******************************************************************************** /* Suggested citation: Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. 2019 Global Multidimensional Poverty Index - Tajikistan DHS 2017 [STATA do-file]. Retrieved from: https://ophi.org.uk/multidimensional-poverty-index/mpi-resources/ For further queries, please 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/Tajikistan DHS 2017" global path_out "G:/GMPI 2.0/cdta" global path_ado "T:/GMPI 2.0/ado" ******************************************************************************** *** TAJIKISTAN DHS 2017 *** ******************************************************************************** ******************************************************************************** *** Step 1: Data preparation *** Selecting variables from KR, BR, IR, & MR recode & merging with PR recode ******************************************************************************** /*Tajikistan DHS 2017: In all households, height and weight measurements were recorded for children age 0-59 months and women age 15-49 (p.3). */ ******************************************************************************** *** Step 1.1 PR - INDIVIDUAL RECODE *** (Children under 5 years) ******************************************************************************** /*The purpose of step 1.1 is to compute anthropometric measures for children under 5 years.*/ use "$path_in/TJPR70FL.DTA", clear *** 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 tab hv120, miss // 6,243 children under 5 are eligible for anthropometric measurement count if hc1!=. //All 6,243 children under 5 have information on age in months tab hv105 if hc1!=. /*A cross check with the age in years reveal that all are within the 5 year age group */ tab hc13 if hc1!=., miss /*Of the 6,243 children, 6,117 (98%) have been measured while 126 have missing information */ tab hc13 if hc1!=. & hc3<=9990, miss tab hc13 if hc1!=. & hc2<=9990, miss /*For example, height and weight data is available for all 6,117 children who have been successfully measured */ /*Following the checks carried out above, we keep only eligible children in this section since the interest is to generate measures for children under 5*/ keep if hv120==1 count //6,243 children under 5 *** Check the variables to calculate the z-scores: *** Variable: SEX *** desc hc27 hv104 /*hc27=sex of the child from biomarker questionnaire; hv104=sex from household roaster */ compare hc27 hv104 //hc27 should match with hv104 tab hc27, miss //"1" for male ;"2" for female tab hc27, nol clonevar gender = hc27 tab gender *** Variable: AGE *** tab hc1, miss codebook hc1 clonevar age_months = hc1 desc age_months sum age_months gen mdate = mdy(hc18, hc17, hc19) gen bdate = mdy(hc30, hc16, hc31) if hc16 <= 31 //Calculate birth date in days from date of interview replace bdate = mdy(hc30, 15, hc31) if hc16 > 31 //If date of birth of child has been expressed as more than 31, we use 15 gen age = (mdate-bdate)/30.4375 //Calculate age in months with days expressed as decimals gen str6 ageunit = "months" lab var ageunit "Months" *** Variable: BODY WEIGHT (KILOGRAMS) *** codebook hc2, tab (9999) gen weight = hc2/10 //We divide it by 10 in order to express it in kilograms tab hc2 if hc2>9990, miss nol //Missing values are 9994 to 9996 replace weight = . if hc2>=9990 //All missing values or out of range are replaced as "." tab hc13 hc2 if hc2>=9990 | hc2==., miss //hw13: result of the measurement desc weight sum weight *** Variable: HEIGHT (CENTIMETERS) codebook hc3, tab (9999) gen height = hc3/10 //We divide it by 10 in order to express it in centimeters tab hc3 if hc3>9990, miss nol //Missing values are 9994 to 9996 replace height = . if hc3>=9990 //All missing values or out of range are replaced as "." tab hc13 hc3 if hc3>=9990 | hc3==., miss desc height sum height *** Variable: MEASURED STANDING/LYING DOWN *** codebook hc15 gen measure = "l" if hc15==1 //Child measured lying down replace measure = "h" if hc15==2 //Child measured standing up replace measure = " " if hc15==9 | hc15==0 | hc15==. //Replace with " " if unknown desc measure tab measure *** Variable: OEDEMA *** lookfor oedema gen oedema = "n" //It assumes no-one has oedema desc oedema tab oedema *** Variable: SAMPLING WEIGHT *** /* We don't require individual weight to compute the z-scores of a child. So we assume all children in the sample have the same weight */ gen sw = 1 desc sw summ sw *** Indicate to STATA where the igrowup_restricted.ado file is stored: ***Source of ado file: 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.