******************************************************************************** /* Citation: Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. 2018 Global Multidimensional Poverty Index - Libya PAPFAM 2014 [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/Libya PAPFAM 2014" global path_out "D:/pov" global path_logs "D:/logs" global path_ado "D:/ado" *** Log file *** log using "$path_logs/lby_papfam14_dataprep.log", replace ******************************************************************************** *** LIBYA PAPFAM 2014 *** ******************************************************************************** ******************************************************************************** *** Step 1: Data preparation *** Selecting variables from KR, BR, IR, & MR recode & merging with PR recode ******************************************************************************** /*The questionnaire instructs the enumerator to collect data on all children under 6. It does not collect any anthropometrics on adults. The report indicate that anthropometric data was successfully collected for 82% of children sampled. The majority of those not measured for height and weight were under 6 months old. */ ******************************************************************************** *** Step 1.1 KR - CHILDREN's RECODE (under 5) ******************************************************************************** use "$path_in/HR.dta", clear rename _all, lower /*The HR dta file for Libya PAPFAM 2014 includes all household members. For the purpose of this section, keep only the child sample. Use variable h108a to identify eligible children. NOTE: Sample size of children 0-5 years reported in PAPFAM report is 15,941 */ desc h108a tab h108a, miss keep if h108a!=. count /*NOTE: The data indicate a total of 15,941 children aged 0-5 years. In terms of age in months, these children are between 0-72 months old. However, the Global MPI's child nutrition indicators (malnutrition and stunting) specify child under 5. In other words, the global focus is only on children between 0-59 months. To be consistent with the Global MPI criteria, we have computed BMI-for-age for children from 60-72 months old. This is done in the next section, that is, Step 1.1b */ desc h105a //Age of member in years desc age_months //Age in months tab age_months h105a, miss /*Children who were between the age of 5 years 1 month and up to 5 years 11 months were identified as 5 years old */ keep if age_months>=0 & age_months<=59 /*NOTE: The final sample count of children aged 0-59 months that is included in the Global MPI estimation for Libya PAPFAM 2014 is 13,486 children*/ *** Generate individual unique key variable required for data merging *** cluster=cluster number; *** hhnum=household number; *** h108=line number of eligible child gen double ind_id = cluster*1000000 + hhnum*100 + hln format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id //NOTE: No duplicate observations gen child_CH=1 //Generate identification variable for observations in child recode count if h102==1 /*NOTE: In the context of Libya PAPFAM 2014, all children aged 0-59 months are permenant residents of their HH*/ /* 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_lby" 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 *** lookfor sex tab h103,miss codebook h103,tab(30) //"1" for male ;"2" for female tab h103, nol clonevar gender = h103 desc gender tab gender *** Variable: AGE *** tab age_months, miss clonevar age = age_months gen str6 ageunit = "months" lab var ageunit "Months" *** Variable: BODY WEIGHT (KILOGRAMS) *** tab h904, miss clonevar weight = h904 replace weight = . if h904>=99.9 //All missing values or out of range are replaced as "." tab h907 h904 if h904>=99.9 | h904==., miss //h907: Result of child measurement desc weight summ weight *** Variable: HEIGHT (CENTIMETERS) tab h905, miss clonevar height = h905 replace height = . if h905>=999.9 //All missing values or out of range are replaced as "." tab h907 h905 if h905>=999.9 | h905==., miss desc height summ height *** Variable: MEASURED STANDING/LYING DOWN *** /*The PAPFAM survey provides a variable that controls for this: h906*/ codebook h906, tab (10) gen measure = "l" if h906==1 //Child measured lying down replace measure = "h" if h906==2 //Child measured standing up replace measure = " " if h906==9 | h906==0 | h906==. //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 *** gen sw = hhweight //For household sample weight desc sw summ sw /*We now run the command to calculate the z-scores with the adofile */ igrowup_restricted reflib datalib datalab gender age ageunit weight height /// measure oedema sw /*We now turn to using the dta file that was created and that contains the calculated z-scores to create the child nutrition variables following WHO standards */ use "$path_out/children_nutri_lby_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 Libya PAPFAM 2014, 959 children are replaced as '.' because they have extreme z-scores which are biologically implausible */ //Retain relevant variables: keep ind_id child_CH cluster hhnum h108a underweight stunting wasting order ind_id child_CH cluster hhnum h108a underweight stunting wasting sort ind_id duplicates report ind_id //Erase files from folder: erase "$path_out/children_nutri_lby_z_rc.xls" erase "$path_out/children_nutri_lby_prev_rc.xls" erase "$path_out/children_nutri_lby_z_rc.dta" //Save temp file for future merging save "$path_out/lby14_CH.dta", replace ******************************************************************************** *** Step 1.1b KR - CHILDREN's RECODE (5-6 years) ******************************************************************************** use "$path_in/HR.dta", clear rename _all, lower /*The HR dta file for Libya PAPFAM 2014 includes all household members. For the purpose of this section, keep only the child sample. Use variable h108a to identify eligible children. */ desc h108a tab h108a, miss keep if h108a!=. count //NOTE: We compute BMI-for-age for children from 60-72 months old. keep if age_months>=60 & age_months<=72 /*NOTE: The final sample count of children aged 60-72 months that is included in the Global MPI estimation for Libya PAPFAM 2014 is 2,454 children*/ *** Generate individual unique key variable required for data merging *** cluster=cluster number; *** hhnum=household number; *** h108=line number of eligible child gen double ind_id = cluster*1000000 + hhnum*100 + hln format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id //NOTE: No duplicate observations gen child_CH=1 //Generate identification variable for observations in child recode count if h102==1 /*NOTE: In the context of Libya PAPFAM 2014, all children aged 60-72 months are permenant residents of their HH*/ /* For this part of the do-file we use the WHO AnthroPlus software. This is to calculate the z-scores for children aged 60-72 months. Source of ado file: https://www.who.int/growthref/tools/en/ */ *** Indicate to STATA where the igrowup_restricted.ado file is stored: adopath + "$path_ado/who2007_stata" /* We use 'reflib' to specify the package directory where the .dta files containing the WHO Growth reference are stored. Note that we use strX to specity the length of the path in string. */ gen str100 reflib = "$path_ado/who2007_stata" lab var reflib "Directory of reference tables" /* We use datalib to specify the working directory where the input STATA data set containing the anthropometric measurement is stored. */ gen str100 datalib = "$path_out" lab var datalib "Directory for datafiles" /* We use datalab to specify the name that will prefix the output files that will be produced from using this ado file*/ gen str30 datalab = "children_nutri_lby" 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 *** lookfor sex tab h103,miss codebook h103,tab(30) //"1" for male ;"2" for female tab h103, nol clonevar gender = h103 desc gender tab gender *** Variable: AGE *** tab age_months, miss //Age is measured in months clonevar age = age_months gen str6 ageunit = "months" lab var ageunit "Months" *** Variable: BODY WEIGHT (KILOGRAMS) *** tab h904, miss clonevar weight = h904 replace weight = . if h904>=99.9 //All missing values or out of range are replaced as "." tab h907 h904 if h904>=99.9 | h904==., miss //h907: Result of child measurement desc weight summ weight *** Variable: HEIGHT (CENTIMETERS) tab h905, miss clonevar height = h905 replace height = . if h905>=999.9 //All missing values or out of range are replaced as "." tab h907 h905 if h905>=999.9 | h905==., miss desc height summ height *** Variable: MEASURED STANDING/LYING DOWN *** codebook h906, tab (10) gen measure = "l" if h906==1 //Child measured lying down replace measure = "h" if h906==2 //Child measured standing up replace measure = " " if h906==9 | h906==0 | h906==. //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 *** gen sw = hhweight //For household sample weight desc sw summ sw /*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 "$path_out/children_nutri_lby_z.dta", clear gen z_bmi = _zbfa replace z_bmi = . if _fbfa==1 lab var z_bmi "z-score bmi-for-age WHO" /*Takes value 1 if BMI-for-age is under 2 stdev below the median & 0 otherwise */ gen low_bmiage = (z_bmi < -2.0) replace low_bmiage = . if z_bmi==. lab var low_bmiage "Teenage low bmi 2sd - WHO" //Retain relevant variables: keep ind_id child_CH age_month low_bmiage order ind_id child_CH age_month low_bmiage sort ind_id //Erase files from folder: erase "$path_out/children_nutri_lby_z.xls" erase "$path_out/children_nutri_lby_prev.xls" erase "$path_out/children_nutri_lby_z.dta" //Save a temp file for merging with PR: save "$path_out/lby14_CH_6Y.dta", replace ******************************************************************************** *** Step 1.2 BR - BIRTH RECODE *** (All females 15-49 years who ever gave birth) ******************************************************************************** /*The purpose of step 1.2 is to identify children of any age who died in the last 5 years prior to the survey date.