******************************************************************************** /* Citation: Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. 2018 Global Multidimensional Poverty Index - Senegal DHS 2016 [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/Senegal DHS 2016" global path_out "D:/pov" global path_logs "D:/logs" global path_ado "D:/ado" *** Log file *** log using "$path_logs/sen_dhs16_dataprep.log", replace ******************************************************************************** *** SENEGAL DHS 2016 *** ******************************************************************************** ******************************************************************************** *** Step 1: Data preparation *** Selecting variables from KR, BR, IR, & MR recode & merging with PR recode ******************************************************************************** /*Senegal DHS 2016: Anthropometric information were recorded for all eligible children age 0-59 months. Anthropometric information was not collected from adult women and men */ ******************************************************************************** *** Step 1.1 KR - CHILDREN's RECODE (under 5) ******************************************************************************** use "$path_in/SNKR7IFL.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 //Senegal DHS 2015-16: 308 observations deleted duplicates report ind_id duplicates tag ind_id, gen(duplicates) tab b16 if duplicates!=0 tab hw13 if duplicates!=0 /*For children not listed in the household, create a false household line. We will check at merging stage. However,these children do not have info on nutrition */ bys ind_id: gen line = (_n) replace ind_id = v001*1000000 + v002*100 + (line+90) if duplicate!=0 //We assume consecutive hh line starting at 90 duplicates report ind_id //We should not have any duplicates at this stage gen child_KR=1 //Generate identification variable for observations in KR recode /* 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_sen" lab var datalab "Working file" *** Next check the variables that WHO ado needs to calculate the z-scores: *** sex, age, weight, height, measurement, oedema & child sampling weight *** Variable: SEX *** tab b4, miss //"1" for male ;"2" for female tab b4, nol clonevar gender = b4 desc gender tab gender *** Variable: AGE *** tab hw1, miss codebook hw1 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 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 replace height = . if hw3>=9990 //All missing values or out of range are replaced as "." tab hw13 hw3 if hw3>=9990 | hw3==., miss desc height summ height *** Variable: MEASURED STANDING/LYING DOWN *** codebook hw15 gen measure = "l" if hw15==1 //Child measured lying down replace measure = "h" if hw15==2 //Child measured standing up replace measure = " " if hw15==9 | hw15==0 | hw15==. //Replace with " " if unknown desc measure tab measure *** Variable: OEDEMA *** lookfor oedema gen str1 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 */ use "$path_out/children_nutri_sen_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 count if _fwei==1 | _flen==1 /* Note: in the context of Senegal DHS 2016, 140 children were replaced as '.' because they have extreme z-scores that are biologically implausible.*/ //Retain relevant variables: keep ind_id child_KR v001 v002 b16 v135 underweight stunting wasting order ind_id child_KR v001 v002 b16 v135 underweight stunting wasting sort ind_id duplicates report ind_id //Erase files from folder: erase "$path_out/children_nutri_sen_z_rc.xls" erase "$path_out/children_nutri_sen_prev_rc.xls" erase "$path_out/children_nutri_sen_z_rc.dta" //Save a temp file for merging with PR: save "$path_out/SEN16_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/SNBR7IFL.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 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 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 Senegal, these figures are identical 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 household 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/SEN16_BR.dta", replace ******************************************************************************** *** Step 1.3 IR - WOMEN's RECODE *** (All eligible females 15-49 years in the household) ******************************************************************************** use "$path_in/SNIR7IFL.dta", clear *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** v003=respondent's line number gen double ind_id = v001*1000000 + v002*100 + v003 format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id gen women_IR=1 //Identification variable for observations in IR recode //Retain relevant variables: keep ind_id women_IR v003 v005 v012 v201 v206 v207 order ind_id women_IR v003 v005 v012 v201 v206 v207 sort ind_id //Save a temp file for merging with PR: save "$path_out/SEN16_IR.dta", replace ******************************************************************************** *** Step 1.4 IR - WOMEN'S RECODE *** (Girls 15-19 years in the household) ******************************************************************************** /*Note: In the case of Senegal DHS 2016, anthropometric data was NOT collected from adults.