*********************************************************************************************************************** ** Human Development Report Office (HDRO), United Nations Development Programme ** Multidimensional Poverty Index 2020 release ** Methodology developed in partnership with the Oxford Poverty and Human Development Initiative, University of Oxford ************************************************************************************************************************ clear all set more off set maxvar 10000 set mem 500m cap log close *** Working Folder Path *** global path_in "C:\Users\cecilia.calderon\Documents\HDRO_MCC\MPI\MPI 2.0\Peru 2018_ENDES\" global path_out "C:\Users\cecilia.calderon\Documents\HDRO_MCC\MPI\MPI 2.0\Peru 2018_ENDES\" global path_logs "C:\Users\cecilia.calderon\Documents\HDRO_MCC\MPI\MPI 2.0\Peru 2018_ENDES\" global path_qc "C:\Users\cecilia.calderon\Documents\HDRO_MCC\MPI\MPI 2.0\Peru 2018_ENDES\" global path_ado "C:" *** Log file *** log using "$path_logs/per_dhs18_dataprep.log", replace ******************************************************************************** *** Peru DHS 2018 *** ******************************************************************************** use "$path_in/RECH0.dta", clear keep hhid hv008 sort hhid save "$path_out/per18_dinterview.dta", replace ******************************************************************************** *** Step 1: Data preparation *** Selecting variables from KR, BR, IR, & MR recode & merging with PR recode ******************************************************************************** /*Peru DHS 2018: Anthropometric information were recorded for: Children 0-59 months and women 15-49. */ ******************************************************************************** *** Step 1.1 KR - CHILDREN's RECODE (under 5) ******************************************************************************** /* This is to try to recover the day and month of measurement but it seems that children cannot be matched between the REC44 and RECH6 datasets, one has the line number of the household and the other the line number of the birth history questionnaire use "$path_in/REC44.dta", clear *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** b16=child's line number in household *gen g=substr(hhid, -2, 2) *destring g, replace gen g1=substr(caseid, -2, 2) destring g1, replace gen ga=substr(caseid, 1, 10) destring ga, replace format ga %20.0g gen double ind_id = ga*1000+g1*10+hwidx format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id duplicates report ind_id duplicates tag ind_id, gen(duplicates) sort ind_id save "", replace*/ use "$path_in/RECH6.dta", clear *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** b16=child's line number in household *gen g=substr(hhid, -2, 2) *destring g, replace gen double ind_id = hhid*100 + hc0 *Check length of variables for a correct generation of the id variable format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id duplicates report ind_id duplicates tag ind_id, gen(duplicates) tab hc13 if duplicates!=0 *keep if hv120==1 gen child_KR=1 //Generate identification variable for observations in KR recode *** Next, indicate to STATA where the igrowup_restricted.ado file is stored: ***Source of ado file: http://www.who.int/childgrowth/software/en/ adopath + "C:\Users\cecilia.calderon\Documents\HDRO_MCC\MPI\WHO igrowup STATA\" *** We will now proceed to create three nutritional variables: *** weight-for-age (underweight), *** weight-for-height (wasting) *** height-for-age (stunting) /* We use 'reflib' to specify the package directory where the .dta files containing the WHO Child Growth Standards are stored. Note that we use strX to specify the length of the path in string. If the path is long, you may specify str55 or more, so it will run. */ gen str100 reflib="C:\igrowup_stata" lab var reflib "Directory of reference tables" /* We use datalib to specify the working directory where the input STATA dataset containing the anthropometric measurement is stored. */ gen str100 datalib = "$path_out" lab var datalib "Directory for datafiles" /* We use datalab to specify the name that will prefix the output files that will be produced from using this ado file (datalab_z_r_rc and datalab_prev_rc)*/ gen str30 datalab = "children_nutri_per" lab var datalab "Working file" *** Next check the variables that WHO ado needs to calculate the z-scores: *** sex, age, weight, height *** Variable: SEX *** tab hc27, miss //"1" for male ;"2" for female tab hc27, nol clonevar gender = hc27 desc gender tab gender *** Variable: AGE *** tab hc1, miss codebook hc1 //Age is measured in months clonevar age_months = hc1 desc age_months summ age_months gen str6 ageunit = "months" lab var ageunit "Months" ** hc17 and hc18 are missing in the RECH6 database, they are available as hw17 and hw18 in the REC44 database but the sample of children is smaller ** for now, I am using the age in months instead of days *gen mdate = mdy(hc18, hc17, hc19) gen bdate = mdy(hc30, hc16, hc31) if hc16 <= 31 //Calculate birth date in days from date of interview *replace bdate = mdy(hc30, 15, hc31) if hc16 > 31 //If date of birth of child has been expressed as more than 31, we use 15 *gen age = (mdate-bdate)/30.4375 //Calculate age in months with days expressed as decimals *replace age = 0 if age<0 *gen age2=hc1a/30.4375 *compare age age_month *drop age2 *** Variable: BODY WEIGHT (KILOGRAMS) *** codebook hc2, tab (10000) gen weight = hc2/10 //We divide it by 10 in order to express it in kilograms tab hc2 if hc2>990,m nol //Missing values are 994 to 996 replace weight = . if hc2>=990 //All missing values or out of range are replaced as "." tab hc13 hc2 if hc2>=990 | hc2==., miss //hc13: result of the measurement desc weight summ weight *** Variable: HEIGHT (CENTIMETERS) codebook hc3, tab (10000) gen height = hc3/10 //We divide it by 10 in order to express it in centimeters tab hc3 if hc3>9990,m nol //Missing values are 9994 to 9996 replace height = . if hc3>=9990 //All missing values or out of range are replaced as "." tab hc13 hc3 if hc3>=9990 | hc3==., miss desc height summ height *** Variable: MEASURED STANDING/LYING DOWN *** codebook hc15 gen measure = "l" if hc15==1 //Child measured lying down replace measure = "h" if hc15==2 //Child measured standing up replace measure = " " if hc15==9 | hc15==0 | hc15==. //Replace with " " if unknown desc measure tab measure *** Variable: OEDEMA *** lookfor oedema gen str1 oedema = "n" //It assumes no-one has oedema desc oedema tab oedema *** Variable: INDIVIDUAL CHILD SAMPLING WEIGHT *** gen sw = 1 *gen sw=hv005/1000000 //For DHS sample weight has to be divided 1000000 desc sw summ sw /*We now run the command to calculate the z-scores with the adofile */ igrowup_restricted reflib datalib datalab gender age_months ageunit weight height measure oedema sw /*We now turn to using the dta file that was created and that contains the calculated z-scores to create the child nutrition variables following WHO standards */ use "$path_out/children_nutri_per_z_rc.dta", clear *** Standard MPI indicator *** //Takes value 1 if the child is under 2 stdev below the median & 0 otherwise gen underweight = (_zwei < -2.0) replace underweight = . if _zwei == . | _fwei==1 lab var underweight "Child is undernourished (weight-for-age) 2sd - WHO" tab underweight, miss gen stunting = (_zlen < -2.0) replace stunting = . if _zlen == . | _flen==1 lab var stunting "Child is stunted (length/height-for-age) 2sd - WHO" tab stunting, miss gen wasting = (_zwfl < - 2.0) replace wasting = . if _zwfl == . | _fwfl == 1 lab var wasting "Child is wasted (weight-for-length/height) 2sd - WHO" tab wasting, miss //Retain relevant variables: keep ind_id child_KR underweight stunting wasting order ind_id child_KR underweight stunting wasting sort ind_id duplicates report ind_id //Erase files from folder: erase "$path_out/children_nutri_per_z_rc.xls" erase "$path_out/children_nutri_per_prev_rc.xls" *erase "$path_out/children_nutri_per_z_rc.dta" //Save a temp file for merging with PR: save "$path_out/per18_KR.dta", replace ******************************************************************************** *** Step 1.2 BR - BIRTH RECODE *** (All females 15-49 years who ever gave