eusilcP: Synthetic EU-SILC data
In simFrame: Simulation Framework

Description Usage Format Details Source References Examples

This data set is synthetically generated from real Austrian EU-SILC (European Union Statistics on Income and Living Conditions) data.

1	data(eusilcP)

A data.frame with 58 654 observations on the following 28 variables:

hid: integer; the household ID.
region: factor; the federal state in which the household is located (levels Burgenland, Carinthia, Lower Austria, Salzburg, Styria, Tyrol, Upper Austria, Vienna and Vorarlberg).
hsize: integer; the number of persons in the household.
eqsize: numeric; the equivalized household size according to the modified OECD scale.
eqIncome: numeric; a simplified version of the equivalized household income.
pid: integer; the personal ID.
id: the household ID combined with the personal ID. The first five digits represent the household ID, the last two digits the personal ID (both with leading zeros).
age: integer; the person's age.
gender: factor; the person's gender (levels male and female).
ecoStat: factor; the person's economic status (levels 1 = working full time, 2 = working part time, 3 = unemployed, 4 = pupil, student, further training or unpaid work experience or in compulsory military or community service, 5 = in retirement or early retirement or has given up business, 6 = permanently disabled or/and unfit to work or other inactive person, 7 = fulfilling domestic tasks and care responsibilities).
citizenship: factor; the person's citizenship (levels AT, EU and Other).
py010n: numeric; employee cash or near cash income (net).
py050n: numeric; cash benefits or losses from self-employment (net).
py090n: numeric; unemployment benefits (net).
py100n: numeric; old-age benefits (net).
py110n: numeric; survivor's benefits (net).
py120n: numeric; sickness benefits (net).
py130n: numeric; disability benefits (net).
py140n: numeric; education-related allowances (net).
hy040n: numeric; income from rental of a property or land (net).
hy050n: numeric; family/children related allowances (net).
hy070n: numeric; housing allowances (net).
hy080n: numeric; regular inter-household cash transfer received (net).
hy090n: numeric; interest, dividends, profit from capital investments in unincorporated business (net).
hy110n: numeric; income received by people aged under 16 (net).
hy130n: numeric; regular inter-household cash transfer paid (net).
hy145n: numeric; repayments/receipts for tax adjustment (net).
main: logical; indicates the main income holder (i.e., the person with the highest income) of each household.

The data set is used as population data in some of the examples in package simFrame. Note that it is included for illustrative purposes only. It consists of 25 000 households, hence it does not represent the true population sizes of Austria and its regions.

Only a few of the large number of variables in the original survey are included in this example data set. Some variable names are different from the standardized names used by the statistical agencies, as the latter are rather cryptic codes. Furthermore, the variables hsize, eqsize, eqIncome and age are not included in the standardized format of EU-SILC data, but have been derived from other variables for convenience. Moreover, some very sparse income components were not included in the the generation of this synthetic data set. Thus the equivalized household income is computed from the available income components.

This is a synthetic data set based on Austrian EU-SILC data from 2006. The original sample was provided by Statistics Austria.

Eurostat (2004) Description of target variables: Cross-sectional and longitudinal. EU-SILC 065/04, Eurostat.

data(eusilcP)
summary(eusilcP)

strata <- stratify(eusilcP, c("region", "gender"))
summary(strata)

Loading required package: Rcpp
Loading required package: lattice
Loading required package: parallel
      hid                  region          hsize           eqsize     
 Min.   :    1   Vienna       :11657   2      :14128   Min.   :1.000  
 1st Qu.: 6262   Lower Austria:11127   4      :13180   1st Qu.:1.500  
 Median :12465   Upper Austria:10310   3      :12429   Median :2.000  
 Mean   :12488   Styria       : 8142   1      : 8602   Mean   :1.943  
 3rd Qu.:18719   Tyrol        : 4796   5      : 6745   3rd Qu.:2.400  
 Max.   :25000   Carinthia    : 4111   6      : 2094   Max.   :4.500  
                 (Other)      : 8511   (Other): 1476                  
    eqIncome           pid            id                 age       
 Min.   :     0   Min.   :1.00   Length:58654       Min.   :-1.00  
 1st Qu.: 13539   1st Qu.:1.00   Class :character   1st Qu.:22.00  
 Median : 18322   Median :2.00   Mode  :character   Median :40.00  
 Mean   : 20163   Mean   :2.07                      Mean   :39.75  
 3rd Qu.: 24277   3rd Qu.:3.00                      3rd Qu.:57.00  
 Max.   :179946   Max.   :9.00                      Max.   :97.00  
                                                                   
