******************************************************************************** /* Citation: Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. 2018 Global Multidimensional Poverty Index - Ukraine MICS 2012 [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/Ukraine MICS 2012" global path_out "G:/pov" global path_logs "G:/logs" global path_ado "G:/ado" *** Log file *** log using "$path_logs/ukr_mics12_dataprep.log", replace ******************************************************************************** *** UKRAINE MICS 2012 *** ******************************************************************************** ******************************************************************************** *** Step 1: Data preparation *** Selecting main variables from CH, WM, HH & MN recode & merging with HL recode ******************************************************************************** /*Ukraine MICS 2012: No anthropometric data was collected for any of the groups. */ ******************************************************************************** *** Step 1.1 CH - CHILDREN's RECODE (under 5) ******************************************************************************** use "$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*100000 + 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 /*Note: Since anthropometric data was not collected for children under 5, the variables generated under this section takes missing value. */ *** Standard MPI indicator *** gen underweight = . lab var underweight "Child is undernourished (weight-for-age) 2sd - WHO" tab underweight, miss gen stunting = . lab var stunting "Child is stunted (length/height-for-age) 2sd - WHO" tab stunting, miss gen wasting = . lab var wasting "Child is wasted (weight-for-length/height) 2sd - WHO" tab wasting, miss //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 "$path_out/UKR12_CH.dta", replace ******************************************************************************** *** Step 1.2 BR - BIRTH RECODE *** (All females 15-49 years who ever gave birth) ******************************************************************************** /*The purpose of step 1.2 is to identify children of any age who died in the last 5 years prior to the survey date.*/ //Ukraine MICS 2012 has no BH file, but PH file has relevant information use "$path_in/ph.dta", clear rename _all, lower *** Generate individual unique key variable required for data merging using: *** hh1=cluster number; *** hh2=household number; *** wm4=women's line number. gen double ind_id = hh1*100000 + hh2*100 + ln format ind_id %20.0g label var ind_id "Individual ID" desc ph4c ph13u ph13n gen date_death = ph4c + ph13n //Date of death = date of birth/end of pregnancy (ph4c) + age at death(ph13n) gen mdead_survey = wdoi-date_death //Months dead from survey = Date of interview (wdoi) - date of death replace mdead_survey = . if (ph13n==0 | ph13n==.) & ph9==1 //Replace children who are alive as '0' and missing cases as '.' gen ydead_survey = mdead_survey/12 //Years dead from survey codebook ph9, tab (10) //bh5 - Child still alive: 1=Yes; 2=No gen child_died = 1 if ph9==2 //1=child dead; 0=child alive replace child_died = 0 if ph9==1 replace child_died = . if ph9==. label define lab_died 0"child is alive" 1"child has died" label values child_died lab_died tab ph9 child_died, miss bysort ind_id: egen tot_child_died = sum(child_died) //For each woman, sum the number of children who died 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 gen women_PH = 1 //Identification variable for observations in PH recode //Retain relevant variables keep ind_id hh1 hh2 women_PH child_died_per_wom child_died_per_wom_5y order ind_id hh1 hh2 women_PH child_died_per_wom child_died_per_wom_5y sort ind_id save "$path_out/UKR12_PH.dta", replace ******************************************************************************** *** Step 1.3 WM - WOMEN's RECODE *** (All eligible females 15-49 years in the household) ******************************************************************************** use "$path_in/wm.dta", clear rename _all, lower *** Generate individual unique key variable required for data merging *** hh1=cluster number; *** hh2=household number; *** ln=respondent's line number gen double ind_id = hh1*100000 + hh2*100 + ln format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id //No duplicate gen women_WM =1 //Identification variable for observations in WM recode tab wb2, miss //233 women with no info on age tab cm1 cm8, miss /* Ukraine MICS 2012: one observation of woman who had never given birth but had a child who later died. This will be corrected later in the dofile */ lookfor marital 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 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 //Retain relevant variables: keep wm7 cm1 cm8 cm9a cm9b ind_id women_WM marital order wm7 cm1 cm8 cm9a cm9b ind_id women_WM marital sort ind_id save "$path_out/UKR12_WM.dta", replace ******************************************************************************** *** Step 1.3 MN - MEN'S RECODE ***(All eligible man: 15-49 years in the household) ******************************************************************************** use "$path_in/mn.dta", clear rename _all, lower *** Generate individual unique key variable required for data merging *** hh1=cluster number; *** hh2=household number; *** ln=respondent's line number gen double ind_id = hh1*100000 + 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 lookfor marital 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 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 //Retain relevant variables: keep mcm1 mcm8 mcm9a mcm9b ind_id men_MN marital order mcm1 mcm8 mcm9a mcm9b ind_id men_MN marital sort ind_id save "$path_out/UKR12_MN.dta", replace ******************************************************************************** *** Step 1.4 HH - HOUSEHOLD RECODE ***(All households interviewed) ******************************************************************************** use "$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*100 + hh2 format hh_id %20.0g lab var hh_id "Household ID" save "$path_out/UKR12_HH.dta", replace ******************************************************************************** *** Step 1.5 HL - HOUSEHOLD MEMBER ******************************************************************************** use "$path_in/hl.dta", clear rename _all, lower gen cty = "Ukraine" gen ccty = "UKR" gen year = "2012" gen survey = "MICS" gen ccnum = 804 *** Generate a household unique key variable at the household level using: ***hh1=cluster number ***hh2=household number gen double hh_id = hh1*100 + 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*100000 + hh2*100 + hl1 format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id //No duplicates sort ind_id ******************************************************************************** *** Step 1.6 DATA MERGING ******************************************************************************** *** Merging PH Recode ***************************************** merge 1:1 ind_id using "$path_out/UKR12_PH.dta" // Using PH file (no BH file for UKR 2012 MICS) drop _merge erase "$path_out/UKR12_PH.dta" *** Merging WM Recode ***************************************** merge 1:1 ind_id using "$path_out/UKR12_WM.dta" tab hl7, miss gen temp = (hl7>0) tab women_WM temp, miss col tab wm7 if temp==1 & women_WM==., miss //Total of eligible women not interviewed drop temp drop _merge erase "$path_out/UKR12_WM.dta" *** Merging HH Recode ***************************************** merge m:1 hh_id using "$path_out/UKR12_HH.dta" tab hh9 if _m==2 drop if _merge==2 //Drop households that were not interviewed drop _merge erase "$path_out/UKR12_HH.dta" *** Merging MN Recode ***************************************** merge 1:1 ind_id using "$path_out/UKR12_MN.dta" drop _merge erase "$path_out/UKR12_MN.dta" *** Merging CH Recode ***************************************** merge 1:1 ind_id using "$path_out/UKR12_CH.dta" drop _merge erase "$path_out/UKR12_CH.dta" sort ind_id ******************************************************************************** *** Step 1.7 CONTROL VARIABLES ******************************************************************************** /* Households are identified as having 'no eligible' members if there are no applicable population, that is, children 0-5 years, adult women 15-49 years or adult men 15-49 years. These households will not have information on relevant indicators of health. As such, these households are considered as non-deprived in those relevant indicators.*/ *** No Eligible Women 15-49 years ***************************************** gen fem_eligible = (hl7>0) if hl7!=. bys hh_id: egen hh_n_fem_eligible = sum(fem_eligible) //Number of eligible women for interview in the hh gen no_fem_eligible = (hh_n_fem_eligible==0) //Takes value 1 if the household had no eligible females for an interview lab var no_fem_eligible "Household has no eligible women" tab no_fem_eligible, miss *** No Eligible Men 15-49 years ***************************************** gen male_eligible = (hl7a>0) if hl7a!=. bys hh_id: egen hh_n_male_eligible = sum(male_eligible) //Number of eligible men for interview in the hh gen no_male_eligible = (hh_n_male_eligible==0) //Takes value 1 if the household had no eligible males for an interview lab var no_male_eligible "Household has no eligible man" tab no_male_eligible, miss *** No Eligible Children 0-5 years ***************************************** gen child_eligible = (hl9>0 | child_CH==1) bys hh_id: egen hh_n_children_eligible = sum(child_eligible) //Number of eligible children for anthropometrics gen no_child_eligible = (hh_n_children_eligible==0) //Takes value 1 if there were no eligible children for anthropometrics lab var no_child_eligible "Household has no children eligible" tab no_child_eligible, miss *** No Eligible Women and Men *********************************************** gen no_adults_eligible = (no_fem_eligible==1 & no_male_eligible==1) //Takes value 1 if the household had no eligible men & women for an interview lab var no_adults_eligible "Household has no eligible women or men" tab no_adults_eligible, miss *** No Eligible Children and Women *********************************************** /*NOTE: In the DHS datasets, we use this variable as a control variable for the nutrition indicator if nutrition data is present for children and women. However, in MICS, we do NOT use this as a control