******************************************************************************** /* Citation: Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. 2019 Global Multidimensional Poverty Index - Armenia DHS 2015-16 [STATA do-file]. Available from OPHI website: http://ophi.org.uk/ For further queries, contact: ophi@qeh.ox.ac.uk */ ******************************************************************************** clear all set more off set maxvar 10000 set mem 500m *** Working Folder Path *** global path_in "T:/GMPI 2.0/rdta/Armenia DHS 2015-16" global path_out "G:/GMPI 2.0/cdta" global path_ado "T:/GMPI 2.0/ado" ******************************************************************************** *** Armenia DHS 2015-16 *** ******************************************************************************** ******************************************************************************** *** Step 1: Data preparation *** Selecting variables from KR, BR, IR, & MR recode & merging with PR recode ******************************************************************************** /*Armenia DHS 2015-16: Anthropometric information were recorded for all women age 15-49 and all children age 6-59 months (p.5). Anthropometric information was not collected from men 15-49, though they were interviewed in a subsample of 1/2 of eligible households (p.5)*/ ******************************************************************************** *** Step 1.1 KR - CHILDREN's RECODE (under 5) ******************************************************************************** use "$path_in/AMKR72FL.DTA", clear *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** b16=child's line number in household gen double ind_id = v001*1000000 + v002*100 + b16 format ind_id %20.0g label var ind_id "Individual ID" drop if b5==0 //Children who are not alive excluded //Armenia DHS 2015-16: 14 observations deleted duplicates report ind_id duplicates tag ind_id, gen(duplicates) tab b16 if duplicates!=0 tab hw13 if duplicates!=0 /*For children not listed in the household, create a false household line. We will check at merging stage. However,these children do not have info on nutrition */ bys ind_id: gen line = (_n) replace ind_id = v001*1000000 + v002*100 + (line+90) if duplicate!=0 //We assume consecutive hh line starting at 90 duplicates report ind_id //We should not have any duplicates at this stage gen child_KR=1 //Generate identification variable for observations in KR recode *** Next, indicate to STATA where the igrowup_restricted.ado file is stored: ***Source: http://www.who.int/childgrowth/software/en/ adopath + "$path_ado/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 = "$path_ado/igrowup_stata" lab var reflib "Directory of reference tables" /* We use datalib to specify the working directory where the input STATA dataset containing the anthropometric measurement is stored. */ gen str100 datalib = "$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_arm" lab var datalab "Working file" *** Next check the variables that WHO ado needs to calculate the z-scores: *** sex, age, weight, height, measurement, oedema & child sampling weight *** Variable: SEX *** tab b4, miss //"1" for male ;"2" for female tab b4, nol clonevar gender = b4 desc gender tab gender *** Variable: AGE *** tab hw1, miss codebook hw1 //Age is measured in months clonevar age_months = hw1 desc age_months summ age_months gen str6 ageunit = "months" lab var ageunit "Months" gen mdate = mdy(hw18, hw17, hw19) gen bdate = mdy(b1, hw16, b2) if hw16 <= 31 //Calculate birth date in days from date of interview replace bdate = mdy(b1, 15, b2) if hw16 > 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 *** Variable: BODY WEIGHT (KILOGRAMS) *** codebook hw2, tab (10000) gen weight = hw2/10 //We divide it by 10 in order to express it in kilograms tab hw2 if hw2>9990, m nol //Missing values are 9994 to 9996 replace weight = . if hw2>=9990 //All missing values or out of range are replaced as "." tab hw13 hw2 if hw2>=9990 | hw2==., miss //hw13: result of the measurement desc weight summ weight *** Variable: HEIGHT (CENTIMETERS) codebook hw3, tab (10000) gen height = hw3/10 //We divide it by 10 in order to express it in centimeters tab hw3 if hw3>9990, m nol //Missing values are 9994 to 9996 replace height = . if hw3>=9990 //All missing values or out of range are replaced as "." tab hw13 hw3 if hw3>=9990 | hw3==., miss desc height summ height *** Variable: MEASURED STANDING/LYING DOWN *** codebook hw15 gen measure = "l" if hw15==1 //Child measured lying down replace measure = "h" if hw15==2 //Child measured standing up replace measure = " " if hw15==9 | hw15==0 | hw15==. //Replace with " " if unknown desc measure tab measure *** Variable: OEDEMA *** lookfor oedema gen oedema = "n" //It assumes no-one has oedema desc oedema tab oedema *** Variable: INDIVIDUAL CHILD SAMPLING WEIGHT *** gen sw = v005/1000000 //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 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 "$path_out/children_nutri_arm_z_rc.dta", clear *** Standard MPI indicator *** //Takes value 1 if the child is under 2 stdev below the median & 0 otherwise gen underweight = (_zwei < -2.0) replace underweight = . if _zwei == . | _fwei==1 lab var underweight "Child is undernourished (weight-for-age) 2sd - WHO" tab underweight, miss gen stunting = (_zlen < -2.0) replace stunting = . if _zlen == . | _flen==1 lab var stunting "Child is stunted (length/height-for-age) 2sd - WHO" tab stunting, miss gen wasting = (_zwfl < - 2.0) replace wasting = . if _zwfl == . | _fwfl == 1 lab var wasting "Child is wasted (weight-for-length/height) 2sd - WHO" tab wasting, miss count if _fwei==1 count if _flen==1 /* Note: In the context of Armenia DHS 2006, 29 children are replaced as '.' because they have extreme z-scores which are biologically implausible.*/ //Retain relevant variables: keep ind_id child_KR v001 v002 b16 v135 underweight* stunting* wasting* order ind_id child_KR v001 v002 b16 v135 underweight* stunting* wasting* sort ind_id save "$path_out/ARM15-16_KR.dta", replace //Erase files from folder: erase "$path_out/children_nutri_arm_z_rc.xls" erase "$path_out/children_nutri_arm_prev_rc.xls" erase "$path_out/children_nutri_arm_z_rc.dta" ******************************************************************************** *** Step 1.2 BR - BIRTH RECODE *** (All females 15-49 years who ever gave birth) ******************************************************************************** /*The purpose of step 1.2 is to identify children of any age who died in the last 5 years prior to the survey date.*/ use "$path_in/AMBR72FL.dta", clear *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** v003=respondent's line number gen double ind_id = v001*1000000 + v002*100 + v003 format ind_id %20.0g label var ind_id "Individual ID" desc b3 b7 gen date_death = b3 + b7 //Date of death = date of birth (b3) + age at death (b7) gen mdead_survey = v008 - date_death //Months dead from survey = Date of interview (v008) - date of death gen ydead_survey = mdead_survey/12 //Years dead from survey gen age_death = b7 label var age_death "Age at death in months" tab age_death, miss //Check whether the age is in months 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 //Armenia DHS 2015-16: these figures are identical //Identify child under 18 mortality in the last 5 years gen child18_died = child_died replace child18_died=0 if age_death>=216 & age_death<. label values child18_died lab_died tab child18_died, miss bysort ind_id: egen tot_child18_died_5y=sum(child18_died) if ydead_survey<=5 /*Total number of children under 18 who died in the past 5 years prior to the interview date */ replace tot_child18_died_5y=0 if tot_child18_died_5y==. & tot_child_died>=0 & tot_child_died<. /*All children who are alive or who died longer than 5 years from the interview date are replaced as '0'*/ replace tot_child18_died_5y=. if child18_died==1 & ydead_survey==. //Replace as '.' if there is no information on when the child died tab tot_child_died tot_child18_died_5y, miss bysort ind_id: egen childu18_died_per_wom_5y = max(tot_child18_died_5y) lab var childu18_died_per_wom_5y "Total child under 18 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 childu18_died_per_wom_5y order ind_id women_BR childu18_died_per_wom_5y sort ind_id save "$path_out/ARM15-16_BR.dta", replace ******************************************************************************** *** Step 1.3 IR - WOMEN's RECODE *** (All eligible females 15-49 years in the household) ******************************************************************************** use "$path_in/AMIR72FL.dta", clear *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** v003=respondent's line number gen double ind_id = v001*1000000 + v002*100 + v003 format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id 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 "$path_out/ARM15-16_IR.dta", replace ******************************************************************************** *** Step 1.4 IR - WOMEN'S RECODE *** (Girls 15-19 years in the household) ******************************************************************************** use "$path_in/AMIR72FL.dta", clear *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** v003=respondent's line number gen double ind_id = v001*1000000 + v002*100 + v003 format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id ***Variables required to calculate the z-scores to produce BMI-for-age: *** Variable: SEX *** gen gender=2 /*Assign all observations as "2" for female, as the IR file contains all women, 15-49 years*/ *** Variable: AGE IN MONTHS *** codebook v006, tab (10) //month of interview codebook v007, tab (10) //year of interview codebook v009, tab (100) //month of birth