in PMA 2020 survey?"
capture encode previous_PMA, gen(previous_PMAv2) lab(yes_no_dnk_nr_list)
/*Additional questions added in July 2016 to core*/
capture confirm var ever_birth
if _rc==0 {
label var ever_birth "Ever given birth"
label var partner_decision "Before using method, did you discuss decision to avoid pregnancy with partner"
label var partner_overall "Using contraception is your decision, husband's decision or together?"
label var rhythm_final "Who made final decision to use rhythm"
label var lam_final "Who made final decision to use LAM"
encode rhythm_final, gen(rhythm_finalv2) lab(decision_list)
encode lam_final, gen(lam_finalv2) lab(decision_list)
rename why_not_decision why_not_decision
label var why_not_decision "Whose decision is it not to use contraception"
label define partner_overall_list 1 "respondent" 2 "husband" 3 "joint" 96 "other" -99 "-99"
encode partner_overall, lab(partner_overall_list) gen(partner_overallv2)
encode why_not_decision, lab(partner_overall_list) gen (why_not_decisionv2)
foreach var in ever_birth partner_decision {
encode `var', gen(`var'v2) lab(yes_no_dnk_nr_list)
}
}
unab vars: *v2
local stubs: subinstr local vars "v2" "", all
foreach var in `stubs'{
rename `var' `var'QZ
order `var'v2, after (`var'QZ)
}
rename *v2 *
drop *QZ
*****************************Change the date variables into Stata time***************************
**Change start and end times into SIF to calculate time
*Have to do the same procedures. Using the end time of the survey as the day of the survey
**Extract portion of string variable that has information on mondth/day/year
gen double todaySIF=clock(today, "YMD")
format todaySIF %tc
gen double startSIF=clock(start, "MDYhms")
gen double manual_dateSIF=clock(manual_date, "MDYhms")
format startSIF %tc
format manual_dateSIF %tc
gen double endSIF=clock(end, "MDYhms")
format endSIF %tc
gen double birthdateSIF=clock(birthdate, "MDY")
format birthdateSIF %tc
gen double husband_cohabit_start_firstSIF=clock(husband_cohabit_start_first, "MDY")
format husband_cohabit_start_firstSIF %tc
replace husband_cohabit_start_firstSIF=. if regexm(husband_cohabit_start_first, "2020")
gen double husband_cohabit_start_recentSIF=clock(husband_cohabit_start_recent, "MDY")
format husband_cohabit_start_recentSIF %tc
replace husband_cohabit_start_recentSIF=. if regexm(husband_cohabit_start_recent, "2020")
capture replace first_birth=recent_birth if children_born==1
capture replace first_birth=recent_birth if birth_events==1
capture replace first_birth=recent_birth if birth_events==1 & children_born==2
gen double first_birthSIF=clock(first_birth, "MDY")
format first_birthSIF %tc
replace first_birthSIF=. if regexm(first_birth, "2020")
gen double recent_birthSIF=clock(recent_birth, "MDY")
format recent_birthSIF %tc
replace recent_birthSIF=. if regexm(recent_birth, "2020")
gen double stop_usingSIF=clock(stop_using, "MDY")
format stop_usingSIF %tc
replace stop_usingSIF=. if regexm(stop_using, "2020")
unab vars: *SIF
local stubs: subinstr local vars "SIF" "", all
foreach var in `stubs'{
order `var'SIF, after (`var')
}
rename todaySIF FQtodaySIF
rename startSIF FQstartSIF
rename manual_dateSIF FQmanual_dateSIF
rename endSIF FQendSIF
rename your_name RE
replace RE=name_typed if your_name_check==0 | your_name_check==.
***************************************************************************************************
********************************* REPROGRAM FEMALE RESPONDENT *********************************
***************************************************************************************************
replace current_recent_method="" if recent_user!=1 & current_user!=1
replace current_method="" if current_user!=1
replace recent_method="" if recent_user!=1
*Current Use
gen femalester=0 if FRS_result==1
gen malester=0 if FRS_result==1
gen IUD=0 if FRS_result==1
gen injectables3=0 if FRS_result==1
gen injectables1=0 if FRS_result==1
gen injectables=0 if FRS_result==1
gen implant=0 if FRS_result==1
gen pill=0 if FRS_result==1
gen malecondom=0 if FRS_result==1
gen femalecondom=0 if FRS_result==1
gen LAM=0 if FRS_result==1
gen EC=0 if FRS_result==1
gen diaphragm=0 if FRS_result==1
gen N_tablet=0 if FRS_result==1
gen foamjelly=0 if FRS_result==1
gen stndrddays=0 if FRS_result==1
gen rhythm=0 if FRS_result==1
gen withdrawal=0 if FRS_result==1
gen othertrad=0 if FRS_result==1
split current_method, gen(current_method_temp)
forval y=1/10{
capture confirm variable current_method_temp`y'
if _rc==0{
replace femalester=1 if current_method_temp`y'=="female_sterilization" & FRS_result==1
replace malester=1 if current_method_temp`y'=="male_sterilization" & FRS_result==1
replace IUD=1 if current_method_temp`y'== "IUD" & FRS_result==1
replace injectables3=1 if current_method_temp`y'== "injectables_3mo" & FRS_result==1
replace injectables1=1 if current_method_temp`y'== "injectables_1mo" & FRS_result==1
replace injectables=1 if current_method_temp`y'=="injectables" & FRS_result==1
replace implant=1 if current_method_temp`y'=="implants" & FRS_result==1
replace pill=1 if current_method_temp`y'=="pill" & FRS_result==1
replace malecondom=1 if current_method_temp`y'=="male_condoms" & FRS_result==1
replace femalecondom=1 if current_method_temp`y'== "female_condoms" & FRS_result==1
replace LAM=1 if current_method_temp`y'== "LAM" & FRS_result==1
replace EC=1 if current_method_temp`y'=="emergency" & FRS_result==1
replace diaphragm=1 if current_method_temp`y'== "diaphragm" & FRS_result==1
replace N_tablet=1 if current_method_temp`y'== "N_tablet" & FRS_result==1
replace foamjelly=1 if current_method_temp`y'=="foam" & FRS_result==1
replace stndrddays=1 if current_method_temp`y'== "beads" & FRS_result==1
replace rhythm=1 if current_method_temp`y'== "rhythm" & FRS_result==1
replace withdrawal=1 if current_method_temp`y'=="withdrawal" & FRS_result==1
replace othertrad=1 if current_method_temp`y'=="other_traditional" & FRS_result==1
}
}
drop current_method_temp*
split why_not_using, gen(why_not_using_)
local x=r(nvars)
foreach var in not_married infrequent_sex menopausal_hysterectomy infecund not_menstruated ///
breastfeeding husband_away fatalistic respondent_opposed partner_opposed others_opposed religion ///
no_knowledge no_source_known side_effects health no_access cost preferred_unavailable ///
no_method_available inconvenient interferes_with_body other {
gen wn`var'=0 if why_not_using!="" & why_not_using!="-99"
forval y=1/`x' {
replace wn`var'=1 if why_not_using_`y'=="`var'"
label values wn`var' yes_no_dnk_nr_list
}
}
drop why_not_using_*
rename wnnot_married why_not_usingnotmarr
rename wninfrequent_sex why_not_usingnosex
rename wnmenopausal_hysterectomy why_not_usingmeno
rename wninfecund why_not_usingsubfec
rename wnnot_menstruated why_not_usingnomens
rename wnbreastfeeding why_not_usingbreastfd
rename wnhusband_away why_not_usinghsbndaway
rename wnfatalistic why_not_usinguptogod
rename wnrespondent_opposed why_not_usingrespopp
rename wnpartner_opposed why_not_usinghusbopp
rename wnothers_opposed why_not_usingotheropp
rename wnreligion why_not_usingrelig
rename wnno_knowledge why_not_usingdkmethod
rename wnno_source_known why_not_usingdksource
rename wnside_effects why_not_usingfearside
rename wnhealth why_not_usinghealth
rename wnno_access why_not_usingaccess
rename wncost why_not_usingcost
rename wnpreferred_unavailable why_not_usingprfnotavail
rename wnno_method_available why_not_usingnomethod
rename wninconvenient why_not_usinginconv
rename wninterferes_with_body why_not_usingbodyproc
rename wnother why_not_usingother
order why_not_usingnotmarr-why_not_usingother, after(why_not_using)
*Awareness
unab vars: heard_*
local stubs: subinstr local vars "heard_" "", all
foreach var in `stubs'{
label var heard_`var' "Have you ever heard of `var'"
encode heard_`var', gen(heard_`var'v2) lab(yes_no_dnk_nr_list)
order heard_`var'v2, after(heard_`var')
drop heard_`var'
rename heard_`var'v2 heard_`var'
}
capture rename heard_gel heard_foamjelly
*Replace skipped questions with values
replace fp_ever_user=1 if (current_user==1 | recent_user==1) & (current_recent_method!="-99")
capture replace age_at_first_use_children=0 if birth_events==0 & fp_ever_user==1
duplicates tag metainstanceName, gen(dupFQ)
duplicates tag link, gen(duplink)
duplicates report
duplicates drop
save `CCRX'_FRQ_$date.dta, replace
|
clear
clear matrix
clear mata
capture log close
set maxvar 15000
set more off
numlabel, add
/*******************************************************************************
*
* FILENAME: CCRX_HHQFQ_Wealth_Unmet_Generation_$date.do
* PURPOSE: PMA2020 HHQ/FQ wealth and unmet need generation in preparation for data analysis
* CREATED: Qingfeng Li (qli28@jhu.edu)
* DATA IN: CCRX_$date_ForAnalysis.dta
* DATA OUT: CCRX_WealthScore_$date.dta
* CCRX_UnmetNeed_$date.dt
* CCRX_WealthWeightAll_$date.dta
* CCRX_WealthWeightCompleteHH_$date.dta
* CCRX_WealthWeightFemale_$date.dta
*******************************************************************************/
*******************************************************************************
* INSTRUCTIONS
*******************************************************************************
*
* 1. Update macros/directories in first section
*
*******************************************************************************
* SET MACROS AND CORRECT DOI: UPDATE THIS SECTION FOR EACH COUNTRY/ROUND
*******************************************************************************
* Set local macros for country and round
local country "Nigeria-Oyo"
local round "Round5Oyo"
local CCRX "NGOyoR5"
local assets "electricity-boatmotor"
local water "water_pipe2dwelling-water_sachet"
local wealthgroup 5
* Set macro for date of HHQ/FQ EC recode dataset that intend to use
local datadate "20Dec2017"
* Set directory for country and round
global datadir "/Users/pma2020/Dropbox (Gates Institute)/Nigeria/Data_NotShared/Round5Oyo/Data/HHQFQ"
cd "$datadir"
* Read in data
use "/Users/pma2020/Dropbox (Gates Institute)/Nigeria/PMANG_Datasets/RoundOyo/Prelim95/NGROyo_NONAME_ECRecode_20Dec2017.dta"
* Rename and save data
save "`CCRX'_NONAME_ECRecode_`datadate'_100Prelim.dta", replace
*If running before weights are incorporated, will gen weights=1
capture confirm var HHweight
if _rc!=0{
gen HHweight=1
gen FQweight=1
}
tempfile temp
save `temp', replace
*******************************************************************************
* RENAME VARIABLES
*******************************************************************************
* Rename variables
capture rename wall_clock wallclock
*******************************************************************************
* STOP UPDATING MACROS
*******************************************************************************
cd "$datadir"
log using "`CCRX'_HHQFQ_Wealth_Unmet_Generation_$date.log", replace
* Set local/global macros for current date
local today=c(current_date)
local c_today= "`today'"
global date=subinstr("`c_today'", " ", "",.)
local todaystata=clock("`today'", "DMY")
* Keep only completed surveys
keep if HHQ_result==1
* First, double check HHtag and metatag
* metatag tags one observation from each form while HHtag tags one observation from each household (in the event that multiple
* forms are submitted for the same household then metatag will not equal HHtag. Be clear on whether you want to identify the
* the number of households identified or the number of forms completed)
* For weight calculation, use metatag
* If metatag does not already exist, generate
capture drop metatag
egen metatag=tag(metainstanceID)
keep if metatag==1
*******************************************************************************
* GENERATE WEALTH QUINTILE
*******************************************************************************
* Tab concatonated assets variable and compare to dichotomous asset variables
tab1 assets
codebook assets
tab1 `assets', mis
foreach var of varlist `assets' {
sum `var', det
local m=r(mean)
replace `var'=int(`m') if `var'==.
}
* Create dichotomous livestock owned variables
foreach var of varlist *_owned {
recode `var' -88 -99 88 99 =.
sum `var', det
local m=r(p50)
recode `var' 0/`m'=0 .=0 else=1, gen(`var'01)
tab1 `var', miss
recode `var' .=0
mean `var'
}
* Main material of the floor
tab1 floor, miss
tab1 floor, nolab
recode floor 10/19 96 =1 -99 .=. else=0, gen(floor_natural)
recode floor 20/29=1 -99 .=. else=0, gen(floor_rudimentary)
recode floor 30/39=1 -99 .=. else=0 ,gen(floor_finished)
//recode floor 11=1 .=. else=0, gen(floor_other)
* Main material of the roof
tab1 roof
tab roof, nolab
recode roof 10/19 96=1 -99 .=. else=0, gen(roof_natural)
recode roof 20/29=1 -99 .=. else=0, gen(roof_rudimentary)
recode roof 30/39=1 .=. else=0, gen(roof_finished)
//recode roof 14=1 .=. else=0, gen(roof_other)
* Main material of the exterior walls
tab1 walls
tab walls, nolab
recode walls 10/19 96=1 -99 .=. else=0, gen(wall_natural)
recode walls 20/29=1 -99 .=. else=0, gen(wall_rudimentary)
recode walls 30/39=1 -99 .=. else=0, gen(wall_finished)
* Recode wall, floor, and roof variables
recode wall_finished .=0
recode wall_rudimentary .=1
recode wall_natural .=0
recode floor_natural .=1
recode floor_rudimentary .=0
recode floor_finished .=0
recode roof_natural .=1
recode roof_rudimentary .=0
recode roof_finished .=0
* Check the page for Country DHS; PDF page 358 of Country 2011 DHS
* Improved drinking water sources include: water from pipe/tap, public tap, borehole or pump, protected well, protected spring or rainwater.