*/ ******************************************************************************** *** Step 1.5 MR - MEN'S RECODE ***(All eligible man: 15-59 years in the household) ******************************************************************************** use "$path_in/SNMR7IFL.dta", clear *** Generate individual unique key variable required for data merging *** mv001=cluster number; *** mv002=household number; *** mv003=respondent's line number gen double ind_id = mv001*1000000 + mv002*100 + mv003 format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id gen men_MR=1 //Identification variable for observations in MR recode //Retain relevant variables: keep ind_id men_MR mv003 mv005 mv012 mv201 mv206 mv207 order ind_id men_MR mv003 mv005 mv012 mv201 mv206 mv207 sort ind_id //Save a temp file for merging with PR: save "$path_out/SEN16_MR.dta", replace ******************************************************************************** *** Step 1.6 MR - MEN'S RECODE ***(Boys 15-19 years in the household) ******************************************************************************** /*Note: In the case of Senegal DHS 2016, anthropometric data was NOT collected for men */ ******************************************************************************** *** Step 1.7 PR - HOUSEHOLD MEMBER'S RECODE ******************************************************************************** use "$path_in/SNPR7IFL.dta", clear gen cty = "Senegal" gen ccty = "SEN" gen year = "2016" gen survey = "DHS" gen ccnum = 686 *** Generate a household unique key variable at the household level using: ***hv001=cluster number ***hv002=household number gen double hh_id = hv001*10000 + hv002 format hh_id %20.0g label var hh_id "Household ID" codebook hh_id *** Generate individual unique key variable required for data merging using: *** hv001=cluster number; *** hv002=household number; *** hvidx=respondent's line number. gen double ind_id = hv001*1000000 + hv002*100 + hvidx format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id sort hh_id ind_id ******************************************************************************** *** Step 1.8 DATA MERGING ******************************************************************************** *** Merging BR Recode ***************************************** merge 1:1 ind_id using "$path_out/SEN16_BR.dta" drop _merge erase "$path_out/SEN16_BR.dta" *** Merging IR Recode ***************************************** merge 1:1 ind_id using "$path_out/SEN16_IR.dta" tab women_IR hv117, miss col drop _merge erase "$path_out/SEN16_IR.dta" *** Merging MR Recode ***************************************** merge 1:1 ind_id using "$path_out/SEN16_MR.dta" tab men_MR hv118, miss col drop _merge erase "$path_out/SEN16_MR.dta" *** Merging KR Recode ***************************************** merge 1:1 ind_id using "$path_out/SEN16_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 //Created 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/SEN16_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 Senegal DHS 2016, 1,349 (3.22%) 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 adult men. These households will not have information on relevant indicators of health. As such, these households are considered as non-deprived in those relevant indicators. */ *** No Eligible Women 15-49 years ***************************************** gen fem_eligible = (hv117==1) bys hh_id: egen hh_n_fem_eligible = sum(fem_eligible) //Number of eligible women for interview in the hh gen no_fem_eligible = (hh_n_fem_eligible==0) //Takes value 1 if the household had no eligible females for an interview lab var no_fem_eligible "Household has no eligible women" tab no_fem_eligible, miss *** No Eligible Men 15-54 years ***************************************** gen male_eligible = (hv118==1) bys hh_id: egen hh_n_male_eligible = sum(male_eligible) //Number of eligible men for interview in the hh gen no_male_eligible = (hh_n_male_eligible==0) //Takes value 1 if the household had no eligible males for an interview lab var no_male_eligible "Household has no eligible man" tab no_male_eligible, miss *** No Eligible Children 0-5 years ***************************************** gen child_eligible = (hv120==1) bys hh_id: egen hh_n_children_eligible = sum(child_eligible) //Number of eligible children for anthropometrics gen no_child_eligible = (hh_n_children_eligible==0) //Takes value 1 if there were no eligible children for anthropometrics lab var no_child_eligible "Household has no children eligible" tab no_child_eligible, miss *** No Eligible Women and Men *********************************************** /*NOTE: In the DHS datasets, we use this variable as a control variable for the child mortality indicator if mortality data was collected from women and men. If child mortality was only collected from women, the we use 'no_fem_eligible' as the eligibility criteria */ gen no_adults_eligible = (no_fem_eligible==1 & no_male_eligible==1) //Takes value 1 if the household had no eligible men & women for an interview lab var no_adults_eligible "Household has no eligible women or men" tab no_adults_eligible, miss *** No Eligible Children and Women *********************************************** /*NOTE: In the DHS datasets, we use this variable as a control variable for the nutrition indicator if nutrition data is present for children and women. In the case of Senegal, this variable is created as empty variable since nutrition data was not collected from adults. */ gen no_child_fem_eligible = . 