******************************************************************************** /* Citation: Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. 2018 Global Multidimensional Poverty Index - Philippines DHS 2013 [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/Philippines DHS 2013" global path_out "D:/pov" global path_logs "D:/logs" global path_ado "D:/ado" *** Log file *** log using "$path_logs/phl_dhs13_dataprep.log", replace ******************************************************************************** *** PHILIPPINES DHS 2013 *** ******************************************************************************** ******************************************************************************** *** Step 1: Data preparation *** Selecting variables from KR, BR, IR, & MR recode & merging with PR recode ******************************************************************************** ******************************************************************************** *** Step 1.1 KR - CHILDREN's RECODE (under 5) ******************************************************************************** /*Philippines DHS 2013: Anthropometric data was not collected from chidren, women and men. Hence the commands under this section have been removed. */ ******************************************************************************** *** Step 1.2 BR - BIRTH RECODE *** (All females 15-49 years who ever gave birth) ******************************************************************************** /*The purpose of step 1.2 is to identify children of any age who died in the last 5 years prior to the survey date.*/ use "$path_in/PHBR61FL.dta", clear *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** v003=respondent's line number gen double ind_id = v001*1000000 + v002*100 + v003 format ind_id %20.0g label var ind_id "Individual ID" desc b3 b7 gen date_death = b3 + b7 //Date of death = date of birth (b3) + age at death (b7) gen mdead_survey = v008 - date_death //Months dead from survey = Date of interview (v008) - date of death gen ydead_survey = mdead_survey/12 //Years dead from survey codebook b5, tab (10) gen child_died = 1 if b5==0 //1=child dead; 0=child alive replace child_died = 0 if b5==1 replace child_died = . if b5==. label define lab_died 1 "child has died" 0 "child is alive" label values child_died lab_died tab b5 child_died, miss /*NOTE: For each woman, sum the number of children who died and compare to the number of sons/daughters whom they reported have died */ bysort ind_id: egen tot_child_died = sum(child_died) egen tot_child_died_2 = rsum(v206 v207) //v206: sons who have died //v207: daughters who have died compare tot_child_died tot_child_died_2 //The figures are identical for Philippines DHS 2013 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 b16 child_died_per_wom child_died_per_wom_5y order ind_id women_BR b16 child_died_per_wom child_died_per_wom_5y sort ind_id //Save a temp file for merging with PR: save "$path_out/PHL13_BR.dta", replace ******************************************************************************** *** Step 1.3 IR - WOMEN's RECODE *** (All eligible females 15-49 years in the household) ******************************************************************************** use "$path_in/PHIR61FL.dta", clear *** Generate individual unique key variable required for data merging *** v001=cluster number; *** v002=household number; *** v003=respondent's line number gen double ind_id = v001*1000000 + v002*100 + v003 //Check length of variables for a correct generation of the id variable format ind_id %20.0g label var ind_id "Individual ID" duplicates report ind_id gen women_IR=1 //Identification variable for observations in IR recode //Marital status of women /* Note: There is no data on the marital status of household members for Philippines DHS 2013 in the PR file. Hence we construct this variable using information from women from the IR file*/ clonevar marital = v501 codebook marital, tab (10) recode marital (0=1)(1=2)(8=.) label define lab_mar 1"never married" 2"currently married" 3"widowed" /// 4"divorced" 5"not living together" /*Note: In the case of the Philippines, women who reported living with a partner was recoded as 'married'. */ label values marital lab_mar label var marital "Marital status of household member (women)" tab v501 marital, miss keep ind_id women_IR v003 v005 v012 v201 v206 v207 marital order ind_id women_IR v003 v005 v012 v201 v206 v207 marital sort ind_id //Save a temp file for merging with PR: save "$path_out/PHL13_IR.dta", replace ******************************************************************************** *** Step 1.4 IR - WOMEN'S RECODE *** (Girls 15-19 