******************************************************************************** /* Suggested citation: Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. 2019 Global Multidimensional Poverty Index - Congo Republic MICS 2014-2015 [STATA do-file]. Retrieved from: https://ophi.org.uk/multidimensional-poverty-index/mpi-resources/ For further queries, please contact: ophi@qeh.ox.ac.uk */ ******************************************************************************** clear all set more off set maxvar 10000 set mem 500m *** Working Folder Path *** global path_in "T:/GMPI 2.0/rdta/Congo Republic MICS 2014-15" global path_out "G:/GMPI 2.0/cdta" global path_ado "T:/GMPI 2.0/ado" ******************************************************************************** *** CONGO MICS 2014-2015 *** ******************************************************************************** ******************************************************************************** *** Step 1: Data preparation *** Selecting main variables from CH, WM, HH & MN recode & merging with HL recode ******************************************************************************** /*Congo Republic MICS 2014-2015: P10 indicates that anthropometric data was collected only for childen under 5 years. */ ******************************************************************************** *** Step 1.1 CH - CHILD RECODE *** (Children under 5 years) ******************************************************************************** /*The purpose of step 1.1 is to compute anthropometric measures for children under 5 years.*/ use "$path_in/ch.dta", clear rename _all, lower *** Generate individual unique key variable required for data merging *** hh1=cluster number; *** hh2=household number; *** ln=child's line number in household gen double ind_id = hh1*1000000 + hh2*100 + ln format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id //No duplicates gen child_CH=1 //Generate identification variable for observations in CH recode *** Check the variables to calculate the z-scores: *** Variable: SEX *** codebook hl4, tab (9) //"1" for male ;"2" for female clonevar gender = hl4 tab gender *** Variable: AGE *** desc cage caged tab cage, miss //Age in months: information missing for 102 children tab caged, miss /*Age in days: information missing for 182 children. (this includes 80 children 9999 with missing age in days). We use age in days as it result in more accurate anthropometric measures. However, we will use information on age in months for the 80 children. */ count if caged < 0 clonevar age_days = caged replace age_days = . if caged==9999 replace age_days = trunc(cage*(365/12)) if caged==9999 sum age_days gen str6 ageunit = "days" lab var ageunit "Days" *** Variable: BODY WEIGHT (KILOGRAMS) *** codebook an3, tab (9999) clonevar weight = an3 replace weight = . if an3>=99 //All missing values or out of range are replaced as "." tab an2 an3 if an3>=99 | an3==., miss //an2: result of the measurement tab uf9 if an2==. & an3==. sum weight *** Variable: HEIGHT (CENTIMETERS) codebook an4, tab (9999) clonevar height = an4 replace height = . if an4>=999 //All missing values or out of range are replaced as "." tab an2 an4 if an4>=999 | an4==., miss tab uf9 if an2==. & an4==. sum height *** Variable: MEASURED STANDING/LYING DOWN codebook an4a gen measure = "l" if an4a==1 //Child measured lying down replace measure = "h" if an4a==2 //Child measured standing up replace measure = " " if an4a==9 | an4a==0 | an4a==. //Replace with " " if unknown desc measure tab measure *** Variable: OEDEMA *** lookfor oedema œdème edema gen str1 oedema = "n" //This variable assumes no one has oedema *** Variable: SAMPLING WEIGHT *** /* We don't require individual weight to compute the z-scores of a child. So we assume all children in the sample have the same weight */ gen sw = 1 sum sw *** Indicate to STATA where the igrowup_restricted.ado file is stored: ***Source of ado file: http://www.who.int/childgrowth/software/en/ adopath + "$path_ado/igrowup_stata" *** We will now proceed to create three nutritional variables: *** weight-for-age (underweight), *** weight-for-height (wasting) *** height-for-age (stunting) *** We specify the first three parameters we need in order to use the ado file: *** reflib, *** datalib, *** datalab /* We use 'reflib' to specify the package directory where the .dta files containing the WHO Child Growth Standards are stored. */ gen str100 reflib = "$path_ado/igrowup_stata" lab var reflib "Directory of reference tables" /* We use datalib to specify the working directory where the input STATA dataset containing the anthropometric measurement is stored. */ gen str100 datalib = "$path_out" lab var datalib "Directory for datafiles" /* We use datalab to specify the name that will prefix the output files that will be produced from using this ado file (datalab_z_r_rc and datalab_prev_rc)*/ gen str30 datalab = "children_nutri_cog" lab var datalab "Working file" /*We now run the command to calculate the z-scores with the adofile */ igrowup_restricted reflib datalib datalab gender age_days 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 "$path_out/children_nutri_cog_z_rc.dta", clear *** Standard MPI indicator *** //Takes value 1 if the child is under 2 stdev below the median & 0 otherwise gen underweight = (_zwei < -2.0) replace underweight = . if _zwei == . | _fwei==1 lab var underweight "Child is undernourished (weight-for-age) 2sd - WHO" tab underweight, miss gen stunting = (_zlen < -2.0) replace stunting = . if _zlen == . | _flen==1 lab var stunting "Child is stunted (length/height-for-age) 2sd - WHO" tab stunting, miss gen wasting = (_zwfl < - 2.0) replace wasting = . if _zwfl == . | _fwfl == 1 lab var wasting "Child is wasted (weight-for-length/height) 2sd - WHO" tab wasting, miss count if _fwei==1 | _flen==1 /*Congo Republic MICS 2014-2015: 128 children were replaced as missing because they have extreme z-scores which are biologically implausible. */ count /*Congo Republic MICS 2014-2015: the number of eligible children is 9,271 as in the country report (p.13 of 542). No mismatch. */ //Retain relevant variables: keep ind_id child_CH ln underweight* stunting* wasting* order ind_id child_CH ln underweight* stunting* wasting* sort ind_id save "$path_out/COG14-15_CH.dta", replace //Erase files from folder: erase "$path_out/children_nutri_cog_z_rc.xls" erase "$path_out/children_nutri_cog_prev_rc.xls" erase "$path_out/children_nutri_cog_z_rc.dta" ******************************************************************************** *** Step 1.2 BH - BIRTH RECODE *** (All females 15-49 years who ever gave birth) ******************************************************************************** /*The purpose of step 1.2 is to identify children under 18 who died in the last 5 years prior to the survey date.*/ use "$path_in/bh.dta", clear rename _all, lower *** Generate individual unique key variable required for data merging using: *** hh1=cluster number; *** hh2=household number; *** ln=women's line number. gen double ind_id = hh1*1000000 + hh2*100 + ln format ind_id %20.0g label var ind_id "Individual ID" desc bh4c bh9c gen date_death = bh4c + bh9c //Date of death = date of birth (bh4c) + age at death (bh9c) gen mdead_survey = wdoi-date_death //Months dead from survey = Date of interview (wdoi) - date of death replace mdead_survey = . if (bh9c==0 | bh9c==.) & bh5==1 /*Replace children who are alive as '.' to distinguish them from children who died at 0 months */ gen ydead_survey = mdead_survey/12 //Years dead from survey gen age_death = bh9c if bh5==2 label var age_death "Age at death in months" tab age_death, miss //Check whether the age is in months codebook bh5, tab (10) gen child_died = 1 if bh5==2 replace child_died = 0 if bh5==1 replace child_died = . if bh5==. label define lab_died 0"child is alive" 1"child has died" label values child_died lab_died tab bh5 child_died, miss bysort ind_id: egen tot_child_died = sum(child_died) //For each woman, sum the number of children who died //Identify child under 18 mortality in the last 5 years gen child18_died = child_died replace child18_died=0 if age_death>=216 & age_death<. label values child18_died lab_died tab child18_died, miss bysort ind_id: egen tot_child18_died_5y=sum(child18_died) if ydead_survey<=5 /*Total number of children under 18 who died in the past 5 years prior to the interview date */ replace tot_child18_died_5y=0 if tot_child18_died_5y==. & tot_child_died>=0 & tot_child_died<. /*All children who are alive or who died longer than 5 years from the interview date are replaced as '0'*/ replace tot_child18_died_5y=. if child18_died==1 & ydead_survey==. //Replace as '.' if there is no information on when the child died tab tot_child_died tot_child18_died_5y, miss bysort ind_id: egen childu18_died_per_wom_5y = max(tot_child18_died_5y) lab var childu18_died_per_wom_5y "Total child under 18 death for each women in the last 5 years (birth recode)" //Keep one observation per women bysort ind_id: gen id=1 if _n==1 keep if id==1 drop id duplicates report ind_id gen women_BH = 1 //Identification variable for observations in BH recode //Retain relevant variables keep ind_id women_BH childu18_died_per_wom_5y order ind_id women_BH childu18_died_per_wom_5y sort ind_id save "$path_out/COG14-15_BH.dta", replace ******************************************************************************** *** Step 1.3 WM - WOMEN's RECODE *** (Eligible females 15-49 years in the household) ******************************************************************************** /*The purpose of step 1.3 is to identify all deaths that are reported by eligible women.*/ use "$path_in/wm.dta", clear rename _all, lower *** Generate individual unique key variable required for data merging using: *** hh1=cluster number; *** hh2=household number; *** ln=women's line number. gen double ind_id = hh1*1000000 + hh2*100 + ln format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id gen women_WM =1 //Identification variable for observations in WM recode tab wb4, miss /*Congo Republic MICS 2014-2015: Fertility and mortality question was collected from women 15-49 years. 133 women with missing age information.