// Contact: Davood Qorbani (https://orcid.org/0000-0002-3499-0167)
// Version: v6.0 (2024.02.28)
// This code needs to be run on the www.microdata.no platform, which uses syntax like the Stata software package.

textblock
Household level aggregated information
---------------------------------------
:::: 
The population of this research is all households who resided in Norway and were registered as residents as of January 1st from 2005 to 2022, with at least one privately owned passenger vehicle in any year (not necessarily in all years). To find these households in the database, we start by omitting all households without any record of vehicle ownership in the mentioned period — reported as of December 31st each year. Then, we are left with a population that comprises every household in Norway that has owned a passenger vehicle at least once during this period — conditioned to the report date.

After this point, various socioeconomic variables are called to the dataset: individual data are aggregated to the household level and then added to the dataset iteratively.

Among these households, any household without a record of of battery electric vehicles (BEV) ownership, i.e., those who owned only emitting vehicles, are called gray adopters in this study. Those with at least one record of BEV ownership in this period are called green adopters, even if they have owned gray vehicles. 

Note: DS stands for Data Set in this script.
endblock

// Connecting to datastore. #Note that v26 is the latest version at the time of developing this code.
require no.ssb.fdb:26 as db

// Start point: 2005-01-01
// Cut off point: 2022-12-31
// #Note that not all values are available in the used database after this date.


textblock
Household Dataset
------------------
::::: is being created.
The household number is the birth number of the contact person in the household.
In the register statistics, household members are identified by formal residential address (i.e., address according to the National Population Register). Since 2014, the household has been identified according to the actual residential address. Household number or id, "BEFOLKNING_HUSHNR" (unit = household), indicates persons who live in the same private home.

Since the unit of analysis is household, "household number" availability mandates the aggregation of other variables. So, even if data on some other variables may be available, one may consider the years this variable is available.

Note: there is another variable, "INNTEKT_HUSHNR", an approximate definition of households that tries to relate household members with the same finance relation.
endblock


// Making temporary copies of the household dataset: It helps aggregate person (individual) level data into household level data without losing any data in the household dataset.


textblock
Household Dataset: People living in different households of various sizes in Norway
------------------
- Household size (a.k.a people_in_household_all_ages) is the number of people living in the household (regardless of age), i.e., people with the same household number.
- The easiest way to get the household size is using BEFOLKNING_PERS_I_HUSHNR. However, the following procedure is a workaround to include only household contact persons in the dataset.
endblock

// First, we create a dataset comprising every registered individual in Norway until 2022. Then, we trim it based on the unique ID of household contact persons.
create-dataset household_DS_all
import db/BEFOLKNING_STATUSKODE 2022-01-01 as population_status_code22
// All registered people in Norway until 2022 are in the dataset now. We can drop unnecessary variables.
drop population_status_code22


// In an iterative process, we tag the contact persons in all households...

create-dataset household_DS_size05
import db/BEFOLKNING_STATUSKODE 2005-01-01 as population_status_code05
// Keeping those who were residents in Norway in 2005.
keep if population_status_code05 == '1'
generate resident_person05 = 1  // counting resident persons in 2005
tabulate resident_person05, missing
import db/BEFOLKNING_HUSHNR 2005-01-01 as household_id05

clone-dataset household_DS_size05 household_DS_finder
clone-dataset household_DS_size05 person_DS_Y05 

generate count_people05 = 1
collapse(sum) count_people05, by(household_id05)
rename count_people05 household_size05
tabulate household_size05, missing
merge household_size05 into household_DS_finder on PERSONID_1
merge household_size05 into household_DS_all on PERSONID_1

delete-dataset household_DS_size05


use household_DS_finder
drop resident_person05
drop population_status_code05
//drop if sysmiss(household_size05)
// Alternative:
//keep if household_size05 >= 0
import db/BEFOLKNING_HUSHNR 2006-01-01 as household_id06, outer_join


create-dataset household_DS_size06
use household_DS_size06
import db/BEFOLKNING_STATUSKODE 2006-01-01 as population_status_code06
// Keeping those who were residents in Norway in 2006.
keep if population_status_code06 == '1'
generate resident_person06 = 1  // counting resident persons in 2006
tabulate resident_person06, missing
import db/BEFOLKNING_HUSHNR 2006-01-01 as household_id06

clone-dataset household_DS_size06 person_DS_Y06 

generate count_people06 = 1
collapse(sum) count_people06, by(household_id06)
rename count_people06 household_size06
//tabulate household_size06, missing
merge household_size06 into household_DS_finder on PERSONID_1
merge household_size06 into household_DS_all on PERSONID_1
delete-dataset household_DS_size06


use household_DS_finder
//drop if sysmiss(household_size05) & sysmiss(household_size06)
import db/BEFOLKNING_HUSHNR 2007-01-01 as household_id07, outer_join


create-dataset household_DS_size07
use household_DS_size07
import db/BEFOLKNING_STATUSKODE 2007-01-01 as population_status_code07
// Keeping those who were residents in Norway in 2007.
keep if population_status_code07 == '1'
generate resident_person07 = 1  // counting resident persons in 2007
tabulate resident_person07, missing
import db/BEFOLKNING_HUSHNR 2007-01-01 as household_id07

clone-dataset household_DS_size07 person_DS_Y07 

generate count_people07 = 1
collapse(sum) count_people07, by(household_id07)
rename count_people07 household_size07
//tabulate household_size07, missing
merge household_size07 into household_DS_finder on PERSONID_1
merge household_size07 into household_DS_all on PERSONID_1
delete-dataset household_DS_size07


use household_DS_finder
//drop if sysmiss(household_size05) & sysmiss(household_size06) & sysmiss(household_size07)
import db/BEFOLKNING_HUSHNR 2008-01-01 as household_id08, outer_join


create-dataset household_DS_size08
use household_DS_size08
import db/BEFOLKNING_STATUSKODE 2008-01-01 as population_status_code08
// Keeping those who were residents in Norway in 2008.
keep if population_status_code08 == '1'
generate resident_person08 = 1  // counting resident persons in 2008
tabulate resident_person08, missing
import db/BEFOLKNING_HUSHNR 2008-01-01 as household_id08

clone-dataset household_DS_size08 person_DS_Y08

generate count_people08 = 1
collapse(sum) count_people08, by(household_id08)
rename count_people08 household_size08
//tabulate household_size08, missing
merge household_size08 into household_DS_finder on PERSONID_1
merge household_size08 into household_DS_all on PERSONID_1
delete-dataset household_DS_size08


use household_DS_finder
//drop if sysmiss(household_size05) & sysmiss(household_size06) & sysmiss(household_size07) & sysmiss(household_size08)
import db/BEFOLKNING_HUSHNR 2009-01-01 as household_id09, outer_join


create-dataset household_DS_size09
use household_DS_size09
import db/BEFOLKNING_STATUSKODE 2009-01-01 as population_status_code09
// Keeping those who were residents in Norway in 2009.
keep if population_status_code09 == '1'
generate resident_person09 = 1  // counting resident persons in 2009
tabulate resident_person09, missing
import db/BEFOLKNING_HUSHNR 2009-01-01 as household_id09

clone-dataset household_DS_size09 person_DS_Y09

generate count_people09 = 1
collapse(sum) count_people09, by(household_id09)
rename count_people09 household_size09
//tabulate household_size09, missing
merge household_size09 into household_DS_finder on PERSONID_1
merge household_size09 into household_DS_all on PERSONID_1
delete-dataset household_DS_size09


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2010-01-01 as household_id10, outer_join


create-dataset household_DS_size10
use household_DS_size10
import db/BEFOLKNING_STATUSKODE 2010-01-01 as population_status_code10
// Keeping those who were residents in Norway in 2010.
keep if population_status_code10 == '1'
generate resident_person10 = 1  // counting resident persons in 2010
tabulate resident_person10, missing
import db/BEFOLKNING_HUSHNR 2010-01-01 as household_id10

clone-dataset household_DS_size10 person_DS_Y10

generate count_people10 = 1
collapse(sum) count_people10, by(household_id10)
rename count_people10 household_size10
//tabulate household_size10, missing
merge household_size10 into household_DS_finder on PERSONID_1
merge household_size10 into household_DS_all on PERSONID_1
delete-dataset household_DS_size10


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2011-01-01 as household_id11, outer_join


create-dataset household_DS_size11
use household_DS_size11
import db/BEFOLKNING_STATUSKODE 2011-01-01 as population_status_code11
// Keeping those who were residents in Norway in 2011.
keep if population_status_code11 == '1'
generate resident_person11 = 1  // counting resident persons in 2011
tabulate resident_person11, missing
import db/BEFOLKNING_HUSHNR 2011-01-01 as household_id11

clone-dataset household_DS_size11 person_DS_Y11

generate count_people11 = 1
collapse(sum) count_people11, by(household_id11)
rename count_people11 household_size11
//tabulate household_size11, missing
merge household_size11 into household_DS_finder on PERSONID_1
merge household_size11 into household_DS_all on PERSONID_1
delete-dataset household_DS_size11


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2012-01-01 as household_id12, outer_join


create-dataset household_DS_size12
use household_DS_size12
import db/BEFOLKNING_STATUSKODE 2012-01-01 as population_status_code12
// Keeping those who were residents in Norway in 2012.
keep if population_status_code12 == '1'
generate resident_person12 = 1  // counting resident persons in 2012
tabulate resident_person12, missing
import db/BEFOLKNING_HUSHNR 2012-01-01 as household_id12

clone-dataset household_DS_size12 person_DS_Y12

generate count_people12 = 1
collapse(sum) count_people12, by(household_id12)
rename count_people12 household_size12
//tabulate household_size12, missing
merge household_size12 into household_DS_finder on PERSONID_1
merge household_size12 into household_DS_all on PERSONID_1
delete-dataset household_DS_size12


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2013-01-01 as household_id13, outer_join


create-dataset household_DS_size13
use household_DS_size13
import db/BEFOLKNING_STATUSKODE 2013-01-01 as population_status_code13
// Keeping those who were residents in Norway in 2013.
keep if population_status_code13 == '1'
generate resident_person13 = 1  // counting resident persons in 2013
tabulate resident_person13, missing
import db/BEFOLKNING_HUSHNR 2013-01-01 as household_id13

clone-dataset household_DS_size13 person_DS_Y13

generate count_people13 = 1
collapse(sum) count_people13, by(household_id13)
rename count_people13 household_size13
//tabulate household_size13, missing
merge household_size13 into household_DS_finder on PERSONID_1
merge household_size13 into household_DS_all on PERSONID_1
delete-dataset household_DS_size13


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2014-01-01 as household_id14, outer_join


create-dataset household_DS_size14
use household_DS_size14
import db/BEFOLKNING_STATUSKODE 2014-01-01 as population_status_code14
// Keeping those who were residents in Norway in 2014.
keep if population_status_code14 == '1'
generate resident_person14 = 1  // counting resident persons in 2014
tabulate resident_person14, missing
import db/BEFOLKNING_HUSHNR 2014-01-01 as household_id14

clone-dataset household_DS_size14 person_DS_Y14

generate count_people14 = 1
collapse(sum) count_people14, by(household_id14)
rename count_people14 household_size14
//tabulate household_size14, missing
merge household_size14 into household_DS_finder on PERSONID_1
merge household_size14 into household_DS_all on PERSONID_1
delete-dataset household_DS_size14


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2015-01-01 as household_id15, outer_join


create-dataset household_DS_size15
use household_DS_size15
import db/BEFOLKNING_STATUSKODE 2015-01-01 as population_status_code15
// Keeping those who were residents in Norway in 2015.
keep if population_status_code15 == '1'
generate resident_person15 = 1  // counting resident persons in 2015
tabulate resident_person15, missing
import db/BEFOLKNING_HUSHNR 2015-01-01 as household_id15

clone-dataset household_DS_size15 person_DS_Y15

generate count_people15 = 1
collapse(sum) count_people15, by(household_id15)
rename count_people15 household_size15
//tabulate household_size15, missing
merge household_size15 into household_DS_finder on PERSONID_1
merge household_size15 into household_DS_all on PERSONID_1
delete-dataset household_DS_size15


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2016-01-01 as household_id16, outer_join


create-dataset household_DS_size16
use household_DS_size16
import db/BEFOLKNING_STATUSKODE 2016-01-01 as population_status_code16
// Keeping those who were residents in Norway in 2016.
keep if population_status_code16 == '1'
generate resident_person16 = 1  // counting resident persons in 2016
tabulate resident_person16, missing
import db/BEFOLKNING_HUSHNR 2016-01-01 as household_id16

clone-dataset household_DS_size16 person_DS_Y16

generate count_people16 = 1
collapse(sum) count_people16, by(household_id16)
rename count_people16 household_size16
//tabulate household_size16, missing
merge household_size16 into household_DS_finder on PERSONID_1
merge household_size16 into household_DS_all on PERSONID_1
delete-dataset household_DS_size16


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2017-01-01 as household_id17, outer_join


create-dataset household_DS_size17
use household_DS_size17
import db/BEFOLKNING_STATUSKODE 2017-01-01 as population_status_code17
// Keeping those who were residents in Norway in 2017.
keep if population_status_code17 == '1'
generate resident_person17 = 1  // counting resident persons in 2017
tabulate resident_person17, missing
import db/BEFOLKNING_HUSHNR 2017-01-01 as household_id17

clone-dataset household_DS_size17 person_DS_Y17

generate count_people17 = 1
collapse(sum) count_people17, by(household_id17)
rename count_people17 household_size17
//tabulate household_size17, missing
merge household_size17 into household_DS_finder on PERSONID_1
merge household_size17 into household_DS_all on PERSONID_1
delete-dataset household_DS_size17


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2018-01-01 as household_id18, outer_join


create-dataset household_DS_size18
use household_DS_size18
import db/BEFOLKNING_STATUSKODE 2018-01-01 as population_status_code18
// Keeping those who were residents in Norway in 2018.
keep if population_status_code18 == '1'
generate resident_person18 = 1  // counting resident persons in 2018
tabulate resident_person18, missing
import db/BEFOLKNING_HUSHNR 2018-01-01 as household_id18

clone-dataset household_DS_size18 person_DS_Y18

generate count_people18 = 1
collapse(sum) count_people18, by(household_id18)
rename count_people18 household_size18
//tabulate household_size18, missing
merge household_size18 into household_DS_finder on PERSONID_1
merge household_size18 into household_DS_all on PERSONID_1
delete-dataset household_DS_size18


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2019-01-01 as household_id19, outer_join


create-dataset household_DS_size19
use household_DS_size19
import db/BEFOLKNING_STATUSKODE 2019-01-01 as population_status_code19
// Keeping those who were residents in Norway in 2019.
keep if population_status_code19 == '1'
generate resident_person19 = 1  // counting resident persons in 2019
tabulate resident_person19, missing
import db/BEFOLKNING_HUSHNR 2019-01-01 as household_id19

clone-dataset household_DS_size19 person_DS_Y19

generate count_people19 = 1
collapse(sum) count_people19, by(household_id19)
rename count_people19 household_size19
//tabulate household_size19, missing
merge household_size19 into household_DS_finder on PERSONID_1
merge household_size19 into household_DS_all on PERSONID_1
delete-dataset household_DS_size19


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2020-01-01 as household_id20, outer_join


create-dataset household_DS_size20
use household_DS_size20
import db/BEFOLKNING_STATUSKODE 2020-01-01 as population_status_code20
// Keeping those who were residents in Norway in 2020.
keep if population_status_code20 == '1'
generate resident_person20 = 1  // counting resident persons in 2020
tabulate resident_person20, missing
import db/BEFOLKNING_HUSHNR 2020-01-01 as household_id20

clone-dataset household_DS_size20 person_DS_Y20

generate count_people20 = 1
collapse(sum) count_people20, by(household_id20)
rename count_people20 household_size20
//tabulate household_size20, missing
merge household_size20 into household_DS_finder on PERSONID_1
merge household_size20 into household_DS_all on PERSONID_1
delete-dataset household_DS_size20


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2021-01-01 as household_id21, outer_join


create-dataset household_DS_size21
use household_DS_size21
import db/BEFOLKNING_STATUSKODE 2021-01-01 as population_status_code21
keep if population_status_code21 == '1'
generate resident_person21 = 1  // counting resident persons in 2021
tabulate resident_person21, missing
import db/BEFOLKNING_HUSHNR 2021-01-01 as household_id21

clone-dataset household_DS_size21 person_DS_Y21

generate count_people21 = 1
collapse(sum) count_people21, by(household_id21)
rename count_people21 household_size21
//tabulate household_size21, missing
merge household_size21 into household_DS_finder on PERSONID_1
merge household_size21 into household_DS_all on PERSONID_1
delete-dataset household_DS_size21


use household_DS_finder
import db/BEFOLKNING_HUSHNR 2022-01-01 as household_id22, outer_join


create-dataset household_DS_size22
use household_DS_size22
import db/BEFOLKNING_STATUSKODE 2022-01-01 as population_status_code22
keep if population_status_code22 == '1'
generate resident_person22 = 1  // counting resident persons in 2022
tabulate resident_person22, missing
import db/BEFOLKNING_HUSHNR 2022-01-01 as household_id22

clone-dataset household_DS_size22 person_DS_Y22

generate count_people22 = 1
collapse(sum) count_people22, by(household_id22)
rename count_people22 household_size22
//tabulate household_size22, missing
merge household_size22 into household_DS_finder on PERSONID_1
merge household_size22 into household_DS_all on PERSONID_1
delete-dataset household_DS_size22


// Who will be in this study?
use household_DS_finder
// Option A: If you want every registered household in Norway from 2005 to 2022, even if they came or left in between, activate the following:
drop if sysmiss(household_size05) & sysmiss(household_size06) & sysmiss(household_size07) & sysmiss(household_size08) & sysmiss(household_size09) & sysmiss(household_size10) & sysmiss(household_size11) & sysmiss(household_size12) & sysmiss(household_size13) & sysmiss(household_size14) & sysmiss(household_size15) & sysmiss(household_size16) & sysmiss(household_size17) & sysmiss(household_size18) & sysmiss(household_size19) & sysmiss(household_size20) & sysmiss(household_size21) & sysmiss(household_size22)
// Option B: If you want only those registered households who lived all years from 2005 to 2022 in Norway, activate the following:
//drop if sysmiss(household_size05) | sysmiss(household_size06) | sysmiss(household_size07) | sysmiss(household_size08) | sysmiss(household_size09) | sysmiss(household_size10) | sysmiss(household_size11) | sysmiss(household_size12) | sysmiss(household_size13) | sysmiss(household_size14) | sysmiss(household_size15) | sysmiss(household_size16) | sysmiss(household_size17) | sysmiss(household_size18) | sysmiss(household_size19) | sysmiss(household_size20) | sysmiss(household_size21) | sysmiss(household_size22)
generate household_this_study = 1


use household_DS_finder
merge household_this_study into household_DS_all on PERSONID_1
delete-dataset household_DS_finder

use household_DS_all
// Keeping only the unique number of those household contact persons who were residents in Norway from 2005 to 2022, irrespective of when they were residents in this period.
drop if sysmiss(household_this_study)
// Now, we have the unique ID of the household contact person from 2005 to 2022. "household_this_study" can be dropped to free up space.
drop household_this_study


// Drop unnecessary variables from the cloned datasets to speed up the process.
use person_DS_Y05
drop population_status_code05

use person_DS_Y06
drop population_status_code06

use person_DS_Y07
drop population_status_code07

use person_DS_Y08
drop population_status_code08

use person_DS_Y09
drop population_status_code09

use person_DS_Y10
drop population_status_code10

use person_DS_Y11
drop population_status_code11

use person_DS_Y12
drop population_status_code12

use person_DS_Y13
drop population_status_code13

use person_DS_Y14
drop population_status_code14

use person_DS_Y15
drop population_status_code15

use person_DS_Y16
drop population_status_code16

use person_DS_Y17
drop population_status_code17

use person_DS_Y18
drop population_status_code18

use person_DS_Y19
drop population_status_code19

use person_DS_Y20
drop population_status_code20

use person_DS_Y21
drop population_status_code21

use person_DS_Y22
drop population_status_code22


// From this step, we call further relevant variables into our main dataset.


// An Option: Making a random draw from the population: It speeds up the analysis.
//sample 0.10 422323
// Note 1: The first argument retains 10% of the observations in the dataset, randomly drawn, based on the second argument – arbitrary seed value 422323.
// Note 2: The first argument specifies how many observations to keep. This can be an integer > 1000 or a proportion (decimal numbers between 0 and 1). The second argument indicates a seed, where one can use positive integers > = 1. Using the same seed and sample size allows the random extract to be the same each time the command is run. If the user wants a new random sample of the same size, they must use a new seed value.


textblock
Private Registered Vehicles in Norway by Fuel Type
-------------------
::::: Passenger Vehicles: 2005 - 2022.

Note 1: There are missing values in the categories, but the total is the same as the graph on "fuel_type".

Note 2: Assigning the fuel types to categories (approach Gray vs. Green) is as follows,
- Green: Electric (05).
- Gray: Gasoline (Petrol) (01), Diesel (02), Paraffin (03), Gas (04), Hybrid Gasoline (07), Hybrid Diesel (08), Other Fuel (09), Biodiesel (10), Biogasoline (11), LPG-gas (12), CNG-gas (13), Methanol (14), Ethanol (15).

Note 3: #Note that Hydrogen (06) is green, but omitted in this analysis because of negligible ownership numbers.
endblock

textblock
::::: Passenger Vehicles 2005.
endblock

// Vehicles 2005
create-dataset vehicle_DS_passenger05

// KJORETOY_KJT_GRUP and KJORETOY_TOT_VEKT: These variables show the division of the car fleet into vehicle groups following the vehicle regulations of 4 October 1994 § 2-2 and regulations on one-off tax - on motor vehicles of 19 March 2001. The codes are used in the Vehicle Register operated by the Norwegian Road Administration. The variables include registered vehicles as of 31 December for each year. Observations with the values ​​0 and unspecified (missing) are omitted.
import db/KJORETOY_KJT_GRUP 2005-12-31 as vehicle_group_code05
keep if vehicle_group_code05 == '101'  // keep only passenger cars (also, no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2005-12-31 as vehicle_individual_id05

import db/KJORETOY_DRIVSTOFF_OMK 2005-12-31 as fuel_type05
define-labels fuel_type_txt '01' 'Gasoline (Petrol)' '02' 'Diesel' '03' 'Paraffin' '04' 'Gas' '05' 'Electric' '06' 'Hydrogen' '07' 'Hybrid Gasoline' '08' 'Hybrid Diesel' '09' 'Other Fuel' '10' 'Biodiesel' '11' 'Biogasoline' '12' 'LPG-gas' '13' 'CNG-gas' '14' 'Methanol' '15' 'Ethanol'
assign-labels fuel_type05 fuel_type_txt

tabulate fuel_type05, missing
piechart fuel_type05

textblock
Private Registered Vehicles in Norway in 2005
-------------------
::::: Gray vs. Green.
endblock

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green05 = fuel_type05

replace emission_categories_gray_green05 = 'Green' if emission_categories_gray_green05 == '05'
replace emission_categories_gray_green05 = 'Gray' if emission_categories_gray_green05 == '01' | emission_categories_gray_green05 == '02' | emission_categories_gray_green05 == '03' | emission_categories_gray_green05 == '04' | emission_categories_gray_green05 == '07' | emission_categories_gray_green05 == '08' | emission_categories_gray_green05 == '09' | emission_categories_gray_green05 == '10' | emission_categories_gray_green05 == '11' | emission_categories_gray_green05 == '12' | emission_categories_gray_green05 == '13' | emission_categories_gray_green05 == '15'
drop if emission_categories_gray_green05 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green05 = 0
replace vehicle_green05 = 1 if emission_categories_gray_green05 == 'Green'
generate vehicle_gray05 = 0
replace vehicle_gray05 = 1 if emission_categories_gray_green05 == 'Gray'

piechart emission_categories_gray_green05
tabulate emission_categories_gray_green05, missing
//tabulate vehicle_green05 vehicle_gray05, missing

// Import the first registration year (model year) of the vehicles in Norway in 2005.
//import db/KJORETOY_FREG_AR 2005-12-31 as vehicle_reg_year05
//histogram vehicle_reg_year05, freq
//histogram vehicle_reg_year05 if emission_categories_gray_green05 == 'Green', freq
//drop if vehicle_reg_year05 < 2005
//summarize vehicle_reg_year05 if vehicle_reg_year05 == 2005

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category to retrieve newly registered vehicles per year.

//generate vehicle_new05 = 0
//replace vehicle_new05 = 1 if vehicle_reg_year05 == 2005
//summarize vehicle_new05 if vehicle_reg_year05 == 2005

//generate vehicle_green_new05 = 0
//replace vehicle_green_new05 = 1 if emission_categories_gray_green05 == 'Green' & vehicle_reg_year05 == 2005
//summarize vehicle_green_new05 if emission_categories_gray_green05 == 'Green' & vehicle_reg_year05 == 2005

//generate vehicle_gray_new05 = 0
//replace vehicle_gray_new05 = 1 if emission_categories_gray_green05 == 'Gray' & vehicle_reg_year05 == 2005
//summarize vehicle_gray_new05 if emission_categories_gray_green05 == 'Gray' & vehicle_reg_year05 == 2005

//tabulate vehicle_green05 vehicle_gray05, missing

// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2005.
clone-dataset vehicle_DS_passenger05 vehicle_DS_passenger_green_owned05
clone-dataset vehicle_DS_passenger05 vehicle_DS_passenger_gray_owned05
//clone-dataset vehicle_DS_passenger05 vehicle_DS_passenger_all_fuel_regist05
//clone-dataset vehicle_DS_passenger05 vehicle_DS_passenger_green_regist05
//clone-dataset vehicle_DS_passenger05 vehicle_DS_passenger_gray_regist05

// Make temporary datasets to calculate categories of passenger vehicles per household in 2005.
clone-dataset person_DS_Y05 household_DS_all_fuel_owners05
clone-dataset person_DS_Y05 household_DS_green_owners05
clone-dataset person_DS_Y05 household_DS_gray_owners05
//clone-dataset person_DS_Y05 household_DS_all_fuel_regist05
//clone-dataset person_DS_Y05 household_DS_green_regist05
//clone-dataset person_DS_Y05 household_DS_gray_regist05


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2005.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2005.
collapse(count) fuel_type05, by(vehicle_individual_id05)
rename fuel_type05 owned_vehicle_all_per_person05
//tabulate owned_vehicle_all_per_person05, missing
merge owned_vehicle_all_per_person05 into household_DS_all_fuel_owners05 on household_id05
delete-dataset vehicle_DS_passenger05

// Then, aggregate the number of all categories of passenger vehicles per household in 2005.
use household_DS_all_fuel_owners05
collapse(sum) owned_vehicle_all_per_person05, by(household_id05)
rename owned_vehicle_all_per_person05 owned_vehicle_all_per_household05
//tabulate owned_vehicle_all_per_household05, missing
histogram owned_vehicle_all_per_household05 if owned_vehicle_all_per_household05 > 0, freq
//piechart owned_vehicle_all_per_household05 if owned_vehicle_all_per_household05 > 0
piechart owned_vehicle_all_per_household05
merge owned_vehicle_all_per_household05 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners05


// Step: Sum the number of green vehicles per person in 2005.
use vehicle_DS_passenger_green_owned05
collapse(sum) vehicle_green05, by(vehicle_individual_id05)
rename vehicle_green05 owned_vehicle_green_per_person05
//tabulate owned_vehicle_green_per_person05, missing
merge owned_vehicle_green_per_person05 into household_DS_green_owners05 on household_id05
delete-dataset vehicle_DS_passenger_green_owned05

// Then, aggregate the number of green vehicles per household in 2005.
use household_DS_green_owners05
collapse(sum) owned_vehicle_green_per_person05, by(household_id05)
rename owned_vehicle_green_per_person05 owned_vehicle_green_per_household05
//tabulate owned_vehicle_green_per_household05, missing
histogram owned_vehicle_green_per_household05 if owned_vehicle_green_per_household05 > 0, freq
piechart owned_vehicle_green_per_household05 if owned_vehicle_green_per_household05 > 0
piechart owned_vehicle_green_per_household05
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household05 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners05


// Step: Sum the number of gray vehicles per person in 2005.
use vehicle_DS_passenger_gray_owned05
collapse(sum) vehicle_gray05, by(vehicle_individual_id05)
rename vehicle_gray05 owned_vehicle_gray_per_person05
//tabulate owned_vehicle_gray_per_person05, missing
merge owned_vehicle_gray_per_person05 into household_DS_gray_owners05 on household_id05
delete-dataset vehicle_DS_passenger_gray_owned05

// Then, aggregate the number of gray vehicles per household in 2005.
use household_DS_gray_owners05
collapse(sum) owned_vehicle_gray_per_person05, by(household_id05)
rename owned_vehicle_gray_per_person05 owned_vehicle_gray_per_household05
merge owned_vehicle_gray_per_household05 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners05


textblock
// Step: Sum the number of all vehicles newly registered per person in 2005.
use vehicle_DS_passenger_all_fuel_regist05
collapse(sum) vehicle_new05, by(vehicle_individual_id05)
rename vehicle_new05 regist_vehicle_all_per_person05
merge regist_vehicle_all_per_person05 into household_DS_all_fuel_regist05 on household_id05
delete-dataset vehicle_DS_passenger_all_fuel_regist05

// Then, aggregate the number of all vehicles newly registered per household in 2005
use household_DS_all_fuel_regist05
collapse(sum) regist_vehicle_all_per_person05, by(household_id05)
rename regist_vehicle_all_per_person05 regist_vehicle_all_per_household05
//tabulate regist_vehicle_all_per_household05, missing
histogram regist_vehicle_all_per_household05 if regist_vehicle_all_per_household05 > 0, freq
piechart regist_vehicle_all_per_household05 if regist_vehicle_all_per_household05 > 0
piechart regist_vehicle_all_per_household05
merge regist_vehicle_all_per_household05 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_regist05

// Step: Sum the number of green vehicles newly registered per person in 2005.
use vehicle_DS_passenger_green_regist05
collapse(sum) vehicle_green_new05, by(vehicle_individual_id05)
rename vehicle_green_new05 regist_vehicle_green_per_person05
merge regist_vehicle_green_per_person05 into household_DS_green_regist05 on household_id05
delete-dataset vehicle_DS_passenger_green_regist05

// Then, aggregate the number of green vehicles newly registered per household in 2005.
use household_DS_green_regist05
collapse(sum) regist_vehicle_green_per_person05, by(household_id05)
rename regist_vehicle_green_per_person05 regist_vehicle_green_per_household05
//tabulate regist_vehicle_green_per_household05, missing
//histogram regist_vehicle_green_per_household05 if regist_vehicle_green_per_household05 > 0, freq
//piechart regist_vehicle_green_per_household05 if regist_vehicle_green_per_household05 > 0
// Merge newly registered regreen vehicles into the main dataset
merge regist_vehicle_green_per_household05 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_regist05

// Step: Sum the number of gray vehicles newly registered per person in 2005.
use vehicle_DS_passenger_gray_regist05
collapse(sum) vehicle_gray_new05, by(vehicle_individual_id05)
rename vehicle_gray_new05 regist_vehicle_gray_per_person05
merge regist_vehicle_gray_per_person05 into household_DS_gray_regist05 on household_id05
delete-dataset vehicle_DS_passenger_gray_regist05

// Then, aggregate the number of gray vehicles newly registered per household in 2005.
use household_DS_gray_regist05
collapse(sum) regist_vehicle_gray_per_person05, by(household_id05)
rename regist_vehicle_gray_per_person05 regist_vehicle_gray_per_household05
//tabulate regist_vehicle_gray_per_household05, missing
//histogram regist_vehicle_gray_per_household05 if regist_vehicle_gray_per_household05 > 0, freq
//piechart regist_vehicle_gray_per_household05 if regist_vehicle_gray_per_household05 > 0
merge regist_vehicle_gray_per_household05 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_regist05
endblock


// Vehicles 2006
create-dataset vehicle_DS_passenger06
import db/KJORETOY_KJT_GRUP 2006-12-31 as vehicle_group_code06
keep if vehicle_group_code06 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2006-12-31 as vehicle_individual_id06

import db/KJORETOY_DRIVSTOFF_OMK 2006-12-31 as fuel_type06
assign-labels fuel_type06 fuel_type_txt

tabulate fuel_type06, missing
piechart fuel_type06

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green06 = fuel_type06
replace emission_categories_gray_green06 = 'Green' if emission_categories_gray_green06 == '05'
replace emission_categories_gray_green06 = 'Gray' if emission_categories_gray_green06 == '01' | emission_categories_gray_green06 == '02' | emission_categories_gray_green06 == '03' | emission_categories_gray_green06 == '04' | emission_categories_gray_green06 == '07' | emission_categories_gray_green06 == '08' | emission_categories_gray_green06 == '09' | emission_categories_gray_green06 == '10' | emission_categories_gray_green06 == '11' | emission_categories_gray_green06 == '12' | emission_categories_gray_green06 == '13' | emission_categories_gray_green06 == '15'
drop if emission_categories_gray_green06 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green06 = 0
replace vehicle_green06 = 1 if emission_categories_gray_green06 == 'Green'
generate vehicle_gray06 = 0
replace vehicle_gray06 = 1 if emission_categories_gray_green06 == 'Gray'

piechart emission_categories_gray_green06
tabulate emission_categories_gray_green06, missing
//tabulate vehicle_green06 vehicle_gray06, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2006.
clone-dataset vehicle_DS_passenger06 vehicle_DS_passenger_green_owned06
clone-dataset vehicle_DS_passenger06 vehicle_DS_passenger_gray_owned06


// Make temporary datasets to calculate categories of passenger vehicles per household in 2006.
clone-dataset person_DS_Y06 household_DS_all_fuel_owners06
clone-dataset person_DS_Y06 household_DS_green_owners06
clone-dataset person_DS_Y06 household_DS_gray_owners06


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2006.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2006.
collapse(count) fuel_type06, by(vehicle_individual_id06)
rename fuel_type06 owned_vehicle_all_per_person06
//tabulate owned_vehicle_all_per_person06, missing
merge owned_vehicle_all_per_person06 into household_DS_all_fuel_owners06 on household_id06
delete-dataset vehicle_DS_passenger06

// Then, aggregate the number of all categories of passenger vehicles per household in 2006.
use household_DS_all_fuel_owners06
collapse(sum) owned_vehicle_all_per_person06, by(household_id06)
rename owned_vehicle_all_per_person06 owned_vehicle_all_per_household06
//tabulate owned_vehicle_all_per_household06, missing
histogram owned_vehicle_all_per_household06 if owned_vehicle_all_per_household06 > 0, freq
//piechart owned_vehicle_all_per_household06 if owned_vehicle_all_per_household06 > 0
piechart owned_vehicle_all_per_household06
merge owned_vehicle_all_per_household06 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners06


