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Analyzing adults in education

This script can be used as a starting point for creating statistics on education for adults. Note that the results generated by the script cannot be considered official statistics without further quality control and processing of the data.

The script is based on an input from some of our users, where we try to create the most comprehensive statistics possible for the group "adults taking education":

We imagine that microdata.no is unable to answer participation in non-formal training or learning-intensive work, but it should be possible to find the number and shares of the population/employees who participate in formal training.

  • How many/what proportion attend the course? Enrolled as of Sept. 1 each year, for example?
  • Which courses, which level do adults take part in?
  • Who attends the course? E.g. labor market status, employment rate, family.

It is a point to distinguish adults from young people who are still in education.

Current information: Age, share of employment, stay from education for x years

Unsure whether it is necessary and possible to distinguish between formal education and formal further education.

We choose to operationalize in the script below by looking at adults as people aged 40 and over who are in education as of 1 September 2023. We also use a few different course variables that can provide relevant information about the education that adults take.

 require no.ssb.fdb:34 as db

create-dataset persondata
import db/BEFOLKNING_STATUSKODE 2023-01-01 as regstat
keep if regstat == '1'
import db/BEFOLKNING_FOEDSELS_AAR_MND as age
replace age = 2023 - int(age / 100) 
generate adult = 0
replace adult = 1 if age >= 40  // Classifying adults as 40+


//Create course dataset as of September 1, 2023 (latest data)
create-dataset coursedata1
import db/NUDB_KURS_NUS 2023-09-01 as course_type
import db/NUDB_KURS_FNR as fnr
destring course_type
collapse (count) course_type -> number_courses (max) course_type, by(fnr)
merge number_courses course_type into persondata

create-dataset coursedata2
import db/NUDB_KURS_UHGRUPPE 2023-09-01 as uh_group
import db/NUDB_KURS_FNR as fnr
keep if inlist(uh_group,'18','19','20','21','22','66','67','68')
tabulate uh_group
collapse (count) uh_group -> number_education, by(fnr)
merge number_education into persondata

create-dataset coursedata3
import db/NUDB_KURS_EIERF 2023-09-01 as ownership
import db/NUDB_KURS_FNR as fnr
keep if inlist(ownership,'3')
tabulate ownership
collapse (count) ownership -> number_private, by(fnr)
merge number_private into persondata

create-dataset coursedata4
import db/NUDB_KURS_ORGANISERING 2023-09-01 as organisation
import db/NUDB_KURS_FNR as fnr
keep if inlist(organisation,'3','4','5')
tabulate organisation
collapse (count) organisation -> number_online, by(fnr)
merge number_online into persondata

use persondata
generate education = number_courses > 0
generate furthereducation = number_education > 0
generate private = number_private > 0
generate online = number_online > 0

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Number of adults (40+) vs young (under 40) studying
endblock
tabulate education adult, freq colpct

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Number of adults (40+) vs young (under 40) studying, distributed by further education
endblock
tabulate furthereducation adult if education, freq colpct

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Number of adults (40+) vs young (under 40) studying, distributed by private vs public school
endblock
tabulate private adult if education, freq colpct

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Number of adults (40+) vs young (under 40) studying, distributed by online vs. education at institution
endblock
tabulate online adult if education, freq colpct

textblock
Number of adults (40+), distributed by the most common course types
endblock
define-labels utdlbl 625202 'Norwegian further education for teachers' 361903 'Health worker profession VG2' 623802 'Practical-pedagogical education for vocational teachers' 625304 'Special education - further education for teachers' 641141 'Bachelor - three-year economics and administration' 621107 'Bachelor - three-year kindergarten teacher education' 682304 'Further education - Police College' 863105 'Ph.D. program - medical subjects' 649999 'Economic and administrative subjects - others unspecified - lower level' 741199 'Economic-administrative subjects - unspecified - higher level' 741132 'Master - 1.5-year management' 369913 'Health and upbringing subjects VG1' 769999 'Health-social and sports subjects - others unspecified - higher level' 641199 'Economic-administrative subjects - unspecified - lower level' 461903 'Health worker profession - VG3' 661120 'Bachelor - three-year nursing' 401105 'Study specialization - study competence providing individual subjects / courses VG3' 699999 'University and college education - unspecified field of study - lower level' 641999 'Economic-administrative subjects - others unspecified - lower level' 399999 'Secondary, basic education - unspecified field of study'
assign-labels course_type utdlbl

tabulate course_type if adult & education, rowsort bottom(20)

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Number of adults (40+) in education, distributed by labor market status and most common professions
endblock
import db/REGSYS_ARB_ARBMARK_STATUS 2023-11-16 as labor_market_status
import db/REGSYS_ARB_YRKE_STYRK08 2023-11-16 as profession

tabulate labor_market_status if adult & education, freq cellpct
tabulate profession if adult & education, rowsort bottom(20)