*/ tab cm1 cm8, miss /*Congo Republic MICS 2014-2015: 18 women report never having given birth but who also have information on child mortality (i.e. anomalies). */ codebook mstatus ma6, tab (10) tab mstatus ma6, miss gen marital = 1 if mstatus == 3 & ma6==. //1: Never married replace marital = 2 if mstatus == 1 & ma6==. //2: Currently married replace marital = 3 if mstatus == 2 & ma6==1 //3: Widowed replace marital = 4 if mstatus == 2 & ma6==2 //4: Divorced replace marital = 5 if mstatus == 2 & ma6==3 //5: Separated/not living together replace mstatus = . if mstatus==9 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 marital, miss tab ma6 marital, miss tab mstatus marital, miss rename marital marital_wom keep wm7* cm1 cm8 cm9a cm9b ind_id women_WM *_wom order wm7* cm1 cm8 cm9a cm9b ind_id women_WM *_wom sort ind_id save "$path_out/COG14-15_WM.dta", replace ******************************************************************************** *** Step 1.4 MN - MEN'S RECODE ***(Eligible man: 15-49 years in the household) ******************************************************************************** /*The purpose of step 1.4 is to identify all deaths that are reported by eligible men.*/ use "$path_in/mn.dta", clear rename _all, lower *** Generate individual unique key variable required for data merging using: *** hh1=cluster number; *** hh2=household number; *** ln=respondent's line number. gen double ind_id = hh1*1000000 + hh2*100 + ln format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id gen men_MN=1 //Identification variable for observations in MR recode tab mwb2, miss /*Congo Republic MICS 2014-2015: Fertility and mortality question was collected from men 15-49 years. 319 men with missing age information.*/ tab mcm1 mcm8, miss /*Congo Republic MICS 2014-2015: 17 men report never fathering a child but who have information on child mortality (i.e. anomalies). */ codebook mmstatus mma6, tab (10) tab mmstatus mma6, miss gen marital = 1 if mmstatus == 3 & mma6==. //1: Never married replace marital = 2 if mmstatus == 1 & mma6==. //2: Currently married replace marital = 3 if mmstatus == 2 & mma6==1 //3: Widowed replace marital = 4 if mmstatus == 2 & mma6==2 //4: Divorced replace marital = 5 if mmstatus == 2 & mma6==3 //5: Separated/not living together replace mmstatus = . if mmstatus==9 //Replace missing observations 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 marital, miss tab mma6 marital, miss tab mmstatus marital, miss rename marital marital_men keep mcm1 mcm8 mcm9a mcm9b ind_id men_MN *_men order mcm1 mcm8 mcm9a mcm9b ind_id men_MN *_men sort ind_id save "$path_out/COG14-15_MN.dta", replace ******************************************************************************** *** Step 1.5 HH - HOUSEHOLD RECODE ***(All households interviewed) ******************************************************************************** use "$path_in/hh.dta", clear rename _all, lower *** Generate individual unique key variable required for data merging *** hh1=cluster number; *** hh2=household number; gen double hh_id = hh1*1000 + hh2 format hh_id %20.0g lab var hh_id "Household ID" duplicates report hh_id save "$path_out/COG14-15_HH.dta", replace ******************************************************************************** *** Step 1.6 HL - HOUSEHOLD MEMBER ******************************************************************************** use "$path_in/hl.dta", clear rename _all, lower *** Generate a household unique key variable at the household level using: ***hh1=cluster number ***hh2=household number gen double hh_id = hh1*1000 + hh2 format hh_id %20.0g label var hh_id "Household ID" *** Generate individual unique key variable required for data merging using: *** hh1=cluster number; *** hh2=household number; *** hl1=respondent's line number. gen double ind_id = hh1*1000000 + hh2*100 + hl1 format ind_id %20.0g label var ind_id "Individual ID" sort ind_id ******************************************************************************** *** Step 1.7 DATA MERGING ******************************************************************************** *** Merging BR Recode ***************************************** merge 1:1 ind_id using "$path_out/COG14-15_BH.dta" drop _merge erase "$path_out/COG14-15_BH.dta" *** Merging WM Recode ***************************************** merge 1:1 ind_id using "$path_out/COG14-15_WM.dta" count if hl7>0 /*11,841 women 15-49 years were eligible for interview. This matches the country report (p.13 of 542) */ drop _merge erase "$path_out/COG14-15_WM.dta" *** Merging HH Recode ***************************************** merge m:1 hh_id using "$path_out/COG14-15_HH.dta" tab hh9 if _m==2 drop if _merge==2 //Drop households that were not interviewed drop _merge erase "$path_out/COG14-15_HH.dta" *** Merging MN Recode ***************************************** merge 1:1 ind_id using "$path_out/COG14-15_MN.dta" drop _merge erase "$path_out/COG14-15_MN.dta" *** Merging CH Recode ***************************************** merge 1:1 ind_id using "$path_out/COG14-15_CH.dta" drop _merge erase "$path_out/COG14-15_CH.dta" sort ind_id ******************************************************************************** *** 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 or 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 *** for child mortality indicator ***************************************** 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 lab var no_fem_eligible "Household has no eligible women" drop fem_eligible hh_n_fem_eligible tab no_fem_eligible, miss *** No eligible men 15-49 years *** for child mortality indicator (if relevant) ***************************************** gen male_eligible = (men_MN==1) bysort hh_id: egen hh_n_male_eligible = sum(male_eligible) //Number of eligible men for interview in the hh gen no_male_eligible = (hh_n_male_eligible==0) //Takes value 1 if the household had no eligible men for an interview lab var no_male_eligible "Household has no eligible man for