// Step: Sum the number of green vehicles per person in 2006.
use vehicle_DS_passenger_green_owned06
collapse(sum) vehicle_green06, by(vehicle_individual_id06)
rename vehicle_green06 owned_vehicle_green_per_person06
//tabulate owned_vehicle_green_per_person06, missing
merge owned_vehicle_green_per_person06 into household_DS_green_owners06 on household_id06
delete-dataset vehicle_DS_passenger_green_owned06

// Then, aggregate the number of green vehicles per household in 2006.
use household_DS_green_owners06
collapse(sum) owned_vehicle_green_per_person06, by(household_id06)
rename owned_vehicle_green_per_person06 owned_vehicle_green_per_household06
//tabulate owned_vehicle_green_per_household06, missing
histogram owned_vehicle_green_per_household06 if owned_vehicle_green_per_household06 > 0, freq
piechart owned_vehicle_green_per_household06 if owned_vehicle_green_per_household06 > 0
piechart owned_vehicle_green_per_household06
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household06 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners06


// Step: Sum the number of gray vehicles per person in 2006.
use vehicle_DS_passenger_gray_owned06
collapse(sum) vehicle_gray06, by(vehicle_individual_id06)
rename vehicle_gray06 owned_vehicle_gray_per_person06
//tabulate owned_vehicle_gray_per_person06, missing
merge owned_vehicle_gray_per_person06 into household_DS_gray_owners06 on household_id06
delete-dataset vehicle_DS_passenger_gray_owned06

// Then, aggregate the number of gray vehicles per household in 2006.
use household_DS_gray_owners06
collapse(sum) owned_vehicle_gray_per_person06, by(household_id06)
rename owned_vehicle_gray_per_person06 owned_vehicle_gray_per_household06
merge owned_vehicle_gray_per_household06 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners06



// Vehicles 2007
create-dataset vehicle_DS_passenger07
import db/KJORETOY_KJT_GRUP 2007-12-31 as vehicle_group_code07
keep if vehicle_group_code07 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2007-12-31 as vehicle_individual_id07

import db/KJORETOY_DRIVSTOFF_OMK 2007-12-31 as fuel_type07
assign-labels fuel_type07 fuel_type_txt

tabulate fuel_type07, missing
piechart fuel_type07

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green07 = fuel_type07
replace emission_categories_gray_green07 = 'Green' if emission_categories_gray_green07 == '05'
replace emission_categories_gray_green07 = 'Gray' if emission_categories_gray_green07 == '01' | emission_categories_gray_green07 == '02' | emission_categories_gray_green07 == '03' | emission_categories_gray_green07 == '04' | emission_categories_gray_green07 == '07' | emission_categories_gray_green07 == '08' | emission_categories_gray_green07 == '09' | emission_categories_gray_green07 == '10' | emission_categories_gray_green07 == '11' | emission_categories_gray_green07 == '12' | emission_categories_gray_green07 == '13' | emission_categories_gray_green07 == '15'
drop if emission_categories_gray_green07 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green07 = 0
replace vehicle_green07 = 1 if emission_categories_gray_green07 == 'Green'
generate vehicle_gray07 = 0
replace vehicle_gray07 = 1 if emission_categories_gray_green07 == 'Gray'

piechart emission_categories_gray_green07
tabulate emission_categories_gray_green07, missing
//tabulate vehicle_green07 vehicle_gray07, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2007.
clone-dataset vehicle_DS_passenger07 vehicle_DS_passenger_green_owned07
clone-dataset vehicle_DS_passenger07 vehicle_DS_passenger_gray_owned07

// Make temporary datasets to calculate categories of passenger vehicles per household in 2007.
clone-dataset person_DS_Y07 household_DS_all_fuel_owners07
clone-dataset person_DS_Y07 household_DS_green_owners07
clone-dataset person_DS_Y07 household_DS_gray_owners07

textblock
::::: Owned private vehicles in Norway by Fuel Type in 2007.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2007.
collapse(count) fuel_type07, by(vehicle_individual_id07)
rename fuel_type07 owned_vehicle_all_per_person07
//tabulate owned_vehicle_all_per_person07, missing
merge owned_vehicle_all_per_person07 into household_DS_all_fuel_owners07 on household_id07
delete-dataset vehicle_DS_passenger07

// Then, aggregate the number of all categories of passenger vehicles per household in 2007.
use household_DS_all_fuel_owners07
collapse(sum) owned_vehicle_all_per_person07, by(household_id07)
rename owned_vehicle_all_per_person07 owned_vehicle_all_per_household07
//tabulate owned_vehicle_all_per_household07, missing
histogram owned_vehicle_all_per_household07 if owned_vehicle_all_per_household07 > 0, freq
//piechart owned_vehicle_all_per_household07 if owned_vehicle_all_per_household07 > 0
piechart owned_vehicle_all_per_household07
merge owned_vehicle_all_per_household07 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners07


// Step: Sum the number of green vehicles per person in 2007.
use vehicle_DS_passenger_green_owned07
collapse(sum) vehicle_green07, by(vehicle_individual_id07)
rename vehicle_green07 owned_vehicle_green_per_person07
//tabulate owned_vehicle_green_per_person07, missing
merge owned_vehicle_green_per_person07 into household_DS_green_owners07 on household_id07
delete-dataset vehicle_DS_passenger_green_owned07

// Then, aggregate the number of green vehicles per household in 2007.
use household_DS_green_owners07
collapse(sum) owned_vehicle_green_per_person07, by(household_id07)
rename owned_vehicle_green_per_person07 owned_vehicle_green_per_household07
//tabulate owned_vehicle_green_per_household07, missing
histogram owned_vehicle_green_per_household07 if owned_vehicle_green_per_household07 > 0, freq
piechart owned_vehicle_green_per_household07 if owned_vehicle_green_per_household07 > 0
piechart owned_vehicle_green_per_household07
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household07 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners07


// Step: Sum the number of gray vehicles per person in 2007.
use vehicle_DS_passenger_gray_owned07
collapse(sum) vehicle_gray07, by(vehicle_individual_id07)
rename vehicle_gray07 owned_vehicle_gray_per_person07
//tabulate owned_vehicle_gray_per_person07, missing
merge owned_vehicle_gray_per_person07 into household_DS_gray_owners07 on household_id07
delete-dataset vehicle_DS_passenger_gray_owned07

// Then, aggregate the number of gray vehicles per household in 2007.
use household_DS_gray_owners07
collapse(sum) owned_vehicle_gray_per_person07, by(household_id07)
rename owned_vehicle_gray_per_person07 owned_vehicle_gray_per_household07
merge owned_vehicle_gray_per_household07 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners07


// Vehicles 2008
create-dataset vehicle_DS_passenger08
import db/KJORETOY_KJT_GRUP 2008-12-31 as vehicle_group_code08
keep if vehicle_group_code08 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2008-12-31 as vehicle_individual_id08

import db/KJORETOY_DRIVSTOFF_OMK 2008-12-31 as fuel_type08
assign-labels fuel_type08 fuel_type_txt

tabulate fuel_type08, missing
piechart fuel_type08

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green08 = fuel_type08
replace emission_categories_gray_green08 = 'Green' if emission_categories_gray_green08 == '05'
replace emission_categories_gray_green08 = 'Gray' if emission_categories_gray_green08 == '01' | emission_categories_gray_green08 == '02' | emission_categories_gray_green08 == '03' | emission_categories_gray_green08 == '04' | emission_categories_gray_green08 == '07' | emission_categories_gray_green08 == '08' | emission_categories_gray_green08 == '09' | emission_categories_gray_green08 == '10' | emission_categories_gray_green08 == '11' | emission_categories_gray_green08 == '12' | emission_categories_gray_green08 == '13' | emission_categories_gray_green08 == '15'
drop if emission_categories_gray_green08 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green08 = 0
replace vehicle_green08 = 1 if emission_categories_gray_green08 == 'Green'
generate vehicle_gray08 = 0
replace vehicle_gray08 = 1 if emission_categories_gray_green08 == 'Gray'

piechart emission_categories_gray_green08
tabulate emission_categories_gray_green08, missing
//tabulate vehicle_green08 vehicle_gray08, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2008.
clone-dataset vehicle_DS_passenger08 vehicle_DS_passenger_green_owned08
clone-dataset vehicle_DS_passenger08 vehicle_DS_passenger_gray_owned08


// Make temporary datasets to calculate categories of passenger vehicles per household in 2008.
clone-dataset person_DS_Y08 household_DS_all_fuel_owners08
clone-dataset person_DS_Y08 household_DS_green_owners08
clone-dataset person_DS_Y08 household_DS_gray_owners08



textblock
::::: Owned private vehicles in Norway by Fuel Type in 2008.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2008.
collapse(count) fuel_type08, by(vehicle_individual_id08)
rename fuel_type08 owned_vehicle_all_per_person08
//tabulate owned_vehicle_all_per_person08, missing
merge owned_vehicle_all_per_person08 into household_DS_all_fuel_owners08 on household_id08
delete-dataset vehicle_DS_passenger08

// Then, aggregate the number of all categories of passenger vehicles per household in 2008.
use household_DS_all_fuel_owners08
collapse(sum) owned_vehicle_all_per_person08, by(household_id08)
rename owned_vehicle_all_per_person08 owned_vehicle_all_per_household08
//tabulate owned_vehicle_all_per_household08, missing
histogram owned_vehicle_all_per_household08 if owned_vehicle_all_per_household08 > 0, freq
//piechart owned_vehicle_all_per_household08 if owned_vehicle_all_per_household08 > 0
piechart owned_vehicle_all_per_household08
merge owned_vehicle_all_per_household08 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners08


// Step: Sum the number of green vehicles per person in 2008.
use vehicle_DS_passenger_green_owned08
collapse(sum) vehicle_green08, by(vehicle_individual_id08)
rename vehicle_green08 owned_vehicle_green_per_person08
//tabulate owned_vehicle_green_per_person08, missing
merge owned_vehicle_green_per_person08 into household_DS_green_owners08 on household_id08
delete-dataset vehicle_DS_passenger_green_owned08

// Then, aggregate the number of green vehicles per household in 2008.
use household_DS_green_owners08
collapse(sum) owned_vehicle_green_per_person08, by(household_id08)
rename owned_vehicle_green_per_person08 owned_vehicle_green_per_household08
//tabulate owned_vehicle_green_per_household08, missing
histogram owned_vehicle_green_per_household08 if owned_vehicle_green_per_household08 > 0, freq
piechart owned_vehicle_green_per_household08 if owned_vehicle_green_per_household08 > 0
piechart owned_vehicle_green_per_household08
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household08 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners08


// Step: Sum the number of gray vehicles per person in 2008.
use vehicle_DS_passenger_gray_owned08
collapse(sum) vehicle_gray08, by(vehicle_individual_id08)
rename vehicle_gray08 owned_vehicle_gray_per_person08
//tabulate owned_vehicle_gray_per_person08, missing
merge owned_vehicle_gray_per_person08 into household_DS_gray_owners08 on household_id08
delete-dataset vehicle_DS_passenger_gray_owned08

// Then, aggregate the number of gray vehicles per household in 2008.
use household_DS_gray_owners08
collapse(sum) owned_vehicle_gray_per_person08, by(household_id08)
rename owned_vehicle_gray_per_person08 owned_vehicle_gray_per_household08
merge owned_vehicle_gray_per_household08 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners08


// Vehicles 2009
create-dataset vehicle_DS_passenger09
import db/KJORETOY_KJT_GRUP 2009-12-31 as vehicle_group_code09
keep if vehicle_group_code09 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2009-12-31 as vehicle_individual_id09

import db/KJORETOY_DRIVSTOFF_OMK 2009-12-31 as fuel_type09
assign-labels fuel_type09 fuel_type_txt

tabulate fuel_type09, missing
piechart fuel_type09

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green09 = fuel_type09
replace emission_categories_gray_green09 = 'Green' if emission_categories_gray_green09 == '05'
replace emission_categories_gray_green09 = 'Gray' if emission_categories_gray_green09 == '01' | emission_categories_gray_green09 == '02' | emission_categories_gray_green09 == '03' | emission_categories_gray_green09 == '04' | emission_categories_gray_green09 == '07' | emission_categories_gray_green09 == '08' | emission_categories_gray_green09 == '09' | emission_categories_gray_green09 == '10' | emission_categories_gray_green09 == '11' | emission_categories_gray_green09 == '12' | emission_categories_gray_green09 == '13' | emission_categories_gray_green09 == '15'
drop if emission_categories_gray_green09 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green09 = 0
replace vehicle_green09 = 1 if emission_categories_gray_green09 == 'Green'
generate vehicle_gray09 = 0
replace vehicle_gray09 = 1 if emission_categories_gray_green09 == 'Gray'

piechart emission_categories_gray_green09
tabulate emission_categories_gray_green09, missing
//tabulate vehicle_green09 vehicle_gray09, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2009.
clone-dataset vehicle_DS_passenger09 vehicle_DS_passenger_green_owned09
clone-dataset vehicle_DS_passenger09 vehicle_DS_passenger_gray_owned09


// Make temporary datasets to calculate categories of passenger vehicles per household in 2009.
clone-dataset person_DS_Y09 household_DS_all_fuel_owners09
clone-dataset person_DS_Y09 household_DS_green_owners09
clone-dataset person_DS_Y09 household_DS_gray_owners09



textblock
::::: Owned private vehicles in Norway by Fuel Type in 2009.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2009.
collapse(count) fuel_type09, by(vehicle_individual_id09)
rename fuel_type09 owned_vehicle_all_per_person09
//tabulate owned_vehicle_all_per_person09, missing
merge owned_vehicle_all_per_person09 into household_DS_all_fuel_owners09 on household_id09
delete-dataset vehicle_DS_passenger09

// Then, aggregate the number of all categories of passenger vehicles per household in 2009.
use household_DS_all_fuel_owners09
collapse(sum) owned_vehicle_all_per_person09, by(household_id09)
rename owned_vehicle_all_per_person09 owned_vehicle_all_per_household09
//tabulate owned_vehicle_all_per_household09, missing
histogram owned_vehicle_all_per_household09 if owned_vehicle_all_per_household09 > 0, freq
//piechart owned_vehicle_all_per_household09 if owned_vehicle_all_per_household09 > 0
piechart owned_vehicle_all_per_household09
merge owned_vehicle_all_per_household09 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners09


// Step: Sum the number of green vehicles per person in 2009.
use vehicle_DS_passenger_green_owned09
collapse(sum) vehicle_green09, by(vehicle_individual_id09)
rename vehicle_green09 owned_vehicle_green_per_person09
//tabulate owned_vehicle_green_per_person09, missing
merge owned_vehicle_green_per_person09 into household_DS_green_owners09 on household_id09
delete-dataset vehicle_DS_passenger_green_owned09

// Then, aggregate the number of green vehicles per household in 2009.
use household_DS_green_owners09
collapse(sum) owned_vehicle_green_per_person09, by(household_id09)
rename owned_vehicle_green_per_person09 owned_vehicle_green_per_household09
//tabulate owned_vehicle_green_per_household09, missing
histogram owned_vehicle_green_per_household09 if owned_vehicle_green_per_household09 > 0, freq
piechart owned_vehicle_green_per_household09 if owned_vehicle_green_per_household09 > 0
piechart owned_vehicle_green_per_household09
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household09 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners09


// Step: Sum the number of gray vehicles per person in 2009.
use vehicle_DS_passenger_gray_owned09
collapse(sum) vehicle_gray09, by(vehicle_individual_id09)
rename vehicle_gray09 owned_vehicle_gray_per_person09
//tabulate owned_vehicle_gray_per_person09, missing
merge owned_vehicle_gray_per_person09 into household_DS_gray_owners09 on household_id09
delete-dataset vehicle_DS_passenger_gray_owned09

// Then, aggregate the number of gray vehicles per household in 2009.
use household_DS_gray_owners09
collapse(sum) owned_vehicle_gray_per_person09, by(household_id09)
rename owned_vehicle_gray_per_person09 owned_vehicle_gray_per_household09
merge owned_vehicle_gray_per_household09 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners09


// Vehicles 2010
create-dataset vehicle_DS_passenger10
import db/KJORETOY_KJT_GRUP 2010-12-31 as vehicle_group_code10
keep if vehicle_group_code10 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2010-12-31 as vehicle_individual_id10

import db/KJORETOY_DRIVSTOFF_OMK 2010-12-31 as fuel_type10
assign-labels fuel_type10 fuel_type_txt

tabulate fuel_type10, missing
piechart fuel_type10

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green10 = fuel_type10
replace emission_categories_gray_green10 = 'Green' if emission_categories_gray_green10 == '05'
replace emission_categories_gray_green10 = 'Gray' if emission_categories_gray_green10 == '01' | emission_categories_gray_green10 == '02' | emission_categories_gray_green10 == '03' | emission_categories_gray_green10 == '04' | emission_categories_gray_green10 == '07' | emission_categories_gray_green10 == '08' | emission_categories_gray_green10 == '09' | emission_categories_gray_green10 == '10' | emission_categories_gray_green10 == '11' | emission_categories_gray_green10 == '12' | emission_categories_gray_green10 == '13' | emission_categories_gray_green10 == '15'
drop if emission_categories_gray_green10 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green10 = 0
replace vehicle_green10 = 1 if emission_categories_gray_green10 == 'Green'
generate vehicle_gray10 = 0
replace vehicle_gray10 = 1 if emission_categories_gray_green10 == 'Gray'

piechart emission_categories_gray_green10
tabulate emission_categories_gray_green10, missing
//tabulate vehicle_green10 vehicle_gray10, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2010.
clone-dataset vehicle_DS_passenger10 vehicle_DS_passenger_green_owned10
clone-dataset vehicle_DS_passenger10 vehicle_DS_passenger_gray_owned10


// Make temporary datasets to calculate categories of passenger vehicles per household in 2010.
clone-dataset person_DS_Y10 household_DS_all_fuel_owners10
clone-dataset person_DS_Y10 household_DS_green_owners10
clone-dataset person_DS_Y10 household_DS_gray_owners10


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2010.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2010.
collapse(count) fuel_type10, by(vehicle_individual_id10)
rename fuel_type10 owned_vehicle_all_per_person10
//tabulate owned_vehicle_all_per_person10, missing
merge owned_vehicle_all_per_person10 into household_DS_all_fuel_owners10 on household_id10
delete-dataset vehicle_DS_passenger10

// Then, aggregate the number of all categories of passenger vehicles per household in 2010.
use household_DS_all_fuel_owners10
collapse(sum) owned_vehicle_all_per_person10, by(household_id10)
rename owned_vehicle_all_per_person10 owned_vehicle_all_per_household10
//tabulate owned_vehicle_all_per_household10, missing
histogram owned_vehicle_all_per_household10 if owned_vehicle_all_per_household10 > 0, freq
//piechart owned_vehicle_all_per_household10 if owned_vehicle_all_per_household10 > 0
piechart owned_vehicle_all_per_household10
merge owned_vehicle_all_per_household10 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners10


// Step: Sum the number of green vehicles per person in 2010.
use vehicle_DS_passenger_green_owned10
collapse(sum) vehicle_green10, by(vehicle_individual_id10)
rename vehicle_green10 owned_vehicle_green_per_person10
//tabulate owned_vehicle_green_per_person10, missing
merge owned_vehicle_green_per_person10 into household_DS_green_owners10 on household_id10
delete-dataset vehicle_DS_passenger_green_owned10

// Then, aggregate the number of green vehicles per household in 2010.
use household_DS_green_owners10
collapse(sum) owned_vehicle_green_per_person10, by(household_id10)
rename owned_vehicle_green_per_person10 owned_vehicle_green_per_household10
//tabulate owned_vehicle_green_per_household10, missing
histogram owned_vehicle_green_per_household10 if owned_vehicle_green_per_household10 > 0, freq
piechart owned_vehicle_green_per_household10 if owned_vehicle_green_per_household10 > 0
piechart owned_vehicle_green_per_household10
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household10 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners10


// Step: Sum the number of gray vehicles per person in 2010.
use vehicle_DS_passenger_gray_owned10
collapse(sum) vehicle_gray10, by(vehicle_individual_id10)
rename vehicle_gray10 owned_vehicle_gray_per_person10
//tabulate owned_vehicle_gray_per_person10, missing
merge owned_vehicle_gray_per_person10 into household_DS_gray_owners10 on household_id10
delete-dataset vehicle_DS_passenger_gray_owned10

// Then, aggregate the number of gray vehicles per household in 2010.
use household_DS_gray_owners10
collapse(sum) owned_vehicle_gray_per_person10, by(household_id10)
rename owned_vehicle_gray_per_person10 owned_vehicle_gray_per_household10
merge owned_vehicle_gray_per_household10 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners10


// Vehicles 2011
create-dataset vehicle_DS_passenger11
import db/KJORETOY_KJT_GRUP 2011-12-31 as vehicle_group_code11
keep if vehicle_group_code11 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2011-12-31 as vehicle_individual_id11

import db/KJORETOY_DRIVSTOFF_OMK 2011-12-31 as fuel_type11
assign-labels fuel_type11 fuel_type_txt

tabulate fuel_type11, missing
piechart fuel_type11

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green11 = fuel_type11
replace emission_categories_gray_green11 = 'Green' if emission_categories_gray_green11 == '05'
replace emission_categories_gray_green11 = 'Gray' if emission_categories_gray_green11 == '01' | emission_categories_gray_green11 == '02' | emission_categories_gray_green11 == '03' | emission_categories_gray_green11 == '04' | emission_categories_gray_green11 == '07' | emission_categories_gray_green11 == '08' | emission_categories_gray_green11 == '09' | emission_categories_gray_green11 == '10' | emission_categories_gray_green11 == '11' | emission_categories_gray_green11 == '12' | emission_categories_gray_green11 == '13' | emission_categories_gray_green11 == '15'
drop if emission_categories_gray_green11 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green11 = 0
replace vehicle_green11 = 1 if emission_categories_gray_green11 == 'Green'
generate vehicle_gray11 = 0
replace vehicle_gray11 = 1 if emission_categories_gray_green11 == 'Gray'

piechart emission_categories_gray_green11
tabulate emission_categories_gray_green11, missing
//tabulate vehicle_green11 vehicle_gray11, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2011.
clone-dataset vehicle_DS_passenger11 vehicle_DS_passenger_green_owned11
clone-dataset vehicle_DS_passenger11 vehicle_DS_passenger_gray_owned11


// Make temporary datasets to calculate categories of passenger vehicles per household in 2011.
clone-dataset person_DS_Y11 household_DS_all_fuel_owners11
clone-dataset person_DS_Y11 household_DS_green_owners11
clone-dataset person_DS_Y11 household_DS_gray_owners11


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2011.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2011.
collapse(count) fuel_type11, by(vehicle_individual_id11)
rename fuel_type11 owned_vehicle_all_per_person11
//tabulate owned_vehicle_all_per_person11, missing
merge owned_vehicle_all_per_person11 into household_DS_all_fuel_owners11 on household_id11
delete-dataset vehicle_DS_passenger11

// Then, aggregate the number of all categories of passenger vehicles per household in 2011.
use household_DS_all_fuel_owners11
collapse(sum) owned_vehicle_all_per_person11, by(household_id11)
rename owned_vehicle_all_per_person11 owned_vehicle_all_per_household11
//tabulate owned_vehicle_all_per_household11, missing
histogram owned_vehicle_all_per_household11 if owned_vehicle_all_per_household11 > 0, freq
//piechart owned_vehicle_all_per_household11 if owned_vehicle_all_per_household11 > 0
piechart owned_vehicle_all_per_household11
merge owned_vehicle_all_per_household11 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners11


// Step: Sum the number of green vehicles per person in 2011.
use vehicle_DS_passenger_green_owned11
collapse(sum) vehicle_green11, by(vehicle_individual_id11)
rename vehicle_green11 owned_vehicle_green_per_person11
//tabulate owned_vehicle_green_per_person11, missing
merge owned_vehicle_green_per_person11 into household_DS_green_owners11 on household_id11
delete-dataset vehicle_DS_passenger_green_owned11

// Then, aggregate the number of green vehicles per household in 2011.
use household_DS_green_owners11
collapse(sum) owned_vehicle_green_per_person11, by(household_id11)
rename owned_vehicle_green_per_person11 owned_vehicle_green_per_household11
//tabulate owned_vehicle_green_per_household11, missing
histogram owned_vehicle_green_per_household11 if owned_vehicle_green_per_household11 > 0, freq
piechart owned_vehicle_green_per_household11 if owned_vehicle_green_per_household11 > 0
piechart owned_vehicle_green_per_household11
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household11 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners11


// Step: Sum the number of gray vehicles per person in 2011.
use vehicle_DS_passenger_gray_owned11
collapse(sum) vehicle_gray11, by(vehicle_individual_id11)
rename vehicle_gray11 owned_vehicle_gray_per_person11
//tabulate owned_vehicle_gray_per_person11, missing
merge owned_vehicle_gray_per_person11 into household_DS_gray_owners11 on household_id11
delete-dataset vehicle_DS_passenger_gray_owned11

// Then, aggregate the number of gray vehicles per household in 2011.
use household_DS_gray_owners11
collapse(sum) owned_vehicle_gray_per_person11, by(household_id11)
rename owned_vehicle_gray_per_person11 owned_vehicle_gray_per_household11
merge owned_vehicle_gray_per_household11 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners11


// Vehicles 2012
create-dataset vehicle_DS_passenger12
import db/KJORETOY_KJT_GRUP 2012-12-31 as vehicle_group_code12
keep if vehicle_group_code12 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2012-12-31 as vehicle_individual_id12

import db/KJORETOY_DRIVSTOFF_OMK 2012-12-31 as fuel_type12
assign-labels fuel_type12 fuel_type_txt

tabulate fuel_type12, missing
piechart fuel_type12

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green12 = fuel_type12
replace emission_categories_gray_green12 = 'Green' if emission_categories_gray_green12 == '05'
replace emission_categories_gray_green12 = 'Gray' if emission_categories_gray_green12 == '01' | emission_categories_gray_green12 == '02' | emission_categories_gray_green12 == '03' | emission_categories_gray_green12 == '04' | emission_categories_gray_green12 == '07' | emission_categories_gray_green12 == '08' | emission_categories_gray_green12 == '09' | emission_categories_gray_green12 == '10' | emission_categories_gray_green12 == '11' | emission_categories_gray_green12 == '12' | emission_categories_gray_green12 == '13' | emission_categories_gray_green12 == '15'
drop if emission_categories_gray_green12 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green12 = 0
replace vehicle_green12 = 1 if emission_categories_gray_green12 == 'Green'
generate vehicle_gray12 = 0
replace vehicle_gray12 = 1 if emission_categories_gray_green12 == 'Gray'

piechart emission_categories_gray_green12
tabulate emission_categories_gray_green12, missing
//tabulate vehicle_green12 vehicle_gray12, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2012.
clone-dataset vehicle_DS_passenger12 vehicle_DS_passenger_green_owned12
clone-dataset vehicle_DS_passenger12 vehicle_DS_passenger_gray_owned12


// Make temporary datasets to calculate categories of passenger vehicles per household in 2012.
clone-dataset person_DS_Y12 household_DS_all_fuel_owners12
clone-dataset person_DS_Y12 household_DS_green_owners12
clone-dataset person_DS_Y12 household_DS_gray_owners12


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2012.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2012.
collapse(count) fuel_type12, by(vehicle_individual_id12)
rename fuel_type12 owned_vehicle_all_per_person12
//tabulate owned_vehicle_all_per_person12, missing
merge owned_vehicle_all_per_person12 into household_DS_all_fuel_owners12 on household_id12
delete-dataset vehicle_DS_passenger12

// Then, aggregate the number of all categories of passenger vehicles per household in 2012.
use household_DS_all_fuel_owners12
collapse(sum) owned_vehicle_all_per_person12, by(household_id12)
rename owned_vehicle_all_per_person12 owned_vehicle_all_per_household12
//tabulate owned_vehicle_all_per_household12, missing
histogram owned_vehicle_all_per_household12 if owned_vehicle_all_per_household12 > 0, freq
//piechart owned_vehicle_all_per_household12 if owned_vehicle_all_per_household12 > 0
piechart owned_vehicle_all_per_household12
merge owned_vehicle_all_per_household12 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners12


// Step: Sum the number of green vehicles per person in 2012.
use vehicle_DS_passenger_green_owned12
collapse(sum) vehicle_green12, by(vehicle_individual_id12)
rename vehicle_green12 owned_vehicle_green_per_person12
//tabulate owned_vehicle_green_per_person12, missing
merge owned_vehicle_green_per_person12 into household_DS_green_owners12 on household_id12
delete-dataset vehicle_DS_passenger_green_owned12

// Then, aggregate the number of green vehicles per household in 2012.
use household_DS_green_owners12
collapse(sum) owned_vehicle_green_per_person12, by(household_id12)
rename owned_vehicle_green_per_person12 owned_vehicle_green_per_household12
//tabulate owned_vehicle_green_per_household12, missing
histogram owned_vehicle_green_per_household12 if owned_vehicle_green_per_household12 > 0, freq
piechart owned_vehicle_green_per_household12 if owned_vehicle_green_per_household12 > 0
piechart owned_vehicle_green_per_household12
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household12 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners12


// Step: Sum the number of gray vehicles per person in 2012.
use vehicle_DS_passenger_gray_owned12
collapse(sum) vehicle_gray12, by(vehicle_individual_id12)
rename vehicle_gray12 owned_vehicle_gray_per_person12
//tabulate owned_vehicle_gray_per_person12, missing
merge owned_vehicle_gray_per_person12 into household_DS_gray_owners12 on household_id12
delete-dataset vehicle_DS_passenger_gray_owned12

// Then, aggregate the number of gray vehicles per household in 2012.
use household_DS_gray_owners12
collapse(sum) owned_vehicle_gray_per_person12, by(household_id12)
rename owned_vehicle_gray_per_person12 owned_vehicle_gray_per_household12
merge owned_vehicle_gray_per_household12 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners12


// Vehicles 2013
create-dataset vehicle_DS_passenger13
import db/KJORETOY_KJT_GRUP 2013-12-31 as vehicle_group_code13
keep if vehicle_group_code13 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2013-12-31 as vehicle_individual_id13

import db/KJORETOY_DRIVSTOFF_OMK 2013-12-31 as fuel_type13
assign-labels fuel_type13 fuel_type_txt

tabulate fuel_type13, missing
piechart fuel_type13

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green13 = fuel_type13
replace emission_categories_gray_green13 = 'Green' if emission_categories_gray_green13 == '05'
replace emission_categories_gray_green13 = 'Gray' if emission_categories_gray_green13 == '01' | emission_categories_gray_green13 == '02' | emission_categories_gray_green13 == '03' | emission_categories_gray_green13 == '04' | emission_categories_gray_green13 == '07' | emission_categories_gray_green13 == '08' | emission_categories_gray_green13 == '09' | emission_categories_gray_green13 == '10' | emission_categories_gray_green13 == '11' | emission_categories_gray_green13 == '12' | emission_categories_gray_green13 == '13' | emission_categories_gray_green13 == '15'
drop if emission_categories_gray_green13 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green13 = 0
replace vehicle_green13 = 1 if emission_categories_gray_green13 == 'Green'
generate vehicle_gray13 = 0
replace vehicle_gray13 = 1 if emission_categories_gray_green13 == 'Gray'

piechart emission_categories_gray_green13
tabulate emission_categories_gray_green13, missing
//tabulate vehicle_green13 vehicle_gray13, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2013.
clone-dataset vehicle_DS_passenger13 vehicle_DS_passenger_green_owned13
clone-dataset vehicle_DS_passenger13 vehicle_DS_passenger_gray_owned13


// Make temporary datasets to calculate categories of passenger vehicles per household in 2013.
clone-dataset person_DS_Y13 household_DS_all_fuel_owners13
clone-dataset person_DS_Y13 household_DS_green_owners13
clone-dataset person_DS_Y13 household_DS_gray_owners13


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2013.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2013.
collapse(count) fuel_type13, by(vehicle_individual_id13)
rename fuel_type13 owned_vehicle_all_per_person13
//tabulate owned_vehicle_all_per_person13, missing
merge owned_vehicle_all_per_person13 into household_DS_all_fuel_owners13 on household_id13
delete-dataset vehicle_DS_passenger13

// Then, aggregate the number of all categories of passenger vehicles per household in 2013.
use household_DS_all_fuel_owners13
collapse(sum) owned_vehicle_all_per_person13, by(household_id13)
rename owned_vehicle_all_per_person13 owned_vehicle_all_per_household13
//tabulate owned_vehicle_all_per_household13, missing
histogram owned_vehicle_all_per_household13 if owned_vehicle_all_per_household13 > 0, freq
//piechart owned_vehicle_all_per_household13 if owned_vehicle_all_per_household13 > 0
piechart owned_vehicle_all_per_household13
merge owned_vehicle_all_per_household13 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners13


// Step: Sum the number of green vehicles per person in 2013.
use vehicle_DS_passenger_green_owned13
collapse(sum) vehicle_green13, by(vehicle_individual_id13)
rename vehicle_green13 owned_vehicle_green_per_person13
//tabulate owned_vehicle_green_per_person13, missing
merge owned_vehicle_green_per_person13 into household_DS_green_owners13 on household_id13
delete-dataset vehicle_DS_passenger_green_owned13

// Then, aggregate the number of green vehicles per household in 2013.
use household_DS_green_owners13
collapse(sum) owned_vehicle_green_per_person13, by(household_id13)
rename owned_vehicle_green_per_person13 owned_vehicle_green_per_household13
//tabulate owned_vehicle_green_per_household13, missing
histogram owned_vehicle_green_per_household13 if owned_vehicle_green_per_household13 > 0, freq
piechart owned_vehicle_green_per_household13 if owned_vehicle_green_per_household13 > 0
piechart owned_vehicle_green_per_household13
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household13 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners13


// Step: Sum the number of gray vehicles per person in 2013.
use vehicle_DS_passenger_gray_owned13
collapse(sum) vehicle_gray13, by(vehicle_individual_id13)
rename vehicle_gray13 owned_vehicle_gray_per_person13
//tabulate owned_vehicle_gray_per_person13, missing
merge owned_vehicle_gray_per_person13 into household_DS_gray_owners13 on household_id13
delete-dataset vehicle_DS_passenger_gray_owned13

// Then, aggregate the number of gray vehicles per household in 2013.
use household_DS_gray_owners13
collapse(sum) owned_vehicle_gray_per_person13, by(household_id13)
rename owned_vehicle_gray_per_person13 owned_vehicle_gray_per_household13
merge owned_vehicle_gray_per_household13 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners13