* Improved water sources do not include: vendor-provided water, bottled water, tanker trucks or unprotected wells and springs.
* Generate dichotomous water source variables
tab water_sources_all, mis
gen water_pipe2dwelling= regexm(water_sources_all, ["piped_indoor"])
gen water_pipe2yard= regexm(water_sources_all, ["piped_yard"])
gen water_publictap= regexm(water_sources_all, ["piped_public"])
gen water_tubewell= regexm(water_sources_all, ["tubewell"])
gen water_protectedwell= regexm(water_sources_all, ["protected_dug_well"])
gen water_unprotectedwell= regexm(water_sources_all, ["unprotected_dug_well"])
gen water_protectedspring= regexm(water_sources_all, ["protected_spring"])
gen water_unprotectedspring= regexm(water_sources_all, ["unprotected_spring"])
gen water_rainwater= regexm(water_sources_all, ["rainwater"])
gen water_tankertruck= regexm(water_sources_all, ["tanker"])
gen water_cart= regexm(water_sources_all, ["cart"])
gen water_surfacewater= regexm(water_sources_all, ["surface_water"])
gen water_bottled= regexm(water_sources_all, ["bottled"])
gen water_sachet= regexm(water_sources_all, ["sachet"])
gen water_refill=regexm(water_sources_all, ["refill"])
* Generate dichotomous toilet facility variables
tab sanitation_all, mis
gen toilet_pipedsewer=regexm(sanitation_all, ["flush_sewer"])
gen toilet_septictank=regexm(sanitation_all, ["flush_septic"])
gen toilet_flushpit=regexm(sanitation_all, ["flushpit"])
gen toilet_elsewhere=regexm(sanitation_all, ["flush_elsewhere"])
gen toilet_unknown=regexm(sanitation_all, ["flush_unknown"])
gen toilet_ventilatedpitlatrine=regexm(sanitation_all, ["vip"])
gen toilet_pitlatrinewslab=regexm(sanitation_all, ["pit_with_slab"])
gen toilet_pitlatrinewoslab=regexm(sanitation_all, ["pit_no_slab"])
gen toilet_compostingtoilet=regexm(sanitation_all, ["composting"])
gen toilet_buckettoilet=regexm(sanitation_all, ["bucket"])
gen toilet_hangingtoilet=regexm(sanitation_all, ["hanging"])
gen toilet_nofacility=regexm(sanitation_all, ["bush"])
gen toilet_other=regexm(sanitation_all, ["other"])
gen toilet_missing=regexm(sanitation_all, ["-99"])
* Rename toilet facility options so not included in wealth quintile generation
rename toilet_nofacility notoilet_nofacility
capture rename toilet_bushwater notoilet_bushwater
rename toilet_missing notoilet_missing
* Create temp file for all variables used to generate wealth quintile
tab1 `water' `assets' floor_* wall_* roof_* toilet_* *_owned01, mis
tempfile tem
save `tem', replace
* Create mean tempfile for all variables used to generate wealth quintile
preserve
collapse (mean) `water' `assets' floor_* wall_* roof_* toilet_* *_owned01
tempfile mean
save `mean', replace
restore
* Create standard deviation tempfile for all variables used to generate wealth quintile
preserve
collapse (sd) `water' `assets' floor_* wall_* roof_* toilet_* *_owned01
tempfile sd
save `sd', replace
restore
* Create count (N) tempfile for all variables used to generate wealth quintile
preserve
collapse (count) `water' `assets' floor_* wall_* roof_* toilet_* *_owned01
tempfile N
save `N', replace
restore
* Create minimum tempfile for all variables used to generate wealth quintile
preserve
collapse (min) `water' `assets' floor_* wall_* roof_* toilet_* *_owned01
tempfile min
save `min', replace
restore
* Create maximum tempfile for all variables used to generate wealth quintile
preserve
collapse (max) `water' `assets' floor_* wall_* roof_* toilet_* *_owned01
tempfile max
save `max', replace
restore
use `mean', clear
append using `sd'
append using `N'
append using `min'
append using `max'
* Use principal component analysis to generate wealth quintile
use `tem', clear
su `water' `assets' floor_* wall_* roof_* toilet_* *_owned01
pca `water' `assets' floor_* wall_* roof_* toilet_* *_owned01
predict score
alpha `water' `assets' floor_* wall_* roof_* toilet_* *_owned01
* Put into quintiles/tertiles based on weighted households
gen wealthcat=`wealthgroup'
if wealthcat==3 {
xtile wealthtertile=score [pw=HHweight], nq(3)
cap label define wealthtert 1 "Lowest tertile" 2 "Middle tertile" 3 "Highest tertile"
label value wealthtertile wealthtert
}
else {
xtile wealthquintile=score [pweight=HHweight], nq(5)
cap label define wealthquint 1 "Lowest quintile" 2 "Lower quintile" 3 "Middle quintile" 4 "Higher quintile" 5 "Highest quintile"
label value wealthquintile wealthquint
}
drop wealthcat
*******************************************************************************
* SAVE AND MERGE WEALTH QUINTILE DATA
*******************************************************************************
* Keep only wealth quintile, pca score variable, and metainstanceID and save as dataset
keep metainstanceID score wealth*
tempfile wealthscore
save `wealthscore', replace
* Merge wealth score dataset into dataset for analysis
use `temp'
*use `CCRX'_`date'_ForAnalysis.dta
merge m:1 metainstanceID using `wealthscore', nogen
save "`CCRX'_WealthWeightAll_$date.dta", replace
*******************************************************************************
* GENERATE UNMET NEED
*******************************************************************************
/* Note from DHS:
Stata program to create Revised unmet need variable as described in
Analytical Study 25: Revising Unmet Need for Family Plnaning
by Bradley, Croft, Fishel, and Westoff, 2012, published by ICF International
measuredhs.com/pubs/pdf/AS25/AS25.pdf
Program written by Sarah Bradley and edited by Trevor Croft, last updated 23 January 2011
SBradley@icfi.com
This program will work for most surveys. If your results do not match
Revising Unmet Need for Family Planning, the survey you are analyzing may require
survey-specific programming. See survey-specific link at measuredhs.com/topics/Unmet-Need.cfm */
* Use weighted female dataset with wealth quintile
use "`CCRX'_WealthWeightAll_$date.dta", clear
keep if FRS_result==1 & HHQ_result==1
tempfile temp
save `temp',replace
* Check for missing values
codebook FQdoi_corrected
* Split FQdoi_corrected
split FQdoi_corrected, gen(doi_)
* Generate doimonth (month of interview) from first split variable
gen doimonth=doi_1
tab1 doimonth, mis
replace doimonth=lower(doimonth)
tab1 doimonth, mis
replace doimonth="12" if doimonth=="dec"
replace doimonth="1" if doimonth=="jan"
replace doimonth="2" if doimonth=="feb"
replace doimonth="3" if doimonth=="mar"
replace doimonth="4" if doimonth=="apr"
replace doimonth="5" if doimonth=="may"
replace doimonth="6" if doimonth=="jun"
replace doimonth="7" if doimonth=="jul"
replace doimonth="8" if doimonth=="aug"
replace doimonth="9" if doimonth=="sep"
replace doimonth="10" if doimonth=="oct"
replace doimonth="11" if doimonth=="nov"
tab1 doimonth, mis
* Generate doiyear (year of interview) from third split variable
gen doiyear=doi_3
* Destring doimonth and doiyear
destring doimonth doiyear, replace
* Calculate doi in century month code (months since January 1900)
gen doicmc=(doiyear-1900)*12+doimonth
tab doicmc, mis
* Check the dates to make sure they make sense and correct if they do not
egen tagdoicmc=tag(doicmc)
*br SubmissionDate wrongdate start end system* doi* this* if tagdoicmc==1
* Drop unecessary variables used to generate doicmc
drop tagdoicmc
drop doi_*
* Confirm have only completed HHQ/FQ surveys
keep if FRS_result==1 & HHQ_result==1
codebook metainstanceID
* Generate unmet need variable
capture g unmet=.
* Set unmet need to NA for unmarried women if survey only included ever-married women
tab FQmarital_status, mis
tab FQmarital_status, mis nolab
recode FQmarital_status -99 =.
* Tab desire for more children variable(s)
des more_children*
tab more_children, mis
tab more_children, mis nolab
* Tab length of time want to wait until next birth variable
tab wait_birth, miss
tab wait_birth, miss nolab
tab wait_birth_pregnant, miss nolab
*******************************************************************************
* GROUP 1: CONTRACEPTIVE USERS
*******************************************************************************
* Using to limit if wants no more, sterilized, or declared infecund
recode unmet .=4 if cp==1 & (more_children==2 | femalester==1 | malester==1 | more_children==3)
* Using to space - all other contraceptive users
recode unmet .=3 if cp==1
*******************************************************************************
* GROUP 2: PREGNANT OR POSTPARTUM AMENORRHEIC (PPA) WOMEN
*******************************************************************************
* Determine who should be in Group 2
* Generate time since last birth (i.e. gap between date of interview and date of last birth)
* Generate month and year of last birth variables
split recent_birth, gen(lastbirth_)
rename lastbirth_1 lastbirthmonth
rename lastbirth_3 lastbirthyear
drop lastbirth_*
* Create numeric month of last birth variable
replace lastbirthmonth=lower(lastbirthmonth)
replace lastbirthmonth="1" if lastbirthmonth=="jan"
replace lastbirthmonth="2" if lastbirthmonth=="feb"
replace lastbirthmonth="3" if lastbirthmonth=="mar"
replace lastbirthmonth="4" if lastbirthmonth=="apr"
replace lastbirthmonth="5" if lastbirthmonth=="may"
replace lastbirthmonth="6" if lastbirthmonth=="jun"
replace lastbirthmonth="7" if lastbirthmonth=="jul"
replace lastbirthmonth="8" if lastbirthmonth=="aug"
replace lastbirthmonth="9" if lastbirthmonth=="sep"
replace lastbirthmonth="10" if lastbirthmonth=="oct"
replace lastbirthmonth="11" if lastbirthmonth=="nov"
replace lastbirthmonth="12" if lastbirthmonth=="dec"
* Destring last birth month and year variables
destring lastbirth*, replace
tab1 lastbirthmonth lastbirthyear
* Replace last birth month and year equal to missing is year is 2020 (i.e. missing)
replace lastbirthmonth=. if lastbirthyear==2020
recode lastbirthyear 2020=.
* Generate last birth data in century month code
gen lastbirthcmc=(lastbirthyear-1900)*12+lastbirthmonth
* Generate time since last birth in months variable
gen tsinceb=doicmc-lastbirthcmc
* Generate time since last period in months from v215, time since last menstrual period
replace menstrual_period=. if menstrual_period==-99
* Tab menstrual_period
tab menstrual_period, mis
tab menstrual_period, mis nolab
**Some women who says years since mp report the actual year
replace menstrual_period_value = menstrual_period_value-doiyear if menstrual_period_value>2000 ///
& menstrual_period_value!=. & menstrual_period==4 // years
* Generate time since menstrual period variable in months
g tsincep = menstrual_period_value if menstrual_period==3 // months
replace tsincep = int(menstrual_period_value/30) if menstrual_period==1 // days
replace tsincep = int(menstrual_period_value/4.3) if menstrual_period==2 // weeks
replace tsincep = menstrual_period_value*12 if menstrual_period==4 // years
* Initialize pregnant (1) or currently postpartum amenorrheic (PPA) women who have not had period since before birth (6)
g pregPPA=1 if pregnant==1 | menstrual_period==6
* For women with missing data or "period not returned" on date of last menstrual period, use information from time since last period
* If last period is before last birth in last 5 years
replace pregPPA=1 if tsincep> tsinceb & tsinceb<60 & tsincep!=. & tsinceb!=.
* Or if said "before last birth" to time since last period in the last 5 years
replace pregPPA=1 if menstrual_period==-99 & tsinceb<60 & tsinceb!=.
* Select only women who are pregnant or PPA for <24 months
g pregPPA24=1 if pregnant==1 | (pregPPA==1 & tsinceb<24)
* Classify based on wantedness of current pregnancy/last birth
* Generate variable for whether or not wanted last/current pregnancy then, later, or not at all
gen wantedlast=pregnancy_current_desired // currently pregnant
gen m10_1=pregnancy_last_desired // not currently pregnant
replace wantedlast = m10_1 if (wantedlast==. | wantedlast==-99) & pregnant!=1
tab wantedlast
replace wantedlast=. if wantedlast==-99
* Recode as no unmet need if wanted current pregnancy/last birth then/at that time
recode unmet .=7 if pregPPA24==1 & wantedlast==1
* Recode as unmet need for spacing if wanted current pregnancy/last birth later
recode unmet .=1 if pregPPA24==1 & wantedlast==2
* Recode as unmet need for limiting if wanted current pregnancy/last birth not at all
recode unmet .=2 if pregPPA24==1 & wantedlast==3
* Recode unmet need as missing value if "wantedlast" missing and if has been post-partum amenorrheic for less then 24 months
recode unmet .=99 if pregPPA24==1 & wantedlast==.
* Determine if sexually active in last 30 days: less than 4 weeks or less than 30 days
gen sexact=0
replace sexact=1 if (last_time_sex==2 & last_time_sex_value<=4 & last_time_sex_value>=0) | (last_time_sex==1 & last_time_sex_value<=30 & last_time_sex_value>=0) ///
| (last_time_sex==3 & last_time_sex_value<=1 & last_time_sex_value>=0)
* If unmarried and not sexually active in last 30 days, assume no need
recode unmet .=97 if FQmarital_status~=1 & FQmarital_status~=2 & sexact!=1
*******************************************************************************
* GROUP 3: DETERMINE FECUNDITY
*******************************************************************************
* Boxes refer to Figure 2 flowchart in DHS Analytics 25 Report
* Box 1 (applicable only to currently married/cohabiting women)
* Married 5+ years ago, no children in past 5 years, never used contraception, excluding pregnant and PPA <24 months
* husband_cohabit_start_current husband_cohabit_start_recent
* husband_cohabit_start_recent is first marriage, FQfirstmarriagemonth and FQfirstmarriageyear are dates of first marriage (women married more than once)
* husband_cohabit_start_current is current marriage (if woman married only once, only have current marriage)
* If first marriage more than five years ago, never used contraception, and never had child, then infecund
* Recode month and year of marriage as missing if year of marriage is 2020 (i.e. missing)
tab1 *marriagemonth *marriageyear, mis
replace firstmarriagemonth=. if firstmarriageyear==2020
replace recentmarriagemonth=. if recentmarriageyear==2020
recode firstmarriageyear 2020=.
recode recentmarriageyear 2020=.