interview" drop male_eligible hh_n_male_eligible tab no_male_eligible, miss *** No eligible children under 5 *** for child nutrition indicator ***************************************** gen child_eligible = (child_CH==1) bysort hh_id: egen hh_n_children_eligible = sum(child_eligible) //Number of eligible children for anthropometrics gen no_child_eligible = (hh_n_children_eligible==0) //Takes value 1 if there were no eligible children for anthropometrics lab var no_child_eligible "Household has no children eligible for anthropometric" drop child_eligible hh_n_children_eligible tab no_child_eligible, miss sort hh_id ******************************************************************************** *** Step 1.9 RENAMING DEMOGRAPHIC VARIABLES *** ******************************************************************************** //Sample weight clonevar weight = hhweight label var weight "Sample weight" //Area: urban or rural desc hh6 clonevar area = hh6 replace area=0 if area==2 label define lab_area 1 "urban" 0 "rural" label values area lab_area label var area "Area: urban-rural" //Sex of household member codebook hl4 clonevar sex = hl4 label var sex "Sex of household member" //Age of household member codebook hl6, tab (100) clonevar age = hl6 replace age = . if age>=98 label var age "Age of household member" //Age group (for global MPI estimation) 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)" //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 compare hhsize hh11 drop member //Subnational region /*The sample for the Congo MICS 2014-15 was designed to cover each of the twelve departments (p. XXVII). */ codebook hh7, tab (99) decode hh7, gen(temp) replace temp = proper(temp) encode temp, gen(region) drop temp lab var region "Region for subnational decomposition" tab region, miss label define lab_reg /// 1"Bouenza" 2"Brazzaville" 3"Cuvette" /// 4"Cuvette-Ouest" 5"Kouilou" 6"Lékoumou" /// 7"Likouala" 8"Niari" 9"Plateaux" /// 10"Pointe-Noire" 11"Pool" 12"Sangha" label values region lab_reg ******************************************************************************** *** Step 2 Data preparation *** *** Standardization of the 10 Global MPI indicators *** Identification of non-deprived & deprived individuals ******************************************************************************** ******************************************************************************** *** Step 2.1 Years of Schooling *** ******************************************************************************** /*In the Republic of Congo, children enter primary school at the age of 6 and secondary school at the age of 12. There are 6 grades in primary school and 7 grades in secondary school (p.222 of MICS report). In primary school, the grades are referred to as: a) preparatory courses 1 and 2 (CP 1 - CP 2) for grades 1 and 2, b) elementary courses 1 and 2 (CE 1- CE 2) for grades 3 and 4, c) middle courses 1 and 2 (CM 1 - CM 2) for grades 5 and 6. In secondary school, the grades are referred to as: a) 6th to 3rd grades ["classe de sixième" to "classe de troisième" in French] for the first cycle ("collège" in French); and b) 2nd to final grade ["classe de seconde" to "classe terminale" in French] for the second cycle ("lycée" in French) */ desc ed4b ed4a codebook ed4a, tab (99) tab age ed3 if ed4a==0, miss //The category 'maternelle' indicate early childhood education, that is, pre-primary clonevar edulevel = ed4a //Highest educational level attended replace edulevel = . if ed4a==. | ed4a==8 //All missing values or out of range are replaced as "." replace edulevel = 0 if ed3==2 //Those who never attended school are replaced as '0' label var edulevel "Highest level of education attended" codebook ed4b, tab (99) clonevar eduhighyear = ed4b //Highest grade attended at that level replace eduhighyear = . if ed4b==. | ed4b==97 | ed4b==98 | ed4b==99 //All missing values or out of range are replaced as "." replace eduhighyear = 0 if ed3==2 //Those who never attended school are replaced as '0' lab var eduhighyear "Highest grade attended for each level of edu" *** Cleaning inconsistencies replace edulevel = 0 if age<10 replace eduhighyear = 0 if age<10 /*The variables edulevel and eduhighyear was replaced with a '0' given that the criteria for this indicator is household member aged 10 years or older */ replace eduhighyear = 0 if edulevel<1 //Early childhood education has no grade *** Now we create the years of schooling tab eduhighyear edulevel, miss gen eduyears = eduhighyear replace eduyears = eduhighyear + 6 if edulevel==2 /*There are 6 grades in primary school; followed by 4 grades in Secondaire 1 (Collège). As such we add 6 years to each of the grades completed at the Secondaire 1 (Collège). */ replace eduyears = eduhighyear + 10 if edulevel==3 /*There are 6 grades in primary school; followed by 4 grades in Secondaire 1 (Collège); and 4 grades in Secondaire 2 (Lycée). This means, individuals would have completed 10 years of schooling before reaching Secondaire 2. As such we add 10 years to each of the grades completed at the Secondaire 2 level.*/ replace eduyears = eduhighyear + 13 if edulevel==4 /*Individuals would have completed 13 years of schooling before reaching Supérieur level. As such we add 13 years to each of the grades completed at the Supérieur level.