******************************************************************************** /* Citation: Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. 2018 Global Multidimensional Poverty Index - India 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 cap log close *** Working Folder Path *** global path_in "T:/GMPI 2.0/data/India DHS 2015-16" global path_out "D:/pov" global path_logs "D:/logs" global path_ado "D:/ado" *** Log file *** log using "$path_logs/ind_dhs15-16_dataprep.log", replace ******************************************************************************** *** INDIA DHS (NFHS) 2015-16 *** ******************************************************************************** ******************************************************************************** *** Step 1: Data preparation *** Selecting variables from KR, BR, IR, & MR recode & merging with PR recode ******************************************************************************** /* NOTE: India DHS (NFHS) 2015-16: Height and weight were measured for all children age 0-59 months, women age 15-49, and a subsample of men age 15-54 who were selected in the state module (p.4). The weight and height of children under five were measured regardless of whether their mothers were interviewed in the survey (p.290). The anthropometric data from women age 15-49 excluded pregnant women and those who had given birth in last two months of the survey (p.298). */ ******************************************************************************** *** Step 1.1 KR - CHILDREN's RECODE (under 5) ******************************************************************************** use "$path_in/IAKR74FL.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 are excluded. 824 observations deleted. duplicates report ind_id duplicates tag ind_id, gen(duplicates) tab b16 if duplicates!=0 tab hw13 if duplicates!=0 /*A number of children are not listed in the household. For children not listed in the household, create a false household line. We will check at merging stage */ bysort 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 //No duplicates at this stage gen child_KR=1 //Generate identification variable for observations in KR recode /* For this part of the do-file we use the WHO Anthro and macros. This is to calculate the z-scores of children under 5. Source of ado file: http://www.who.int/childgrowth/software/en/ */ *** Indicate to STATA where the igrowup_restricted.ado file is stored: 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_ind" 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 and all missing values are "." tab b4, nol clonevar gender = b4 desc gender tab gender *** Variable: AGE *** tab hw1, miss codebook hw1 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 (9999) gen weight = hw2/10 //We divide it by 10 in order to express it in kilograms tab hw2 if hw2>9990, miss 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 (9999) gen height = hw3/10 //We divide it by 10 in order to express it in centimeters tab hw3 if hw3>9990, miss 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 count if hw3>9990 | hw2>=9990 /* NOTE: In India DHS 2015/16, a total of 10,612 chidren between the age of 0-5 years have missing observations for weight or height or the combination of both.*/ *** 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_ind_z_rc.dta", clear 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 v001 v002 b16 v135 underweight stunting wasting order ind_id child_KR v001 v002 b16 v135 underweight stunting wasting sort ind_id duplicates report ind_id //erase files from folder: erase "$path_out/children_nutri_ind_z_rc.xls" erase "$path_out/children_nutri_ind_prev_rc.xls" erase "$path_out/children_nutri_ind_z_rc.dta" //Save a temp file for merging with PR: save "$path_out/IND15-16_KR.dta", replace ******************************************************************************** *** 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/IABR74FL.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 v008 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 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 India DHS 2015-16, the figures are not identical for 538 women. gen diff= tot_child_died - tot_child_died_2 tab diff, miss count if diff!=0 /*It could be the case that the mismatch between the "tot_child_died" and "tot_child_died_2" variables are because "tot_child_died" is constructed using women's birth information. On the other hand, "tot_child_died_2" may be based on any child who died, including children who have been adopted. However, in the case of mismatch, we continue to follow the "tot_child_died" variable as the variable is based on women's birth history data */ drop diff 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 order ind_id women_BR b16 child_died_per_wom child_died_per_wom_5y sort ind_id //Save a temp file for merging with PR: save "$path_out/IND15-16_BR.dta", replace ******************************************************************************** *** Step 1.3 IR - WOMEN's RECODE *** (All eligible females 15-49 years in the household) ******************************************************************************** use "$path_in/IAIR74FL.DTA", clear *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** v003=respondent's line number gen double ind_id = v001*1000000 + v002*100 + v003 format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id gen women_IR=1 //Identification variable for observations in IR recode keep ind_id women_IR v003 v005 v012 v201 v206 v207 order ind_id women_IR v003 v005 v012 v201 v206 v207 sort ind_id //Save a temp file for merging with PR: save "$path_out/IND15-16_IR.dta", replace ******************************************************************************** *** Step 1.4 IR - WOMEN'S RECODE *** (Girls 15-19 years in the household) ******************************************************************************** use "$path_in/IAIR74FL.