STATISTICS POLAND-Poland

 Labour Market Department, STATISTICS POLAND in cooperation with experts from STATISTICS POLAND and Central Statistical Computing Centre.

STATISTICS POLAND
00-925 Warsaw, Aleja Niepodległości 208

The Structure of earnings survey (SES) is conducted every four years in the Member States of the European Union (EU) and provides comparable information at EU level on relationships between the level of earnings, individual characteristics of employees (sex, age, occupation, length of service, educational level) and their employer (economic activity, size of the enterprise, etc.).

• for economic activities: Statistical Classification of Economic Activities in the European Community, Rev. 2 (2008) (NACE)
• for occupation: International Standard Classification of Occupations 2008 (ISCO-08) – pages 3-34
• for education: International Standard Classification of Education 2011 (ISCED 2011) - Section 9. (pages 71-72)
• for regions: Nomenclature of Territorial Units for Statistics, by regional level, version 2013 (NUTS)

NACE Rev. 2 sections B-S for Eurostat

NACE Rev. 2 sections A-S for national purposes

All the definitions are in line with Commission Regulation (EC) No 1738/2005.

local units

local units with at least 9 employees from the National Business Register

Poland

reference month - October 2018

reference year - 2018

not applicable

Two-stage sampling was applied in the SES 2018 as in the previous SES, with stratification on the first stage. The first-stage sampling units constitutes local units, while on the second stage are drawn employees who met particular requirements of the survey i.e. work the whole month in October (all persons from sampled units up to 40 employed participate in the survey without sampling, persons from units employing more than 40 persons are sampled).

a) size of sample

The 20.0 percent sample for the SES 2018 included about 47.0 thousand units. The responses were given by about 25.6 thousand units. The average unit that gave re­sponse comprised 146 employed persons, of which 122 were employees. In particular units the survey embraced about 868.8 thousand of sampled employees who worked for the whole month (October 2018). After generalization, the results are representative for population of about 8.4 million of employees (full- and part-time paid employees without converting part-time paid employees into full-time paid employees; refers to coverage of NACE A-S). The data on earnings are presented after recalculation into full-time work.

b) sampling scheme

The following variables are considered in the sample selection: the kind of activity, the ownership sector, and the size of the establishment regarding the number of the employed.

Two–stage sampling scheme was applied to sample selection, with stratification at the first stage. The first–stage sampling units were establishments; while at the second stage were selected employees who were employed for the entire month in October 2018.

Establishments were selected separately in each population. As a population is understood a population of local units defined by:

– NACE sections (rev. 2),

– ownership sector (public and private).

There were following exceptions from this rule:

1) in sections F, G, H, I, J, K, L, M, N, P, Q, R, S of private sector within strata 1 and 2 populations constitute particular NACE subsections,

2) in section C of private sector within strata from 1 to 5 populations constitute particular NACE subsections,

3) in sections O, P, Q, R of public sector within strata 1 and 2 populations constitute particular NACE groups,

4) in sections G, P, Q of private sector populations constitute particular NACE groups.

There were 184 populations overall in the survey.

Sampling of local units – the first stage sampling.

The first-stage sampling frame was created on the basis of the National Business Register -(system BJS- the data base of statistical units, kept by Statistics Poland). All information necessary for the SES purposes concerning entities which were transmitted from the BJS system into the sampling frame. Information on the number of employees in a particular local unit was transmitted from results survey on the number of employees. In case of lack of information about a particular local unit in the survey on the number of employees, information on the number of the employed included in BJS was used.

Sampling of local units was carried out separately in each population. Within every population local units were divided into strata according to the number of the employed. Table below presents the outline of strata in the SES 2018.

STRATA IN THE SES 2018– I stage of sampling

The N_ih value i.e. the number of reporting units (local units) in h-th stratum of i-th population was determined for every stratum of a given population. According to assumptions a sample in every population should cover about 10 percent of employed persons fulfilling the survey conditions. Thus, sampling fractions on the first and second stages were determined in order to:

(1)

Values  f1_ih  and M2_ih = 1/f2_ih. However in strata from 6 up to 14 i.e. in local units with over 1100 employed persons  different  sampling fractions  were allowed i.e. less or more than 10 per cent of the employed. In these strata the first stage sampling was not carried out but all the local units from these strata were counted into a sample. Moreover, in case of discrepancies between an actual number of employees and the figure written in the sampling frame equation (1) was not fulfilled.