*/ 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_tjk" lab var datalab "Working file" /*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 "$path_out/children_nutri_tjk_z_rc.dta", clear *** Standard MPI indicator *** //Takes value 1 if the child is under 2 stdev below the median & 0 otherwise gen underweight = (_zwei < -2.0) replace underweight = . if _zwei == . | _fwei==1 lab var underweight "Child is undernourished (weight-for-age) 2sd - WHO" tab underweight, miss gen stunting = (_zlen < -2.0) replace stunting = . if _zlen == . | _flen==1 lab var stunting "Child is stunted (length/height-for-age) 2sd - WHO" tab stunting, miss gen wasting = (_zwfl < - 2.0) replace wasting = . if _zwfl == . | _fwfl == 1 lab var wasting "Child is wasted (weight-for-length/height) 2sd - WHO" tab wasting, miss count if _fwei==1 | _flen==1 /*Note: Tajikistan DHS 2017: 38 children were replaced as missing because they have extreme z-scores which are biologically implausible. */ //Retain relevant variables: keep ind_id underweight* stunting* wasting* order ind_id underweight* stunting* wasting* sort ind_id duplicates report ind_id save "$path_out/TJK17_PR_child.dta", replace //Erase files from folder: erase "$path_out/children_nutri_tjk_z_rc.xls" erase "$path_out/children_nutri_tjk_prev_rc.xls" erase "$path_out/children_nutri_tjk_z_rc.dta" ******************************************************************************** *** Step 1.2 BR - BIRTH RECODE *** (All females 15-49 years who ever gave birth) ******************************************************************************** /*The purpose of step 1.2 is to identify children under 18 who died in the last 5 years prior to the survey date.*/ use "$path_in/TJBR70FL.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 codebook b5, tab (10) gen child_died = 1 if b5==0 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 //Tajikistan DHS 2017: 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 "$path_out/TJK17_BR.dta", replace ******************************************************************************** *** Step 1.3 IR - WOMEN's RECODE *** (Eligible female 15-49 years in the household) ******************************************************************************** /*The purpose of step 1.3 is to identify all deaths that are reported by eligible women.*/ use "$path_in/TJIR70FL.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 tab v012, miss codebook v201 v206 v207,tab (999) //Women 15-49 years provided information on child mortality. 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 "$path_out/TJK17_IR.dta", replace ******************************************************************************** *** Step 1.4 PR - INDIVIDUAL RECODE *** (Girls 15-19 years in the household) ******************************************************************************** /*The purpose of step 1.4 is to compute bmi-for-age for girls 15-19 years. */ use "$path_in/TJPR70FL.dta", clear *** Generate individual unique key variable required for data merging using: gen double ind_id = hv001*1000000 + hv002*100 + hvidx format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id *** Identify anthropometric sample for girls count if hv105>=15 & hv105<=19 & hv104==2 tab ha13 if hv105>=15 & hv105<=19 & hv104==2, miss /*Total number of girls 15-19 years who live in household selected for male survey and have anthropometric data: 1,959 */ tab ha13 hv117 if hv105>=15 & hv105<=19 & hv104==2, miss tab ha13 hv103 if hv105>=15 & hv105<=19 & hv104==2, miss /*41 of the 1,959 women 15-19 years are identified as non-eligible for the female interview as they did not sleep the night before in the household. Hence they will not have data on child mortality but they have anthropometric information as they were measured. */ *** Keep relevant sample keep if hv105>=15 & hv105<=19 & hv104==2 count //Total girls 15-19 years: 1,959 ***Variables required to calculate the z-scores to produce BMI-for-age: *** Variable: SEX *** codebook hv104, tab (9) clonevar gender = hv104 //2:female *** Variable: AGE *** desc hv807c ha32 gen age_month = hv807c - ha32 lab var age_month "Age in months, individuals 15-19 years (girls)" tab age_month, miss /*Note: For a couple of observations, we find that the age in months is beyond 228 months. In this secton, while calculating the z-scores, these cases will be excluded. However, in section 2.3, we will take the BMI information of these girls. */ *** Variable: AGE UNIT *** gen str6 ageunit = "months" lab var ageunit "Months" *** Variable: BODY WEIGHT (KILOGRAMS) *** codebook ha2, tab (9999) count if ha2>9990 tab ha13 if ha2>9990, miss gen weight = ha2/10 if ha2<9990 /*Weight information from girls. We divide it by 10 in order to express it in kilograms. Missing values or out of range are identified as "." */ sum weight *** Variable: HEIGHT (CENTIMETERS) codebook ha3, tab (9999) count if ha3>9990 tab ha13 if ha3>9990, miss gen height = ha3/10 if ha3<9990 /*Height information from girls. We divide it by 10 in order to express it in centimeters. Missing values or out of range are identified as "." */ sum height *** Variable: OEDEMA // We assume all individuals in the sample have no oedema gen oedema = "n" tab oedema *** Variable: SAMPLING WEIGHT *** /* We don't require individual weight to compute the z-scores. We assume all individuals in the sample have the same sample weight */ gen sw = 1 sum sw /* For this part of the do-file we use the WHO AnthroPlus software. This is to calculate the z-scores for young individuals aged 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 + "$path_ado/who2007_stata" /* We use 'reflib' to specify the package directory where the .dta files containing the WHO Growth reference are stored. Note that we use strX to specify the length of the path in string. */ gen str100 reflib = "$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 = "$path_out" lab var datalib "Directory for datafiles" /* We use datalab to specify the name that will prefix the output files that will be produced from using this ado file*/ gen str30 datalab = "girl_nutri_tjk" lab var datalab "Working file" /*We now run the command to calculate the z-scores with the adofile */ who2007 reflib datalib datalab gender age_month ageunit weight height oedema sw /*We now turn to using the dta file that was created and that contains the calculated z-scores to compute BMI-for-age*/ use "$path_out/girl_nutri_tjk_z.dta", clear gen z_bmi = _zbfa replace z_bmi = . if _fbfa==1 /*Tajikistan DHS 2017: 1 girl 15-19 years were replaced as missing because she has extreme z-scores which are biologically implausible. */ lab var z_bmi "z-score bmi-for-age WHO" *** Standard MPI indicator *** /*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 girl_PR=1 //Identification variable for girls 15-19 years in PR recode //Retain relevant variables: keep ind_id girl_PR age_month low_bmiage* order ind_id girl_PR age_month low_bmiage* sort ind_id save "$path_out/TJK17_PR_girls.dta", replace //Erase files from folder: erase "$path_out/girl_nutri_tjk_z.xls" erase "$path_out/girl_nutri_tjk_prev.xls" erase "$path_out/girl_nutri_tjk_z.dta" ******************************************************************************** *** Step 1.5 MR - MEN'S RECODE ***(Eligible man 15-59 years in the household) ******************************************************************************** //Tajikistan DHS 2017: No male recode. ******************************************************************************** *** Step 1.6 PR - INDIVIDUAL RECODE *** (Boys 15-19 years in the household) ******************************************************************************** //Tajikistan DHS 2017: Anthropometric data was not collected from men. ******************************************************************************** *** Step 1.7 PR - HOUSEHOLD MEMBER'S RECODE ******************************************************************************** use "$path_in/TJPR70FL.dta", clear *** Generate a household unique key variable at the household level using: ***hv001=cluster number ***hv002=household number gen double hh_id = hv001*1000 + hv002 format hh_id %20.0g label var hh_id "Household ID" codebook hh_id *** Generate individual unique key variable required for data merging using: *** hv001=cluster number; *** hv002=household number; *** hvidx=respondent's line number. gen double ind_id = hv001*1000000 + hv002*100 + hvidx format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id sort hh_id ind_id ********************************************************************************* *** Step 1.8 DATA MERGING ******************************************************************************** *** Merging BR Recode ***************************************** merge 1:1 ind_id using "$path_out/TJK17_BR.dta" drop _merge erase "$path_out/TJK17_BR.dta" *** Merging IR Recode ***************************************** merge 1:1 ind_id using "$path_out/TJK17_IR.dta" tab women_IR hv117, miss col tab ha65 if hv117==1 & women_IR ==., miss //Total number of eligible women not interviewed drop _merge erase "$path_out/TJK17_IR.dta" /*Check if the number of women in BR recode matches the number of those who provided birth history information in IR recode. */ count if women_BR==1 count if v201!=0 & v201!