// Vehicles 2014
create-dataset vehicle_DS_passenger14
import db/KJORETOY_KJT_GRUP 2014-12-31 as vehicle_group_code14
keep if vehicle_group_code14 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2014-12-31 as vehicle_individual_id14

import db/KJORETOY_DRIVSTOFF_OMK 2014-12-31 as fuel_type14
assign-labels fuel_type14 fuel_type_txt

tabulate fuel_type14, missing
piechart fuel_type14

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green14 = fuel_type14
replace emission_categories_gray_green14 = 'Green' if emission_categories_gray_green14 == '05'
replace emission_categories_gray_green14 = 'Gray' if emission_categories_gray_green14 == '01' | emission_categories_gray_green14 == '02' | emission_categories_gray_green14 == '03' | emission_categories_gray_green14 == '04' | emission_categories_gray_green14 == '07' | emission_categories_gray_green14 == '08' | emission_categories_gray_green14 == '09' | emission_categories_gray_green14 == '10' | emission_categories_gray_green14 == '11' | emission_categories_gray_green14 == '12' | emission_categories_gray_green14 == '13' | emission_categories_gray_green14 == '15'
drop if emission_categories_gray_green14 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green14 = 0
replace vehicle_green14 = 1 if emission_categories_gray_green14 == 'Green'
generate vehicle_gray14 = 0
replace vehicle_gray14 = 1 if emission_categories_gray_green14 == 'Gray'

piechart emission_categories_gray_green14
tabulate emission_categories_gray_green14, missing
//tabulate vehicle_green14 vehicle_gray14, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2014.
clone-dataset vehicle_DS_passenger14 vehicle_DS_passenger_green_owned14
clone-dataset vehicle_DS_passenger14 vehicle_DS_passenger_gray_owned14


// Make temporary datasets to calculate categories of passenger vehicles per household in 2014.
clone-dataset person_DS_Y14 household_DS_all_fuel_owners14
clone-dataset person_DS_Y14 household_DS_green_owners14
clone-dataset person_DS_Y14 household_DS_gray_owners14


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2014.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2014.
collapse(count) fuel_type14, by(vehicle_individual_id14)
rename fuel_type14 owned_vehicle_all_per_person14
//tabulate owned_vehicle_all_per_person14, missing
merge owned_vehicle_all_per_person14 into household_DS_all_fuel_owners14 on household_id14
delete-dataset vehicle_DS_passenger14

// Then, aggregate the number of all categories of passenger vehicles per household in 2014.
use household_DS_all_fuel_owners14
collapse(sum) owned_vehicle_all_per_person14, by(household_id14)
rename owned_vehicle_all_per_person14 owned_vehicle_all_per_household14
//tabulate owned_vehicle_all_per_household14, missing
histogram owned_vehicle_all_per_household14 if owned_vehicle_all_per_household14 > 0, freq
//piechart owned_vehicle_all_per_household14 if owned_vehicle_all_per_household14 > 0
piechart owned_vehicle_all_per_household14
merge owned_vehicle_all_per_household14 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners14


// Step: Sum the number of green vehicles per person in 2014.
use vehicle_DS_passenger_green_owned14
collapse(sum) vehicle_green14, by(vehicle_individual_id14)
rename vehicle_green14 owned_vehicle_green_per_person14
//tabulate owned_vehicle_green_per_person14, missing
merge owned_vehicle_green_per_person14 into household_DS_green_owners14 on household_id14
delete-dataset vehicle_DS_passenger_green_owned14

// Then, aggregate the number of green vehicles per household in 2014.
use household_DS_green_owners14
collapse(sum) owned_vehicle_green_per_person14, by(household_id14)
rename owned_vehicle_green_per_person14 owned_vehicle_green_per_household14
//tabulate owned_vehicle_green_per_household14, missing
histogram owned_vehicle_green_per_household14 if owned_vehicle_green_per_household14 > 0, freq
piechart owned_vehicle_green_per_household14 if owned_vehicle_green_per_household14 > 0
piechart owned_vehicle_green_per_household14
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household14 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners14


// Step: Sum the number of gray vehicles per person in 2014.
use vehicle_DS_passenger_gray_owned14
collapse(sum) vehicle_gray14, by(vehicle_individual_id14)
rename vehicle_gray14 owned_vehicle_gray_per_person14
//tabulate owned_vehicle_gray_per_person14, missing
merge owned_vehicle_gray_per_person14 into household_DS_gray_owners14 on household_id14
delete-dataset vehicle_DS_passenger_gray_owned14

// Then, aggregate the number of gray vehicles per household in 2014.
use household_DS_gray_owners14
collapse(sum) owned_vehicle_gray_per_person14, by(household_id14)
rename owned_vehicle_gray_per_person14 owned_vehicle_gray_per_household14
merge owned_vehicle_gray_per_household14 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners14


// Vehicles 2015
create-dataset vehicle_DS_passenger15
import db/KJORETOY_KJT_GRUP 2015-12-31 as vehicle_group_code15
keep if vehicle_group_code15 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2015-12-31 as vehicle_individual_id15

import db/KJORETOY_DRIVSTOFF_OMK 2015-12-31 as fuel_type15
assign-labels fuel_type15 fuel_type_txt

tabulate fuel_type15, missing
piechart fuel_type15

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green15 = fuel_type15
replace emission_categories_gray_green15 = 'Green' if emission_categories_gray_green15 == '05'
replace emission_categories_gray_green15 = 'Gray' if emission_categories_gray_green15 == '01' | emission_categories_gray_green15 == '02' | emission_categories_gray_green15 == '03' | emission_categories_gray_green15 == '04' | emission_categories_gray_green15 == '07' | emission_categories_gray_green15 == '08' | emission_categories_gray_green15 == '09' | emission_categories_gray_green15 == '10' | emission_categories_gray_green15 == '11' | emission_categories_gray_green15 == '12' | emission_categories_gray_green15 == '13' | emission_categories_gray_green15 == '15'
drop if emission_categories_gray_green15 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green15 = 0
replace vehicle_green15 = 1 if emission_categories_gray_green15 == 'Green'
generate vehicle_gray15 = 0
replace vehicle_gray15 = 1 if emission_categories_gray_green15 == 'Gray'

piechart emission_categories_gray_green15
tabulate emission_categories_gray_green15, missing
//tabulate vehicle_green15 vehicle_gray15, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2015.
clone-dataset vehicle_DS_passenger15 vehicle_DS_passenger_green_owned15
clone-dataset vehicle_DS_passenger15 vehicle_DS_passenger_gray_owned15


// Make temporary datasets to calculate categories of passenger vehicles per household in 2015.
clone-dataset person_DS_Y15 household_DS_all_fuel_owners15
clone-dataset person_DS_Y15 household_DS_green_owners15
clone-dataset person_DS_Y15 household_DS_gray_owners15


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2015.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2015.
collapse(count) fuel_type15, by(vehicle_individual_id15)
rename fuel_type15 owned_vehicle_all_per_person15
//tabulate owned_vehicle_all_per_person15, missing
merge owned_vehicle_all_per_person15 into household_DS_all_fuel_owners15 on household_id15
delete-dataset vehicle_DS_passenger15

// Then, aggregate the number of all categories of passenger vehicles per household in 2015.
use household_DS_all_fuel_owners15
collapse(sum) owned_vehicle_all_per_person15, by(household_id15)
rename owned_vehicle_all_per_person15 owned_vehicle_all_per_household15
//tabulate owned_vehicle_all_per_household15, missing
histogram owned_vehicle_all_per_household15 if owned_vehicle_all_per_household15 > 0, freq
//piechart owned_vehicle_all_per_household15 if owned_vehicle_all_per_household15 > 0
piechart owned_vehicle_all_per_household15
merge owned_vehicle_all_per_household15 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners15


// Step: Sum the number of green vehicles per person in 2015.
use vehicle_DS_passenger_green_owned15
collapse(sum) vehicle_green15, by(vehicle_individual_id15)
rename vehicle_green15 owned_vehicle_green_per_person15
//tabulate owned_vehicle_green_per_person15, missing
merge owned_vehicle_green_per_person15 into household_DS_green_owners15 on household_id15
delete-dataset vehicle_DS_passenger_green_owned15

// Then, aggregate the number of green vehicles per household in 2015.
use household_DS_green_owners15
collapse(sum) owned_vehicle_green_per_person15, by(household_id15)
rename owned_vehicle_green_per_person15 owned_vehicle_green_per_household15
//tabulate owned_vehicle_green_per_household15, missing
histogram owned_vehicle_green_per_household15 if owned_vehicle_green_per_household15 > 0, freq
piechart owned_vehicle_green_per_household15 if owned_vehicle_green_per_household15 > 0
piechart owned_vehicle_green_per_household15
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household15 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners15


// Step: Sum the number of gray vehicles per person in 2015.
use vehicle_DS_passenger_gray_owned15
collapse(sum) vehicle_gray15, by(vehicle_individual_id15)
rename vehicle_gray15 owned_vehicle_gray_per_person15
//tabulate owned_vehicle_gray_per_person15, missing
merge owned_vehicle_gray_per_person15 into household_DS_gray_owners15 on household_id15
delete-dataset vehicle_DS_passenger_gray_owned15

// Then, aggregate the number of gray vehicles per household in 2015.
use household_DS_gray_owners15
collapse(sum) owned_vehicle_gray_per_person15, by(household_id15)
rename owned_vehicle_gray_per_person15 owned_vehicle_gray_per_household15
merge owned_vehicle_gray_per_household15 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners15


// Vehicles 2016
create-dataset vehicle_DS_passenger16
import db/KJORETOY_KJT_GRUP 2016-12-31 as vehicle_group_code16
keep if vehicle_group_code16 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2016-12-31 as vehicle_individual_id16

import db/KJORETOY_DRIVSTOFF_OMK 2016-12-31 as fuel_type16
assign-labels fuel_type16 fuel_type_txt

tabulate fuel_type16, missing
piechart fuel_type16

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green16 = fuel_type16
replace emission_categories_gray_green16 = 'Green' if emission_categories_gray_green16 == '05'
replace emission_categories_gray_green16 = 'Gray' if emission_categories_gray_green16 == '01' | emission_categories_gray_green16 == '02' | emission_categories_gray_green16 == '03' | emission_categories_gray_green16 == '04' | emission_categories_gray_green16 == '07' | emission_categories_gray_green16 == '08' | emission_categories_gray_green16 == '09' | emission_categories_gray_green16 == '10' | emission_categories_gray_green16 == '11' | emission_categories_gray_green16 == '12' | emission_categories_gray_green16 == '13' | emission_categories_gray_green16 == '15'
drop if emission_categories_gray_green16 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green16 = 0
replace vehicle_green16 = 1 if emission_categories_gray_green16 == 'Green'
generate vehicle_gray16 = 0
replace vehicle_gray16 = 1 if emission_categories_gray_green16 == 'Gray'

piechart emission_categories_gray_green16
tabulate emission_categories_gray_green16, missing
//tabulate vehicle_green16 vehicle_gray16, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2016.
clone-dataset vehicle_DS_passenger16 vehicle_DS_passenger_green_owned16
clone-dataset vehicle_DS_passenger16 vehicle_DS_passenger_gray_owned16


// Make temporary datasets to calculate categories of passenger vehicles per household in 2016.
clone-dataset person_DS_Y16 household_DS_all_fuel_owners16
clone-dataset person_DS_Y16 household_DS_green_owners16
clone-dataset person_DS_Y16 household_DS_gray_owners16


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2016.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2016.
collapse(count) fuel_type16, by(vehicle_individual_id16)
rename fuel_type16 owned_vehicle_all_per_person16
//tabulate owned_vehicle_all_per_person16, missing
merge owned_vehicle_all_per_person16 into household_DS_all_fuel_owners16 on household_id16
delete-dataset vehicle_DS_passenger16

// Then, aggregate the number of all categories of passenger vehicles per household in 2016.
use household_DS_all_fuel_owners16
collapse(sum) owned_vehicle_all_per_person16, by(household_id16)
rename owned_vehicle_all_per_person16 owned_vehicle_all_per_household16
//tabulate owned_vehicle_all_per_household16, missing
histogram owned_vehicle_all_per_household16 if owned_vehicle_all_per_household16 > 0, freq
//piechart owned_vehicle_all_per_household16 if owned_vehicle_all_per_household16 > 0
piechart owned_vehicle_all_per_household16
merge owned_vehicle_all_per_household16 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners16


// Step: Sum the number of green vehicles per person in 2016.
use vehicle_DS_passenger_green_owned16
collapse(sum) vehicle_green16, by(vehicle_individual_id16)
rename vehicle_green16 owned_vehicle_green_per_person16
//tabulate owned_vehicle_green_per_person16, missing
merge owned_vehicle_green_per_person16 into household_DS_green_owners16 on household_id16
delete-dataset vehicle_DS_passenger_green_owned16

// Then, aggregate the number of green vehicles per household in 2016.
use household_DS_green_owners16
collapse(sum) owned_vehicle_green_per_person16, by(household_id16)
rename owned_vehicle_green_per_person16 owned_vehicle_green_per_household16
//tabulate owned_vehicle_green_per_household16, missing
histogram owned_vehicle_green_per_household16 if owned_vehicle_green_per_household16 > 0, freq
piechart owned_vehicle_green_per_household16 if owned_vehicle_green_per_household16 > 0
piechart owned_vehicle_green_per_household16
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household16 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners16


// Step: Sum the number of gray vehicles per person in 2016.
use vehicle_DS_passenger_gray_owned16
collapse(sum) vehicle_gray16, by(vehicle_individual_id16)
rename vehicle_gray16 owned_vehicle_gray_per_person16
//tabulate owned_vehicle_gray_per_person16, missing
merge owned_vehicle_gray_per_person16 into household_DS_gray_owners16 on household_id16
delete-dataset vehicle_DS_passenger_gray_owned16

// Then, aggregate the number of gray vehicles per household in 2016.
use household_DS_gray_owners16
collapse(sum) owned_vehicle_gray_per_person16, by(household_id16)
rename owned_vehicle_gray_per_person16 owned_vehicle_gray_per_household16
merge owned_vehicle_gray_per_household16 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners16


// Vehicles 2017
create-dataset vehicle_DS_passenger17
import db/KJORETOY_KJT_GRUP 2017-12-31 as vehicle_group_code17
keep if vehicle_group_code17 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2017-12-31 as vehicle_individual_id17

import db/KJORETOY_DRIVSTOFF_OMK 2017-12-31 as fuel_type17
assign-labels fuel_type17 fuel_type_txt

tabulate fuel_type17, missing
piechart fuel_type17

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green17 = fuel_type17
replace emission_categories_gray_green17 = 'Green' if emission_categories_gray_green17 == '05'
replace emission_categories_gray_green17 = 'Gray' if emission_categories_gray_green17 == '01' | emission_categories_gray_green17 == '02' | emission_categories_gray_green17 == '03' | emission_categories_gray_green17 == '04' | emission_categories_gray_green17 == '07' | emission_categories_gray_green17 == '08' | emission_categories_gray_green17 == '09' | emission_categories_gray_green17 == '10' | emission_categories_gray_green17 == '11' | emission_categories_gray_green17 == '12' | emission_categories_gray_green17 == '13' | emission_categories_gray_green17 == '15'
drop if emission_categories_gray_green17 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green17 = 0
replace vehicle_green17 = 1 if emission_categories_gray_green17 == 'Green'
generate vehicle_gray17 = 0
replace vehicle_gray17 = 1 if emission_categories_gray_green17 == 'Gray'

piechart emission_categories_gray_green17
tabulate emission_categories_gray_green17, missing
//tabulate vehicle_green17 vehicle_gray17, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2017.
clone-dataset vehicle_DS_passenger17 vehicle_DS_passenger_green_owned17
clone-dataset vehicle_DS_passenger17 vehicle_DS_passenger_gray_owned17


// Make temporary datasets to calculate categories of passenger vehicles per household in 2017.
clone-dataset person_DS_Y17 household_DS_all_fuel_owners17
clone-dataset person_DS_Y17 household_DS_green_owners17
clone-dataset person_DS_Y17 household_DS_gray_owners17


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2017.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2017.
collapse(count) fuel_type17, by(vehicle_individual_id17)
rename fuel_type17 owned_vehicle_all_per_person17
//tabulate owned_vehicle_all_per_person17, missing
merge owned_vehicle_all_per_person17 into household_DS_all_fuel_owners17 on household_id17
delete-dataset vehicle_DS_passenger17

// Then, aggregate the number of all categories of passenger vehicles per household in 2017.
use household_DS_all_fuel_owners17
collapse(sum) owned_vehicle_all_per_person17, by(household_id17)
rename owned_vehicle_all_per_person17 owned_vehicle_all_per_household17
//tabulate owned_vehicle_all_per_household17, missing
histogram owned_vehicle_all_per_household17 if owned_vehicle_all_per_household17 > 0, freq
//piechart owned_vehicle_all_per_household17 if owned_vehicle_all_per_household17 > 0
piechart owned_vehicle_all_per_household17
merge owned_vehicle_all_per_household17 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners17


// Step: Sum the number of green vehicles per person in 2017.
use vehicle_DS_passenger_green_owned17
collapse(sum) vehicle_green17, by(vehicle_individual_id17)
rename vehicle_green17 owned_vehicle_green_per_person17
//tabulate owned_vehicle_green_per_person17, missing
merge owned_vehicle_green_per_person17 into household_DS_green_owners17 on household_id17
delete-dataset vehicle_DS_passenger_green_owned17

// Then, aggregate the number of green vehicles per household in 2017.
use household_DS_green_owners17
collapse(sum) owned_vehicle_green_per_person17, by(household_id17)
rename owned_vehicle_green_per_person17 owned_vehicle_green_per_household17
//tabulate owned_vehicle_green_per_household17, missing
histogram owned_vehicle_green_per_household17 if owned_vehicle_green_per_household17 > 0, freq
piechart owned_vehicle_green_per_household17 if owned_vehicle_green_per_household17 > 0
piechart owned_vehicle_green_per_household17
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household17 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners17


// Step: Sum the number of gray vehicles per person in 2017.
use vehicle_DS_passenger_gray_owned17
collapse(sum) vehicle_gray17, by(vehicle_individual_id17)
rename vehicle_gray17 owned_vehicle_gray_per_person17
//tabulate owned_vehicle_gray_per_person17, missing
merge owned_vehicle_gray_per_person17 into household_DS_gray_owners17 on household_id17
delete-dataset vehicle_DS_passenger_gray_owned17

// Then, aggregate the number of gray vehicles per household in 2017.
use household_DS_gray_owners17
collapse(sum) owned_vehicle_gray_per_person17, by(household_id17)
rename owned_vehicle_gray_per_person17 owned_vehicle_gray_per_household17
merge owned_vehicle_gray_per_household17 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners17


// Vehicles 2018
create-dataset vehicle_DS_passenger18
import db/KJORETOY_KJT_GRUP 2018-12-31 as vehicle_group_code18
keep if vehicle_group_code18 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2018-12-31 as vehicle_individual_id18

import db/KJORETOY_DRIVSTOFF_OMK 2018-12-31 as fuel_type18
assign-labels fuel_type18 fuel_type_txt

tabulate fuel_type18, missing
piechart fuel_type18

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green18 = fuel_type18
replace emission_categories_gray_green18 = 'Green' if emission_categories_gray_green18 == '05'
replace emission_categories_gray_green18 = 'Gray' if emission_categories_gray_green18 == '01' | emission_categories_gray_green18 == '02' | emission_categories_gray_green18 == '03' | emission_categories_gray_green18 == '04' | emission_categories_gray_green18 == '07' | emission_categories_gray_green18 == '08' | emission_categories_gray_green18 == '09' | emission_categories_gray_green18 == '10' | emission_categories_gray_green18 == '11' | emission_categories_gray_green18 == '12' | emission_categories_gray_green18 == '13' | emission_categories_gray_green18 == '15'
drop if emission_categories_gray_green18 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green18 = 0
replace vehicle_green18 = 1 if emission_categories_gray_green18 == 'Green'
generate vehicle_gray18 = 0
replace vehicle_gray18 = 1 if emission_categories_gray_green18 == 'Gray'

piechart emission_categories_gray_green18
tabulate emission_categories_gray_green18, missing
//tabulate vehicle_green18 vehicle_gray18, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2018.
clone-dataset vehicle_DS_passenger18 vehicle_DS_passenger_green_owned18
clone-dataset vehicle_DS_passenger18 vehicle_DS_passenger_gray_owned18


// Make temporary datasets to calculate categories of passenger vehicles per household in 2018.
clone-dataset person_DS_Y18 household_DS_all_fuel_owners18
clone-dataset person_DS_Y18 household_DS_green_owners18
clone-dataset person_DS_Y18 household_DS_gray_owners18


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2018.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2018.
collapse(count) fuel_type18, by(vehicle_individual_id18)
rename fuel_type18 owned_vehicle_all_per_person18
//tabulate owned_vehicle_all_per_person18, missing
merge owned_vehicle_all_per_person18 into household_DS_all_fuel_owners18 on household_id18
delete-dataset vehicle_DS_passenger18

// Then, aggregate the number of all categories of passenger vehicles per household in 2018.
use household_DS_all_fuel_owners18
collapse(sum) owned_vehicle_all_per_person18, by(household_id18)
rename owned_vehicle_all_per_person18 owned_vehicle_all_per_household18
//tabulate owned_vehicle_all_per_household18, missing
histogram owned_vehicle_all_per_household18 if owned_vehicle_all_per_household18 > 0, freq
//piechart owned_vehicle_all_per_household18 if owned_vehicle_all_per_household18 > 0
piechart owned_vehicle_all_per_household18
merge owned_vehicle_all_per_household18 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners18


// Step: Sum the number of green vehicles per person in 2018.
use vehicle_DS_passenger_green_owned18
collapse(sum) vehicle_green18, by(vehicle_individual_id18)
rename vehicle_green18 owned_vehicle_green_per_person18
//tabulate owned_vehicle_green_per_person18, missing
merge owned_vehicle_green_per_person18 into household_DS_green_owners18 on household_id18
delete-dataset vehicle_DS_passenger_green_owned18

// Then, aggregate the number of green vehicles per household in 2018.
use household_DS_green_owners18
collapse(sum) owned_vehicle_green_per_person18, by(household_id18)
rename owned_vehicle_green_per_person18 owned_vehicle_green_per_household18
//tabulate owned_vehicle_green_per_household18, missing
histogram owned_vehicle_green_per_household18 if owned_vehicle_green_per_household18 > 0, freq
piechart owned_vehicle_green_per_household18 if owned_vehicle_green_per_household18 > 0
piechart owned_vehicle_green_per_household18
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household18 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners18


// Step: Sum the number of gray vehicles per person in 2018.
use vehicle_DS_passenger_gray_owned18
collapse(sum) vehicle_gray18, by(vehicle_individual_id18)
rename vehicle_gray18 owned_vehicle_gray_per_person18
//tabulate owned_vehicle_gray_per_person18, missing
merge owned_vehicle_gray_per_person18 into household_DS_gray_owners18 on household_id18
delete-dataset vehicle_DS_passenger_gray_owned18

// Then, aggregate the number of gray vehicles per household in 2018.
use household_DS_gray_owners18
collapse(sum) owned_vehicle_gray_per_person18, by(household_id18)
rename owned_vehicle_gray_per_person18 owned_vehicle_gray_per_household18
merge owned_vehicle_gray_per_household18 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners18


// Vehicles 2019
create-dataset vehicle_DS_passenger19
import db/KJORETOY_KJT_GRUP 2019-12-31 as vehicle_group_code19
keep if vehicle_group_code19 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2019-12-31 as vehicle_individual_id19

import db/KJORETOY_DRIVSTOFF_OMK 2019-12-31 as fuel_type19
assign-labels fuel_type19 fuel_type_txt

tabulate fuel_type19, missing
piechart fuel_type19

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green19 = fuel_type19
replace emission_categories_gray_green19 = 'Green' if emission_categories_gray_green19 == '05'
replace emission_categories_gray_green19 = 'Gray' if emission_categories_gray_green19 == '01' | emission_categories_gray_green19 == '02' | emission_categories_gray_green19 == '03' | emission_categories_gray_green19 == '04' | emission_categories_gray_green19 == '07' | emission_categories_gray_green19 == '08' | emission_categories_gray_green19 == '09' | emission_categories_gray_green19 == '10' | emission_categories_gray_green19 == '11' | emission_categories_gray_green19 == '12' | emission_categories_gray_green19 == '13' | emission_categories_gray_green19 == '15'
drop if emission_categories_gray_green19 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green19 = 0
replace vehicle_green19 = 1 if emission_categories_gray_green19 == 'Green'
generate vehicle_gray19 = 0
replace vehicle_gray19 = 1 if emission_categories_gray_green19 == 'Gray'

piechart emission_categories_gray_green19
tabulate emission_categories_gray_green19, missing
//tabulate vehicle_green19 vehicle_gray19, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2019.
clone-dataset vehicle_DS_passenger19 vehicle_DS_passenger_green_owned19
clone-dataset vehicle_DS_passenger19 vehicle_DS_passenger_gray_owned19


// Make temporary datasets to calculate categories of passenger vehicles per household in 2019.
clone-dataset person_DS_Y19 household_DS_all_fuel_owners19
clone-dataset person_DS_Y19 household_DS_green_owners19
clone-dataset person_DS_Y19 household_DS_gray_owners19


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2019.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2019.
collapse(count) fuel_type19, by(vehicle_individual_id19)
rename fuel_type19 owned_vehicle_all_per_person19
//tabulate owned_vehicle_all_per_person19, missing
merge owned_vehicle_all_per_person19 into household_DS_all_fuel_owners19 on household_id19
delete-dataset vehicle_DS_passenger19

// Then, aggregate the number of all categories of passenger vehicles per household in 2019.
use household_DS_all_fuel_owners19
collapse(sum) owned_vehicle_all_per_person19, by(household_id19)
rename owned_vehicle_all_per_person19 owned_vehicle_all_per_household19
//tabulate owned_vehicle_all_per_household19, missing
histogram owned_vehicle_all_per_household19 if owned_vehicle_all_per_household19 > 0, freq
//piechart owned_vehicle_all_per_household19 if owned_vehicle_all_per_household19 > 0
piechart owned_vehicle_all_per_household19
merge owned_vehicle_all_per_household19 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners19


// Step: Sum the number of green vehicles per person in 2019.
use vehicle_DS_passenger_green_owned19
collapse(sum) vehicle_green19, by(vehicle_individual_id19)
rename vehicle_green19 owned_vehicle_green_per_person19
//tabulate owned_vehicle_green_per_person19, missing
merge owned_vehicle_green_per_person19 into household_DS_green_owners19 on household_id19
delete-dataset vehicle_DS_passenger_green_owned19

// Then, aggregate the number of green vehicles per household in 2019.
use household_DS_green_owners19
collapse(sum) owned_vehicle_green_per_person19, by(household_id19)
rename owned_vehicle_green_per_person19 owned_vehicle_green_per_household19
//tabulate owned_vehicle_green_per_household19, missing
histogram owned_vehicle_green_per_household19 if owned_vehicle_green_per_household19 > 0, freq
piechart owned_vehicle_green_per_household19 if owned_vehicle_green_per_household19 > 0
piechart owned_vehicle_green_per_household19
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household19 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners19


// Step: Sum the number of gray vehicles per person in 2019.
use vehicle_DS_passenger_gray_owned19
collapse(sum) vehicle_gray19, by(vehicle_individual_id19)
rename vehicle_gray19 owned_vehicle_gray_per_person19
//tabulate owned_vehicle_gray_per_person19, missing
merge owned_vehicle_gray_per_person19 into household_DS_gray_owners19 on household_id19
delete-dataset vehicle_DS_passenger_gray_owned19

// Then, aggregate the number of gray vehicles per household in 2019.
use household_DS_gray_owners19
collapse(sum) owned_vehicle_gray_per_person19, by(household_id19)
rename owned_vehicle_gray_per_person19 owned_vehicle_gray_per_household19
merge owned_vehicle_gray_per_household19 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners19


// Vehicles 2020
create-dataset vehicle_DS_passenger20
import db/KJORETOY_KJT_GRUP 2020-12-31 as vehicle_group_code20
keep if vehicle_group_code20 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2020-12-31 as vehicle_individual_id20

import db/KJORETOY_DRIVSTOFF_OMK 2020-12-31 as fuel_type20
assign-labels fuel_type20 fuel_type_txt

tabulate fuel_type20, missing
piechart fuel_type20

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green20 = fuel_type20
replace emission_categories_gray_green20 = 'Green' if emission_categories_gray_green20 == '05'
replace emission_categories_gray_green20 = 'Gray' if emission_categories_gray_green20 == '01' | emission_categories_gray_green20 == '02' | emission_categories_gray_green20 == '03' | emission_categories_gray_green20 == '04' | emission_categories_gray_green20 == '07' | emission_categories_gray_green20 == '08' | emission_categories_gray_green20 == '09' | emission_categories_gray_green20 == '10' | emission_categories_gray_green20 == '11' | emission_categories_gray_green20 == '12' | emission_categories_gray_green20 == '13' | emission_categories_gray_green20 == '15'
drop if emission_categories_gray_green20 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green20 = 0
replace vehicle_green20 = 1 if emission_categories_gray_green20 == 'Green'
generate vehicle_gray20 = 0
replace vehicle_gray20 = 1 if emission_categories_gray_green20 == 'Gray'

piechart emission_categories_gray_green20
tabulate emission_categories_gray_green20, missing
//tabulate vehicle_green20 vehicle_gray20, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2020.
clone-dataset vehicle_DS_passenger20 vehicle_DS_passenger_green_owned20
clone-dataset vehicle_DS_passenger20 vehicle_DS_passenger_gray_owned20


// Make temporary datasets to calculate categories of passenger vehicles per household in 2020.
clone-dataset person_DS_Y20 household_DS_all_fuel_owners20
clone-dataset person_DS_Y20 household_DS_green_owners20
clone-dataset person_DS_Y20 household_DS_gray_owners20


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2020.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2020.
collapse(count) fuel_type20, by(vehicle_individual_id20)
rename fuel_type20 owned_vehicle_all_per_person20
//tabulate owned_vehicle_all_per_person20, missing
merge owned_vehicle_all_per_person20 into household_DS_all_fuel_owners20 on household_id20
delete-dataset vehicle_DS_passenger20

// Then, aggregate the number of all categories of passenger vehicles per household in 2020.
use household_DS_all_fuel_owners20
collapse(sum) owned_vehicle_all_per_person20, by(household_id20)
rename owned_vehicle_all_per_person20 owned_vehicle_all_per_household20
//tabulate owned_vehicle_all_per_household20, missing
histogram owned_vehicle_all_per_household20 if owned_vehicle_all_per_household20 > 0, freq
//piechart owned_vehicle_all_per_household20 if owned_vehicle_all_per_household20 > 0
piechart owned_vehicle_all_per_household20
merge owned_vehicle_all_per_household20 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners20


// Step: Sum the number of green vehicles per person in 2020.
use vehicle_DS_passenger_green_owned20
collapse(sum) vehicle_green20, by(vehicle_individual_id20)
rename vehicle_green20 owned_vehicle_green_per_person20
//tabulate owned_vehicle_green_per_person20, missing
merge owned_vehicle_green_per_person20 into household_DS_green_owners20 on household_id20
delete-dataset vehicle_DS_passenger_green_owned20

// Then, aggregate the number of green vehicles per household in 2020.
use household_DS_green_owners20
collapse(sum) owned_vehicle_green_per_person20, by(household_id20)
rename owned_vehicle_green_per_person20 owned_vehicle_green_per_household20
//tabulate owned_vehicle_green_per_household20, missing
histogram owned_vehicle_green_per_household20 if owned_vehicle_green_per_household20 > 0, freq
piechart owned_vehicle_green_per_household20 if owned_vehicle_green_per_household20 > 0
piechart owned_vehicle_green_per_household20
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household20 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners20


// Step: Sum the number of gray vehicles per person in 2020.
use vehicle_DS_passenger_gray_owned20
collapse(sum) vehicle_gray20, by(vehicle_individual_id20)
rename vehicle_gray20 owned_vehicle_gray_per_person20
//tabulate owned_vehicle_gray_per_person20, missing
merge owned_vehicle_gray_per_person20 into household_DS_gray_owners20 on household_id20
delete-dataset vehicle_DS_passenger_gray_owned20

// Then, aggregate the number of gray vehicles per household in 2020.
use household_DS_gray_owners20
collapse(sum) owned_vehicle_gray_per_person20, by(household_id20)
rename owned_vehicle_gray_per_person20 owned_vehicle_gray_per_household20
merge owned_vehicle_gray_per_household20 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners20


// Vehicles 2021
create-dataset vehicle_DS_passenger21
import db/KJORETOY_KJT_GRUP 2021-12-31 as vehicle_group_code21
keep if vehicle_group_code21 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2021-12-31 as vehicle_individual_id21

import db/KJORETOY_DRIVSTOFF_OMK 2021-12-31 as fuel_type21
assign-labels fuel_type21 fuel_type_txt

tabulate fuel_type21, missing
piechart fuel_type21

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green21 = fuel_type21
replace emission_categories_gray_green21 = 'Green' if emission_categories_gray_green21 == '05'
replace emission_categories_gray_green21 = 'Gray' if emission_categories_gray_green21 == '01' | emission_categories_gray_green21 == '02' | emission_categories_gray_green21 == '03' | emission_categories_gray_green21 == '04' | emission_categories_gray_green21 == '07' | emission_categories_gray_green21 == '08' | emission_categories_gray_green21 == '09' | emission_categories_gray_green21 == '10' | emission_categories_gray_green21 == '11' | emission_categories_gray_green21 == '12' | emission_categories_gray_green21 == '13' | emission_categories_gray_green21 == '15'
drop if emission_categories_gray_green21 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green21 = 0
replace vehicle_green21 = 1 if emission_categories_gray_green21 == 'Green'
generate vehicle_gray21 = 0
replace vehicle_gray21 = 1 if emission_categories_gray_green21 == 'Gray'

piechart emission_categories_gray_green21
tabulate emission_categories_gray_green21, missing
//tabulate vehicle_green21 vehicle_gray21, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2021.
clone-dataset vehicle_DS_passenger21 vehicle_DS_passenger_green_owned21
clone-dataset vehicle_DS_passenger21 vehicle_DS_passenger_gray_owned21


// Make temporary datasets to calculate categories of passenger vehicles per household in 2021.
clone-dataset person_DS_Y21 household_DS_all_fuel_owners21
clone-dataset person_DS_Y21 household_DS_green_owners21
clone-dataset person_DS_Y21 household_DS_gray_owners21


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2021.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2021.
collapse(count) fuel_type21, by(vehicle_individual_id21)
rename fuel_type21 owned_vehicle_all_per_person21
//tabulate owned_vehicle_all_per_person21, missing
merge owned_vehicle_all_per_person21 into household_DS_all_fuel_owners21 on household_id21
delete-dataset vehicle_DS_passenger21

// Then, aggregate the number of all categories of passenger vehicles per household in 2021.
use household_DS_all_fuel_owners21
collapse(sum) owned_vehicle_all_per_person21, by(household_id21)
rename owned_vehicle_all_per_person21 owned_vehicle_all_per_household21
//tabulate owned_vehicle_all_per_household21, missing
histogram owned_vehicle_all_per_household21 if owned_vehicle_all_per_household21 > 0, freq
//piechart owned_vehicle_all_per_household21 if owned_vehicle_all_per_household21 > 0
piechart owned_vehicle_all_per_household21
merge owned_vehicle_all_per_household21 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners21