codebook v010, tab (100) //year of birth gen imonth = mdy(v006, 1, v007) //month of interview (v006) //year of interview (v007) gen bmonth = mdy(v009, 1, v010) //month of birth (v009) //year of birth (v010) gen age_month = (imonth-bmonth)/30.4375 //Calculate age in months lab var age_month "Age in months, individuals 15-19 years" *** Variable: AGE UNIT *** gen str6 ageunit = "months" lab var ageunit "Months" *** Variable: BODY WEIGHT (KILOGRAMS) *** codebook v437, tab (1000) gen weight = v437/10 //We divide it by 10 in order to express it in kilograms replace weight = . if v437>=9990 //All missing values or out of range are replaced as "." summ weight *** Variable: HEIGHT (CENTIMETERS) codebook v438, tab (1000) gen height = v438/10 //We divide it by 10 in order to express it in centimeters replace height = . if v438>=9990 //All missing values or out of range are replaced as "." summ height *** Variable: OEDEMA gen oedema = "n" tab oedema *** Variable: SAMPLING WEIGHT *** gen sw = v005/1000000 //For DHS sample weight has to be divided 1000000 summ sw count if v012>=15 & v012<=19 //Total number of girls in the IR recode //Armenia DHS 2015-16: 743 keep if v012>=15 & v012<=19 //Keep only girls between age 15-19 years to compute BMI-for-age *** Next, indicate to STATA where the igrowup_restricted.ado file is stored: ***Source: https://www.who.int/growthref/tools/en/ adopath + "$path_ado/who2007_stata" /* We use 'reflib' to specify the package directory where the .dta files containing the WHO Growth reference are stored. Note that we use strX to specify the length of the path in string. */ gen str100 reflib = "$path_ado/who2007_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 = "$path_out" lab var datalib "Directory for datafiles" /* We use datalab to specify the name that will prefix the output files that will be produced from using this ado file*/ gen str30 datalab = "girl_nutri_arm" lab var datalab "Working file" /*We now run the command to calculate the z-scores with the adofile */ who2007 reflib datalib datalab gender age_month ageunit weight height oedema sw /*We now turn to using the dta file that was created and that contains the calculated z-scores to compute BMI-for-age*/ use "$path_out/girl_nutri_arm_z.dta", clear gen z_bmi = _zbfa replace z_bmi = . if _fbfa==1 lab var z_bmi "z-score bmi-for-age WHO" *** Standard MPI indicator *** gen low_bmiage = (z_bmi < -2.0) //Takes value 1 if the child 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 15-19 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 save "$path_out/ARM15-16_IR_girls.dta", replace //Erase files from folder: erase "$path_out/girl_nutri_arm_z.xls" erase "$path_out/girl_nutri_arm_prev.xls" erase "$path_out/girl_nutri_arm_z.dta" ******************************************************************************** *** Step 1.5 MR - MEN'S RECODE ***(All eligible man: 15-49 years in the household) ******************************************************************************** use "$path_in/AMMR72FL.dta", clear *** Generate individual unique key variable required for data merging *** mv001=cluster number; *** mv002=household number; *** mv003=respondent's line number gen double ind_id = mv001*1000000 + mv002*100 + mv003 format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id gen men_MR=1 //Identification variable for observations in MR recode count if mv135==1 //Armenia DHS 2015-16: 2743 non-usual resident keep ind_id men_MR mv003 mv005 mv012 mv201 mv206 mv207 order ind_id men_MR mv003 mv005 mv012 mv201 mv206 mv207 sort ind_id save "$path_out/ARM15-16_MR.dta", replace ******************************************************************************** *** Step 1.6 MR - MEN'S RECODE ***(Boys 15-19 years in the household) ******************************************************************************** /*Note: In the case of Armenia 2015-16, anthropometric data was NOT collected for men.*/ ******************************************************************************** *** Step 1.7 PR - HOUSEHOLD MEMBER'S RECODE ******************************************************************************** use "$path_in/AMPR72FL.dta", clear *** Generate a household unique key variable at the household level using: ***hv001=cluster number ***hv002=household number gen double hh_id = hv001*10000 + hv002 format hh_id %20.0g label var hh_id "Household ID" codebook hh_id *** Generate individual unique key variable required for data merging using: *** hv001=cluster number; *** hv002=household number; *** hvidx=respondent's line number. gen double ind_id = hv001*1000000 + hv002*100 + hvidx format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id sort hh_id ind_id ******************************************************************************** *** 1.8 DATA MERGING ******************************************************************************** *** Merging BR Recode ***************************************** merge 1:1 ind_id using "$path_out/ARM15-16_BR.dta" drop _merge erase "$path_out/ARM15-16_BR.dta" *** Merging IR Recode ***************************************** merge 1:1 ind_id using "$path_out/ARM15-16_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 "$path_out/ARM15-16_IR.dta" *** Merging IR Recode: 15-19 years girls ***************************************** merge 1:1 ind_id using "$path_out/ARM15-16_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 /*Note: In Armenia DHS 2015-16, 3 girls 15-19 were identified as measured, eligible, and completed interview and are present in the PR recode, but they were not present in the IR recode. It should be noted that they have BMI information but not BMI-for-age.*/ drop _merge erase "$path_out/ARM15-16_IR_girls.dta" *** Merging MR Recode ***************************************** merge 1:1 ind_id using "$path_out/ARM15-16_MR.dta" tab men_MR hv118, miss col drop _merge erase "$path_out/ARM15-16_MR.dta" *** Merging KR Recode ***************************************** merge 1:1 ind_id using "$path_out/ARM15-16_KR.dta" count if b16==0 & child_KR==1 //The children without household line are unique to the KR recode replace hh_id = v001*10000 + v002 if b16==0 & child_KR==1 //Create hd_id for children without household line tab child_KR hv120 if hc60<30, miss col tab hc60 if hv120==1 & child_KR==. /*If caretaker is not the mother/mother not in the household then the child is not in the KR recode */ tab hc13 hc60 if hv120==1 & child_KR==. sum hc5 if hc13==0 & hv120==1 & child_KR==. replace hv102 = v135 if b16==0 & child_KR==1 tab child_KR underweight if b16==0 & child_KR==1, miss drop _merge erase "$path_out/ARM15-16_KR.dta" sort ind_id ******************************************************************************** *** 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. In the context of Armenia DHS 2015-16, 87 (0.3%) individuals who were non-usual residents were dropped from the sample */ ******************************************************************************** *** 1.10 SUBSAMPLE VARIABLE *** ******************************************************************************** /* In the context of Armenia DHS 2015-16, height and weight measurements were collected from children (0-5) and women (15-49) in all the sampled households. Hence there is no presence of subsmaple. */ gen subsample=. label var subsample "Households selected as part of nutrition subsample" tab subsample, miss ******************************************************************************** *** 1.11 CONTROL VARIABLES ******************************************************************************** /* Households are identified as having 'no eligible' members if there is no applicable population, that is, children 0-5 years, adult women 15-49 years or men 15-54 / 15-59 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 ***************************************** gen fem_eligible = (hv117==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 15-54 / 15-54 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 hemoglobin data. For example, in some country data, only half of the household or one third or two third of the households is assessed for hemoglobin 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 ******************************************************************************** *** 1.12 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 //Redefine the coding and labels to 1/0 label define lab_area 1 "urban" 0 "rural" label values area lab_area label var area "Area: urban-rural" //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)" //Marital status of household member clonevar marital = hv115 codebook marital, tab (20) recode marital (0=1)(1=2)(8=.) label define lab_mar 1"never married" 2"currently married" 3"widowed" /// 4"divorced" 5"not living together" label values marital lab_mar label var marital "Marital status of household member" tab hv115 marital, m //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 /* NOTE: The sample for the Armenia DHS 2015-16 was designed to produce representative estimates of key indicators at the national level, for Yerevan, and for total urban and total rural areas. Many indicators can be estimated at the regional (marz) level (there are 11 regions), but this is not representative (p.5). As such, for the purpose of the global MPI, despite the presence of subnational information (hv024), we generated the 'region' variable as an empty variable. */ codebook hv024, tab (100) gen region = . lab var region "Region for subnational decomposition" decode hv024, gen(temp) replace temp = proper(temp) encode temp, gen(region_2) codebook region_2, tab (100) tab hv024 region_2, miss lab var region_2 "Regions in Armenia - not representative" drop temp ******************************************************************************** *** Step 