*/ use "$path_in/BH.dta", clear rename _all, lower *** Generate individual unique key variable required for data merging *** cluster=cluster number; *** hhnum=household number; *** wln=respondent's line number gen double ind_id = cluster*1000000 + hhnum*100 + wln format ind_id %20.0g label var ind_id "Individual ID" lookfor interview /*NOTE: In the context of Libya PAPFAM 2014 There are two dates of interview: xhintc - Date of interview (CMC) xwintc - Woman Date of Interview (CMC) For accuracy purpose, we go with the Woman Date of Interview (CMC). */ desc xw215c xw220c xhintc xwintc compare xhintc xwintc gen date_death = xw215c + xw220c //Date of death = date of birth (xw215c) + age at death (xw220c) gen mdead_survey = xwintc - date_death //Months dead from survey = Date of interview (xwintc) - date of death gen ydead_survey = mdead_survey/12 //Years dead from survey sum ydead_survey //There is one case with negative "years dead" drop if ydead_survey<0 codebook w216, tab (10) gen child_died = 1 if w216==2 //Redefine the coding and labels (1=child dead; 0=child alive) replace child_died = 0 if w216==1 replace child_died = . if w216==. label define lab_died 1 "child has died" 0 "child is alive" label values child_died lab_died tab w216 child_died, miss bysort ind_id: egen tot_child_died_5y=sum(child_died) if ydead_survey<=5 /*For each woman, sum the number of children who died in the past 5 years prior to the interview date */ replace tot_child_died_5y=0 if tot_child_died_5y==. & tot_child_died>=0 & tot_child_died<. /*All children who are alive and died longer than 5 years from the interview date are replaced as '0'*/ replace tot_child_died_5y=. if child_died==1 & ydead_survey==. //Replace as '.' if there is no information on when the child died tab tot_child_died tot_child_died_5y, miss bysort ind_id: egen child_died_per_wom = max(tot_child_died) lab var child_died_per_wom "Total child death for each women (birth recode)" bysort ind_id: egen child_died_per_wom_5y = max(tot_child_died_5y) lab var child_died_per_wom_5y "Total child death for each women in the last 5 years (birth recode)" //Keep one observation per women bysort ind_id: gen id=1 if _n==1 keep if id==1 drop id duplicates report ind_id gen women_BR = 1 //Identification variable for observations in BR recode //Retain relevant variables keep ind_id women_BR child_died_per_wom child_died_per_wom_5y order ind_id women_BR child_died_per_wom child_died_per_wom_5y sort ind_id //Save temp file for future merging save "$path_out/LBY14_BR.dta", replace ******************************************************************************** *** Step 1.3 IR - WOMEN's RECODE *** (All eligible females 15-49 years in the household) ******************************************************************************** use "$path_in/WOM.dta", clear rename _all, lower *** Generate individual unique key variable required for data merging *** cluster=cluster number; *** hhnum=household number; *** wln=respondent's line number gen double ind_id = cluster*1000000 + hhnum*100 + wln format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id gen women_WM=1 //Identification variable for observations in IR recode tab w124 w206, miss //Check whether all ever married women are present in the sample //Retain relevant variables: keep ind_id women_WM wmweight wresult w201 w206 w207a w207b order ind_id women_WM wmweight wresult w201 w206 w207a w207b sort ind_id //Save temp file for future merging save "$path_out/LBY14_WM.dta", replace ******************************************************************************** *** Step 1.4 HH - Household's recode *** ******************************************************************************** use "$path_in/HH.dta", clear rename _all, lower *** Generate individual unique key variable required for data merging *** cluster=cluster number; *** hhnum=household number; gen double hh_id = cluster*100 + hhnum format hh_id %20.0g lab var hh_id "Household ID" duplicates report hh_id //Save temp file for future merging save "$path_out/LBY14_HH.dta", replace ******************************************************************************** *** Step 1.5 HR - Household Member's recode **** ******************************************************************************** use "$path_in/HR.dta", clear rename _all, lower gen cty = "Libya" gen ccty = "LBY" gen year = "2014" gen survey = "PAPFAM" gen ccnum = 434 *** Generate a household unique key variable at the household level using: ***cluster=cluster number ***hhnum=household number gen double