// Step: Sum the number of green vehicles per person in 2021.
use vehicle_DS_passenger_green_owned21
collapse(sum) vehicle_green21, by(vehicle_individual_id21)
rename vehicle_green21 owned_vehicle_green_per_person21
//tabulate owned_vehicle_green_per_person21, missing
merge owned_vehicle_green_per_person21 into household_DS_green_owners21 on household_id21
delete-dataset vehicle_DS_passenger_green_owned21

// Then, aggregate the number of green vehicles per household in 2021.
use household_DS_green_owners21
collapse(sum) owned_vehicle_green_per_person21, by(household_id21)
rename owned_vehicle_green_per_person21 owned_vehicle_green_per_household21
//tabulate owned_vehicle_green_per_household21, missing
histogram owned_vehicle_green_per_household21 if owned_vehicle_green_per_household21 > 0, freq
piechart owned_vehicle_green_per_household21 if owned_vehicle_green_per_household21 > 0
piechart owned_vehicle_green_per_household21
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household21 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners21


// Step: Sum the number of gray vehicles per person in 2021.
use vehicle_DS_passenger_gray_owned21
collapse(sum) vehicle_gray21, by(vehicle_individual_id21)
rename vehicle_gray21 owned_vehicle_gray_per_person21
//tabulate owned_vehicle_gray_per_person21, missing
merge owned_vehicle_gray_per_person21 into household_DS_gray_owners21 on household_id21
delete-dataset vehicle_DS_passenger_gray_owned21

// Then, aggregate the number of gray vehicles per household in 2021.
use household_DS_gray_owners21
collapse(sum) owned_vehicle_gray_per_person21, by(household_id21)
rename owned_vehicle_gray_per_person21 owned_vehicle_gray_per_household21
merge owned_vehicle_gray_per_household21 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners21


// Vehicles 2022
create-dataset vehicle_DS_passenger22
import db/KJORETOY_KJT_GRUP 2022-12-31 as vehicle_group_code22
keep if vehicle_group_code22 == '101' // keep only passenger cars (also no ambulances)

// To find individuals owning passenger vehicles in Norway, vehicles are linked to people using KJORETOY_KJORETOYID_FNR.
import db/KJORETOY_KJORETOYID_FNR 2022-12-31 as vehicle_individual_id22

import db/KJORETOY_DRIVSTOFF_OMK 2022-12-31 as fuel_type22
assign-labels fuel_type22 fuel_type_txt

tabulate fuel_type22, missing
piechart fuel_type22

// Assign vehicles to emission categories (approach Gray vs. Green)
generate emission_categories_gray_green22 = fuel_type22
replace emission_categories_gray_green22 = 'Green' if emission_categories_gray_green22 == '05'
replace emission_categories_gray_green22 = 'Gray' if emission_categories_gray_green22 == '01' | emission_categories_gray_green22 == '02' | emission_categories_gray_green22 == '03' | emission_categories_gray_green22 == '04' | emission_categories_gray_green22 == '07' | emission_categories_gray_green22 == '08' | emission_categories_gray_green22 == '09' | emission_categories_gray_green22 == '10' | emission_categories_gray_green22 == '11' | emission_categories_gray_green22 == '12' | emission_categories_gray_green22 == '13' | emission_categories_gray_green22 == '15'
drop if emission_categories_gray_green22 == '06'

// Tag vehicles by the numerical value of 0 (false) and 1 (true) within each emission category.
generate vehicle_green22 = 0
replace vehicle_green22 = 1 if emission_categories_gray_green22 == 'Green'
generate vehicle_gray22 = 0
replace vehicle_gray22 = 1 if emission_categories_gray_green22 == 'Gray'

piechart emission_categories_gray_green22
tabulate emission_categories_gray_green22, missing
//tabulate vehicle_green22 vehicle_gray22, missing


// Make temporary clones of vehicle_DS_passenger for further analysis of the year 2022.
clone-dataset vehicle_DS_passenger22 vehicle_DS_passenger_green_owned22
clone-dataset vehicle_DS_passenger22 vehicle_DS_passenger_gray_owned22


// Make temporary datasets to calculate categories of passenger vehicles per household in 2022.
clone-dataset person_DS_Y22 household_DS_all_fuel_owners22
clone-dataset person_DS_Y22 household_DS_green_owners22
clone-dataset person_DS_Y22 household_DS_gray_owners22


textblock
::::: Owned private vehicles in Norway by Fuel Type in 2022.
endblock


// Step: At this step, passenger vehicles per person are calculated – irrespective of fuel type in 2022.
collapse(count) fuel_type22, by(vehicle_individual_id22)
rename fuel_type22 owned_vehicle_all_per_person22
//tabulate owned_vehicle_all_per_person22, missing
merge owned_vehicle_all_per_person22 into household_DS_all_fuel_owners22 on household_id22
delete-dataset vehicle_DS_passenger22

// Then, aggregate the number of all categories of passenger vehicles per household in 2022.
use household_DS_all_fuel_owners22
collapse(sum) owned_vehicle_all_per_person22, by(household_id22)
rename owned_vehicle_all_per_person22 owned_vehicle_all_per_household22
//tabulate owned_vehicle_all_per_household22, missing
histogram owned_vehicle_all_per_household22 if owned_vehicle_all_per_household22 > 0, freq
//piechart owned_vehicle_all_per_household22 if owned_vehicle_all_per_household22 > 0
piechart owned_vehicle_all_per_household22
merge owned_vehicle_all_per_household22 into household_DS_all on PERSONID_1
delete-dataset household_DS_all_fuel_owners22


// Step: Sum the number of green vehicles per person in 2022.
use vehicle_DS_passenger_green_owned22
collapse(sum) vehicle_green22, by(vehicle_individual_id22)
rename vehicle_green22 owned_vehicle_green_per_person22
//tabulate owned_vehicle_green_per_person22, missing
merge owned_vehicle_green_per_person22 into household_DS_green_owners22 on household_id22
delete-dataset vehicle_DS_passenger_green_owned22

// Then, aggregate the number of green vehicles per household in 2022.
use household_DS_green_owners22
collapse(sum) owned_vehicle_green_per_person22, by(household_id22)
rename owned_vehicle_green_per_person22 owned_vehicle_green_per_household22
//tabulate owned_vehicle_green_per_household22, missing
histogram owned_vehicle_green_per_household22 if owned_vehicle_green_per_household22 > 0, freq
piechart owned_vehicle_green_per_household22 if owned_vehicle_green_per_household22 > 0
piechart owned_vehicle_green_per_household22
// Merge green vehicle ownership data into the main dataset
merge owned_vehicle_green_per_household22 into household_DS_all on PERSONID_1
delete-dataset household_DS_green_owners22


// Step: Sum the number of gray vehicles per person in 2022.
use vehicle_DS_passenger_gray_owned22
collapse(sum) vehicle_gray22, by(vehicle_individual_id22)
rename vehicle_gray22 owned_vehicle_gray_per_person22
//tabulate owned_vehicle_gray_per_person22, missing
merge owned_vehicle_gray_per_person22 into household_DS_gray_owners22 on household_id22
delete-dataset vehicle_DS_passenger_gray_owned22

// Then, aggregate the number of gray vehicles per household in 2022.
use household_DS_gray_owners22
collapse(sum) owned_vehicle_gray_per_person22, by(household_id22)
rename owned_vehicle_gray_per_person22 owned_vehicle_gray_per_household22
merge owned_vehicle_gray_per_household22 into household_DS_all on PERSONID_1
delete-dataset household_DS_gray_owners22


//Streamlining of the dataset: Keep only those households who at least once owned one vehicle over the time period...
use household_DS_all
keep if owned_vehicle_all_per_household05 >= 1 | owned_vehicle_all_per_household06 >= 1 | owned_vehicle_all_per_household07 >= 1 | owned_vehicle_all_per_household08 >= 1 | owned_vehicle_all_per_household09 >= 1 | owned_vehicle_all_per_household10 >= 1 | owned_vehicle_all_per_household11 >= 1 | owned_vehicle_all_per_household12 >= 1 | owned_vehicle_all_per_household13 >= 1 | owned_vehicle_all_per_household14 >= 1 | owned_vehicle_all_per_household15 >= 1 | owned_vehicle_all_per_household16 >= 1 | owned_vehicle_all_per_household17 >= 1 | owned_vehicle_all_per_household18 >= 1 | owned_vehicle_all_per_household19 >= 1 | owned_vehicle_all_per_household20 >= 1 | owned_vehicle_all_per_household21 >= 1 | owned_vehicle_all_per_household22 >= 1


// From this step, we only call information of those households with a record of vehicle ownership


// 2005 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y05 immigration_category_DS05
use immigration_category_DS05
import db/BEFOLKNING_INVKAT as immigration_category05 // This variable shows various combinations of own or parents' country of birth.
define-labels immigration_category_txt 'A' 'Born in Norway with two Norwegian-born parents' 'B' 'Immigrants' 'C' 'Norwegian-born with immigrant parents' 'E' 'Foreign-born with one Norwegian-born parent' 'F' 'Norwegian-born with one foreign-born parent' 'G' 'Foreign-born with two Norwegian-born parents'
assign-labels immigration_category05 immigration_category_txt
piechart immigration_category05
tabulate immigration_category05, missing

generate person_background05 = 0
replace person_background05 = 1 if immigration_category05 == 'B' | immigration_category05 == 'C' | immigration_category05 == 'E' | immigration_category05 == 'F' | sysmiss(immigration_category05)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background05

collapse(sum) person_background05, by(household_id05)
generate household_background05 = 0
replace household_background05 = 1 if person_background05 >= 1
piechart household_background05
//summarize household_background05
merge household_background05 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS05


// 2006 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y06 immigration_category_DS06
use immigration_category_DS06
import db/BEFOLKNING_INVKAT as immigration_category06 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category06 immigration_category_txt
piechart immigration_category06
tabulate immigration_category06, missing

generate person_background06 = 0
replace person_background06 = 1 if immigration_category06 == 'B' | immigration_category06 == 'C' | immigration_category06 == 'E' | immigration_category06 == 'F' | sysmiss(immigration_category06)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background06

collapse(sum) person_background06, by(household_id06)
generate household_background06 = 0
replace household_background06 = 1 if person_background06 >= 1
piechart household_background06
//summarize household_background06
merge household_background06 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS06


// 2007 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y07 immigration_category_DS07
use immigration_category_DS07
import db/BEFOLKNING_INVKAT as immigration_category07 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category07 immigration_category_txt
piechart immigration_category07
tabulate immigration_category07, missing

generate person_background07 = 0
replace person_background07 = 1 if immigration_category07 == 'B' | immigration_category07 == 'C' | immigration_category07 == 'E' | immigration_category07 == 'F' | sysmiss(immigration_category07)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background07

collapse(sum) person_background07, by(household_id07)
generate household_background07 = 0
replace household_background07 = 1 if person_background07 >= 1
piechart household_background07
//summarize household_background07
merge household_background07 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS07


// 2008 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y08 immigration_category_DS08
use immigration_category_DS08
import db/BEFOLKNING_INVKAT as immigration_category08 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category08 immigration_category_txt
piechart immigration_category08
tabulate immigration_category08, missing

generate person_background08 = 0
replace person_background08 = 1 if immigration_category08 == 'B' | immigration_category08 == 'C' | immigration_category08 == 'E' | immigration_category08 == 'F' | sysmiss(immigration_category08)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background08

collapse(sum) person_background08, by(household_id08)
generate household_background08 = 0
replace household_background08 = 1 if person_background08 >= 1
piechart household_background08
//summarize household_background08
merge household_background08 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS08


// 2009 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y09 immigration_category_DS09
use immigration_category_DS09
import db/BEFOLKNING_INVKAT as immigration_category09 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category09 immigration_category_txt
piechart immigration_category09
tabulate immigration_category09, missing

generate person_background09 = 0
replace person_background09 = 1 if immigration_category09 == 'B' | immigration_category09 == 'C' | immigration_category09 == 'E' | immigration_category09 == 'F' | sysmiss(immigration_category09)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background09

collapse(sum) person_background09, by(household_id09)
generate household_background09 = 0
replace household_background09 = 1 if person_background09 >= 1
piechart household_background09
//summarize household_background09
merge household_background09 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS09


// 2010 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y10 immigration_category_DS10
use immigration_category_DS10
import db/BEFOLKNING_INVKAT as immigration_category10 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category10 immigration_category_txt
piechart immigration_category10
tabulate immigration_category10, missing

generate person_background10 = 0
replace person_background10 = 1 if immigration_category10 == 'B' | immigration_category10 == 'C' | immigration_category10 == 'E' | immigration_category10 == 'F' | sysmiss(immigration_category10)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background10

collapse(sum) person_background10, by(household_id10)
generate household_background10 = 0
replace household_background10 = 1 if person_background10 >= 1
piechart household_background10
//summarize household_background10
merge household_background10 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS10


// 2011 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y11 immigration_category_DS11
use immigration_category_DS11
import db/BEFOLKNING_INVKAT as immigration_category11 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category11 immigration_category_txt
piechart immigration_category11
tabulate immigration_category11, missing

generate person_background11 = 0
replace person_background11 = 1 if immigration_category11 == 'B' | immigration_category11 == 'C' | immigration_category11 == 'E' | immigration_category11 == 'F' | sysmiss(immigration_category11)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background11

collapse(sum) person_background11, by(household_id11)
generate household_background11 = 0
replace household_background11 = 1 if person_background11 >= 1
piechart household_background11
//summarize household_background11
merge household_background11 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS11


// 2012 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y12 immigration_category_DS12
use immigration_category_DS12
import db/BEFOLKNING_INVKAT as immigration_category12 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category12 immigration_category_txt
piechart immigration_category12
tabulate immigration_category12, missing

generate person_background12 = 0
replace person_background12 = 1 if immigration_category12 == 'B' | immigration_category12 == 'C' | immigration_category12 == 'E' | immigration_category12 == 'F' | sysmiss(immigration_category12)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background12

collapse(sum) person_background12, by(household_id12)
generate household_background12 = 0
replace household_background12 = 1 if person_background12 >= 1
piechart household_background12
//summarize household_background12
merge household_background12 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS12


// 2013 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y13 immigration_category_DS13
use immigration_category_DS13
import db/BEFOLKNING_INVKAT as immigration_category13 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category13 immigration_category_txt
piechart immigration_category13
tabulate immigration_category13, missing

generate person_background13 = 0
replace person_background13 = 1 if immigration_category13 == 'B' | immigration_category13 == 'C' | immigration_category13 == 'E' | immigration_category13 == 'F' | sysmiss(immigration_category13)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background13

collapse(sum) person_background13, by(household_id13)
generate household_background13 = 0
replace household_background13 = 1 if person_background13 >= 1
piechart household_background13
//summarize household_background13
merge household_background13 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS13


// 2014 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y14 immigration_category_DS14
use immigration_category_DS14
import db/BEFOLKNING_INVKAT as immigration_category14 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category14 immigration_category_txt
piechart immigration_category14
tabulate immigration_category14, missing

generate person_background14 = 0
replace person_background14 = 1 if immigration_category14 == 'B' | immigration_category14 == 'C' | immigration_category14 == 'E' | immigration_category14 == 'F' | sysmiss(immigration_category14)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background14

collapse(sum) person_background14, by(household_id14)
generate household_background14 = 0
replace household_background14 = 1 if person_background14 >= 1
piechart household_background14
//summarize household_background14
merge household_background14 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS14


// 2015 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y15 immigration_category_DS15
use immigration_category_DS15
import db/BEFOLKNING_INVKAT as immigration_category15 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category15 immigration_category_txt
piechart immigration_category15
tabulate immigration_category15, missing

generate person_background15 = 0
replace person_background15 = 1 if immigration_category15 == 'B' | immigration_category15 == 'C' | immigration_category15 == 'E' | immigration_category15 == 'F' | sysmiss(immigration_category15)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background15

collapse(sum) person_background15, by(household_id15)
generate household_background15 = 0
replace household_background15 = 1 if person_background15 >= 1
piechart household_background15
//summarize household_background15
merge household_background15 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS15


// 2016 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y16 immigration_category_DS16
use immigration_category_DS16
import db/BEFOLKNING_INVKAT as immigration_category16 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category16 immigration_category_txt
piechart immigration_category16
tabulate immigration_category16, missing

generate person_background16 = 0
replace person_background16 = 1 if immigration_category16 == 'B' | immigration_category16 == 'C' | immigration_category16 == 'E' | immigration_category16 == 'F' | sysmiss(immigration_category16)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background16

collapse(sum) person_background16, by(household_id16)
generate household_background16 = 0
replace household_background16 = 1 if person_background16 >= 1
piechart household_background16
//summarize household_background16
merge household_background16 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS16


// 2017 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y17 immigration_category_DS17
use immigration_category_DS17
import db/BEFOLKNING_INVKAT as immigration_category17 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category17 immigration_category_txt
piechart immigration_category17
tabulate immigration_category17, missing

generate person_background17 = 0
replace person_background17 = 1 if immigration_category17 == 'B' | immigration_category17 == 'C' | immigration_category17 == 'E' | immigration_category17 == 'F' | sysmiss(immigration_category17)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background17

collapse(sum) person_background17, by(household_id17)
generate household_background17 = 0
replace household_background17 = 1 if person_background17 >= 1
piechart household_background17
//summarize household_background17
merge household_background17 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS17


// 2018 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y18 immigration_category_DS18
use immigration_category_DS18
import db/BEFOLKNING_INVKAT as immigration_category18 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category18 immigration_category_txt
piechart immigration_category18
tabulate immigration_category18, missing

generate person_background18 = 0
replace person_background18 = 1 if immigration_category18 == 'B' | immigration_category18 == 'C' | immigration_category18 == 'E' | immigration_category18 == 'F' | sysmiss(immigration_category18)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background18

collapse(sum) person_background18, by(household_id18)
generate household_background18 = 0
replace household_background18 = 1 if person_background18 >= 1
piechart household_background18
//summarize household_background18
merge household_background18 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS18


// 2019 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y19 immigration_category_DS19
use immigration_category_DS19
import db/BEFOLKNING_INVKAT as immigration_category19 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category19 immigration_category_txt
piechart immigration_category19
tabulate immigration_category19, missing

generate person_background19 = 0
replace person_background19 = 1 if immigration_category19 == 'B' | immigration_category19 == 'C' | immigration_category19 == 'E' | immigration_category19 == 'F' | sysmiss(immigration_category19)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background19

collapse(sum) person_background19, by(household_id19)
generate household_background19 = 0
replace household_background19 = 1 if person_background19 >= 1
piechart household_background19
//summarize household_background19
merge household_background19 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS19


// 2020 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y20 immigration_category_DS20
use immigration_category_DS20
import db/BEFOLKNING_INVKAT as immigration_category20 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category20 immigration_category_txt
piechart immigration_category20
tabulate immigration_category20, missing

generate person_background20 = 0
replace person_background20 = 1 if immigration_category20 == 'B' | immigration_category20 == 'C' | immigration_category20 == 'E' | immigration_category20 == 'F' | sysmiss(immigration_category20)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background20

collapse(sum) person_background20, by(household_id20)
generate household_background20 = 0
replace household_background20 = 1 if person_background20 >= 1
piechart household_background20
//summarize household_background20
merge household_background20 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS20


// 2021 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y21 immigration_category_DS21
use immigration_category_DS21
import db/BEFOLKNING_INVKAT as immigration_category21 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category21 immigration_category_txt
piechart immigration_category21
tabulate immigration_category21, missing

generate person_background21 = 0
replace person_background21 = 1 if immigration_category21 == 'B' | immigration_category21 == 'C' | immigration_category21 == 'E' | immigration_category21 == 'F' | sysmiss(immigration_category21)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background21

collapse(sum) person_background21, by(household_id21)
generate household_background21 = 0
replace household_background21 = 1 if person_background21 >= 1
piechart household_background21
//summarize household_background21
merge household_background21 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS21


// 2022 'International Background' vs. 'Local Background'
clone-dataset person_DS_Y22 immigration_category_DS22
use immigration_category_DS22
import db/BEFOLKNING_INVKAT as immigration_category22 // This variable shows various combinations of own or parents' country of birth.
assign-labels immigration_category22 immigration_category_txt
piechart immigration_category22
tabulate immigration_category22, missing

generate person_background22 = 0
replace person_background22 = 1 if immigration_category22 == 'B' | immigration_category22 == 'C' | immigration_category22 == 'E' | immigration_category22 == 'F' | sysmiss(immigration_category22)
// 1 if any 'International Background', 0 if 'Local Background'
piechart person_background22

collapse(sum) person_background22, by(household_id22)
generate household_background22 = 0
replace household_background22 = 1 if person_background22 >= 1
piechart household_background22
//summarize household_background22
merge household_background22 into household_DS_all on PERSONID_1
delete-dataset immigration_category_DS22


textblock
Finding people who work in a municipality other than the residence municipality (dummy =1)
-----------
::::: All Norway

We know that those under 18 and retired are the people with the most missing values in work. The following procedure helps to identify those who actually work, but in a different municipality than the one they are formally registered.

EFOLKNING_KOMMNR_FORMELL (379 categories): This variable shows the person's municipality of residence according to the population register (formal address). as of YYYY.01.01. This is a "Status" variable.

REGSYS_ARBKOMM (443 categories, from 2000 to 2014): This variable indicates the municipality where the enterprise in which a person works is located. This applies to the main employment relationship (it is mainly the employment relationship with the highest agreed working hours). The variable includes residents aged 15-74 who are employed, main employment in November. Observations with the values 0 and unstated (missing) are omitted.

REGSYS_ARB_ARBKOMM (379 categories, from 2015 to 2022): This variable indicates the municipality where the business in which a person works is located. Applies to the main employment relationship (it is essentially the employment relationship with the highest agreed working hours). as of YYYY.11.16. This is a "Status" variable. The variable includes residents aged 15-74 who are employed. Observations with the values 0 and unstated (missing) are omitted.

#Note the registration date differences!

endblock
// Examples to limit to specific municipalities:
//keep if person_residence_municipality20 == '0301'  // Oslo
//keep if person_residence_municipality20 == '5001'  // Trondheim


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2005.
clone-dataset person_DS_Y05 household_DS_municipality05
use household_DS_municipality05
import db/BEFOLKNING_KOMMNR_FORMELL 2005-01-01 as person_residence_municipality05
import db/REGSYS_ARBKOMM 2005-11-16 as person_work_municipality05
tabulate person_residence_municipality05, missing
tabulate person_work_municipality05, missing
generate person_residence_work05 = 1
replace person_residence_work05 = 0 if person_residence_municipality05 == person_work_municipality05 | sysmiss(person_residence_municipality05) | sysmiss(person_work_municipality05)
tabulate person_residence_work05, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2005.
collapse(sum) person_residence_work05, by(household_id05)
generate household_residence_work05 = 0
replace household_residence_work05 = 1 if person_residence_work05 >= 1
piechart household_residence_work05
tabulate household_residence_work05
merge household_residence_work05 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality05


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2006.
clone-dataset person_DS_Y06 household_DS_municipality06
use household_DS_municipality06
import db/BEFOLKNING_KOMMNR_FORMELL 2006-01-01 as person_residence_municipality06
import db/REGSYS_ARBKOMM 2006-11-16 as person_work_municipality06
//tabulate person_residence_municipality06, missing
//tabulate person_work_municipality06, missing
generate person_residence_work06 = 1
replace person_residence_work06 = 0 if person_residence_municipality06 == person_work_municipality06 | sysmiss(person_residence_municipality06) | sysmiss(person_work_municipality06)
tabulate person_residence_work06, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2006.
collapse(sum) person_residence_work06, by(household_id06)
generate household_residence_work06 = 0
replace household_residence_work06 = 1 if person_residence_work06 >= 1
piechart household_residence_work06
tabulate household_residence_work06
merge household_residence_work06 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality06


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2007.
clone-dataset person_DS_Y07 household_DS_municipality07
use household_DS_municipality07
import db/BEFOLKNING_KOMMNR_FORMELL 2007-01-01 as person_residence_municipality07
import db/REGSYS_ARBKOMM 2007-11-16 as person_work_municipality07
//tabulate person_residence_municipality07, missing
//tabulate person_work_municipality07, missing
generate person_residence_work07 = 1
replace person_residence_work07 = 0 if person_residence_municipality07 == person_work_municipality07 | sysmiss(person_residence_municipality07) | sysmiss(person_work_municipality07)
tabulate person_residence_work07, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2007.
collapse(sum) person_residence_work07, by(household_id07)
generate household_residence_work07 = 0
replace household_residence_work07 = 1 if person_residence_work07 >= 1
piechart household_residence_work07
tabulate household_residence_work07
merge household_residence_work07 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality07


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2008.
clone-dataset person_DS_Y08 household_DS_municipality08
use household_DS_municipality08
import db/BEFOLKNING_KOMMNR_FORMELL 2008-01-01 as person_residence_municipality08
import db/REGSYS_ARBKOMM 2008-11-16 as person_work_municipality08
//tabulate person_residence_municipality08, missing
//tabulate person_work_municipality08, missing
generate person_residence_work08 = 1
replace person_residence_work08 = 0 if person_residence_municipality08 == person_work_municipality08 | sysmiss(person_residence_municipality08) | sysmiss(person_work_municipality08)
tabulate person_residence_work08, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2008.
collapse(sum) person_residence_work08, by(household_id08)
generate household_residence_work08 = 0
replace household_residence_work08 = 1 if person_residence_work08 >= 1
piechart household_residence_work08
tabulate household_residence_work08
merge household_residence_work08 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality08


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2009.
clone-dataset person_DS_Y09 household_DS_municipality09
use household_DS_municipality09
import db/BEFOLKNING_KOMMNR_FORMELL 2009-01-01 as person_residence_municipality09
import db/REGSYS_ARBKOMM 2009-11-16 as person_work_municipality09
//tabulate person_residence_municipality09, missing
//tabulate person_work_municipality09, missing
generate person_residence_work09 = 1
replace person_residence_work09 = 0 if person_residence_municipality09 == person_work_municipality09 | sysmiss(person_residence_municipality09) | sysmiss(person_work_municipality09)
tabulate person_residence_work09, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2009.
collapse(sum) person_residence_work09, by(household_id09)
generate household_residence_work09 = 0
replace household_residence_work09 = 1 if person_residence_work09 >= 1
piechart household_residence_work09
tabulate household_residence_work09
merge household_residence_work09 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality09


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2010.
clone-dataset person_DS_Y10 household_DS_municipality10
use household_DS_municipality10
import db/BEFOLKNING_KOMMNR_FORMELL 2010-01-01 as person_residence_municipality10
import db/REGSYS_ARBKOMM 2010-11-16 as person_work_municipality10
//tabulate person_residence_municipality10, missing
//tabulate person_work_municipality10, missing
generate person_residence_work10 = 1
replace person_residence_work10 = 0 if person_residence_municipality10 == person_work_municipality10 | sysmiss(person_residence_municipality10) | sysmiss(person_work_municipality10)
tabulate person_residence_work10, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2010.
collapse(sum) person_residence_work10, by(household_id10)
generate household_residence_work10 = 0
replace household_residence_work10 = 1 if person_residence_work10 >= 1
piechart household_residence_work10
tabulate household_residence_work10
merge household_residence_work10 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality10


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2011.
clone-dataset person_DS_Y11 household_DS_municipality11
use household_DS_municipality11
import db/BEFOLKNING_KOMMNR_FORMELL 2011-01-01 as person_residence_municipality11
import db/REGSYS_ARBKOMM 2011-11-16 as person_work_municipality11
//tabulate person_residence_municipality11, missing
//tabulate person_work_municipality11, missing
generate person_residence_work11 = 1
replace person_residence_work11 = 0 if person_residence_municipality11 == person_work_municipality11 | sysmiss(person_residence_municipality11) | sysmiss(person_work_municipality11)
tabulate person_residence_work11, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2011.
collapse(sum) person_residence_work11, by(household_id11)
generate household_residence_work11 = 0
replace household_residence_work11 = 1 if person_residence_work11 >= 1
piechart household_residence_work11
tabulate household_residence_work11
merge household_residence_work11 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality11


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2012.
clone-dataset person_DS_Y12 household_DS_municipality12
use household_DS_municipality12
import db/BEFOLKNING_KOMMNR_FORMELL 2012-01-01 as person_residence_municipality12
import db/REGSYS_ARBKOMM 2012-11-16 as person_work_municipality12
//tabulate person_residence_municipality12, missing
//tabulate person_work_municipality12, missing
generate person_residence_work12 = 1
replace person_residence_work12 = 0 if person_residence_municipality12 == person_work_municipality12 | sysmiss(person_residence_municipality12) | sysmiss(person_work_municipality12)
tabulate person_residence_work12, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2012.
collapse(sum) person_residence_work12, by(household_id12)
generate household_residence_work12 = 0
replace household_residence_work12 = 1 if person_residence_work12 >= 1
piechart household_residence_work12
tabulate household_residence_work12
merge household_residence_work12 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality12


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2013.
clone-dataset person_DS_Y13 household_DS_municipality13
use household_DS_municipality13
import db/BEFOLKNING_KOMMNR_FORMELL 2013-01-01 as person_residence_municipality13
import db/REGSYS_ARBKOMM 2013-11-16 as person_work_municipality13
//tabulate person_residence_municipality13, missing
//tabulate person_work_municipality13, missing
generate person_residence_work13 = 1
replace person_residence_work13 = 0 if person_residence_municipality13 == person_work_municipality13 | sysmiss(person_residence_municipality13) | sysmiss(person_work_municipality13)
tabulate person_residence_work13, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2013.
collapse(sum) person_residence_work13, by(household_id13)
generate household_residence_work13 = 0
replace household_residence_work13 = 1 if person_residence_work13 >= 1
piechart household_residence_work13
tabulate household_residence_work13
merge household_residence_work13 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality13


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2014.
clone-dataset person_DS_Y14 household_DS_municipality14
use household_DS_municipality14
import db/BEFOLKNING_KOMMNR_FORMELL 2014-01-01 as person_residence_municipality14
import db/REGSYS_ARBKOMM 2014-11-16 as person_work_municipality14
//tabulate person_residence_municipality14, missing
//tabulate person_work_municipality14, missing
generate person_residence_work14 = 1
replace person_residence_work14 = 0 if person_residence_municipality14 == person_work_municipality14 | sysmiss(person_residence_municipality14) | sysmiss(person_work_municipality14)
tabulate person_residence_work14, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2014.
collapse(sum) person_residence_work14, by(household_id14)
generate household_residence_work14 = 0
replace household_residence_work14 = 1 if person_residence_work14 >= 1
piechart household_residence_work14
tabulate household_residence_work14
merge household_residence_work14 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality14


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2015.
clone-dataset person_DS_Y15 household_DS_municipality15
use household_DS_municipality15
import db/BEFOLKNING_KOMMNR_FORMELL 2015-01-01 as person_residence_municipality15
import db/REGSYS_ARB_ARBKOMM 2015-11-16 as person_work_municipality15
//tabulate person_residence_municipality15, missing
//tabulate person_work_municipality15, missing
generate person_residence_work15 = 1
replace person_residence_work15 = 0 if person_residence_municipality15 == person_work_municipality15 | sysmiss(person_residence_municipality15) | sysmiss(person_work_municipality15)
tabulate person_residence_work15, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2015.
collapse(sum) person_residence_work15, by(household_id15)
generate household_residence_work15 = 0
replace household_residence_work15 = 1 if person_residence_work15 >= 1
piechart household_residence_work15
tabulate household_residence_work15
merge household_residence_work15 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality15


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2016.
clone-dataset person_DS_Y16 household_DS_municipality16
use household_DS_municipality16
import db/BEFOLKNING_KOMMNR_FORMELL 2016-01-01 as person_residence_municipality16
import db/REGSYS_ARB_ARBKOMM 2016-11-16 as person_work_municipality16
//tabulate person_residence_municipality16, missing
//tabulate person_work_municipality16, missing
generate person_residence_work16 = 1
replace person_residence_work16 = 0 if person_residence_municipality16 == person_work_municipality16 | sysmiss(person_residence_municipality16) | sysmiss(person_work_municipality16)
tabulate person_residence_work16, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2016.
collapse(sum) person_residence_work16, by(household_id16)
generate household_residence_work16 = 0
replace household_residence_work16 = 1 if person_residence_work16 >= 1
piechart household_residence_work16
tabulate household_residence_work16
merge household_residence_work16 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality16


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2017.
clone-dataset person_DS_Y17 household_DS_municipality17
use household_DS_municipality17
import db/BEFOLKNING_KOMMNR_FORMELL 2017-01-01 as person_residence_municipality17
import db/REGSYS_ARB_ARBKOMM 2017-11-16 as person_work_municipality17
//tabulate person_residence_municipality17, missing
//tabulate person_work_municipality17, missing
generate person_residence_work17 = 1
replace person_residence_work17 = 0 if person_residence_municipality17 == person_work_municipality17 | sysmiss(person_residence_municipality17) | sysmiss(person_work_municipality17)
tabulate person_residence_work17, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2017.
collapse(sum) person_residence_work17, by(household_id17)
generate household_residence_work17 = 0
replace household_residence_work17 = 1 if person_residence_work17 >= 1
piechart household_residence_work17
tabulate household_residence_work17
merge household_residence_work17 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality17


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2018.
clone-dataset person_DS_Y18 household_DS_municipality18
use household_DS_municipality18
import db/BEFOLKNING_KOMMNR_FORMELL 2018-01-01 as person_residence_municipality18
import db/REGSYS_ARB_ARBKOMM 2018-11-16 as person_work_municipality18
//tabulate person_residence_municipality18, missing
//tabulate person_work_municipality18, missing
generate person_residence_work18 = 1
replace person_residence_work18 = 0 if person_residence_municipality18 == person_work_municipality18 | sysmiss(person_residence_municipality18) | sysmiss(person_work_municipality18)
tabulate person_residence_work18, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2018.
collapse(sum) person_residence_work18, by(household_id18)
generate household_residence_work18 = 0
replace household_residence_work18 = 1 if person_residence_work18 >= 1
piechart household_residence_work18
tabulate household_residence_work18
merge household_residence_work18 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality18


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2019.
clone-dataset person_DS_Y19 household_DS_municipality19
use household_DS_municipality19
import db/BEFOLKNING_KOMMNR_FORMELL 2019-01-01 as person_residence_municipality19
import db/REGSYS_ARB_ARBKOMM 2019-11-16 as person_work_municipality19
//tabulate person_residence_municipality19, missing
//tabulate person_work_municipality19, missing
generate person_residence_work19 = 1
replace person_residence_work19 = 0 if person_residence_municipality19 == person_work_municipality19 | sysmiss(person_residence_municipality19) | sysmiss(person_work_municipality19)
tabulate person_residence_work19, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2019.
collapse(sum) person_residence_work19, by(household_id19)
generate household_residence_work19 = 0
replace household_residence_work19 = 1 if person_residence_work19 >= 1
piechart household_residence_work19
tabulate household_residence_work19
merge household_residence_work19 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality19


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2020.
clone-dataset person_DS_Y20 household_DS_municipality20
use household_DS_municipality20
import db/BEFOLKNING_KOMMNR_FORMELL 2020-01-01 as person_residence_municipality20
import db/REGSYS_ARB_ARBKOMM 2020-11-16 as person_work_municipality20
//tabulate person_residence_municipality20, missing
//tabulate person_work_municipality20, missing
generate person_residence_work20 = 1
replace person_residence_work20 = 0 if person_residence_municipality20 == person_work_municipality20 | sysmiss(person_residence_municipality20) | sysmiss(person_work_municipality20)
tabulate person_residence_work20, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2020.
collapse(sum) person_residence_work20, by(household_id20)
generate household_residence_work20 = 0
replace household_residence_work20 = 1 if person_residence_work20 >= 1
piechart household_residence_work20
tabulate household_residence_work20
merge household_residence_work20 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality20


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2021.
clone-dataset person_DS_Y21 household_DS_municipality21
use household_DS_municipality21
import db/BEFOLKNING_KOMMNR_FORMELL 2021-01-01 as person_residence_municipality21
import db/REGSYS_ARB_ARBKOMM 2021-11-16 as person_work_municipality21
//tabulate person_residence_municipality21, missing
//tabulate person_work_municipality21, missing
generate person_residence_work21 = 1
replace person_residence_work21 = 0 if person_residence_municipality21 == person_work_municipality21 | sysmiss(person_residence_municipality21) | sysmiss(person_work_municipality21)
tabulate person_residence_work21, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2021.
collapse(sum) person_residence_work21, by(household_id21)
generate household_residence_work21 = 0
replace household_residence_work21 = 1 if person_residence_work21 >= 1
piechart household_residence_work21
tabulate household_residence_work21
merge household_residence_work21 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality21


// Find individual persons who worked in a municipality other than the residence municipality (=1) in 2022.
clone-dataset person_DS_Y22 household_DS_municipality22
use household_DS_municipality22
import db/BEFOLKNING_KOMMNR_FORMELL 2022-01-01 as person_residence_municipality22
import db/REGSYS_ARB_ARBKOMM 2022-11-16 as person_work_municipality22
tabulate person_residence_municipality22, missing
tabulate person_work_municipality22, missing
generate person_residence_work22 = 1
replace person_residence_work22 = 0 if person_residence_municipality22 == person_work_municipality22 | sysmiss(person_residence_municipality22) | sysmiss(person_work_municipality22)
tabulate person_residence_work22, missing

// Find households with at least one member who worked in a municipality other than the residence municipality (=1) in 2022.
collapse(sum) person_residence_work22, by(household_id22)
generate household_residence_work22 = 0
replace household_residence_work22 = 1 if person_residence_work22 >= 1
piechart household_residence_work22
tabulate household_residence_work22
merge household_residence_work22 into household_DS_all on PERSONID_1
delete-dataset household_DS_municipality22


textblock
Household Dataset: Income, wealth, and debt
------------------
::::: Income, wealth, and debt of household members are called and then aggregated onto the household level at this step. 