variable. This is because nutrition data is only collected from children. However, we continue to generate this variable in this do-file so as to be consistent*/ 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. However, in MICS, we do NOT use this as a control variable. This is because nutrition data is only collected from children. However, we continue to generate this variable in this do-file so as to be consistent*/ 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 the MICS surveys do not collect hemoglobin data. As such, this variable takes missing value. However, we continue to generate this variable in this do-file so as to be consistent*/ gen no_hem_eligible = . lab var no_hem_eligible "Household has no eligible individuals for hemoglobin measurements" drop fem_eligible hh_n_fem_eligible male_eligible hh_n_male_eligible /// child_eligible hh_n_children_eligible sort hh_id ******************************************************************************** *** Step 1.8 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" //Relationship to the head of household desc hl3 clonevar relationship = hl3 codebook relationship, tab (20) recode relationship (1=1)(2=2)(3=3)(13=3)(4/12=4)(14=5)(98=.)(99=.) label define lab_rel 1"head" 2"spouse" 3"child" 4"extended family" /// 5"not related" label values relationship lab_rel label var relationship "Relationship to the head of household" tab hl3 relationship, miss //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 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 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 codebook hh7, tab (100) decode hh7, gen(temp) replace temp = proper(temp) encode temp, gen(region) lab var region "Region for subnational decomposition" tab hh7 region, miss drop temp ******************************************************************************** *** Step 2 Data preparation *** *** Standardization of the 10 Global MPI indicators *** Identification of non-deprived & deprived individuals ******************************************************************************** ******************************************************************************** *** Step 2.1 Years of Schooling *** ******************************************************************************** /* According to Ukraine MICS 2012 survey report, children start school at ages 6 or 7 at the discretion of their parents/caretakers, primary school is for ages 7-10, and secondary school is for ages 11-17 (p. 165). The survey used the following groupings: primary (grades 1-4), secondary (5-12), PTU (1-3), technical/vocational school (1-4), higher education (1+) */ /* Note Ukraine MICS 2012: for ed4a, PTU is secondary special education, starting after nine years of educ (4 primary and first 5 years of secondary) and texnikum (4) is technical school */ tab ed4b ed4a, miss /* Note large number of missing values for all key education variables ed4a - 13.80% ed4b - 15% ed3 - 13% */ tab age ed6a if ed5==1, miss //Check: For those currently in school and their level of schooling clonevar edulevel = ed4a //Highest educational level attended replace edulevel = . if ed4a==. | ed4a==8 |ed4a==9 //ed4a=8/98/99 are missing values replace edulevel = 0 if ed3==2 //Those who never attended school are replaced as '0' label var edulevel "Highest educational level attended" clonevar eduhighyear = ed4b //Highest grade of education completed replace eduhighyear = . if ed4b==. | ed4b==98 | ed4b==99 //ed4b=97/98/99 are missing values replace eduhighyear = 0 if ed3==2 //Those who never attended school are replaced as '0' lab var eduhighyear "Highest year of education completed" *** Cleaning inconsistencies replace eduhighyear = 0 if age<10 /*The variable "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 *** Now we create the years of schooling tab eduhighyear edulevel, miss gen eduyears = eduhighyear replace eduyears = 0 if edulevel<2 & eduhighyear==. /*Assuming 0 year if they only attend preschool or primary but the last year is unknown*/ replace eduyears = eduhighyear + 9 if (edulevel==3) /*PTU (special secondary education starts after 9 years of general education */ replace eduyears = eduhighyear + 11 if (edulevel==4 | edulevel==5) /*University after 11 years of education, 4 years of primary + 5 years of lower secondary and 2 years of upper secondary */ replace eduyears = 0 if edulevel==0 & eduyears==. replace eduyears = . if edulevel==. & eduhighyear==. //Replaced as missing value when level of education is missing *** 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" /*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 //Values for 0 are less than 1% drop temp temp2 hhs /*The entire household is considered deprived if no household member aged 10 years or older has completed SIX years of schooling. */ gen years_edu6 = (eduyears>=6) replace years_edu6 = . if eduyears==. bysort hh_id: egen hh_years_edu6_1 = max(years_edu6) gen hh_years_edu6 = (hh_years_edu6_1==1) replace hh_years_edu6 = . if hh_years_edu6_1==. replace hh_years_edu6 = . if hh_years_edu6==0 & no_missing_edu==0 //Final variable missing if household has info for < 2/3 of members 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 (10) 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 