hh_id = cluster*100 + hhnum format hh_id %20.0g label var hh_id "Household ID" *** Generate individual unique key variable required for data merging using: *** cluster=cluster number; *** hhnum=household number; *** hln=respondent's line number. gen double ind_id = cluster*1000000 + hhnum*100 + hln format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id ******************************************************************************** *** Step 1.6 DATA MERGING ******************************************************************************** *** Merging BR Recode ***************************************** merge 1:1 ind_id using "$path_out/LBY14_BR.dta" drop _merge erase "$path_out/LBY14_BR.dta" *** Merging WM Recode ***************************************** merge 1:1 ind_id using "$path_out/LBY14_WM.dta" tab wresult women_WM, miss col bys hh_id: egen temp=sum(women_WM) tab h101w temp, miss tab h101c temp, miss count if temp==0 & h101c >=1 //NOTE: There is 1,326 women not eligible but with child measures drop temp _merge erase "$path_out/LBY14_WM.dta" *** Merging HH Recode ***************************************** merge m:1 hh_id using "$path_out/LBY14_HH.dta" tab hresult if _m==2 drop if _merge==2 //Drop households that were not interviewed drop _merge erase "$path_out/LBY14_HH.dta" *** Merging CH Under 5 Recode ***************************************** merge 1:1 ind_id using "$path_out/LBY14_CH.dta" drop _merge erase "$path_out/LBY14_CH.dta" sort ind_id *** Merging CH 5-6 years Recode ***************************************** merge 1:1 ind_id using "$path_out/LBY14_CH_6Y.dta" drop _merge erase "$path_out/LBY14_CH_6Y.dta" sort ind_id ******************************************************************************** *** Step 1.7 KEEPING ONLY DE JURE HOUSEHOLD MEMBERS *** ******************************************************************************** //Permanent (de jure) household members clonevar resident = h102 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. Note: However, in Libya PAPFAM 2014, all householders are permanent members. */ ******************************************************************************** *** Step 1.8 CONTROL VARIABLES ******************************************************************************** /* Households are identified as having 'no eligible' members if there are no applicable population, that is, children 0-5 years, adult women 15-49 years or men 15-59 years. These households will not have information on relevant indicators of health. As such, these households are considered as non-deprived in those relevant indicators.*/ *** No Eligible Women ***************************************** gen fem_eligible = (women_WM==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 tab no_fem_eligible, miss tab no_fem_eligible h101c, miss /* NOTE: There is 1,326 individuals living in households without eligible women but have child who was eligible for anthropometric measures. */ lab var no_fem_eligible "Household has no eligible women" *** No Eligible Men ***************************************** //NOTE: Libya PAPFAM 2014 have no male recode file gen no_male_eligible = . lab var no_male_eligible "Household has no eligible man" tab no_male_eligible, miss *** No Eligible Children Under 5 ***************************************** gen child_eligible = 0 replace child_eligible = 1 if h101c>=1 & (age_months>=0 & age_months<=59) 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 tab no_child_eligible,miss lab var no_child_eligible "Household has no children under 5 eligible" *** No Eligible Children 5-6 years ***************************************** gen child_eligible_6y = 0 replace child_eligible_6y = 1 if h101c>=1 & (age_months>=60 & age_months<=72) bys hh_id: egen hh_n_children_eligible_6y = sum(child_eligible_6y) //Number of eligible children for anthropometrics gen no_child_eligible_6y = (hh_n_children_eligible_6y==0) //Takes value 1 if there were no eligible children for anthropometrics tab no_child_eligible_6y, miss lab var no_child_eligible_6y "Household has no children 5-6 years eligible" *** No Eligible Women and Men *********************************************** gen no_adults_eligible = (no_fem_eligible==1 & no_male_eligible==1) lab var no_adults_eligible "Household has no eligible women or men" tab no_adults_eligible, miss /*Libya PAPFAM 2014 enumerated men, as household members but did not collect child mortality information from men */ *** No Eligible Children and Women *********************************************** /*NOTE: In the DHS datasets, we use this variable as a control variable for the nutrition indicator if nutrition data is present for children and women.