*/ *** Checking for further inconsistencies replace eduyears = . if age<=eduyears & age>0 /*There are cases in which the years of schooling are greater than the age of the individual. This is clearly a mistake in the data. 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" tab eduyears, miss /*A control variable is created on whether there is information on years of education for at least 2/3 of the household members aged 10 years and older */ gen temp = 1 if eduyears!=. & age>=10 & age!=. bysort hh_id: egen no_missing_edu = sum(temp) /*Total household members who are 10 years and older with no missing years of education */ gen temp2 = 1 if age>=10 & age!=. bysort hh_id: egen hhs = sum(temp2) /*Total number of household members who are 10 years and older */ replace no_missing_edu = no_missing_edu/hhs replace no_missing_edu = (no_missing_edu>=2/3) /*Identify whether there is information on years of education for at least 2/3 of the household members aged 10 years and older */ tab no_missing_edu, miss label var no_missing_edu "No missing edu for at least 2/3 of the HH members aged 10 years & older" drop temp temp2 hhs *** Standard MPI *** /*The entire household is considered deprived if no household member aged 10 years or older has completed SIX years of schooling. */ ******************************************************************* gen years_edu6 = (eduyears>=6) /* The years of schooling indicator takes a value of "1" if at least someone in the hh has reported 6 years of education or more */ replace years_edu6 = . if eduyears==. bysort hh_id: egen hh_years_edu6_1 = max(years_edu6) gen hh_years_edu6 = (hh_years_edu6_1==1) replace hh_years_edu6 = . if hh_years_edu6_1==. replace hh_years_edu6 = . if hh_years_edu6==0 & no_missing_edu==0 lab var hh_years_edu6 "Household has at least one member with 6 years of edu" tab hh_years_edu6, miss ******************************************************************************** *** Step 2.2 Child School Attendance *** ******************************************************************************** codebook ed5, tab (99) gen attendance = . replace attendance = 1 if ed5==1 //Replace attendance with '1' if currently attending school replace attendance = 0 if ed5==2 //Replace attendance with '0' if currently not attending school replace attendance = 0 if ed3==2 //Replace attendance with '0' if never ever attended school replace attendance = 0 if age<5 | age>24 //Replace attendance with '0' for individuals who are not of school age label define lab_attend 1 "currently attending" 0 "not currently attending" label values attendance lab_attend label var attendance "Attended school during current school year" tab attendance, miss *** Standard MPI *** /*The entire household is considered deprived if any school-aged child is not attending school up to class 8. */ ******************************************************************* gen child_schoolage = (schage>=6 & schage<=14) /* Note: In Congo, the official school entrance age to primary school is 6 years. So, age range is 6-14 (=6+8) Source 1: Country report p. 222 Source 2: "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!=. 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) //It takes value 1 if the household has children in school age lab var hh_children_schoolage "Household has children in school age" gen child_not_atten = (attendance==0) if child_schoolage==1 replace child_not_atten = . if attendance==. & child_schoolage==1 bysort hh_id: egen any_child_not_atten = max(child_not_atten) gen hh_child_atten = (any_child_not_atten==0) replace hh_child_atten = . if any_child_not_atten==. replace hh_child_atten = 1 if hh_children_schoolage==0 replace hh_child_atten = . if hh_child_atten==1 & no_missing_atten==0 /*If the household has been intially identified as non-deprived, but has missing school attendance for at least 2/3 of the school aged children, then we replace this household with a value of '.' because there is insufficient information to conclusively conclude that the household is not deprived */ lab var hh_child_atten "Household has all school age children up to class 8 in school" tab hh_child_atten, miss /*Note: The indicator takes value 1 if ALL children in school age are attending school and 0 if there is at least one child not attending. Households with no children receive a value of 1 as non-deprived. The indicator has a missing value only when there are all missing values on children attendance in households that have children in school age. */ ******************************************************************************** *** Step 2.3 Nutrition *** ******************************************************************************** ******************************************************************************** *** Step 2.3a Child Nutrition *** ******************************************************************************** *** Child Underweight Indicator *** ************************************************************************ *** Standard MPI *** bysort hh_id: egen temp = max(underweight) gen hh_no_underweight = (temp==0) //Takes value 1 if no child in the hh is underweight replace hh_no_underweight = . if temp==. replace hh_no_underweight = 1 if no_child_eligible==1 //Households with no eligible children will receive a value of 1 lab var hh_no_underweight "Household has no child underweight - 2 stdev" drop temp *** Child Stunting Indicator *** ************************************************************************ *** Standard MPI *** bysort hh_id: egen temp = max(stunting) gen hh_no_stunting = (temp==0) //Takes value 1 if no child in the hh is stunted replace hh_no_stunting = . if temp==. replace hh_no_stunting = 1 if no_child_eligible==1 //Households with no eligible children will receive a value of 1 lab var hh_no_stunting "Household has no child stunted - 2 stdev" drop temp *** Child Either Underweight or Stunted Indicator *** ************************************************************************ *** Standard MPI *** gen hh_no_uw_st = 1 if hh_no_stunting==1 & hh_no_underweight==1 replace hh_no_uw_st = 0 if hh_no_stunting==0 | hh_no_underweight==0 //Takes value 0 if child in the hh is stunted or underweight replace hh_no_uw_st = . if hh_no_stunting==. & hh_no_underweight==. replace hh_no_uw_st = 1 if no_child_eligible==1 //Households with no eligible children will receive a value of 1 lab var hh_no_uw_st "Household has no child underweight or stunted" ******************************************************************************** *** Step 2.3b Household Nutrition Indicator *** ******************************************************************************** *** Standard MPI *** /* The indicator takes value 1 if the household has no child under 5 who has either height-for-age or weight-for-age that is under 2 stdev below the median. It also takes value 1 for the households that have no eligible children. The indicator takes a value of 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 individuals malnourished" tab hh_nutrition_uw_st, miss ******************************************************************************** *** Step 2.4 Child Mortality *** ******************************************************************************** codebook cm9a cm9b mcm9a mcm9b /*cm9a or mcm9a: number of sons who have died cm9b or mcm9b: number of daughters who have died */ egen temp_f = rowtotal(cm9a cm9b), missing //Total child mortality reported by eligible women replace temp_f = 0 if cm1==1 & cm8==2 | cm1==2 /*Assign a value of "0" for: - all eligible women who have ever gave birth but reported no child death - all eligible women who never ever gave birth */ replace temp_f = 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_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 temp_m = rowtotal(mcm9a mcm9b), missing //Total child mortality reported by eligible men replace temp_m = 0 if mcm1==1 & mcm8==2 | mcm1==2 /*Assign a value of "0" for: - all eligible men who ever fathered children but reported no child death - all eligible men who never fathered children */ replace temp_m = 0 if no_male_eligible==1 /*Assign a value of "0" for: - individuals living in households that have non-eligible women */ 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" tab child_mortality, miss *** Standard MPI *** /* The standard MPI indicator takes a value of "0" if women in the household reported mortality among children under 18 in the last 5 years from the survey year. The indicator takes a value of "1" if eligible women within the household reported (i) no child mortality or (ii) if any child died longer than 5 years from the survey year or (iii) if any child 18 years and older died in the last 5 years. 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. */ ************************************************************************ tab childu18_died_per_wom_5y, miss /* The 'childu18_died_per_wom_5y' variable was constructed in Step 1.2 using information from individual women who ever gave birth in the BH 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 BH file. So at this stage, it is important that we aggregate the information that was obtained from the BH file at the household level. This ensures that women who were not present in the BH file is assigned with a value, following the information provided by other women in the household.*/ replace childu18_died_per_wom_5y = 0 if cm1==2 /*Assign a value of "0" for: - all eligible women who never ever gave birth */ replace childu18_died_per_wom_5y = 0 if no_fem_eligible==1 /*Assign a value of "0" for: - individuals living in households that have non-eligible women */ bysort hh_id: egen childu18_mortality_5y = sum(childu18_died_per_wom_5y), missing replace childu18_mortality_5y = 0 if childu18_mortality_5y==. & child_mortality==0 /*Replace all households as 0 death if women has missing value and men reported no death in those households */ label var childu18_mortality_5y "Under 18 child mortality within household past 5 years reported by women" tab childu18_mortality_5y, miss gen hh_mortality_u18_5y = (childu18_mortality_5y==0) replace hh_mortality_u18_5y = . if childu18_mortality_5y==. lab var hh_mortality_u18_5y "Household had no under 18 child mortality in the last 5 years" tab hh_mortality_u18_5y, miss ******************************************************************************** *** Step 2.5 Electricity *** ******************************************************************************** *** Standard MPI *** /*Members of the household are considered deprived if the household has no electricity */ **************************************** clonevar electricity = hc8a codebook electricity, tab (9) replace electricity = 0 if electricity==2 //0=no; 1=yes replace electricity = . if electricity==9 //Replace missing values label var electricity "Household has