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 (20) //month of interview codebook v007, tab (10) //year of interview codebook v009, tab (20) //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 (9999) 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 (9999) 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 *** Keep only relevant sample: teenagers 15 - 19 years *** count if v012>=15 & v012<=19 //Total number of girls in the IR recode keep if v012>=15 & v012<=19 //Keep only girls between age 15-19 years to compute BMI-for-age /* For this part of the do-file we use the WHO AnthroPlus software. This is to calculate the z-scores for 15-19 years. Source of ado file: https://www.who.int/growthref/tools/en/ */ *** Indicate to STATA where the igrowup_restricted.ado file is stored: 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 specity 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_ind" 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 "$path_out/girl_nutri_ind_z.dta", clear gen z_bmi = _zbfa replace z_bmi = . if _fbfa==1 lab var z_bmi "z-score bmi-for-age WHO" /*Takes value 1 if BMI-for-age is under 2 stdev below the median & 0 otherwise */ gen low_bmiage = (z_bmi < -2.0) replace low_bmiage = . if z_bmi==. lab var low_bmiage "Teenage low bmi 2sd - WHO" gen teen_IR=1 //Identification variable for observations in IR recode (only 15-19 years) //Retain relevant variables: keep ind_id teen_IR age_month low_bmiage order ind_id teen_IR age_month low_bmiage sort ind_id //erase files from folder: erase "$path_out/girl_nutri_ind_z.xls" erase "$path_out/girl_nutri_ind_prev.xls" erase "$path_out/girl_nutri_ind_z.dta" //Save a temp file for merging with PR: save "$path_out/IND15-16_IR_girls.dta", replace ******************************************************************************** *** Step 1.5 MR - MEN'S RECODE ***(All eligible man: 15-54 years in the household) ******************************************************************************** use "$path_in/IAMR74FL.DTA", clear tab mv012, miss //Age of individual men: 15-54 years *** 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 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 a temp file for merging with PR: save "$path_out/IND15-16_MR.dta", replace ******************************************************************************** *** Step 1.6a MR - MEN'S RECODE ***(Boys 15-19 years in the household) ******************************************************************************** /*Note: In the case of India 2015-16, anthropometric data was collected for men. However, to compute the BMI-for-age, we will need to extract the weight and height variable from the PR file for boys 15 -19 years as it is not present in the individual MR file */ use "$path_in/IAPR74FL.DTA", clear gen double ind_id = hv001*1000000 + hv002*100 + hvidx format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id count if hv104==1 & hv105>=15 & hv105<=59 //Total number of men 15-59 years in the PR recode keep if hv104==1 & hv105>=15 & hv105<=59 //Keep only men between the age 15-59 years to merge with the MR recode count if hb2!=. //hb2: man's weight in kilograms count if hb3!=. //hb3: man's height in centimeters codebook hb13, tab (10) //hb13: result of measurement - height/weight gen teen_MR_temp = 1 keep ind_id teen_MR_temp hb2 hb3 hb13 //Retain the height and weight variables sort ind_id //Save a temp file for merging with MR: save "$path_out/temp.dta", replace ******************************************************************************** *** Step 1.6b MR - MEN'S RECODE ***(Boys 15-19 years in the household) ******************************************************************************** use "$path_in/IAMR74FL.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 = mv001*1000000 + mv002*100 + mv003 format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id merge 1:1 ind_id using "$path_out/temp.dta" keep if _merge==3 /*Note: The male individuals extracted from the PR file are many more than the male individuals in the MR file. This is because the MR file only contains individual men who were selected as part of the male subsample. On the otherhand, the PR file contains all members of the household that was interviewed. For the purpose of measuring BMI-for-age, our primary interest is the men who were selected as part of the male subsample. As such, we only keep the individuals who matched at _merge==3, that is, the 112,122 individual men from the male subsample. */ drop _merge ***Variables required to calculate the z-scores to produce BMI-for-age: *** Variable: SEX *** gen gender=1 /*Assign all observations as "1" for male, as the MR file contains all men, 15-54*/ *** Variable: AGE IN MONTHS *** codebook mv006, tab (12) //month of interview codebook mv007, tab (10) //year of interview