*/ gen no_child_fem_eligible = (no_child_eligible==1 & no_fem_eligible==1) lab var no_child_fem_eligible "Household has no children or women eligible" tab no_child_fem_eligible, miss *** No Eligible Women, Men or Children *********************************************** /*NOTE: In the DHS datasets, we use this variable as a control variable for the nutrition indicator if nutrition data is present for children, women and men. */ gen no_eligibles = (no_fem_eligible==1 & no_male_eligible==1 & no_child_eligible==1) lab var no_eligibles "Household has no eligible women, men, or children" tab no_eligibles, miss *** No Eligible Subsample ***************************************** //Note that PAPFAM surveys do not collect hemoglobin data from women 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 child_eligible hh_n_children_eligible sort hh_id ind_id ******************************************************************************** *** Step 1.9 SUBSAMPLE VARIABLE *** ******************************************************************************** /* In the context of Libya PAPFAM 2014, height and weight measurements were collected from all children (0-72 months). As such there is no presence of subsample. */ gen subsample = . label var subsample "Households selected as part of nutrition subsample" tab subsample, miss ******************************************************************************** *** Step 1.10 RENAMING DEMOGRAPHIC VARIABLES *** ******************************************************************************** //Sample weight clonevar weight = hhweight label var weight "Sample weight" //Area: urban or rural codebook area , tab (5) 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 = h104 codebook relationship, tab (20) recode relationship (1=1)(2=2)(3=3)(4/7=4)(8=5)(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 h104 relationship, miss //Sex of household member //Ensure coding is "1" Male, "2" Female codebook h103 clonevar sex = h103 label var sex "Sex of household member" //Age of household member codebook h105a, tab (100) clonevar age = h105a 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 desc h106 clonevar marital = h106 codebook marital, tab (10) 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 h106 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 /*NOTE: Libya PAPFAM 2014 was designed to provide estimates of key indicators for the country as a whole, and for urban and rural of each of the 21 districts sampled.*/ lookfor region codebook district, tab (99) clonevar region = district lab var region "Region for subnational decomposition" codebook region, tab (100) ******************************************************************************** *** Step 2 Data preparation *** *** Standardization of the 10 Global MPI indicators *** Identification of non-deprived & deprived individuals ******************************************************************************** ******************************************************************************** *** Step 2.1 Years of Schooling *** ******************************************************************************** /*PAPFAM does not provide the number of years of education so we need to construct that variable from the edulevel and eduhighyear variables */ codebook h110a , tab (99) /*School attendance: Currently attending; Attended in the past; Never attended; DK/Missing */ codebook h110ba , tab (99) /*Highest level reached: KG;Basic;Secondary;Higher institute;University+;DK/Missing */ tab h110ba h110a, miss tab age if h110a==. /*Missing value in school attendance variable is because the data was not collected from children aged 0-5 years. */ *** Creating educational level variable *** clonevar edulevel = h110ba //Highest educational level attended replace edulevel = . if h110ba==8 | h110ba==9 | h110ba==. //Check for the categories related to missing values replace edulevel = 0 if h110a==3 //Assign edulevel=0 for individuals who never ever attended school replace edulevel = 0 if age < 10 /*The variable "edulevel" was replaced with a '0' given that the criteria for the years of schooling indicator is household member aged 10 years or older */ label define lab_edulevel 0 "None" 1 "Primary" 2 "Secondary" 3 "Higher" 4"University" label values edulevel lab_edulevel label var edulevel "Highest educational level attended" *** Creating educational grade variable *** tab h110bb h110ba,m //Check the relationship between highest grade and level clonevar eduhighyear = h110bb //Highest grade finished successfully replace eduhighyear = . if h110bb==. | h110bb==98 //Check for the categories related to missing values replace eduhighyear = 0 if h110a==3 //Assign eduhighyear=0 for individuals who never ever attended school replace eduhighyear = 10 if h110bb==88 & edulevel==4 //Assign eduhighyear=10 for individuals who have postgraduate degree replace eduhighyear = 0 if age < 10 /*The variable "eduhighyear" was replaced with a '0' given that the criteria for the years of schooling indicator is household member aged 10 years or older */ replace eduhighyear = 0 if edulevel<1 //Cleaning inconsistencies replace eduhighyear=. if (eduhighyear!