earn_ses2014_esqrs_at

National Reference Metadata in ESS Standard for Quality Reports Structure (ESQRS)

Compiling agency: Statistics Austria


Eurostat metadata
Reference metadata
1. Contact
2. Statistical presentation
3. Statistical processing
4. Quality management
5. Relevance
6. Accuracy and reliability
7. Timeliness and punctuality
8. Coherence and comparability
9. Accessibility and clarity
10. Cost and Burden
11. Confidentiality
12. Comment
Related Metadata
Annexes (including footnotes)
 



For any question on data and metadata, please contact: EUROPEAN STATISTICAL DATA SUPPORT

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1. Contact Top
1.1. Contact organisation

Statistics Austria

1.2. Contact organisation unit

Directorate Social Statistics
Living Conditions, Social Protection

1.5. Contact mail address

STATISTICS AUSTRIA 

Social Statistics

Guglgasse 13

1110 Vienna

AUSTRIA


2. Statistical presentation Top
2.1. Data description

The Austrian Structure of Earnings Survey (SES) has been conducted every four years since 2002.The survey is based on Council Regulation (EC) No 530/1999 concerning structural statistics on earnings and on labour costs and Commission Regulation (EC) No 1738/2005. The survey covers all mandatory variables. Since 2010, the optional variable 'citizenship' has also been available.

This quality report refers to the SES for the reference year 2014. It is based on the Commission Regulation (EC) No 698/2006 of 5 May 2006 Implementing Council Regulation (EC) No 530/1999 as regards quality evaluation of structural statistics on labour costs and earnings.

2.2. Classification system

Statistical classification of economic activities in the European Community (NACE Rev. 2), International Standard Classification of Occupations (ISCO-08), International Standard Classification of Education (ISCED11), Nomenclature of Units for Territorial Statistics (NUTS).

2.3. Coverage - sector

Enterprises with at least 10 employees in the sections B to S (excluding O) of NACE Rev. 2.

2.4. Statistical concepts and definitions

The concept and the definitions are set out in the Council Regulation (EC) No 530/1999 of 9 March 1999 concerning structural statistics on earnings and on labour costs, the Commission Regulation (EC) No 1738/2005 of 21 October 2005 amending Regulation (EC) No 1916/2000 as regards the definition and transmission of information on the structure of earnings and the Structure of Earnings Survey 2014 Implementing Arrangements.

2.5. Statistical unit

Enterprises or local units and employees in these units.

2.6. Statistical population

Employees in enterprises with at least 10 employees in the sections B to S (excluding O) of NACE Rev. 2.

2.7. Reference area

Austria.

2.8. Coverage - Time

2014

2.9. Base period

Not applicable.


3. Statistical processing Top
3.1. Source data

The SES 2014 was conducted as a combination of primary and secondary statistics. The use of secondary data meant that the survey could be restricted to those characteristics which cannot be obtained from sources of administrative data. With the exception of the variable "citizenship", optional variables were usually not included in the survey.

  • Primary data

Earnings: gross earnings in reference month, earnings related to overtime, special payments for shift work

Hours worked: full-time or part-time employee, % share of a full-timer's normal hours, number of hours paid, number of overtime hours paid

Workplace-related characteristics: occupation, length of service in the enterprise, type of employment contract, annual days of holiday leave

Enterprise-related variables: geographical location of the local unit, principal economic activity of the local unit, form of economic and financial control, collective pay agreement

 

  • Administrative data

Business register: geographical location of the enterprise, principal economic activity of the enterprise

Social security data: size of the enterprise, sex, age, citizenship

Wage tax data: number of weeks, gross annual earnings, annual bonuses and allowances

Education register: highest successfully completed level of education

3.2. Frequency of data collection

Reference year: 2014.

Reference month: October 2014.

3.3. Data collection

In Austria, the SES is conducted on the basis of a two-stage random sampling approach of enterprises (first stage) and employees (second stage). The population used for sampling comprises around 43 100 enterprises with at least 10 employees in NACE Rev. 2 sections B-N and P-S and the approximately 2.5 million employees of these enterprises. Some 11 800 enterprises and 216 000 employees were selected for sampling.

As a first step, a stratified random sample of enterprises was drawn from the business register. The variables economic activity (NACE Rev. 2), location at level NUTS 1 and size of the enterprise were used as stratification criteria.

