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DwB-Training Cource on EU-SILC , February 13-15, 2013

Working with household income variables Practical computing session I – Part 3 Heike Wirth GESIS – Leibniz Institut für Sozialwissenschaften. DwB-Training Cource on EU-SILC , February 13-15, 2013 Romanian Social Data Archive at the Departement of Sociology University of Bucharest, Romania.

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DwB-Training Cource on EU-SILC , February 13-15, 2013

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  1. Working with household income variablesPractical computing session I – Part 3Heike WirthGESIS – Leibniz Institut für Sozialwissenschaften DwB-Training Cource on EU-SILC , February 13-15, 2013 Romanian Social Data Archive at the Departement of Sociology University of Bucharest, Romania

  2. EU-Silc is the most important data source regarding comparative description and analysis of poverty across the European countries • Headline indicator AROPE (at risk of poverty or social exclusion) based on • At risik of poverty • Severe material deprivation • Very low work intensity

  3. highest AROPE rates: BG, RO, LV • lowest AROPE rates: CZ, SE, NL

  4. At risk of poverty (HX080) • less than 60% of the national median equivalised household income Severe material deprivation (HX110) • couldn’t afford at least 4 out of 9 material deprivation items (HS010 – HS110) Very low work intensity (RX050) • ratio of workable months and actually worked months < 0.2 • All three indicators are included in the data as well as the combined indicator AROPE (PX070) • In the following the focus is on ‘at risk of poverty’ indicator or rather its components

  5. Some Questions regarding Poverty • Does poverty vary widely between different country in Europe • How is income poverty correlated with gender, age, education, urban and other characteristics? • What are the main sources of income for the poor? • How does unemployment affect poverty? • How is income poverty linked to health conditions & access to health services? • Consistency of poverty • How long people are poor? • How often people are poor over a period?

  6. Types of exploring poverty profiles • By poverty status: Poor versus non-poor • Summarize the distributions of characteristics, e.g. household type, household size, activity status, educational level, urbanisation degree of residence, age … • By characteristics: e.g. age, health condition, number of children, educational level, occupation of household head, region of residence. • Report the poverty rate for each components.

  7. Hands on – Good to know • All income variables in the UDB are in € (EURO) • For countries not members of the euro area the conversion factor can be found in variables HX010 & PX010. • Income data (euro) i. e. HY020 * HX010 = income data (national currency) • HY010 – Total householdgrossincome • HY020 – Total disposablehouseholdincome • HY022 – Total disposable HH incomebeforesocialtransfer • These variables do not include PY080 although publishedindicators were calculated with PY080 • Remember PY080G was summarized across household members and added as the variable 'pens' to the household data

  8. Please note: • HY025 =Within-household non-response inflation factor • Factor by which it is necessary to multiply • the total gross income (HY010); • the total disposable income (HY020) or • the total disposable income before social transfers (HY022) to compensate the non-response in individual questionnaires • If the data are not corrected by the effect of non-responding individuals within a household, income of individuals not interviewed is not added up into the total household Income. • For further details see guidelines

  9. Hands on – (for more hints see the exercise sheet) ***********************************************************. * Exercise 3.1a * * Compute the equivalised household income (OECD scale) by yourself, * name the new computed variable: HX090_new * * * Check your results by comparing the means of HY020, HX090_new, HX090 * by HH type(HX060), & * by country (HB020) * * Did you get what you expected? ***********************************************************.

  10. Hands on – Exercise 3.1a * open the household dataset. GET FILE='data_path/household_data.sav'. compute HX090_new = ((HY020+PENS)* HY025)/HX050. var lab HX090_new 'oecd scale'. mean tables HY020 HX090 HX090_new by HX060/cells = mean . mean tables HY020 HX090 HX090_new by HB020/cells = mean .

  11. Hands on - Exercise 3.1b ************************************************************************************. * Exercise 3.1b * * Compare the means of the equivalised Income according to the OECD Scale * with equivalised Income based on * * Per-Capita scale (divide disposable income by number of household members), and * LIS-equivalence scale (divide disposable income by square root of number of household members) * * across Household Type * across countries * what are the main findings? * * Compute the mean of the equivalised disposable income depending on the * number of unemployed persons (unemp) in the household, by country * * Collapse categories for number of unemployed persons: 0, 1, >=2 ***********************************************************************************.

  12. Hands on - Exercise 3.1b * Per capita income. compute pc_income = ((HY020+PENS)* HY025)/HX040. * SQUARE ROOT EQUIVALISED HOUSEHOLD-INCOME. compute HX040_2 = sqrt(HX040). compute lis_income = ((HY020+PENS)*HY025)/HX040_2. var lab pc_income 'per capita income'/ lis_income 'squareroothhsize'. means tables HX090 pc_incomelis_income by HX060 HB020/ cells = mean . temporary. select if (HB020 ne 'DK'). means tables HX090 pc_incomelis_income by HX060 HB020/ cells = mean . * Equivalised Income by number of unemployed persons in the household *. recode unemp (0=0) (1=1) (2 thru highest = 2)into unemp_n. val lab unemp_n 2 '>=2'. * check your new variable *. cross unemp by unemp_n. means tables = hx090 by unemp_n by hb020/cells = mean.

  13. Hands on - Exercise 3.1c - Optional *************************************************************************** * * Now compute the equivalised income based on the disposable income before social * transfers excl. old-age-benefits (HY022) * Name the new variable HY22_equi * * Remember to take into account the variable 'pens' as well as the inflation factor (HY025). * * Compare the means of HX090 and HY022_equi by country * What are the main findings? * * Compute the mean of the equivalised disposable income (before social transfers) by * number of unemployed persons (unemp_n) in the household, by country **************************************************************************.