2 Data preparation *** *** Standardization of the 10 Global MPI indicators *** Identification of non-deprived & deprived individuals ******************************************************************************** ******************************************************************************** *** Step 2.1 Years of Schooling *** ******************************************************************************** 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 /*The variable "eduyears" was replaced with a '.' if total years of education was more than individual's age */ replace eduyears = 0 if age < 10 /*The variable "eduyears" was replaced with a '0' given that the criteria for this indicator is household member aged 10 years or older */ /*A control variable is created on whether there is information on years of education for at least 2/3 of the household members aged 10 years and older */ gen temp = 1 if eduyears!=. & age>=10 & age!=. bysort hh_id: egen no_missing_edu = sum(temp) /*Total household members who are 10 years and older with no missing years of education */ gen temp2 = 1 if age>=10 & age!=. bysort hh_id: egen hhs = sum(temp2) //Total number of household members who are 10 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 10 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 10 years & older" drop temp temp2 hhs *** Standard MPI *** /*The entire household is considered deprived if no household member aged 10 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 *** 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 Armenia, 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: Armenia DHS 2015-16 has no anthropometric data for men, 15-49 years */ lookfor body mass codebook ha40 gen hb40 = . foreach var in ha40 hb40 { gen inf_`var' = 1 if `var'!=. bys sex: tab age inf_`var' //Check if it is restricted to only an age group or full sample drop inf_`var' } *** *** Standard MPI: 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>=99.97 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==1 /*Under this section, households that don't have eligible female population is 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 (15-49 years) */ *** Standard MPI: BMI Indicator for Men 15-54/15-59 years *** ******************************************************************* gen m_bmi = hb40/100 lab var m_bmi "Male's BMI" gen m_low_bmi = (m_bmi<18.5) replace m_low_bmi = . if m_bmi==. | m_bmi>=99.97 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, 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 */ *** New Standard MPI: BMI-for-age for individuals 15-19 years *** and BMI for individuals 20-49 years *** ******************************************************************* 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 */ replace low_bmi_byage = 1 if low_bmiage==1 & age_month!=. //Replace variable "low_bmi_byage = 1" if eligible teenagers have low BMI replace low_bmi_byage = 0 if low_bmiage==0 & age_month!=. /*Replace variable "low_bmi_byage = 0" if teenagers are identified as having low BMI but normal BMI-for-age */ /*Note: The following control variable is applied when there is BMI information for women and 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==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, miss tab hh_no_low_bmiage, miss /*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 *** ******************************************************************************** *** Standard MPI: 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 *** Standard MPI: 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 *** Standard MPI: 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 *** ******************************************************************************** *** Standard MPI *** /* Members of the household are considered deprived if the household has a child under 5 whose height-for-age or weight-for-age is under two standard deviation below the median, or has teenager with BMI-for-age that is under two standard deviation below the median, or has adults with BMI threshold that is below 18.5 kg/m2. Households that have no eligible adult AND no eligible children are considered non-deprived. 