=. & women_IR==1 /*Check if the number of women in BR and IR recode who provided birth history information matches with the number of eligible women identified by hv117. */ count if hv117==1 count if women_BR==1 | v201==0 count if (women_BR==1 | v201==0) & hv117==1 tab v201 if hv117==1, miss tab v201 ha65 if hv117==1, miss /*Note: Some 0.8% eligible women did not provide information on their birth history. This will result in missing value for the child mortality indicator that we will construct later */ *** Merging 15-19 years: girls ***************************************** merge 1:1 ind_id using "$path_out/TJK17_PR_girls.dta" tab ha13 girl_PR if hv105>=15 & hv105<=19 & hv104==2, miss col drop _merge erase "$path_out/TJK17_PR_girls.dta" *** Merging MR Recode ***************************************** //Tajikistan DHS 2017: No male recode. gen religion_men=. gen ethnic_men=. gen insurance_men=. *** Merging 15-19 years: boys ***************************************** //Tajikistan DHS 2017: Anthropometric data was not collected from men gen age_month_b = . lab var age_month_b "Age in months, individuals 15-19 years (boys)" gen low_bmiage_b = . lab var low_bmiage_b "Teenage low bmi 2sd - WHO (boys)" gen low_bmiage_b_u = . lab var low_bmiage_b_u "Teenage very low bmi 3sd - WHO (boys)" *** Merging KR Recode ***************************************** merge 1:1 ind_id using "$path_out/TJK17_PR_child.dta" tab hv120, miss tab hc13 if hv120==1, miss drop _merge erase "$path_out/TJK17_PR_child.dta" sort ind_id ******************************************************************************** *** Step 1.9 KEEP ONLY DE JURE HOUSEHOLD MEMBERS *** ******************************************************************************** /*The Global MPI is based on de jure (permanent) household members only. As such, non-usual residents will be excluded from the sample. */ clonevar resident = hv102 tab resident, miss label var resident "Permanent (de jure) household member" drop if resident!=1 /*Tajikistan DHS 2017: 575 (1.28%) individuals who were non-usual residents were dropped from the sample. */ ******************************************************************************** *** Step 1.10 KEEP HOUSEHOLDS SELECTED FOR ANTHROPOMETRIC SUBSAMPLE *** *** if relevant ******************************************************************************** /*In a number of DHS surveys, only a subsample of households were selected for anthropometric measure. In such cases, the Global MPI estimation is based on this subsample. As such, we only retain the relevant subsample of households for analyses. */ /* Tajikistan DHS 2017: height and weight measurements were collected from all children under 5 and women 15-49 years. There is no presence of subsample. */ gen subsample = . label var subsample "Households selected as part of nutrition subsample" ******************************************************************************** *** Step 1.11 CONTROL VARIABLES ******************************************************************************** /* Households are identified as having 'no eligible' members if there are no applicable population, that is, children 0-5 years and adult women 15-49 years. These households will not have information on relevant indicators of health. As such, these households are considered as non-deprived in those relevant indicators. */ *** No eligible women 15-59 years *** for adult nutrition indicator *********************************************** tab ha13, miss tab ha13 if hv105>=15 & hv105<=49 & hv104==2, miss gen fem_nutri_eligible = (ha13!=.) tab fem_nutri_eligible, miss bysort hh_id: egen hh_n_fem_nutri_eligible = sum(fem_nutri_eligible) gen no_fem_nutri_eligible = (hh_n_fem_nutri_eligible==0) //Takes value 1 if the household had no eligible women for anthropometrics lab var no_fem_nutri_eligible "Household has no eligible women for anthropometric" drop fem_nutri_eligible hh_n_fem_nutri_eligible tab no_fem_nutri_eligible, miss *** No eligible women 15-49 years *** for child mortality indicator ***************************************** tab hv117, miss tab hv103 hv117, miss /*Only women who slept the night before in the household were eligible for female interview (women's questionnaire). */ 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 women for an interview lab var no_fem_eligible "Household has no eligible women for interview" drop fem_eligible hh_n_fem_eligible tab no_fem_eligible, miss *** No eligible men *** for adult nutrition indicator (if relevant) *********************************************** //Tajikistan DHS 2017: No anthropometric data for adults. gen no_male_nutri_eligible = . lab var no_male_nutri_eligible "Household has no eligible men for anthropometric" *** No eligible men *** for child mortality indicator (if relevant) ***************************************** //Tajikistan DHS 2017: no male recode gen no_male_eligible = . lab var no_male_eligible "Household has no eligible man" *** No