birth) ******************************************************************************** use "$path_in/REC21.dta", clear *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** v003=respondent's line number *gen g=substr(caseid, -4, 2) *destring g, replace *** Generate a household unique key variable at the household level using: ***v001=cluster number ***v002=household number *gen double ind_id = v001*10000000 + v002*1000 + g*100 + v003 *Check length of variables for a correct generation of the id variable gen g1=substr(caseid, -2, 2) destring g1, replace gen ga=substr(caseid, 1, 10) destring ga, replace format ga %20.0g gen double ind_id = ga*100+g1 format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id duplicates report ind_id bidx desc b3 b7 gen date_death = b3 + b7 //Date of death = date of birth (b3) + age at death (b7) gen mdead_survey = bcmc - date_death //Months dead from survey = Date of interview (bcmc) - date of death gen ydead_survey = mdead_survey/12 //Years dead from survey codebook b5, tab (10) gen child_died = 1 if b5==0 //Redefine the coding and labels (1=child dead; 0=child alive) replace child_died = 0 if b5==1 replace child_died = . if b5==. label define lab_died 1 "child has died" 0 "child is alive" label values child_died lab_died tab b5 child_died, miss /*NOTE: For each woman, sum the number of children who died and compare to the number of sons/daughters whom they reported have died */ bysort ind_id: egen tot_child_died = sum(child_died) *egen tot_child_died_2 = rsum(v206 v207) //v206: sons who have died //v207: daughters who have died *compare tot_child_died tot_child_died_2 //In Peru DHS 2018, these figures are identical replace child_died=0 if b7>=216 & b7<. /* counting only deaths of children <18y (216 months) */ bysort ind_id: egen tot_child_died_5y=sum(child_died) if ydead_survey<=5 /*For each woman, sum the number of children who died in the past 5 years prior to the interview date */ replace tot_child_died_5y=0 if tot_child_died_5y==. & tot_child_died>=0 & tot_child_died<. /*All children who are alive and died longer than 5 years from the interview date are replaced as '0'*/ replace tot_child_died_5y=. if child_died==1 & ydead_survey==. //Replace as '.' if there is no information on when the child died tab tot_child_died tot_child_died_5y, miss bysort ind_id: egen child_died_per_wom = max(tot_child_died) lab var child_died_per_wom "Total child death for each women (birth recode)" bysort ind_id: egen child_died_per_wom_5y = max(tot_child_died_5y) lab var child_died_per_wom_5y "Total child death for each women in the last 5 years (birth recode)" //Keep one observation per women bysort ind_id: gen id=1 if _n==1 keep if id==1 drop id duplicates report ind_id gen women_BR = 1 //Identification variable for observations in BR recode //Retain relevant variables keep ind_id women_BR b16 child_died_per_wom child_died_per_wom_5y b7 order ind_id women_BR b16 child_died_per_wom child_died_per_wom_5y b7 sort ind_id //Save a temp file for merging with PR: save "$path_out/per18_BR.dta", replace ******************************************************************************** *** Step 1.3 IR - WOMEN's RECODE *** (All eligible females 12-49 years in the household) ******************************************************************************** use "$path_in/RE223132.dta", clear ** 38,777 women aged 12-49y gen g1=substr(caseid, -2, 2) destring g1, replace gen ga=substr(caseid, 1, 10) destring ga, replace format ga %20.0g gen double ind_id = ga*100+g1 format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id duplicates report ind_id *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** v003=respondent's line number *gen g=substr(caseid, -4, 2) *destring g, replace *gen double ind_id = v001*10000000 + v002*1000 + g*100 + v003 *Check length of variables for a correct generation of the id variable* *format ind_id %20.0g gen women_IR=1 //Identification variable for observations in IR recode keep ind_id women_IR /*v003 v005 v012*/ v201 v206 v207 order ind_id women_IR /*v003 v005 v012*/ v201 v206 v207 sort ind_id //Save a temp file for merging with PR: save "$path_out/per18_IR.dta", replace ******************************************************************************** *** Step 1.4 IR - WOMEN'S RECODE *** (Girls 12-19 years in the household) ******************************************************************************** use "$path_in/RECH5.dta", clear ** 41,334 women aged 12-49y *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** v003=respondent's line number *gen g=substr(hhid, -2, 2) *destring g, replace *gen double ind_id = hv001*10000000 + hv002*1000 + g*100 + hvidx *Check length of variables for a correct generation of the id variable *format ind_id %20.0g sort hhid merge hhid using "$path_out/per18_dinterview.dta" drop if _merge==2 drop _merge gen double ind_id = hhid*100 + ha0 format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id duplicates report ind_id keep if ha1>=12 & ha1<=19 ***Variables required to calculate the z-scores to produce BMI-for-age: *** Variable: SEX *** gen gender=2 *** Variable: AGE IN MONTHS *** *compare hv807c hv008 /* date of biomarker vs date of interview, they should be identical */ gen age_month=hv008-ha32 lab var age_month "Age in months, individuals 12-19 years" *** Variable: AGE UNIT *** gen str6 ageunit = "months" lab var ageunit "Months" *** Variable: BODY WEIGHT (KILOGRAMS) *** codebook ha2, tab (999) gen weight = ha2/10 //We divide it by 10 in order to express it in kilograms replace weight = . if ha2>=9990 //All missing values or out of range are replaced as "." summ weight *** Variable: HEIGHT (CENTIMETERS) codebook ha3, tab (999) gen height = ha3/10 //We divide it by 10 in order to express it in centimeters replace height = . if ha3>=9990 //All missing values or out of range are replaced as "." summ height *** Variable: OEDEMA lookfor oedema gen oedema = "n" tab oedema *** Variable: SAMPLING WEIGHT *** gen sw = 1 *gen sw = hv005/1000000 //For DHS sample weight has to be divided 1000000* summ sw *** Next, indicate to STATA where the igrowup_restricted.ado file is stored: adopath + "C:\Users\cecilia.calderon\Documents\HDRO_MCC\MPI\WHO 2007 stata\" /* We use 'reflib' to specify the package directory where the .dta files containing the WHO Growth reference are stored. Note that we use strX to specity the length of the path in string. */ gen str100 reflib="C:\WHO 2007 Stata" lab var reflib "Directory of reference tables" /* We use datalib to specify the working directory where the input STATA data set containing the anthropometric measurement is stored. */ gen str100 datalib = "$path_out" lab var datalib "Directory for datafiles" /* We use datalab to specify the name that will prefix the output files that will be produced from using this ado file*/ gen str30 datalab = "girl_nutri_per" lab var datalab "Working file" /*We now run the command to calculate the z-scores with the adofile */ who2007 reflib datalib datalab gender age_month ageunit weight height oedema sw /*We now turn to using the dta file that was created and that contains the calculated z-scores to compute BMI-for-age*/ use "$path_out/girl_nutri_per_z.dta", clear gen z_bmi = _zbfa replace z_bmi = . if _fbfa==1 lab var z_bmi "z-score bmi-for-age WHO" gen low_bmiage = (z_bmi < -2.0) /*Takes value 1 if BMI-for-age is under 2 stdev below the median & 0 otherwise */ replace low_bmiage = . if z_bmi==. lab var low_bmiage "Teenage low bmi 2sd - WHO" gen teen_IR=1 //Identification variable for observations in IR recode (only 12-19 years) //Retain relevant variables: keep ind_id teen_IR age_month low_bmiage order ind_id teen_IR age_month low_bmiage sort ind_id //Erase files from folder: erase "$path_out/girl_nutri_per_z.xls" erase "$path_out/girl_nutri_per_prev.xls" erase "$path_out/girl_nutri_per_z.dta" //Save a temp file for merging with PR: save "$path_out/per18_IR_girls.dta", replace ***************** ** Women 20-49 ** ***************** use "$path_in/RECH5.dta", clear ** 41,334 women aged 12-49y sort hhid merge hhid using "$path_out/per18_dinterview.dta" drop if _merge==2 drop _merge gen double ind_id = hhid*100 + ha0 format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id duplicates report ind_id ***Variables required to calculate the z-scores to produce BMI-for-age: *** Variable: SEX *** gen gender=2 *** Variable: AGE IN MONTHS *** *compare hv807c hv008 /* date of biomarker vs date of interview, they should be identical */ gen age_month=hv008-ha32 lab var age_month "Age in months, individuals 12-19 years" *keep if (ha1>=20 & ha1<=49) | (age_month>=229 & age_month<.) keep hhid ind_id ha13 ha40 ha2 ha3 ha0 ha1 sort hhid save "$path_out/per18_IR_women20_49.dta", replace ******************************************************************************** *** Step 1.5 MR - MEN'S RECODE ***(All eligible man: 15-49 years in the household) ******************************************************************************** ** no MR recode ** HH variables ** use "$path_in/RECH0.dta", clear keep hhid hv008 hv009 hv010 hv011 hv012 hv013 hv014 hv015 hv017 hv020 hv021 hv023 hv024 hv025 hv026 hv027 hv028 hv035 hv040 hv041 hv042 hv043 hv044 hv005 sort hhid gen hh_id = hhid save "$path_out/per18_hh.dta", replace use "$path_in/RECH23.dta", clear keep hhid hv201 hv202 hv204 hv205 hv206 hv207 hv208 hv209 hv210 hv211 hv212 hv213 hv214 hv215 hv216 hv217 hv218 hv221 hv225 hv226 hv235 hv236 hv237 hv238 hv243a hv243b hv243c hv243d hv244 sh56 sh58 sh59 sh61j sh61p sh61q sh61s sh63 sh64 sh70 sh77f hv270 hv271 sort hhid gen hh_id = hhid save "$path_out/per18_ls.dta", replace ******************************************************************************** *** Step 1.7 PR - HOUSEHOLD MEMBER'S RECODE ******************************************************************************** use "$path_in/RECH1.dta", clear gen cty = "Peru" gen ccty = "PER" gen year = "2018" gen survey = "DHS" gen ccnum = 604 *** Generate a household unique key variable at the household level using: ***hv001=cluster number ***hv002=household number *gen g=substr(hhid, -2, 2) *destring g, replace *gen double hh_id = hv001*10000000 + hv002*1000 + g*100 *Check length of variables for a correct generation of the id variable gen hh_id = hhid format hh_id %20.0g label var hh_id "Household ID" codebook hh_id *** Generate individual unique key variable required for data merging using: *** hv001=cluster number; *** hv002=household number; *** hvidx=respondent's line number. *gen double ind_id = hv001*10000000 + hv002*1000 + g*100 + hvidx *Check length of variables for a correct generation of the id variable gen double ind_id = hhid*100+ hvidx format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id sort hh_id ind_id ******************************************************************************** *** 1.8 DATA MERGING ******************************************************************************** *** Merging BR Recode ***************************************** merge 1:1 ind_id using "$path_out/per18_BR.dta" drop _merge erase "$path_out/per18_BR.dta" *** Merging IR Recode ***************************************** merge 1:1 ind_id using "$path_out/per18_IR.dta" tab women_IR hv117, miss col *tab ha65 if hv117==1 & women_IR ==., miss //Total number of eligible women not interviewed *tab ha65 ha13 if women_IR == . & hv117==1, miss drop _merge erase "$path_out/per18_IR.dta" *** Merging IR Recode: 15-19 years girls ***************************************** merge 1:1 ind_id using "$path_out/per18_IR_girls.dta" tab teen_IR hv117 if hv105>=15 & hv105<=19, miss col *tab ha65 if hv117==1 & teen_IR ==. & (hv105>=15 & hv105<=19), miss //Total number of eligible girls not interviewed *tab ha65 ha13 if hv117==1 & teen_IR ==. & (hv105>=15 & hv105<=19), miss *tab ha40 if ha65==1 & ha13 ==0 & hv117==1 & teen_IR ==. & (hv105>=15 & hv105<=19), miss drop _merge erase "$path_out/per18_IR_girls.dta" merge 1:1 ind_id using "$path_out/per18_IR_women20_49.dta" drop _merge *** Merging KR Recode ***************************************** merge 1:1 ind_id using "$path_out/per18_KR.dta" count if b16==0 & child_KR==1 //The children without household line are unique to the KR recode drop _merge erase "$path_out/per18_KR.dta" sort hhid ** Household variables ** merge hhid using "$path_out/per18_hh.dta" ta _merge ta hv015 _merge,m drop if _merge==2 /* drop not completed interviews */ drop _merge sort hhid merge hhid using "$path_out/per18_ls.dta" ta _merge drop if _merge==2 /* drop not completed interviews */ drop _merge ******************************************************************************** *** Step 1.9 KEEPING ONLY DE JURE HOUSEHOLD MEMBERS *** ******************************************************************************** //Permanent (de jure) household members clonevar resident = hv102 codebook resident, tab (10) label var resident "Permanent (de jure) household member" drop if resident!=1 tab resident, miss /*Note: The Global MPI is based on de jure (permanent) household members only. As such, non-usual residents will be excluded from the sample.*/ ******************************************************************************** *** 1.10 CONTROL VARIABLES ******************************************************************************** /* Households are identified as having 'no eligible' members if there are no applicable population, that is, children 0-5 years, adult women 15-49 years or men 15-49 years. These households will not have information on relevant indicators of health. As such, these households are considered as non-deprived in those relevant indicators.*/ *** No Eligible Women 15-49 years ***************************************** *replace hv117 = 1 if women_IR==1 & hv117==0 /* these are women 12-14 who were interviewed for fertility and mortality, I am making them eligible because they provided information */ *gen fem_eligible = (hv117==1) gen fem_eligible = (hv105>=12 & hv105<=49 & hv104==2 & hv103==1) bys hh_id: egen hh_n_fem_eligible = sum(fem_eligible) //Number of eligible women for interview in the hh gen no_fem_eligible = (hh_n_fem_eligible==0) //Takes value 1 if the household had no eligible females for an interview lab var no_fem_eligible "Household has no eligible women" tab no_fem_eligible, miss *** No Eligible Men 15-49 years ***************************************** gen male_eligible = (hv118==1) bys hh_id: egen hh_n_male_eligible = sum(male_eligible) //Number of eligible men for interview in the hh gen no_male_eligible = (hh_n_male_eligible==0) //Takes value 1 if the household had no eligible males for an interview lab var no_male_eligible "Household has no eligible man" tab no_male_eligible, miss *** No Eligible Children 0-5 years ***************************************** gen child_eligible = (hv120==1) bys hh_id: egen hh_n_children_eligible = sum(child_eligible) //Number of eligible children for anthropometrics gen no_child_eligible = (hh_n_children_eligible==0) //Takes value 1 if there were no eligible children for anthropometrics lab var no_child_eligible "Household has no children eligible" tab no_child_eligible, miss *** No Eligible Women and Men *********************************************** /*NOTE: In the DHS datasets, we use this variable as a control variable for the child mortality indicator if mortality data was collected from women and men. If child mortality was only colelcted from women, the we use 'no_fem_eligible' as the eligibility criteria */ gen no_adults_eligible = (no_fem_eligible==1 & no_male_eligible==1) //Takes value 1 if the household had no eligible men & women for an interview lab var no_adults_eligible "Household has no eligible women or men" tab no_adults_eligible, miss *** No Eligible Children and Women *********************************************** /*NOTE: In the DHS datasets, we use this variable as a control variable for the nutrition indicator if nutrition data is present for children and women.