 

Sampling frame: Number of enterprises in the sample by economic activity and size of the enterprise in %

NACE Rev. 2 Size of the enterprise
Total 10-19 20-49 50-99 100-249 250-499 500-999 1 000+
enterprises in the sample in %
Total 27.4 18.5 27.8 37.1 58.6 77.2 91.0 100.0
B 100.0 100.0 100.0 100.0 100.0 100.0 . .
C 38.2 24.3 28.7 51.3 75.8 80.4 90.2 100.0
D 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
E 100.0 100.0 100.0 100.0 100.0 100.0 100.0 .
F 15.6 9.7 19.8 16.2 38.0 100.0 100.0 100.0
G 22.7 17.1 23.8 30.3 51.0 60.6 100.0 100.0
H 24.5 18.2 25.6 25.9 48.7 84.8 100.0 100.0
I 19.7 15.3 23.1 24.5 49.0 100.0 100.0 100.0
J 36.1 21.2 40.3 54.3 71.3 100.0 100.0 100.0
K 38.0 21.5 30.8 42.0 60.2 100.0 100.0 100.0
L 54.1 36.8 58.4 100.0 100.0 100.0 . .
M 25.8 16.5 32.6 47.8 68.2 100.0 100.0 100.0
N 18.2 9.4 19.9 14.7 27.6 52.5 82.1 100.0
P 55.5 40.6 57.5 85.4 100.0 100.0 100.0 100.0
Q 17.3 8.4 17.2 15.8 22.8 38.3 65.1 100.0
R 66.1 53.2 67.9 100.0 100.0 100.0 100.0 .
S 32.6 23.3 26.0 63.4 97.0 100.0 100.0 100.0

 

In a second step, a simple random sample of employees was drawn within the selected enterprises. Depending on the size of the enterprise, the corresponding number of employees per enterprise was selected. In order not to over-burden enterprises, the maximum number of selected employees per enterprise was limited to 80.

 

Selection of employees within the enterprises

Size of enterprise Every nth element selected
10 to 19 1
20 to 49 2
50 to 99 5
     100 and more employees 10

 

Drawing the sample at the level of the local unit was not possible, because at the moment of sampling employees could be unambiguously allocated only at enterprise level. Enterprises with local units in different NACE divisions / NUTS 2 regions were, pursuant to the national implementing regulation[1]), required to allocate the employees selected to the local unit. This affected around 22% of the enterprises.

For data transmission, 99% of the enterprises used the web-based questionnaire (eQuest-Web).


[1] Order of the Federal Minister for Economic Affairs and Labour on the Structure of Earnings Survey in industry and parts of the service sector (Verdienststrukturstatistik-Verordnung), Federal Law Gazette (BGBl.) II No 66/2007, as amended by BGBl. II No 99/2011

3.4. Data validation

To , the already tried and tested web-questionnaire (eQuest-Web) was used for the primary survey. To prevent errors the web-based questionnaire incorporated explanatory notes and plausibility tests on the individual survey parameters. Moreover, integrated classifications allowed the automatic recognition of NACE codes and occupations according to ISCO-08. A hotline was also available to provide information to enterprises.

The submitted data were subjected to several layers of plausibility testing at both micro and macro levels for the ex-post identification of any measuring errors. The first step was for the survey data to be examined and corrected at micro level. In a second step the survey data were linked to the secondary data and subjected to another plausibility test. After weighting the data, aggregates were subject to a third plausibility test (macro plausibility).

3.5. Data compilation

The following variables were calculated based on primary or administrative data:

Gross hourly earnings: gross earnings in the reference month / number of hours paid

Length of service in the enterprise: 31 October 2014 - entry date (career breaks exceeding one year have been deducted)

Share of a full-timer's normal hours: contractually agreed working time / normal statutory or collectively agreed working time * 100

Number of weeks to which the gross annunal earnings relate: In order to determine the number of weeks to which the gross annual earnings relate, information was available from social security data (date of joining or leaving the enterprise), from wage tax data (reference period) and from survey data (entry date). In 84.8% of cases, all three sources concurred. In case of discrepancies, the number of weeks was determined by comparing the monthly earnings derived from primary data and the monthly earnings calculated from secondary data (gross annual earnings). The figure with the smallest difference was chosen (6.4% social security data, 7.3% wage tax data, 0.5% survey data). In 1.0% of cases the number of weeks was calculated on the basis of gross monthly earnings and gross annual earnings.