- Note 1: The unit changes from individual to household.
- Note 2: The descriptive analysis indicates that there are individuals (and households) with negative income!
- Note 3: SSB has several income-related variables, including - but not limited to - capital income (kapitalinntekter "INNTEKT_WKAPINNT"), wage income (Lønnsinntekter "INNTEKT_WLONN"), occupational income (Yrkesinntekter "INNTEKT_WYRKINNT"), total income (samlet inntekt "INNTEKT_WSAMINNT"), income after tax (inntekt etter skatt "INNTEKT_WIES"), and gross income (bruttoinntekt "SKATT_BRUTTOINNTEKT"). We use income after tax in our analysis.
endblock


// Calculating household level income (unit type = household) in temporary datasets: From 2005 to 2022

clone-dataset person_DS_Y05 household_DS_income05
use household_DS_income05
import db/INNTEKT_WIES 2005-12-31 as income_household_member05

histogram income_household_member05, width(100000) freq // This command generates a histogram of income with a width of NOK 100'000.

// The following four lines of codes reveal those individuals with negative incomes in the year of analysis.

//histogram income_household_member05 if income_household_member05 <= 0, width(100000) freq

//generate income_household_member_category_100k = int(income_household_member/100000)
//histogram income_household_member_category_100k, width(1) freq
//tabulate income_household_member_category_100k, missing

// Aggregating income of individual household members onto the household level.
collapse(sum) income_household_member05, by(household_id05)
rename income_household_member05 household_income05

histogram household_income05, width(100000) freq // This command generates a histogram of income with a width of NOK 100'000.
summarize household_income05
merge household_income05 into household_DS_all on PERSONID_1
delete-dataset household_DS_income05


// And the following years ...

clone-dataset person_DS_Y06 household_DS_income06
use household_DS_income06
import db/INNTEKT_WIES 2006-12-31 as income_household_member06
collapse(sum) income_household_member06, by(household_id06)
rename income_household_member06 household_income06
summarize household_income06
merge household_income06 into household_DS_all on PERSONID_1
delete-dataset household_DS_income06


clone-dataset person_DS_Y07 household_DS_income07
use household_DS_income07
import db/INNTEKT_WIES 2007-12-31 as income_household_member07
collapse(sum) income_household_member07, by(household_id07)
rename income_household_member07 household_income07
summarize household_income07
merge household_income07 into household_DS_all on PERSONID_1
delete-dataset household_DS_income07


clone-dataset person_DS_Y08 household_DS_income08
use household_DS_income08
import db/INNTEKT_WIES 2008-12-31 as income_household_member08
collapse(sum) income_household_member08, by(household_id08)
rename income_household_member08 household_income08
summarize household_income08
merge household_income08 into household_DS_all on PERSONID_1
delete-dataset household_DS_income08


clone-dataset person_DS_Y09 household_DS_income09
use household_DS_income09
import db/INNTEKT_WIES 2009-12-31 as income_household_member09
collapse(sum) income_household_member09, by(household_id09)
rename income_household_member09 household_income09
summarize household_income09
merge household_income09 into household_DS_all on PERSONID_1
delete-dataset household_DS_income09


clone-dataset person_DS_Y10 household_DS_income10
use household_DS_income10
import db/INNTEKT_WIES 2010-12-31 as income_household_member10
collapse(sum) income_household_member10, by(household_id10)
rename income_household_member10 household_income10
summarize household_income10
merge household_income10 into household_DS_all on PERSONID_1
delete-dataset household_DS_income10


clone-dataset person_DS_Y11 household_DS_income11
use household_DS_income11
import db/INNTEKT_WIES 2011-12-31 as income_household_member11
collapse(sum) income_household_member11, by(household_id11)
rename income_household_member11 household_income11
summarize household_income11
merge household_income11 into household_DS_all on PERSONID_1
delete-dataset household_DS_income11


clone-dataset person_DS_Y12 household_DS_income12
use household_DS_income12
import db/INNTEKT_WIES 2012-12-31 as income_household_member12
collapse(sum) income_household_member12, by(household_id12)
rename income_household_member12 household_income12
summarize household_income12
merge household_income12 into household_DS_all on PERSONID_1
delete-dataset household_DS_income12


clone-dataset person_DS_Y13 household_DS_income13
use household_DS_income13
import db/INNTEKT_WIES 2013-12-31 as income_household_member13
collapse(sum) income_household_member13, by(household_id13)
rename income_household_member13 household_income13
summarize household_income13
merge household_income13 into household_DS_all on PERSONID_1
delete-dataset household_DS_income13


clone-dataset person_DS_Y14 household_DS_income14
use household_DS_income14
import db/INNTEKT_WIES 2014-12-31 as income_household_member14
collapse(sum) income_household_member14, by(household_id14)
rename income_household_member14 household_income14
summarize household_income14
merge household_income14 into household_DS_all on PERSONID_1
delete-dataset household_DS_income14


clone-dataset person_DS_Y15 household_DS_income15
use household_DS_income15
import db/INNTEKT_WIES 2015-12-31 as income_household_member15
collapse(sum) income_household_member15, by(household_id15)
rename income_household_member15 household_income15
summarize household_income15
merge household_income15 into household_DS_all on PERSONID_1
delete-dataset household_DS_income15


clone-dataset person_DS_Y16 household_DS_income16
use household_DS_income16
import db/INNTEKT_WIES 2016-12-31 as income_household_member16
collapse(sum) income_household_member16, by(household_id16)
rename income_household_member16 household_income16
summarize household_income16
merge household_income16 into household_DS_all on PERSONID_1
delete-dataset household_DS_income16


clone-dataset person_DS_Y17 household_DS_income17
use household_DS_income17
import db/INNTEKT_WIES 2017-12-31 as income_household_member17
collapse(sum) income_household_member17, by(household_id17)
rename income_household_member17 household_income17
summarize household_income17
merge household_income17 into household_DS_all on PERSONID_1
delete-dataset household_DS_income17


clone-dataset person_DS_Y18 household_DS_income18
use household_DS_income18
import db/INNTEKT_WIES 2018-12-31 as income_household_member18
collapse(sum) income_household_member18, by(household_id18)
rename income_household_member18 household_income18
summarize household_income18
merge household_income18 into household_DS_all on PERSONID_1
delete-dataset household_DS_income18


clone-dataset person_DS_Y19 household_DS_income19
use household_DS_income19
import db/INNTEKT_WIES 2019-12-31 as income_household_member19
collapse(sum) income_household_member19, by(household_id19)
rename income_household_member19 household_income19
summarize household_income19
merge household_income19 into household_DS_all on PERSONID_1
delete-dataset household_DS_income19


clone-dataset person_DS_Y20 household_DS_income20
use household_DS_income20
import db/INNTEKT_WIES 2020-12-31 as income_household_member20
collapse(sum) income_household_member20, by(household_id20)
rename income_household_member20 household_income20
summarize household_income20
merge household_income20 into household_DS_all on PERSONID_1
delete-dataset household_DS_income20


clone-dataset person_DS_Y21 household_DS_income21
use household_DS_income21
import db/INNTEKT_WIES 2021-12-31 as income_household_member21
collapse(sum) income_household_member21, by(household_id21)
rename income_household_member21 household_income21
summarize household_income21
histogram household_income21, width(100000) freq
merge household_income21 into household_DS_all on PERSONID_1
summarize household_income21 if household_income21 <= 0
delete-dataset household_DS_income21


// Calculating household level wealth (unit type = household) in temporary datasets: From 2005 to 2020 ...
// #Note that Wealth data for 2021 is not available in database v26

clone-dataset person_DS_Y05 household_DS_wealth05
use household_DS_wealth05
import db/INNTEKT_BRUTTOFORM 2005-12-31 as wealth_household_member05 // This variable is the gross taxable wealth of individual persons. There are two other similar variables, "taxable gross assets SKATT_BRUTTOFORMUE", and "taxable gross financial capital SKATT_BRUTTO_FINANSKAPITAL" in SSB's data set. We use gross taxable wealth in our analysis.

collapse(sum) wealth_household_member05, by(household_id05)
rename wealth_household_member05 household_wealth05
merge household_wealth05 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth05


clone-dataset person_DS_Y06 household_DS_wealth06
use household_DS_wealth06
import db/INNTEKT_BRUTTOFORM 2006-12-31 as wealth_household_member06
collapse(sum) wealth_household_member06, by(household_id06)
rename wealth_household_member06 household_wealth06
merge household_wealth06 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth06


clone-dataset person_DS_Y07 household_DS_wealth07
use household_DS_wealth07
import db/INNTEKT_BRUTTOFORM 2007-12-31 as wealth_household_member07
collapse(sum) wealth_household_member07, by(household_id07)
rename wealth_household_member07 household_wealth07
merge household_wealth07 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth07


clone-dataset person_DS_Y08 household_DS_wealth08
use household_DS_wealth08
import db/INNTEKT_BRUTTOFORM 2008-12-31 as wealth_household_member08
collapse(sum) wealth_household_member08, by(household_id08)
rename wealth_household_member08 household_wealth08
merge household_wealth08 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth08


clone-dataset person_DS_Y09 household_DS_wealth09
use household_DS_wealth09
import db/INNTEKT_BRUTTOFORM 2009-12-31 as wealth_household_member09
collapse(sum) wealth_household_member09, by(household_id09)
rename wealth_household_member09 household_wealth09
merge household_wealth09 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth09


clone-dataset person_DS_Y10 household_DS_wealth10
use household_DS_wealth10
import db/INNTEKT_BRUTTOFORM 2010-12-31 as wealth_household_member10
collapse(sum) wealth_household_member10, by(household_id10)
rename wealth_household_member10 household_wealth10
merge household_wealth10 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth10


clone-dataset person_DS_Y11 household_DS_wealth11
use household_DS_wealth11
import db/INNTEKT_BRUTTOFORM 2011-12-31 as wealth_household_member11
collapse(sum) wealth_household_member11, by(household_id11)
rename wealth_household_member11 household_wealth11
merge household_wealth11 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth11


clone-dataset person_DS_Y12 household_DS_wealth12
use household_DS_wealth12
import db/INNTEKT_BRUTTOFORM 2012-12-31 as wealth_household_member12
collapse(sum) wealth_household_member12, by(household_id12)
rename wealth_household_member12 household_wealth12
merge household_wealth12 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth12


clone-dataset person_DS_Y13 household_DS_wealth13
use household_DS_wealth13
import db/INNTEKT_BRUTTOFORM 2013-12-31 as wealth_household_member13
collapse(sum) wealth_household_member13, by(household_id13)
rename wealth_household_member13 household_wealth13
merge household_wealth13 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth13


clone-dataset person_DS_Y14 household_DS_wealth14
use household_DS_wealth14
import db/INNTEKT_BRUTTOFORM 2014-12-31 as wealth_household_member14
collapse(sum) wealth_household_member14, by(household_id14)
rename wealth_household_member14 household_wealth14
merge household_wealth14 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth14


clone-dataset person_DS_Y15 household_DS_wealth15
use household_DS_wealth15
import db/INNTEKT_BRUTTOFORM 2015-12-31 as wealth_household_member15
collapse(sum) wealth_household_member15, by(household_id15)
rename wealth_household_member15 household_wealth15
merge household_wealth15 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth15


clone-dataset person_DS_Y16 household_DS_wealth16
use household_DS_wealth16
import db/INNTEKT_BRUTTOFORM 2016-12-31 as wealth_household_member16
collapse(sum) wealth_household_member16, by(household_id16)
rename wealth_household_member16 household_wealth16
merge household_wealth16 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth16


clone-dataset person_DS_Y17 household_DS_wealth17
use household_DS_wealth17
import db/INNTEKT_BRUTTOFORM 2017-12-31 as wealth_household_member17
collapse(sum) wealth_household_member17, by(household_id17)
rename wealth_household_member17 household_wealth17
merge household_wealth17 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth17


clone-dataset person_DS_Y18 household_DS_wealth18
use household_DS_wealth18
import db/INNTEKT_BRUTTOFORM 2018-12-31 as wealth_household_member18
collapse(sum) wealth_household_member18, by(household_id18)
rename wealth_household_member18 household_wealth18
merge household_wealth18 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth18


clone-dataset person_DS_Y19 household_DS_wealth19
use household_DS_wealth19
import db/INNTEKT_BRUTTOFORM 2019-12-31 as wealth_household_member19
collapse(sum) wealth_household_member19, by(household_id19)
rename wealth_household_member19 household_wealth19
merge household_wealth19 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth19


clone-dataset person_DS_Y20 household_DS_wealth20
use household_DS_wealth20
import db/INNTEKT_BRUTTOFORM 2020-12-31 as wealth_household_member20
collapse(sum) wealth_household_member20, by(household_id20)
rename wealth_household_member20 household_wealth20
merge household_wealth20 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth20


// Wealth data for 2021 is not available in database v.26
textblock
clone-dataset person_DS_Y21 household_DS_wealth21
use household_DS_wealth21
import db/INNTEKT_BRUTTOFORM 2021-12-31 as wealth_household_member21
collapse(sum) wealth_household_member21, by(household_id21)
rename wealth_household_member21 household_wealth21
merge household_wealth21 into household_DS_all on PERSONID_1
delete-dataset household_DS_wealth21
endblock


// Calculating household level debt (unit type = household) in temporary datasets: From 2005 to 2021

clone-dataset person_DS_Y05 household_DS_debt05
use household_DS_debt05
import db/SKATT_GJELD 2005-12-31 as debt_household_member05 // Debt to Norwegian and foreign creditors as well as unit owners' share of the housing association's debt. In the tax statistics for individuals, debt from 2017 includes debt reduction due to valuation discounts. https://www.ssb.no/a/metadata/conceptvariable/vardok/17/nb. The variable covers all taxable persons during the income year. Including persons with dnr. Observations with the values ​​0 and unspecified (missing) are omitted.

collapse(sum) debt_household_member05, by(household_id05)
rename debt_household_member05 household_debt05

histogram household_debt05, width(100000) freq // This command generates a histogram of income with a width of NOK 100'000.

merge household_debt05 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt05


clone-dataset person_DS_Y06 household_DS_debt06
use household_DS_debt06
import db/SKATT_GJELD 2006-12-31 as debt_household_member06
collapse(sum) debt_household_member06, by(household_id06)
rename debt_household_member06 household_debt06
merge household_debt06 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt06


clone-dataset person_DS_Y07 household_DS_debt07
use household_DS_debt07
import db/SKATT_GJELD 2007-12-31 as debt_household_member07
collapse(sum) debt_household_member07, by(household_id07)
rename debt_household_member07 household_debt07
merge household_debt07 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt07


clone-dataset person_DS_Y08 household_DS_debt08
use household_DS_debt08
import db/SKATT_GJELD 2008-12-31 as debt_household_member08
collapse(sum) debt_household_member08, by(household_id08)
rename debt_household_member08 household_debt08
merge household_debt08 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt08


clone-dataset person_DS_Y09 household_DS_debt09
use household_DS_debt09
import db/SKATT_GJELD 2009-12-31 as debt_household_member09
collapse(sum) debt_household_member09, by(household_id09)
rename debt_household_member09 household_debt09
merge household_debt09 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt09


clone-dataset person_DS_Y10 household_DS_debt10
use household_DS_debt10
import db/SKATT_GJELD 2010-12-31 as debt_household_member10
collapse(sum) debt_household_member10, by(household_id10)
rename debt_household_member10 household_debt10
merge household_debt10 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt10


clone-dataset person_DS_Y11 household_DS_debt11
use household_DS_debt11
import db/SKATT_GJELD 2011-12-31 as debt_household_member11
collapse(sum) debt_household_member11, by(household_id11)
rename debt_household_member11 household_debt11
merge household_debt11 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt11


clone-dataset person_DS_Y12 household_DS_debt12
use household_DS_debt12
import db/SKATT_GJELD 2012-12-31 as debt_household_member12
collapse(sum) debt_household_member12, by(household_id12)
rename debt_household_member12 household_debt12
merge household_debt12 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt12


clone-dataset person_DS_Y13 household_DS_debt13
use household_DS_debt13
import db/SKATT_GJELD 2013-12-31 as debt_household_member13
collapse(sum) debt_household_member13, by(household_id13)
rename debt_household_member13 household_debt13
merge household_debt13 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt13


clone-dataset person_DS_Y14 household_DS_debt14
use household_DS_debt14
import db/SKATT_GJELD 2014-12-31 as debt_household_member14
collapse(sum) debt_household_member14, by(household_id14)
rename debt_household_member14 household_debt14
merge household_debt14 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt14


clone-dataset person_DS_Y15 household_DS_debt15
use household_DS_debt15
import db/SKATT_GJELD 2015-12-31 as debt_household_member15
collapse(sum) debt_household_member15, by(household_id15)
rename debt_household_member15 household_debt15
merge household_debt15 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt15


clone-dataset person_DS_Y16 household_DS_debt16
use household_DS_debt16
import db/SKATT_GJELD 2016-12-31 as debt_household_member16
collapse(sum) debt_household_member16, by(household_id16)
rename debt_household_member16 household_debt16
merge household_debt16 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt16


clone-dataset person_DS_Y17 household_DS_debt17
use household_DS_debt17
import db/SKATT_GJELD 2017-12-31 as debt_household_member17
collapse(sum) debt_household_member17, by(household_id17)
rename debt_household_member17 household_debt17
merge household_debt17 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt17


clone-dataset person_DS_Y18 household_DS_debt18
use household_DS_debt18
import db/SKATT_GJELD 2018-12-31 as debt_household_member18
collapse(sum) debt_household_member18, by(household_id18)
rename debt_household_member18 household_debt18
merge household_debt18 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt18


clone-dataset person_DS_Y19 household_DS_debt19
use household_DS_debt19
import db/SKATT_GJELD 2019-12-31 as debt_household_member19
collapse(sum) debt_household_member19, by(household_id19)
rename debt_household_member19 household_debt19
merge household_debt19 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt19


clone-dataset person_DS_Y20 household_DS_debt20
use household_DS_debt20
import db/SKATT_GJELD 2020-12-31 as debt_household_member20
collapse(sum) debt_household_member20, by(household_id20)
rename debt_household_member20 household_debt20
merge household_debt20 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt20


clone-dataset person_DS_Y21 household_DS_debt21
use household_DS_debt21
import db/SKATT_GJELD 2021-12-31 as debt_household_member21
collapse(sum) debt_household_member21, by(household_id21)
rename debt_household_member21 household_debt21
merge household_debt21 into household_DS_all on PERSONID_1
delete-dataset household_DS_debt21


textblock
Household Dataset: highest completed education in the household
------------------
::::: The highest completed education for each person is called and then aggregated onto the household level at this step. By doing so, the highest completed education in the household is identified.

Note: NUS2000 (NUS code) is the code for the highest completed education according to the definition of education level drawn up in 2006. For more information, see https://www.ssb.no/utdanning/artikler-og-publikasjoner/hvordan-klassifiseres-en-persons-hoyeste-utdanningsniva

Note: the data related to education (according to NUS2000) has 5,322 categories.
endblock

// Importing the highest completed education for each person: needed for aggregation and finding the highest level of education in the household

clone-dataset person_DS_Y05 household_DS_edu05
use household_DS_edu05
import db/NUDB_BU 2005-12-31 as education_level_person05
//tabulate education_level_person, missing

// Squeezing the total categories of "education_level_person"
generate education_level_person_aggregated05 = substr(education_level_person05, 1, 1)
// Note: Level 0 is actually "kindergarten/preschool education or no education".
// See more at https://www.ssb.no/en/utdanning/artikler-og-publikasjoner/facts-about-education-in-norway-2021
define-labels education_level_aggregated_txt '0' 'No school edu.' '1' 'Primary edu.' '2' 'Lower secondary edu.' '3' 'Upper secondary edu.' '4' 'Post secondary (Prof. degree)' '5' 'Post secondary (Higher prof. degree)' '6' 'Bachelor degree' '7' 'Master degree' '8' 'PhD degree'
assign-labels education_level_person_aggregated05 education_level_aggregated_txt
tabulate education_level_person_aggregated05, missing
piechart education_level_person_aggregated05
// Following, the "_numeric" will be used for correlation & regression.

// Find the highest level of education in the household in 2005.
collapse(max) education_level_person_aggregated05, by(household_id05)
rename education_level_person_aggregated05 household_highest_edu05

//assign-labels household_highest_edu05 education_level_aggregated_txt
//tabulate household_highest_edu05, missing

generate household_highest_edu_numeric05 = household_highest_edu05
destring household_highest_edu_numeric05
//merge household_highest_edu05 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric05 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu05


// 2006 edu
clone-dataset person_DS_Y06 household_DS_edu06
use household_DS_edu06
import db/NUDB_BU 2006-12-31 as education_level_person06 
generate education_level_person_aggregated06 = substr(education_level_person06, 1, 1)
assign-labels education_level_person_aggregated06 education_level_aggregated_txt
tabulate education_level_person_aggregated06, missing

// Find the highest level of education in the household in 2006.
collapse(max) education_level_person_aggregated06, by(household_id06)
rename education_level_person_aggregated06 household_highest_edu06

//assign-labels household_highest_edu06 education_level_aggregated_txt
//tabulate household_highest_edu06, missing

generate household_highest_edu_numeric06 = household_highest_edu06
destring household_highest_edu_numeric06
//merge household_highest_edu06 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric06 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu06


// 2007 edu
clone-dataset person_DS_Y07 household_DS_edu07
use household_DS_edu07
import db/NUDB_BU 2007-12-31 as education_level_person07 
generate education_level_person_aggregated07 = substr(education_level_person07, 1, 1)
assign-labels education_level_person_aggregated07 education_level_aggregated_txt
tabulate education_level_person_aggregated07, missing

// Find the highest level of education in the household in 2007.
collapse(max) education_level_person_aggregated07, by(household_id07)
rename education_level_person_aggregated07 household_highest_edu07

//assign-labels household_highest_edu07 education_level_aggregated_txt
//tabulate household_highest_edu07, missing

generate household_highest_edu_numeric07 = household_highest_edu07
destring household_highest_edu_numeric07
//merge household_highest_edu07 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric07 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu07


// 2008 edu
clone-dataset person_DS_Y08 household_DS_edu08
use household_DS_edu08
import db/NUDB_BU 2008-12-31 as education_level_person08 
generate education_level_person_aggregated08 = substr(education_level_person08, 1, 1)
assign-labels education_level_person_aggregated08 education_level_aggregated_txt
tabulate education_level_person_aggregated08, missing

// Find the highest level of education in the household in 2008.
collapse(max) education_level_person_aggregated08, by(household_id08)
rename education_level_person_aggregated08 household_highest_edu08

//assign-labels household_highest_edu08 education_level_aggregated_txt
//tabulate household_highest_edu08, missing

generate household_highest_edu_numeric08 = household_highest_edu08
destring household_highest_edu_numeric08
//merge household_highest_edu08 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric08 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu08


// 2009 edu
clone-dataset person_DS_Y09 household_DS_edu09
use household_DS_edu09
import db/NUDB_BU 2009-12-31 as education_level_person09 
generate education_level_person_aggregated09 = substr(education_level_person09, 1, 1)
assign-labels education_level_person_aggregated09 education_level_aggregated_txt
tabulate education_level_person_aggregated09, missing

// Find the highest level of education in the household in 2009.
collapse(max) education_level_person_aggregated09, by(household_id09)
rename education_level_person_aggregated09 household_highest_edu09

//assign-labels household_highest_edu09 education_level_aggregated_txt
//tabulate household_highest_edu09, missing

generate household_highest_edu_numeric09 = household_highest_edu09
destring household_highest_edu_numeric09
//merge household_highest_edu09 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric09 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu09


// 2010 edu
clone-dataset person_DS_Y10 household_DS_edu10
use household_DS_edu10
import db/NUDB_BU 2010-12-31 as education_level_person10 
generate education_level_person_aggregated10 = substr(education_level_person10, 1, 1)
assign-labels education_level_person_aggregated10 education_level_aggregated_txt
tabulate education_level_person_aggregated10, missing

// Find the highest level of education in the household in 2010.
collapse(max) education_level_person_aggregated10, by(household_id10)
rename education_level_person_aggregated10 household_highest_edu10

//assign-labels household_highest_edu10 education_level_aggregated_txt
//tabulate household_highest_edu10, missing

generate household_highest_edu_numeric10 = household_highest_edu10
destring household_highest_edu_numeric10
//merge household_highest_edu10 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric10 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu10


// 2011 edu
clone-dataset person_DS_Y11 household_DS_edu11
use household_DS_edu11
import db/NUDB_BU 2011-12-31 as education_level_person11 
generate education_level_person_aggregated11 = substr(education_level_person11, 1, 1)
assign-labels education_level_person_aggregated11 education_level_aggregated_txt
tabulate education_level_person_aggregated11, missing

// Find the highest level of education in the household in 2011.
collapse(max) education_level_person_aggregated11, by(household_id11)
rename education_level_person_aggregated11 household_highest_edu11

//assign-labels household_highest_edu11 education_level_aggregated_txt
//tabulate household_highest_edu11, missing

generate household_highest_edu_numeric11 = household_highest_edu11
destring household_highest_edu_numeric11
//merge household_highest_edu11 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric11 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu11


// 2012 edu
clone-dataset person_DS_Y12 household_DS_edu12
use household_DS_edu12
import db/NUDB_BU 2012-12-31 as education_level_person12 
generate education_level_person_aggregated12 = substr(education_level_person12, 1, 1)
assign-labels education_level_person_aggregated12 education_level_aggregated_txt
tabulate education_level_person_aggregated12, missing

// Find the highest level of education in the household in 2012.
collapse(max) education_level_person_aggregated12, by(household_id12)
rename education_level_person_aggregated12 household_highest_edu12

//assign-labels household_highest_edu12 education_level_aggregated_txt
//tabulate household_highest_edu12, missing

generate household_highest_edu_numeric12 = household_highest_edu12
destring household_highest_edu_numeric12
//merge household_highest_edu12 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric12 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu12


// 2013 edu
clone-dataset person_DS_Y13 household_DS_edu13
use household_DS_edu13
import db/NUDB_BU 2013-12-31 as education_level_person13 
generate education_level_person_aggregated13 = substr(education_level_person13, 1, 1)
assign-labels education_level_person_aggregated13 education_level_aggregated_txt
tabulate education_level_person_aggregated13, missing

// Find the highest level of education in the household in 2013.
collapse(max) education_level_person_aggregated13, by(household_id13)
rename education_level_person_aggregated13 household_highest_edu13

//assign-labels household_highest_edu13 education_level_aggregated_txt
//tabulate household_highest_edu13, missing

generate household_highest_edu_numeric13 = household_highest_edu13
destring household_highest_edu_numeric13
//merge household_highest_edu13 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric13 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu13


// 2014 edu
clone-dataset person_DS_Y14 household_DS_edu14
use household_DS_edu14
import db/NUDB_BU 2014-12-31 as education_level_person14 
generate education_level_person_aggregated14 = substr(education_level_person14, 1, 1)
assign-labels education_level_person_aggregated14 education_level_aggregated_txt
tabulate education_level_person_aggregated14, missing

// Find the highest level of education in the household in 2014.
collapse(max) education_level_person_aggregated14, by(household_id14)
rename education_level_person_aggregated14 household_highest_edu14

//assign-labels household_highest_edu14 education_level_aggregated_txt
//tabulate household_highest_edu14, missing

generate household_highest_edu_numeric14 = household_highest_edu14
destring household_highest_edu_numeric14
//merge household_highest_edu14 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric14 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu14


// 2015 edu
clone-dataset person_DS_Y15 household_DS_edu15
use household_DS_edu15
import db/NUDB_BU 2015-12-31 as education_level_person15 
generate education_level_person_aggregated15 = substr(education_level_person15, 1, 1)
assign-labels education_level_person_aggregated15 education_level_aggregated_txt
tabulate education_level_person_aggregated15, missing

// Find the highest level of education in the household in 2015.
collapse(max) education_level_person_aggregated15, by(household_id15)
rename education_level_person_aggregated15 household_highest_edu15

//assign-labels household_highest_edu15 education_level_aggregated_txt
//tabulate household_highest_edu15, missing

generate household_highest_edu_numeric15 = household_highest_edu15
destring household_highest_edu_numeric15
//merge household_highest_edu15 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric15 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu15


// 2016 edu
clone-dataset person_DS_Y16 household_DS_edu16
use household_DS_edu16
import db/NUDB_BU 2016-12-31 as education_level_person16 
generate education_level_person_aggregated16 = substr(education_level_person16, 1, 1)
assign-labels education_level_person_aggregated16 education_level_aggregated_txt
tabulate education_level_person_aggregated16, missing

// Find the highest level of education in the household in 2016.
collapse(max) education_level_person_aggregated16, by(household_id16)
rename education_level_person_aggregated16 household_highest_edu16

//assign-labels household_highest_edu16 education_level_aggregated_txt
//tabulate household_highest_edu16, missing

generate household_highest_edu_numeric16 = household_highest_edu16
destring household_highest_edu_numeric16
//merge household_highest_edu16 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric16 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu16


// 2017 edu
clone-dataset person_DS_Y17 household_DS_edu17
use household_DS_edu17
import db/NUDB_BU 2017-12-31 as education_level_person17 
generate education_level_person_aggregated17 = substr(education_level_person17, 1, 1)
assign-labels education_level_person_aggregated17 education_level_aggregated_txt
tabulate education_level_person_aggregated17, missing

// Find the highest level of education in the household in 2017.
collapse(max) education_level_person_aggregated17, by(household_id17)
rename education_level_person_aggregated17 household_highest_edu17

//assign-labels household_highest_edu17 education_level_aggregated_txt
//tabulate household_highest_edu17, missing

generate household_highest_edu_numeric17 = household_highest_edu17
destring household_highest_edu_numeric17
//merge household_highest_edu17 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric17 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu17


// 2018 edu
clone-dataset person_DS_Y18 household_DS_edu18
use household_DS_edu18
import db/NUDB_BU 2018-12-31 as education_level_person18 
generate education_level_person_aggregated18 = substr(education_level_person18, 1, 1)
assign-labels education_level_person_aggregated18 education_level_aggregated_txt
tabulate education_level_person_aggregated18, missing

// Find the highest level of education in the household in 2018.
collapse(max) education_level_person_aggregated18, by(household_id18)
rename education_level_person_aggregated18 household_highest_edu18

//assign-labels household_highest_edu18 education_level_aggregated_txt
//tabulate household_highest_edu18, missing

generate household_highest_edu_numeric18 = household_highest_edu18
destring household_highest_edu_numeric18
//merge household_highest_edu18 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric18 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu18


// 2019 edu
clone-dataset person_DS_Y19 household_DS_edu19
use household_DS_edu19
import db/NUDB_BU 2019-12-31 as education_level_person19 
generate education_level_person_aggregated19 = substr(education_level_person19, 1, 1)
assign-labels education_level_person_aggregated19 education_level_aggregated_txt
tabulate education_level_person_aggregated19, missing

// Find the highest level of education in the household in 2019.
collapse(max) education_level_person_aggregated19, by(household_id19)
rename education_level_person_aggregated19 household_highest_edu19

//assign-labels household_highest_edu19 education_level_aggregated_txt
//tabulate household_highest_edu19, missing

generate household_highest_edu_numeric19 = household_highest_edu19
destring household_highest_edu_numeric19
//merge household_highest_edu19 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric19 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu19


// 2020 edu
clone-dataset person_DS_Y20 household_DS_edu20
use household_DS_edu20
import db/NUDB_BU 2020-12-31 as education_level_person20 
generate education_level_person_aggregated20 = substr(education_level_person20, 1, 1)
assign-labels education_level_person_aggregated20 education_level_aggregated_txt
tabulate education_level_person_aggregated20, missing

// Find the highest level of education in the household in 2020.
collapse(max) education_level_person_aggregated20, by(household_id20)
rename education_level_person_aggregated20 household_highest_edu20