electricity" tab electricity, miss ******************************************************************************** *** Step 2.6 Sanitation *** ******************************************************************************** /* Improved sanitation facilities include flush or pour flush toilets to sewer systems, septic tanks or pit latrines, ventilated improved pit latrines, pit latrines with a slab, and composting toilets. These facilities are only considered improved if it is private, that is, it is not shared with other households. Source: https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01.pdf Note: In cases of mismatch between the country report and the internationally agreed guideline, we followed the report. */ desc ws8 ws9 clonevar toilet = ws8 clonevar shared_toilet = ws9 codebook shared_toilet, tab(99) recode shared_toilet (2=0) tab ws9 shared_toilet, miss nol *** Standard MPI *** /*Members of the household are considered deprived if the household's sanitation facility is not improved (according to the SDG guideline) or it is improved but shared with other households*/ ******************************************************************** codebook toilet, tab(99) gen toilet_mdg = ((toilet<=22 | toilet==31) & 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<=22 | toilet==31) & shared_toilet==1 /*Household is assigned a value of '0' if it uses improved sanitation but shares toilet with other households */ replace toilet_mdg = 0 if toilet == 14 /* The report indicate (p.141) that flush to somewhere else is considered as unimproved sanitation facility */ 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" tab toilet toilet_mdg, miss /* Note: In Congo, the report considers the category open defecation (95) as neither improved nor non-improved (p. 141). But we consider it as non-improved following the internationally agreed guideline. The category other (96) is considered by the report, and thus by us too, as non-improved */ ******************************************************************************** *** Step 2.7 Drinking Water *** ******************************************************************************** /* Improved drinking water sources include the following: piped water into dwelling, yard or plot; public taps or standpipes; boreholes or tubewells; protected dug wells; protected springs; packaged water; delivered water and rainwater which is located on premises or is less than a 30-minute walk from home roundtrip. Source: https://unstats.un.org/sdgs/metadata/files/Metadata-06-01-01.pdf Note: In cases of mismatch between the country report and the internationally agreed guideline, we followed the report. */ clonevar water = ws1 clonevar timetowater = ws4 clonevar ndwater = ws2 tab ws2 if water==91 /*Households using bottled water are only considered to be using improved water when they use water from an improved source for cooking and personal hygiene. This is because the quality of bottled water is not known. However, it is important to note that households using bottled water for drinking are classified as unimproved source if this is explicitly indicated in the country report. */ *** Standard MPI *** /* Members of the household are considered deprived if the household does not have access to improved drinking water (according to the SDG guideline) or safe drinking water is at least a 30-minute walk from home, roundtrip */ ******************************************************************** codebook water, tab(99) gen water_mdg = 1 if water==11 | water==12 | water==13 | water==14 | /// water==21 | water==31 | water==41 | water==51 | /// water==91 /*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, packaged water.*/ replace water_mdg = 0 if water==32 | water==42 | water==61 | /// water==81 | water==96 /*Deprived if it is unprotected well, unprotected spring, tanker truck surface water (river/lake, etc),other*/ codebook timetowater, tab (999) replace water_mdg = 0 if water_mdg==1 & timetowater >= 30 & timetowater!=. & /// 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==91 & /// (ndwater==32 | ndwater==42 | ndwater==61 | /// ndwater==81 | 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 = hc3 codebook floor, tab(99) gen floor_imp = 1 replace floor_imp = 0 if floor==11 | 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. We followed the report's definitions of natural or rudimentary materials. */ clonevar wall = hc5 codebook wall, tab(99) gen wall_imp = 1 replace wall_imp = 0 if wall<=28 | wall==96 /*Deprived if natural/rudimentary material or other, as per country report (p 420 )*/ 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. We followed the report's definitions of natural and rudimentary materials. */ clonevar roof = hc4 codebook roof, tab(99) gen roof_imp = 1 replace roof_imp = 0 if roof<=25 | roof==96 /* Deprived if natural/rudimentary material or other, as per country report (p 419 )*/ replace roof_imp = . if roof==. | roof==99 lab var roof_imp "Household has roof that it is not of low quality materials" tab roof roof_imp, miss *** Standard MPI *** /* Members of the household is deprived in housing if the roof, floor OR walls are constructed from low quality materials.