eligible children under 5 *** for child nutrition indicator ***************************************** 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 for anthropometric" drop child_eligible hh_n_children_eligible tab no_child_eligible, miss *** No eligible women and men *** for adult nutrition indicator *********************************************** gen no_adults_eligible = (no_fem_nutri_eligible==1) lab var no_adults_eligible "Household has no eligible women or men for anthropometrics" tab no_adults_eligible, miss *** No Eligible Children and Women *** for child and women nutrition indicator *********************************************** gen no_child_fem_eligible = (no_child_eligible==1 & no_fem_nutri_eligible==1) lab var no_child_fem_eligible "Household has no children or women eligible for anthropometric" tab no_child_fem_eligible, miss *** No Eligible Women, Men or Children *** for nutrition indicator *********************************************** gen no_eligibles = (no_fem_nutri_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 *** for hemoglobin ***************************************** 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 members for hemoglobin test lab var no_hem_eligible "Household has no eligible individuals for hemoglobin test" drop hem_eligible hh_n_hem_eligible tab no_hem_eligible, miss sort hh_id ind_id ******************************************************************************** *** Step 1.12 RENAMING DEMOGRAPHIC VARIABLES *** ******************************************************************************** //Sample weight /*Note: DHS sample weight are calculated to six decimals but are presented in the standard recode files without the decimal point. As such, all DHS weights should be divided by 1,000,000 before applying the weights to calculation or analysis. */ desc hv005 clonevar weight = hv005 replace weight = weight/1000000 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" //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) /*Please ensure recoding follows these 5 broad categories: 1"never married" 2"currently married" 3"widowed" 4"divorced" 5"not living together" */ 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 //Subnational region /* The sample for Tajikistan DHS 2017 was designed to yield representative results for the urban and rural areas separately, and for each of the four administrative regions (Districts of Republican Subordination (DRS), Sughd, Khatlon, and Gorno-Badakhshan Autonomous Oblast (GBAO)) and Dushanbe (p.1). */ codebook hv024, tab (99) clonevar region = hv024 tab hv024 region, miss lab var region "Region for subnational decomposition" ******************************************************************************** *** 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 (99) clonevar attendance = hv121 recode attendance (2=1) label define lab_attend 1 "currently attending" 0 "not currently attending" label values attendance lab_attend label var attendance "Attended school during current school year" codebook attendance, tab (99) *** 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>=7 & age<=15) /* Note: In Tajikistan, the official school entrance age to primary school is 7 years. So, age range is 7-15 (=7+8) Source: Country report (p.12) and 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 *** ******************************************************************************** codebook ha40 foreach var in ha40 { gen inf_`var' = 1 if `var'!=. bysort sex: tab age inf_`var' //Tajikistan DHS 2017: women 15-49 years drop inf_`var' } *** *** BMI Indicator for Women 15-59 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" gen f_low_bmi_u = (f_bmi<17) replace f_low_bmi_u = . if f_bmi==. | f_bmi>=99.97 lab var f_low_bmi_u "BMI of women <17" //Note: The BMI threshold applied for destitution is 17 instead of 18.5 *** BMI Indicator for Men *** ******************************************************************* //Note: Tajikistan DHS 2017 has no anthropometric data for men. gen m_bmi = . lab var m_bmi "Male's BMI" gen m_low_bmi = . lab var m_low_bmi "BMI of male < 18.5" gen m_low_bmi_u = . lab var m_low_bmi_u "BMI of male <17" *** 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 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. If there is no male anthropometric data, then 0 changes are made.*/ /*Note: The following command replaces BMI with BMI-for-age for those between the age group of 15-19 by their age in months where information is available */ //Replacement for girls: replace low_bmi_byage = 1 if low_bmiage==1 & age_month!=. replace low_bmi_byage = 0 if low_bmiage==0 & age_month!