*/ gen no_child_fem_eligible = (no_child_eligible==1 & no_fem_eligible==1) lab var no_child_fem_eligible "Household has no children or women eligible" tab no_child_fem_eligible, miss *** No Eligible Women, Men or Children *********************************************** /*NOTE: In the DHS datasets, we use this variable as a control variable for the nutrition indicator if nutrition data is present for children, women and men. */ gen no_eligibles = (no_fem_eligible==1 & no_male_eligible==1 & no_child_eligible==1) lab var no_eligibles "Household has no eligible women, men, or children" tab no_eligibles, miss *** No Eligible Subsample ***************************************** /*hv042 (household selected for hemoglobin) is essentially a variable that indicates whether there is selection of a subsample for anthropometric data. */ gen hem_eligible =(hv042==1) bys hh_id: egen hh_n_hem_eligible = sum(hem_eligible) gen no_hem_eligible = (hh_n_hem_eligible==0) //Takes value 1 if the HH had no eligible females for hemoglobin test lab var no_hem_eligible "Household has no eligible individuals for hemoglobin measurements" tab no_hem_eligible, miss drop fem_eligible hh_n_fem_eligible male_eligible hh_n_male_eligible /// child_eligible hh_n_children_eligible hem_eligible hh_n_hem_eligible sort hh_id ind_id ******************************************************************************** *** 1.11 SUBSAMPLE VARIABLE *** ******************************************************************************** /* In Peru DHS 2018, height and weight measurements were collected from children (0-5), women (15-49) and men (15-49) for 50% of the sample. */ gen subsample=hv042 label var subsample "Households selected as part of nutrition subsample" tab subsample, miss ******************************************************************************** *** 1.12 RENAMING DEMOGRAPHIC VARIABLES *** ******************************************************************************** //Sample weight desc hv005 clonevar weight = hv005 label var weight "Sample weight" //Area: urban or rural desc hv025 codebook hv025, tab (5) clonevar area = hv025 replace area=0 if area==2 label define lab_area 1 "urban" 0 "rural" label values area lab_area label var area "Area: urban-rural" //Relationship to the head of household clonevar relationship = hv101 codebook relationship, tab (20) recode relationship (1=1)(2=2)(3=3)(11=3)(4/10=4)(12=5)(15=6) label define lab_rel 1"head" 2"spouse" 3"child" 4"extended family" /// 5"not related" 6"maid" label values relationship lab_rel label var relationship "Relationship to the head of household" tab hv101 relationship, miss //Sex of household member codebook hv104, tab (10) clonevar sex = hv104 label var sex "Sex of household member" //Age of household member codebook hv105, tab (1000) clonevar age = hv105 replace age = . if age>=98 label var age "Age of household member" //Age group recode age (0/4 = 1 "0-4")(5/9 = 2 "5-9")(10/14 = 3 "10-14") /// (15/17 = 4 "15-17")(18/59 = 5 "18-59")(60/max=6 "60+"), gen(agec7) lab var agec7 "age groups (7 groups)" recode age (0/9 = 1 "0-9") (10/17 = 2 "10-17")(18/59 = 3 "18-59") /// (60/max=4 "60+"), gen(agec4) lab var agec4 "age groups (4 groups)" //Total number of de jure hh members in the household gen member = 1 bysort hh_id: egen hhsize = sum(member) label var hhsize "Household size" tab hhsize, miss drop member //Subnational region lookfor region codebook hv024, tab (100) gen region = hv024 lab var region "Region for subnational decomposition" tab hv024 region, miss ******************************************************************************** *** Step 2 Data preparation *** *** Standardization of the 10 Global MPI indicators *** Identification of non-deprived & deprived individuals ******************************************************************************** ******************************************************************************** *** Step 2.1 Years of Schooling *** ******************************************************************************** ** school entrance age: 6y ** duration of primary: 6y codebook hv108, tab(30) clonevar eduyears = hv108 *total number of years of education replace eduyears = . if eduyears>30 *recode any unreasonable years of highest education as missing value replace eduyears = . if eduyears>=age & age>0 replace eduyears = 0 if age < 10 replace eduyears = 0 if (age==10 | age==11) & eduyears < 6 /*The variable "eduyears" was replaced with a '0' given that the criteria for this indicator is household member aged 12 years or older */ replace eduyears=6 if age>=10 & age<. & (hv106==2 | hv106==3) & (hv108==. | hv108==98 | hv108==99) /* there a few people with missing years of schooling but according to hv106 we know there are in secondary or higher so they completed at least 6 yrs of schooling, I am imputing them a value of 6 years since this is sufficient for the MPI to be considered not deprived */ /*A control variable is created on whether there is information on years of education for at least 2/3 of the household members. */ gen temp = 1 if (eduyears!=. & (age>=12 & age!=.)) | (((age==10 | age==11) & eduyears>=6 & eduyears<.)) bysort hh_id: egen no_missing_edu = sum(temp) /*Total household members who are 12 years and older with no missing years of education but recognizing as an achievement if the member is 10 or 11 and already completed 6 yrs of schooling */ gen temp2 = 1 if (age>=12 & age!=.) | (((age==10 | age==11) & eduyears>=6 & eduyears<.)) bysort hh_id: egen hhs = sum(temp2) *Total number of household members who are 12 years and older replace no_missing_edu = no_missing_edu/hhs replace no_missing_edu = (no_missing_edu>=2/3) /*Identify whether there is information on years of education for at least 2/3 of the household members aged 12 years and older */ tab no_missing_edu, miss label var no_missing_edu "No missing edu for at least 2/3 of the HH members aged 12 years & older" drop temp temp2 hhs /*The entire household is considered deprived if no household member aged 12 years or older has completed SIX years of schooling. */ gen years_edu6 = (eduyears>=6) /* The years of schooling indicator takes a value of "1" if at least someone in the hh has reported 6 years of education or more */ replace years_edu6 = . if eduyears==. bysort hh_id: egen hh_years_edu6_1 = max(years_edu6) gen hh_years_edu6 = (hh_years_edu6_1==1) replace hh_years_edu6 = . if hh_years_edu6_1==. replace hh_years_edu6 = . if hh_years_edu6==0 & no_missing_edu==0 lab var hh_years_edu6 "Household has at least one member with 6 years of edu" ******************************************************************************** *** Step 2.2 Child School Attendance *** ******************************************************************************** codebook hv121, tab (10) clonevar attendance = hv121 recode attendance (2=1) codebook attendance, tab (10) replace attendance = 0 if (attendance==9 | attendance==.) & hv109==0 /*In some countries, they don't assess attendance for those with no educational attainment. These are replaced with a '0' */ replace attendance = . if attendance==9 & hv109!=0 //Replace missing values *** Old & New Standard MPI *** ******************************************************************* /*The entire household is considered deprived if any school-aged child is not attending school up to class 8. */ gen child_schoolage = (age>=6 & age<=14) /* Note: In Peru, the official school entrance age is 6 years. So, age range is 6-14 (=6+8) Source: http://data.uis.unesco.org/?ReportId=163. */ /*A control variable is created on whether there is no information on school attendance for at least 2/3 of the school age children */ count if child_schoolage==1 & attendance==. //Understand how many eligible school aged children are not attending school gen temp = 1 if child_schoolage==1 & attendance!