In order to sample local units in strata from (1) to (5)  value n_ih was calculated i.e. the number of units for drawing from h-th stratum of i-th population.   

(2)

The value e_ih  in the equation (2) is a random zero-one variable. It was introduced in order to random rounding  of the n_ih value to the integer number. Then n_ih whole numbers from the [1; N_ih] interval were sampled without replacement. They matched  conventional ranking numbers of units in the sampling frame. In the same way were sampled local units in all strata in subsequent populations.

• Sampling of employees in local units – the second stage sampling.

In the previous surveys on wages by occupations employees were sampled centrally, i.e. in the Central Statistical Office. Random numbers were sent to previously sampled local units.The numbers constituted ranking numbers of employees on specially prepared registers of employees.These registers were a second stage sampling frame. Such approach was not effective because:

• registers with ranking numbers of the sampled employees had to be sent to local units,
• there were significant discrepancies between the numbers of employees written in the sampling frame and the real number of employees meeting survey requirements.

Thus, this problem was solved for SES 1999 and onwards  surveys by ensuring sampled local units access via Internet to a special program for sampling employees.

The input parameter of this program was value Pj, i.e. the number of employees in the j-th local unit, who worked the whole October. Now the program performs following tasks:

1) reads in data on employees and sorts records according to 6-digit code ISCO’08 of the performed occupation,

2) depending on a given value Pj imputes value M2 i.e inversion of sampling fraction and then calculates value mpj i.e. the number of employees to be sampled in j-th local unit:

(3)

Relations between Pj and M2 for the SES 2018 is given below in table:

STRATA IN THE SES 2018 – II stage of sampling

(3)  generates a string of random numbers {No.} according to the following rule: 

(4) 

where: a_jk  -  is a random integer number from interval [1; M2].

Values No.jk are subsequent numbers (ranking numbers) of employees drawn to a sample in j-th local unit. Sampling according to the above program represents stratified random sampling, in which  one element is drawn from a stratum of M2 size. 

There was specially prepared instruction for sampling of employees for local units which could not use the above program. Sampling according to this instruction represented systematic sampling with interval size M2 and random beginning 1.

• Generalization method

The main parameter estimated on the basis of the survey  results is the number of employees characterised with the specified determinant. This parameter is estimated according to the formula:

(1)

where:

x_khi - the number of the employed with the specified characteristics in the i-th unit in the h-th stratum of  the k-th population,

W_khi - weight appointed to the employee selected to the survey in the i-th establishment of the h-th stratum of the k-th population.

If the survey was full (universe), the weight set in the formula (1) would be described by the formula:

(2)

Because the survey is not full (universe) for different reasons, the weight W_khi  was described by the formula:

(3)

whereas:

(4) 

where:

P_1kh – estimated number of the employed in the units surveyed in the h-th stratum of the k-th population,

P_2kh – estimated number of the employed in the units not surveyed because of the refusal in the h-th stratum of the k-th population,

P_3kh – estimated number of the employed in the units not surveyed because of the lack of contact in the h-th stratum of the k-th population,

P_4kh – estimated number of the employed in the units not surveyed because of the lack of activity, liquidation of the establishment, bankruptcy, etc. in  the h-th stratum of the k-th population.

The above values were estimated on the basis of the data on the number of the employed included in the sample frame. The equations (3) – (4) indicate that weights correction included lack of responses caused by refusals and in proportion to the number of the surveyed units and refusals, lack of responses caused by lack of contact with the selected unit.

every 2 years

The data is collected in a two-stage sample statistical survey using an online questionnaire.