tab age ed5, miss //Check individuals who are not of school age replace attendance = 0 if age<5 | age>24 //Replace attendance with '0' for individuals who are not of school age tab attendance, miss 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" /*The entire household is considered deprived if any school-aged child is not attending school up to class 8. */ gen child_schoolage = (age>=7 & age<=15) /* Note: In Ukraine, the official school entrance age for primary school is 7 years. This is indicated in the country report (p.166). So, age range is 7-15 (=7+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 //Values for 0 are less than 1% 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 *** ******************************************************************************** /*Ukraine MICS 2012 has no information on nutrition. Hence we create empty variables under this section */ gen hh_no_underweight = . lab var hh_no_underweight "Household has no child underweight - 2 stdev" gen hh_no_stunting = . lab var hh_no_stunting "Household has no child stunted - 2 stdev" gen hh_no_uw_st = . lab var hh_no_uw_st "Household has no child underweight or stunted" gen hh_nutrition_uw_st = . lab var hh_nutrition_uw_st "Household has no child underweight/stunted or adult deprived by BMI/BMI-for-age" ******************************************************************************** *** Step 2.4 Child Mortality *** ******************************************************************************** codebook 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 reported by women & men" tab child_mortality, miss /*Deprived if any children died in the household */ ************************************************************************ gen hh_mortality = (child_mortality==0) /*Household is replaced with a value of "1" if there is no incidence of child mortality*/ replace hh_mortality = . if child_mortality==. replace hh_mortality = 1 if no_adults_eligible==1 //Replace households as non-deprived if there is no eligible population lab var hh_mortality "Household had no child mortality" tab hh_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.*/ replace child_died_per_wom_5y = 0 if cm1==2 /*Assign a value of "0" for: - all eligible women who never ever gave birth */ replace child_died_per_wom_5y = 0 if no_fem_eligible==1 /*Assign a value of "0" for: - individuals living in households that have non-eligible women */ bysort hh_id: egen child_mortality_5y = sum(child_died_per_wom_5y), missing replace child_mortality_5y = 0 if child_mortality_5y==. & child_mortality==0 /*Replace all households as 0 death if women has missing value and men reported no death in those households */ label var child_mortality_5y "Total child mortality within household past 5 years reported by women" tab child_mortality_5y, miss /* The new standard MPI indicator takes a value of "1" if eligible women within the household reported no child mortality or if any child died longer than 5 years from the survey year. The indicator takes a value of "0" if women in the household reported any child mortality in the last 5 years from the survey year. Households were replaced with a value of "1" if eligible men within the household reported no child mortality in the absence of information from women. The indicator takes a missing value if there was missing information on reported death from eligible individuals. */ gen hh_mortality_5y = (child_mortality_5y==0) replace hh_mortality_5y = . if child_mortality_5y==. tab hh_mortality_5y, miss lab var hh_mortality_5y "Household had no child mortality in the last 5 years" ******************************************************************************** *** Step 2.5 Electricity *** ******************************************************************************** /*Members of the household are considered deprived if the household has no electricity */ clonevar electricity = hc8a codebook electricity, tab (10) replace electricity = 0 if electricity==2 replace electricity = . if electricity==9 label var electricity "Household has electricity" ******************************************************************************** *** Step 2.6 Sanitation *** ******************************************************************************** /*Members of the household are considered deprived if the household's sanitation facility is not improved, according to MDG guidelines, or it is improved but shared with other household. We also checked the country reports on how the sanitation categories have been grouped. In cases of mismatch, we have followed the country report */ clonevar toilet = ws8 codebook toilet, tab(30) codebook ws9, tab(30) clonevar shared_toilet = ws9 recode shared_toilet (2=0) recode shared_toilet (9=.) tab ws9 shared_toilet, miss nol //0=no;1=yes;.=missing gen toilet_mdg = ((toilet<23 | toilet==31) & shared_toilet!