years in the household) ******************************************************************************** /*Philippines DHS 2013: Anthropometric data was not collected from chidren, women and men. Hence the commands under this section have been removed. */ ******************************************************************************** *** Step 1.5 MR - MEN'S RECODE ***(All eligible man in the household) ******************************************************************************** /* Note: There is no MR dta file for Philippines DHS 2013. Hence the commands under this section have been removed. */ ******************************************************************************** *** Step 1.6 MR - MEN'S RECODE ***(Boys 15-19 years in the household) ******************************************************************************** /* Note: There is no MR dta file for Philippines DHS 2013. Hence the commands under this section have been removed. */ ******************************************************************************** *** Step 1.7 PR - HOUSEHOLD MEMBER'S RECODE ******************************************************************************** use "$path_in/PHPR62FL.dta", clear gen cty = "Philippines" gen ccty = "PHL" gen year = "2013" gen survey = "DHS" gen ccnum = 608 *** Generate a household unique key variable at the household level using: ***hv001=cluster number ***hv002=household number gen double hh_id = hv001*10000 + hv002 format hh_id %20.0g label var hh_id "Household ID" codebook hh_id *** Generate individual unique key variable required for data merging using: *** hv001=cluster number; *** hv002=household number; *** hvidx=respondent's line number. gen double ind_id = hv001*1000000 + hv002*100 + hvidx format ind_id %20.0g label var ind_id "Individual ID" codebook ind_id count if hv102==1 //Usual resident or visitor sort hh_id ind_id ******************************************************************************** *** Step 1.8 DATA MERGING ******************************************************************************** *** Merging BR Recode ***************************************** merge 1:1 ind_id using "$path_out/PHL13_BR.dta" drop _merge erase "$path_out/PHL13_BR.dta" *** Merging IR Recode ***************************************** merge 1:1 ind_id using "$path_out/PHL13_IR.dta" tab women_IR hv117, miss col tab ha65 if hv117==1 & women_IR==., miss //Total number of eligible women not interviewed tab ha65 ha13 if women_IR== . & hv117==1, miss drop _merge erase "$path_out/PHL13_IR.dta" sort ind_id ******************************************************************************** *** Step 1.9 CONTROL VARIABLES ******************************************************************************** /* Households are identified as having 'no eligible' members if there are no applicable population, that is, children 0-5 years, adult women 15-49 years or adult men. These households will not have information on relevant indicators of health. As such, these households are considered as non-deprived in those relevant indicators.*/ *** No Eligible Women 15-49 years ***************************************** gen fem_eligible = (hv117==1) bysort hh_id: egen hh_n_fem_eligible = sum(fem_eligible) //Number of eligible women for interview in the hh gen no_fem_eligible = (hh_n_fem_eligible==0) //Takes value 1 if the household had no eligible females for an interview lab var no_fem_eligible "Household has no eligible women" tab no_fem_eligible, miss *** No Eligible Men ***************************************** //NOTE: Philippines DHS 2013 have no male recode file. //If this is the case, this variable takes missing value gen no_male_eligible = . lab var no_male_eligible "Household has no eligible man" tab no_male_eligible, miss *** No Eligible Children 0-5 years ***************************************** //NOTE: Philippines DHS 2013 have no anthropometric data. //If this is the case, this variable takes missing value gen no_child_eligible = . lab var no_child_eligible "Household has no children eligible" tab no_child_eligible, miss *** No Eligible Women and Men *********************************************** //NOTE: Philippines DHS 2013 have no male recode file. //If this is the case, this variable takes missing value gen no_adults_eligible = . lab var no_adults_eligible "Household has no eligible women or men" tab no_adults_eligible, miss *** No Eligible Children and Women *********************************************** //NOTE: Philippines DHS 2013 have no anthropometric data. //If this is the case, this variable takes missing value gen no_child_fem_eligible = . lab var no_child_fem_eligible "Household has no children or women eligible" tab no_child_fem_eligible, miss *** No Eligible Women, Men or Children *********************************************** //NOTE: Philippines DHS 2013 have no anthropometric data. //If this is the case, this variable takes missing value gen no_eligibles = . lab var no_eligibles "Household has no eligible women, men, or children" tab no_eligibles, miss *** No Eligible Subsample ***************************************** //NOTE: Philippines DHS 2013 have no anthropometric data. 