*/ ************************************************************** gen housing_1 = 1 replace housing_1 = 0 if floor_imp==0 | wall_imp==0 | roof_imp==0 replace housing_1 = . if floor_imp==. & wall_imp==. & roof_imp==. lab var housing_1 "Household has roof, floor & walls that it is not low quality material" tab housing_1, miss ******************************************************************************** *** Step 2.9 Cooking Fuel *** ******************************************************************************** /* Solid fuel are solid materials burned as fuels, which includes coal as well as solid biomass fuels (wood, animal dung, crop wastes and charcoal). Source: https://apps.who.int/iris/bitstream/handle/10665/141496/9789241548885_eng.pdf */ lookfor cooking combustible clonevar cookingfuel = hc6 *** Standard MPI *** /* Members of the household are considered deprived if the household uses solid fuels and solid biomass fuels for cooking. */ ***************************************************************** codebook cookingfuel, tab(99) gen cooking_mdg = 1 replace cooking_mdg = 0 if cookingfuel>5 & cookingfuel<95 replace cooking_mdg = . if cookingfuel==. |cookingfuel==99 lab var cooking_mdg "Household cooks with clean fuels" /* Deprived if: coal/lignite, charcoal, wood, straw/shrubs/grass, agricultural crop, animal dung */ tab cookingfuel cooking_mdg, miss ******************************************************************************** *** Step 2.10 Assets ownership *** ******************************************************************************** *** Television/LCD TV/plasma TV/color TV/black & white tv lookfor tv television plasma lcd télé codebook hc8c clonevar television = hc8c lab var television "Household has television" *** Radio/walkman/stereo/kindle lookfor radio walkman stereo stéréo codebook hc8b clonevar radio = hc8b lab var radio "Household has radio" *** Handphone/telephone/iphone/mobilephone/ipod lookfor telephone téléphone mobilephone ipod codebook hc8d hc9b clonevar telephone = hc8d replace telephone=1 if telephone!=1 & hc9b==1 //hc9b=mobilephone. Combine information on telephone and mobilephone. tab hc9b hc8d if telephone==1,miss lab var telephone "Household has telephone (landline/mobilephone)" *** Refrigerator/icebox/fridge lookfor refrigerator réfrigérateur codebook hc8e clonevar refrigerator = hc8e lab var refrigerator "Household has refrigerator" *** Car/van/lorry/truck lookfor car voiture truck van codebook hc9f clonevar car = hc9f lab var car "Household has car" *** Bicycle/cycle rickshaw lookfor bicycle bicyclette codebook hc9c clonevar bicycle = hc9c lab var bicycle "Household has bicycle" *** Motorbike/motorized bike/autorickshaw lookfor motorbike moto codebook hc9d clonevar motorbike = hc9d lab var motorbike "Household has motorbike" //Note: motorbike or scooter *** Computer/laptop/tablet lookfor computer ordinateur laptop ipad tablet gen computer = . lab var computer "Household has computer" //Congo Republic MICS 2014-15: No data on computer *** Animal cart lookfor cart brouette charrette codebook hc9e gen animal_cart = hc9e lab var animal_cart "Household has animal cart" foreach var in television radio telephone refrigerator car /// bicycle motorbike computer animal_cart { replace `var' = 0 if `var'==2 label define lab_`var' 0"No" 1"Yes" label values `var' lab_`var' replace `var' = . if `var'==9 | `var'==99 | `var'==8 | `var'==98 } //Labels defined and missing values replaced *** Standard MPI *** /* Members of the household are considered deprived in assets if the household does not own more than one of: radio, TV, telephone, bike, motorbike, refrigerator, computer or animal cart and does not own a car or truck.*/ ***************************************************************************** egen n_small_assets2 = rowtotal(television radio telephone refrigerator bicycle motorbike computer animal_cart), missing lab var n_small_assets2 "Household Number of Small Assets Owned" gen hh_assets2 = (car==1 | n_small_assets2 > 1) replace hh_assets2 = . if car==. & n_small_assets2==. lab var hh_assets2 "Household Asset Ownership: HH has car or more than 1 small assets incl computer & animal cart" ******************************************************************************** *** Step 2.11 Rename and keep variables for MPI calculation ******************************************************************************** //Retain data on sampling design: desc psu stratum clonevar strata = stratum //Retain year, month & date of interview: desc hh5y hh5m hh5d clonevar year_interview = hh5y clonevar month_interview = hh5m clonevar date_interview = hh5d //Generate presence of subsample gen subsample = . *** Rename key global MPI indicators for estimation *** recode hh_mortality_u18_5y (0=1)(1=0) , gen(d_cm) recode hh_nutrition_uw_st (0=1)(1=0) , gen(d_nutr) recode hh_child_atten (0=1)(1=0) , gen(d_satt) recode hh_years_edu6 (0=1)(1=0) , gen(d_educ) recode electricity (0=1)(1=0) , gen(d_elct) recode water_mdg (0=1)(1=0) , gen(d_wtr) recode toilet_mdg (0=1)(1=0) , gen(d_sani) recode housing_1 (0=1)(1=0) , gen(d_hsg) recode cooking_mdg (0=1)(1=0) , gen(d_ckfl) recode hh_assets2 (0=1)(1=0) , gen(d_asst) *** Generate coutry and survey details for estimation *** char _dta[cty] "Congo" char _dta[ccty] "COG" char _dta[year] "2014-2015" char _dta[survey] "MICS" char _dta[ccnum] "178" char _dta[type] "micro" *** Sort, compress and save data for estimation *** sort ind_id compress la da "Micro data for `_dta[ccty]' (`_dta[ccnum]'). Last save: `c(filedate)'." save "$path_out/cog_mics14-15.dta", replace