3.6. Adjustment

[Not requested]


4. Quality management Top
4.1. Quality assurance

Not available.

4.2. Quality management - assessment

[Not requested]


5. Relevance Top
5.1. Relevance - User Needs

The results of the SES are used at national level mainly by various federal ministries, employer and employee organisations, universities and other research institutes, the media, enterprises and private individuals.

5.2. Relevance - User Satisfaction

Information about the profile of users or the level of satisfaction with the data provided is not available for Austria.

5.3. Completeness

[Not requested]

5.3.1. Data completeness - rate

[Not requested]


6. Accuracy and reliability Top

In Austria, responding to the SES is a legal requirement. The unit-response rate was 98%.

6.1. Accuracy - overall

[Not requested]

6.2. Sampling error

Provisional data.

6.2.1. Sampling error - indicators

Coefficients of variation in %

 

Variables Gross monthly earnings Gross hourly earnings in the reference month
(B42) (B43)
Total 0.28 0.23
Full-time/part-time  
Full-time - total 0.27 0.27
Full-time - women 0.43 0.42
Full-time - men 0.31 0.31
Part-time - total 0.58 0.39
Part-time - women 0.56 0.39
Part-time - men 1.46 0.97
NACE Rev. 2  
B 1.59 1.59
C 0.42 0.40
D 1.29 1.22
E 0.88 0.79
F 0.81 0.70
G 0.73 0.58
H 1.15 1.03
I 1.10 0.58
J 1.12 0.97
K 1.27 1.10
L 1.70 1.40
M 1.34 1.08
N 1.88 1.27
P 1.82 1.07
Q 1.25 0.87
R 1.64 1.37
S 1.23 0.99
Occupation (ISCO-08)  
1 Managers 1.00 0.98
2 Professionals 0.75 0.56
3 Technicians and associate professionals 0.50 0.41
4 Clerical support workers 0.54 0.44
5 Service and sales workers 0.72 0.45
7 Craft and related trades workers 0.41 0.35
8 Plant and machine operators and assemblers 0.67 0.60
9 Elementary occupations 0.83 0.45
Age classes  
15-19 0.96 0.82
20-29 0.44 0.26
30-39 0.42 0.33
40-49 0.45 0.38
50-59 0.58 0.49
60+ 2.61 1.84
NUTS 1  
1 Eastern Austria 0.47 0.38
2 Southern Austria 0.50 0.40
3 Western Austria 0.41 0.33
Highest completed level of education (ISCED11)   
G1 ISCED 0-2 0.55 0.40
G2 ISCED 3-4 0.30 0.23
G3 ISCED 5-6 0.58 0.45
G4 ISCED 7-8 0.80 0.65
Size of the enterprise  
10-49 0.43 0.33
50-249 0.65 0.54
250-499 0.75 0.60
500-999 0.61 0.59
1000+ 0.59 0.48
6.3. Non-sampling error

Over-coverage was found with regard to just 0.7% of the enterprises in the sample. Statistical units in section O (public administration and defence; compulsory social security) were not part of the population, which meant that local units whose higher-level unit was allocated to section O in the business register were not recorded. According to the business register, this led to under-coverage of 8.6% of employees.

6.3.1. Coverage error

The sample of enterprises is based on the business register. Therefore coverage errors are directly connected to the quality of the register data. In order to achieve a high level of completeness and relevance, Statistics Austria conducts ongoing technical comparisons between the business register and external sources of administrative data (register of companies, tax and social security data etc.). Moreover, information from economic surveys makes a substantial contribution to keeping the business register up to date.

6.3.1.1. Over-coverage - rate

Over-coverage was found with regard to 0.7% of the enterprises in the sample. These enterprises were no longer active at the time of the survey and thus did not respond (see also point 6.3.3. Non-response errors).

Statistical units in section O (public administration and defence; compulsory social security) were not part of the population, which meant that local units whose higher-level unit was allocated to section O in the business register were not recorded. According to the business register, this led to under-coverage of 8.6% of employees.