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 sort hh_id ind_id ******************************************************************************** *** Step 1.10 SUBSAMPLE VARIABLE *** ******************************************************************************** /* In the context of Phillipines DHS 2013, anthropometric measures was not collected as part of the survey. Hence, there is no anthropometric subsample selection. */ gen subsample= . label var subsample "Households selected as part of nutrition subsample" ******************************************************************************** *** Step 1.11 KEEPING ONLY DE JURE HOUSEHOLD MEMBERS *** ******************************************************************************** //Permanent (de jure) household members clonevar resident = hv102 label var resident "Permanent (de jure) household member" drop if resident!=1 /*Note: The Global MPI is based on de jure (permanent) household members only. As such, non-usual residents will be excluded from the sample. In the context of Phillipines DHS 2013, 2,382 (3.30%) individuals who were non-usual residents were dropped from the sample */ ******************************************************************************** *** Step 1.12 RENAMING DEMOGRAPHIC VARIABLES *** ******************************************************************************** //Sample weight desc hv005 clonevar weight = hv005 label var weight "Sample weight" //Area: urban or rural desc hv025 codebook hv025, tab (5) clonevar area = hv025 replace area=0 if area==2 label define lab_area 1 "urban" 0 "rural" label values area lab_area label var area "Area: urban-rural" //Relationship to the head of household clonevar relationship = hv101 codebook relationship, tab (20) recode relationship (1=1)(2=2)(3=3)(11=3)(4/10=4)(12=5) label define lab_rel 1"head" 2"spouse" 3"child" 4"extended family" /// 5"not related" 6"maid" label values relationship lab_rel label var relationship "Relationship to the head of household" tab hv101 relationship, miss //Sex of household member codebook hv104 clonevar sex = hv104 label var sex "Sex of household member" //Age of household member codebook hv105, tab (100) clonevar age = hv105 replace age = . if age>=98 label var age "Age of household member" //Age group recode age (0/4 = 1 "0-4")(5/9 = 2 "5-9")(10/14 = 3 "10-14") /// (15/17 = 4 "15-17")(18/59 = 5 "18-59")(60/max=6 "60+"), gen(agec7) lab var agec7 "age groups (7 groups)" recode age (0/9 = 1 "0-9") (10/17 = 2 "10-17")(18/59 = 3 "18-59") /// (60/max=4 "60+"), gen(agec4) lab var agec4 "age groups (4 groups)" //Marital status of household member /*NOTE: Philippines DHS 2013 have no data on marital status in the PR file. So the variable was constructed in the IR file using information from women*/ desc marital //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 lookfor region codebook hv024, tab (99) /*NOTE: The Philippines has 17 administrative regions(p.1). The sample was designed to provide data representative of the country and its 17 administrative regions (p.6).*/ clonevar region = hv024 /*NOTE: Remove the numbers that were included with the name of the regions. So relabel the names */ label define lab_reg 1 "National Capital Region" 2 "Cordillera Admin Region" /// 3 "Ilocos Region" 4 "Cagayan Valley" /// 5 "Central Luzon" 6 "Calabarzon" /// 7 "Mimaropa" 8 "Bicol" /// 9 "Western Visayas" 10 "Central Visayas" /// 11 "Eastern Visayas" 12 "Zamboanga Peninsula" /// 13 "Northern Mindanao" 14 "Davao" /// 15 "Soccsksargen" 16 "Caraga" 17 "Armm" label values region lab_reg lab var region "Region for subnational decomposition" ******************************************************************************** *** Step 2 Data preparation *** *** Standardization of the 10 Global MPI indicators *** Identification of non-deprived & deprived individuals ******************************************************************************** ******************************************************************************** *** Step 2.1 Years of Schooling *** ******************************************************************************** codebook hv108, tab(30) clonevar eduyears = hv108 //Total number of years of education replace eduyears = . if eduyears>30 //Recode any unreasonable years of highest education as missing value replace eduyears = . if eduyears>=age & age>0 /*The variable "eduyears" was replaced with a '.' if total years of education was more than individual's age */ replace eduyears = 0 if age < 10 /*The variable "eduyears" was replaced with a '0' given that the criteria for this indicator is household member aged 10 years or older */ /*A control variable is created on whether there is information on years of education for at least 2/3 of the household members aged 10 years and older */ gen temp = 1 if eduyears!