codebook mv009, tab (100) //month of birth codebook mv010, tab (100) //year of birth gen imonth = mdy(mv006, 1, mv007) //month of interview (mv006) //year of interview (mv007) gen bmonth = mdy(mv009, 1, mv010) //month of birth (mv009) //year of birth (mv010) 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) *** /*In the case of India DHS 2015-16, the usual mv437 variable (body weight) does not exist in the MR file. Hence we use the hb2 (man's weight in kilograms) variable that were extracted from the PR file. */ codebook hb2, tab (9999) gen weight = hb2/10 //We divide it by 10 in order to express it in kilograms replace weight = . if hb2>=9990 //All missing values or out of range are replaced as "." summ weight *** Variable: HEIGHT (CENTIMETERS) /*In the case of India DHS 2015-16, the usual mv438 variable (height) does not exist in the MR file. Hence we use the hb3 (man's height in centimeters) variable that were extracted from the PR file. */ codebook hb3, tab (9999) gen height = hb3/10 //We divide it by 10 in order to express it in centimeters replace height = . if hb3>=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 = mv005/1000000 //For DHS sample weight has to be divided 1000000 summ sw *** Keep only relevant sample: teenagers 15 - 19 years *** count if mv012>=15 & mv012<=19 //Total number of boys in the MR recode keep if mv012>=15 & mv012<=19 //Keep only boys between age 15-19 years to compute BMI-for-age /* For this part of the do-file we use the WHO AnthroPlus software. This is to calculate the z-scores for 15-19 years. Source of ado file: https://www.who.int/growthref/tools/en/ */ *** Indicate to STATA where the igrowup_restricted.ado file is stored: 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 specity 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 = "boy_nutri_ind" 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 "$path_out/boy_nutri_ind_z.dta", clear gen z_bmi = _zbfa replace z_bmi = . if _fbfa==1 lab var z_bmi "z-score bmi-for-age WHO" /*Takes value 1 if BMI-for-age is under 2 stdev below the median & 0 otherwise */ gen low_bmiage = (z_bmi < -2.0) replace low_bmiage = . if z_bmi==. lab var low_bmiage "Teenage low bmi 2sd - WHO" gen teen_MR = 1 //Identification variable for observations in IR recode (only 15-19 years) //Retain relevant variables: keep ind_id teen_MR age_month low_bmiage order ind_id teen_MR age_month low_bmiage sort ind_id //erase files from folder: erase "$path_out/boy_nutri_ind_z.xls" erase "$path_out/boy_nutri_ind_prev.xls" erase "$path_out/temp.dta" erase "$path_out/boy_nutri_ind_z.dta" //Save a temp file for merging with PR: save "$path_out/IND15-16_MR_boys.dta", replace ******************************************************************************** *** Step 1.7 PR - HOUSEHOLD MEMBER'S RECODE ******************************************************************************** use "$path_in/IAPR74FL.DTA", clear gen cty = "India" gen ccty = "IND" gen year = "2015-2016" gen survey = "DHS" gen ccnum = 356 *** Generate a household unique key variable at the household level using: ***hv001=cluster number ***hv002=household number gen double hh_id = hv001*10000 + hv002 format hh_id %20.0g label var hh_id "Household ID" codebook hh_id *** Generate individual unique key variable required for data merging using: *** hv001=cluster number; *** hv002=household number; *** hvidx=respondent's line number. gen double ind_id = hv001*1000000 + hv002*100 + hvidx format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id sort hh_id ind_id ******************************************************************************** *** Step 1.8 DATA MERGING ******************************************************************************** *** Merging BR Recode ***************************************** merge 1:1 ind_id using "$path_out/IND15-16_BR.dta" drop _merge erase "$path_out/IND15-16_BR.dta" *** Merging IR Recode ***************************************** merge 1:1 ind_id using "$path_out/IND15-16_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 "$path_out/IND15-16_IR.dta" *** Merging IR Recode: 15-19 years girls ***************************************** merge 1:1 ind_id using "$path_out/IND15-16_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 India DHS 2015-16, 2,262 girls 15-19 were identified as measured and are present in the PR recode, but they were not present in the IR recode. It should be noted that 2,185 of these girls have BMI information but not BMI-for-age.*/ drop _merge erase "$path_out/IND15-16_IR_girls.dta" *** Merging MR Recode ***************************************** merge 1:1 ind_id using "$path_out/IND15-16_MR.dta" tab men_MR hv118, miss col tab hb65 if hv118==1 & men_MR ==. //Total of eligible men not interviewed tab hb13 hb65 if hv118==1 & men_MR ==., miss drop _merge erase "$path_out/IND15-16_MR.dta" *** Merging MR Recode: 15-19 years boys ***************************************** merge 1:1 ind_id using "$path_out/IND15-16_MR_boys.dta" tab teen_MR hv118 if hv105>=15 & hv105<=19, miss col tab hb65 if hv118==1 & teen_MR==. & (hv105>=15 & hv105<=19), miss //Total number of eligible boys not interviewed tab hb13 if teen_MR== . & hv118==1 & (hv105>=15 & hv105<=19), miss *tab hb40 if hb65==1 & hb13==0 & hv118==1 & teen_MR ==. & (hv105>=15 & hv105<=19), miss /*Note: In India DHS 2015-16, 410 boys 15-19 were identified as measured and are present in the PR recode, but they were not present in the IR recode. It should be noted that 386 of these boys have BMI information but not BMI-for-age.