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. In the case of Senegal, this variable is created as empty variable since nutrition data was not collected from adults. */ gen no_eligibles = . lab var no_eligibles "Household has no eligible women, men, or children" tab no_eligibles, miss *** No Eligible Subsample ***************************************** /*hv042 (household selected for hemoglobin) is essentially a variable that indicates whether there is selection of a subsample for anthropometric data. In the case of Senegal, anthropometric data was collected from all children under 5 hence there is no subsample selection. In this case, the variable is created as an empty variable. */ gen no_hem_eligible = . 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 sort hh_id ind_id ******************************************************************************** *** Step 1.11 SUBSAMPLE VARIABLE *** ******************************************************************************** /* The hv042 (household selected for hemoglobin) is a variable that indicates whether there is selection of subsample for anthropometric measures. In the case of Senegal, anthropometric data was collected from all children under 5 hence there is no subsample selection. In this case, the subsample variable is created as an empty variable. */ 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 9=2)(3=3)(11=3)(4/8=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 (20) recode marital (0=1)(1=2) 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 //Religion of the household head lookfor religion gen religion_hh = . label var religion_hh "Religion of household head" //Ethnicity of the household head lookfor ethnicity gen ethnic_hh = . label var ethnic_hh "Ethnicity of household head" //Subnational region /*NOTE: Senegal has 14 administrative regions, but the data is representative by 4 "grands regions" or categories of regions, as defined on page 7 of the country report */ codebook hv024, tab (99) gen region = . replace region = 1 if hv024==11 | hv024==8 | hv024==4 replace region = 2 if hv024==1 | hv024==7 replace region = 3 if hv024==3 | hv024==9 | hv024==6 | hv024==12 replace region = 4 if hv024==5 | hv024==10 | hv024==13 | hv024==14 | hv024==2 label define lab_reg 1"Nord" 2"Ouest" 3"Centre" 4"Sud" label values region lab_reg lab var region "Region for subnational decomposition" codebook region, tab (100) 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 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) replace years_edu6 = . if eduyears==. bysort hh_id: egen hh_years_edu6_1 = max(years_edu6) gen hh_years_edu6 = (hh_years_edu6_1==1) replace hh_years_edu6 = . if hh_years_edu6_1==. replace hh_years_edu6 = . if hh_years_edu6==0 & no_missing_edu==0 lab var hh_years_edu6 "Household has at least one member with 6 years of edu" ******************************************************************************** *** Step 2.2 Child School Attendance *** ******************************************************************************** codebook hv121, tab (10) clonevar attendance = hv121 recode attendance (2=1) codebook attendance, tab (10) replace attendance = 0 if (attendance==9 | attendance==.) & hv109==0 /*In some countries, they don't assess attendance for those with no educational attainment. These are replaced with a '0'*/ replace attendance = . if attendance==9 & hv109!=0 //Missing values replaced /*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) /* In Senegal, 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 2.3b Child Nutrition *** ******************************************************************************** *** Child Underweight Indicator *** ************************************************************************ bysort hh_id: egen temp = max(underweight) gen hh_no_underweight = (temp==0) //Takes value 1 if no child in the hh is underweight replace hh_no_underweight = . if temp==. replace hh_no_underweight = 1 if no_child_eligible==1 //Households with no eligible children will receive a value of 1 lab var hh_no_underweight "Household has no child underweight - 2 stdev" drop temp *** Child Stunting Indicator *** ************************************************************************ bysort hh_id: egen temp = max(stunting) gen hh_no_stunting = (temp==0) //Takes value 1 if no child in the hh is stunted replace hh_no_stunting = . if temp==. replace hh_no_stunting = 1 if no_child_eligible==1 lab var hh_no_stunting "Household has no child stunted - 2 stdev" drop temp *** Child Either Stunted or Underweight Indicator *** ************************************************************************ gen uw_st = 1 if stunting==1 | underweight==1 replace uw_st = 0 if stunting==0 & underweight==0 replace uw_st = . if stunting==. & underweight==. bysort hh_id: egen temp = max(uw_st) gen hh_no_uw_st = (temp==0) //Takes value 1 if no child in the hh is underweight or stunted replace hh_no_uw_st = . if temp==. replace hh_no_uw_st = 1 if no_child_eligible==1 //Households with no eligible children will receive a value of 1 lab var hh_no_uw_st "Household has no child underweight or stunted" drop temp ******************************************************************************** *** Step 2.3c Household Nutrition Indicator *** ******************************************************************************** /* The indicator takes value 1 if there is no children under 5 underweight or stunted. It also takes value 1 for the households that have no eligible children. The indicator takes value missing "." only if all 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_uw_st==0 replace hh_nutrition_uw_st = . if hh_no_uw_st==. replace hh_nutrition_uw_st = 1 if no_child_eligible==1 /*We replace households that do not have the applicable population, that is, children 0-5, as non-deprived in nutrition*/ lab var hh_nutrition_uw_st "Household