=. & age>=10 & age!=. bysort hh_id: egen no_missing_edu = sum(temp) /*Total household members who are 10 years and older with no missing years of education */ gen temp2 = 1 if age>=10 & age!=. bysort hh_id: egen hhs = sum(temp2) /*Total number of household members who are 10 years and older */ replace no_missing_edu = no_missing_edu/hhs replace no_missing_edu = (no_missing_edu>=2/3) /*Identify whether there is information on years of education for at least 2/3 of the household members aged 10 years and older */ tab no_missing_edu, miss label var no_missing_edu "No missing edu for at least 2/3 of the HH members aged 10 years & older" drop temp temp2 hhs /*The entire household is considered deprived if no household member aged 10 years or older has completed SIX years of schooling. */ gen years_edu6 = (eduyears>=6) /* The years of schooling indicator takes a value of "1" if at least someone in the hh has reported 6 years of education or more */ replace years_edu6 = . if eduyears==. bysort hh_id: egen hh_years_edu6_1 = max(years_edu6) gen hh_years_edu6 = (hh_years_edu6_1==1) replace hh_years_edu6 = . if hh_years_edu6_1==. replace hh_years_edu6 = . if hh_years_edu6==0 & no_missing_edu==0 lab var hh_years_edu6 "Household has at least one member with 6 years of edu" ******************************************************************************** *** Step 2.2 Child School Attendance *** ******************************************************************************** codebook hv121, tab (10) clonevar attendance = hv121 //Note: Philippines DHS 2013 has no data on school attendance gen hh_child_atten=. lab var hh_child_atten "Household has all school age children up to class 8 in school" ******************************************************************************** *** Step 2.3 Nutrition *** ******************************************************************************** //Philippines DHS 2013 has no information on nutrition. 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 *** ******************************************************************************** /*Note: Philippines DHS 2013 has no male recode file. Hence, the child mortality information is exclusively from women.*/ codebook v206 v207 //Total child mortality reported by eligible women egen temp_f = rowtotal(v206 v207), missing replace temp_f = 0 if v201==0 bysort hh_id: egen child_mortality_f = sum(temp_f), missing lab var child_mortality_f "Occurrence of child mortality reported by women" tab child_mortality_f, miss drop temp_f 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 in the BR file. The missing values represent eligible woman who have never ever given birth and so are not present in the BR file. But these 'missing women' may be living in households where there are other women with child mortality information from the BR file. So at this stage, it is important that we aggregate the information that was obtained from the BR file at the household level. This ensures that women who were not present in the BR file is assigned with a value, following the information provided by other women in the household.*/ replace child_died_per_wom_5y = 0 if v201==0 /*Assign a value of "0" for: - all eligible women who never ever gave birth */ replace child_died_per_wom_5y = 0 if no_fem_eligible==1 /*Assign a value of "0" for: - individuals living in households that have non-eligible women */ bysort hh_id: egen child_mortality_5y = sum(child_died_per_wom_5y), missing replace child_mortality_5y = 0 if child_mortality_5y==. & child_mortality==0 /*Replace all households as 0 death if women has missing value and men reported no death in those households. However, in the context of the Philippines DHS 2013, there was no changes since child mortality information was collected only from women. */ label var child_mortality_5y "Total child mortality within household past 5 years reported by women" tab child_mortality_5y, miss /* The new standard MPI indicator takes a value of "1" if eligible women within the household reported no child