=. & edulevel==.) & (eduhighyear!=0 & edulevel==.) //Cleaning further inconsistencies replace eduhighyear=. if edulevel==. //Cleaning further inconsistencies lab var eduhighyear "Highest year of education completed" tab eduhighyear edulevel,miss *** Finally: creating the years of schooling variable *** gen eduyears = eduhighyear /*NOTE: Children in Libya attend primary school between the ages of 6 and 15, that is for 9 years. Hence replacements are necessary because eduhighyear does not consider consecutive years after edulevel>=1 */ replace eduyears = 0 if edulevel<1 replace eduyears = 9+eduhighyear if edulevel==2 replace eduyears = 13+eduhighyear if edulevel==3 /*These higher institutions offer programmes in many vocational specialities for a period of three years after obtaining the secondary school certificate. So one may assume that this category should be +13 following secondary completion */ replace eduyears = 13+eduhighyear if edulevel==4 /*The bachelor degree (university, category 4) requires four years of study in most programmes after obtaining the secondary school certificate. So one may assume that this category should be +13 as well following secondary completion */ replace eduyears = . if edulevel==. & eduhighyear==. replace eduyears = . if age<=eduyears & age>0 /*There are cases in which the years of schooling are greater than the age of the individual, which is clearly a mistake in the data. There might also be individuals that show too much schooling given their age e.g. a 7 year-old with 5 years of schooling). Please check whether this is the case and correct when necessary */ 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 */ lab var eduyears "Total number of years of education accomplished" /*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) /* 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 *** ******************************************************************************** /* Note that the school attendance variable used for Libya PAPFAM 2014 is: hv110a (School attendance). The information was collected from all individuals aged 6 years and older */ codebook h110a, tab (10) clonevar attendance = h110a //1=attending, 0=not attending recode attendance (2=0) (3=0) //2='attended in the past'; 3='never attended' replace attendance = . if attendance==9 //9, 99 and 8, 98 are missing or non-applicable /*The entire household is considered deprived if any school-aged child is not attending school up to class 8. */ gen child_schoolage = (age>=6 & age<=14) /* Note: In Libya, 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 *** ******************************************************************************** /*Please note that the PAPFAM datasets do not collect nutrition data from adults. In the context of countries with PAPFAM datasets, the entire household is considered deprived if any child under 5 for whom there is nutritional information is malnourished or children 5-6 years have low BMI-for-age in the household.*/ ******************************************************************************** *** Step 2.3a Child (under 5) Nutrition *** ******************************************************************************** *** Child Underweight Indicator *** ************************************************************************ /* Libya PAPFAM 2014 collected nutrition data from children under 6. In this section, the construction of the nutrition indicator will be on children under 5. Households with no eligible children will receive a value of 1 */ 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 2.3b Child 5-6 years Nutrition *** ******************************************************************************** *** Child BMI-for-age Indicator *** ************************************************************************ /* Libya PAPFAM 2014 collected nutrition data from children under 6. In this section, the construction of the nutrition indicator will be for children between 5 - 6 years. Households with no eligible children will receive a value of 1 */ bysort hh_id: egen temp = max(low_bmiage) gen hh_no_low_bmiage = (temp==0) //Takes value 1 if no child in the hh has low BMI-for-age replace hh_no_low_bmiage = . if temp==. replace hh_no_low_bmiage = 1 if no_child_eligible_6y==1 /* Households with no eligible children will receive a value of 1 */ lab var hh_no_low_bmiage "Household has no child low BMI-for-age" drop temp ******************************************************************************** *** Step 2.3c Household Nutrition Indicator *** ******************************************************************************** /* In the context of Libya PAPFAM 2014, the final nutrition indicator had around 10 percent missing value. The report indicate