=. /*Note: The following control variable is applied when there is BMI information for adults 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_adults_eligible==1 //Households take a value of '1' if there is no eligible adult 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_bmiage, miss /*NOTE that hh_no_low_bmiage takes value 1 if: (a) no any eligible individuals in the household has (observed) low BMI or (b) there are no eligible individuals in the household. The variable takes values 0 for those households that have at least one adult with observed low BMI. The variable has a missing value only when there is missing info on BMI for ALL eligible adults in the household */ ******************************************************************************** *** Step 2.3b Child Nutrition *** ******************************************************************************** *** Child Underweight Indicator *** ************************************************************************ *** Standard MPI *** 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 *** ************************************************************************ *** Standard MPI *** 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 //Households with no eligible children will receive a value of 1 lab var hh_no_stunting "Household has no child stunted - 2 stdev" drop temp *** Child Either Underweight or Stunted Indicator *** ************************************************************************ *** Standard MPI *** gen hh_no_uw_st = 1 if hh_no_stunting==1 & hh_no_underweight==1 replace hh_no_uw_st = 0 if hh_no_stunting==0 | hh_no_underweight==0 //Takes value 0 if child in the hh is stunted or underweight replace hh_no_uw_st = . if hh_no_stunting==. & hh_no_underweight==. 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" ******************************************************************************** *** 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 indicator as missing if household has eligible adult and child with missing nutrition information */ replace hh_nutrition_uw_st = . if hh_no_low_bmiage==. & hh_no_uw_st==1 & no_child_eligible==1 /*Replace indicator as missing if household has eligible adult with missing nutrition information and no eligible child for anthropometric measures */ replace hh_nutrition_uw_st = . if hh_no_uw_st==. & hh_no_low_bmiage==1 & no_adults_eligible==1 /*Replace indicator as missing if household has eligible child with missing nutrition information and no eligible adult for anthropometric measures */ replace hh_nutrition_uw_st = 1 if no_eligibles==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 individuals malnourished" tab hh_nutrition_uw_st, miss ******************************************************************************** *** Step 2.4 Child Mortality *** ******************************************************************************** codebook v206 v207 /*v206: number of sons who have died v207 : number of daughters who have died */ egen temp_f = rowtotal(v206 v207), missing //Total child mortality reported by eligible women replace temp_f = 0 if v201==0 //This line replaces women who have never given birth 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 egen child_mortality = rowmax(child_mortality_f) lab var child_mortality "Total child mortality within household" 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 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 *** ******************************************************************************** *** Standard MPI *** /*Members of the household are considered deprived if the household has no electricity */ *************************************************** clonevar electricity = hv206 codebook electricity, tab (9) 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. */ desc hv205 hv225 clonevar toilet = hv205 clonevar shared_toilet = hv225 codebook shared_toilet, tab(99) *** 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*/ ******************************************************************** codebook toilet, tab(99) 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==. //Household is assigned a value of '.' if it has missing information lab var toilet_mdg "Household has improved sanitation" 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. */ desc hv201 hv204 hv202 clonevar water = hv201 clonevar timetowater = hv204 clonevar ndwater = hv202 tab hv202 if water==71, miss /*Households using bottled water are only considered to be using improved water when they use water from an improved source for cooking and personal hygiene. This is because the quality of bottled water is not known. */ *** 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 */ ******************************************************************** codebook water, tab(99) gen water_mdg = 1 if water==11 | water==12 | water==13 | water==14 | /// water==21 | water==31 | water==41 | water==51 | water==71 replace water_mdg = 0 if water==32 | water==42 | water==43 | /// water==61 | water==62 /*Deprived if it is "unprotected well", "unprotected spring", "tanker truck" "surface water (river/lake, etc)", "cart with small tank","other" */ codebook timetowater, tab(9999) replace water_mdg = 0 if water_mdg==1 & timetowater >= 30 & timetowater!