The example illustrates how to proceed in order to analyse the probability of getting a job and earning over NOK 500,000 one year after one is in a state without a job. The age group we are looking at is 16-60.<\/p>\n\n\n\n
The analysis checks for various demographic characteristics as well as status on the labour market (unemployed, ordinary labour market measures, vocationally disabled, other jobseeker conditions, as well as work disability).<\/p>\n\n\n\n
Some descriptive statistics are first created, and finally a logit analysis including marginal effects is run (the option As can be seen, all explanatory variables have significant estimated coefficient values. The value of Pseudo R2 shows that the model explains approx. 18% of the total variation for the dependent variable. Such values \u200b\u200bare not unusual in socio-economic analyses.<\/p>\n\n\nmfx(dydx)<\/code> used for this).<\/p>\n\n\n\n\/\/Connect to database\r\nrequire no.ssb.fdb:23 as db\r\n\r\n\/\/Create population of persons 16-60 \u00e5r without a job in November 2018, and resident in Norway per 1. Januar 2019\r\ncreate-dataset demographydata\r\nimport db\/BEFOLKNING_FOEDSELS_AAR_MND as birth_year_month\r\nimport db\/BEFOLKNING_STATUSKODE 2019-01-01 as regstat\r\nimport db\/REGSYS_ARB_ARBMARK_STATUS 2018-11-16 as labourstat\r\n\r\ngenerate age = 2018 - int(birth_year_month \/ 100)\r\ngenerate job = 0\r\nreplace job = 1 if labourstat == '1' | labourstat == '2'\r\nkeep if age >= 16 & age <= 60 & regstat == '1' & job == 0\r\n\r\nhistogram age, discrete\r\n\r\n\/\/Import relevant variables (demography data are mostly measured per 1\/1 each year)\r\nimport db\/BEFOLKNING_KJOENN as gender\r\nimport db\/BEFOLKNING_INVKAT as imm_cat\r\nimport db\/SIVSTANDFDT_SIVSTAND 2018-11-16 as civstat\r\nimport db\/BEFOLKNING_BARN_I_REGSTAT_FAMNR 2019-01-01 as children\r\nimport db\/NUDB_BU 2018-11-16 as edu\r\nimport db\/NUDB_SOSBAK as social_background\r\nimport db\/BEFOLKNING_KOMMNR_FAKTISK 2019-01-01 as municipality\r\nimport db\/ARBSOEK2001FDT_HOVED 2018-11-16 as work_seeker_stat\r\nimport db\/UFOERP2011FDT_GRAD 2018-11-16 as disability_level\r\nimport db\/INNTEKT_BRUTTOFORM 2018-12-31 as wealth\r\nimport db\/INNTEKT_WYRKINNT 2019-12-31 as work_income19\r\n\r\n\/\/Create a dependent variable with two outcomes (dummy variable): High work income vs. low work income\r\nhistogram work_income19, width(100000) freq\r\nsummarize work_income19\r\ngenerate high_income = 0\r\nreplace high_income = 1 if work_income19 > 500000\r\npiechart high_income\r\n\r\n\/\/Adapt the independent variables so that they suit the statistical model (most of them need to be tranformed into dummy variables)\r\ngenerate male = 0\r\nreplace male = 1 if gender == '1'\r\npiechart male\r\n\r\ndestring civstat\r\ngenerate married = 0\r\nreplace married = 1 if civstat == 2\r\nreplace married = civstat if sysmiss(civstat)\r\npiechart married\r\n\r\ngenerate immigrant = 0\r\nreplace immigrant = 1 if imm_cat == 'B'\r\npiechart immigrant\r\n\r\ntabulate children, missing\r\ngenerate child = 0\r\nreplace child = 1 if children == 1\r\n\r\ngenerate more_children = 0\r\nreplace more_children = 1 if children > 1\r\n\r\ndestring edu\r\ngenerate high_edu = 0\r\nreplace high_edu = 1 if edu >= 700000 & edu < 900000\r\nreplace high_edu = edu if sysmiss(edu)\r\npiechart high_edu\r\n\r\ngenerate high_edu_parents = 0\r\nreplace high_edu_parents = 1 if social_background == '1'\r\npiechart high_edu_parents\r\n\r\ngenerate oslo = 0\r\nreplace oslo = 1 if municipality == '0301'\r\n\r\ngenerate bergen = 0\r\nreplace bergen = 1 if municipality == '1201'\r\n\r\ngenerate stavanger = 0\r\nreplace stavanger = 1 if municipality == '1103'\r\n\r\ngenerate trondheim = 0\r\nreplace trondheim = 1 if municipality == '5001'\r\n\r\nbarchart(sum) oslo bergen stavanger trondheim\r\n\r\ndestring work_seeker_stat\r\ntabulate work_seeker_stat, missing\r\n\r\ngenerate unempl = 0\r\nreplace unempl = 1 if work_seeker_stat == 1\r\n\r\ngenerate measure = 0\r\nreplace measure = 1 if work_seeker_stat == 3\r\n\r\ngenerate voc_disabled = 0\r\nreplace voc_disabled = 1 if work_seeker_stat == 5 | work_seeker_stat >= 10\r\n\r\ngenerate other_workseekers = 0\r\nreplace other_workseekers = 1 if work_seeker_stat == 2 | work_seeker_stat == 4 | work_seeker_stat == 7\r\n\r\ngenerate disabled = 1\r\nreplace disabled = 0 if sysmiss(disability_level)\r\n\r\nbarchart(sum) unempl measure voc_disabled other_workseekers disabled\r\n\r\nhistogram wealth, width(100000) freq\r\nsummarize wealth\r\ngenerate wealth_high = 0\r\nreplace wealth_high = 1 if wealth > 1000000\r\nreplace wealth_high = wealth if sysmiss(wealth)\r\npiechart wealth_high\r\n\r\n\/\/Use sankey diagram to show transitions between states\r\nsankey work_seeker_stat high_income\r\nsankey high_edu high_income\r\n\r\n\/\/Run logit analysis where the dependent variable is allways listed first (needs to be dummy)\r\nlogit high_income male married age immigrant child more_children high_edu high_edu_parents oslo bergen stavanger trondheim unempl measure voc_disabled other_workseekers disabled wealth_high, mfx(dydx)<\/code><\/pre>\n<\/div>","protected":false},"parent":0,"menu_order":123,"template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":""},"class_list":["post-1298","eksempel","type-eksempel","status-publish","hentry"],"acf":[],"yoast_head":"\n