The data is validated following the validation rules provide in Eurostat's Implementing arrangements for SES.

not available 

not applicable

The data quality is ensured by the careful validation. 

not available

Description of the users

Users of the SES data can be divided into the following groups:

a) national users:

• government bodies such as The Chancellery of the President of Poland, The Chancellery of the Prime Minister, Ministry of Development, Ministry of Economic Development, Labour and Technology, Ministry of Finance, National Bank of Poland, Ministry of Education and Science, Ministry of Health, law courts;
• research centres such as the Polish Academy of Science, universities, high schools;
• employers especially employers  who want to create new business;
• trade unions;
• students, pupils;
• mass media.

b) international users:

• European Union bodies – European Council, European Commission (including EUROSTAT);
• International bodies such as International Labour Organization (ILO), OECD, UNICEF, UNECE, The World Bank, Monetary International Fund;
• foreign  employers;
• foreign research centres, universities, high schools.

Description of users’ needs

Generally, internal users are interested in the level of earnings by occupations and by different socio-demographic characteristics such as age, sex, level of education, length of service and their impact on the situation of different occupational groups on the labour market e.g.

Ministry of Economic Development, Labour and Technology examines the  situation of working women and men in occupational groups (ISCO’08) and types of activities (NACE Rev.2) and its impact on potential unemployment and fixing the minimum wage;

Ministry of Finance examines mainly earnings by occupations in different types of activities;

Ministry of Education and Science examines labour demand in relation to the  level of earnings by occupations and by educational level;

Research centres  are interested in analyses of earnings by occupations and other characteristics of employees and their place of work to realise different research projects, besides they conduct international comparisons especially on the level of earnings by occupations and by sex in European Union Countries and Candidate Countries;

Employers  conduct  analyses of earnings by occupations by type of activity to carry out adequate, human resources policy in their enterprises;

Students, postgraduate students and candidates for a doctor's degree use data on the structure of earnings in their master and PhD thesis.

The needs of users are satisfied in a professional way. Users obtain all information on the structure of earnings that are indispensable for conducting adequate calculations, analyses or policy. Information is available mainly in electronic form and in the form of methodological publication, yearbooks of which labour yearbooks.

1. All mandatory variables are available from Polish SES 2018.

2. Optional variables which are missing in table A describing local units (they are not available from the Polish SES 2018):

• A17 – an affiliation of the local unit to a group,
• Key_B – Key identifying the enterprise.

3. Optional variables which are missing in table B  describing sampled employees in local units (they are not available from the Polish SES 2018):

• B24 - management position/supervisory position,
• B29 - citizenship,
• B34 - other annual days of paid absence,
• B412 - annual payments in kind,
• B423 - compulsory social contributions and taxes paid by the employer on behalf of the employee,
• B4231 - compulsory social security contributions,
• B4232 - taxes.

4. ISCO’08 occupation code 0 (army forces) is not covered in SES 2018.

see 5.3

not available

Data obtained from sample surveys, such as the structure of earnings survey by occupations, are biased with: sampling and non-sampling errors which determine the accuracy of the survey. Thus, the limitation and reduction of these errors significantly influence the improvement of data quality and correct interpretation of survey results.

Sampling errors are related to the sample size and sampling schemes. Their nature consists in the fact that incomplete information concerning a phenomenon influences on lack of confidence regarding the relevance of estimates obtained from a sample survey. Thus, the results of a sample survey should be treated as only approximate estimation on a value of an unknown parameter of the population. Therefore, on one hand, we should be aware of incomplete reliability of results (i.e. differences between values gained from a sample and actual values in population, possible to obtain only from a full survey), while on the other hand, we should try to obtain maximum credibility of data through adequate sampling.

Generally, sampling errors can be limited through the extension of sample size or the appliance of more effective sampling frames.

Because the sampling design has an important impact on sampling errors, chapter 3.1 presents a detailed description of the sampling method in SES.

Evaluation of sampling errors in the SES 2018 is carried out on the basis of the relative standard error. Standard error determines a range of variation of a sample mean estimator around a real mean in the population (standard error square is called the variance of estimated mean).

The standard error is a measure of data precision. The lower the standard error is the higher precision is and vice versa – the higher the standard error the lower precision.