According to the employee estimation model in the business register[1] the under-coverage relates to the service sector mainly, in particular to education (section P -52%) and to parts of public health and social services (section Q -26%). In the education sector, it is mainly employees in public schools and childcare facilities who are not covered[2]. In health and social work the same applies to some part of employees in public homes and hospitals. Smaller economic sectors were also affected. In the area of "arts, entertainments and recreation" (section R -20%), employees of public libraries, archives, museums and swimming baths are missing. In industry, municipal employees in section E "water supply and waste disposal" (-18%) were not recorded.

This can lead to either an over-estimation or under-estimation of earnings in these sectors.

 


[1] Employees are allocated to a local unit on the basis of an allocation key. This allocation key is based on an estimation model according to which the employees of the enterprise are allocated to the local units.

[2] Section P "education" includes private and faith-based childcare establishments and schools, all universities and colleges, driving schools and the adult education sector.

6.3.1.2. Common units - proportion

[Not requested]

6.3.2. Measurement error

To prevent measuring errors, the already tried and tested web-based questionnaire was used for the primary survey. The submitted data were subjected to several layers of plausibility testing at both micro and macro levels for the ex-post identification of any measuring errors (see point 3.4. Data validation).

The secondary data were, in general, of very high quality. The business register is being continually improved in order to make it more comprehensive and up-to-date (see point 6.3.1. Coverage error). The social security data come from monthly notifications to the social security institutions, meaning that employment relationships can be recorded precisely to the day. Tests are carried out upstream in the Statistics Austria database system to ensure the good quality of these data. For wage tax, data from the Austrian wage tax statistics were used which had already been verified by the tax statistics department of Statistics Austria. In this way, optimum data quality could be ensured in the calculation of gross annual earnings and annual bonuses and allowances.

The data from the education register are based on data from the 2001 census, which are constantly updated and supplemented according to the information sent by schools, universities and other educational establishments. The ongoing qualification of employees from other countries can be taken into account only if the training was completed in Austria, the qualification is officially recognised, the Public Employment Service Austria (AMS) provides the information or if an academic degree is entered in the Central Register of Residents (ZMR) (see point 6.3.3.2. Item non-response).

6.3.3. Non response error

Enterprises are legally obligated to provide information [1]. In addition, the following measures were taken to limit non response: firstly, reminder letters to enterprises that did not reply by the response deadline; then dunning letters sent to those enterprises that had not replied to the reminder letter; thirdly, reminder telephone calls and, fourthly, reports to the authorities for suspected failure to comply with information obligations (see also point 7.2. Punctuality).

 


[1] Regulation of the Federal Minister for Economic Affairs and Labour on the Structure of Earnings Survey in industry and parts of the service sector (Verdienststrukturstatistik-Verordnung), Federal Law Gazette (BGBl.) II No 66/2007, as amended by BGBl. II No 99/2011

6.3.3.1. Unit non-response - rate

The overall unit response rate was 98.3%. The unit non-response rate of 1.7% (199 enterprises) can be broken down further into 0.7% over-coverage (see point 6.3.1. Coverage errors) and 1.0% refusals.

 

Unit-Response rate

NACE Rev. 2 Size of the enterprise
Total 10-19 20-49 50-99 100-249 250-499 500-999 1 000+
response rate in %
Total 98.3 97.4 98.4 98.9 99.2 99.7 99.7 99.2
B 99.2 98.0 100.0 100.0 100.0 100.0 100.0 .
C 99.1 97.8 99.2 99.2 99.8 99.5 100.0 100.0
D 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
E 98.6 97.8 99.1 100.0 100.0 100.0 100.0 .
F 97.8 97.5 97.8 98.6 97.5 97.6 100.0 100.0
G 98.8 98.6 98.2 99.5 99.4 100.0 100.0 100.0
H 95.6 95.3 95.0 94.6 96.5 100.0 100.0 100.0
I 96.7 95.0 98.0 100.0 97.3 100.0 100.0 100.0
J 98.9 97.1 99.4 100.0 100.0 100.0 100.0 100.0
K 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
L 98.4 97.3 98.0 100.0 100.0 100.0 100.0 .
M 98.3 98.1 98.0 98.4 98.9 100.0 100.0 100.0
N 97.9 93.3 100.0 95.8 100.0 100.0 95.7 100.0
P 97.7 97.1 97.5 97.6 97.7 100.0 100.0 100.0
Q 98.8 98.5 99.1 100.0 100.0 100.0 100.0 94.1
R 98.0 97.5 98.2 96.8 100.0 100.0 100.0 100.0
S 98.4 97.4 100.0 98.3 98.4 100.0 100.0 100.0
6.3.3.2. Item non-response - rate

See point 6.3.4.1. Imputation.