=. /*Generate a variable that captures the number of eligible school aged children who are attending school */ bysort hh_id: egen no_missing_atten = sum(temp) /*Total school age children with no missing information on school attendance */ gen temp2 = 1 if child_schoolage==1 bysort hh_id: egen hhs = sum(temp2) //Total number of household members who are of school age replace no_missing_atten = no_missing_atten/hhs replace no_missing_atten = (no_missing_atten>=2/3) /*Identify whether there is missing information on school attendance for more than 2/3 of the school age children */ tab no_missing_atten, miss label var no_missing_atten "No missing school attendance for at least 2/3 of the school aged children" drop temp temp2 hhs bysort hh_id: egen hh_children_schoolage = sum(child_schoolage) replace hh_children_schoolage = (hh_children_schoolage>0) //Control variable: //It takes value 1 if the household has children in school age lab var hh_children_schoolage "Household has children in school age" gen child_not_atten = (attendance==0) if child_schoolage==1 replace child_not_atten = . if attendance==. & child_schoolage==1 bysort hh_id: egen any_child_not_atten = max(child_not_atten) gen hh_child_atten = (any_child_not_atten==0) replace hh_child_atten = . if any_child_not_atten==. replace hh_child_atten = 1 if hh_children_schoolage==0 replace hh_child_atten = . if hh_child_atten==1 & no_missing_atten==0 /*If the household has been intially identified as non-deprived, but has missing school attendance for at least 2/3 of the school aged children, then we replace this household with a value of '.' because there is insufficient information to conclusively conclude that the household is not deprived */ lab var hh_child_atten "Household has all school age children up to class 8 in school" tab hh_child_atten, miss /*Note: The indicator takes value 1 if ALL children in school age are attending school and 0 if there is at least one child not attending. Households with no children receive a value of 1 as non-deprived. The indicator has a missing value only when there are all missing values on children attendance in households that have children in school age. */ ******************************************************************************** *** Step 2.3 Nutrition *** ******************************************************************************** ******************************************************************************** *** Step 2.3a Adult Nutrition *** ******************************************************************************** /*Note: Peru DHS 2018 has anthropometric data for women 12-49 years */ lookfor body mass codebook ha40 *gen hb40 = . foreach var in ha40 { gen inf_`var' = 1 if `var'!=. bys sex: tab age inf_`var' drop inf_`var' } *** ELIGIBILITY FOR BMI *** ** WOMEN gen fem_eligible_bmi = 1 if ha13<. replace fem_eligible_bmi = 0 if ha13==. replace fem_eligible_bmi = 0 if age>49 & age<. bys hh_id: egen hh_n_fem_eligible_bmi = sum(fem_eligible_bmi) //Number of eligible women for BMI in the hh gen no_fem_eligible_bmi = (hh_n_fem_eligible_bmi==0) //Takes value 1 if the household had no eligible females for an interview lab var no_fem_eligible_bmi "Household has no eligible women" tab no_fem_eligible_bmi, miss *** No Eligible Women, Men or Children for BMI *********************************************** /*NOTE: In the DHS datasets, we use this variable as a control variable for the nutrition indicator if nutrition data is present for children, women and men. */ gen no_eligibles_bmi = (no_fem_eligible_bmi==1 & no_child_eligible==1) lab var no_eligibles_bmi "Household has no eligible women, men, or children for BMI" tab no_eligibles_bmi, miss *** BMI Indicator for Women 15-49 years *** ******************************************************************* gen f_bmi = ha40/100 //Low BMI of women 15-49 years lab var f_bmi "Women's BMI" gen f_low_bmi = (f_bmi<18.5) replace f_low_bmi = . if f_bmi==. | f_bmi>=99.90 replace f_low_bmi = . if age>49 & age<. lab var f_low_bmi "BMI of women < 18.5" bysort hh_id: egen low_bmi = max(f_low_bmi) gen hh_no_low_bmi = (low_bmi==0) /*Under this section, households take a value of '1' if no women in the household has low bmi */ replace hh_no_low_bmi = . if low_bmi==. /*Under this section, households take a value of '.' if there is no information from eligible women*/ replace hh_no_low_bmi = 1 if no_fem_eligible_bmi==1 /*Under this section, households that don't have eligible female population are identified as non-deprived in nutrition. */ drop low_bmi lab var hh_no_low_bmi "Household has no adult with low BMI" tab hh_no_low_bmi, miss /*Figures are exclusively based on information from eligible adult women (12-49 years) */ *** BMI Indicator for Men 15-49 years *** ******************************************************************* gen m_bmi = . lab var m_bmi "Male's BMI" gen m_low_bmi = . lab var m_low_bmi "BMI of male < 18.5" bysort hh_id: egen low_bmi = max(m_low_bmi) replace hh_no_low_bmi = 0 if low_bmi==1 /*Under this section, households take a value of '0' if there's any male with low bmi*/ replace hh_no_low_bmi = 1 if low_bmi==0 & hh_no_low_bmi==. /*Under this section, households take a value of '1' if no male has low BMI & info is missing for women */ drop low_bmi tab hh_no_low_bmi if subsample==1, miss /*Figures are based on information from eligible adult women and eligible men. For countries that do not have male recode or lack anthropometric data for men, then the figures are exclusively from women. */ *** BMI-for-age for individuals 15-19 years and BMI for individuals 20-49 years *** ******************************************************************* *replace age_month=age_month_boys if age_month_boys<. & age_month==. *replace low_bmiage=low_bmiage_boys if low_bmiage_boys<. & low_bmiage==. gen low_bmi_byage = 0 lab var low_bmi_byage "Individuals with low BMI or BMI-for-age" replace low_bmi_byage = 1 if f_low_bmi==1 //Replace variable "low_bmi_byage = 1" if eligible women have low BMI /*Note: The following command will result in 0 changes when there is no BMI information from men*/ replace low_bmi_byage = 1 if low_bmi_byage==0 & m_low_bmi==1 //Replace variable "low_bmi_byage = 1" if eligible men have low BMI /*Note: The following command replaces BMI with BMI-for-age for those between the age group of 15-19 by their age in months where information is available */ //Replacement for girls: replace low_bmi_byage = 1 if low_bmiage==1 & age_month!=. replace low_bmi_byage = 0 if low_bmiage==0 & age_month!=. //Replacements for boys: *replace low_bmi_byage = 1 if low_bmiage_b==1 & age_month_b!=. *replace low_bmi_byage = 0 if low_bmiage_b==0 & age_month_b!=. /*Note: The following control variable is applied when there is BMI information for women and men, as well as BMI-for-age for teenagers */ replace low_bmi_byage = . if f_low_bmi==. & low_bmiage==. bysort hh_id: egen low_bmi = max(low_bmi_byage) gen hh_no_low_bmiage = (low_bmi==0) /*Households take a value of '1' if all eligible adults and teenagers in the household has normal bmi or bmi-for-age */ replace hh_no_low_bmiage = . if low_bmi==. /*Households take a value of '.' if there is no information from eligible individuals in the household */ replace hh_no_low_bmiage = 1 if no_fem_eligible_bmi==1 /*Households take a value of '1' if there is no eligible population.*/ drop low_bmi lab var hh_no_low_bmiage "Household has no adult with low BMI or BMI-for-age" tab hh_no_low_bmi if subsample==1, m tab hh_no_low_bmiage if subsample==1, m /*NOTE that hh_no_low_bmi takes value 1 if: (a) no any eligible adult in the household has (observed) low BMI or (b) there are no eligible adults in the household. One has to check and adjust the dofile so all people who are eligible and/or measured are included. It is particularly important to check if male are measured and what age group among males and females. The variable takes values 0 for those households that have at least one adult with observed low BMI. The variable has a missing value only when there is missing info on BMI for ALL eligible adults in the household */ ******************************************************************************** *** Step 2.3b Child Nutrition *** ******************************************************************************** *** Child Underweight Indicator *** ************************************************************************ bysort hh_id: egen temp = max(underweight) gen hh_no_underweight = (temp==0) //Takes value 1 if no child in the hh is underweight replace hh_no_underweight = . if temp==. replace hh_no_underweight = 1 if no_child_eligible==1 /* Households with no eligible children will receive a value of 1 */ lab var hh_no_underweight "Household has no child underweight - 2 stdev" drop temp *** Child Stunting Indicator *** ************************************************************************ bysort hh_id: egen temp = max(stunting) gen hh_no_stunting = (temp==0) //Takes value 1 if no child in the hh is stunted replace hh_no_stunting = . if temp==. replace hh_no_stunting = 1 if no_child_eligible==1 lab var hh_no_stunting "Household has no child stunted - 2 stdev" drop temp *** Child Either Stunted or Underweight Indicator *** ************************************************************************ gen uw_st = 1 if stunting==1 | underweight==1 replace uw_st = 0 if stunting==0 & underweight==0 replace uw_st = . if stunting==. & underweight==. bysort hh_id: egen temp = max(uw_st) gen hh_no_uw_st = (temp==0) //Takes value 1 if no child in the hh is underweight or stunted replace hh_no_uw_st = . if temp==. replace hh_no_uw_st = 1 if no_child_eligible==1 //Households with no eligible children will receive a value of 1 lab var hh_no_uw_st "Household has no child underweight or stunted" drop temp ******************************************************************************** *** Step 2.3c Household Nutrition Indicator *** ******************************************************************************** /* The indicator takes value 1 if there is no low BMI-for-age among teenagers, no low BMI among adults or no children under 5 underweight or stunted. It also takes value 1 for the households that have no eligible adult AND no eligible children. The indicator takes a value of missing "." only if all eligible adults and eligible children have missing information in their respective nutrition variable. */ gen hh_nutrition_uw_st = 1 if (hh_no_low_bmiage==1 & hh_no_uw_st==1) | (hh_no_low_bmiage==. & hh_no_uw_st==1 & no_child_eligible==0) | (hh_no_low_bmiage==1 & hh_no_uw_st==. & no_fem_eligible_bmi==0) replace hh_nutrition_uw_st = 0 if hh_no_low_bmiage==0 | hh_no_uw_st==0 replace hh_nutrition_uw_st = . if hh_no_low_bmiage==. & hh_no_uw_st==. replace hh_nutrition_uw_st = 1 if no_eligibles_bmi==1 /*If country have collected anthropometric data from women, child 0-5 & a subsample of men, we only replace households which do not have any of these three applicable population as non-deprived*/ lab var hh_nutrition_uw_st "Household has no child underweight/stunted or adult deprived by BMI/BMI-for-age" ******************************************************************************** *** Step 2.4 Child Mortality *** ******************************************************************************** codebook v206 v207 //v206 or mv206: number of sons who have died //v207 or mv207: number of daughters who have died //Total child mortality reported by eligible women egen temp_f = rowtotal(v206 v207), missing replace temp_f = 0 if v201==0 bysort hh_id: egen child_mortality_f = sum(temp_f), missing lab var child_mortality_f "Occurrence of child mortality reported by women" tab child_mortality_f, miss drop temp_f //Total child mortality reported by eligible men /*egen temp_m = rowtotal(mv206 mv207), missing replace temp_m = 0 if mv201==0 bysort hh_id: egen child_mortality_m = sum(temp_m), missing lab var child_mortality_m "Occurrence of child mortality reported by men" tab child_mortality_m, miss drop temp_m*/ egen child_mortality = rowmax(child_mortality_f) lab var child_mortality "Total child mortality within household reported by women & men" tab child_mortality if subsample==1, miss /*Deprived if any children died in the household */ ************************************************************************ gen hh_mortality = (child_mortality==0) /*Household is replaced with a value of "1" if there is no incidence of child mortality*/ replace hh_mortality = . if child_mortality==. replace hh_mortality = 1 if no_fem_eligible==1 /*Change eligibility to "no_fem_eligible==1" if child mortality indicator is constructed solely using information from women */ lab var hh_mortality "Household had no child mortality" tab hh_mortality if subsample==1, miss /*Deprived if any children died in the household in the last 5 years from the survey year */ ************************************************************************ tab child_died_per_wom_5y, miss /* The 'child_died_per_wom_5y' variable was constructed in Step 1.2 using information from individual women who ever gave birth in the BR file. The missing values represent eligible woman who have never ever given birth and so are not present in the BR file. But these 'missing women' may be living in households where there are other women with child mortality information from the BR file. So at this stage, it is important that we aggregate the information that was obtained from the BR file at the household level. This ensures that women who were not present in the BR file is assigned with a value, following the information provided by other women in the household.*/ replace child_died_per_wom_5y = 0 if v201==0 /*Assign a value of "0" for: - all eligible women who never ever gave birth */ replace child_died_per_wom_5y = 0 if no_fem_eligible==1 /*Assign a value of "0" for: - individuals living in households that have non-eligible women */ bysort hh_id: egen child_mortality_5y = sum(child_died_per_wom_5y), missing replace child_mortality_5y = 0 if child_mortality_5y==. & child_mortality==0 /*Replace all households as 0 death if women has missing value and men reported no death in those households */ label var child_mortality_5y "Total child mortality within household past 5 years reported by women" tab child_mortality_5y if subsample==1, miss /* The new standard MPI indicator takes a value of "1" if eligible women within the household reported no child mortality or if any child died longer than 5 years from the survey year. The indicator takes a value of "0" if women in the household reported any child mortality in the last 5 years from the survey year. Households were replaced with a value of "1" if eligible men within the household reported no child mortality in the absence of information from women. The indicator takes a missing value if there was missing information on reported death from eligible individuals. */ gen hh_mortality_5y = (child_mortality_5y==0) replace hh_mortality_5y = . if child_mortality_5y==. tab hh_mortality_5y if subsample==1, miss lab var hh_mortality_5y "Household had no child mortality in the last 5 years" ******************************************************************************** *** Step 2.5 Electricity *** ******************************************************************************** /*Members of the household are considered deprived if the household has no electricity */ clonevar electricity = hv206 codebook electricity, tab (10) label var electricity "Household has electricity" ******************************************************************************** *** Step 2.6 Sanitation *** ******************************************************************************** /*Members of the household are considered deprived if the household's sanitation facility is not improved, according to MDG guidelines, or it is improved but shared with other household. In cases of mismatch between the MDG guideline and country report, we followed the country report. */ clonevar toilet = hv205 codebook toilet, tab(30) codebook hv225, tab(30) clonevar shared_toilet = hv225 //0=no;1=yes;.=missing gen toilet_mdg = 1 if (toilet==11 | toilet==12 | toilet==21 | toilet==22) & shared_toilet!=1 replace toilet_mdg = 0 if toilet==23 | toilet==24 | toilet==31 | toilet==32 | toilet==96 replace toilet_mdg = 0 if shared_toilet==1 *replace toilet_mdg = . if toilet==. | toilet==99 lab var toilet_mdg "Household has improved sanitation with MDG Standards" tab toilet toilet_mdg, miss /* Dentro de la vivienda | 85,514 58.86 58.86 11 y Fuera de la vivienda | 6,420 4.42 63.28 12 y Letrina ventilada | 8,155 5.61 68.89 21 y Pozo septico | 5,785 3.98 72.87 22 y Letrina - ciego o negro | 27,840 19.16 92.03 23 n Letrina sobre rio o lago | 50 0.03 92.07 24 n Rio o canal | 259 0.18 92.25 31 n Sin servicio | 10,921 7.52 99.76 32 n OTRO | 344 0.24 100.00 96 n -------------------------+----------------------------------- Total | 145,288 100.00 */ ******************************************************************************** *** Step 2.7 Drinking Water *** ******************************************************************************** /*Members of the household are considered deprived if the household does not have access to safe drinking water according to MDG guidelines, or safe drinking water is more than a 30-minute walk from home roundtrip. In cases of mismatch between the MDG guideline and country report, we followed the country report.