    gender         ecoStat      citizenship       py010n           py050n      
 male  :28539   1      :20900   AT   :44066   Min.   :     0   Min.   : -6895  
 female:30115   5      :12836   EU   : 1257   1st Qu.:     0   1st Qu.:     0  
                7      : 4607   Other: 3162   Median :  2382   Median :     0  
                2      : 4362   NA's :10169   Mean   :  9062   Mean   :  1288  
                4      : 2921                 3rd Qu.: 16820   3rd Qu.:     0  
                (Other): 2859                 Max.   :199075   Max.   :129874  
                NA's   :10169                 NA's   :10169    NA's   :10169   
     py090n            py100n           py110n            py120n        
 Min.   :    0.0   Min.   :     0   Min.   :    0.0   Min.   :    0.00  
 1st Qu.:    0.0   1st Qu.:     0   1st Qu.:    0.0   1st Qu.:    0.00  
 Median :    0.0   Median :     0   Median :    0.0   Median :    0.00  
 Mean   :  444.6   Mean   :  3713   Mean   :   72.9   Mean   :   51.22  
 3rd Qu.:    0.0   3rd Qu.:     0   3rd Qu.:    0.0   3rd Qu.:    0.00  
 Max.   :29887.1   Max.   :101777   Max.   :22546.8   Max.   :46398.44  
 NA's   :10169     NA's   :10169    NA's   :10169     NA's   :10169     
     py130n            py140n             hy040n             hy050n      
 Min.   :    0.0   Min.   :    0.00   Min.   : -2962.5   Min.   :-11857  
 1st Qu.:    0.0   1st Qu.:    0.00   1st Qu.:     0.0   1st Qu.:     0  
 Median :    0.0   Median :    0.00   Median :     0.0   Median :     0  
 Mean   :  393.7   Mean   :   41.73   Mean   :   879.9   Mean   :  2826  
 3rd Qu.:    0.0   3rd Qu.:    0.00   3rd Qu.:     0.0   3rd Qu.:  4558  
 Max.   :53183.6   Max.   :18643.46   Max.   :129586.6   Max.   :118309  
 NA's   :10169     NA's   :10169                                         
     hy070n             hy080n             hy090n              hy110n        
 Min.   :    0.00   Min.   :     0.0   Min.   :  -457.46   Min.   :    0.00  
 1st Qu.:    0.00   1st Qu.:     0.0   1st Qu.:     0.75   1st Qu.:    0.00  
 Median :    0.00   Median :     0.0   Median :    58.45   Median :    0.00  
 Mean   :   93.12   Mean   :   744.6   Mean   :   462.45   Mean   :   32.97  
 3rd Qu.:    0.00   3rd Qu.:     0.0   3rd Qu.:   234.78   3rd Qu.:    0.00  
 Max.   :17954.97   Max.   :124206.2   Max.   :112011.03   Max.   :14506.49  
                                                                             
     hy130n            hy145n            main        
 Min.   :-5489.6   Min.   :-29519.3   Mode :logical  
 1st Qu.:    0.0   1st Qu.:  -256.8   FALSE:33654    
 Median :    0.0   Median :     0.0   TRUE :25000    
 Mean   :  339.1   Mean   :  -108.8                  
 3rd Qu.:    0.0   3rd Qu.:     0.0                  
 Max.   :40762.9   Max.   : 49768.0                  
                                                     
          region gender Size
1     Burgenland   male  947
2  Lower Austria   male 5547
3         Vienna   male 5546
4      Carinthia   male 1950
5         Styria   male 3994
6  Upper Austria   male 5085
7       Salzburg   male 1954
8          Tyrol   male 2257
9     Vorarlberg   male 1259
10    Burgenland female  994
11 Lower Austria female 5580
12        Vienna female 6111
13     Carinthia female 2161
14        Styria female 4148
15 Upper Austria female 5225
16      Salzburg female 2071
17         Tyrol female 2539
18    Vorarlberg female 1286