The standard error in the SES 2018 is in line with the Commission Regulation No 698/2006 and amounts to less than 3% for the most of variables (for more detailed information please see Annex 2 and Annex 3).

Probability sampling

Variance

Coefficients of variation are defined as the ratio of the square root of the variance of the estimator to the expected value. It is estimated by the ratio of the square root of the estimate of the sampling variance to the estimated value. Both numerator and denominator are provided, together with the resulting coefficient of variation. The estimation of the sampling variance takes into account the sampling design.

According to the Commission Regulation No 698/2006 coefficients of variation refer to monthly and hourly earnings broken down by:

• full time/part-time and sex;
• NACE Rev.2 sections;
• occupation (ISCO-08 at the 1-digit level);
• age band (≤ 19, 20-29, 30-39, 40-49, 50-59, ≥ 60 years old);
• macroregion (NUTS 2016 at the 1-digit level,  PL2, PL4, PL5, PL6, PL7, PL8, PL9; optional);
• education (ISCED 2011, 1 to 4; optional);
• and size band of the enterprise (1-9 (if appropriate), 10-49, 50-249, 250-499, 500-999, 1000+).

Detailed variance analyses are presented in Annex 3 (with NACE A and without NACE A) attached to this report. In Annex 1 (with NACE A and without NACE A) are presented paid employment and average gross wages and salaries for October 2018. In Annex 2 (with NACE A and without NACE A) are presented standard deviation of gross earnings for October 2018. The relative indicators of wages differentiation were calculated using individual data, and not on the basis of the frequency distributions of total gross wages for October 2018.

Generally, the sampling errors for the basic indicators of earnings of October 2018 from SES 2018 were the following (coefficients of variation of gross earnings in %):

- monthly gross earnings (aggregation A-S  →STATISTICS POLAND): 80.7%

- monthly gross earnings (aggregation B-S → EUROSTAT): 80.7%

- hourly gross earnings (aggregation A-S →STATISTICS POLAND): 81.0%

- hourly gross earnings (aggregation B-S → EUROSTAT): 80.9%

The highest coefficients of variation of average monthly gross earnings by type of activity (NACE Rev.2 B-S → EUROSTAT)  took place in:

- Trade; repair of motor vehicles (G): 97.4%

- Financial and insurance activities (K): 93.5%

- Accommodation and catering (I): 89.3%

- Information and communication (J): 85.4%

- Professional, scientific and technical activities (M): 82.9%

- Construction (F): 81.3%

The smallest sampling errors of monthly gross earnings by type of activity were in:

- Mining and quarrying (B): 40.2%

- Education (P): 42.9%

- Arts, entertainment and recreation (R): 48.6%

- Electricity, gas, steam, and air conditioning supply (D): 50.3%

- Public administration and defense; compulsory social security (O): 50.6%

- Water supply, sewerage; waste management and remediation activities (E): 51.2%

The highest coefficients of variation of average monthly gross earnings by type of activity (NACE Rev.2 A-S → STATISTICS POLAND) took place in:

- Trade; repair of motor vehicles (G): 97.1%

- Financial and insurance activities (K): 93.4%

- Accommodation and catering (I): 88.4%

- Information and communication (J): 85.8%

- Professional, scientific and technical activities (M): 83.3%

- Construction (F): 81.6%

The smallest sampling errors of monthly gross earnings by type of activity were in:

-

- Mining and quarrying (B): 40.5%

- Education (P): 43.0%

- Arts, entertainment and recreation (R): 48.7%

- Electricity, gas, steam, and air conditioning supply (D): 50.2%

- Public administration and defense; compulsory social security (O): 50.6%

- Water supply, sewerage; waste management and remediation activities (E): 51.2%

Non- probability sampling

Non-probability sampling is not used.

No data from registers have been used, except for the setting up of the frame population.

Non-sampling errors are divided into coverage errors, measurements and processing errors, non-response errors and model assumption errors. They are described below.

Generally,  coverage errors are divided into  overcoverage and  undercoverage  errors.