*/ clonevar water = hv201 clonevar timetowater = hv204 codebook water, tab(100) clonevar ndwater = hv202 /* Red dentro de vivienda | 105,449 72.58 72.58 11 y Red fuera de la vivienda pero dentro de | 6,821 4.69 77.27 12 y Pilon, grifo publico | 2,974 2.05 79.32 13 y Pozo dentro de vivienda | 2,336 1.61 80.93 21 n Pozo publico | 1,133 0.78 81.71 22 n Manantial | 2,413 1.66 83.37 41 n Rio, presa, lago,estanque, arroyo, cana | 3,889 2.68 86.05 43 n Agua de lluvia | 237 0.16 86.21 51 y Camion cisterna | 2,616 1.80 88.01 61 n Agua embotellada | 13,158 9.06 97.07 71 y/n Otro | 4,262 2.93 100.00 96 n ----------------------------------------+----------------------------------- Total | 145,288 100.00 */ gen water_mdg = 1 if water==11 | water==12 | water==13 | water==51 | (water==71 & (ndwater==11 | ndwater==12 | ndwater==13 | ndwater==51 | ndwater==.)) replace water_mdg = 0 if water==21 | water==22 | water==41 | water==43 | water==61 | water==96 | (water==71 & (ndwater==21 | ndwater==22 | ndwater==41 | ndwater==43 | ndwater==61 | ndwater==96)) replace water_mdg = 0 if (water_mdg==1 | water_mdg==.) & timetowater >= 30 & timetowater!=. & timetowater!=996 & timetowater!=998 & timetowater!=999 //Deprived if water is at more than 30 minutes' walk (roundtrip) replace water_mdg = . if water==. | water==99 lab var water_mdg "Household has drinking water with MDG standards (considering distance)" tab water water_mdg, miss ******************************************************************************** *** Step 2.8 Housing *** ******************************************************************************** /* Members of the household are considered deprived if the household has a dirt, sand or dung floor */ clonevar floor = hv213 codebook floor, tab(99) gen floor_imp = 1 replace floor_imp = 0 if floor==11 | floor==12 | floor==96 //Deprived if "mud/earth", "sand", "dung", "other" replace floor_imp = . if floor==. | floor==99 lab var floor_imp "Household has floor that it is not earth/sand/dung" tab floor floor_imp, miss /* Members of the household are considered deprived if the household has wall made of natural or rudimentary materials */ clonevar wall = hv214 codebook wall, tab(99) gen wall_imp = 1 replace wall_imp = 0 if wall<=26 | wall==41 | wall==96 /*Deprived if "no wall" "cane/palms/trunk" "mud/dirt" "grass/reeds/thatch" "pole/bamboo with mud" "stone with mud" "plywood" "cardboard" "carton/plastic" "uncovered adobe" "canvas/tent" "unburnt bricks" "reused wood" "other"*/ replace wall_imp = . if wall==. | wall==99 lab var wall_imp "Household has wall that it is not of low quality materials" tab wall wall_imp, miss /* Members of the household are considered deprived if the household has roof made of natural or rudimentary materials */ clonevar roof = hv215 codebook roof, tab(99) gen roof_imp = 1 replace roof_imp = 0 if roof<=26 | roof==41 | roof==96 /*Deprived if "no roof" "thatch/palm leaf" "mud/earth/lump of earth" "sod/grass" "plastic/polythene sheeting" "rustic mat" "cardboard" "canvas/tent" "wood planks/reused wood" "unburnt bricks" "other"*/ replace roof_imp = . if roof==. | roof==99 lab var roof_imp "Household has roof that it is not of low quality materials" tab roof roof_imp, miss /*Household is deprived in housing if the roof, floor OR walls uses low quality materials.*/ gen housing_1 = 1 replace housing_1 = 0 if floor_imp==0 | wall_imp==0 | roof_imp==0 replace housing_1 = . if floor_imp==. & wall_imp==. & roof_imp==. lab var housing_1 "Household has roof, floor & walls that it is not low quality material" tab housing_1, miss ******************************************************************************** *** Step 2.9 Cooking Fuel *** ******************************************************************************** /* Members of the household are considered deprived if the household cooks with solid fuels: wood, charcoal, crop residues or dung. "Indicators for Monitoring the Millennium Development Goals", p. 63 */ clonevar cookingfuel = hv226 codebook cookingfuel, tab(99) gen cooking_mdg = 1 if cookingfuel<=5 | cookingfuel==95 | cookingfuel==96 replace cooking_mdg = 0 if (cookingfuel>5 & cookingfuel<=11) replace cooking_mdg = . if cookingfuel==. | cookingfuel==99 lab var cooking_mdg "Househod has cooking fuel according to MDG standards" tab cookingfuel cooking_mdg, miss ******************************************************************************** *** Step 2.10 Assets ownership *** ******************************************************************************** /* Members of the household are considered deprived if the household does not own more than one of: radio, TV, telephone, bike, motorbike or refrigerator and does not own a car or truck. */ //Check that for standard assets in living standards: "no"==0 and yes=="1" codebook hv208 hv207 hv221 hv243a hv209 hv212 hv210 hv211 hv243c sh61p clonevar television = hv208 gen bw_television = . clonevar radio = hv207 clonevar telephone = hv221 clonevar mobiletelephone = hv243a clonevar refrigerator = hv209 clonevar car = hv212 clonevar bicycle = hv210 clonevar motorbike = hv211 clonevar computer = sh61p clonevar animal_cart = hv243c * animal cart not available * foreach var in television radio telephone mobiletelephone refrigerator /// car bicycle motorbike computer animal_cart { replace `var' = . if `var'==9 | `var'==99 | `var'==8 | `var'==98 } //9 , 99 and 8, 98 are missing values //Group telephone and mobiletelephone as a single variable replace telephone=1 if telephone==0 & mobiletelephone==1 replace telephone=1 if telephone==. & mobiletelephone==1 /* Members of the household are considered deprived in assets if the household does not own more than one of: radio, TV, telephone, bike, motorbike, refrigerator, computer or animal_cart and does not own a car or truck.*/ egen n_small_assets2 = rowtotal(television radio telephone refrigerator bicycle motorbike computer animal_cart), missing lab var n_small_assets2 "Household Number of Small Assets Owned" gen hh_assets2 = (car==1 | n_small_assets2 > 1) replace hh_assets2 = . if car==. & n_small_assets2==. lab var hh_assets2 "Household Asset Ownership: HH has car or more than 1 small assets incl computer & animal cart" ******************************************************************************** *** Step 2.11 Rename and keep variables for MPI calculation ******************************************************************************** //Retain data on sampling design: desc /*hv022*/ hv021 *clonevar strata = hv022 clonevar psu = hv021 //Retain year, month & date of interview: desc /*hv007 hv006*/ hv008 *clonevar year_interview = hv007 *clonevar month_interview = hv006 clonevar date_interview = hv008 *** Rename key global MPI indicators for estimation *** recode hh_mortality_5y (0=1)(1=0) , gen(d_cm) recode hh_nutrition_uw_st (0=1)(1=0) , gen(d_nutr) recode hh_child_atten (0=1)(1=0) , gen(d_satt) recode hh_years_edu6 (0=1)(1=0) , gen(d_educ) recode electricity (0=1)(1=0) , gen(d_elct) recode water_mdg (0=1)(1=0) , gen(d_wtr) recode toilet_mdg (0=1)(1=0) , gen(d_sani) recode housing_1 (0=1)(1=0) , gen(d_hsg) recode cooking_mdg (0=1)(1=0) , gen(d_ckfl) recode hh_assets2 (0=1)(1=0) , gen(d_asst) *** Keep selected variables for global MPI estimation *** keep hh_id ind_id ccty ccnum cty survey year subsample psu weight area relationship sex age agec7 agec4 hhsize region date_interview d_cm d_nutr d_satt d_educ d_elct d_wtr d_sani d_hsg d_ckfl d_asst hh_mortality_5y hh_nutrition_uw_st hh_child_atten hh_years_edu6 electricity water_mdg toilet_mdg housing_1 cooking_mdg hh_assets2 order hh_id ind_id ccty ccnum cty survey year subsample psu weight area relationship sex age agec7 agec4 hhsize region date_interview d_cm d_nutr d_satt d_educ d_elct d_wtr d_sani d_hsg d_ckfl d_asst hh_mortality_5y hh_nutrition_uw_st hh_child_atten hh_years_edu6 electricity water_mdg toilet_mdg housing_1 cooking_mdg hh_assets2 *** Sort, compress and save data for estimation *** sort ind_id compress save "$path_out/per_dhs18_pov.dta", replace log close ******************************************************************************** *** MPI Calculation (TTD file) ******************************************************************************** **SELECT COUNTRY POV FILE RUN ON LOOP FOR MORE COUNTRIES use "$path_out\per_dhs18_pov.dta", clear ******************************************************************************** *** Define Sample Weight and total population *** ******************************************************************************** gen sample_weight = weight/1000000 //only DHS gen country = "Peru" gen countrycode = "PER" /* change to weight if MICS*/ ******************************************************************************** *** List of the 10 indicators included in the MPI *** ******************************************************************************** gen edu_1 = hh_years_edu6 gen atten_1 = hh_child_atten gen cm_1 = hh_mortality_5y /* change countries with no child mortality 5 year to child mortality ever*/ gen nutri_1 = hh_nutrition_uw_st gen elec_1 = electricity gen toilet_1 = toilet_mdg gen water_1 = water_mdg gen house_1 = housing_1 gen fuel_1 = cooking_mdg gen asset_1 = hh_assets2 global est_1 edu_1 atten_1 cm_1 nutri_1 elec_1 toilet_1 water_1 house_1 fuel_1 asset_1 ******************************************************************************** *** List of sample without missing values *** ******************************************************************************** foreach j of numlist 1 { gen sample_`j' = (edu_`j'!