6.3.4. Processing error

Due to the complete changeover to a web-based questionnaire, that incorporates plausibility tests on the individual characteristics, it was possible to reduce the item non-response of the survey data. On the other hand, administrative data showed a slight increase of missing values. 

6.3.4.1. Imputation - rate

Missing information about the variable 'length of service in the enterprise' was calculated on the basis of information from social security data on the date of joining or leaving the enterprise. Information on the variable 'type of employment contract' was also completed with the aid of social security data. For the variable 'annual days of holiday leave', the statutory holiday entitlement was imputed, taking into account age and length of service.

 

Item imputation rate (survey data)

Variables Total

absolute

Imputed values

absolute

Item imputation rate

in %

Length of service 211 202 238 0.1
Type of employment contract 211 202 375 0.2
Annual days of holiday leave 211 202 1 520 0.7

 

Wage tax data that could not be linked by the personal ID were matched using the enterprise ID and the variables age and sex. Thus, 2.1% could be clearly assigned by statistical matching. The remaining gaps in administrative data were completed using different imputation procedures.

The variables 'gross annual earnings' and 'annual bonuses and allowances' were imputed using regression analysis. The basis of the calculation was the information on gross monthly earnings from the survey in combination with information on the date of joining or leaving the enterprise.

Missing information on the 'highest completed level of education' was imputed based on  a multinomial regression, using the predictors sex, gross hourly earnings, age-classes, occupation and citizenship (Austrian, EU-15 and other).

 

Item imputation rate (administrative data)

Variables Total

absolute

Imputed values

absolute

Item imputation rate

in %

Gross annual earnings 211 202 8 838 4.2
Annual bonuses and allowances 211 202 8 838 4.2
Highest successfully completed level of education 211 202 17 863 8.5

 

Information on education was missing for 0.7% of employees with Austrian citizenship and 46.4% of employees with other citizenship. This is because the education register receives ongoing notifications only from educational institutions in Austria. The ongoing qualification of employees from other countries can be taken into account only if the training was completed in Austria, the qualification is officially recognised, the Public Employment Service Austria (AMS) provides the information or if an academic degree is entered in the Central Register of Residents (ZMR) (see point 6.3.2. Measurement error). The value of the variable 'highest completed level of education' is therefore significantly restricted for non-Austrians. This means that the earnings of employees who are not Austrian, broken down by level of education, could be either over- or under-recorded.

6.3.5. Model assumption error

To correct for unit non-response the net sample was adjusted to the population given by the selection framework. The weighting of the data, like the sampling, took place in several steps. The first step was to reproduce the sample design, by giving each enterprise the base weight of the sampled unit Wk, representing the reciprocal selection probability in the stratum h (Wk=Nh/nh). Using the total number of employees per enterprise as a basis, a sample weight was also calculated for each employee in the sample. Using the base weight Wk of the enterprise k, the number of employees Bk of this enterprise and the number of employees bk of the enterprise k in the sample, the base weight Wbhk  was calculated for each employee in the sample unit.

Wbhk= Wk*Bk/bk

Based on the allocation of employees to local units by enterprises, a breakdown by economic activity and region at the level of the local unit could be conducted within the meaning of the European legal basis. For the weighting of the local units summarised as aggregates, it was necessary, in a next step, to calibrate the weights such that the sum of the weights of each NUTS 1 region cross-referenced with NACE sections matched the corresponding population.

Then, the second step of the weighting was to modify the basic weighting Wbhk, which initially was allocated to each aggregate, so that the sum of the weights of these units corresponded both with the corresponding figures from the population in the cross-referencing of the NACE sections and NUTS 1 region and the sum of all male and female employees in the population.[1]

 


[1] Weighting by gender has been carried out since 2006, as the Structure of Earnings Survey has been used as the source for calculating the gender pay gap throughout the EU.

6.4. Seasonal adjustment

[Not requested]

6.5. Data revision - policy

[Not requested]

6.6. Data revision - practice

[Not requested]

6.6.1. Data revision - average size

[Not requested]


7. Timeliness and punctuality Top
7.1. Timeliness

The final data were delivered to Eurostat on 29 June 2016 (t+18).