* Generate marriage century month code variable
gen marriagecmc=(firstmarriageyear-1900)*12+firstmarriagemonth
replace marriagecmc=(recentmarriageyear-1900)*12 + recentmarriagemonth if marriage_history==1
* Generate time since marriage century month code variable
gen v512=int((doicmc-marriagecmc)/12)
tab v512, mis //years since marriage
* Generate dichotomous infecund variable
g infec=1 if (FQmarital_status==1 | FQmarital_status==2) & v512>=5 & v512!=. & (tsinceb>59 | ever_birth==0) & fp_ever_used==0 & pregPPA24!=1
* Box 2
* Declared infecund on future desires for children
replace infec=1 if more_children==3 // v605==7
* Box 3
* Menopausal/hysterectomy as reason not using contraception - slightly different recoding in DHS III and IV+
replace infec=1 if why_not_usingmeno==1
* Box 4
* Time since last period is >=6 months and not PPA
replace infec=1 if tsincep>=6 & tsincep!=. & pregPPA!=1
* Box 5
* Menopausal/hysterectomy for time since last period response
replace infec=1 if menstrual_period==5
* Never menstruated for time since last period response, unless had a birth in the last 5 years
replace infec=1 if menstrual_period==7 & (tsinceb>59 | tsinceb==.)
* Box 6
* Time since last birth>= 60 months and last period was before last birth
replace infec=1 if menstrual_period==6 & tsinceb>=60 & tsinceb!=.
* Never had a birth, but last period reported as before last birth - assume code should have been something other than 6
replace infec=1 if menstrual_period==6 & ever_birth==0
* Exclude pregnant and PP amenorrheic < 24 months
replace infec=. if pregPPA24==1
* Recode unmet need
recode unmet .=9 if infec==1
*******************************************************************************
* GROUP 4: FECUND WOMEN
*******************************************************************************
* Recode as no unmet need if wants child within 2 years
recode unmet .=7 if more_children==1 & ((wait_birth==1 & wait_birth_value<24) | (wait_birth==2 & wait_birth_value<2) | (wait_birth==3) ) // v605==1, wants within 2 years; wait_birth: 4 months; 5 years
* Recode as unmet need for spacing if wants in 2+ years, wants undecided timing, or unsure if wants
recode unmet .=1 if more_children==-88 | (more_children==1 & ( (wait_birth==1 & wait_birth_value>=24) | (wait_birth==2 & wait_birth_value>=2)) | (wait_birth==-88) | (wait_birth==-99)) //v605>=2 & v605<=4
* Recode as unmet need for limiting if wants no more children
recode unmet .=2 if more_children==2
* Recode any reamining missing values as "99"
recode unmet .=99
* Label unmet need
capture la def unmet ///
1 "unmet need for spacing" ///
2 "unmet need for limiting" ///
3 "using for spacing" ///
4 "using for limiting" ///
7 "no unmet need" ///
9 "infecund or menopausal" ///
97 "not sexually active" ///
98 "unmarried - EM sample or no data" ///
99 "missing"
la val unmet unmet
* Generate and lable dichotomous unmet need variable
capture recode unmet (1/2=1 "unmet need") (else=0 "no unmet need"), g(unmettot)
* Tab unmet need variable for married or unmarried women
tab unmet
tab unmet if FQmarital_status==1 | FQmarital_status==2
* Keep only certain variables
keep unmet unmettot FQmetainstanceID doi* *cmc tsince*
* Drop duplicates
duplicates drop FQmetainstanceID, force
*******************************************************************************
* SAVE AND MERGE UNMET NEED DATA, CLOSE LOG
*******************************************************************************
* Save unmet need dataset
tempfile unmet
saveold `unmet', replace
* Merge unmet need dataset with weighted, wealth quintile dataset containing all data
use "`CCRX'_WealthWeightAll_$date.dta"
merge m:1 FQmetainstanceID using `unmet', nogen
saveold "`CCRX'_WealthWeightAll_$date.dta", replace version(12)
log close
|
clear
clear matrix
clear mata
capture log close
set maxvar 15000
set more off
numlabel, add
/*******************************************************************************
*
* FILENAME: PMA_HHQFQ_2Page_Analysis_$date.do
* PURPOSE: PMA2020 HHQ/FQ two page data analysis
* CREATED: Linnea Zimmerman (lzimme12@jhu.edu)
* DATA IN: CCRX_WealthWeightFemale_$date.dta
* DATA OUT: CCRX_HHQFQ_2Page_Analysis_$date.dta
*
*******************************************************************************/
*******************************************************************************
* INSTRUCTIONS
*******************************************************************************
*
* 1. Update macros/directories in first section
* 2. If using source of method by method figure, uncomment last section
* and update coding
*
*******************************************************************************
* SET MACROS: UPDATE THIS SECTION FOR EACH COUNTRY/ROUND
*******************************************************************************
* Set macros for country and round
local country "Nigeria"
local round "Round5Oyo"
local CCRX "NGOyoR5"
* Set macros for contraceptive methods
local mcp "(current_methodnumEC>=1 & current_methodnumEC>>=19)"
local tcp "(current_methodnumEC>=30 & current_methodnumEC<=39)"
* Set macro for date of most recently generated weighted HHQ/FQ dataset with wealth and unmet need variables that intend to use
local datadate "23Feb2018"
* Set macros for data sets
local householddata "/Users/ealarson/Documents/Côte d'Ivoire/Data_NotShared/Round1/HHQFQ/Analysis - 02.23.2018/CIR1_WealthWeightAll_8Aug2018.dta"
* Set macro for directory/file of program to calculate medians
local medianfile "/Users/ealarson/Dropbox (Gates Institute)/1 DataManagement_General/DataRoutines/PMA_SOI_2P/DoFiles/Current/PMA2020_MedianDefineFn_simple_9Mar2015.do"
*local medianfile "C:\Users\Shulin\Dropbox (Gates Institute)/DataRoutines/PMA_SOI_2P/DoFiles/Current/PMA2020_MedianDefineFn_simple_9Mar2015.do"
* Set directory for country and round
global datadir "/Users/ealarson/Documents/Côte d'Ivoire/Data_NotShared/Round1/HHQFQ/Analysis - 02.23.2018"
cd "$datadir"
* Set local/global macros for current date
local today=c(current_date)
local c_today= "`today'"
global date=subinstr("`c_today'", " ", "",.)
* Create log
log using "`CCRX'_HHQFQ_2Page_Analysis.log", replace
*******************************************************************************
* PREPARE DATA FOR ANALYSIS
*******************************************************************************
* First use household data to show response rates
use "`householddata'",clear
preserve
keep if metatag==1
gen responserate=0 if HHQ_result>=1 & HHQ_result<6
replace responserate=1 if HHQ_result==1
label define responselist 0 "Not complete" 1 "Complete"
label val responserate responselist
tabout responserate using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", replace ///
cells(freq col) h2("Household response rate") f(0 1) clab(n %)
restore
* Response rate among all women
gen FQresponserate=0 if eligible==1 & last_night==1
replace FQresponserate=1 if FRS_result==1 & last_night==1
label define responselist 0 "Not complete" 1 "Complete"
label val FQresponserate responselist
tabout FQresponserate if HHQ_result==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append ///
cells(freq col) h2("Female response rate") f(0 1) clab(n %)
* Restrict analysis to women who completed questionnaire and households with completed questionnaire
keep if FRS_result==1 & HHQ_result==1
* Restrict analysis to women who slept in the house the night before (de facto)
keep if last_night==1
* Save data set so can replicate analysis results later
save "`CCRX'_HHQFQ_2Page_Analysis_$date.dta", replace
* Check for duplicates
duplicates report FQmetainstanceID
codebook FQmetainstanceID
* Set survey weights
svyset EA [pweight=FQweight]
* Generate variable that represents number of observations
gen one=FRS_result
label var one "All women"
* Generate dichotomous "married" variable to represent all women married or currently living with a man
gen married=(FQmarital_status==1 | FQmarital_status==2)
label variable married "Married or currently living with a man"
* Generate dichotomous sexually active unmarried women variable
cap drop umsexactive
gen umsexactive=0
replace umsexact=1 if married==0 & ((last_time_sex==2 & last_time_sex_value<=4 & last_time_sex_value>=0) | (last_time_sex==1 & last_time_sex_value<=30 & last_time_sex_value>=0) ///
| (last_time_sex==3 & last_time_sex_value<=1 & last_time_sex_value>=0))
*Generate sexually active variable
gen sexactive= (last_time_sex==2 & last_time_sex_value<=4 & last_time_sex_value>=0) | (last_time_sex==1 & last_time_sex_value<=30 & last_time_sex_value>=0) ///
| (last_time_sex==3 & last_time_sex_value<=1 & last_time_sex_value>=0)
* Generate 0/1 urban/rural variable
capture confirm var ur
if _rc==0 {
gen urban=ur==1
label variable urban "Urban/rural place of residence"
label define urban 1 "Urban" 0 "Rural"
label value urban urban
tab urban, mis
}
else {
gen urban=1
label variable urban "No urban/rural breakdown"
}
* Label yes/no response options
capture label define yesno 0 "No" 1 "Yes"
foreach x in married umsexactive sexactive {
label values `x' yesno
}
* Tabout count of all women, unweighted
*tabout one using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", replace cells(freq) h2("All women (unweighted)") f(0)
/* Tabout count of all women, weighted (should be same as unweighted count of all women)
tabout one [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append cells(freq) h2("All women (weighted)") f(0)
* Tabout count of all married women, unweighted
*tabout married if married==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append cells (freq) h2("Married women (unweighted)") f(0)
* Tabout count of all married women, weighted
tabout married if married==1 [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append cells(freq) h2("Married women (weighted)") f(0)
* Tabout count of unmarried sexually active women, unweighted
*tabout umsexactive if umsexactive==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append cells(freq) h2("Unmarried sexually active (unweighted)") f(0)
* Tabout count of unmarried sexually active women, weighted
tabout umsexactive if umsexactive==1 [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append cells(freq) h2("Unmarried sexually active (weighted)") f(0)
*/
*******************************************************************************
* FIRST PAGE
*******************************************************************************
*******************************************************************************
* CONTRACEPTIVE PREVALENCE RATE
*******************************************************************************
* Set macros for contraceptive methods
local mcp "(current_methodnumEC>=1 & current_methodnumEC<=19)"
local tcp "(current_methodnumEC>=30 & current_methodnumEC<=39)"
* Generate numeric current method variable that includes emergency contraception
capture gen current_methodnumEC=current_recent_methodEC if cp==1
label variable current_methodnumEC "Current contraceptive method, including EC (numeric)"
* Generate dichotomous current use of modern contraceptive variable
capture gen mcp=`mcp'
label variable mcp "Current use of modern contraceptive method"
* Generate dichotomous current use of traditional contraceptive variable
capture gen tcp=`tcp'
label variable tcp "Current use of traditional contraceptive method"
* Generate dichotomous current use of any contraceptive variable
capture gen cp= current_methodnumEC>=1 & current_methodnumEC<40
label variabl cp "Current use of any contraceptive method"
* Generate dichotomous current use of long acting contraceptive variable
capture drop longacting
capture gen longacting=current_methodnumEC>=1 & current_methodnumEC<=4
label variable longacting "Current use of long acting contraceptive method"
* Label yes/no response options
foreach x in cp mcp tcp longacting {
label values `x' yes_no_dnk_nr_list
}
* Tabout weighted proportion of contracpetive use (overall, modern, traditional, long acting) among all women
tabout cp mcp longacting [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) h2("CPR/mCPR/Long-acting - all women (weighted)")
* Tabout weighted proportion of contracpetive use (overall, modern, traditional, long acting) among married women
tabout cp mcp longacting if married==1 [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) h2("CPR/mCPR/Long-acting - married women (weighted)")
*******************************************************************************
* UNMET NEED
*******************************************************************************
* Label variables for tabout
label variable unmet "Unmet need (categorical)"
label variable unmettot "Unmet need (dichotomous)"
* Generate total demand = current use + unmet need
gen totaldemand=0
replace totaldemand=1 if cp==1 | unmettot==1
label variable totaldemand "Has contraceptive demand, i.e. current user or unmet need"
* Generate total demand staisfied - CONFIRM INDICATOR CODED CORRECTLY
gen totaldemand_sat=0 if totaldemand==1
replace totaldemand_sat=1 if totaldemand==1 & mcp==1
label variable totaldemand_sat "Contraceptive demand satisfied by modern method"
* Generate categorical unmet need, traditional method, modern method variable
gen cont_unmet=0 if married==1
replace cont_unmet=1 if unmettot==1
replace cont_unmet=2 if tcp==1
replace cont_unmet=3 if mcp==1
label variable cont_unmet "Unmet need, traditional method, and modern method prevalence among married women"
label define cont_unmetl 0 "None" 1 "Unmet need" 2 "Traditional contraceptive use" 3 "Modern contraceptive use"
label values cont_unmet cont_unmetl
* Label yes/no response options
foreach x in totaldemand totaldemand_sat {
label values `x' yesno
}
* Tabout weighted proportion of unmet need (categorical and dichotomous) among all women
tabout unmettot unmet [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) h2("Unmet need (categorical and dichotomous) - all women (weighted)")
* Tabout weighted proportion of unmet need (categorical dichotomous) among married women
tabout unmettot unmet [aw=FQweight] if married==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) h2("Unmet need (categorical and dichotomous) - married women (weighted)")
* Tabout weighted proportion of unmet need (categorical dichotomous) among unmarried sexually active women
*capture tabout unmettot unmet [aw=FQweight] if umsexactive==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) h2("Unmet need (categorical and dichotomous) - unmarried sexually active women (weighted)")
* Tabout weighted proportion of total demand among all women
tabout totaldemand [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) h2("Total demand for contraception - all women (weighted)")
* Tabout weighted proportion of total demand among all women
tabout totaldemand_sat [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) h2("Contraceptive demand satisfied by modern method- all women (weighted)")
* Tabout weighted proportion of total demand among married women
tabout totaldemand [aw=FQweight] if married==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) h2("Total demand for contraception - married women (weighted)")
* Tabout weighted proportion of total demand among married women
tabout totaldemand_sat [aw=FQweight] if married==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) h2("Contraceptive demand satisfied by modern method- married women (weighted)")
* Tabout weighted proportion of total demand among married women
tabout totaldemand_sat wealth [aw=FQweight] if married==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(col) f(1) clab(%) npos(row) h1("Contraceptive demand satisfied by wealth - married women (weighted)")
*******************************************************************************
* FERTILITY INDICATORS
*******************************************************************************
* Generate variables like in DHS, bith date in cmc
gen birthmonth2=birthmonth
replace birthmonth=6 if birthmonth==-88
gen v011=(birthyear-1900)*12 + birthmonth
*******************************************************************************
* UNINTENDED BIRTHS
*******************************************************************************
* Codebook recent births unintended: last birth/current pregnancy wanted then, later, not at all
codebook pregnancy_last_desired
codebook pregnancy_current_desired
* Recode "-99" as "." to represent missing
recode pregnancy_last_desired -99 =.
recode pregnancy_current_desired -99 =.
