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Introduction to the training dataset Alexander Mack.

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Presentation on theme: "Introduction to the training dataset Alexander Mack."— Presentation transcript:

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

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