R: Synthetic SES survey data

ses {laeken} | R Documentation

## Synthetic SES survey data

### Description

This data set is a subset of synthetically generated real Austrian SES (Structural Earnings Survey) data.

### Usage

    data(ses)

### Format

A data frame with 115691 observations on the following 28 variables.

`location`

  geographical location with levels `AT1` (eastern Austria), `AT2` (southern Austria), and `AT3` (western Austria).

`NACE1`

  economic branch given in NACE (C - O) 1-digit classification.

`size`

  employment size range in 5 categories.

`economicFinanc`

  form of economic and financial control (levels `A` = public and financial control, `B` = private control).

`payAgreement`

  collective bargaining agreement with levels `A` = national level pay agreement or interconfederal agreement, `B` = industry agreement, `C` = agreement of individual industries in individual regions, `D` = enterprise or single employer agreement, `E` = agreement applying only to workers in the local unit, `F` = any other type of agreement, `N` = no collective agreement exists

`IDunit`

  ID for place of employment.

`sex`

  gender with levels `female` and `male`.

`age`

  age in age classes.

`education`

  highest education.

`occupation`

  occupation with levels `11` = Legislators and seniors officials, `12` = Corporate managers, `13` = Managers of small enterprises, `21` = Physical, mathematical and engineering science professionals, `22` = Life science and health professionals, `23` = Teaching professionals, `24` = Other professionals, `31` = Physical and engineering science associate professionals, `32` = Life science and health associate professionals, `33` = Teaching associate professionals, `34` = Other associate professionals, `41` = Office clerks, `42` = Customer services clerks, `51` = Personal and protective services workers, `52` = Models, salespersons and demonstrators, `61` = Skilled agricultural and fishery workers, `71` = Extraction and building trades workers, `72` = Metal, machinery and related trades workers, `73` = Precision, handicraft, craft printing and related trades workers, `74` = Other craft and related trades workers, `81` = Stationary plant and related operators, `82` = Machine operators and assemblers, `83` = Drivers and mobile plant operators, `91` = Sales and services elementary occupations, `92` = Agricultural, fishery and related labourers, `93` = Labourers in mining, construction, manufacturing and transport

`contract`

  type of contract. Levels `A` = indefinite duration, employment contract, `B` = temporary fixed duration `C` = apprentice.

`fullPart`

  full-time working time (FT) or part-time employee (PT).

`lengthService`

  The total length of service in the enterprises in the reference month is be based on the number of completed years of service.

`weeks`

  the number of weeks in the reference year to which the gross annual earnings relate is mentioned. That is the employee's working time actually paid during the year and should correspond to the actual gross annual earnings.

`hoursPaid`

  the number of hours paid in the reference month which means these hours actually paid including all normal and overtime hours worked and remunerated by the employee during the month.

`overtimeHours`

  the number of overtime hours paid in the reference month. Overtime hours are those worked in addition to those of the normal working month.

`shareNormalHours`

  the share of a full timer's normal hours. The hours contractually worked of a part-time employee are expressed as percentages of the number of normal hours worked by a full-time employee in the local unit.

`holiday`

  the annual days of holiday leave (in full days).

`notPaid`

  examples of annual bonuses and allowances are Christmas and holiday bonuses, 13th and 14th month payments and productivity bonuses, hence any periodic, irregular and exceptional bonuses and other payments that do not feature every pay period. Besides the main difference between annual earnings and monthly earnings is the inclusion of payments that do not regularly occur in each pay period.

`earningsOvertime`

  earnings related to overtime.

`paymentsShiftWork`

  These special payments for shift work are premium payments during the reference month for shirt work, night work or weekend work where they are not treated as overtime.

`earningsMonth`

  the gross earnings in the reference month covers remuneration in cash paid during the reference month before any tax deductions and social security deductions and social security contributions payable by wage earners and retained by the employer.

`earnings`

  gross annual earnings in the reference year.

`earningsHour`

  hourly earnings, being the quotient of monthly earnings and the number of hours paid in the reference month.

`weightsEmployers`

  sampling weights in the first stage at employer level.

`weightsEmployees`

  sampling weights corresponding to the second stage at employee level.

`weights`

  the final sampling weights, which is the product of `weightsEmployers` and `weighsEmployees`.

### Details

The Structural Earnings Survey (SES) is conducted in almost all European Countries, and the most important figures are reported to Eurostat. SES is a complex survey of enterprises and establishments with more than 10 employees, NACE C-O, including a large sample of employees. In many countries, a two-stage design is used where in the first stage a stratified sample of enterprises and establishments on NACE 1-digit level, NUTS 1 and employment size range is used, and large enterprises have higher inclusion probabilities. In stage 2, systematic sampling is applied in each enterprise using unequal inclusion probabilities regarding employment size range categories.

The data set in the package consists of enterprise and employees data from 500 places of work. Note that this is a subset of synthetic data set that is simulated from the original Austrian SES data.

### Author(s)

Matthias Templ, Karoline Geissler

### Source

This is a synthetic data set based on Austrian SES data from 2006.

### References

A. Alfons and M. Templ (2013) Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken. Journal of Statistical Software, 54(15), 1–25. URL http://www.jstatsoft.org/v54/i15/

T. Geissberger (2009) Verdienststrukturerhebung 2006, Struktur und Verteilung der Verdienste in Oesterreich, Statistik Austria, ISBN 978-3-902587-97-8.

M. Templ (2012) Comparison of perturbation methods based on pre-defined quality indicators, UNECE Work Session on Statistical Data Editing, Tarragona, Spain.

### Examples

    data(ses)
    summary(ses)

[Package laeken version 0.4.6 Index]