* Generate wantedness variable that combines results from last birth and current pregnancy questions
gen wanted=1 if pregnancy_last_desired==1 | pregnancy_current_desired==1
replace wanted=2 if pregnancy_last_desired==2 | pregnancy_current_desired==2
replace wanted=3 if pregnancy_last_desired==3 | pregnancy_current_desired==3
label variable wanted "Intendedness of previous birth/current pregnancy (categorical): then, later, not at all"
label def wantedlist 1 "then" 2 "later" 3 "not at all"
label val wanted wantedlist
tab wanted, mis
* Generate dichotomous intendedness variables that combines births wanted "later" or "not at all"
gen unintend=1 if wanted==2 | wanted==3
replace unintend=0 if wanted==1
label variable unintend "Intendedness of previous birth/current pregnancy (dichotomous)"
label define unintendl 0 "intended" 1 "unintended"
label values unintend unintendl
* Tabout intendedness and wantedness among women who had a birth in the last 5 years or are currently pregnant
tabout unintend wanted [aw=FQweight] if tsinceb<60 | pregnant==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) ///
h2("Intendedness (dichotomous and categorical) among women who had a birth in the last 5 years or are currently pregnant (weighted)")
*******************************************************************************
* CURRENT USE AND UNMET NEED AMONG MARRIED WOMEN BY WEALTH
*******************************************************************************
* Tabout current use and unmet need among married women of reproductive age, by wealth quintile (weighted)
tabout wealth cont_unmet[aw=FQweight] if married==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(row) f(1) clab(%) npos(row) h1("Unmet need, traditional method, and modern method prevalence among married women (weighted)")
*******************************************************************************
* METHOD MIX PIE CHART
*******************************************************************************
* Label variables
label variable current_recent_method "Current or recent method"
* Tabout current/recent method if using modern contraceptive method, among married women
tabout current_methodnumEC [aweight=FQweight] if mcp==1 & married==1 & cp==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) h2("Method mix - married women (weighted)")
* Tabout current/recent method if using modern contraceptive method, among unmarried sexually active women
capture tabout current_methodnumEC [aweight=FQweight] if mcp==1 & umsexactive==1 & cp==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) h2("Method mix - unmarried sexually active women (weighted)")
gen current_methodnumEC2=current_methodnumEC
replace current_methodnumEC2=0 if current_methodnumEC2==. | current_methodnumEC2==-99
replace current_methodnumEC2=30 if current_methodnumEC>=30 & current_methodnumEC<=39
label copy methods_list methods_list2
label define methods_list2 0 "Not using" 30 "Traditional methods", modify
label val current_methodnumEC2 methods_list2
*******************************************************************************
* SECOND PAGE
*******************************************************************************
*******************************************************************************
* CHOICE INDICATORS BY WEALTH
*******************************************************************************
* Method chosen self of jointly
tab fp_final_decision if cp==1
gen methodchosen=1 if fp_final_decision==1 | fp_final_decision==4 | fp_final_decision==5
replace methodchosen=0 if fp_final_decision==2 | fp_final_decision==3
replace methodchosen=0 if fp_final_decision==-99 | fp_final_decision==6
label variable methodchosen "Who chose method?"
label define methodchosenl 0 "Not self" 1 "Self, self/provider, self/partner"
label values methodchosen methodchosenl
* Generate dichotomous would return to provider/refer relative to provider variable
recode return_to_provider -88 -99=0
recode refer_to_relative -88 -99=0
gen returnrefer=1 if return_to_provider==1 & refer_to_relative==1 & cp==1
replace returnrefer=0 if cp==1 & (return_to_provider==0 | refer_to_relative==0)
label variable returnrefer "Would return to provider and refer a friend or family member"
label values returnrefer yesno
* Tabout who chose method (weighted) by wealth quintile among current users
tabout methodchosen wealth [aweight=FQweight] if mcp==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(col) f(1) clab(%) npos(row) h1("Method chosen - current modern user (weighted)")
* Tabout obtained method of choice by wealth (weighted) among current users
recode fp_obtain_desired -88 -99=0
tabout fp_obtain_desired wealth [aweight=FQweight] if mcp==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(col) f(1) clab(%) npos(row) h1("Obtained method of choice by wealth - current modern user (weighted)")
* Tabout told of other methods by wealth (weighted) among current users
recode fp_told_other_methods -88 -99=0
tabout fp_told_other_methods wealth [aweight=FQweight] if mcp==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(col) f(1) npos(row) h1("Told of other methods by wealth - current modern user (weighted)")
* Tabout counseled on side effects by wealth (weighted) among current users
recode fp_side_effects -88 -99=0
tabout fp_side_effects wealth [aweight=FQweight] if mcp==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(col) f(1) npos(row) h1("Told about side effects by wealth - current modern user (weighted)")
gen fees_paid_lastvisit=0 if method_fees==0
replace fees_paid_lastvisit=1 if method_fees>0 & method_fees!=.
replace fees_paid_lastvisit=1 if method_fees==-88
label var fees_paid_lastvisit "Did you pay for services the last time you obtained FP?"
label define yesno_list 1 "yes" 0 "no"
label val fees_paid_lastvisit yesno_list
* Tabout paid for services by wealth (weighted) among current users
tabout fees_paid_lastvisit wealth [aweight=FQweight] if mcp==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(col) f(1) npos(row) h1("REWORDED QUESTION FROM PREVIOUS ROUNDS Paid for FP services at last visit by wealth - current modern user (weighted)")
* Tabout would return to provider by wealth (weighted) among current users
tabout returnrefer wealth [aweight=FQweight] if mcp==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(col) f(1) npos(row) h1("Return to/refer provider by wealth - current modern user (weighted)")
*******************************************************************************
* RECEIVED A METHOD FROM A PUBLIC SDP
*******************************************************************************
* Generate dichotomous variable for public versus not public source of family planning
recode fp_provider_rw (1/19=1 "public") (-88 -99=0) (nonmiss=0 "not public"), gen(publicfp_rw)
label variable publicfp_rw "Respondent or partner for method for first time from public family planning provider"
* Tabout whether received contraceptive method from public facility by wealth (weighted) among current users
tabout publicfp_rw wealth if mcp==1 [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(col) f(1) npos(row) h1("Respondent/partner received method from public facility initially by wealth - current modern user (weighted)")
* Tabout percent unintended births is the only indicator in the section not restricted to current users (all others restricted to current users)
tabout unintend wealth [aweight=FQweight] if tsinceb<60 | pregnant==1 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(col) f(1) npos(row) h1("Percent unintended by wealth - current user (weighted)")
save "`CCRX'_HHQFQ_2Page_Analysis_$date.dta", replace
*******************************************************************************
* REASON FOR NON-USE
*******************************************************************************
* Collapse reasons into five categories
* Perceived not at risk (not married, lactating, infrequent/no sex, husband away, menopausal, subfecund, fatalistic)
* Infrequent/no sex/husband away
gen nosex=0 if why_not_using!=""
replace nosex=1 if (why_not_usinghsbndaway==1 | why_not_usingnosex==1)
* Menopausal/subfecund/amenorhheic
gen meno=0 if why_not_using!=""
replace meno=1 if why_not_usingmeno==1 | why_not_usingnomens==1 | why_not_usingsubfec==1
* Lactating
gen lactate=0 if why_not_using!=""
replace lactate=1 if why_not_usingbreastfd==1
* Combined no need
gen noneed=0 if why_not_using!=""
replace noneed=1 if nosex==1|meno==1|lactate==1|why_not_usinguptogod==1
label variable noneed "Perceived not at risk"
tab noneed [aw=FQweight]
* Not married is separate category
gen notmarried=0 if why_not_using!=""
replace notmarried=1 if why_not_usingnotmarr==1
label variable notmarried "Reason not using: not married"
tab notmarried [aw=FQweight]
* Method related includes fear of side effects, health concers, interferes wth bodies natural processes, inconvenient to use
* Health concerns
gen methodrelated=0 if why_not_using!=""
replace methodrelated=1 if (why_not_usinghealth==1 | why_not_usingbodyproc==1| why_not_usingfearside==1 | why_not_usinginconv==1)
label variable methodrelated "Reason not using: method or health-related concerns"
tab methodrelated [aw=FQweight]
* Opposition includes personal, partner, other, religious
gen opposition=0 if why_not_using!=""
replace opposition=1 if why_not_usingrespopp==1|why_not_usinghusbopp==1|why_not_usingotheropp==1| why_not_usingrelig==1
label variable opposition "Reason not using: opposition to use"
tab opposition [aw=FQweight]
* Access/knowledge
gen accessknowledge=0 if why_not_using!=""
replace accessknowledge=1 if why_not_usingdksource==1 | why_not_usingdkmethod==1 | why_not_usingaccess==1 | why_not_usingcost==1 | ///
why_not_usingprfnotavail==1 | why_not_usingnomethod==1
label variable accessknowledge "Reason not using: lack of access/knowledge"
tab accessknowledge [aw=FQweight]
* Other/no response/don't know
gen othernoresp=0 if why_not_using!=""
replace othernoresp=1 if ( why_not_usingother==1 | why_not_using=="-88" | why_not_using=="-99" )
label variable othernoresp "Reason not using: other"
tab othernoresp [aweight=FQweight]
* Label yes/no response options
foreach x in noneed nosex notmarried methodrelated opposition accessknowledge othernoresp {
label values `x' yesno
}
* Tabout reasons for not using contraception among all women wanting to delay the next birth for 2 or more yeras
tabout notmarried noneed methodrelated opposition accessknowledge othernoresp [aweight=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", ///
append oneway c(freq col) f(0 1) npos(row) h2("Reasons for non-use - among all women wanting to delay (weighted)")
*Drop ammened birthmonth and keep original coding
drop birthmonth
rename birthmonth2 birthmonth
save "`CCRX'_HHQFQ_2Page_Analysis_$date.dta", replace
*******************************************************************************
* MEANS AND MEDIANS
*******************************************************************************
* Generate age at first marriage by "date of first marriage - date of birth"
* Get the date for those married only once from FQcurrent*
* Get the date for those married more than once from FQfirst*
* marriage cmc already defined in unmet need
* Run code to generate medians
run "`medianfile'"
capture drop one
* Generate median age of first marriage
capture drop agemarriage
gen agemarriage=(marriagecmc-v011)/12
label variable agemarriage "Age at first marriage (25 to 49 years)"
*hist agemarriage if FQ_age>=25 & FQ_age<50
save, replace
save tem, replace
**Install the new command needed for the change
ssc install listtab, all replace
* Median age at marriage among all women who have married
preserve
pma2020mediansimple tem agemarriage 25
gen urban="All Women"
tempfile total
save `total', replace
restore
preserve
keep if urban==0
capture codebook metainstanceID
if _rc!=2000{
save tem, replace
pma2020mediansimple tem agemarriage 25
gen urban="Rural"
tempfile rural
save `rural', replace
}
restore
preserve
keep if urban==1
capture codebook metainstanceID
if _rc!=2000{
save tem, replace
pma2020mediansimple tem agemarriage 25
gen urban="Urban"
tempfile urban
save `urban', replace
}
restore
preserve
use `total', clear
capture append using `rural'
capture append using `urban'
listtab urban median , appendto("`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls") rstyle(tabdelim) headlines("Median age at marriage among all women who have married- by urban/rural (weighted)") footlines(" ")
restore
*Median age at first sex among all women who have had sex
preserve
keep if age_at_first_sex>0 & age_at_first_sex<50
save tem, replace
pma2020mediansimple tem age_at_first_sex 15
gen urban="All Women"
tempfile total
save `total', replace
restore
preserve
keep if age_at_first_sex>0 & age_at_first_sex<50 & urban==0
capture codebook metainstanceID
if _rc!=2000 {
save tem, replace
pma2020mediansimple tem age_at_first_sex 15
gen urban="Rural"
tempfile rural
save `rural', replace
}
restore
preserve
keep if age_at_first_sex>0 & age_at_first_sex<50 & urban==1
capture codebook metainstanceID
if _rc!=2000 {
save tem, replace
pma2020mediansimple tem age_at_first_sex 15
gen urban="Urban"
tempfile urban
save `urban',replace
}
restore
preserve
use `total', clear
capture append using `rural'
capture append using `urban'
listtab urban median , appendto("`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls") rstyle(tabdelim) headlines("Median age at first sex - among all women who have had sex by urban/rural(weighted)") footlines(" ")
restore
* Median age at first contraceptive use among all women who have ever use contraception
preserve
keep if fp_ever_used==1 & age_at_first_use>0
save tem, replace
pma2020mediansimple tem age_at_first_use 15
gen urban="All Women"
tempfile total
save `total', replace
restore
preserve
keep if fp_ever_used==1 & age_at_first_use>0 & urban==0
capture codebook metainstanceID
if _rc!=2000 {
save tem, replace
pma2020mediansimple tem age_at_first_use 15
gen urban="Rural"
tempfile rural
save `rural', replace
}
restore
preserve
keep if fp_ever_used==1 & age_at_first_use>0 & urban==1
capture codebook metainstanceID
if _rc!=2000 {
save tem, replace
pma2020mediansimple tem age_at_first_use 15
gen urban="Urban"
tempfile urban
save `urban', replace
}
restore
preserve
use `total', clear
append using `rural'
append using `urban'
listtab urban median , appendto("`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls") rstyle(tabdelim) headlines("Median age at first contraceptive use - among all women who have used contraception by urban/rural (weighted)") footlines(" ")
restore