  14. Hands on - Exercise 3.1c - Optional * equivalised income before social transfers *. compute HY022_equi = ((HY022+PENS)* HY025)/HX050. means tables = HY022_equi HX090 by HB020/ cells = mean . means tables = HX090 HY022_equi by unemp_n by hb020 /cells = mean.

  15. Exercise 3.1d - Optional - At Risk of Poverty ************************************************************************************ * The at-risk-of-poverty rate is the share of people with an equivalised disposable income * (after social transfer) below 60 % of the national median equivalised disposable income after social transfers * This poverty indicator is included in the EU-SILC data (HX080). * Below you find an example of how to compute this poverty line * Step 1: compute the national median, name it median_sample *. aggregate outfile = * mode = addvariables overwrite = yes /break = HB010 HB020 /median_sample = median(HX090). means tables median_sample by hb020. step 2: compute the poverty line, name it povline_60 *. compute povline_60 = median_sample * 0.6. step 3: compute the poverty- indicator - name it at_risk_60 *. compute at_risk_60 = 0. if (hx090 lt povline_60) at_risk_60 = 1.

  16. Exercise 3.1d - Optional - At Risk of Poverty **************************************************** * compare the findings with poverty indicator (HX080) provided in the data • Reason for the discrepancies ? • * * Compute the percentage of households at risk of poverty depending on the number of unemployed persons (unemp_n) by country * ***************************************************. * % of households at risk by number of unemployed persons in hh, by country *. cross unemp_n by HX080 povline_60 by HB020 / cells = row.

  17. Exercise 3.1e - Optional - Make ends meet ************************************************************************************. * Subjective indicators regarding the financial situation of the household * HS120: Ability to make ends meet (scale 1-6: with great difficulties (1) ... very easily(6)) * HS130: Lowest monthly income to make ends meet * * Compute the average ability to make ends meet depending on whether a household is at * risk of poverty or not, by country * * Focus on households with difficulties or great difficulties to make ends meet. * Compare the average of the lowest monthly income needed (HS130) to the disposable income, by * country. * * Do the same for households which make ends meet easily or very easily * Main points ? ************************************************************************************.

  18. Exercise 3.1e - Optional - Make ends meet * average ability to make ends meet by risk at poverty & country *. means table HS120 by HX080 by HB020 / cells = mean. * Which income variable seems approriate for this comparison?. * Equivalised income (HX090) * Total disposable income (HY020) respectively the published HY020 * HY022_pens = ((HY020+PENS)* HY025) * what else ?. compute monthly = (HX090/12). compute HY020_pub = ((HY020+Pens)*HY025)/12. * compute HY020_m = (HY020/12). * All households *. means HX090 monthly HY020_pub HS130 by HB020 /cells = mean.

  19. Exercise 3.1e - Optional - Make ends meet * only households with difficulties or great difficulties *. compute difficult = (HS120 le 2). compute easily = (HS120 ge 5). val lab difficult 1 'difficulties' 0 'no difficulties'. val lab easily 1 'easily' 0 'not so easily'. cross hs120 by difficult easily. filter by difficult. means monthly HY020_pub HS130 by HB020 /cells = mean. filter by easily. means monthly HY020_pub HS130 by HB020 /cells = mean. filter off.

  20. Exercise optional *******************************************************. • Calculate Income Quintile for households, by country • Calculate the mean of equivalised disposable income for the lowest & highest income quintile, by country • Calculate the mean of lowest monthly income to make ends meet by income quintile & country • Calculate percentage of households with difficulties/great difficulites to make ends meet, by income quintile and country • e) Severe material deprivation rate, by income quintile and country *******************************************************.

  21. Exercise optional *compute the quintiles for each country based on HX090 *. * Option 1 - use the comand split file *. split file by hb020. RANK VARIABLES= HX090 /NTILES(5) INTO Quintile /print = yes. freq quintile. split file off.

  22. Exercise optional ************************************************************************ Option 2 - Rank variables (however HB020 = string variable) => use HB020_num ************************************************************************. autorecode variables = HB020 / into HB020_num. RANK VARIABLES= HX090 by HB020_num /NTILES(5) INTO Quintil. cross HB020 by quintil. * mean of equivalised disposable income for the lowest & highest income quintile, by country *. Temporary. select if (quintil = 1 or quintil = 5). means tables hx090 by hb020 by quintil /cells = means.

  23. Exercise optional * Calculate the mean of lowest monthly income to make ends meet by income quintile & country *. means tables HY020_pub HS130 by HB020 by quintile /cells = means. * difficulties/great difficulites to make ends meet, by income quintile and country *. cross hb020 by difficult easily by quintil / cells = row.

  24. You did it … … at least for today Source: : http://www.hierschreibenwir.de/node/32556

  25. Types of equivalence measure >= 14 >=14 <= 14 Household size: 4; income 4000 € Per-capita income = 4000/4 = 500 € Lis-equival. income = 4000/Square root(4) = 2000 € OECD (new)=4000/(1+1*0.5+2*0.3)=4000/2.1= 1905 € Source: : http://www.hierschreibenwir.de/node/32556

  26. If not taking HY25 into account 2000 € 0 € 2000 € Household size: 4; income 2000 € Per-capita income = 2000/4 = 1000 € Lis-equival. income = 2000/Square root(4) = 2000 € OECD (new)=2000/(1+(1*0.5)+(2*0.3))=2000/2.1= 1905 € Source: : http://www.hierschreibenwir.de/node/32556

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