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 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_child_fem_eligible==1 /*We replace households that do not have the applicable population, that is, women 15-49 & children 0-5, as non-deprived in nutrition*/ 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 mv206 mv207 //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 child_mortality_m) lab var child_mortality "Total child mortality within household reported by women & men" tab child_mortality, miss *** Standard MPI *** /* Members of the household are considered deprived if women in the household reported mortality among children under 18 in the last 5 years from the survey year. Members of the household is considered non-deprived if eligible women within the household reported (i) no child mortality or (ii) if any child died longer than 5 years from the survey year or (iii) if any child 18 years and older died in the last 5 years. In adddition, members of the household were identified as non-deprived if eligible men within the household reported no child mortality in the absence of information from women. Households that have no eligible women or adult are considered non-deprived. The indicator takes a missing value if there was missing information on reported death from eligible individuals. */ ************************************************************************ tab childu18_died_per_wom_5y, miss /* The 'childu18_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 childu18_died_per_wom_5y = 0 if v201==0 /*Assign a value of "0" for: - all eligible women who never ever gave birth */ *replace childu18_died_per_wom_5y = 0 if hv115==0 & hv104==2 & hv105>=15 & hv105<=49 /*This line replaces never-married women with 0 child death. If in your country dataset, child mortality information was only collected from ever-married women (check report), please activate this command line.*/ replace childu18_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 childu18_mortality_5y = sum(childu18_died_per_wom_5y), missing replace childu18_mortality_5y = 0 if childu18_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 childu18_mortality_5y "Under 18 child mortality within household past 5 years reported by women" tab childu18_mortality_5y, miss gen hh_mortality_u18_5y = (childu18_mortality_5y==0) replace hh_mortality_u18_5y = . if childu18_mortality_5y==. lab var hh_mortality_u18_5y "Household had no under 18 child mortality in the last 5 years" tab hh_mortality_u18_5y, miss ******************************************************************************** *** Step 2.5 Electricity *** ******************************************************************************** /*Members of the household are considered deprived if the household has no electricity */ *** Standard MPI *** /*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 *** ******************************************************************************** /* Improved sanitation facilities include flush or pour flush toilets to sewer systems, septic tanks or pit latrines, ventilated improved pit latrines, pit latrines with a slab, and composting toilets. These facilities are only considered improved if it is private, that is, it is not shared with other households. Source: https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01.pdf Note: In cases of mismatch between the country report and the internationally agreed guideline, we followed the report. */ clonevar toilet = hv205 codebook toilet, tab(30) codebook hv225, tab(30) clonevar shared_toilet = hv225 *** Standard MPI *** /*Members of the household are considered deprived if the household's sanitation facility is not improved (according to the SDG guideline) or it is improved but shared with other households*/ ******************************************************************** gen toilet_mdg = (toilet<23 & shared_toilet!=1) /*Household is assigned a value of '1' if it uses improved sanitation and does not share toilet with other households */ replace toilet_mdg = 0 if toilet<23 & shared_toilet==1 /*Household is assigned a value of '0' if it uses improved sanitation but shares toilet with other households */ replace toilet_mdg = 0 if toilet == 14 | toilet == 15 /*Household is assigned a value of '0' if it uses non-improved sanitation: "flush to somewhere else" and "flush don't know where" */ replace toilet_mdg = . if toilet==. | toilet==99 //Household is assigned a value of '.' if it has missing information lab var toilet_mdg "Household has improved sanitation with MDG Standards" tab toilet toilet_mdg, miss ******************************************************************************** *** Step 2.7 Drinking Water *** ******************************************************************************** /* Improved drinking water sources include the following: piped water into dwelling, yard or plot; public taps or standpipes; boreholes or tubewells; protected dug wells; protected springs; packaged water; delivered water and rainwater which is located on premises or is less than a 30-minute walk from home roundtrip. Source: https://unstats.un.org/sdgs/metadata/files/Metadata-06-01-01.pdf Note: In cases of mismatch between the country report and the internationally agreed guideline, we followed the report. */ clonevar water = hv201 clonevar timetowater = hv204 codebook water, tab(100) clonevar ndwater = hv202 //Non-drinking water. tab hv202 if water==71 /*Because the quality of bottled water is not known, households using bottled water for