* Generate age at first birth by subtracting birth date from age at first birth and dividing by hours in a year
capture drop agefirstbirth
capture replace first_birthSIF=recent_birthSIF if birth_events==1
capture replace first_birthSIF=recent_birthSIF if children_born==1
gen agefirstbirth=hours(first_birthSIF-birthdateSIF)/8765.81
* Median age at first birth among all women who have ever given birth
preserve
keep if ever_birth==1
save tem, replace
pma2020mediansimple tem agefirstbirth 25
gen urban="All Women"
tempfile total
save `total', replace
restore
preserve
keep if ever_birth==1 & birth_events_rw!=. & birth_events_rw!=-99 & urban==0
capture codebook metainstanceID
if _rc!=2000 {
save tem, replace
pma2020mediansimple tem agefirstbirth 25
gen urban="Rural"
tempfile rural
save `rural', replace
}
restore
preserve
keep if ever_birth==1 & birth_events_rw!=. & birth_events_rw!=-99 & urban==1
capture codebook metainstanceID
if _rc!=2000 {
save tem, replace
pma2020mediansimple tem agefirstbirth 25
gen urban="Urban"
tempfile urban
save `urban', replace
}
restore
preserve
use `total', clear
append using `rural'
append using `urban'
listtab urban median , appendto("`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls") rstyle(tabdelim) headlines("Median age at first birth - among all women who have given birth by urban/rural(weighted)") footlines(" ")
restore
* Percent of women age 18-24 having first birth by age 18
capture drop birth18
gen birth18=0 if FQ_age>=18 & FQ_age<25
replace birth18=1 if agefirstbirth<18 & birth18==0
label variable birth18 "Birth by age 18 (18-24)"
tab birth18 [aw=FQweight]
tab urban birth18 [aw=FQweight], row
* Percent received FP information from visiting provider or health care worker at facility
recode visited_by_health_worker -99=0
recode facility_fp_discussion -99=0
gen healthworkerinfo=0
replace healthworkerinfo=1 if visited_by_health_worker==1 | facility_fp_discussion==1
label variable healthworkerinfo "Received family planning info from provider in last 12 months"
tab healthworkerinfo [aweight=FQweight]
tab urban healthworkerinfo [aweight=FQweight], row
* Percent with exposure to family planning media in past few months
gen fpmedia=0
replace fpmedia=1 if fp_ad_radio==1 | fp_ad_magazine==1 | fp_ad_tv==1
label variable fpmedia "Exposed to family planning media in last few months"
tab fpmedia [aw=FQweight]
tab urban fpmedia [aw=FQweight], row
* Label yes/no response options
foreach x in healthworkerinfo fpmedia {
label values `x' yesno
}
* Tabout mean no. of living children at first contraceptive use among women who have ever used contraception
replace age_at_first_use_children=0 if ever_birth==0 & fp_ever_used==1
tabout urban [aweight=FQweight] if fp_ever_used==1 & age_at_first_use_children>=0 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append sum c(mean age_at_first_use_children) f(3) npos(row) h2("Mean number of children at first contraceptive use - among all women who have used contraception (weighted)")
* Tabout birth by age 18 among all women by urban/rural, weighted
tabout birth18 urban [aweight=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(col) f(1) npos(row) h1("Birth by age 18 (18-24) - among all women (weighted)")
* Tabout received family planning information from provider in last 12 months among all women by urban/rural, weighted
tabout healthworkerinfo urban [aweight=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(col) f(1) npos(row) h1("Received FP info. from provider in last 12 months - among all women (weighted)")
* Tabout received family planning information from provider in last 12 months among all women by urban/rural, weighted
tabout fpmedia urban [aweight=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append c(col) f(1) npos(row) h1("Exposed to FP media in last few months - among all women (weighted)")
*******************************************************************************
* ALTERNATIVE INDICATORS IF FEWER THAN 50 UNMARRIED SEXUALLY ACTIVE WOMEN
*******************************************************************************
* Percent women 18-24 who are married by age 18
gen married18=0 if FQ_age>=18 & FQ_age<25
replace married18=1 if agemarriage<18 & married18==0
label variable married18 "Married by age 18"
tab married18 [aw=FQweight]
tab urban married18 [aw=FQweight], row
* Percent women 18-24 who have had first birth by age 18
*Already defined
* Percent women 18-24 who have had first contraceptive use by age 18
gen fp18=0 if FQ_age>=18 & FQ_age<25
replace fp18=1 if age_at_first_use>0 & age_at_first_use<18 & fp18==0
label variable fp18 "Used contraception by age 18"
tab fp18 [aw=FQweight]
tab urban fp18 [aw=FQweight], row
* Percent women who had first sex by age 18
gen sex18=0 if FQ_age>=18 & FQ_age<25
replace sex18=1 if age_at_first_sex>0 & age_at_first_sex<18 & sex18==0
label variable sex18 "Had first sex by age 18"
tab sex18 [aw=FQweight]
tab urban sex18 [aw=FQweight], row
* Label yes/no response options
foreach x in married18 birth18 fp18 sex18 {
label values `x' yesno
}
* Tabout married by 18, first birth before 18, contraceptive use by 18, first sex by 18 among women age 18-24 (weighted)
tabout married18 sex18 fp18 birth18 [aw=FQweight] if FQ_age>=18 & FQ_age<25 using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append oneway c(col) f(1) clab(%) npos(row) ///
h2("Married by 18, first sex by 18, contraceptive use by 18, first birth before 18 - women age 18-24 (weighted)")
* Age specific rates of long, short, tcp and unmet need
gen lstu=1 if longacting==1
replace lstu=2 if longacting!=1 & mcp==1
replace lstu=3 if tcp==1
replace lstu=4 if unmettot==1
replace lstu=5 if lstu==.
label define lstul 1 "Long acting" 2 "Short acting" 3 "Traditional" 4 "Unmet need" 5 "Not using/no need"
label val lstu lstul
egen age5=cut(FQ_age), at(15(5)50)
tabout age5 lstu [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append ///
cells(row) h1("Use/need by age - all women (weighted)") f(2)
*******************************************************************************
* DEMOGRAPHIC VARIABLES
*******************************************************************************
recode school -99=.
tabout age5 [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append ///
c(freq col) f(0 1) clab(n %) npos(row) h2("Distribution of de facto women by age - weighted")
tabout school [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append ///
c(freq col) f(0 1) clab(n %) npos(row) h2("Distribution of de facto women by education - weighted")
tabout FQmarital_status [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append ///
c(freq col) f(0 1) clab(n %) npos(row) h2("Distribution of de facto women by marital status - weighted")
tabout wealth [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append ///
c(freq col) f(0 1) clab(n %) npos(row) h2("Distribution of de facto women by wealth - weighted")
tabout sexactive [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append ///
c(freq col) f(0 1) clab(n %) npos(row) h2("Distribution of de facto women by sexual activity - weighted")
capture tabout urban [aw=FQweight] using "`CCRX'_HHQFQ_2Page_Analysis_Output_$date.xls", append ///
c(freq col) f(0 1) clab(n %) npos(row) h2("Distribution of de facto women by urban/rural - weighted")
*******************************************************************************
* CLOSE
*******************************************************************************
log close
|
****PMA 2020 Indonesia Data Quality Checks****
***Version 20 January 2014****
**Third do file in series***
/*This do file labels each variable in the household roster*/
clear
set more off
cd "$datadir"
local today=c(current_date)
local c_today= "`today'"
local date=subinstr("`c_today'", " ", "",.)
local todaystata=clock("`today'", "DMY")
local CCRX $CCRX
local HHQcsv $HHQcsv
local HHQcsv2 $HHQcsv2
clear
capture noisily insheet using "$csvdir/`HHQcsv'_hh_rpt.csv", comma case
if _rc==0 {
tostring *, replace force
save `CCRX'_HHQmember.dta, replace
}
/*If you need to add an extra version of the forms, this will check if that
version number exists and add it. If the version does not, it will continue*/
clear
capture noisily insheet using "$csvdir/`HHQcsv2'_hh_rpt.csv", comma case
if _rc==0 {
tostring *, replace force
append using `CCRX'_HHQmember.dta, force
save, replace
}
use `CCRX'_HHQmember.dta, clear
************************************************************************************
rename mb_grp* *
rename ms_grp* *
rename nm_grp* *
capture confirm var EA_transfer
if _rc!=0{
capture rename quartier EA
}
**Assign variable labels
***Clean the HHQ Member information
label var PARENT_KEY "Unique id household - ODK generate"
label var firstname "First name of household memeber"
label var respondent_match "Is respondent in household"
label var gender "Sex of household member"
label var age "Age"
label var marital_status "Marital Status"
label var relationship "Relationship to head of household"
label var head_check "Head of household?"
capture label var family_id "Family ID"
capture rename usual_member usually_live
label var usually_live "Usually live in household"
label var last_night "Slept in the household night before"
label var eligible "Eligible female respondent"
label var FRS_form_name "Linking ID for Eligible Woman"
destring age, replace
destring respondent_match, replace
destring head_check, replace
capture destring family_id, replace
capture destring EA, replace
destring eligible, replace
numlabel, add
label define yes_no_dnk_nr_list 0 no 1 yes -88 "-88" -99 "-99"
encode more_hh_members, gen(more_hh_membersv2) lab(yes_no_dnk_nr_list)
label define gender_list 1 male 2 female -88 "-88" -99 "-99"
encode gender, gen(genderv2) lab(gender_list)
label define marital_status_list 5 never_married 1 currently_married 2 currently_living_with_partner 3 divorced 4 widow -99 "-99"
encode marital_status, gen(marital_statusv2) lab(marital_status_list)
label define relationship_list 1 head 2 spouse 3 child 4 child_in_law 5 grandchild 6 parent 7 parent_in_law 8 sibling 9 other 10 help -88 "-88" -99 "-99"
encode relationship, gen(relationshipv2) lab(relationship_list)
encode usually_live, gen(usually_livev2) lab(yes_no_dnk_nr_list)
encode last_night, gen(last_nightv2) lab(yes_no_dnk_nr_list)
unab vars: *v2
local stubs: subinstr local vars "v2" "", all
foreach var in `stubs'{
rename `var' `var'QZ
order `var'v2, after(`var'QZ)
}
rename *v2 *
drop *QZ
*Check for observations that are all duplicates
duplicates report
duplicates drop
rename PARENT_KEY metainstanceID
rename KEY member_number
duplicates drop member_number, force
rename link_transfer link
drop *_transfer*
rename link link_transfer
drop SETOFhh_rpt
drop firstname_raw
save `CCRX'_HHQmember_`date', replace
|
****PMA 2020 Data Quality Checks****
***Version Written in Bootcamp July 21-23, 2014****
**Second do file in series***
clear matrix
clear
cd "$datadir"
/*This do file imports the Household Questionnaire (without the roster) into Stata and then cleans it*/
set more off
/*Import Household Questionnaire csv file into Stata. There is a separate spreadsheet that has
the roster information for each person. Input now, but will merge later. The loop is programmed to look
for questionnaire versions up to version 30. If versions beyond version 30 are added, the values in the forval
command below will need to be updated */
/*generating a dataset with one variable so that all datasets can append to it. Cannot append
to a completely empty dataset */
local CCRX $CCRX
local HHQcsv $HHQcsv
local HHQcsv2 $HHQcsv2
set obs 1
gen x=.
save `CCRX'_HHQ.dta, replace
*Create a temporary file
tempfile tempHHQ
/*If there are multiple versions during the same round, they should all be named the same thing other than ///
the version number. */
clear
capture noisily insheet using "$csvdir/`HHQcsv'.csv", comma case
tostring *, replace force
save `tempHHQ', replace
use `CCRX'_HHQ.dta
append using `tempHHQ', force
save, replace
/*If you need to add an extra version of the forms, this will check if that
version number exists and add it. If the version does not, it will continue*/
clear
capture noisily insheet using "$csvdir/`HHQcsv2'.csv", comma case
if _rc==0 {
tostring *, replace force
save `tempHHQ', replace
use `CCRX'_HHQ.dta
append using `tempHHQ', force
save, replace
}
**Drop the single empty observation and empty variable x
use `CCRX'_HHQ.dta
drop in 1
drop x
save, replace
***Generate variable for country and round
gen country="$country"
label var country "Country"
gen round="$round"
label var round "Round of data collection"
order country round, first
*****DRC ONLY
capture rename quartier EA*
capture rename water_*refill water_*15
capture rename name_grp* *
rename date_group* *
rename *grp* **
rename assets_assets* assets*
rename s_* *
capture rename sanitation_all_sanitation_all sanitation_all
rename w_* *
rename livestock_* *
**Assign variable labels
label var times_visited "Visit number"
label var your_name "Resident Enumerator name"
label var your_name_check "To RE: Is this your name?"
label var name_typed "RE name is not correct"
label var system_date "Date and Time"
label var system_date_check "Confirm correct date and time"
label var manual_date "If no, enter correct date"
******
label var EA "EA"
label var structure "Structure number"
label var household "Household number"
label var hh_duplicate_check "Have you already submitted a form for this structure/household?"
label var resubmit_reasons "Reason for resubmission"
label var available "Respondent present and available at least once"
capture label var previous_survey "Previous PMA survey?"
label var begin_interview "May I begin"
label var consent_obtained "Consent obtained"
label var witness_auto "Interviewer name"
label var witness_manual "Name check"
label var num_HH_members "Number of household members in the roster"
label var heads "Number of heads of households"
label var names "Names in household"
label var respondent_in_roster "Check that respondent is in the roster"
label var roster_complete "Check that Roster is completed"
label var assets "List of Household Assets"
label var assets_check "Question 10 Check"
capture label var hh_location_ladder "Location of house on wealth ladder: 1 = poorest, 10 = wealthiest"
label var owned_ask "Do you own livestock"
label var owned_livestock_own "Total number of livestock owned"
label var floor "Main floor material"
label var roof "Main roof material"
label var walls "Main exterior wall material"
label var handwashing_place_rw "Can you show me where members most often wash their hands?"