*/ drop _merge erase "$path_out/IND15-16_MR_boys.dta" *** Merging KR Recode ***************************************** merge 1:1 ind_id using "$path_out/IND15-16_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/IND15-16_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 India DHS 2015-16, 93,223 (3.25%) individuals who were non-usual residents were dropped from the sample */ ******************************************************************************** *** Step 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 15-54 / 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. For further details see Alkire and Santos (2010)*/ *** No Eligible Women 15-49 years ***************************************** gen fem_eligible = (hv117==1) bysort 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 years ***************************************** gen male_eligible = (hv118==1) bysort 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) bysort 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 *********************************************** 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) bysort 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 ******************************************************************************** *** Step 1.11 SUBSAMPLE VARIABLE *** ******************************************************************************** /* In the context of India DHS 2015-16, height and weight measurements was collected from all women age 15-49 and children 0-5. Hence in this case, there is no subsample selection. */ gen subsample = . label var subsample "Households selected as part of nutrition subsample" tab subsample, miss ******************************************************************************** *** Step 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)(15/16=4)(12=5)(17=6)(98=.) 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 clonevar sex = hv104 label var sex "Sex of household member" //Age of household member codebook hv105, tab (100) 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 (10) 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, miss //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 //Caste of the household head lookfor caste clonevar caste_hh = sh46 label var caste_hh "Caste of household head" clonevar caste_scst = sh36 label var caste_scst "Caste grouping" //Subnational region /*NOTE: The sample for the India DHS 2015-16 was designed to provide estimates of key indicators for the country as a whole, for urban and rural areas separately, and for each of the 36 states/union territories and the 157 districts (p.1). However, it is not clear in the report whether the nutrition indicator for men is representative at the district level. */ clonevar region = hv024 lab var region "Region for subnational decomposition" codebook region, tab (99) 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 *** ******************************************************************************** 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 //Check that values for 0 are less than 1% 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 /*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==8 & hv109!=0 //9, 99 and 8, 98 are missing or non-applicable /*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 India, the official school entrance age is 6 years. So, age range is 6-14 (=6+8) Source: "http://data.uis.unesco.org/?ReportId=163" Go to Education>Education>System>Official entrance age to primary education. Look at the starting age and add 8. */ /*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 *** ******************************************************************************** lookfor body mass codebook ha40 hb40 foreach var in ha40 hb40 { gen inf_`var' = 1 if `var'!=. bysort sex: tab age inf_`var' //women: 15-49 //men:15-54 (subsample using state module, see p.3 & 4 of report) drop inf_`var' } *** *** BMI Indicator for Women 15-49 years *** ******************************************************************* gen f_bmi = ha40/100 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) */ *** BMI Indicator for Men 15-54 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 */ *** BMI-for-age for individuals 15-19 years and BMI for individuals 20-49 years *** ******************************************************************* gen low_bmi_byage = 0 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 men, as well as BMI-for-age for teenagers */ replace low_bmi_byage = . if f_low_bmi==. & m_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_adults_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 *** ******************************************************************************** /*NOTE that the hh_no_underweight or hh_no_stunting variables takes value 1 if: (a) no any eligible children in the hh is undernourished or (b) there are no eligible children in the hh. The variable takes values 0 for those households that have at least one measured child undernourished. The variable has missing values only when there is missing info in nutrition for ALL eligible children in the household */ *** 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 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==1 //Replace households as non-deprived if there is no eligible population 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 /*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, 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, 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) replace electricity = . if electricity==9 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. We also checked the country reports on how the sanitation categories have been grouped. In cases of mismatch, we have followed the country report */ clonevar toilet = hv205 codebook toilet, tab(30) codebook hv225, tab(30) clonevar shared_toilet = hv225 //0=no;1=yes;.