In order to keep the period of time between the reference period and the publication of data at national level as short as possible, first results were presented in press conference and a press release on 28 July 2016.

7.1.1. Time lag - first result

[Not requested]

7.1.2. Time lag - final result

[Not requested]

7.2. Punctuality

Overview of the data collection process

  Dates for the field work Submission deadlines Response rate
Transmission of questionnaires 13 April 15 15 May 2015 62%
First reminder 1 June 2015 22 June 2015 86%
Second reminder 6 July 2015 27 July 2015 95%
Reminder calls 3 - 31 August 2015 7 September 2015 98%
Reports to the authorities 17 September 2015 - -
Data processing (matching,  plausibility checks, imputation, weighting) October 2015 - June 2016 - -
Transmission of data to Eurostat 29 June 2016 - -
7.2.1. Punctuality - delivery and publication

[Not requested]


8. Coherence and comparability Top
8.1. Comparability - geographical

The comparability of the results between Member States is restricted because of under-coverage in NACE Rev. 2 sections P, Q, R and E (see point 6.3.1. Coverage error).

Statistical units in section O were not part of the population, which meant that local units whose superordinate enterprise was allocated to section O in the business register were not recorded. The proportion of employees in those economic sectors (except R) was therefore much lower in Austria than the EU average.

The exclusion of section O from the survey population also affects the results for certain professional groups such as teaching professionals (ISCO 23), health professionals (ISCO 22), health associate professionals (ISCO 32) and personal care workers (ISCO 53).

 

Proportion of employees by economic activity in %

 Q: Eurostat. Not including Greece and Croatia. Enquiry of 28 November 2016.

8.1.1. Asymmetry for mirror flow statistics - coefficient

[Not requested]

8.2. Comparability - over time

The changes to the definitions between the 2002, 2006, 2010 and 2014 surveys are mainly the result of amendments to legal acts and classifications (NACE, ISCO, ISCED). Pursuant to Article 3 of Regulation (EC) No 530/1999, the inclusion of sections M – O of NACE Rev. 1 was optional for the SES 2002. Furthermore, a derogation from Article 6 has been in force for Austria in 2002, whereby the statistical unit could relate to the enterprise rather than to the local unit.

Coverage

  • 2002 sections C-K of NACE Rev. 1
  • 2006 sections C-K and M-O of NACE Rev. 1.1
  • since 2010 B-N and P-S of NACE Rev. 2

Statistical units

  • 2002 enterprises
  • since 2006 enterprises/local units

Weighting

  • 2002 enterprises/employees
  • since 2006 local units/employees by sex

Classifications

  • 2002 NACE Rev. 1, 2006 NACE Rev. 1.1 / since 2010 NACE Rev. 2
  • 2002 / 2006 ISCO-88, 2010 / 2014 ISCO-08
  • 2002 - 2010 ISCED 97, 2014 ISCED 11

 

  Gross hourly earnings (mean)
2006 2010 2014
Total 13.26 14.77 15.93
Women 10.99 12.45 13.65
Men 14.74 16.37 17.55
Full-time/part-time   
Full-time 13.91 15.58 16.84
Full-time – women 11.35 13.02 14.19
Full-time – men 14.89 16.6 17.88
Part-time 11.09 12.43 13.62
Part-time – women 10.57 11.84 13.14
Part-time – men 13.06 14.37 15.08
Age categories   
15-19 5.32 5.77 6.40
20-29 10.61 11.7 12.41
30-39 13.69 15.13 16.00
40-49 15.08 16.6 17.88
50-59 15.73 17.33 18.73
60+ 17.63 19.57 20.53
NUTS 1   
1 Eastern Austria 13.83 15.46 16.40
2 Southern Austria 12.4 13.93 15.28
3 Western Austria 13.07 14.47 15.77
Size of the enterprise    
10-49 11.59 12.96 13.83
50-249 13.3 14.69 15.92
250-499 13.51 16.18 16.84
500-999 14.49 15.5 17.60
1000+ 14.38 15.8 17.16
8.2.1. Length of comparable time series

[Not requested]

8.3. Coherence - cross domain

 [Not requested]

8.4. Coherence - sub annual and annual statistics

[Not requested]

8.5. Coherence - National Accounts

The characteristics 'wages and salaries' per employee in the National Accounts and 'gross annual earnings' in the SES are comparable only with certain caveats. Such comparisons lead to a discrepancy of -4%.