=1) replace toilet_mdg = 0 if toilet == 14 | toilet== 15 replace toilet_mdg = 0 if (toilet<22 | toilet==31) & shared_toilet==1 replace toilet_mdg = . if toilet==. | toilet==99 lab var toilet_mdg "Household has improved sanitation with MDG Standards" tab toilet toilet_mdg, miss ******************************************************************************** *** Step 2.7 Drinking Water *** ******************************************************************************** /*Members of the household are considered deprived if the household does not have access to safe drinking water according to MDG guidelines, or safe drinking water is more than a 30-minute walk from home roundtrip. */ clonevar water = ws1 clonevar timetowater = ws4 codebook water, tab(99) clonevar ndwater = ws2 /* Ukraine MICS 2012: observations if bottled water used for drinking */ tab ws2 if water==91 /*Because the quality of bottled water is not known, households using bottled water for drinking are classified as using an improved or unimproved source according to their water source for non-drinking activities such as cooking and hand washing. However, it is important to note that households using bottled water for drinking are classified as unimproved source if this is explicitly mentioned in the country report. */ gen water_mdg = 1 if water==11 | water==12 | water==13 | water==14 | /// water==21 | water==31 | water==41 | water==91 /*Non deprived if water is "piped into dwelling", "piped to yard/plot", "piped to neighbour", "public tap/standpipe", "tube well or borehole", "protected well", "protected spring", "bottled water" */ replace water_mdg = 0 if water==32 | water==42 | water==61 | water==71 | /// water==81 | water==96 /*Deprived if it is "unprotected well", "unprotected spring", "tanker truck" "cart with small tank/drum", "surface water (river/lake, etc)", "other" */ 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==61) /*Households using bottled water for drinking are classified as using an improved or unimproved source according to their water source for non-drinking activities */ /* Ukraine MICS 2012: for households that use bottled water for both drinking water and non-drinking water, they are considered non-deprived for consistency with countries without non-drinking water data. */ 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<=12 | floor==96 //Deprived if "mud/earth/clay", "sand", "dung", "other" replace floor_imp = . if floor==99 replace floor_imp = . if floor==. lab var floor_imp "Household has floor that it is not earth/sand/dung" tab floor floor_imp, miss /* Members of the household are considered deprived if the household has wall made of natural or rudimentary materials */ clonevar wall = hc5 codebook wall, tab(99) gen wall_imp = 1 replace wall_imp = 0 if wall<=26 | wall==96 /*Deprived if "no wall" "cane/palms/trunk" "mud/dirt" "grass/reeds/thatch" "pole/bamboo with mud" "stone with mud" "cardboard" "carton/plastic" "uncovered adobe" "canvas/tent" "unburnt bricks" "other" "plywood" */ replace wall_imp = . if wall==99 replace wall_imp = . if wall==. lab var wall_imp "Household has wall that it is not of low quality materials" tab wall wall_imp, miss /* Members of the household are considered deprived if the household has roof made of natural or rudimentary materials */ clonevar roof = hc4 codebook roof, tab(99) gen roof_imp = 1 replace roof_imp = 0 if roof<=23 | roof==96 /*Deprived if "no roof" "thatch/palm leaf" "mud/earth/lump of earth" "sod/grass" "plastic/polythene sheeting" "rustic mat" "cardboard" "canvas/tent" "unburnt bricks" "other"*/ replace roof_imp = . if roof==99 replace roof_imp = . if roof==. lab var roof_imp "Household has roof that it is not of low quality materials" tab roof roof_imp, miss /*Household is deprived in housing if the roof, floor OR walls uses low quality materials.*/ gen housing_1 = 1 replace housing_1 = 0 if floor_imp==0 | wall_imp==0 | roof_imp==0 replace housing_1 = . if floor_imp==. & wall_imp==. & roof_imp==. lab var housing_1 "Household has roof, floor & walls that are 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 = hc6 codebook cookingfuel, tab(99) gen cooking_mdg = 1 replace cooking_mdg = 0 if cookingfuel>5 & cookingfuel<95 replace cooking_mdg = . if cookingfuel==. | cookingfuel==99 lab var cooking_mdg "Household has cooking fuel according to MDG standards" /* Non deprived if: 1 "electricity", 2 "lpg", 3 "natural gas", 4 "biogas", 95 "no food cooked in household" Deprived if: 6 "coal/lignite", 7 "charcoal", 8 "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. */ //Check that for standard assets in living standards: "no"==0 and yes=="1" codebook hc8c hc8b hc8e hc9b hc9d hc8f hc9f hc9c hc8j hc11 clonevar television = hc8c replace television = 1 if hc8d==1 /* Ukraine MICS 2012: variable hc8d is flatscreen tv and variable hc8c is crt tv set.*/ gen bw_television = . clonevar radio = hc8b clonevar telephone = hc8e clonevar mobiletelephone = hc9b clonevar refrigerator = hc8f clonevar car = hc9f clonevar bicycle = hc9c clonevar motorbike = hc9d gen computer = hc8j replace computer = 1 if hc8k==1 replace computer = 1 if hc8l==1 /* Ukraine MICS 2012: there are three different variables for computer - hc8j (desktop computer), hc8k (laptop computer), and hc8l (tablet computer). These will all be included in this variable. */ clonevar animal_cart = hc9e 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: desc psu stratum rename stratum strata //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_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/ukr_mics12_pov.dta", replace log close