//assign-labels household_highest_edu20 education_level_aggregated_txt
//tabulate household_highest_edu20, missing

generate household_highest_edu_numeric20 = household_highest_edu20
destring household_highest_edu_numeric20
//merge household_highest_edu20 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric20 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu20


// 2021 edu
clone-dataset person_DS_Y21 household_DS_edu21
use household_DS_edu21
import db/NUDB_BU 2021-12-31 as education_level_person21 
//tabulate education_level_person, missing
generate education_level_person_aggregated21 = substr(education_level_person21, 1, 1)
assign-labels education_level_person_aggregated21 education_level_aggregated_txt
tabulate education_level_person_aggregated21, missing

// Find the highest level of education in the household in 2021.
collapse(max) education_level_person_aggregated21, by(household_id21)
rename education_level_person_aggregated21 household_highest_edu21

//assign-labels household_highest_edu21 education_level_aggregated_txt
//tabulate household_highest_edu21, missing

generate household_highest_edu_numeric21 = household_highest_edu21
destring household_highest_edu_numeric21
//merge household_highest_edu21 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric21 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu21


// 2022 edu  // #Note that the latest data is until 2022-09-01
clone-dataset person_DS_Y22 household_DS_edu22
use household_DS_edu22
import db/NUDB_BU 2022-09-01 as education_level_person22 
//tabulate education_level_person, missing
generate education_level_person_aggregated22 = substr(education_level_person22, 1, 1)
assign-labels education_level_person_aggregated22 education_level_aggregated_txt
tabulate education_level_person_aggregated22, missing

// Find the highest level of education in the household in 2022.
collapse(max) education_level_person_aggregated22, by(household_id22)
rename education_level_person_aggregated22 household_highest_edu22

//assign-labels household_highest_edu22 education_level_aggregated_txt
//tabulate household_highest_edu22, missing

generate household_highest_edu_numeric22 = household_highest_edu22
destring household_highest_edu_numeric22
//merge household_highest_edu22 into household_DS_all on PERSONID_1
merge household_highest_edu_numeric22 into household_DS_all on PERSONID_1
delete-dataset household_DS_edu22


textblock
Adding further demographical information ...
-------------------
::::: Types of household
endblock

clone-dataset person_DS_Y05 household_DS_type05
use household_DS_type05

// Importing detailed breakdown of household types: 24 categories
import db/BEFOLKNING_REGSTAT_HUSHTYP 2005-01-01 as household_type05, outer_join
// Define and assign the English translation to each category.
define-labels household_type_txt '1.1.1' 'Living alone under 30 years' '1.1.2' 'Living alone 30-44 years' '1.1.3' 'Living alone 45-66 years' '1.1.4' 'Living alone 67 years and over' '1.2.1' 'Couple without children, oldest person under 30 years' '1.2.2' 'Couple without children, oldest person 30-44 years' '1.2.3' 'Couple without children, oldest person 45-66 years' '1.2.4' 'Couple without children, oldest person 67 years and over' '1.3.1' 'Married couple with small children (youngest children 0-5 years)' '1.3.2' 'Cohabiting couple with small children (youngest child 0-5 years)' '1.4.1' 'Married couple with older children (youngest children 6-17 years)' '1.4.2' 'Cohabiting couple with older children (youngest child 6-17 years)' '1.5.1' 'Mother with small children (youngest child 0-5 years)' '1.5.2' 'Father with small children (youngest child 0-5 years)' '1.6.1' 'Mother with older children (youngest child 6-17 years)' '1.6.2' 'Father with older children (youngest child 6-17 years)' '1.7.1' 'Married couple with adult children (youngest child 18 years and over)' '1.7.2' 'Cohabiting couple with adult children (youngest child 18 years and over)' '1.7.3' 'Mother with adult children (youngest child 18 years and over)' '1.7.4' 'Father with adult children (youngest child 18 years and over)' '2.1.1' 'Households with two or more single-person families' '2.1.2' 'Other multi-family households without children 0-17 years' '2.2.0' 'Multi-family households with small children (youngest children under 0-5 years)' '2.3.0' 'Multi-family households with older children (youngest children 6-17 years)'
assign-labels household_type05 household_type_txt

//tabulate household_type, missing
generate resident05 = 1
barchart(count) resident05, over(household_type05)

// Squeezing the total number of "household_type": according to the age category of children in the household
// Note: According to SSB, the age of the youngest child determines the type of household.


generate household_type_by_children05 = household_type05
replace household_type_by_children05 = '0 Without children' if household_type_by_children05 == '1.1.1' | household_type_by_children05 == '1.1.2' | household_type_by_children05 == '1.1.3' | household_type_by_children05 == '1.1.4' | household_type_by_children05 == '1.2.1' | household_type_by_children05 == '1.2.2' | household_type_by_children05 == '1.2.3' | household_type_by_children05 == '1.2.4' | household_type_by_children05 == '2.1.1' | household_type_by_children05 == '2.1.2'
replace household_type_by_children05 = '1 With small children' if household_type_by_children05 == '1.3.1' | household_type_by_children05 == '1.3.2' | household_type_by_children05 == '1.5.1' | household_type_by_children05 == '1.5.2' | household_type_by_children05 == '2.2.0'
replace household_type_by_children05 = '2 With older children' if household_type_by_children05 == '1.4.1' | household_type_by_children05 == '1.4.2' | household_type_by_children05 == '1.6.1' | household_type_by_children05 == '1.6.2' | household_type_by_children05 == '2.3.0'
replace household_type_by_children05 = '3 With adult children' if household_type_by_children05 == '1.7.1' | household_type_by_children05 == '1.7.2' | household_type_by_children05 == '1.7.3' | household_type_by_children05 == '1.7.4'
tabulate household_type_by_children05, missing
merge household_type_by_children05 into household_DS_all on PERSONID_1
generate type_have_children05 = 0
replace type_have_children05 = 1 if  household_type05 == '1.3.1' | household_type05 == '1.3.2' | household_type05 == '1.5.1' | household_type05 == '1.5.2' | household_type05 == '2.2.0' | household_type05 == '1.4.1' | household_type05 == '1.4.2' | household_type05 == '1.6.1' | household_type05 == '1.6.2' | household_type05 == '2.3.0' | household_type05 == '1.7.1' | household_type05 == '1.7.2' | household_type05 == '1.7.3' | household_type05 == '1.7.4'
tabulate type_have_children05, missing
merge type_have_children05 into household_DS_all on PERSONID_1
delete-dataset household_DS_type05


// and, the following years ...

// Household type by children 2006
clone-dataset person_DS_Y06 household_DS_type06
use household_DS_type06
import db/BEFOLKNING_REGSTAT_HUSHTYP 2006-01-01 as household_type06
assign-labels household_type06 household_type_txt

generate household_type_by_children06 = household_type06
replace household_type_by_children06 = '0 Without children' if household_type_by_children06 == '1.1.1' | household_type_by_children06 == '1.1.2' | household_type_by_children06 == '1.1.3' | household_type_by_children06 == '1.1.4' | household_type_by_children06 == '1.2.1' | household_type_by_children06 == '1.2.2' | household_type_by_children06 == '1.2.3' | household_type_by_children06 == '1.2.4' | household_type_by_children06 == '2.1.1' | household_type_by_children06 == '2.1.2'
replace household_type_by_children06 = '1 With small children' if household_type_by_children06 == '1.3.1' | household_type_by_children06 == '1.3.2' | household_type_by_children06 == '1.5.1' | household_type_by_children06 == '1.5.2' | household_type_by_children06 == '2.2.0'
replace household_type_by_children06 = '2 With older children' if household_type_by_children06 == '1.4.1' | household_type_by_children06 == '1.4.2' | household_type_by_children06 == '1.6.1' | household_type_by_children06 == '1.6.2' | household_type_by_children06 == '2.3.0'
replace household_type_by_children06 = '3 With adult children' if household_type_by_children06 == '1.7.1' | household_type_by_children06 == '1.7.2' | household_type_by_children06 == '1.7.3' | household_type_by_children06 == '1.7.4'
tabulate household_type_by_children06, missing
merge household_type_by_children06 into household_DS_all on PERSONID_1
generate type_have_children06 = 0
replace type_have_children06 = 1 if  household_type06 == '1.3.1' | household_type06 == '1.3.2' | household_type06 == '1.5.1' | household_type06 == '1.5.2' | household_type06 == '2.2.0' | household_type06 == '1.4.1' | household_type06 == '1.4.2' | household_type06 == '1.6.1' | household_type06 == '1.6.2' | household_type06 == '2.3.0' | household_type06 == '1.7.1' | household_type06 == '1.7.2' | household_type06 == '1.7.3' | household_type06 == '1.7.4'
tabulate type_have_children06, missing
merge type_have_children06 into household_DS_all on PERSONID_1
delete-dataset household_DS_type06


// Household type by children 2007
clone-dataset person_DS_Y07 household_DS_type07
use household_DS_type07
import db/BEFOLKNING_REGSTAT_HUSHTYP 2007-01-01 as household_type07
assign-labels household_type07 household_type_txt

generate household_type_by_children07 = household_type07
replace household_type_by_children07 = '0 Without children' if household_type_by_children07 == '1.1.1' | household_type_by_children07 == '1.1.2' | household_type_by_children07 == '1.1.3' | household_type_by_children07 == '1.1.4' | household_type_by_children07 == '1.2.1' | household_type_by_children07 == '1.2.2' | household_type_by_children07 == '1.2.3' | household_type_by_children07 == '1.2.4' | household_type_by_children07 == '2.1.1' | household_type_by_children07 == '2.1.2'
replace household_type_by_children07 = '1 With small children' if household_type_by_children07 == '1.3.1' | household_type_by_children07 == '1.3.2' | household_type_by_children07 == '1.5.1' | household_type_by_children07 == '1.5.2' | household_type_by_children07 == '2.2.0'
replace household_type_by_children07 = '2 With older children' if household_type_by_children07 == '1.4.1' | household_type_by_children07 == '1.4.2' | household_type_by_children07 == '1.6.1' | household_type_by_children07 == '1.6.2' | household_type_by_children07 == '2.3.0'
replace household_type_by_children07 = '3 With adult children' if household_type_by_children07 == '1.7.1' | household_type_by_children07 == '1.7.2' | household_type_by_children07 == '1.7.3' | household_type_by_children07 == '1.7.4'
tabulate household_type_by_children07, missing
merge household_type_by_children07 into household_DS_all on PERSONID_1
generate type_have_children07 = 0
replace type_have_children07 = 1 if  household_type07 == '1.3.1' | household_type07 == '1.3.2' | household_type07 == '1.5.1' | household_type07 == '1.5.2' | household_type07 == '2.2.0' | household_type07 == '1.4.1' | household_type07 == '1.4.2' | household_type07 == '1.6.1' | household_type07 == '1.6.2' | household_type07 == '2.3.0' | household_type07 == '1.7.1' | household_type07 == '1.7.2' | household_type07 == '1.7.3' | household_type07 == '1.7.4'
tabulate type_have_children07, missing
merge type_have_children07 into household_DS_all on PERSONID_1
delete-dataset household_DS_type07


// Household type by children 2008
clone-dataset person_DS_Y08 household_DS_type08
use household_DS_type08
import db/BEFOLKNING_REGSTAT_HUSHTYP 2008-01-01 as household_type08
assign-labels household_type08 household_type_txt

generate household_type_by_children08 = household_type08
replace household_type_by_children08 = '0 Without children' if household_type_by_children08 == '1.1.1' | household_type_by_children08 == '1.1.2' | household_type_by_children08 == '1.1.3' | household_type_by_children08 == '1.1.4' | household_type_by_children08 == '1.2.1' | household_type_by_children08 == '1.2.2' | household_type_by_children08 == '1.2.3' | household_type_by_children08 == '1.2.4' | household_type_by_children08 == '2.1.1' | household_type_by_children08 == '2.1.2'
replace household_type_by_children08 = '1 With small children' if household_type_by_children08 == '1.3.1' | household_type_by_children08 == '1.3.2' | household_type_by_children08 == '1.5.1' | household_type_by_children08 == '1.5.2' | household_type_by_children08 == '2.2.0'
replace household_type_by_children08 = '2 With older children' if household_type_by_children08 == '1.4.1' | household_type_by_children08 == '1.4.2' | household_type_by_children08 == '1.6.1' | household_type_by_children08 == '1.6.2' | household_type_by_children08 == '2.3.0'
replace household_type_by_children08 = '3 With adult children' if household_type_by_children08 == '1.7.1' | household_type_by_children08 == '1.7.2' | household_type_by_children08 == '1.7.3' | household_type_by_children08 == '1.7.4'
tabulate household_type_by_children08, missing
merge household_type_by_children08 into household_DS_all on PERSONID_1
generate type_have_children08 = 0
replace type_have_children08 = 1 if  household_type08 == '1.3.1' | household_type08 == '1.3.2' | household_type08 == '1.5.1' | household_type08 == '1.5.2' | household_type08 == '2.2.0' | household_type08 == '1.4.1' | household_type08 == '1.4.2' | household_type08 == '1.6.1' | household_type08 == '1.6.2' | household_type08 == '2.3.0' | household_type08 == '1.7.1' | household_type08 == '1.7.2' | household_type08 == '1.7.3' | household_type08 == '1.7.4'
tabulate type_have_children08, missing
merge type_have_children08 into household_DS_all on PERSONID_1
delete-dataset household_DS_type08


// Household type by children 2009
clone-dataset person_DS_Y09 household_DS_type09
use household_DS_type09
import db/BEFOLKNING_REGSTAT_HUSHTYP 2009-01-01 as household_type09
assign-labels household_type09 household_type_txt

generate household_type_by_children09 = household_type09
replace household_type_by_children09 = '0 Without children' if household_type_by_children09 == '1.1.1' | household_type_by_children09 == '1.1.2' | household_type_by_children09 == '1.1.3' | household_type_by_children09 == '1.1.4' | household_type_by_children09 == '1.2.1' | household_type_by_children09 == '1.2.2' | household_type_by_children09 == '1.2.3' | household_type_by_children09 == '1.2.4' | household_type_by_children09 == '2.1.1' | household_type_by_children09 == '2.1.2'
replace household_type_by_children09 = '1 With small children' if household_type_by_children09 == '1.3.1' | household_type_by_children09 == '1.3.2' | household_type_by_children09 == '1.5.1' | household_type_by_children09 == '1.5.2' | household_type_by_children09 == '2.2.0'
replace household_type_by_children09 = '2 With older children' if household_type_by_children09 == '1.4.1' | household_type_by_children09 == '1.4.2' | household_type_by_children09 == '1.6.1' | household_type_by_children09 == '1.6.2' | household_type_by_children09 == '2.3.0'
replace household_type_by_children09 = '3 With adult children' if household_type_by_children09 == '1.7.1' | household_type_by_children09 == '1.7.2' | household_type_by_children09 == '1.7.3' | household_type_by_children09 == '1.7.4'
tabulate household_type_by_children09, missing
merge household_type_by_children09 into household_DS_all on PERSONID_1
generate type_have_children09 = 0
replace type_have_children09 = 1 if  household_type09 == '1.3.1' | household_type09 == '1.3.2' | household_type09 == '1.5.1' | household_type09 == '1.5.2' | household_type09 == '2.2.0' | household_type09 == '1.4.1' | household_type09 == '1.4.2' | household_type09 == '1.6.1' | household_type09 == '1.6.2' | household_type09 == '2.3.0' | household_type09 == '1.7.1' | household_type09 == '1.7.2' | household_type09 == '1.7.3' | household_type09 == '1.7.4'
tabulate type_have_children09, missing
merge type_have_children09 into household_DS_all on PERSONID_1
delete-dataset household_DS_type09


// Household type by children 2010
clone-dataset person_DS_Y10 household_DS_type10
use household_DS_type10
import db/BEFOLKNING_REGSTAT_HUSHTYP 2010-01-01 as household_type10
assign-labels household_type10 household_type_txt

generate household_type_by_children10 = household_type10
replace household_type_by_children10 = '0 Without children' if household_type_by_children10 == '1.1.1' | household_type_by_children10 == '1.1.2' | household_type_by_children10 == '1.1.3' | household_type_by_children10 == '1.1.4' | household_type_by_children10 == '1.2.1' | household_type_by_children10 == '1.2.2' | household_type_by_children10 == '1.2.3' | household_type_by_children10 == '1.2.4' | household_type_by_children10 == '2.1.1' | household_type_by_children10 == '2.1.2'
replace household_type_by_children10 = '1 With small children' if household_type_by_children10 == '1.3.1' | household_type_by_children10 == '1.3.2' | household_type_by_children10 == '1.5.1' | household_type_by_children10 == '1.5.2' | household_type_by_children10 == '2.2.0'
replace household_type_by_children10 = '2 With older children' if household_type_by_children10 == '1.4.1' | household_type_by_children10 == '1.4.2' | household_type_by_children10 == '1.6.1' | household_type_by_children10 == '1.6.2' | household_type_by_children10 == '2.3.0'
replace household_type_by_children10 = '3 With adult children' if household_type_by_children10 == '1.7.1' | household_type_by_children10 == '1.7.2' | household_type_by_children10 == '1.7.3' | household_type_by_children10 == '1.7.4'
tabulate household_type_by_children10, missing
merge household_type_by_children10 into household_DS_all on PERSONID_1
generate type_have_children10 = 0
replace type_have_children10 = 1 if  household_type10 == '1.3.1' | household_type10 == '1.3.2' | household_type10 == '1.5.1' | household_type10 == '1.5.2' | household_type10 == '2.2.0' | household_type10 == '1.4.1' | household_type10 == '1.4.2' | household_type10 == '1.6.1' | household_type10 == '1.6.2' | household_type10 == '2.3.0' | household_type10 == '1.7.1' | household_type10 == '1.7.2' | household_type10 == '1.7.3' | household_type10 == '1.7.4'
tabulate type_have_children10, missing
merge type_have_children10 into household_DS_all on PERSONID_1
delete-dataset household_DS_type10


// Household type by children 2011
clone-dataset person_DS_Y11 household_DS_type11
use household_DS_type11
import db/BEFOLKNING_REGSTAT_HUSHTYP 2011-01-01 as household_type11
assign-labels household_type11 household_type_txt

generate household_type_by_children11 = household_type11
replace household_type_by_children11 = '0 Without children' if household_type_by_children11 == '1.1.1' | household_type_by_children11 == '1.1.2' | household_type_by_children11 == '1.1.3' | household_type_by_children11 == '1.1.4' | household_type_by_children11 == '1.2.1' | household_type_by_children11 == '1.2.2' | household_type_by_children11 == '1.2.3' | household_type_by_children11 == '1.2.4' | household_type_by_children11 == '2.1.1' | household_type_by_children11 == '2.1.2'
replace household_type_by_children11 = '1 With small children' if household_type_by_children11 == '1.3.1' | household_type_by_children11 == '1.3.2' | household_type_by_children11 == '1.5.1' | household_type_by_children11 == '1.5.2' | household_type_by_children11 == '2.2.0'
replace household_type_by_children11 = '2 With older children' if household_type_by_children11 == '1.4.1' | household_type_by_children11 == '1.4.2' | household_type_by_children11 == '1.6.1' | household_type_by_children11 == '1.6.2' | household_type_by_children11 == '2.3.0'
replace household_type_by_children11 = '3 With adult children' if household_type_by_children11 == '1.7.1' | household_type_by_children11 == '1.7.2' | household_type_by_children11 == '1.7.3' | household_type_by_children11 == '1.7.4'
tabulate household_type_by_children11, missing
merge household_type_by_children11 into household_DS_all on PERSONID_1
generate type_have_children11 = 0
replace type_have_children11 = 1 if  household_type11 == '1.3.1' | household_type11 == '1.3.2' | household_type11 == '1.5.1' | household_type11 == '1.5.2' | household_type11 == '2.2.0' | household_type11 == '1.4.1' | household_type11 == '1.4.2' | household_type11 == '1.6.1' | household_type11 == '1.6.2' | household_type11 == '2.3.0' | household_type11 == '1.7.1' | household_type11 == '1.7.2' | household_type11 == '1.7.3' | household_type11 == '1.7.4'
tabulate type_have_children11, missing
merge type_have_children11 into household_DS_all on PERSONID_1
delete-dataset household_DS_type11


// Household type by children 2012
clone-dataset person_DS_Y12 household_DS_type12
use household_DS_type12
import db/BEFOLKNING_REGSTAT_HUSHTYP 2012-01-01 as household_type12
assign-labels household_type12 household_type_txt

generate household_type_by_children12 = household_type12
replace household_type_by_children12 = '0 Without children' if household_type_by_children12 == '1.1.1' | household_type_by_children12 == '1.1.2' | household_type_by_children12 == '1.1.3' | household_type_by_children12 == '1.1.4' | household_type_by_children12 == '1.2.1' | household_type_by_children12 == '1.2.2' | household_type_by_children12 == '1.2.3' | household_type_by_children12 == '1.2.4' | household_type_by_children12 == '2.1.1' | household_type_by_children12 == '2.1.2'
replace household_type_by_children12 = '1 With small children' if household_type_by_children12 == '1.3.1' | household_type_by_children12 == '1.3.2' | household_type_by_children12 == '1.5.1' | household_type_by_children12 == '1.5.2' | household_type_by_children12 == '2.2.0'
replace household_type_by_children12 = '2 With older children' if household_type_by_children12 == '1.4.1' | household_type_by_children12 == '1.4.2' | household_type_by_children12 == '1.6.1' | household_type_by_children12 == '1.6.2' | household_type_by_children12 == '2.3.0'
replace household_type_by_children12 = '3 With adult children' if household_type_by_children12 == '1.7.1' | household_type_by_children12 == '1.7.2' | household_type_by_children12 == '1.7.3' | household_type_by_children12 == '1.7.4'
tabulate household_type_by_children12, missing
merge household_type_by_children12 into household_DS_all on PERSONID_1
generate type_have_children12 = 0
replace type_have_children12 = 1 if  household_type12 == '1.3.1' | household_type12 == '1.3.2' | household_type12 == '1.5.1' | household_type12 == '1.5.2' | household_type12 == '2.2.0' | household_type12 == '1.4.1' | household_type12 == '1.4.2' | household_type12 == '1.6.1' | household_type12 == '1.6.2' | household_type12 == '2.3.0' | household_type12 == '1.7.1' | household_type12 == '1.7.2' | household_type12 == '1.7.3' | household_type12 == '1.7.4'
tabulate type_have_children12, missing
merge type_have_children12 into household_DS_all on PERSONID_1
delete-dataset household_DS_type12


// Household type by children 2013
clone-dataset person_DS_Y13 household_DS_type13
use household_DS_type13
import db/BEFOLKNING_REGSTAT_HUSHTYP 2013-01-01 as household_type13
assign-labels household_type13 household_type_txt

generate household_type_by_children13 = household_type13
replace household_type_by_children13 = '0 Without children' if household_type_by_children13 == '1.1.1' | household_type_by_children13 == '1.1.2' | household_type_by_children13 == '1.1.3' | household_type_by_children13 == '1.1.4' | household_type_by_children13 == '1.2.1' | household_type_by_children13 == '1.2.2' | household_type_by_children13 == '1.2.3' | household_type_by_children13 == '1.2.4' | household_type_by_children13 == '2.1.1' | household_type_by_children13 == '2.1.2'
replace household_type_by_children13 = '1 With small children' if household_type_by_children13 == '1.3.1' | household_type_by_children13 == '1.3.2' | household_type_by_children13 == '1.5.1' | household_type_by_children13 == '1.5.2' | household_type_by_children13 == '2.2.0'
replace household_type_by_children13 = '2 With older children' if household_type_by_children13 == '1.4.1' | household_type_by_children13 == '1.4.2' | household_type_by_children13 == '1.6.1' | household_type_by_children13 == '1.6.2' | household_type_by_children13 == '2.3.0'
replace household_type_by_children13 = '3 With adult children' if household_type_by_children13 == '1.7.1' | household_type_by_children13 == '1.7.2' | household_type_by_children13 == '1.7.3' | household_type_by_children13 == '1.7.4'
tabulate household_type_by_children13, missing
merge household_type_by_children13 into household_DS_all on PERSONID_1
generate type_have_children13 = 0
replace type_have_children13 = 1 if  household_type13 == '1.3.1' | household_type13 == '1.3.2' | household_type13 == '1.5.1' | household_type13 == '1.5.2' | household_type13 == '2.2.0' | household_type13 == '1.4.1' | household_type13 == '1.4.2' | household_type13 == '1.6.1' | household_type13 == '1.6.2' | household_type13 == '2.3.0' | household_type13 == '1.7.1' | household_type13 == '1.7.2' | household_type13 == '1.7.3' | household_type13 == '1.7.4'
tabulate type_have_children13, missing
merge type_have_children13 into household_DS_all on PERSONID_1
delete-dataset household_DS_type13


// Household type by children 2014
clone-dataset person_DS_Y14 household_DS_type14
use household_DS_type14
import db/BEFOLKNING_REGSTAT_HUSHTYP 2014-01-01 as household_type14
assign-labels household_type14 household_type_txt

generate household_type_by_children14 = household_type14
replace household_type_by_children14 = '0 Without children' if household_type_by_children14 == '1.1.1' | household_type_by_children14 == '1.1.2' | household_type_by_children14 == '1.1.3' | household_type_by_children14 == '1.1.4' | household_type_by_children14 == '1.2.1' | household_type_by_children14 == '1.2.2' | household_type_by_children14 == '1.2.3' | household_type_by_children14 == '1.2.4' | household_type_by_children14 == '2.1.1' | household_type_by_children14 == '2.1.2'
replace household_type_by_children14 = '1 With small children' if household_type_by_children14 == '1.3.1' | household_type_by_children14 == '1.3.2' | household_type_by_children14 == '1.5.1' | household_type_by_children14 == '1.5.2' | household_type_by_children14 == '2.2.0'
replace household_type_by_children14 = '2 With older children' if household_type_by_children14 == '1.4.1' | household_type_by_children14 == '1.4.2' | household_type_by_children14 == '1.6.1' | household_type_by_children14 == '1.6.2' | household_type_by_children14 == '2.3.0'
replace household_type_by_children14 = '3 With adult children' if household_type_by_children14 == '1.7.1' | household_type_by_children14 == '1.7.2' | household_type_by_children14 == '1.7.3' | household_type_by_children14 == '1.7.4'
tabulate household_type_by_children14, missing
merge household_type_by_children14 into household_DS_all on PERSONID_1
generate type_have_children14 = 0
replace type_have_children14 = 1 if  household_type14 == '1.3.1' | household_type14 == '1.3.2' | household_type14 == '1.5.1' | household_type14 == '1.5.2' | household_type14 == '2.2.0' | household_type14 == '1.4.1' | household_type14 == '1.4.2' | household_type14 == '1.6.1' | household_type14 == '1.6.2' | household_type14 == '2.3.0' | household_type14 == '1.7.1' | household_type14 == '1.7.2' | household_type14 == '1.7.3' | household_type14 == '1.7.4'
tabulate type_have_children14, missing
merge type_have_children14 into household_DS_all on PERSONID_1
delete-dataset household_DS_type14


// Household type by children 2015
clone-dataset person_DS_Y15 household_DS_type15
use household_DS_type15
import db/BEFOLKNING_REGSTAT_HUSHTYP 2015-01-01 as household_type15
assign-labels household_type15 household_type_txt

generate household_type_by_children15 = household_type15
replace household_type_by_children15 = '0 Without children' if household_type_by_children15 == '1.1.1' | household_type_by_children15 == '1.1.2' | household_type_by_children15 == '1.1.3' | household_type_by_children15 == '1.1.4' | household_type_by_children15 == '1.2.1' | household_type_by_children15 == '1.2.2' | household_type_by_children15 == '1.2.3' | household_type_by_children15 == '1.2.4' | household_type_by_children15 == '2.1.1' | household_type_by_children15 == '2.1.2'
replace household_type_by_children15 = '1 With small children' if household_type_by_children15 == '1.3.1' | household_type_by_children15 == '1.3.2' | household_type_by_children15 == '1.5.1' | household_type_by_children15 == '1.5.2' | household_type_by_children15 == '2.2.0'
replace household_type_by_children15 = '2 With older children' if household_type_by_children15 == '1.4.1' | household_type_by_children15 == '1.4.2' | household_type_by_children15 == '1.6.1' | household_type_by_children15 == '1.6.2' | household_type_by_children15 == '2.3.0'
replace household_type_by_children15 = '3 With adult children' if household_type_by_children15 == '1.7.1' | household_type_by_children15 == '1.7.2' | household_type_by_children15 == '1.7.3' | household_type_by_children15 == '1.7.4'
tabulate household_type_by_children15, missing
merge household_type_by_children15 into household_DS_all on PERSONID_1
generate type_have_children15 = 0
replace type_have_children15 = 1 if  household_type15 == '1.3.1' | household_type15 == '1.3.2' | household_type15 == '1.5.1' | household_type15 == '1.5.2' | household_type15 == '2.2.0' | household_type15 == '1.4.1' | household_type15 == '1.4.2' | household_type15 == '1.6.1' | household_type15 == '1.6.2' | household_type15 == '2.3.0' | household_type15 == '1.7.1' | household_type15 == '1.7.2' | household_type15 == '1.7.3' | household_type15 == '1.7.4'
tabulate type_have_children15, missing
merge type_have_children15 into household_DS_all on PERSONID_1
delete-dataset household_DS_type15


// Household type by children 2016
clone-dataset person_DS_Y16 household_DS_type16
use household_DS_type16
import db/BEFOLKNING_REGSTAT_HUSHTYP 2016-01-01 as household_type16
assign-labels household_type16 household_type_txt

generate household_type_by_children16 = household_type16
replace household_type_by_children16 = '0 Without children' if household_type_by_children16 == '1.1.1' | household_type_by_children16 == '1.1.2' | household_type_by_children16 == '1.1.3' | household_type_by_children16 == '1.1.4' | household_type_by_children16 == '1.2.1' | household_type_by_children16 == '1.2.2' | household_type_by_children16 == '1.2.3' | household_type_by_children16 == '1.2.4' | household_type_by_children16 == '2.1.1' | household_type_by_children16 == '2.1.2'
replace household_type_by_children16 = '1 With small children' if household_type_by_children16 == '1.3.1' | household_type_by_children16 == '1.3.2' | household_type_by_children16 == '1.5.1' | household_type_by_children16 == '1.5.2' | household_type_by_children16 == '2.2.0'
replace household_type_by_children16 = '2 With older children' if household_type_by_children16 == '1.4.1' | household_type_by_children16 == '1.4.2' | household_type_by_children16 == '1.6.1' | household_type_by_children16 == '1.6.2' | household_type_by_children16 == '2.3.0'
replace household_type_by_children16 = '3 With adult children' if household_type_by_children16 == '1.7.1' | household_type_by_children16 == '1.7.2' | household_type_by_children16 == '1.7.3' | household_type_by_children16 == '1.7.4'
tabulate household_type_by_children16, missing
merge household_type_by_children16 into household_DS_all on PERSONID_1
generate type_have_children16 = 0
replace type_have_children16 = 1 if  household_type16 == '1.3.1' | household_type16 == '1.3.2' | household_type16 == '1.5.1' | household_type16 == '1.5.2' | household_type16 == '2.2.0' | household_type16 == '1.4.1' | household_type16 == '1.4.2' | household_type16 == '1.6.1' | household_type16 == '1.6.2' | household_type16 == '2.3.0' | household_type16 == '1.7.1' | household_type16 == '1.7.2' | household_type16 == '1.7.3' | household_type16 == '1.7.4'
tabulate type_have_children16, missing
merge type_have_children16 into household_DS_all on PERSONID_1
delete-dataset household_DS_type16


// Household type by children 2017
clone-dataset person_DS_Y17 household_DS_type17
use household_DS_type17
import db/BEFOLKNING_REGSTAT_HUSHTYP 2017-01-01 as household_type17
assign-labels household_type17 household_type_txt

generate household_type_by_children17 = household_type17
replace household_type_by_children17 = '0 Without children' if household_type_by_children17 == '1.1.1' | household_type_by_children17 == '1.1.2' | household_type_by_children17 == '1.1.3' | household_type_by_children17 == '1.1.4' | household_type_by_children17 == '1.2.1' | household_type_by_children17 == '1.2.2' | household_type_by_children17 == '1.2.3' | household_type_by_children17 == '1.2.4' | household_type_by_children17 == '2.1.1' | household_type_by_children17 == '2.1.2'
replace household_type_by_children17 = '1 With small children' if household_type_by_children17 == '1.3.1' | household_type_by_children17 == '1.3.2' | household_type_by_children17 == '1.5.1' | household_type_by_children17 == '1.5.2' | household_type_by_children17 == '2.2.0'
replace household_type_by_children17 = '2 With older children' if household_type_by_children17 == '1.4.1' | household_type_by_children17 == '1.4.2' | household_type_by_children17 == '1.6.1' | household_type_by_children17 == '1.6.2' | household_type_by_children17 == '2.3.0'
replace household_type_by_children17 = '3 With adult children' if household_type_by_children17 == '1.7.1' | household_type_by_children17 == '1.7.2' | household_type_by_children17 == '1.7.3' | household_type_by_children17 == '1.7.4'
tabulate household_type_by_children17, missing
merge household_type_by_children17 into household_DS_all on PERSONID_1
generate type_have_children17 = 0
replace type_have_children17 = 1 if  household_type17 == '1.3.1' | household_type17 == '1.3.2' | household_type17 == '1.5.1' | household_type17 == '1.5.2' | household_type17 == '2.2.0' | household_type17 == '1.4.1' | household_type17 == '1.4.2' | household_type17 == '1.6.1' | household_type17 == '1.6.2' | household_type17 == '2.3.0' | household_type17 == '1.7.1' | household_type17 == '1.7.2' | household_type17 == '1.7.3' | household_type17 == '1.7.4'
tabulate type_have_children17, missing
merge type_have_children17 into household_DS_all on PERSONID_1
delete-dataset household_DS_type17