has no child underweight or stunted" ******************************************************************************** *** 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) 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 Senegal report, the category "latrine with manual flush" is considered improved (table 2.2, p.13) */ gen toilet_mdg = (toilet<=24 & 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<=24 & shared_toilet==1 /*Household is assigned a value of '0' if it uses improved sanitation but shares toilet with other households */ 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(100) clonevar ndwater = hv202 //Non-drinking water tab hv202 if water==71 /*Because the quality of bottled water is not known, households using bottled water for drinking are classified as using an improved or unimproved source according to their water source for non-drinking activities such as cooking and hand washing. However, it is important to note that households using bottled water for drinking are classified as unimproved source if this is explicitly mentioned in the country report. */ gen water_mdg = 1 if water==11 | water==12 | water==13 | water==14 | /// water==21 | water==31 | water==41 | water==51 | water==71 /*Non deprived if water is "piped into dwelling", "piped to yard/plot", "public tap/standpipe", "tube well or borehole", "protected well", "protected spring", "rainwater", "bottled water" */ replace water_mdg = 0 if water==32 | water==42 | water==43 | /// water==61 | water==62 | water==96 /*Deprived if it is "unprotected well", "unprotected spring", "tanker truck" "surface water (river/lake, etc)", "cart with small tank","other" */ replace water_mdg = 0 if water_mdg==1 & timetowater >= 30 & timetowater!=. & /// timetowater!=996 & timetowater!=998 & timetowater!=999 //Deprived if water is at more than 30 minutes' walk (roundtrip) replace water_mdg = . if water==. | water==99 replace water_mdg = 0 if water==71 & /// (ndwater==32 | ndwater==42 | ndwater==43 | /// ndwater==61 | ndwater==62 | ndwater==96) /*Households using bottled water for drinking are classified as using an improved or unimproved source according to their water source for non-drinking activities */ lab var water_mdg "Household has drinking water with MDG standards (considering distance)" tab water water_mdg, miss ******************************************************************************** *** Step 2.8 Housing *** ******************************************************************************** /* Members of the household are considered deprived if the household has a dirt, sand or dung floor */ clonevar floor = hv213 codebook floor, tab(99) gen floor_imp = 1 replace floor_imp = 0 if floor<=12 | floor==96 //Deprived if "mud/earth", "sand", "dung", "other" replace floor_imp = . if floor==. | floor==99 lab var floor_imp "Household has floor that it is not earth/sand/dung" tab floor floor_imp, miss /* Members of the household are considered deprived if the household has walls 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 */ clonevar roof = hv215 codebook roof, tab(99) gen roof_imp = 1 replace roof_imp = 0 if roof<=23 | roof==96 /*Deprived if "no roof" "thatch/palm leaf" "mud/earth/lump of earth" "sod/grass" "plastic/polythene sheeting" "rustic mat" "cardboard" "canvas/tent" "wood planks/reused wood" "unburnt bricks" "other"*/ replace roof_imp = . if roof==. | roof==99 lab var roof_imp "Household has roof that it is not of low quality materials" tab roof roof_imp, miss /*Household is deprived in housing if the roof, floor OR walls uses low quality materials.*/ gen housing_1 = 1 replace housing_1 = 0 if floor_imp==0 | wall_imp==0 | roof_imp==0 replace housing_1 = . if floor_imp==. & wall_imp==. & roof_imp==. lab var housing_1 "Household has roof, floor & walls that it is not low quality material" tab housing_1, miss ******************************************************************************** *** Step 2.9 Cooking Fuel *** ******************************************************************************** /* Members of the household are considered deprived if the household cooks with solid fuels: wood, charcoal, crop residues or dung. "Indicators for Monitoring the Millennium Development Goals", p. 63 */ clonevar cookingfuel = hv226 codebook cookingfuel, tab(99) gen cooking_mdg = 1 replace cooking_mdg = 0 if cookingfuel>5 & cookingfuel<95 replace cooking_mdg = . if cookingfuel==. | cookingfuel==99 lab var cooking_mdg "Household has cooking fuel by MDG standards" /* Non deprived if: "electricity", "lpg", "natural gas", "biogas", "kerosene" , "no food cooked in household", "other" Deprived if: "coal/lignite", "charcoal", "wood", "straw/shrubs/grass" "agricultural crop", "animal dung" */ tab cookingfuel cooking_mdg, miss /*Note that in Senegal DHS 2016, the category 'other' cooking fuel is not identified as solid fuel. Hence this particular category is identified as 'non-deprived' */ ******************************************************************************** *** 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. */ clonevar television = hv208 gen bw_television = . clonevar radio = hv207 clonevar telephone = hv221 clonevar mobiletelephone = hv243a clonevar refrigerator = hv209 clonevar car = hv212 clonevar bicycle = hv210 clonevar motorbike = hv211 clonevar computer = sh110n 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 /// 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 /// 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/sen_dhs16_pov.dta", replace log close