=. & /// timetowater!=996 & timetowater!=998 /*Deprived if water source is 30 minutes or more from home, roundtrip. Please check the value assigned to 'in premises'. If this is different from 996, add to the condition accordingly */ replace water_mdg = . if water==. replace water_mdg = 0 if water==71 & /// (ndwater==32 | ndwater==42 | ndwater==43 | /// ndwater==61 | ndwater==62) /*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 safe drinking water on premises" 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<=12 //Deprived if mud/earth, sand, dung, other replace floor_imp = . if floor==. 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. We followed the report's definitions 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, uncovered adobe, plywood, stone with mud, cardboard, reused wood, other */ replace wall_imp = . if wall==. 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. We followed the report's definitions of natural and rudimentary materials. */ clonevar roof = hv215 codebook roof, tab(99) gen roof_imp = 1 replace roof_imp = 0 if roof<=24 /*Deprived if no roof, thatch/palm leaf, sod, cardboard, wood planks */ replace roof_imp = . if roof==. 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 combustible clonevar cookingfuel = hv226 *** Standard MPI *** /* Members of the household are considered deprived if the household uses solid fuels and solid biomass fuels for cooking. */ ***************************************************************** codebook cookingfuel, tab(99) gen cooking_mdg = 1 replace cooking_mdg = 0 if cookingfuel>5 & cookingfuel<95 replace cooking_mdg = . if cookingfuel==. lab var cooking_mdg "Household cooks with clean fuels" /* 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 */ *** Television/LCD TV/plasma TV/color TV/black & white tv lookfor tv television plasma lcd codebook hv208 clonevar television = hv208 lab var television "Household has television" *** Radio/walkman/stereo/kindle lookfor radio walkman stereo codebook hv207 clonevar radio = hv207 lab var radio "Household has radio" *** Handphone/telephone/iphone/mobilephone/ipod lookfor telephone téléphone mobilephone ipod codebook hv221 hv243a clonevar telephone = hv221 replace telephone=1 if telephone!=1 & hv243a==1 //hv243a=mobilephone. Combine information on telephone and mobilephone. tab hv243a hv221 if telephone==1,miss lab var telephone "Household has telephone (landline/mobilephone)" *** Refrigerator/icebox/fridge lookfor refrigerator réfrigérateur codebook hv209 clonevar refrigerator = hv209 lab var refrigerator "Household has refrigerator" *** Car/van/lorry/truck lookfor car voiture truck van //Note: car (sh122f) and truck (sh122h) has been aggregated into hv212 codebook hv212 clonevar car = hv212 lab var car "Household has car" *** Bicycle/cycle rickshaw lookfor bicycle bicyclette codebook hv210 clonevar bicycle = hv210 lab var bicycle "Household has bicycle" *** Motorbike/motorized bike/autorickshaw lookfor motorbike moto codebook hv211 clonevar motorbike = hv211 lab var motorbike "Household has motorbike" *** Computer/laptop/tablet lookfor computer ordinateur laptop ipad tablet codebook hv243e clonevar computer = hv243e lab var computer "Household has computer" *** Animal cart lookfor cart codebook hv243c clonevar animal_cart = hv243c lab var animal_cart "Household has animal cart" *** 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 /*Final check to see the total number of missing values for each variable. Variables should not have at this stage high proportion of missing (unless the information was not collected for the country). If larger than 3%, please check whether it is genuinely missing information. */ mdesc psu strata area age /// hh_years_edu6 hh_child_atten hh_no_uw_st hh_nutrition_uw_st /// hh_mortality_u18_5y electricity toilet_mdg water_mdg housing_1 /// cooking_mdg hh_assets2 television radio telephone refrigerator bicycle /// motorbike car computer animal_cart *** 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] "Tajikistan" char _dta[ccty] "TJK" char _dta[year] "2017" char _dta[survey] "DHS" char _dta[ccnum] "762" 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/tjk_dhs17.dta", replace