=. & atten_`j'!=. & cm_`j'!=. & nutri_`j'!=. & elec_`j'!=. & toilet_`j'!=. & water_`j'!=. & house_`j'!=. & fuel_`j'!=. & asset_`j'!=.) replace sample_`j' = . if subsample==0 /* Note: If the anthropometric data was collected from a subsample of the total population that was sampled, then the final analysis only includes the subsample population. */ *** Percentage sample after dropping missing values *** sum sample_`j' [iw = sample_weight] gen per_sample_weighted_`j' = r(mean) sum sample_`j' gen per_sample_`j' = r(mean) } *** ******************************************************************************** *** Define deprivation matrix 'g0' *** which takes values 1 if individual is deprived in the particular *** indicator according to deprivation cutoff z as defined during step 2 *** ******************************************************************************** foreach j of numlist 1 { foreach var in ${est_`j'} { gen g0`j'_`var' = 1 if `var'==0 replace g0`j'_`var' = 0 if `var'==1 } } *** Raw Headcount Ratios foreach j of numlist 1 { foreach var in ${est_`j'} { sum g0`j'_`var' if sample_`j'==1 [iw = sample_weight] gen raw`j'_`var' = r(mean)*100 lab var raw`j'_`var' "Raw Headcount: Percentage of people who are deprived in `var'" } } ******************************************************************************** *** Define vector 'w' of dimensional and indicator weight *** ******************************************************************************** /*If survey lacks one or more indicators, weights need to be adjusted within / each dimension such that each dimension weighs 1/3 and the indicator weights add up to one (100%). CHECK COUNTRY FILE*/ foreach j of numlist 1 { // DIMENSION EDUCATION foreach var in edu_`j' atten_`j' { capture drop w`j'_`var' gen w`j'_`var' = 1/6 } // DIMENSION HEALTH foreach var in cm_`j' nutri_`j' { capture drop w`j'_`var' gen w`j'_`var' = 1/6 } // DIMENSION LIVING STANDARD foreach var in elec_`j' toilet_`j' water_`j' house_`j' fuel_`j' asset_`j' { capture drop w`j'_`var' gen w`j'_`var' = 1/18 } } ******************************************************************************** *** Generate the weighted deprivation matrix 'w' * 'g0' ******************************************************************************** foreach j of numlist 1 { foreach var in ${est_`j'} { gen w`j'_g0_`var' = w`j'_`var' * g0`j'_`var' replace w`j'_g0_`var' = . if sample_`j'!=1 /*The estimation is based only on observations that have non-missing values for all variables in varlist_pov*/ } } ******************************************************************************** *** Generate the vector of individual weighted deprivation count 'c' ******************************************************************************** foreach j of numlist 1 { egen c_vector_`j' = rowtotal(w`j'_g0_*) replace c_vector_`j' = . if sample_`j'!=1 *drop w_g0_* } ******************************************************************************** *** Identification step according to poverty cutoff k (20 33.33 50) *** ******************************************************************************** foreach j of numlist 1 { foreach k of numlist 20 33 50 { gen multidimensionally_poor_`j'_`k' = (c_vector_`j'>=`k'/100) replace multidimensionally_poor_`j'_`k' = . if sample_`j'!=1 //Takes value 1 if individual is multidimensional poor } } ******************************************************************************** *** Generate the censored vector of individual weighted deprivation count 'c(k)' ******************************************************************************** foreach j of numlist 1 { foreach k of numlist 20 33 50 { gen c_censured_vector_`j'_`k' = c_vector_`j' replace c_censured_vector_`j'_`k' = 0 if multidimensionally_poor_`j'_`k'==0 } //Provide a score of zero if a person is not poor } * ******************************************************************************** *** Define censored deprivation matrix 'g0(k)' *** ******************************************************************************** foreach j of numlist 1 { foreach var in ${est_`j'} { gen g0`j'_k_`var' = g0`j'_`var' replace g0`j'_k_`var' = 0 if multidimensionally_poor_`j'_33==0 replace g0`j'_k_`var' = . if sample_`j'!=1 } } ******************************************************************************** *** Generates Multidimensional Poverty Index (MPI), *** Headcount (H) and Intensity of Poverty (A) *** ******************************************************************************** *** Multidimensional Poverty Index (MPI) *** foreach j of numlist 1 { foreach k of numlist 20 33 50 { sum c_censured_vector_`j'_`k' [iw = sample_weight] if sample_`j'==1 gen MPI_`j'_`k' = r(mean) lab var MPI_`j'_`k' "MPI with k=`k'" } sum c_censured_vector_`j'_33 [iw = sample_weight] if sample_`j'==1 gen MPI_`j' = r(mean) lab var MPI_`j' "`j' Multidimensional Poverty Index (MPI = H*A): Range 0 to 1" *** Headcount (H) *** sum multidimensionally_poor_`j'_33 [iw = sample_weight] if sample_`j'==1 gen H_`j' = r(mean)*100 lab var H_`j' "`j' Headcount ratio: % Population in multidimensional poverty (H)" *** Intensity of Poverty (A) *** sum c_censured_vector_`j'_33 [iw = sample_weight] if multidimensionally_poor_`j'_33==1 & sample_`j'==1 gen A_`j' = r(mean)*100 lab var A_`j' "`j' Intensity of deprivation among the poor (A): Average % of weighted deprivations" *** Population vulnerable to poverty (who experience 20-32.9% intensity of deprivations) *** gen temp = 0 replace temp = 1 if c_vector_`j'>=0.2 & c_vector_`j'<0.3332 replace temp = . if sample_`j'!=1 sum temp [iw = sample_weight] gen vulnerable_`j' = r(mean)*100 lab var vulnerable_`j' "`j' % Population vulnerable to poverty (who experience 20-32.9% intensity of deprivations)" drop temp *** Population in severe poverty (with intensity 50% or higher) *** gen temp = 0 replace temp = 1 if c_vector_`j'>0.49 replace temp = . if sample_`j'!=1 sum temp [iw = sample_weight] gen severe_`j' = r(mean)*100 lab var severe_`j' "`j' % Population in severe poverty (with intensity 50% or higher)" drop temp } * *** Censored Headcount *** foreach j of numlist 1 { foreach var in ${est_`j'} { sum g0`j'_k_`var' [iw = sample_weight] if sample_`j'==1 gen cen`j'_`var' = r(mean)*100 lab var cen`j'_`var' "Censored Headcount: Percentage of people who are poor and deprived in `var'" } } *** Dimensional Contribution *** foreach j of numlist 1 { foreach var in ${est_`j'} { gen cont`j'_`var' = (w`j'_`var' * cen`j'_`var')/MPI_`j' if sample_`j'==1 lab var cont`j'_`var' "% Contribution in MPI of indicator..." } } *** Prepare results to export *** keep subsample country year survey per_sample_weighted* per_sample* MPI* H* A* vulnerable* severe* raw* cen* cont* keep if subsample==1 *gen temp = (_n) *keep if temp==1 *drop temp order MPI_1 H_1 A_1 vulnerable_1 severe_1 cont1_nutr cont1_cm_1 cont1_edu_1 cont1_atten_1 cont1_fuel_1 cont1_toilet_1 cont1_water_1 cont1_elec_1 cont1_house_1 cont1_asset_1 per_sample_1 per_sample_weighted_1 raw1_nutri_1 raw1_cm_1 raw1_edu_1 raw1_atten_1 raw1_fuel_1 raw1_toilet_1 raw1_water_1 raw1_elec_1 raw1_house_1 raw1_asset_1 cen1_nutri_1 cen1_cm_1 cen1_edu_1 cen1_atten_1 cen1_fuel_1 cen1_toilet_1 cen1_water_1 cen1_elec_1 cen1_house_1 cen1_asset_1 codebook, compact clear