that the high missing value was because eligible children were not present for measurement. */ tab h907 if child_CH==1 //h907: Results of child measurement /* 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 | hh_no_low_bmiage==0 replace hh_nutrition_uw_st = . if hh_no_uw_st==. & hh_no_low_bmiage==. replace hh_nutrition_uw_st = 1 if no_child_eligible==1 & no_child_eligible_6y==1 /*We replace households that do not have the applicable population, as non-deprived in nutrition*/ lab var hh_nutrition_uw_st "Household has no child underweight or stunted" ******************************************************************************** *** Step 2.4 Child Mortality *** ******************************************************************************** /*In the context of Libya PAPFAM 2014, information on child mortality was collected only from women */ codebook w206 w207a w207b // w206: Had children who died // w207a: number of sons who have died // w207b: number of daughters who have died egen temp_f = rowtotal(w207a w207b), missing //Total child mortality reported by eligible women replace temp_f = 0 if (w201==1 & w206!=1) | (w201==2 & w206!=1) replace temp_f = 0 if w201==. & w206==. & marital==1 & temp_f==. /*Assign a value of "0" for: - all eligible women who ever had a life birth but reported no child death - all eligible women who never had a life birth and reported no death (presumably this group are women who never ever gave birth) - all elegible but never married women were not asked birth history questions and hence we assume there is no child mortality among this group*/ 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 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. The missing values represent eligible woman who have never ever given birth. 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 at the household level. This ensures that women who don't have a birth history is assigned with a value, following the information provided by other women in the household.*/ replace child_died_per_wom_5y = 0 if (w201==1 & w206!=1) | /// (w201==2 & w206!=1) /*Assign a value of "0" for: - all eligible women who ever had a life birth but reported no child death - all eligible women who never had a life birth and reported no death (presumably this group are women who never ever gave birth) */ replace child_died_per_wom_5y = 0 if no_fem_eligible==1 /*It is assumed that households have 0 death if there is no eligible women in those households */ 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. However, in the case of Libya, child mortality information was provided only by women. As such, no changes. */ 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. The indicator takes a missing value if there was missing information on reported death from eligible women. */ 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 Note: Libya PAPFAM 2014 has no direct question on whether household has electricity or not. As the best alternative, the electricity indicator for Libya PAPFAM 2014 was drawn from the h617 variable: Main type of lighting. The categoreis are: Electricity; Kerosene; Gas; Oil/Candles; Other; No lighting. As such, the category 'Electricity' is recoded as 'Yes electricity' and all other categories are recoded as 'No electricity' */ lookfor electricity lookfor lighting codebook h617, tab (10) //h617 - Main type of lighting clonevar electricity = h617 recode electricity (2/8=0) replace electricity = . if electricity==9 codebook electricity, tab (10) label var electricity "Household has electricity" ******************************************************************************** *** Step 2.6 Sanitation *** ******************************************************************************** /*Members of the household are considered deprived if the household's sanitation facility is not improved, according to MDG guidelines, or it is improved but shared with other household. In cases of mismatch between the MDG guideline and country report, we followed the country report. */ lookfor toilet clonevar toilet = h614 codebook toilet, tab (30) codebook h615, tab(10) //0=no;1=yes;.=missing //Note: replace public toilet as 'shared' recode h615(2=0)(9=.),gen(shared_toilet) replace shared_toilet=1 if h614==5 & h615==. tab h615 shared_toilet, miss /* NOTE: The toilet categories for Libya PAPFAM 2014 are different from the standardised version found in DHS and MICS. The categories are: 1 FT connected 2 FT not connected 3 Toilet connected 4 Toilet connected to closed pit 5 Public toilet 6 Open air 96 Other Following the country report, the categories of public toilet, open air & other are coded as non-improved sanitation. */ gen toilet_mdg = toilet<=4 & 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<=4 & 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==. //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. In cases of mismatch between the MDG guideline and country report, we followed the country report.