=missing /*NOTE: In the Indian DHS 2015-16 report, open defecation is identified neither as improved or unimproved sanitation facilities. However, open defecation is coded as unimproved (deprived) following the MDG guidelines*/ gen toilet_mdg = ((toilet<23 | toilet==41) & 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 | toilet==41) & 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 *** ******************************************************************************** /*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. */ clonevar water = hv201 clonevar timetowater = hv204 codebook water, tab(99) clonevar ndwater = hv202 //Non-drinking water: no observation for India DHS 2015-16 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, there is no data on the water source for non-drinking activities for India DHS 2015-16. According to the country report, because the quality of bottled water is not known, households using bottled water are classified as using unimproved source in accordance with the practice of the WHO-UNICEF Joint Monitoring Programme for Water Supply and Sanitation(p.24) or p.55 of the PDF file of the report. We follow the country approach. The category other water sources is neither listed as improved or non-improved in the country report (Table 2.1, p.24). As such, we follow the MDG standard where 'other drinking sources' are listed as unimproved source. The category 'community RO plant' as source of drinking water is listed as improved source in the country report (Table 2.1, p.24). As such we follow the country report. */ gen water_mdg = 1 if water==11 | water==12 | water==13 | water==21 | /// water==31 | water==41 | water==51 | water==72 /*Non deprived if water is "piped into dwelling", "piped to yard/plot", "public tap/standpipe", "tube well or borehole", "protected well", "protected spring", "rainwater", "community RO plant" - following report (p.24) */ replace water_mdg = 0 if water==32 | water==42 | water==43 | /// water==61 | water==62 | water==96 | water==71 /*Deprived if it is "unprotected well", "unprotected spring", "tanker truck" "surface water (river/lake, etc)", "cart with small tank","other" 71"bottled water" - following report (p.24) */ 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 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<=13 | floor==96 //Deprived if "mud/earth", "sand", "dung", "other" /* NOTE: The non-improved categories in India DHS 2015-16 include: 11"mud/clay/earth"; 12"sand"; 13"dung" */ 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==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. Note: In the context of India, the DHS country specific questionnaire has identified roof made from 'loosely packed stone' as rudimentary roofing material (p. 11). As such this category is identified as non-imporved in our work. */ clonevar roof = hv215 codebook roof, tab(99) gen roof_imp = 1 replace roof_imp = 0 if roof<=25 | roof==96 /*Deprived if "no roof" "thatch/palm leaf" "mud/earth/lump of earth" "sod/grass" "plastic/polythene sheeting" "rustic mat" "wood planks" "cardboard" "canvas/tent" "unburnt bricks" "other" "loosely packed stone"*/ 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 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" */ /*NOTE: In India DHS 2015-16, three of the cooking fuel categories, that is, "kerosene", "other" and "no food cooked in house" was neiter listed as clean fuel or solid fuel (Table 2.3, p.26 or p57 of PDF file). As such for the purpose of the standard MPI, we follow the MDG approcah of coding these 3 categories as clean fuel for cooking (non-deprived). */ 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 hv244 clonevar television = hv208 clonevar bw_television = sh37i clonevar radio = hv207 clonevar telephone = hv221 clonevar mobiletelephone = hv243a clonevar refrigerator = hv209 clonevar car = hv212 clonevar bicycle = hv210 clonevar motorbike = hv211 clonevar computer = sh37o 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 } //Missing values replaced //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 caste_hh caste_scst /// strata psu weight area relationship sex age agec7 agec4 marital hhsize /// region year_interview month_interview date_interview /// d_cm d_nutr d_satt d_educ d_elct d_wtr d_sani d_hsg d_ckfl d_asst order hh_id ind_id ccty ccnum cty survey year subsample caste_hh caste_scst /// strata psu weight area relationship sex age agec7 agec4 marital hhsize /// region year_interview month_interview date_interview /// d_cm d_nutr d_satt d_educ d_elct d_wtr d_sani d_hsg d_ckfl d_asst *** Sort, compress and save data for estimation *** sort ind_id compress save "$path_out/ind_dhs15-16_pov.dta", replace log close