// Household type by children 2018
clone-dataset person_DS_Y18 household_DS_type18
use household_DS_type18
import db/BEFOLKNING_REGSTAT_HUSHTYP 2018-01-01 as household_type18
assign-labels household_type18 household_type_txt

generate household_type_by_children18 = household_type18
replace household_type_by_children18 = '0 Without children' if household_type_by_children18 == '1.1.1' | household_type_by_children18 == '1.1.2' | household_type_by_children18 == '1.1.3' | household_type_by_children18 == '1.1.4' | household_type_by_children18 == '1.2.1' | household_type_by_children18 == '1.2.2' | household_type_by_children18 == '1.2.3' | household_type_by_children18 == '1.2.4' | household_type_by_children18 == '2.1.1' | household_type_by_children18 == '2.1.2'
replace household_type_by_children18 = '1 With small children' if household_type_by_children18 == '1.3.1' | household_type_by_children18 == '1.3.2' | household_type_by_children18 == '1.5.1' | household_type_by_children18 == '1.5.2' | household_type_by_children18 == '2.2.0'
replace household_type_by_children18 = '2 With older children' if household_type_by_children18 == '1.4.1' | household_type_by_children18 == '1.4.2' | household_type_by_children18 == '1.6.1' | household_type_by_children18 == '1.6.2' | household_type_by_children18 == '2.3.0'
replace household_type_by_children18 = '3 With adult children' if household_type_by_children18 == '1.7.1' | household_type_by_children18 == '1.7.2' | household_type_by_children18 == '1.7.3' | household_type_by_children18 == '1.7.4'
tabulate household_type_by_children18, missing
merge household_type_by_children18 into household_DS_all on PERSONID_1
generate type_have_children18 = 0
replace type_have_children18 = 1 if  household_type18 == '1.3.1' | household_type18 == '1.3.2' | household_type18 == '1.5.1' | household_type18 == '1.5.2' | household_type18 == '2.2.0' | household_type18 == '1.4.1' | household_type18 == '1.4.2' | household_type18 == '1.6.1' | household_type18 == '1.6.2' | household_type18 == '2.3.0' | household_type18 == '1.7.1' | household_type18 == '1.7.2' | household_type18 == '1.7.3' | household_type18 == '1.7.4'
tabulate type_have_children18, missing
merge type_have_children18 into household_DS_all on PERSONID_1
delete-dataset household_DS_type18


// Household type by children 2019
clone-dataset person_DS_Y19 household_DS_type19
use household_DS_type19
import db/BEFOLKNING_REGSTAT_HUSHTYP 2019-01-01 as household_type19
assign-labels household_type19 household_type_txt

generate household_type_by_children19 = household_type19
replace household_type_by_children19 = '0 Without children' if household_type_by_children19 == '1.1.1' | household_type_by_children19 == '1.1.2' | household_type_by_children19 == '1.1.3' | household_type_by_children19 == '1.1.4' | household_type_by_children19 == '1.2.1' | household_type_by_children19 == '1.2.2' | household_type_by_children19 == '1.2.3' | household_type_by_children19 == '1.2.4' | household_type_by_children19 == '2.1.1' | household_type_by_children19 == '2.1.2'
replace household_type_by_children19 = '1 With small children' if household_type_by_children19 == '1.3.1' | household_type_by_children19 == '1.3.2' | household_type_by_children19 == '1.5.1' | household_type_by_children19 == '1.5.2' | household_type_by_children19 == '2.2.0'
replace household_type_by_children19 = '2 With older children' if household_type_by_children19 == '1.4.1' | household_type_by_children19 == '1.4.2' | household_type_by_children19 == '1.6.1' | household_type_by_children19 == '1.6.2' | household_type_by_children19 == '2.3.0'
replace household_type_by_children19 = '3 With adult children' if household_type_by_children19 == '1.7.1' | household_type_by_children19 == '1.7.2' | household_type_by_children19 == '1.7.3' | household_type_by_children19 == '1.7.4'
tabulate household_type_by_children19, missing
merge household_type_by_children19 into household_DS_all on PERSONID_1
generate type_have_children19 = 0
replace type_have_children19 = 1 if  household_type19 == '1.3.1' | household_type19 == '1.3.2' | household_type19 == '1.5.1' | household_type19 == '1.5.2' | household_type19 == '2.2.0' | household_type19 == '1.4.1' | household_type19 == '1.4.2' | household_type19 == '1.6.1' | household_type19 == '1.6.2' | household_type19 == '2.3.0' | household_type19 == '1.7.1' | household_type19 == '1.7.2' | household_type19 == '1.7.3' | household_type19 == '1.7.4'
tabulate type_have_children19, missing
merge type_have_children19 into household_DS_all on PERSONID_1
delete-dataset household_DS_type19


// Household type by children 2020
clone-dataset person_DS_Y20 household_DS_type20
use household_DS_type20
import db/BEFOLKNING_REGSTAT_HUSHTYP 2020-01-01 as household_type20
assign-labels household_type20 household_type_txt

generate household_type_by_children20 = household_type20
replace household_type_by_children20 = '0 Without children' if household_type_by_children20 == '1.1.1' | household_type_by_children20 == '1.1.2' | household_type_by_children20 == '1.1.3' | household_type_by_children20 == '1.1.4' | household_type_by_children20 == '1.2.1' | household_type_by_children20 == '1.2.2' | household_type_by_children20 == '1.2.3' | household_type_by_children20 == '1.2.4' | household_type_by_children20 == '2.1.1' | household_type_by_children20 == '2.1.2'
replace household_type_by_children20 = '1 With small children' if household_type_by_children20 == '1.3.1' | household_type_by_children20 == '1.3.2' | household_type_by_children20 == '1.5.1' | household_type_by_children20 == '1.5.2' | household_type_by_children20 == '2.2.0'
replace household_type_by_children20 = '2 With older children' if household_type_by_children20 == '1.4.1' | household_type_by_children20 == '1.4.2' | household_type_by_children20 == '1.6.1' | household_type_by_children20 == '1.6.2' | household_type_by_children20 == '2.3.0'
replace household_type_by_children20 = '3 With adult children' if household_type_by_children20 == '1.7.1' | household_type_by_children20 == '1.7.2' | household_type_by_children20 == '1.7.3' | household_type_by_children20 == '1.7.4'
tabulate household_type_by_children20, missing
merge household_type_by_children20 into household_DS_all on PERSONID_1
generate type_have_children20 = 0
replace type_have_children20 = 1 if  household_type20 == '1.3.1' | household_type20 == '1.3.2' | household_type20 == '1.5.1' | household_type20 == '1.5.2' | household_type20 == '2.2.0' | household_type20 == '1.4.1' | household_type20 == '1.4.2' | household_type20 == '1.6.1' | household_type20 == '1.6.2' | household_type20 == '2.3.0' | household_type20 == '1.7.1' | household_type20 == '1.7.2' | household_type20 == '1.7.3' | household_type20 == '1.7.4'
tabulate type_have_children20, missing
merge type_have_children20 into household_DS_all on PERSONID_1
delete-dataset household_DS_type20


// Household type by children 2021
clone-dataset person_DS_Y21 household_DS_type21
use household_DS_type21
import db/BEFOLKNING_REGSTAT_HUSHTYP 2021-01-01 as household_type21
assign-labels household_type21 household_type_txt

generate household_type_by_children21 = household_type21
replace household_type_by_children21 = '0 Without children' if household_type_by_children21 == '1.1.1' | household_type_by_children21 == '1.1.2' | household_type_by_children21 == '1.1.3' | household_type_by_children21 == '1.1.4' | household_type_by_children21 == '1.2.1' | household_type_by_children21 == '1.2.2' | household_type_by_children21 == '1.2.3' | household_type_by_children21 == '1.2.4' | household_type_by_children21 == '2.1.1' | household_type_by_children21 == '2.1.2'
replace household_type_by_children21 = '1 With small children' if household_type_by_children21 == '1.3.1' | household_type_by_children21 == '1.3.2' | household_type_by_children21 == '1.5.1' | household_type_by_children21 == '1.5.2' | household_type_by_children21 == '2.2.0'
replace household_type_by_children21 = '2 With older children' if household_type_by_children21 == '1.4.1' | household_type_by_children21 == '1.4.2' | household_type_by_children21 == '1.6.1' | household_type_by_children21 == '1.6.2' | household_type_by_children21 == '2.3.0'
replace household_type_by_children21 = '3 With adult children' if household_type_by_children21 == '1.7.1' | household_type_by_children21 == '1.7.2' | household_type_by_children21 == '1.7.3' | household_type_by_children21 == '1.7.4'
tabulate household_type_by_children21, missing
merge household_type_by_children21 into household_DS_all on PERSONID_1
generate type_have_children21 = 0
replace type_have_children21 = 1 if  household_type21 == '1.3.1' | household_type21 == '1.3.2' | household_type21 == '1.5.1' | household_type21 == '1.5.2' | household_type21 == '2.2.0' | household_type21 == '1.4.1' | household_type21 == '1.4.2' | household_type21 == '1.6.1' | household_type21 == '1.6.2' | household_type21 == '2.3.0' | household_type21 == '1.7.1' | household_type21 == '1.7.2' | household_type21 == '1.7.3' | household_type21 == '1.7.4'
tabulate type_have_children21, missing
merge type_have_children21 into household_DS_all on PERSONID_1
delete-dataset household_DS_type21


// Household type by children 2022
clone-dataset person_DS_Y22 household_DS_type22
use household_DS_type22
import db/BEFOLKNING_REGSTAT_HUSHTYP 2022-01-01 as household_type22
assign-labels household_type22 household_type_txt

generate resident22 = 1
barchart(count) resident22, over(household_type22)

generate household_type_by_children22 = household_type22
replace household_type_by_children22 = '0 Without children' if household_type_by_children22 == '1.1.1' | household_type_by_children22 == '1.1.2' | household_type_by_children22 == '1.1.3' | household_type_by_children22 == '1.1.4' | household_type_by_children22 == '1.2.1' | household_type_by_children22 == '1.2.2' | household_type_by_children22 == '1.2.3' | household_type_by_children22 == '1.2.4' | household_type_by_children22 == '2.1.1' | household_type_by_children22 == '2.1.2'
replace household_type_by_children22 = '1 With small children' if household_type_by_children22 == '1.3.1' | household_type_by_children22 == '1.3.2' | household_type_by_children22 == '1.5.1' | household_type_by_children22 == '1.5.2' | household_type_by_children22 == '2.2.0'
replace household_type_by_children22 = '2 With older children' if household_type_by_children22 == '1.4.1' | household_type_by_children22 == '1.4.2' | household_type_by_children22 == '1.6.1' | household_type_by_children22 == '1.6.2' | household_type_by_children22 == '2.3.0'
replace household_type_by_children22 = '3 With adult children' if household_type_by_children22 == '1.7.1' | household_type_by_children22 == '1.7.2' | household_type_by_children22 == '1.7.3' | household_type_by_children22 == '1.7.4'
tabulate household_type_by_children22, missing
merge household_type_by_children22 into household_DS_all on PERSONID_1
generate type_have_children22 = 0
replace type_have_children22 = 1 if  household_type22 == '1.3.1' | household_type22 == '1.3.2' | household_type22 == '1.5.1' | household_type22 == '1.5.2' | household_type22 == '2.2.0' | household_type22 == '1.4.1' | household_type22 == '1.4.2' | household_type22 == '1.6.1' | household_type22 == '1.6.2' | household_type22 == '2.3.0' | household_type22 == '1.7.1' | household_type22 == '1.7.2' | household_type22 == '1.7.3' | household_type22 == '1.7.4'
tabulate type_have_children22, missing
merge type_have_children22 into household_DS_all on PERSONID_1
delete-dataset household_DS_type22


textblock
Urban Settlement
------------------
The urban settlement is also called this stage.

::::: There are three (3) categories of settlement:
- s: a person not resident in an urban area (also it means, the person resident in scattered areas),
- t: a person living in an urban area,
- u: person unplaced in dense/scattered area, due to lack of coordinates.

Note: In later steps, this data is aggregated at the household level.

Note: data on urban settlement for 2010 is not available. https://microdata.no/discovery/variable/no.ssb.fdb/26/BEFOLKNING_TS_KODE?searchString=BEFOLKNING_TS_KODE

endblock

clone-dataset person_DS_Y05 household_DS_urban_settlement05
use household_DS_urban_settlement05
import db/BEFOLKNING_TS_KODE 2005-01-01 as urban_settlement05
define-labels urban_settlement_txt 's' nonUrban 't' Urban 'u' Unknown
assign-labels urban_settlement05 urban_settlement_txt
drop if urban_settlement05 == "u" // Drop missing "Unknown", i.e., missing values
// Create an independent dummy variable for urbanism: "t" is urban.
generate live_in_urban05 = 0
replace live_in_urban05 = 1 if urban_settlement05 == "t"
piechart urban_settlement05
merge live_in_urban05 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement05


clone-dataset person_DS_Y06 household_DS_urban_settlement06
use household_DS_urban_settlement06
import db/BEFOLKNING_TS_KODE 2006-01-01 as urban_settlement06
assign-labels urban_settlement06 urban_settlement_txt
drop if urban_settlement06 == "u"
generate live_in_urban06 = 0
replace live_in_urban06 = 1 if urban_settlement06 == "t"
merge live_in_urban06 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement06

clone-dataset person_DS_Y07 household_DS_urban_settlement07
use household_DS_urban_settlement07
import db/BEFOLKNING_TS_KODE 2007-01-01 as urban_settlement07
assign-labels urban_settlement07 urban_settlement_txt
drop if urban_settlement07 == "u"
generate live_in_urban07 = 0
replace live_in_urban07 = 1 if urban_settlement07 == "t"
merge live_in_urban07 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement07

clone-dataset person_DS_Y08 household_DS_urban_settlement08
use household_DS_urban_settlement08
import db/BEFOLKNING_TS_KODE 2008-01-01 as urban_settlement08
assign-labels urban_settlement08 urban_settlement_txt
drop if urban_settlement08 == "u"
generate live_in_urban08 = 0
replace live_in_urban08 = 1 if urban_settlement08 == "t"
merge live_in_urban08 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement08

clone-dataset person_DS_Y09 household_DS_urban_settlement09
use household_DS_urban_settlement09
import db/BEFOLKNING_TS_KODE 2009-01-01 as urban_settlement09
assign-labels urban_settlement09 urban_settlement_txt
drop if urban_settlement09 == "u"
generate live_in_urban09 = 0
replace live_in_urban09 = 1 if urban_settlement09 == "t"
merge live_in_urban09 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement09

// #Note that data on urban settlement for 2010 is not available. https://microdata.no/discovery/variable/no.ssb.fdb/19/BEFOLKNING_TS_KODE?searchString=BEFOLKNING_TS_KODE
//clone-dataset person_DS_Y10 household_DS_urban_settlement10
//use household_DS_urban_settlement10
//generate live_in_urban10 = 0
//merge live_in_urban10 into household_DS_all on PERSONID_1
//delete-dataset household_DS_urban_settlement10

clone-dataset person_DS_Y11 household_DS_urban_settlement11
use household_DS_urban_settlement11
import db/BEFOLKNING_TS_KODE 2011-01-01 as urban_settlement11
assign-labels urban_settlement11 urban_settlement_txt
drop if urban_settlement11 == "u"
generate live_in_urban11 = 0
replace live_in_urban11 = 1 if urban_settlement11 == "t"
piechart urban_settlement11
merge live_in_urban11 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement11

clone-dataset person_DS_Y12 household_DS_urban_settlement12
use household_DS_urban_settlement12
import db/BEFOLKNING_TS_KODE 2012-01-01 as urban_settlement12
assign-labels urban_settlement12 urban_settlement_txt
drop if urban_settlement12 == "u"
generate live_in_urban12 = 0
replace live_in_urban12 = 1 if urban_settlement12 == "t"
merge live_in_urban12 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement12

clone-dataset person_DS_Y13 household_DS_urban_settlement13
use household_DS_urban_settlement13
import db/BEFOLKNING_TS_KODE 2013-01-01 as urban_settlement13
assign-labels urban_settlement13 urban_settlement_txt
drop if urban_settlement13 == "u"
generate live_in_urban13 = 0
replace live_in_urban13 = 1 if urban_settlement13 == "t"
merge live_in_urban13 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement13

clone-dataset person_DS_Y14 household_DS_urban_settlement14
use household_DS_urban_settlement14
import db/BEFOLKNING_TS_KODE 2014-01-01 as urban_settlement14
assign-labels urban_settlement14 urban_settlement_txt
drop if urban_settlement14 == "u"
generate live_in_urban14 = 0
replace live_in_urban14 = 1 if urban_settlement14 == "t"
merge live_in_urban14 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement14

clone-dataset person_DS_Y15 household_DS_urban_settlement15
use household_DS_urban_settlement15
import db/BEFOLKNING_TS_KODE 2015-01-01 as urban_settlement15
assign-labels urban_settlement15 urban_settlement_txt
drop if urban_settlement15 == "u"
generate live_in_urban15 = 0
replace live_in_urban15 = 1 if urban_settlement15 == "t"
merge live_in_urban15 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement15

clone-dataset person_DS_Y16 household_DS_urban_settlement16
use household_DS_urban_settlement16
import db/BEFOLKNING_TS_KODE 2016-01-01 as urban_settlement16
assign-labels urban_settlement16 urban_settlement_txt
drop if urban_settlement16 == "u"
generate live_in_urban16 = 0
replace live_in_urban16 = 1 if urban_settlement16 == "t"
piechart urban_settlement16
merge live_in_urban16 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement16

clone-dataset person_DS_Y17 household_DS_urban_settlement17
use household_DS_urban_settlement17
import db/BEFOLKNING_TS_KODE 2017-01-01 as urban_settlement17
assign-labels urban_settlement17 urban_settlement_txt
drop if urban_settlement17 == "u"
generate live_in_urban17 = 0
replace live_in_urban17 = 1 if urban_settlement17 == "t"
merge live_in_urban17 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement17

clone-dataset person_DS_Y18 household_DS_urban_settlement18
use household_DS_urban_settlement18
import db/BEFOLKNING_TS_KODE 2018-01-01 as urban_settlement18
assign-labels urban_settlement18 urban_settlement_txt
drop if urban_settlement18 == "u"
generate live_in_urban18 = 0
replace live_in_urban18 = 1 if urban_settlement18 == "t"
merge live_in_urban18 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement18

clone-dataset person_DS_Y19 household_DS_urban_settlement19
use household_DS_urban_settlement19
import db/BEFOLKNING_TS_KODE 2019-01-01 as urban_settlement19
assign-labels urban_settlement19 urban_settlement_txt
drop if urban_settlement19 == "u"
generate live_in_urban19 = 0
replace live_in_urban19 = 1 if urban_settlement19 == "t"
merge live_in_urban19 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement19

clone-dataset person_DS_Y20 household_DS_urban_settlement20
use household_DS_urban_settlement20
import db/BEFOLKNING_TS_KODE 2020-01-01 as urban_settlement20
assign-labels urban_settlement20 urban_settlement_txt
drop if urban_settlement20 == "u"
generate live_in_urban20 = 0
replace live_in_urban20 = 1 if urban_settlement20 == "t"
merge live_in_urban20 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement20

clone-dataset person_DS_Y21 household_DS_urban_settlement21
use household_DS_urban_settlement21
import db/BEFOLKNING_TS_KODE 2021-01-01 as urban_settlement21
assign-labels urban_settlement21 urban_settlement_txt
drop if urban_settlement21 == "u"
generate live_in_urban21 = 0
replace live_in_urban21 = 1 if urban_settlement21 == "t"
merge live_in_urban21 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement21

clone-dataset person_DS_Y22 household_DS_urban_settlement22
use household_DS_urban_settlement22
import db/BEFOLKNING_TS_KODE 2022-01-01 as urban_settlement22
assign-labels urban_settlement22 urban_settlement_txt
drop if urban_settlement22 == "u"
generate live_in_urban22 = 0
replace live_in_urban22 = 1 if urban_settlement22 == "t"
merge live_in_urban22 into household_DS_all on PERSONID_1
delete-dataset household_DS_urban_settlement22


// Do some housekeeping: delete datasets that are no longer needed.
delete-dataset person_DS_Y05
delete-dataset person_DS_Y06
delete-dataset person_DS_Y07
delete-dataset person_DS_Y08
delete-dataset person_DS_Y09
delete-dataset person_DS_Y10
delete-dataset person_DS_Y11
delete-dataset person_DS_Y12
delete-dataset person_DS_Y13
delete-dataset person_DS_Y14
delete-dataset person_DS_Y15
delete-dataset person_DS_Y16
delete-dataset person_DS_Y17
delete-dataset person_DS_Y18
delete-dataset person_DS_Y19
delete-dataset person_DS_Y20
delete-dataset person_DS_Y21
delete-dataset person_DS_Y22


use household_DS_all
// We generate natural logarithm of income in this step. To avoid missing values generated by the natural logarithm transformation, negative values of income are set to 1.

summarize household_income05 if household_income05 <= 0
replace household_income05 = 1 if household_income05 <= 0
generate household_income_natlog05 = ln(household_income05) 

summarize household_income06 if household_income06 <= 0
replace household_income06 = 1 if household_income06 <= 0
generate household_income_natlog06 = ln(household_income06) 

summarize household_income07 if household_income07 <= 0
replace household_income07 = 1 if household_income07 <= 0
generate household_income_natlog07 = ln(household_income07) 

summarize household_income08 if household_income08 <= 0
replace household_income08 = 1 if household_income08 <= 0
generate household_income_natlog08 = ln(household_income08) 

summarize household_income09 if household_income09 <= 0
replace household_income09 = 1 if household_income09 <= 0
generate household_income_natlog09 = ln(household_income09) 

summarize household_income10 if household_income10 <= 0
replace household_income10 = 1 if household_income10 <= 0
generate household_income_natlog10 = ln(household_income10) 

summarize household_income11 if household_income11 <= 0
replace household_income11 = 1 if household_income11 <= 0
generate household_income_natlog11 = ln(household_income11) 

summarize household_income12 if household_income12 <= 0
replace household_income12 = 1 if household_income12 <= 0
generate household_income_natlog12 = ln(household_income12) 

summarize household_income13 if household_income13 <= 0
replace household_income13 = 1 if household_income13 <= 0
generate household_income_natlog13 = ln(household_income13) 

summarize household_income14 if household_income14 <= 0
replace household_income14 = 1 if household_income14 <= 0
generate household_income_natlog14 = ln(household_income14) 

summarize household_income15 if household_income15 <= 0
replace household_income15 = 1 if household_income15 <= 0
generate household_income_natlog15 = ln(household_income15) 

summarize household_income16 if household_income16 <= 0
replace household_income16 = 1 if household_income16 <= 0
generate household_income_natlog16 = ln(household_income16) 

summarize household_income17 if household_income17 <= 0
replace household_income17 = 1 if household_income17 <= 0
generate household_income_natlog17 = ln(household_income17) 

summarize household_income18 if household_income18 <= 0
replace household_income18 = 1 if household_income18 <= 0
generate household_income_natlog18 = ln(household_income18) 

summarize household_income19 if household_income19 <= 0
replace household_income19 = 1 if household_income19 <= 0
generate household_income_natlog19 = ln(household_income19) 

summarize household_income20 if household_income20 <= 0
replace household_income20 = 1 if household_income20 <= 0
generate household_income_natlog20 = ln(household_income20) 

summarize household_income21 if household_income21 <= 0
replace household_income21 = 1 if household_income21 <= 0
generate household_income_natlog21 = ln(household_income21) 

//summarize household_income22 if household_income22 <= 0
//replace household_income22 = 1 if household_income22 <= 0
//generate household_income_natlog22 = ln(household_income22) 


// For the Green panel data
clone-dataset household_DS_all household_DS_2005_2022_atleast_once_green_owners_wide

// For the Gray descriptive analysis
clone-dataset household_DS_all household_DS_2005_2022_always_gray_owners_wide


// Continue with the Green panel data
// Streamling of the forked dataset "Green": Keep any "once_owner" household who ever owned any green vehicle over the period...
use household_DS_2005_2022_atleast_once_green_owners_wide
keep if owned_vehicle_green_per_household05 >= 1 | owned_vehicle_green_per_household06 >= 1 | owned_vehicle_green_per_household07 >= 1 | owned_vehicle_green_per_household08 >= 1 | owned_vehicle_green_per_household09 >= 1 | owned_vehicle_green_per_household10 >= 1 | owned_vehicle_green_per_household11 >= 1 | owned_vehicle_green_per_household12 >= 1 | owned_vehicle_green_per_household13 >= 1 | owned_vehicle_green_per_household14 >= 1 | owned_vehicle_green_per_household15 >= 1 | owned_vehicle_green_per_household16 >= 1 | owned_vehicle_green_per_household17 >= 1 | owned_vehicle_green_per_household18 >= 1 | owned_vehicle_green_per_household19 >= 1 | owned_vehicle_green_per_household20 >= 1 | owned_vehicle_green_per_household21 >= 1 | owned_vehicle_green_per_household22 >= 1

// For the Green descriptive analysis
clone-dataset household_DS_2005_2022_atleast_once_green_owners_wide household_DS_2005_2022_atleast_once_green_owners_descriptive

// The following is not needed for the green panel.
use household_DS_2005_2022_atleast_once_green_owners_wide
drop owned_vehicle_gray_per_household05
drop owned_vehicle_gray_per_household06
drop owned_vehicle_gray_per_household07
drop owned_vehicle_gray_per_household08
drop owned_vehicle_gray_per_household09
drop owned_vehicle_gray_per_household10
drop owned_vehicle_gray_per_household11
drop owned_vehicle_gray_per_household12
drop owned_vehicle_gray_per_household13
drop owned_vehicle_gray_per_household14
drop owned_vehicle_gray_per_household15
drop owned_vehicle_gray_per_household16
drop owned_vehicle_gray_per_household17
drop owned_vehicle_gray_per_household18
drop owned_vehicle_gray_per_household19
drop owned_vehicle_gray_per_household20
drop owned_vehicle_gray_per_household21
drop owned_vehicle_gray_per_household22

drop owned_vehicle_all_per_household05
drop owned_vehicle_all_per_household06
drop owned_vehicle_all_per_household07
drop owned_vehicle_all_per_household08
drop owned_vehicle_all_per_household09
drop owned_vehicle_all_per_household10
drop owned_vehicle_all_per_household11
drop owned_vehicle_all_per_household12
drop owned_vehicle_all_per_household13
drop owned_vehicle_all_per_household14
drop owned_vehicle_all_per_household15
drop owned_vehicle_all_per_household16
drop owned_vehicle_all_per_household17
drop owned_vehicle_all_per_household18
drop owned_vehicle_all_per_household19
drop owned_vehicle_all_per_household20
drop owned_vehicle_all_per_household21
drop owned_vehicle_all_per_household22

// income, wealth and debt in NOK, and a detailed household_type_by_children are not needed for this panel data anlysis.

drop household_income05
drop household_income06
drop household_income07
drop household_income08
drop household_income09
drop household_income10
drop household_income11
drop household_income12
drop household_income13
drop household_income14
drop household_income15
drop household_income16
drop household_income17
drop household_income18
drop household_income19
drop household_income20
drop household_income21
//drop household_income22

drop household_wealth05
drop household_wealth06
drop household_wealth07
drop household_wealth08
drop household_wealth09
drop household_wealth10
drop household_wealth11
drop household_wealth12
drop household_wealth13
drop household_wealth14
drop household_wealth15
drop household_wealth16
drop household_wealth17
drop household_wealth18
drop household_wealth19
drop household_wealth20
//drop household_wealth21
//drop household_wealth22

drop household_debt05
drop household_debt06
drop household_debt07
drop household_debt08
drop household_debt09
drop household_debt10
drop household_debt11
drop household_debt12
drop household_debt13
drop household_debt14
drop household_debt15
drop household_debt16
drop household_debt17
drop household_debt18
drop household_debt19
drop household_debt20
drop household_debt21
//drop household_debt22

drop household_type_by_children05
drop household_type_by_children06
drop household_type_by_children07
drop household_type_by_children08
drop household_type_by_children09
drop household_type_by_children10
drop household_type_by_children11
drop household_type_by_children12
drop household_type_by_children13
drop household_type_by_children14
drop household_type_by_children15
drop household_type_by_children16
drop household_type_by_children17
drop household_type_by_children18
drop household_type_by_children19
drop household_type_by_children20
drop household_type_by_children21
drop household_type_by_children22

reshape-to-panel household_size household_background household_residence_work household_income_natlog household_highest_edu_numeric type_have_children live_in_urban owned_vehicle_green_per_household

//A new streamling of the dataset: Any dissolved household over the time period should be dropped on years with missing data.
drop if sysmiss(household_size)

tabulate live_in_urban if date@panel == 10

// Urban data for 2010 and income data for 2022 is not reported in v.26. So, we drop all data fror these years from the panel regression analysis.

use household_DS_2005_2022_atleast_once_green_owners_wide
drop if date@panel == 10
drop if date@panel == 22
drop if sysmiss(household_size)


// For the Gray panel data analysis
// Streamling of the forked dataset "gray": Drop any "once_owner" household who ever owned any green vehicle over the period...
use household_DS_2005_2022_always_gray_owners_wide
drop if owned_vehicle_green_per_household05 >= 1 | owned_vehicle_green_per_household06 >= 1 | owned_vehicle_green_per_household07 >= 1 | owned_vehicle_green_per_household08 >= 1 | owned_vehicle_green_per_household09 >= 1 | owned_vehicle_green_per_household10 >= 1 | owned_vehicle_green_per_household11 >= 1 | owned_vehicle_green_per_household12 >= 1 | owned_vehicle_green_per_household13 >= 1 | owned_vehicle_green_per_household14 >= 1 | owned_vehicle_green_per_household15 >= 1 | owned_vehicle_green_per_household16 >= 1 | owned_vehicle_green_per_household17 >= 1 | owned_vehicle_green_per_household18 >= 1 | owned_vehicle_green_per_household19 >= 1 | owned_vehicle_green_per_household20 >= 1 | owned_vehicle_green_per_household21 >= 1 | owned_vehicle_green_per_household22 >= 1

// For Gray descriptive analysis
clone-dataset household_DS_2005_2022_always_gray_owners_wide household_DS_2005_2022_always_gray_owners_descriptive

// Since this group have never had a green car, we can drop all owned_vehicle_green_per_household variables for Gray panel analysis.
use household_DS_2005_2022_always_gray_owners_wide
drop owned_vehicle_green_per_household05
drop owned_vehicle_green_per_household06
drop owned_vehicle_green_per_household07
drop owned_vehicle_green_per_household08
drop owned_vehicle_green_per_household09
drop owned_vehicle_green_per_household10
drop owned_vehicle_green_per_household11
drop owned_vehicle_green_per_household12
drop owned_vehicle_green_per_household13
drop owned_vehicle_green_per_household14
drop owned_vehicle_green_per_household15
drop owned_vehicle_green_per_household16
drop owned_vehicle_green_per_household17
drop owned_vehicle_green_per_household18
drop owned_vehicle_green_per_household19
drop owned_vehicle_green_per_household20
drop owned_vehicle_green_per_household21
drop owned_vehicle_green_per_household22

drop owned_vehicle_all_per_household05
drop owned_vehicle_all_per_household06
drop owned_vehicle_all_per_household07
drop owned_vehicle_all_per_household08
drop owned_vehicle_all_per_household09
drop owned_vehicle_all_per_household10
drop owned_vehicle_all_per_household11
drop owned_vehicle_all_per_household12
drop owned_vehicle_all_per_household13
drop owned_vehicle_all_per_household14
drop owned_vehicle_all_per_household15
drop owned_vehicle_all_per_household16
drop owned_vehicle_all_per_household17
drop owned_vehicle_all_per_household18
drop owned_vehicle_all_per_household19
drop owned_vehicle_all_per_household20
drop owned_vehicle_all_per_household21
drop owned_vehicle_all_per_household22

// Again, income, wealth and debt in NOK, and a detailed household_type_by_children are not needed for this panel data anlysis.

drop household_income05
drop household_income06
drop household_income07
drop household_income08
drop household_income09
drop household_income10
drop household_income11
drop household_income12
drop household_income13
drop household_income14
drop household_income15
drop household_income16
drop household_income17
drop household_income18
drop household_income19
drop household_income20
drop household_income21
//drop household_income22

drop household_wealth05
drop household_wealth06
drop household_wealth07
drop household_wealth08
drop household_wealth09
drop household_wealth10
drop household_wealth11
drop household_wealth12
drop household_wealth13
drop household_wealth14
drop household_wealth15
drop household_wealth16
drop household_wealth17
drop household_wealth18
drop household_wealth19
drop household_wealth20
//drop household_wealth21
//drop household_wealth22

drop household_debt05
drop household_debt06
drop household_debt07
drop household_debt08
drop household_debt09
drop household_debt10
drop household_debt11
drop household_debt12
drop household_debt13
drop household_debt14
drop household_debt15
drop household_debt16
drop household_debt17
drop household_debt18
drop household_debt19
drop household_debt20
drop household_debt21
//drop household_debt22

drop household_type_by_children05
drop household_type_by_children06
drop household_type_by_children07
drop household_type_by_children08
drop household_type_by_children09
drop household_type_by_children10
drop household_type_by_children11
drop household_type_by_children12
drop household_type_by_children13
drop household_type_by_children14
drop household_type_by_children15
drop household_type_by_children16
drop household_type_by_children17
drop household_type_by_children18
drop household_type_by_children19
drop household_type_by_children20
drop household_type_by_children21
drop household_type_by_children22

reshape-to-panel household_size household_background household_residence_work household_income_natlog household_highest_edu_numeric type_have_children live_in_urban owned_vehicle_gray_per_household

//A new streamling of the dataset: Any dissolved household over the time period should be dropped on years with missing data.
drop if sysmiss(household_size)


// Urban data for 2010 and income data for 2022 is not reported in v.26. So, we drop all data fror these years from the panel regression analysis.
use household_DS_2005_2022_always_gray_owners_wide
drop if date@panel == 10
drop if date@panel == 22
drop if sysmiss(household_size)


textblock
VIF and Tolerance
-------
For Green
endblock

use household_DS_2005_2022_atleast_once_green_owners_descriptive

regress owned_vehicle_green_per_household21 household_income_natlog21 household_size21 household_residence_work21 household_highest_edu_numeric21 type_have_children21 live_in_urban21 household_background21, vif

textblock
VIF and Tolerance
-------
For Gray
endblock

use household_DS_2005_2022_always_gray_owners_descriptive

regress owned_vehicle_gray_per_household21 household_income_natlog21 household_size21 household_residence_work21 household_highest_edu_numeric21 type_have_children21 live_in_urban21 household_background21, vif


textblock
some tests for instrument(s) 
-------
unrestricted vs restricted model
endblock

textblock
For Green
-------
unrestricted vs restricted model: results
endblock

use household_DS_2005_2022_atleast_once_green_owners_descriptive

tabulate household_background21, missing
piechart household_background21
histogram household_background21, freq

// Unrestricted model for Green
regress household_income_natlog21 household_size21  household_residence_work21 household_highest_edu_numeric21 type_have_children21 live_in_urban21 household_background21, het_iid het_fstat

regress-predict household_income_natlog21 household_size21  household_residence_work21 household_highest_edu_numeric21 type_have_children21 live_in_urban21 household_background21, residuals(residuals_income_background_green)

histogram residuals_income_background_green, normal
correlate household_background21 residuals_income_background_green, sig
drop residuals_income_background_green // No longer needed!

// Restricted model for Green
regress household_income_natlog21 household_size21  household_residence_work21 household_highest_edu_numeric21 type_have_children21 live_in_urban21, het_iid het_fstat


textblock
For Gray
-------
unrestricted vs restricted model: results
endblock

use household_DS_2005_2022_always_gray_owners_descriptive

tabulate household_background21, missing
piechart household_background21
histogram household_background21, freq

// Unrestricted model for Gray
regress household_income_natlog21 household_size21  household_residence_work21 household_highest_edu_numeric21 type_have_children21 live_in_urban21 household_background21, het_iid het_fstat

regress-predict household_income_natlog21 household_size21  household_residence_work21 household_highest_edu_numeric21 type_have_children21 live_in_urban21 household_background21, residuals(residuals_income_background_gray)

histogram residuals_income_background_gray, normal
correlate household_background21 residuals_income_background_gray, sig
drop residuals_income_background_gray // No longer needed!