label var handwashing_place_observations "At handwashing observe soap, water, sanitation"
rename water_main_drinking_select water_sources_main_drinking
rename water_main_other water_sources_main_other
label var water_sources_all "Which water sources used for any purpose"
label var number_of_sources "Number of sources in water_sources_all"
label var water_sources_main_drinking "Main source of drinking water"
label var water_sources_main_other "Main source of cooking/ handwashing water"
label var source_labels "Water sources mentioned"
label var water_main_drinking "Main drinking water among all sources mentioned"
label var water_uses "Use of main water source"
label var water_months_avail "Availability of main water source during year"
label var water_reliability "Availability of main water source when expected"
label var water_collection "Minutes in round trip to main water source"
label var sanitation_all "Use any of the following toilet facilities"
capture label var sanitation_all_other "Other toilet facility specified"
label var number_of_sanitation "Total number of toilet facilities"
label var sanitation_main "Main toilet facility"
label var sanitation_labels "Toilet facilities mentioned"
capture label var sanitation_vip_check "Latrine has ventilation pipe"
capture label var sanitation_pit_with_slab_check "Latrine has cement slab"
capture label var sanitation_where "Location of toilet facility"
drop the_sanitation
destring sanitation_empty_value, replace
label define dwmy_list 1 days 2 weeks 3 months 4 years -88 "-88" -99 "-99"
label define sanitation_empty_list 1 months 2 years -77 "-77" -88 "-88" -99 "-99"
encode sanitation_empty_units, gen(sanitation_empty_unitsv2) lab(sanitation_empty_list)
label var sanitation_empty_units "Days, weeks, months, or years since toilet facility was emptied"
label var sanitation_empty_value "Number of days, weeks, months, or years since toilet facility was emptied"
label var sanitation_empty_who "Last person to empty toilet facility"
label var sanitation_empty_where "Last place toilet facilities were emptied to"
capture label var sanitation_empty_where_other "Other emptied location specified"
label define sanitation_who 1 neighbors 2 provider 3 other -88 "-88" -99 "-99"
encode sanitation_empty_who, gen(sanitation_empty_whov2) lab(sanitation_who)
label define sanitation_where 1 covered_hold 2 open_water 3 open_ground ///
4 taken_facility 5 taken_dnk 6 other -88 "-88" -99 "-99"
encode sanitation_empty_where, gen(sanitation_empty_wherev2) lab(sanitation_where)
label var sanitation_frequency_cc "How often use toilet facility"
label var shared_san "Share toilet facility with other households/public"
label var shared_san_hh "Number of HH that share toilet facility"
label var bush_use "How many people use bush"
label var minAge "Minimum age of children listed in household"
label var thankyou "Thank you"
label var locationLatitude "Latitude"
label var locationLongitude "Longitude"
label var locationAltitude "Altitude"
label var locationAccuracy "Accuracy"
capture label var HH_photo "Hyperlink to photo"
label var HHQ_result "HHQ Result"
label var start "Start time"
label var end "End time"
label var deviceid "deviceid"
label var simserial "simserial"
label var metainstanceID "Household Unique ID - ODK"
capture label var handwashing_container_show "Moveable container available in the household"
*e. All variables are forced to string to make sure nothing is dropped when appending. Most variables need to be
*destrung and/or encoded before analysis. Destring variables (if numbers are stored as string) or encode (if values are in character but can be categories)
capture destring version, replace
destring times_visited, replace
destring structure, replace
destring household, replace
destring num_HH_members, replace
destring heads, replace
destring assets_check, replace
destring EA, replace
destring owned_livestock_own, replace
destring number_of_sources, replace
destring number_of_sanitation, replace
destring shared_san_hh, replace
destring water_collection*, replace
destring bush_use, replace
destring minAge, replace
destring consent_obtained, replace
*Assign value lables. This should be done before the encoding step to ensure the right number of values are encoded
capture drop label, _all
label define yes_no_dnk_nr_list 0 no 1 yes -88 "-88" -99 "-99"
foreach var of varlist your_name_check system_date_check hh_duplicate_check available ///
begin_interview owned_ask roster_complete {
encode `var', gen(`var'v2) lab(yes_no_dnk_nr_list)
}
label val consent_obtained yes_no_dnk_nr_list
capture encode garden, gen(gardenv2) lab(yes_no_dnk_nr_list)
label define resubmit_reason_list 1 new_members 2 correction 3 dissappeared 4 not_received 5 other
replace water_sources_main_drinking=water_sources_all if number_of_sources==1
replace water_sources_main_other=water_sources_all if number_of_sources==1
label define water_source_list 1 piped_indoor 2 piped_yard 3 piped_public 4 tubewell ///
5 protected_dug_well 6 unprotected_dug_well 7 protected_spring ///
8 unprotected_spring 9 rainwater 10 tanker 11 cart 12 surface_water ///
13 bottled 14 sachet 15 refill -99 "-99"
encode water_sources_main_drinking, gen(water_sources_main_drinkingv2) lab(water_source_list)
encode water_sources_main_other, gen(water_sources_main_otherv2) lab(water_source_list)
replace sanitation_main=sanitation_all if number_of_sanitation==1
label define sanitation_list 1 flush_sewer 2 flush_septic 3 flush_elsewhere 4 flush_unknown 5 vip ///
6 pit_with_slab 7 pit_no_slab 8 composting 9 bucket 10 hanging 11 other 12 bush 13 flushpit 14 bush_water_body -99 "-99"
encode sanitation_main, gen(sanitation_mainv2) lab(sanitation_list)
label define handwash_list 1 observed_fixed 2 observed_mobile 3 not_here 4 no_permission ///
5 not_observed_other -99 "-99"
encode handwashing_place_rw, gen(handwashing_place_rwv2) lab(handwash_list)
label define frequency_of_use_list_v2 1 always 2 mostly 3 occasionally -99 "-99"
label define shared_san_list 1 not_shared 2 shared_under_ten_HH 3 shared_above_ten_HH ///
4 shared_public -99 "-99"
encode sanitation_frequency_cc, gen(sanitation_frequency_ccv2) lab(frequency_of_use_list_v2)
encode shared_san, gen(shared_sanv2) lab(shared_san_list)
replace shared_sanv2=. if shared_san==""
capture encode shared_san_fp, gen(shared_san_fpv2) lab(shared_san_list)
label define continuity_list 1 always 2 predictable 3 unpredictable -99 "-99"
encode water_reliability, gen(water_reliabilityv2) lab(continuity_list)
*************
label define hhr_result_list 1 completed 2 not_at_home 3 postponed 4 refused 5 partly_completed 6 vacant 7 destroyed ///
8 not_found 9 absent_extended_period
encode (HHQ_result), gen(HHQ_resultv2) lab(hhr_result_list)
*Participated in previous survey
capture label var previous_survey "Previously participated in PMA 2020 survey - household"
capture encode previous_survey, gen(previous_surveyv2) lab(yes_no_dnk_nr_list)
unab vars: *v2
local stubs: subinstr local vars "v2" "", all
foreach var in `stubs'{
rename `var' `var'QZ
order `var'v2, after(`var'QZ)
}
rename *v2 *
drop *QZ
foreach use in drinking cooking washing livestock business gardening {
gen water_uses_`use'=.
replace water_uses_`use'=0 if water_uses!=""
replace water_uses_`use'=1 if (regexm(water_uses, "`use'"))
replace water_uses_`use'=. if water_uses=="-99"
}
*****************************Changing time variables*****************************
**Change the date variables into clock time
**Change date variable of upload from scalar to stata time (SIF)
*Drop the day of the week of the interview and the UST
gen double SubmissionDateSIF=clock(SubmissionDate, "MDYhms")
format SubmissionDateSIF %tc
**Change start and end times into SIF to calculate time
*Have to do the same procedures. Using the end time of the survey as the day of the survey
gen double startSIF=clock(start, "MDYhms")
format startSIF %tc
gen double endSIF=clock(end, "MDYhms")
format endSIF %tc
rename your_name RE
replace RE=name_typed if your_name_check==0 | your_name_check==.
**Check any complete duplicates, duplicates of metainstanceid, and duplicates of structure and household numbers
duplicates report
duplicates report metainstanceID
duplicates tag metainstanceID, gen (dupmeta)
*******Round specific questions
capture confirm var collect_water_dry
if _rc==0{
label var collect_water_dry "Time collect water - DRY season"
label var collect_water_dry_value "Value - collect water dry"
label var collect_water_wet "Time collect water - WET season"
label var collect_water_wet_value "Value - collect water wet"
label define collect_water_list 1 minutes 2 hours 3 someone_else 4 no_one -88 "-88" -99 "-99"
encode collect_water_dry, gen(collect_water_dryv2) lab(collect_water_list)
encode collect_water_wet, gen(collect_water_wetv2) lab(collect_water_list)
destring collect_water_dry_value, replace
destring collect_water_wet_value, replace
}
*Child Feces
label var child_feces "What do you do with children's waste"
*child_feces is multi-select, need to change to binary
gen child_feces_burn=0 if child_feces!=""
replace child_feces_burn=1 if (regexm(child_feces, ["burn"]))
gen child_feces_latdisp=0 if child_feces!=""
replace child_feces_latdisp=1 if (regexm(child_feces, ["latrine_disposal"]))
gen child_feces_bury=0 if child_feces!=""
replace child_feces_bury=1 if (regexm(child_feces, ["bury"]))
gen child_feces_garbage=0 if child_feces!=""
replace child_feces_garbage=1 if (regexm(child_feces, ["garbage"]))
gen child_feces_manure=0 if child_feces!=""
replace child_feces_manure=1 if (regexm(child_feces, ["manure"]))
gen child_feces_leave=0 if child_feces!=""
replace child_feces_leave=1 if (regexm(child_feces, ["leave"]))
gen child_feces_waste_water=0 if child_feces!=""
replace child_feces_waste_water=1 if (regexm(child_feces, ["waste_water"]))
gen child_feces_latused=0 if child_feces!=""
replace child_feces_latused=1 if (regexm(child_feces, ["latrine_used"]))
*r}
save `CCRX'_HHQ_$date.dta, replace
|
** GPS checks for HHQ survey
** Authors Ann Rogers - Sally Ann Safi - Beth Larson - Julien Nobili
** Requirements: Listing.dta file (needs to be generated beforehand)
** version 1.1 (April 2019)
**********************Set directory**************************************
local datadir $datadir
local dofiles $dofiledir
local csv_results $datadir
local Geo_ID $Geo_ID
local CCRX $CCRX
*****************Preparation of the Listing file*************************
* Import full country round listing file; Requires to first generate a clean version of the listing.dta, using the Listing.do;
** Then save the output in your datadir (non-dropbox) **
*** NEEDS TO BE UPDATED BEFORE USE ***
clear
use BFR5_Listing_22Apr2019.dta
drop if HH_SDP=="SDP"
duplicates drop
destring GPS_HHLatitude, replace
destring GPS_HHLongitude, replace
* Generate XY average per EA (EA centroids creation)
bysort EA: egen centro_latt=mean(GPS_HHLatitude) if GPS_HHLatitude!=0
bysort EA: egen centro_long=mean(GPS_HHLongitude) if GPS_HHLongitude!=0
* Save one observation per EA with average geo-coordinates
egen tag=tag(EA)
preserve
keep if tag==1
keep EA centro_latt centro_long
* Save as temp_listing
tempfile temp_listing_centroids
save `temp_listing_centroids.dta', replace
restore
drop if Occupied_YN_HH=="no"
drop if GPS_HHLatitude==. | GPS_HHLongitude==.
* Convert vars to string and concatenate vars EA + structure_number
egen conc= concat(EA number_structure_HH), punct("-")
* Keep useful vars and drop duplicates
keep EA GPS_HHLatitude GPS_HHLongitude conc
duplicates drop conc, force
* Save as temp_listing_ready
tempfile temp_listing_ready
save `temp_listing_ready.dta', replace
**********************Preparation of the HHQ file****************
* Use cleaned HHQ dataset from PARENT
clear
use "`datadir'/`CCRX'_HHQ_$date.dta"
* Keep vars and generate concatanated vars EA + Structure_number
egen conc= concat(EA structure), punct("-")
keep RE `Geo_ID' EA locationLatitude locationLongitude locationAccuracy metainstanceID conc
******* Merge: temp_listing_centroids + HHQ file, then listing_ready + (HHQ+ listing centro)**************
merge m:1 EA using `temp_listing_centroids.dta', gen(centroid_merge)
drop centroid_merge
merge m:1 conc using `temp_listing_ready.dta', gen(ready_merge)
drop if ready_merge==2
************* Gen distances vars (distance from HH to Centroid, and HH to listing's structure***********
destring locationLatitude, replace
destring locationLongitude, replace
destring locationAccuracy, replace
gen distance_2_cent=(((locationLatitude-centro_latt)^2+(locationLongitude-centro_long)^2)^(1/2))*111295
gen distance_2_list=(((locationLatitude-GPS_HHLatitude)^2+(locationLongitude-GPS_HHLongitude)^2)^(1/2))*111295
*********** Generate mean and standard-dev using var distance_cent *************
bysort EA: egen mean_distance_cent=mean(distance_2_cent)
bysort EA: egen sd_distance_cent=sd(distance_2_cent)
************************ Genarate Issues vars **********************************
gen missing_coordinates=1 if locationLatitude==. | locationLongitude==.
gen poor_accuracy=1 if locationAccuracy>6 & !missing(locationAccuracy)
gen EA_size_issue=1 if mean_distance_cent<sd_distance_cent
gen HH_suspect_location=1 if ((distance_2_cent-mean_distance_cent)/sd_distance_cent)>=2
gen No_correspondence_HH2listing=1 if ready_merge==1
gen HH_toofar_listing=1 if distance_2_list >101 & !missing(distance_2_list)
********* Keep useful vars and save output files (GIS monitoring) **************
keep RE `Geo_ID' EA locationLatitude locationLongitude locationAccuracy metainstanceID missing_coordinates poor_accuracy EA_size_issue HH_suspect_location No_correspondence_HH2listing HH_toofar_listing
save `CCRX'_HHQ_GISFullcheck_$date.dta, replace
export delimited using `CCRX'_HHQ_GISFullcheck_$date.csv, replace
************* Output spreadsheet (errors by RE) Errors.xls *********************
collapse(count) missing_coordinates poor_accuracy EA_size_issue HH_suspect_location No_correspondence_HH2listing HH_toofar_listing, by(RE)
export excel RE missing_coordinates poor_accuracy EA_size_issue HH_suspect_location No_correspondence_HH2listing HH_toofar_listing using `CCRX'_HHQFQErrors_$date.xls, firstrow(variables) sh(GPS_check_by_RE) sheetreplace
********************************* Voilà ! ****************************************
|
clear
clear matrix
clear mata
capture log close
set maxvar 15000
set more off
numlabel, add
*******************************************************************************
*
* FILENAME: CCRX_Listing_v1_$date.do
* PURPOSE: Generates listing database and
* CREATED: Linnea Zimmerman (lzimme12@jhu.edu)
* DATA IN: CDR4_Listing_v#.csv
* DATA OUT: CDR4_Listing_$date.dta
* UPDATES: v2-01Nov2017 Linnea Zimmerman added code to incorporate second csv file
*
*******************************************************************************
*******************************************************************************
* INSTRUCTIONS
*******************************************************************************
*
* 1. Update macros/directories in first section
*
*******************************************************************************
* SET MACROS AND CORRECT DOI: UPDATE THIS SECTION FOR EACH COUNTRY/ROUND
*******************************************************************************
clear matrix
clear
set more off
local today=c(current_date)
local c_today= "`today'"
local date=subinstr("`c_today'", " ", "",.)
di "`date'"
global date=subinstr("`c_today'", " ", "",.)
