1. Introduction to the training dataset Alexander Mack

2. Overview 1. Overview of EU-SILC data 2. Comparison UDB and training data 3. Technical details for training session

3. 1. Overview of EU-SILC The EU-SILC consist of four sub datasets: Personal Data (udb_c10p_silc_course) 305 variables; demographics; income; work and unemployment; health Personal Register (udb_c10r_silc_course) 51 variables; includes persons under age 16; demographic information; childcare; immigration; work intensity; person weights; information on proxy interviews Household Data (udb_c10h_silc_course) 218 variables; Income; subjective economic situation; household assets; housing situation Household Register (udb_c10d_silc_course) 10 variables; HH-weight; sampling; NUTS, degree of urbanisation

4. 1.Overview of EU-SILC data UDB 2010 includes the following 30 countries: AT, BE, BG, CY, CZ, DE, DK, EE, ES, FI, FR, GR, HU, IE, IS, IT, LT, LU, LV, MT, NL, NO, PL, PT, RO, SE, SI, SK, UK UDB 2010 covers a total of 576531 person records Sample Sizes for individual countries range from 8840 (MT) to 47551 (IT)

5. 2. Differences between UDB and training data Training data contains only the following countries 14 countries: AT, BG, CY, CZ, DK, EE, ES, FI, FR, GR, HU, LT, MT, PL For each country a subsample of 1500 households was drawn (exception MT: 1141) Dropped variables: PSU-1; PSU-2; order of selection of PSU; Furthermore the training data contain only households with a maximum of 5 persons  Training data is not a representative sample and thus cannot be used for scientific analysis

6. 2. Comparison UDB and training data HouseholdsIndividuals UDBTraining DataUDBTraining Data AT6,1881,50014,0853,305 BG6,1711,50016,3563,617 CY3,7801,50011,0883,116 CZ9,0981,50021,3793,442 DK5,8671,50014,7573,686 EE4,9721,50013,4743,761 ES13,5971,50037,0263,917 FI10,9891,50027,0093,522 FR11,0471,50026,5313,404 GR7,0051,50017,6113,715 HU9,8131,50024,7513,627 LT5,3141,50013,2353,603 MT3,7811,14110,3843,034 PL12,9301,50037,3793,940

7. 2. Comparison UDB and training data WomenAverage Age UDBTraining DataUDBTraining Data AT51.89%52.38%40.5441.39 BG52.20%52.81%45.7547.71 CY52.04%51.70%39.4140.24 CZ52.06%52.21%42.5843.37 DK50.23%51.41%39.7140.45 EE52.87%53.26%39.5641.85 ES51.33%51.26%41.6041.88 FI49.34%50.23%39.4041.00 FR51.74%51.67%39.7341.08 GR51.41%51.58%43.7744.08 HU54.11%54.34%40.5842.04 LT53.00%53.51%44.8045.76 MT50.99%50.63%41.9942.99 PL52.23%52.84%40.0541.923

8. 2. Comparison UDB and training data Average HH sizeEquivalized HH income in € UDBTraining DataUDBTraining Data AT2.282.2023347.9923656.79 BG2.652.413273.9193376.776 CY2.932.8218642.1319024.89 CZ2.352.297500.9027506.106 DK2.522.4629123.9429675.08 EE2.712.516091.4816133.798 ES2.722.6114454.8214659.99 FI2.462.3525258.4325589.49 FR2.402.2724435.2924980.76 GR2.512.4812970.6113084.48 HU2.522.424606.114638.638 LT2.492.404943.1364873.46 MT2.752.6611169.5111236.86 PL2.892.634956.9865125.38

9. 3. Technical details for training session With SPSS: FILE HANDLE data_path / NAME=D:\DWB- Training\SILC\Data\SPSS files'. GET FILE='data_path/filename.sav‘ will open your dataset With Stata: CD "D:\DWB-Training\SILC\Data\Stata files“ use filename.dta, replace will open your dataset