The main reasons for this discrepancy are:

  • Statistical units in National Accounts are companies (or ‘establishments’), which are units between the enterprise level and the level of local units.
  • The coverage of National Accounts includes enterprises with fewer than 10 employees.
  •  'Wages and salaries' in National Accounts include estimates for tips or gratuities, in particular in section I (Accommodation and food service activities) and the divisions 49.32 (Taxi operation) and 96.02 (Hairdressing and other beauty treatment) in NACE Rev. 2.
  • The number of employees in National Accounts corresponds to the annual average whereas SES exclusively relates to employees in the reference month (October). Seasonal variations thus arise as a result of the choice of reference period.
  • Differences occur also regarding the calculation of full-time units (FTU), e.g. hours paid / hours worked, annual average / full year.
  • The National Accounts include units whose enterprises come under Section O. This plays a role in NACE sections P, Q, R and E in particular.

 

Comparison of wages and salaries per employee in National Accounts and gross annual earnings in the SES

 

NACE Rev. 2 National Accounts  SES Eurostat Difference
in %
National Accounts  SES STAT  Difference
in %
Wages and salaries (D11) FTU Mean annual earning (FTU, full year) Wages and salaries (D11) Gross Annual Earnings in the reference year
Total 41.055 42.392 3 34.734 33.418 -4
B 50.938 51.411 1 48.371 45.060 -7
C 44.469 45.355 2 41.798 40.503 -3
D 66.313 65.485 -1 62.272 60.342 -3
E 42.087 38.871 -8 38.042 34.016 -11
F 37.909 39.407 4 34.622 32.324 -7
G 35.938 36.744 2 29.528 28.590 -3
H 38.504 40.147 4 35.398 34.691 -2
I 31.930 25.345 -21 24.069 15.580 -35
J 55.853 58.372 5 50.511 49.676 -2
K 62.187 62.665 1 53.833 54.532 1
L 41.959 47.407 13 31.975 37.426 17
M 48.676 56.545 16 39.773 44.133 11
N 30.503 31.741 4 24.234 20.332 -16
P 49.647 47.054 -5 40.773 28.406 -30
Q 38.812 39.587 2 30.276 27.852 -8
R 42.286 41.534 -2 32.516 26.556 -18
S 27.889 38.895 39 21.749 28.898 33
8.6. Coherence - internal

[Not requested]


9. Accessibility and clarity Top

The results were published at national level in various media.

9.1. Dissemination format - News release

The most important results were presented in the form of a press conference and a press release (English version).

9.2. Dissemination format - Publications

Internet

An overview of the key findings (text and tables in HTML, PDF and Excel formats) can be found on the website of Statistics Austria (English version).

Statistische Nachrichten

The main results of the Structure of Earnings Survey 2014 were published in volume 10/2016 of 'Statistische Nachrichten' (English summary).

Printed publications

Detailed results and tables will be presented in the publication entitled 'Verdienststrukturerhebung 2014 – Struktur und Verteilung der Verdienste in Österreich' (Print version, including CD ROM with tables in Excel format; free PDF file available from our website).

Statistical Yearbook

Selected results are published in the Statistical Yearbook in the chapters 'Income; earnings" and 'Employment and the labour market' (print version, including CD ROM with tables in Excel format).

9.3. Dissemination format - online database

STATCUBE database

The data can also be found on the Statistics Austria website under Publications & Services, STATcube– Statistical Database

9.3.1. Data tables - consultations

[Not requested]

9.4. Dissemination format - microdata access

[Not requested]

9.5. Dissemination format - other

Not available

9.6. Documentation on methodology

Metadata

Meta data (definitions, explanations, methods, quality) can be found on the website of Statistics Austria (German version).

9.7. Quality management - documentation

[Not requested]

9.7.1. Metadata completeness - rate

[Not requested]

9.7.2. Metadata - consultations

[Not requested]


10. Cost and Burden Top

[Not requested]


11. Confidentiality Top

 

11.1. Confidentiality - policy

[Not requested]

11.2. Confidentiality - data treatment

[Not requested]


12. Comment Top

Annex: Plausibility of variables



Annexes:
Plausibility of variables


Related metadata Top


Annexes Top