**Create a folder where you will store Round 1 Data
*In that folder, create a folder called CSV_Files and a folder called Listing
*Within the Listing folder, create a folder called Archived_data
*Use this folder as your data directory and update the global datadir
local Country DRC
local CCRX CDR7
local Round Round7
local Listingcsv CDR7_Listing_v6
local Listingcsv2 CDR7_Listing_v6
local dofiledate 17Jun2015
local GeoID "level1 level2 level3 level4 EA"
local StructureHH "number_structure_HH"
local StructureSDP "number_SDP"
**************** Update to the directories where:
**The csv files are saved
global csvdir "/Users/ealarson/Dropbox (Gates Institute)/7 DRC/PMADataManagement_DRC/Round7/Data/CSV_Files"
**Where you want the datasets to save (DO NOT SAVE ON DROPBOX)
global datadir "/Users/ealarson/Documents/DRC/Data_NotShared/Round7/Listing"
**Where the do files are saved
global dofiledir "/Users/ealarson/Dropbox (Gates Institute)/7 DRC/PMADataManagement_DRC/Round7/Cleaning_DoFiles/Current"
*******************************************************************************************
******* Stop Updating Macros Here *******
*******************************************************************************************
cd "$datadir/Listing"
*Create folder called Archived_Data in the Listing Folder
* Zip all of the old versions of the datasets. Updates the archive rather than replacing so all old versions are still in archive
capture zipfile `CCRX'*, saving (Archived_Data/ArchivedListing_$date.zip, replace)
*Delete old versions. Old version still saved in ArchivedData.zip
shell rm `CCRX'_*
*Added second csv file for multiple versions
tempfile tempList
clear
capture insheet using "$csvdir/`Listingcsv'.csv", comma case
tostring *, replace force
save `CCRX'_Listing_$date.dta, replace
clear
capture insheet using "$csvdir/`Listingcsv2'.csv", comma case
if _rc==0 {
tostring *, replace force
save `tempList', replace
use `CCRX'_Listing_$date.dta
append using `tempList', force
save `CCRX'_Listing_$date.dta, replace
}
**********************
clear
**Merge in household information into structure information
clear
capture insheet using "$csvdir/`Listingcsv'_HH_grp.csv", comma case
tostring *, replace force
save `CCRX'_ListingHH_$date, replace
clear
capture insheet using "$csvdir/`Listingcsv2'_HH_grp.csv", comma case
if _rc==0 {
tostring *, replace force
save `tempList', replace
use `CCRX'_ListingHH_$date.dta
append using `tempList', force
save `CCRX'_ListingHH_$date.dta, replace
}
use `CCRX'_ListingHH_$date.dta
save, replace
**The first database is just the structures, there should not be any duplicate households because households need to be merged in
use `CCRX'_Listing_$date
***Make sure Block Census code for Indonesia is consistent in all entries
capture replace blok_sensus=upper(blok_sensus)
capture rename ea EA
capture rename name_grp* *
capture rename B_grpB your_name
capture rename B_grpB2 name_typed
capture rename B3 your_name_check
replace your_name=name_typed if your_name_check=="no"
rename your_name RE
*Check for duplicate
destring number_structure_HH, replace
destring number_HH, replace
destring number_SDP, replace
duplicates drop metainstanceID, force
save, replace
clear
use `CCRX'_ListingHH_$date
capture confirm PARENT_KEY
capture duplicates drop KEY, force
rename PARENT_KEY metainstanceID
save, replace
use `CCRX'_Listing_$date.dta
*merge in household group information into structure information
merge 1:m metainstanceID using `CCRX'_ListingHH_$date
drop if EA=="9999"
save `CCRX'_ListingCombined_$date, replace
******************************************************************************************************
*****************CLEAN THE LISTING FORM OF DUPLICATE SUBMISSIONS OR NUMBERING ERRORS*************
**Clean duplicates that are listed below usng the Listing File
run "$dofiledir/`CCRX'_CleaningByRE_LISTING_`dofiledate'.do"
save, replace
*******************************************************************************************************
use `CCRX'_ListingCombined_$date.dta
egen metatag=tag(metainstanceID)
save, replace
keep if metatag==1
sort `GeoID' `StructureHH'
rename duplicate_check IsThisResubmission
duplicates tag `GeoID' `StructureHH' HH_SDP if HH_SDP=="HH", gen(dupHHstructure)
capture duplicates tag `GeoID' `StructureSDP' HH_SDP if HH_SDP=="SDP", gen(dupSDPstructure)
**Keep only the structure numbers that are duplicates. Even if they are multiHH dwellings, there should not be duplicate structure numbers
**First export out list of duplicate households
preserve
capture confirm variable dupHHstructure
if _rc!=111{
keep if dupHHstructure!=0 & dupHHstructure!=.
sort RE `GeoID' `StructureHH'
save `CCRX'_ListingDuplicates_$date.dta, replace
**Dropping the unnecessary variables
order metainstanceID RE `GeoID' `StructureHH' HH_SDP Occupied_YN_HH mult_HH number_HH address_description_HH* IsThis* dupHHstructure
keep metainstanceID RE `GeoID' `StructureHH' HH_SDP Occupied_YN_HH mult_HH number_HH address_description_HH* IsThis* dupHHstructure
capture noisily export excel using "`CCRX'_ListingErrors_$date.xls", sh("DupHHStructures") firstrow(variables) replace
if _rc!=198{
restore
}
if _rc==198 {
clear
set obs 1
gen x="NO DUPLICATE HOUSEHOLD STRUCTURES"
export excel using `CCRX'_ListingErrors_$date.xls, firstrow(variables) sh("DupHHStructures") sheetreplace
restore
}
}
else{
restore
}
***Second export list of duplicate SDPs. Do separately because there may not be SDPs immediatley, and the code will have errors
*if combine SDP and HH code
capture confirm var dupSDPstructure
if _rc!=111{
preserve
keep if dupSDPstructure!=0
sort `GeoID' RE `StructureSDP'
save `tempList', replace
use `CCRX'_ListingDuplicates_$date.dta, replace
append using `tempList'
save, replace
**Dropping the unnecessary variables
restore
preserve
keep if dupSDPstructure!=0 & dupSDPstructure!=.
order metainstanceID RE `GeoID' HH_SDP `StructureSDP' address_description_SDP* IsThis*
keep metainstanceID RE `GeoID' HH_SDP `StructureSDP' address_description_SDP* IsThis* dupSDP*
capture noisily export excel using "`CCRX'_ListingErrors_$date.xls", sh("DupSDPStructures") firstrow(variables) sheetreplace
if _rc!=198{
restore
}
if _rc==198 {
clear
set obs 1
gen x="NO DUPLICATE SDP STRUCTURES"
export excel using `CCRX'_ListingErrors_$date.xls, firstrow(variables) sh("DupSDPStructures") sheetreplace
restore
}
}
preserve
gen HH=1 if HH_SDP=="HH"
gen SDP=1 if HH_SDP=="SDP"
collapse (count) HH SDP, by(RE `GeoID')
sort RE EA
rename HH HHStructures
rename SDP SDPStructures
save `CCRX'_ListingErrors_$date.dta, replace
restore
clear
use `CCRX'_ListingCombined_$date, replace
*check to see if multihousehold dwellings have more than one entry
gen HH=1 if HH_SDP=="HH"
gen SDP=1 if HH_SDP=="SDP"
save, replace
bysort metainstanceID: egen HHcount=total(HH)
preserve
sort `GeoID' RE number_structure_HH
order RE `GeoID' HH_SDP `StructureHH' Occupied_YN_HH mult_HH number_HH address*
keep RE `GeoID' HH_SDP `StructureHH' Occupied_YN_HH mult_HH number_HH metatag HHcount address*
capture export excel using "`CCRX'_ListingErrors_$date.xls" if metatag==1 & HHcount==1 & mult_HH=="yes", sh(MultiHHError) sheetreplace firstrow(variables)
if _rc!=198{
restore
}
if _rc==198 {
clear
set obs 1
gen x="NO STRUCTURES LISTED AS MULTI-HOUSEHOLD WITH ONLY ONE HOUSEHOLD"
export excel using `CCRX'_ListingErrors_$date.xls, firstrow(variables) sh("MultiHHError") sheetreplace
restore
}
**Generating the average number of households in the EA and the number of errors (multi with only one HH)
generate multiHHerr=1 if mult_HH=="yes" & HHcount==1
save, replace
tempfile collapse
preserve
keep if HH==1
collapse (count) HH multiHHerr, by (RE `GeoID' )
rename HH NumberHHperEA
rename multiHHerr SingleHHlabeledMulti
save `collapse', replace
restore
use `CCRX'_ListingErrors_$date.dta
merge 1:1 RE `GeoID' using `collapse', nogen
sort `GeoID' RE
save, replace
use `CCRX'_ListingCombined_$date.dta
**Get average number of households per structure. Have to keep only one structure
preserve
keep if HH==1 & metatag==1
collapse (mean) HHcount, by (RE `GeoID' )
rename HHcount AverageHHperStrc
save `collapse', replace
restore
use `CCRX'_ListingErrors_$date.dta
merge 1:1 RE `GeoID' using `collapse', nogen
sort `GeoID' RE
save, replace
*Export out forms and total the number of forms where the date is entered incorrectly
use `CCRX'_ListingCombined_$date.dta
preserve
gen datetag=.
split start, gen(start_)
replace datetag=1 if start_3!="2015"
drop start_*
split end, gen(end_)
replace datetag=1 if end_3!="2015"
drop end_*
keep if datetag==1 & metatag==1
capture collapse (sum) datetag if metatag==1, by (RE `GeoID')
if _rc!=2000{
save `collapse', replace
use `CCRX'_ListingErrors_$date
merge 1:1 RE `GeoID' using `collapse', nogen
save, replace
}
else {
clear
use `CCRX'_ListingErrors_$date
gen datetag=.
save, replace
}
export excel using "`CCRX'_ListingErrors_$date.xls", sh(TotalsByRE) sheetreplace firstrow(variables)
restore
*Export out forms and total the total number of forms uploaded
use `CCRX'_ListingCombined_$date.dta
preserve
egen EAtag=tag(`GeoID')
tab EAtag
collapse (sum) HH EAtag SDP
label var HH "Total Households Listed and Uploaded"
label var SDP "Total Private SDP Listed and Uploaded"
label var EAtag "Total EAs with any forms uploaded"
order EAtag HH SDP
save `collapse', replace
export excel using "`CCRX'_ListingErrors_$date.xls", sh(OverallTotal) sheetreplace firstrow(varlabels)
restore
|
/*
The purpose of this do file is to select households for reinterview.
1. Update the macros below to be country and round specific
2. Update the EA that you are selecting
3. Update the variable names that you want to export and keep
4. The information will export to an excel spreadsheet with the EA name/number
5. Send that information to the supervisor. They will need to complete the entire
reinterview questionnaire but will be able to directly compare some information
in the field.
**Use the combined dataset with names to identify households for selection
for reinterview.