mortality or if any child died longer than 5 years from the survey year. The indicator takes a value of "0" if women in the household reported any child mortality in the last 5 years from the survey year. Households were replaced with a value of "1" if eligible men within the household reported no child mortality in the absence of information from women. The indicator takes a missing value if there was missing information on reported death from eligible individuals. */ gen hh_mortality_5y = (child_mortality_5y==0) replace hh_mortality_5y = . if child_mortality_5y==. tab hh_mortality_5y, miss lab var hh_mortality_5y "Household had no child mortality in the last 5 years" ******************************************************************************** *** Step 2.5 Electricity *** ******************************************************************************** /*Members of the household are considered deprived if the household has no electricity */ clonevar electricity = hv206 codebook electricity, tab (10) replace electricity = . if electricity==9 label var electricity "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. */ clonevar toilet = hv205 codebook toilet, tab(30) codebook hv225, tab(30) clonevar shared_toilet = hv225 //0=no;1=yes;.=missing /*Note: Phillippines DHS 2013 report does not identify the category 'public toilet' as improve nor non-improved. But we consider it as non-improved since it is a shared facility */ gen toilet_mdg = ((toilet<23 | toilet==41) & shared_toilet!=1) /*Household is assigned a value of '1' if it uses improved sanitation and does not share toilet with other households */ replace toilet_mdg = 0 if (toilet<23 | toilet==41) & shared_toilet==1 /*Household is assigned a value of '0' if it uses improved sanitation but shares toilet with other households */ replace toilet_mdg = 0 if toilet == 14 | toilet == 15 /*Household is assigned a value of '0' if it uses non-improved sanitation: "flush to somewhere else" and "flush don't know where" */ replace toilet_mdg = . if toilet==. | toilet==99 //Household is assigned a value of '.' if it has missing information lab var toilet_mdg "Household has improved sanitation with MDG Standards" tab toilet toilet_mdg, miss ******************************************************************************** *** Step 2.7 Drinking Water *** ******************************************************************************** /*Members of the household are considered deprived if the household does not have access to safe drinking water according to MDG guidelines, or safe drinking water is more than a 30-minute walk from home roundtrip. In cases of mismatch between the MDG guideline and country report, we followed the country report.*/ clonevar water = hv201 clonevar timetowater = hv204 codebook water, tab(100) clonevar ndwater = hv202 //Note: Philippines DHS 2013 has no observations for hv202 gen water_mdg = 1 if water==11 | water==12 | water==13 | water==21 | /// water==31 | water==32 | water==41 | water==51| water==71 /*Non deprived if water is "piped into dwelling", "piped to yard/plot", "public tap/standpipe", "tube well or borehole", "protected well", "protected spring", "rainwater", "bottled water" Note: Philippines DHS 2013 have a category on 'semi-protected well'. Following the report, this particular category was identified as improved source of drinking water (p.8)*/ replace water_mdg = 0 if water==33 | water==42 | water==43 | /// water==61 | water==62 | water==96 /*Deprived if it is "unprotected well", "unprotected spring", "tanker truck" "surface water (river/lake, etc)", "cart with small tank","other" */ replace water_mdg = 0 if water_mdg==1 & timetowater >= 30 & timetowater!=. & /// timetowater!=996 & timetowater!=998 & timetowater!=999 //Deprived if water is at more than 30 minutes' walk (roundtrip) replace water_mdg = . if water==. | water==99 lab var water_mdg "Household has drinking water with MDG standards (considering distance)" tab water water_mdg, miss ******************************************************************************** *** Step 2.8 Housing *** ******************************************************************************** /* Members of the household are considered deprived if the household has a dirt, sand or dung floor */ clonevar floor = hv213 codebook floor, tab(99) gen floor_imp = 1 replace floor_imp = 0 if floor<=11 | floor==96 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 wall made of natural or rudimentary materials */ clonevar wall = hv214 codebook wall, tab(99) gen wall_imp = 1 replace wall_imp = 0 if wall<=25 | wall==96 replace wall_imp = . if wall==. | wall==99 lab var wall_imp "Household has wall that it is not of low quality materials" tab wall wall_imp, miss /* Members of the household are considered deprived if the household has roof made of natural or rudimentary materials */ clonevar roof = hv215 codebook roof, tab(99) gen roof_imp = 1 replace roof_imp = 0 if roof<=24 | roof==96 replace roof_imp = . if roof==. | roof==99 lab var roof_imp "Household has roof that it is not of low quality materials" tab roof roof_imp, miss /*Household is deprived in housing if the roof, floor OR walls uses low quality materials.