*/ lookfor water clonevar water = h608 codebook water, tab (30) clonevar timetowater = h610 codebook timetowater, tab (9999) /*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 ndwater = . /*NOTE: -Libya PAPFAM 2014 do not have a variable on the use of water for non-drinking activities. So no observation for ndwater variable. -The Libya PAPFAM 2014 report indicate that public tab, piped supply, protected and supervised wells including rain water are improved source of drinking water. -The Libya PAPFAM 2014 report considers bottled water as non-improved source of drinking water. */ gen water_mdg = 1 if water==1 | water==2 | water==3 | water==4 | /// water==5 | water==8 /*Non deprived if water is "piped supply","public tap", "artesian well", "regular well", "protected spring", "rainwater", */ replace water_mdg = 0 if water==6 | water==7 | water==9 | /// water==10 | water==96 /*Deprived if water is "unprotected well", "unprotected spring", "lake/pool", "tanker truck","bottled water", "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 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 */ /* NOTE: The Libya PAPFAM 2014 show that some 4000+ individuals have missing data for floor. This is because, the data was not collected from households if their type of dwelling is hut, tent, temporary shelter or other. Given the precarious conditions of these dwellings, the flooring of these dwellings were re-coded as non-improved rather than treating it as 'missing'. */ clonevar floor = h603 codebook floor, tab (10) codebook h601, tab (99) gen floor_imp = 1 replace floor_imp = 0 if floor==1 | floor==6 //Deprived if "mud/earth", "sand", "dung", "other" replace floor_imp = . if floor==. | floor==9 replace floor_imp = 0 if floor==. & h601 >=5 /*Specific to Libya PAPFAM 2014: Deprived if type of dwelling is hut, tent, temporary shelter or other */ lab var floor_imp "Household has floor that it is not earth/sand/dung" tab floor floor_imp, miss /* Members of the household are considered deprived if the household has wall made of natural or rudimentary materials */ gen wall = . //Libya PAPFAM 2014 has no data on wall gen wall_imp = . lab var wall_imp "Household has wall that it is not of low quality materials" /* Members of the household are considered deprived if the household has roof made of natural or rudimentary materials */ gen roof = . //Libya PAPFAM 2014 has no data on roof gen roof_imp = . lab var roof_imp "Household has roof that it is not of low quality materials" /*Household is deprived in housing if the roof, floor OR walls uses low quality materials. Since Libya PAPFAM 2014 do not have information on walls and roof, we replace the MPI indicator on housing with information on floor. */ gen housing_1 = floor_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 = h619 codebook cookingfuel, tab(9) gen cooking_mdg = 1 replace cooking_mdg = 0 if cookingfuel>=4 & cookingfuel<6 replace cooking_mdg = . if cookingfuel==. | cookingfuel==99 lab var cooking_mdg "Household has cooking fuel by MDG standards" /* Non deprived if: gas from cylendre; gas; kaz/ kerosene; other Deprived if: coal; wood; */ tab cookingfuel cooking_mdg, miss ******************************************************************************** *** Step 2.10 Assets ownership *** ******************************************************************************** /* Members of the household are considered deprived if the household does not own more than one of: radio, TV, telephone, bike, motorbike or refrigerator and does not own a car or truck. */ /*It is useful to state onset that Libya PAPFAM 2014 has no data for motorbike, animal cart, motorboat and the specific type and number of livestock*/ //Check that for standard assets in living standards: "no"==0 and yes=="1" codebook h625_2 h625_1 h625_10 h625_11 h625_5 h629_2 h629_1 h625_14 h628 h629_12 clonevar television = h625_2 gen bw_television = . clonevar radio = h625_1 clonevar telephone = h625_10 clonevar mobiletelephone = h625_11 clonevar refrigerator = h625_5 clonevar car = h629_2 clonevar bicycle = h629_1 gen motorbike = . //NOTE: Libya PAPFAM 2014 has no data on ownership of motorcycle clonevar computer = h625_14 gen animal_cart = . //Libya PAPFAM 2014 has no observation for animal cart foreach var in television radio telephone mobiletelephone refrigerator /// car bicycle motorbike computer animal_cart { replace `var' = 0 if `var'==2 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: clonevar strata = district clonevar psu = cluster //Retain year, month & date of interview: clonevar year_interview = hinty clonevar month_interview = hintm clonevar date_interview = xhintc *** 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/lby_papfam14_pov.dta", replace log close