*May 2, 2016 updated error to get geographic identifiers to export
*/
*******************************************************************************
* SET MACROS: UPDATE THIS SECTION FOR EACH EA THAT NEEDS RE_INTERVIEW INFORMATION
*******************************************************************************
*Dataset date - this is the date of the data you are using to select the
*the households to reinterview
local datadate 2May2016
*Update the EA name/number
local EA "Odapu_Ogaji"
*******************************************************************************
* SET MACROS: UPDATE THIS SECTION FOR EACH COUNTRY/ROUND - ONLY NEEDS TO BE DONE ONCE
*******************************************************************************
*BEFORE USE THE FOLLOWING NEED TO BE UPDATED:
*Country
local Country Nigeria
*Round Number
local Round Round3
*Country and Round Abbreviation
local CCRX NGR3
*Combined Dataset Name (must be the version with names)
local CombinedDataset `CCRX'_Combined_`datadate'.dta
*Geographic Identifier
global GeoID "state LGA Locality EA"
*Geographic Identifier lower than EA to household
global GEOID_SH "structure household"
*variables to keep
local varstokeep "RE firstname gender age eligible usual last_night"
**Directory where the Combined dataset is stored
global datadir "/Users/pma2020/Dropbox (Gates Institute)/Nigeria/Data_NotShared/Round3/Data/HHQFQ"
**Directory where the excel files will be exported to
global selectiondir "/Users/pma2020/Dropbox (Gates Institute)/Nigeria/Data_NotShared/Round3/Data/Selection"
*******************************************************************************************
******* Stop Updating Macros Here *******
*******************************************************************************************
cd "$selectiondir"
set seed 7261982
use "$datadir/`CombinedDataset'", clear
tempfile completetemp
save `completetemp', replace
keep if EA=="`EA'"
keep if metatag==1
sample 5, count
gen reinterview=1
tempfile temp
keep metainstanceID reinterview
save `temp', replace
use `completetemp', clear
merge m:1 metainstanceID using `temp', nogen
keep if reinterview==1
keep $GeoID $GeoID_SH `varstokeep'
export excel using `CCRX'_ReinterviewInformation_`EA'.xls, firstrow(variables) replace
|
clear
clear matrix
clear mata
capture log close
set maxvar 15000
set more off
numlabel, add
/*******************************************************************************
*
* FILENAME: CCRX_Selection_11May2015
* PURPOSE: Generate database of households selected for interview
* CREATED: Linnea Zimmerman (lzimme12@jhu.edu)
* DATA IN: CCRX_Selection_vX.csv
* DATA OUT: `CCRX'_Selection_$date.dta
* UPDATES: 08/11/2015 by Linnea Zimmerman
*
*******************************************************************************/
*******************************************************************************
* INSTRUCTIONS
*******************************************************************************
*
* 1. Update macros/directories in first section
*
*******************************************************************************
* SET MACROS: UPDATE THIS SECTION FOR EACH COUNTRY/ROUND
*******************************************************************************
clear matrix
clear
set more off
local Country Niger
local CCRX NER2
local Selectioncsv Selection_NER2_v13
local Round Round2
*Supervisory area
local supervisoryarea "level3"
*Update the data directory
global datadir "~/Dropbox (Gates Institute)/`Country'/Data_NotShared/`Round'/Data"
**Global directory for the dropbox where the csv files are originally stored
global csvdir "~/Dropbox (Gates Institute)/`Country'/pmadatamanagement_`Country'/`Round'/Data/CSV_Files"
*Country specific data directory
*global datadir "~/PMA2020/`Round'/Data"
**Do file directory
global dofiledir "~/Dropbox (Gates Institute)/`Country'/pmadatamanagement_`Country'/`Round'/Cleaning_DoFiles/Current"
cd "$datadir/Selection"
*******************************************************************************
*Stop Updating here
*******************************************************************************
*Create folder called Archived_Data in the Listing Folder
* Zip all of the old versions of the datasets. Updates the archive rather than replacing so all old versions are still in archive
*capture zipfile `CCRX'*, saving (Archived_Data/ArchivedSelection_$date, replace)
*Delete old versions. Old version still saved in ArchivedData.zip
capture shell rm `CCRX'_*
clear
capture insheet using "$csvdir/`Selectioncsv'.csv", comma case
tostring _all, replace
save `CCRX'_Selection_$date.dta,replace
clear
capture insheet using "$csvdir/`Selectioncsv'_HH_selectionrpt.csv", comma case
tostring _all, replace
save `CCRX'_SelectionHousehold_$date.dta,replace
tempfile temp
rename PARENT_KEY metainstanceID
save `temp', replace
use `CCRX'_Selection_$date.dta
merge 1:m metainstanceID using `temp', nogen
save, replace
tostring name_typed, replace
rename name_grp* *
rename date_group* *
rename HH_selectiongrp* *
replace your_name=name_typed if your_name_check!="yes"
replace system_date=manual_date if system_date_check!="yes"
replace RE_name=RE_name_other if RE_name_other!=""
rename your_name Supervisor
drop SETOFHH_selectionrpt your_name_check name_typed system_date_check manual_date ///
HH_selectionprompt HH_selectioncheck_grp v* goback goback_2 SDP_selection_prompt start end ///
deviceid simserial phonenumber KEY RE* max_num_HH too* *logging SDP_check
order SubmissionDate system_date all_selected_HH, last
order metainstanceID, first
sort Supervisor `supervisoryarea' EA structure household
save, replace
|
/*Water sources code updated to be consistent with Sri Vedachalam code*/
capture drop water_source_list_cc
input str60 water_source_list_cc
piped_indoor
piped_yard
piped_public
tubewell
protected_dug_well
unprotected_dug_well
protected_spring
unprotected_spring
rainwater
tanker
cart
surface_water
bottled
sachet
refill
-99
end
* Replace water_sources_main_drnk equal to response in water_sources_all if only provided one source
gen water_sources_single=water_sources_all if !regexm(water_sources_all,"") & water_sources_all!="-99" & water_sources_all!=""
forvalue i=1/16 {
replace water_sources_single="`i'" if water_sources_single==water_source_list[`i']
}
destring water_sources_single, replace
replace water_sources_main_drinking=water_sources_single if water_sources_main_drinking==.
destring water_months_avail, replace
* Replace water_source_main_other equal to response in water_sources_all if only provided one source
replace water_sources_main_other=water_sources_single if water_sources_main_other==.
gen main_drinking_rc=.
replace main_drinking_rc=1 if water_sources_main_drinking==1 | water_sources_main_drinking==2
replace main_drinking_rc=2 if water_sources_main_drinking==3
replace main_drinking_rc=3 if water_sources_main_drinking==4
replace main_drinking_rc=4 if water_sources_main_drinking ==5
replace main_drinking_rc=5 if water_sources_main_drinking ==7
replace main_drinking_rc=6 if water_sources_main_drinking ==9
replace main_drinking_rc=7 if water_sources_main_drinking==13
replace main_drinking_rc=8 if water_sources_main_drinking==6
replace main_drinking_rc=9 if water_sources_main_drinking==8
replace main_drinking_rc=10 if water_sources_main_drinking==10 | water_sources_main_drinking==11
replace main_drinking_rc=11 if water_sources_main_drinking==12
replace main_drinking_rc=12 if water_sources_main_drinking==14
replace main_drinking_rc=13 if water_sources_main_drinking==15
gen main_drinking_other_rc=.
replace main_drinking_other_rc=1 if water_sources_main_other==1 | water_sources_main_other==2
replace main_drinking_other_rc=2 if water_sources_main_other==3
replace main_drinking_other_rc=3 if water_sources_main_other==4
replace main_drinking_other_rc=4 if water_sources_main_other ==5
replace main_drinking_other_rc=5 if water_sources_main_other ==7
replace main_drinking_other_rc=6 if water_sources_main_other ==9
replace main_drinking_other_rc=7 if water_sources_main_other==13
replace main_drinking_other_rc=8 if water_sources_main_other==6
replace main_drinking_other_rc=9 if water_sources_main_other==8
replace main_drinking_other_rc=10 if water_sources_main_other==10 | water_sources_main_other==11
replace main_drinking_other_rc=11 if water_sources_main_other==12
replace main_drinking_other_rc=12 if water_sources_main_other==14
replace main_drinking_other_rc=13 if water_sources_main_other==15
* Generate percentage values for (total) improved versus not improved main water sources for drinking purposes
gen main_drinking_class=.
replace main_drinking_class=1 if main_drinking_rc==1 | main_drinking_rc==2 | main_drinking_rc==3 | ///
main_drinking_rc==4 | main_drinking_rc==5 | main_drinking_rc==6 | main_drinking_rc==7 | main_drinking_rc==10 | main_drinking_rc==12 | main_drinking_rc==13
replace main_drinking_class=0 if main_drinking_rc==8 | main_drinking_rc==9 | main_drinking_rc==11
* Label new water variables
lab var main_drinking_rc "Main source of drinking water (recode)"
lab var main_drinking_class "Classification of main source of drinking water (improved/unimproved)"
* Label new water varibles' response options
label define classl 1 "Improved" 0 "Unimproved"
label val main_drinking_class classl
label define water_sourcel 1 "Piped into dwelling/yard" 2 "Public tap/standpipe" ///
3 "Tubewell/bore hole" 4 "Protected well" 5 "Protected spring" 6 "Rainwater" 7 "Bottled (Improved and unimproved)" ///
8 "Unprotected well" 9 "Unprotected spring" 10 "Tanker truck/cart with small tank" 11 "Surface water" ///
12 "Sachet" 13 "Refill"
label val main_drinking_rc water_sourcel
label val main_drinking_other_rc water_sourcel
* Order new water variables
capture order water_sources_single main_drinking_rc main_drinking_class, after(water_collection_14)
capture water_sources_single main_drinking_rc main_drinking_class, after(water_collection_15)
***Sanitation
* Generate variables for improved (not shared/shared) and unimproved sanitation facilities
gen sanitation_main_rc=.
* Facilities considered improved
* 1: flush/pour flush to piped sewer system
* 2: flush/ pour flush to septic tank or pit latrine
* 3: ventilated improved pit latrine
* 4: pit latrine with slab
* 5: composting toilet
replace sanitation_main_rc=1 if (sanitation_main==1 | sanitation_all=="flush_sewer") & shared_san==1
replace sanitation_main_rc=2 if (sanitation_main==2 | sanitation_all=="flush_septic") & shared_san==1
capture replace sanitation_main_rc=2 if (sanitation_main==13 | sanitation_all=="flushpit) & (shared_san_fp==1)
replace sanitation_main_rc=3 if (sanitation_main==5 | sanitation_all=="flushpit") & shared_san==1
replace sanitation_main_rc=4 if (sanitation_main==6 | sanitation_all=="vip") & shared_san==1
replace sanitation_main_rc=5 if (sanitation_main==8 | sanitation_all=="composting") & shared_san==1
* Facilities that would be considered improved if they were not shared by two or more households
* 6: flush/pour flush to piped sewer system
* 7: flush/ pour flush to septic tank or pit latrine
* 8: ventilated improved pit latrine
* 9: pit latrine with slab
* 10: composting toilet
replace sanitation_main_rc=6 if (sanitation_main==1 | sanitation_all=="flush_sewer") & (shared_san!=1 & shared_san!=.)
replace sanitation_main_rc=7 if (sanitation_main==2 | sanitation_all=="flush_septic") & (shared_san!=1 & shared_san!=.)
capture replace sanitation_main_rc=7 if (sanitation_main==13 | sanitation_all=="flushpit") & (shared_san_fp!=1 & shared_san_fp!=.)
replace sanitation_main_rc=8 if (sanitation_main==5 | sanitation_all=="vip") & (shared_san!=1 & shared_san!=.)
replace sanitation_main_rc=9 if (sanitation_main==6 | sanitation_all=="pit_with_slab") & (shared_san!=1 & shared_san!=.)
replace sanitation_main_rc=10 if (sanitation_main==8 | sanitation_all=="composting") & (shared_san!=1 & shared_san!=.)
* Non improved facilities
* 11: Flush/ pour flush not to sewer/septic tank
* 12: Pit latrine without slab/bucket toilet
* 13: Hanging toilet/hanging latrine
* 14: No facility/bush/field
* 15: Bush(water body)
* 16: Other
* -99: Missing
replace sanitation_main_rc=11 if sanitation_main==3 | sanitation_main==4 | sanitation_all=="flush_elsewhere" | sanitation_all=="flush_unknown"
replace sanitation_main_rc=12 if sanitation_main==7 | sanitation_main==9 | sanitation_all=="pit_no_slab" | sanitation_all=="bucket"
replace sanitation_main_rc=13 if sanitation_main==10 | sanitation_all=="hanging"
replace sanitation_main_rc=14 if sanitation_main==12 | sanitation_all=="bush"
replace sanitation_main_rc=15 if sanitation_main==14 | sanitation_all=="bush_water_body"
replace sanitation_main_rc=16 if sanitation_main==11 | sanitation_all=="other"
* The 16 sanitation facilities are now aggregated into 4 main types using the DHS classification
* 1= Improved, not shared facility
* 2= Improved, Shared facility
* 3= Non-improved facility
* 4= Open defecation
gen sanitation_main_class=.
replace sanitation_main_class=1 if sanitation_main_rc==1 | sanitation_main_rc==2 | sanitation_main_rc==3 | sanitation_main_rc==4 | sanitation_main_rc==5
replace sanitation_main_class=2 if sanitation_main_rc==6 | sanitation_main_rc==7 | sanitation_main_rc==8 | sanitation_main_rc==9 | sanitation_main_rc==10
replace sanitation_main_class=3 if sanitation_main_rc==11 | sanitation_main_rc==12 |sanitation_main_rc==13 | sanitation_main_rc==16 | sanitation_main_rc==-99
replace sanitation_main_class=4 if sanitation_main_rc==14 | sanitation_main_rc==15 // includes only bush (#14) and bush_water_body (#15)[in case of ID]; other (#16) and missing (-99) are categorized as unimproved as per DHS criteria
label define sanitation_main_classl 1 "Improved, not shared facility" 2 "Shared Facility" 3 "Non-improved facility" 4 "Open defecation"
label values sanitation_main_class sanitation_main_classl
* Label new sanitation variables
lab var sanitation_main_rc "Main source of sanitation (recode)"
lab var sanitation_main_class "Classification of the main source of sanitation"
* Label new sanitation varibles' response options
*fixed typo changed "sanitation_main_class" to "sanitation_main_classl":
3 "Non-improved facility" 4 "open defecation"
label val sanitation_main_class sanitation_main_classl
label define sanitation_main_rcl 1 "Flush/pour flush to piped sewer system" ///
2 "Flush/pour flush to septic tank" 3 "Ventilated improved pit latrine" 4 "Pit latrine with slab" ///
5 "Composting toilet" 6 "Shared-flush/pour flush to piped sewer system" ///
7 "Shared-flush/ pour flush to septic tank" 8 "Shared-ventilated improved pit latrine" ///
9 "Shared-pit latrine with slab" 10 "Shared-composting toilet" 11 "Flush/ pour flush not to sewer/septic tank" ///
12 "Pit latrine without slab/bucket toilet" 13 "Hanging toilet/hanging latrine" 14 "No facility/bush/field" ///
15 "Bush (water body)" 16 "Other" -99 "Missing"
label values sanitation_main_rc sanitation_main_rcl
* Order new sanitation varriables
order sanitation_main_rc sanitation_main_class, after(sanitation_frequency_cc)
* Drop unnecessary WASH variables
capture drop water_source_list_cc
capture drop water_sources_single
capture drop watersourcelist
*ncode sanitation_where
label define sanitation_where_list 1 "dwelling" 2 "yard" -77 "-77" -99 "-99"
encode sanitation_where, gen(sanitation_wherev2) lab(sanitation_where_list)
label var sanitation_wherev2 "Where is your toilet"
unab vars: *v2
local stubs: subinstr local vars "v2" "", all
foreach var in `stubs'{
rename `var' `var'QZ
order `var'v2, after (`var'QZ)
}
rename *v2 *
save, replace
|