*/ gen housing_1 = 1 replace housing_1 = 0 if floor_imp==0 | wall_imp==0 | roof_imp==0 replace housing_1 = . if floor_imp==. & wall_imp==. & roof_imp==. lab var housing_1 "Household has roof, floor & walls that it is not low quality material" tab housing_1, miss ******************************************************************************** *** Step 2.9 Cooking Fuel *** ******************************************************************************** /* Members of the household are considered deprived if the household cooks with solid fuels: wood, charcoal, crop residues or dung. "Indicators for Monitoring the Millennium Development Goals", p. 63 */ clonevar cookingfuel = hv226 codebook cookingfuel, tab(99) gen cooking_mdg = 1 replace cooking_mdg = 0 if cookingfuel>5 & cookingfuel<95 replace cooking_mdg = . if cookingfuel==. | cookingfuel==99 lab var cooking_mdg "Household has cooking fuel by MDG standards" /* Deprived if: "coal/lignite", "charcoal", "wood", "straw/shrubs/grass" "agricultural crop", "animal dung" */ 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 hv208 hv207 hv221 hv243a hv209 hv212 hv210 hv211 hv244 clonevar television = hv208 gen bw_television = . clonevar radio = hv207 clonevar telephone = hv221 clonevar mobiletelephone = hv243a clonevar refrigerator = hv209 clonevar car = hv212 clonevar bicycle = hv210 clonevar motorbike = hv211 gen computer = . //Note: Philippines DHS 2013 has no observation on computer clonevar animal_cart = hv243c foreach var in television radio telephone mobiletelephone refrigerator /// car bicycle motorbike computer animal_cart { replace `var' = . if `var'==9 | `var'==99 | `var'==8 | `var'==98 } //Missing values replaced //Combine information on telephone and mobiletelephone replace telephone=1 if telephone==0 & mobiletelephone==1 replace telephone=1 if telephone==. & mobiletelephone==1 /* Members of the household are considered deprived in assets if the household does not own more than one of: radio, TV, telephone, bike, motorbike, refrigerator, computer or animal_cart and does not own a car or truck.*/ egen n_small_assets2 = rowtotal(television radio telephone refrigerator bicycle motorbike computer animal_cart), missing lab var n_small_assets2 "Household Number of Small Assets Owned" gen hh_assets2 = (car==1 | n_small_assets2 > 1) replace hh_assets2 = . if car==. & n_small_assets2==. lab var hh_assets2 "Household Asset Ownership: HH has car or more than 1 small assets incl computer & animal cart" ******************************************************************************** *** Step 2.11 Rename and keep variables for MPI calculation ******************************************************************************** //Retain data on sampling design: desc hv022 hv021 clonevar strata = hv022 clonevar psu = hv021 //Retain year, month & date of interview: desc hv007 hv006 hv008 clonevar year_interview = hv007 clonevar month_interview = hv006 clonevar date_interview = hv008 *** Rename key global MPI indicators for estimation *** recode hh_mortality_5y (0=1)(1=0) , gen(d_cm) recode hh_nutrition_uw_st (0=1)(1=0) , gen(d_nutr) recode hh_child_atten (0=1)(1=0) , gen(d_satt) recode hh_years_edu6 (0=1)(1=0) , gen(d_educ) recode electricity (0=1)(1=0) , gen(d_elct) recode water_mdg (0=1)(1=0) , gen(d_wtr) recode toilet_mdg (0=1)(1=0) , gen(d_sani) recode housing_1 (0=1)(1=0) , gen(d_hsg) recode cooking_mdg (0=1)(1=0) , gen(d_ckfl) recode hh_assets2 (0=1)(1=0) , gen(d_asst) *** Keep selected variables for global MPI estimation *** keep hh_id ind_id ccty ccnum cty survey year subsample /// strata psu weight area relationship sex age agec7 agec4 marital hhsize /// region year_interview month_interview date_interview /// d_cm d_nutr d_satt d_educ d_elct d_wtr d_sani d_hsg d_ckfl d_asst order hh_id ind_id ccty ccnum cty survey year subsample /// strata psu weight area relationship sex age agec7 agec4 marital hhsize /// region year_interview month_interview date_interview /// d_cm d_nutr d_satt d_educ d_elct d_wtr d_sani d_hsg d_ckfl d_asst *** Sort, compress and save data for estimation *** sort ind_id compress save "$path_out/phl_dhs13_pov.dta", replace log close