// Restricted model for Gray
regress household_income_natlog21 household_size21  household_residence_work21 household_highest_edu_numeric21 type_have_children21 live_in_urban21, het_iid het_fstat


textblock
The pairwise correlations ...
-------
including the instrument(s).
endblock

textblock
The pairwise correlations ...
-------

endblock

use household_DS_all

// Year 2020
correlate owned_vehicle_all_per_household20 owned_vehicle_gray_per_household20 owned_vehicle_green_per_household20 household_size20 household_residence_work20 household_income20 household_income_natlog20 household_wealth20 household_debt20 household_highest_edu_numeric20 type_have_children20 live_in_urban20 household_background20, pairwise

// Year 2021
correlate owned_vehicle_all_per_household21 owned_vehicle_gray_per_household21 owned_vehicle_green_per_household21 household_size21 household_residence_work21 household_income21 household_income_natlog21 household_highest_edu_numeric21 type_have_children21 live_in_urban21 household_background21, pairwise 


textblock
The pairwise correlations ...
-------
including the instrument(s): "at least once green owners" dataset
endblock

use household_DS_2005_2022_atleast_once_green_owners_descriptive

// Year 2020
correlate owned_vehicle_green_per_household20 household_size20 household_residence_work20 household_income20 household_income_natlog20 household_wealth20 household_debt20 household_highest_edu_numeric20 type_have_children20 live_in_urban20 household_background20, pairwise

correlate owned_vehicle_green_per_household20 household_background20, sig
correlate household_income_natlog20 household_background20, sig
correlate household_income20 household_background20, sig

// Year 2021
correlate owned_vehicle_green_per_household21 household_size21 household_residence_work21 household_income21 household_income_natlog21 household_highest_edu_numeric21 type_have_children21 live_in_urban21 household_background21, pairwise 

correlate owned_vehicle_green_per_household21 household_background21, sig
correlate household_income_natlog21 household_background21, sig
correlate household_income21 household_background21, sig


textblock
The pairwise correlations ...
-------
including the instrument(s): "always gray owners" dataset
endblock

use household_DS_2005_2022_always_gray_owners_descriptive

// Year 2020
correlate owned_vehicle_gray_per_household20 household_size20 household_residence_work20 household_income20 household_income_natlog20 household_wealth20 household_debt20 household_highest_edu_numeric20 type_have_children20 live_in_urban20 household_background20, pairwise

correlate owned_vehicle_gray_per_household20 household_background20, sig
correlate household_income_natlog20 household_background20, sig
correlate household_income20 household_background20, sig

// Year 2021
correlate owned_vehicle_gray_per_household21 household_size21 household_residence_work21 household_income21 household_income_natlog21 household_highest_edu_numeric21 type_have_children21 live_in_urban21 household_background21, pairwise 

correlate owned_vehicle_gray_per_household21 household_background21, sig
correlate household_income_natlog21 household_background21, sig
correlate household_income21 household_background21, sig


//use household_DS_2005_2022_always_gray_owners
//generate some_results = invFtail(1,22251180,0.05)
//tabulate some_results



textblock
Green vehicles ownership Panel Data: Ignoring income endogeneity
---------------------------------------
2005 to 2021 (except for 2010)
endblock

use household_DS_2005_2022_atleast_once_green_owners_wide

// Perform hausman-test
// dependent variable: Green vehicle ownership
hausman owned_vehicle_green_per_household household_size household_residence_work household_income_natlog household_highest_edu_numeric type_have_children live_in_urban


textblock
IV regression: 1st stage for Green
-------
"household background" is the instrument.
endblock

hausman household_income_natlog household_size household_residence_work household_highest_edu_numeric type_have_children live_in_urban household_background

regress-panel household_income_natlog household_size household_residence_work household_highest_edu_numeric type_have_children live_in_urban household_background, fe

regress-panel-predict household_income_natlog household_size household_residence_work household_highest_edu_numeric type_have_children live_in_urban household_background, fe predicted(household_income_natlog_HAT) residuals(residuals_income_natlog_stage1_green)

histogram residuals_income_natlog_stage1_green, normal

textblock
IV regression: 2nd stage for Green
-------
"household background" is the instrument.
endblock

hausman owned_vehicle_green_per_household household_size household_residence_work household_income_natlog_HAT household_highest_edu_numeric type_have_children live_in_urban



textblock
Gray vehicles ownership Panel Data: Ignoring income endogeneity
---------------------------------------
2005 to 2021 (except for 2010)
endblock

use household_DS_2005_2022_always_gray_owners_wide

// Perform hausman-test
// dependent variable: gray vehicle ownership
hausman owned_vehicle_gray_per_household household_size household_residence_work household_income_natlog household_highest_edu_numeric type_have_children live_in_urban


textblock
IV regression: 1st stage for Gray
-------
"household background" is the instrument.
endblock

hausman household_income_natlog household_size household_residence_work household_highest_edu_numeric type_have_children live_in_urban household_background

regress-panel household_income_natlog household_size  household_residence_work household_highest_edu_numeric type_have_children live_in_urban household_background, fe

regress-panel-predict household_income_natlog household_size household_residence_work household_highest_edu_numeric type_have_children live_in_urban household_background, fe predicted(household_income_natlog_HAT) residuals(residuals_income_natlog_stage1_gray)

histogram residuals_income_natlog_stage1_gray, normal

textblock
IV regression: 2nd stage for Gray
-------
...
endblock

hausman owned_vehicle_gray_per_household household_size household_residence_work household_income_natlog_HAT household_highest_edu_numeric type_have_children live_in_urban



textblock
Descriptive analysis for the paper: Green Adopters
---------------------------------------
2005 to 2022: An overview
endblock

use household_DS_2005_2022_atleast_once_green_owners_descriptive


summarize household_income05 household_income06 household_income07 household_income08 household_income09 household_income10 household_income11 household_income12 household_income13 household_income14 household_income15 household_income16 household_income17 household_income18 household_income19 household_income20 household_income21

summarize household_wealth05 household_wealth06 household_wealth07 household_wealth08 household_wealth09 household_wealth10 household_wealth11 household_wealth12 household_wealth13 household_wealth14 household_wealth15 household_wealth16 household_wealth17 household_wealth18 household_wealth19 household_wealth20

summarize household_debt05 household_debt06 household_debt07 household_debt08 household_debt09 household_debt10 household_debt11 household_debt12 household_debt13 household_debt14 household_debt15 household_debt16 household_debt17 household_debt18 household_debt19 household_debt20 household_debt21

summarize household_highest_edu_numeric05 household_highest_edu_numeric06 household_highest_edu_numeric07 household_highest_edu_numeric08 household_highest_edu_numeric09 household_highest_edu_numeric10 household_highest_edu_numeric11 household_highest_edu_numeric12 household_highest_edu_numeric13 household_highest_edu_numeric14 household_highest_edu_numeric15 household_highest_edu_numeric16 household_highest_edu_numeric17 household_highest_edu_numeric18 household_highest_edu_numeric19 household_highest_edu_numeric20 household_highest_edu_numeric21 household_highest_edu_numeric22

tabulate household_highest_edu_numeric05 if household_size05 >= 1, missing
tabulate household_highest_edu_numeric06 if household_size06 >= 1, missing
tabulate household_highest_edu_numeric07 if household_size07 >= 1, missing
tabulate household_highest_edu_numeric08 if household_size08 >= 1, missing
tabulate household_highest_edu_numeric09 if household_size09 >= 1, missing
tabulate household_highest_edu_numeric10 if household_size10 >= 1, missing
tabulate household_highest_edu_numeric11 if household_size11 >= 1, missing
tabulate household_highest_edu_numeric12 if household_size12 >= 1, missing
tabulate household_highest_edu_numeric13 if household_size13 >= 1, missing
tabulate household_highest_edu_numeric14 if household_size14 >= 1, missing
tabulate household_highest_edu_numeric15 if household_size15 >= 1, missing
tabulate household_highest_edu_numeric16 if household_size16 >= 1, missing
tabulate household_highest_edu_numeric17 if household_size17 >= 1, missing
tabulate household_highest_edu_numeric18 if household_size18 >= 1, missing
tabulate household_highest_edu_numeric19 if household_size19 >= 1, missing
tabulate household_highest_edu_numeric20 if household_size20 >= 1, missing
tabulate household_highest_edu_numeric21 if household_size21 >= 1, missing
tabulate household_highest_edu_numeric22 if household_size22 >= 1, missing

sankey household_highest_edu_numeric05 household_highest_edu_numeric22


summarize household_size05 household_size06 household_size07 household_size08 household_size09 household_size10 household_size11 household_size12 household_size13 household_size14 household_size15 household_size16 household_size17 household_size18 household_size19 household_size20 household_size21 household_size22

tabulate household_size05 if household_size05 >= 1, missing
tabulate household_size06 if household_size06 >= 1, missing
tabulate household_size07 if household_size07 >= 1, missing
tabulate household_size08 if household_size08 >= 1, missing
tabulate household_size09 if household_size09 >= 1, missing
tabulate household_size10 if household_size10 >= 1, missing
tabulate household_size11 if household_size11 >= 1, missing
tabulate household_size12 if household_size12 >= 1, missing
tabulate household_size13 if household_size13 >= 1, missing
tabulate household_size14 if household_size14 >= 1, missing
tabulate household_size15 if household_size15 >= 1, missing
tabulate household_size16 if household_size16 >= 1, missing
tabulate household_size17 if household_size17 >= 1, missing
tabulate household_size18 if household_size18 >= 1, missing
tabulate household_size19 if household_size19 >= 1, missing
tabulate household_size20 if household_size20 >= 1, missing
tabulate household_size21 if household_size21 >= 1, missing
tabulate household_size22 if household_size22 >= 1, missing


summarize household_residence_work05 household_residence_work06 household_residence_work07 household_residence_work08 household_residence_work09 household_residence_work10 household_residence_work11 household_residence_work12 household_residence_work13 household_residence_work14 household_residence_work15 household_residence_work16 household_residence_work17 household_residence_work18 household_residence_work19 household_residence_work20 household_residence_work21 household_residence_work22

tabulate household_residence_work05 if household_size05 >= 1, missing
tabulate household_residence_work06 if household_size06 >= 1, missing
tabulate household_residence_work07 if household_size07 >= 1, missing
tabulate household_residence_work08 if household_size08 >= 1, missing
tabulate household_residence_work09 if household_size09 >= 1, missing
tabulate household_residence_work10 if household_size10 >= 1, missing
tabulate household_residence_work11 if household_size11 >= 1, missing
tabulate household_residence_work12 if household_size12 >= 1, missing
tabulate household_residence_work13 if household_size13 >= 1, missing
tabulate household_residence_work14 if household_size14 >= 1, missing
tabulate household_residence_work15 if household_size15 >= 1, missing
tabulate household_residence_work16 if household_size16 >= 1, missing
tabulate household_residence_work17 if household_size17 >= 1, missing
tabulate household_residence_work18 if household_size18 >= 1, missing
tabulate household_residence_work19 if household_size19 >= 1, missing
tabulate household_residence_work20 if household_size20 >= 1, missing
tabulate household_residence_work21 if household_size21 >= 1, missing
tabulate household_residence_work22 if household_size22 >= 1, missing


piechart household_background05
piechart household_background10
piechart household_background15
piechart household_background20
piechart household_background22

tabulate household_background05 if household_size05 >= 1, missing
tabulate household_background06 if household_size06 >= 1, missing
tabulate household_background07 if household_size07 >= 1, missing
tabulate household_background08 if household_size08 >= 1, missing
tabulate household_background09 if household_size09 >= 1, missing
tabulate household_background10 if household_size10 >= 1, missing
tabulate household_background11 if household_size11 >= 1, missing
tabulate household_background12 if household_size12 >= 1, missing
tabulate household_background13 if household_size13 >= 1, missing
tabulate household_background14 if household_size14 >= 1, missing
tabulate household_background15 if household_size15 >= 1, missing
tabulate household_background16 if household_size16 >= 1, missing
tabulate household_background17 if household_size17 >= 1, missing
tabulate household_background18 if household_size18 >= 1, missing
tabulate household_background19 if household_size19 >= 1, missing
tabulate household_background20 if household_size20 >= 1, missing
tabulate household_background21 if household_size21 >= 1, missing
tabulate household_background22 if household_size22 >= 1, missing

sankey household_background05 household_background20
sankey household_background05 household_background22


tabulate household_type_by_children05 if household_size05 >= 1, missing
tabulate household_type_by_children06 if household_size06 >= 1, missing
tabulate household_type_by_children07 if household_size07 >= 1, missing
tabulate household_type_by_children08 if household_size08 >= 1, missing
tabulate household_type_by_children09 if household_size09 >= 1, missing
tabulate household_type_by_children10 if household_size10 >= 1, missing
tabulate household_type_by_children11 if household_size11 >= 1, missing
tabulate household_type_by_children12 if household_size12 >= 1, missing
tabulate household_type_by_children13 if household_size13 >= 1, missing
tabulate household_type_by_children14 if household_size14 >= 1, missing
tabulate household_type_by_children15 if household_size15 >= 1, missing
tabulate household_type_by_children16 if household_size16 >= 1, missing
tabulate household_type_by_children17 if household_size17 >= 1, missing
tabulate household_type_by_children18 if household_size18 >= 1, missing
tabulate household_type_by_children19 if household_size19 >= 1, missing
tabulate household_type_by_children20 if household_size20 >= 1, missing
tabulate household_type_by_children21 if household_size21 >= 1, missing
tabulate household_type_by_children22 if household_size22 >= 1, missing

sankey household_type_by_children05 household_type_by_children10
sankey household_type_by_children10 household_type_by_children15
sankey household_type_by_children15 household_type_by_children20
sankey household_type_by_children20 household_type_by_children22
sankey household_type_by_children05 household_type_by_children22


piechart live_in_urban05
piechart live_in_urban11
piechart live_in_urban15
piechart live_in_urban20
piechart live_in_urban22

tabulate live_in_urban05 if household_size05 >= 1, missing
tabulate live_in_urban06 if household_size06 >= 1, missing
tabulate live_in_urban07 if household_size07 >= 1, missing
tabulate live_in_urban08 if household_size08 >= 1, missing
tabulate live_in_urban09 if household_size09 >= 1, missing
//tabulate live_in_urban10 if household_size10 >= 1, missing
tabulate live_in_urban11 if household_size11 >= 1, missing
tabulate live_in_urban12 if household_size12 >= 1, missing
tabulate live_in_urban13 if household_size13 >= 1, missing
tabulate live_in_urban14 if household_size14 >= 1, missing
tabulate live_in_urban15 if household_size15 >= 1, missing
tabulate live_in_urban16 if household_size16 >= 1, missing
tabulate live_in_urban17 if household_size17 >= 1, missing
tabulate live_in_urban18 if household_size18 >= 1, missing
tabulate live_in_urban19 if household_size19 >= 1, missing
tabulate live_in_urban20 if household_size20 >= 1, missing
tabulate live_in_urban21 if household_size21 >= 1, missing
tabulate live_in_urban22 if household_size22 >= 1, missing

sankey live_in_urban05 live_in_urban22


summarize owned_vehicle_all_per_household05 owned_vehicle_all_per_household06 owned_vehicle_all_per_household07 owned_vehicle_all_per_household08 owned_vehicle_all_per_household09 owned_vehicle_all_per_household10 owned_vehicle_all_per_household11 owned_vehicle_all_per_household12 owned_vehicle_all_per_household13 owned_vehicle_all_per_household14 owned_vehicle_all_per_household15 owned_vehicle_all_per_household16 owned_vehicle_all_per_household17 owned_vehicle_all_per_household18 owned_vehicle_all_per_household19 owned_vehicle_all_per_household20 owned_vehicle_all_per_household21 owned_vehicle_all_per_household22

summarize owned_vehicle_gray_per_household05 owned_vehicle_gray_per_household06 owned_vehicle_gray_per_household07 owned_vehicle_gray_per_household08 owned_vehicle_gray_per_household09 owned_vehicle_gray_per_household10 owned_vehicle_gray_per_household11 owned_vehicle_gray_per_household12 owned_vehicle_gray_per_household13 owned_vehicle_gray_per_household14 owned_vehicle_gray_per_household15 owned_vehicle_gray_per_household16 owned_vehicle_gray_per_household17 owned_vehicle_gray_per_household18 owned_vehicle_gray_per_household19 owned_vehicle_gray_per_household20 owned_vehicle_gray_per_household21 owned_vehicle_gray_per_household22


sankey owned_vehicle_gray_per_household05 owned_vehicle_gray_per_household20
sankey owned_vehicle_gray_per_household05 owned_vehicle_gray_per_household22

summarize owned_vehicle_green_per_household05 owned_vehicle_green_per_household06 owned_vehicle_green_per_household07 owned_vehicle_green_per_household08 owned_vehicle_green_per_household09 owned_vehicle_green_per_household10 owned_vehicle_green_per_household11 owned_vehicle_green_per_household12 owned_vehicle_green_per_household13 owned_vehicle_green_per_household14 owned_vehicle_green_per_household15 owned_vehicle_green_per_household16 owned_vehicle_green_per_household17 owned_vehicle_green_per_household18 owned_vehicle_green_per_household19 owned_vehicle_green_per_household20 owned_vehicle_green_per_household21 owned_vehicle_green_per_household22


sankey owned_vehicle_green_per_household05 owned_vehicle_green_per_household10
sankey owned_vehicle_green_per_household10 owned_vehicle_green_per_household15
sankey owned_vehicle_green_per_household15 owned_vehicle_green_per_household20
sankey owned_vehicle_green_per_household20 owned_vehicle_green_per_household22

sankey owned_vehicle_green_per_household05 owned_vehicle_green_per_household22

// To find the number of households with zero green vehicle in this sub-poulation
summarize household_size05 if owned_vehicle_green_per_household05 == 0
summarize household_size06 if owned_vehicle_green_per_household06 == 0
summarize household_size07 if owned_vehicle_green_per_household07 == 0
summarize household_size08 if owned_vehicle_green_per_household08 == 0
summarize household_size09 if owned_vehicle_green_per_household09 == 0
summarize household_size10 if owned_vehicle_green_per_household10 == 0
summarize household_size11 if owned_vehicle_green_per_household11 == 0
summarize household_size12 if owned_vehicle_green_per_household12 == 0
summarize household_size13 if owned_vehicle_green_per_household13 == 0
summarize household_size14 if owned_vehicle_green_per_household14 == 0
summarize household_size15 if owned_vehicle_green_per_household15 == 0
summarize household_size16 if owned_vehicle_green_per_household16 == 0
summarize household_size17 if owned_vehicle_green_per_household17 == 0
summarize household_size18 if owned_vehicle_green_per_household18 == 0
summarize household_size19 if owned_vehicle_green_per_household19 == 0
summarize household_size20 if owned_vehicle_green_per_household20 == 0
summarize household_size21 if owned_vehicle_green_per_household21 == 0
summarize household_size22 if owned_vehicle_green_per_household22 == 0

// To find the number of households with at least one green vehicle in this sub-poulation
summarize household_size05 if owned_vehicle_green_per_household05 >= 1
summarize household_size06 if owned_vehicle_green_per_household06 >= 1
summarize household_size07 if owned_vehicle_green_per_household07 >= 1
summarize household_size08 if owned_vehicle_green_per_household08 >= 1
summarize household_size09 if owned_vehicle_green_per_household09 >= 1
summarize household_size10 if owned_vehicle_green_per_household10 >= 1
summarize household_size11 if owned_vehicle_green_per_household11 >= 1
summarize household_size12 if owned_vehicle_green_per_household12 >= 1
summarize household_size13 if owned_vehicle_green_per_household13 >= 1
summarize household_size14 if owned_vehicle_green_per_household14 >= 1
summarize household_size15 if owned_vehicle_green_per_household15 >= 1
summarize household_size16 if owned_vehicle_green_per_household16 >= 1
summarize household_size17 if owned_vehicle_green_per_household17 >= 1
summarize household_size18 if owned_vehicle_green_per_household18 >= 1
summarize household_size19 if owned_vehicle_green_per_household19 >= 1
summarize household_size20 if owned_vehicle_green_per_household20 >= 1
summarize household_size21 if owned_vehicle_green_per_household21 >= 1
summarize household_size22 if owned_vehicle_green_per_household22 >= 1



textblock
Descriptive analysis for the paper: Gray Adopters
---------------------------------------
2005 to 2022: An overview
endblock

use household_DS_2005_2022_always_gray_owners_descriptive


summarize household_income05 household_income06 household_income07 household_income08 household_income09 household_income10 household_income11 household_income12 household_income13 household_income14 household_income15 household_income16 household_income17 household_income18 household_income19 household_income20 household_income21

summarize household_wealth05 household_wealth06 household_wealth07 household_wealth08 household_wealth09 household_wealth10 household_wealth11 household_wealth12 household_wealth13 household_wealth14 household_wealth15 household_wealth16 household_wealth17 household_wealth18 household_wealth19 household_wealth20

summarize household_debt05 household_debt06 household_debt07 household_debt08 household_debt09 household_debt10 household_debt11 household_debt12 household_debt13 household_debt14 household_debt15 household_debt16 household_debt17 household_debt18 household_debt19 household_debt20 household_debt21

summarize household_highest_edu_numeric05 household_highest_edu_numeric06 household_highest_edu_numeric07 household_highest_edu_numeric08 household_highest_edu_numeric09 household_highest_edu_numeric10 household_highest_edu_numeric11 household_highest_edu_numeric12 household_highest_edu_numeric13 household_highest_edu_numeric14 household_highest_edu_numeric15 household_highest_edu_numeric16 household_highest_edu_numeric17 household_highest_edu_numeric18 household_highest_edu_numeric19 household_highest_edu_numeric20 household_highest_edu_numeric21 household_highest_edu_numeric22

tabulate household_highest_edu_numeric05 if household_size05 >= 1, missing
tabulate household_highest_edu_numeric06 if household_size06 >= 1, missing
tabulate household_highest_edu_numeric07 if household_size07 >= 1, missing
tabulate household_highest_edu_numeric08 if household_size08 >= 1, missing
tabulate household_highest_edu_numeric09 if household_size09 >= 1, missing
tabulate household_highest_edu_numeric10 if household_size10 >= 1, missing
tabulate household_highest_edu_numeric11 if household_size11 >= 1, missing
tabulate household_highest_edu_numeric12 if household_size12 >= 1, missing
tabulate household_highest_edu_numeric13 if household_size13 >= 1, missing
tabulate household_highest_edu_numeric14 if household_size14 >= 1, missing
tabulate household_highest_edu_numeric15 if household_size15 >= 1, missing
tabulate household_highest_edu_numeric16 if household_size16 >= 1, missing
tabulate household_highest_edu_numeric17 if household_size17 >= 1, missing
tabulate household_highest_edu_numeric18 if household_size18 >= 1, missing
tabulate household_highest_edu_numeric19 if household_size19 >= 1, missing
tabulate household_highest_edu_numeric20 if household_size20 >= 1, missing
tabulate household_highest_edu_numeric21 if household_size21 >= 1, missing
tabulate household_highest_edu_numeric22 if household_size22 >= 1, missing

sankey household_highest_edu_numeric05 household_highest_edu_numeric22


summarize household_size05 household_size06 household_size07 household_size08 household_size09 household_size10 household_size11 household_size12 household_size13 household_size14 household_size15 household_size16 household_size17 household_size18 household_size19 household_size20 household_size21 household_size22

tabulate household_size05 if household_size05 >= 1, missing
tabulate household_size06 if household_size06 >= 1, missing
tabulate household_size07 if household_size07 >= 1, missing
tabulate household_size08 if household_size08 >= 1, missing
tabulate household_size09 if household_size09 >= 1, missing
tabulate household_size10 if household_size10 >= 1, missing
tabulate household_size11 if household_size11 >= 1, missing
tabulate household_size12 if household_size12 >= 1, missing
tabulate household_size13 if household_size13 >= 1, missing
tabulate household_size14 if household_size14 >= 1, missing
tabulate household_size15 if household_size15 >= 1, missing
tabulate household_size16 if household_size16 >= 1, missing
tabulate household_size17 if household_size17 >= 1, missing
tabulate household_size18 if household_size18 >= 1, missing
tabulate household_size19 if household_size19 >= 1, missing
tabulate household_size20 if household_size20 >= 1, missing
tabulate household_size21 if household_size21 >= 1, missing
tabulate household_size22 if household_size22 >= 1, missing


summarize household_residence_work05 household_residence_work06 household_residence_work07 household_residence_work08 household_residence_work09 household_residence_work10 household_residence_work11 household_residence_work12 household_residence_work13 household_residence_work14 household_residence_work15 household_residence_work16 household_residence_work17 household_residence_work18 household_residence_work19 household_residence_work20 household_residence_work21 household_residence_work22

tabulate household_residence_work05 if household_size05 >= 1, missing
tabulate household_residence_work06 if household_size06 >= 1, missing
tabulate household_residence_work07 if household_size07 >= 1, missing
tabulate household_residence_work08 if household_size08 >= 1, missing
tabulate household_residence_work09 if household_size09 >= 1, missing
tabulate household_residence_work10 if household_size10 >= 1, missing
tabulate household_residence_work11 if household_size11 >= 1, missing
tabulate household_residence_work12 if household_size12 >= 1, missing
tabulate household_residence_work13 if household_size13 >= 1, missing
tabulate household_residence_work14 if household_size14 >= 1, missing
tabulate household_residence_work15 if household_size15 >= 1, missing
tabulate household_residence_work16 if household_size16 >= 1, missing
tabulate household_residence_work17 if household_size17 >= 1, missing
tabulate household_residence_work18 if household_size18 >= 1, missing
tabulate household_residence_work19 if household_size19 >= 1, missing
tabulate household_residence_work20 if household_size20 >= 1, missing
tabulate household_residence_work21 if household_size21 >= 1, missing
tabulate household_residence_work22 if household_size22 >= 1, missing


piechart household_background05
piechart household_background10
piechart household_background15
piechart household_background20
piechart household_background22

tabulate household_background05 if household_size05 >= 1, missing
tabulate household_background06 if household_size06 >= 1, missing
tabulate household_background07 if household_size07 >= 1, missing
tabulate household_background08 if household_size08 >= 1, missing
tabulate household_background09 if household_size09 >= 1, missing
tabulate household_background10 if household_size10 >= 1, missing
tabulate household_background11 if household_size11 >= 1, missing
tabulate household_background12 if household_size12 >= 1, missing
tabulate household_background13 if household_size13 >= 1, missing
tabulate household_background14 if household_size14 >= 1, missing
tabulate household_background15 if household_size15 >= 1, missing
tabulate household_background16 if household_size16 >= 1, missing
tabulate household_background17 if household_size17 >= 1, missing
tabulate household_background18 if household_size18 >= 1, missing
tabulate household_background19 if household_size19 >= 1, missing
tabulate household_background20 if household_size20 >= 1, missing
tabulate household_background21 if household_size21 >= 1, missing
tabulate household_background22 if household_size22 >= 1, missing

sankey household_background05 household_background20
sankey household_background05 household_background22


tabulate household_type_by_children05 if household_size05 >= 1, missing
tabulate household_type_by_children06 if household_size06 >= 1, missing
tabulate household_type_by_children07 if household_size07 >= 1, missing
tabulate household_type_by_children08 if household_size08 >= 1, missing
tabulate household_type_by_children09 if household_size09 >= 1, missing
tabulate household_type_by_children10 if household_size10 >= 1, missing
tabulate household_type_by_children11 if household_size11 >= 1, missing
tabulate household_type_by_children12 if household_size12 >= 1, missing
tabulate household_type_by_children13 if household_size13 >= 1, missing
tabulate household_type_by_children14 if household_size14 >= 1, missing
tabulate household_type_by_children15 if household_size15 >= 1, missing
tabulate household_type_by_children16 if household_size16 >= 1, missing
tabulate household_type_by_children17 if household_size17 >= 1, missing
tabulate household_type_by_children18 if household_size18 >= 1, missing
tabulate household_type_by_children19 if household_size19 >= 1, missing
tabulate household_type_by_children20 if household_size20 >= 1, missing
tabulate household_type_by_children21 if household_size21 >= 1, missing
tabulate household_type_by_children22 if household_size22 >= 1, missing

sankey household_type_by_children05 household_type_by_children10
sankey household_type_by_children10 household_type_by_children15
sankey household_type_by_children15 household_type_by_children20
sankey household_type_by_children20 household_type_by_children22
sankey household_type_by_children05 household_type_by_children22


piechart live_in_urban05
piechart live_in_urban11
piechart live_in_urban15
piechart live_in_urban20
piechart live_in_urban22

tabulate live_in_urban05 if household_size05 >= 1, missing
tabulate live_in_urban06 if household_size06 >= 1, missing
tabulate live_in_urban07 if household_size07 >= 1, missing
tabulate live_in_urban08 if household_size08 >= 1, missing
tabulate live_in_urban09 if household_size09 >= 1, missing
//tabulate live_in_urban10 if household_size10 >= 1, missing
tabulate live_in_urban11 if household_size11 >= 1, missing
tabulate live_in_urban12 if household_size12 >= 1, missing
tabulate live_in_urban13 if household_size13 >= 1, missing
tabulate live_in_urban14 if household_size14 >= 1, missing
tabulate live_in_urban15 if household_size15 >= 1, missing
tabulate live_in_urban16 if household_size16 >= 1, missing
tabulate live_in_urban17 if household_size17 >= 1, missing
tabulate live_in_urban18 if household_size18 >= 1, missing
tabulate live_in_urban19 if household_size19 >= 1, missing
tabulate live_in_urban20 if household_size20 >= 1, missing
tabulate live_in_urban21 if household_size21 >= 1, missing
tabulate live_in_urban22 if household_size22 >= 1, missing

sankey live_in_urban05 live_in_urban22


summarize owned_vehicle_all_per_household05 owned_vehicle_all_per_household06 owned_vehicle_all_per_household07 owned_vehicle_all_per_household08 owned_vehicle_all_per_household09 owned_vehicle_all_per_household10 owned_vehicle_all_per_household11 owned_vehicle_all_per_household12 owned_vehicle_all_per_household13 owned_vehicle_all_per_household14 owned_vehicle_all_per_household15 owned_vehicle_all_per_household16 owned_vehicle_all_per_household17 owned_vehicle_all_per_household18 owned_vehicle_all_per_household19 owned_vehicle_all_per_household20 owned_vehicle_all_per_household21 owned_vehicle_all_per_household22

summarize owned_vehicle_gray_per_household05 owned_vehicle_gray_per_household06 owned_vehicle_gray_per_household07 owned_vehicle_gray_per_household08 owned_vehicle_gray_per_household09 owned_vehicle_gray_per_household10 owned_vehicle_gray_per_household11 owned_vehicle_gray_per_household12 owned_vehicle_gray_per_household13 owned_vehicle_gray_per_household14 owned_vehicle_gray_per_household15 owned_vehicle_gray_per_household16 owned_vehicle_gray_per_household17 owned_vehicle_gray_per_household18 owned_vehicle_gray_per_household19 owned_vehicle_gray_per_household20 owned_vehicle_gray_per_household21 owned_vehicle_gray_per_household22

// Note that we know this group has never had a green car...
summarize owned_vehicle_green_per_household05 owned_vehicle_green_per_household06 owned_vehicle_green_per_household07 owned_vehicle_green_per_household08 owned_vehicle_green_per_household09 owned_vehicle_green_per_household10 owned_vehicle_green_per_household11 owned_vehicle_green_per_household12 owned_vehicle_green_per_household13 owned_vehicle_green_per_household14 owned_vehicle_green_per_household15 owned_vehicle_green_per_household16 owned_vehicle_green_per_household17 owned_vehicle_green_per_household18 owned_vehicle_green_per_household19 owned_vehicle_green_per_household20 owned_vehicle_green_per_household21 owned_vehicle_green_per_household22

sankey owned_vehicle_gray_per_household05 owned_vehicle_gray_per_household22

// To find the number of households with zero gray vehicle in this sub-poulation
summarize household_size05 if owned_vehicle_gray_per_household05 == 0
summarize household_size06 if owned_vehicle_gray_per_household06 == 0
summarize household_size07 if owned_vehicle_gray_per_household07 == 0
summarize household_size08 if owned_vehicle_gray_per_household08 == 0
summarize household_size09 if owned_vehicle_gray_per_household09 == 0
summarize household_size10 if owned_vehicle_gray_per_household10 == 0
summarize household_size11 if owned_vehicle_gray_per_household11 == 0
summarize household_size12 if owned_vehicle_gray_per_household12 == 0
summarize household_size13 if owned_vehicle_gray_per_household13 == 0
summarize household_size14 if owned_vehicle_gray_per_household14 == 0
summarize household_size15 if owned_vehicle_gray_per_household15 == 0
summarize household_size16 if owned_vehicle_gray_per_household16 == 0
summarize household_size17 if owned_vehicle_gray_per_household17 == 0
summarize household_size18 if owned_vehicle_gray_per_household18 == 0
summarize household_size19 if owned_vehicle_gray_per_household19 == 0
summarize household_size20 if owned_vehicle_gray_per_household20 == 0
summarize household_size21 if owned_vehicle_gray_per_household21 == 0
summarize household_size22 if owned_vehicle_gray_per_household22 == 0

// To find the number of households with at least one gray vehicle in this sub-poulation: We know that the households of this population do not own any green vehicles
summarize household_size05 if owned_vehicle_gray_per_household05 >= 1
summarize household_size06 if owned_vehicle_gray_per_household06 >= 1
summarize household_size07 if owned_vehicle_gray_per_household07 >= 1
summarize household_size08 if owned_vehicle_gray_per_household08 >= 1
summarize household_size09 if owned_vehicle_gray_per_household09 >= 1
summarize household_size10 if owned_vehicle_gray_per_household10 >= 1
summarize household_size11 if owned_vehicle_gray_per_household11 >= 1
summarize household_size12 if owned_vehicle_gray_per_household12 >= 1
summarize household_size13 if owned_vehicle_gray_per_household13 >= 1
summarize household_size14 if owned_vehicle_gray_per_household14 >= 1
summarize household_size15 if owned_vehicle_gray_per_household15 >= 1
summarize household_size16 if owned_vehicle_gray_per_household16 >= 1
summarize household_size17 if owned_vehicle_gray_per_household17 >= 1
summarize household_size18 if owned_vehicle_gray_per_household18 >= 1
summarize household_size19 if owned_vehicle_gray_per_household19 >= 1
summarize household_size20 if owned_vehicle_gray_per_household20 >= 1
summarize household_size21 if owned_vehicle_gray_per_household21 >= 1
summarize household_size22 if owned_vehicle_gray_per_household22 >= 1