drinking are classified as using an improved or unimproved source according to their water source for non-drinking activities such as cooking and hand washing. However, it is important to note that households using bottled water for drinking are classified as unimproved source if this is explicitly mentioned in the country report. */ /* In Armenia, all the households that use bottled water for drinking get their non-drinking water piped into dwelling, so they are considered non-deprived */ *** Standard MPI *** /* Members of the household are considered deprived if the household does not have access to improved drinking water (according to the SDG guideline) or safe drinking water is at least a 30-minute walk from home, roundtrip */ ******************************************************************** gen water_mdg = 1 if water==11 | water==12 | water==14 | water==21 | /// water==31 | water==41 | water==71 /*Non deprived if water is "piped into dwelling", "piped to yard/plot", "public tap/standpipe", "tube well or borehole", "protected well", "protected spring", "rainwater", 71"bottled water" */ replace water_mdg = 0 if water==32 | water==42 | water==61 | /// water==96 /*Deprived if it is "unprotected well", "unprotected spring", "tanker truck" "surface water (river/lake, etc)", "cart with small tank","other" */ replace water_mdg = 0 if water_mdg==1 & 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 replace water_mdg = 0 if water==71 & /// (ndwater==32 | ndwater==42 | ndwater==61 | ndwater==96) /*Households using bottled water for drinking are classified as using an improved or unimproved source according to their water source for non-drinking activities */ 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 */ lookfor floor clonevar floor = hv213 codebook floor, tab(100) gen floor_imp = 1 replace floor_imp = 0 if floor==11 //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 walls made of natural or rudimentary materials */ lookfor wall clonevar wall = hv214 codebook wall, tab(100) gen wall_imp = 1 replace wall_imp = 0 if wall<=26 | 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 */ lookfor roof clonevar roof = hv215 codebook roof, tab(100) gen roof_imp = 1 replace roof_imp = 0 if roof<=23 /*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 *** Standard MPI *** /* Members of the household is deprived in housing if the roof, floor OR walls are constructed from 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 *** ******************************************************************************** /* Solid fuel are solid materials burned as fuels, which includes coal as well as solid biomass fuels (wood, animal dung, crop wastes and charcoal). Source: https://apps.who.int/iris/bitstream/handle/10665/141496/9789241548885_eng.pdf */ lookfor cooking clonevar cookingfuel = hv226 codebook cookingfuel, tab(99) *** Standard MPI *** /* Members of the household are considered deprived if the household uses solid fuels and solid biomass fuels for cooking. */ ***************************************************************** gen cooking_mdg = 1 replace cooking_mdg = 0 if cookingfuel>5 & cookingfuel<95 replace cooking_mdg = . if cookingfuel==. | cookingfuel==99 lab var cooking_mdg "Household has cooking fuel by MDG standards" /* Non deprived if: "electricity", "lpg", "natural gas", "biogas", "kerosene" , "no food cooked in household", "other" Deprived if: "coal/lignite", "charcoal", "wood", "straw/shrubs/grass" "agricultural crop", "animal dung" */ tab cookingfuel cooking_mdg, miss ******************************************************************************** *** Step 2.10 Assets ownership *** ******************************************************************************** /*Assets that are included in the global MPI: Radio, TV, telephone, bicycle, motorbike, refrigerator, car, computer and animal cart */ 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 = hv243e clonevar animal_cart = hv243c 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 } //Replace missing values //Combine information on telephone and mobiletelephone replace telephone=1 if telephone==0 & mobiletelephone==1 replace telephone=1 if telephone==. & mobiletelephone==1 *** Standard MPI *** /* 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 DHS wealth index: desc hv270 clonevar windex=hv270 desc hv271 clonevar windexf=hv271 //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_u18_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) *** Generate coutry and survey details for estimation *** char _dta[cty] "Armenia" char _dta[ccty] "ARM" char _dta[year] "2015-2016" char _dta[survey] "DHS" char _dta[ccnum] "051" char _dta[type] "micro" *** Sort, compress and save data for estimation *** sort ind_id compress la da "Micro data for `_dta[ccty]' (`_dta[ccnum]'). Last save: `c(filedate)'." save "$path_out/arm_dhs15-16.dta", replace