Overcoverage errors relate  to units present in the frame and which, in fact, do not belong to the target population or to units not existing in practice (e.g. units that have not been contacted at all, units that are in scope but classified in the wrong sampling strata, duplication in the sampling frame, dead and inactive units ).

In the SES 2018 lack of active constituted 1.8% of the selected sample (the ratio of 841 lack of active to the selected sample of 47.048 units).

Undercoverage errors refer to units not included in the frame, but which should be (e.g. delays in birth registration, lost registration applications). For these units  no information is obtained.

As for the methods of limitation and reduction of coverage errors, errors due to lack of answers from the whole unit are eliminated mainly through updating addresses in a sample frame and methods of results weighting described in details in the item 6.3.3. Errors deriving from lack of answers regarding items are limited through grossing-up correction.

In terms of employees covered by the sample survey their rate amounts about 29.6% of the number of employees in the units in scope i.e. units the survey embraced about 868.8 thousand of sampled employees who worked for the whole month (October 2018). After generalisation, the results are representative for population about 8.4 million of employees persons (for aggregation NACE Rev.2 A-S).

Generally, the errors regarding to unclear, illegible questions and explanations were reduced during the data collection period.

As for the respondent errors, these errors are connected with misunderstandings of methodological note and misinterpretation. Respondents sometimes give incomplete answers in case of time consuming questions. These type of errors are eliminated during the control phase. If errors are caused by averse attitudes of respondents, a survey objective is explained once again, together with respondents’ role in a survey and clearance of any doubts concerning the survey. 

In case of paper forms sent by the post, all doubts regarding variables and addresses of reporting units were explained during phone calls of respondents with the staff of the statistical office and during the e-mail contact.

Overall sampling rate (including those units exhaustively covered): 20.0%

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Measurement errors are divided into: the survey instrument (questionnaire) errors, the respondent errors, the information system and the mode of data collection errors.

As for the survey instrument-questionnaires errors, the questionnaire in the SES is designed in such way to eliminate these types of errors because the detailed explanatory notes are attached to this questionnaire to increase its clarity.

Variables that are corrected very often are following:

• overtime hours;
• basic wages;
• prizes and bonuses;
• annual bonuses that should be given on our form in the amount of 1/12th part of total annual bonuses (they are calculated for October 2018).

Below are presented variables that were often corrected by following reasons:

The variables that were corrected very seldom refer to: work seniority bonuses, the year of birth, sex, level of education.

Detailed classification of non-response units is following:

non- response units consist of 21.484 units about 45.7% of the selected sample by reasons given below:

refusals – 20.205 i.e. 42.9 % of the selected sample;

lack of activity - 841 i.e. 1.8 % of the selected sample;

lack of contact - 438 i.e. 0.9% of the selected sample.

Description of the methods used for re-weighting for non-response is closely connected with the creation of generalising ratios in the SES 2018. Ex-post stratification is here used.

not available

not available

Processing  errors are errors in post-data-collection processes such as data entry, coding, keying, editing, weighting  and tabulating.

As for errors deriving from data compiling and processing, there are  some problems with coding. The code of occupation is given by  reporting units on the basis of the name of performed occupation. The code is checked with the corresponding nomenclature but in some cases descriptions given by reporting units are not enough clear to establish the right code. In these cases additional explanations are required. There are not problems with keying, editing, weighting, tabulating because wrong controlling assumption in a computer program or wrong interpretation of the results are removed immediately during the phase control and data are tested again in conducted surveys. It is very difficult to control an occupation with adequate level of education. We  apply elastic approach to this matter because in practice, people with the long work seniority have the high position that is not in accordance with their low educational level. 

Sometimes, vocational and elementary education is linked with an occupation ISCO 3 (technicians and associate professionals) or tertiary education is linked with occupations: ISCO 7 (craft and related trades workers), ISCO 8 (plant and machine operators and assemblers), ISCO 9 (elementary occupations). All such cases are checked and explained  by the staff of Statistical Office in Bydgoszcz.

No imputation applied.

• A representative month is selected.
• NACE Rev.2 Sections A-S of which B to S are fully covered for units with 10 or more employees.
• Small local units, in this case local units with less than 10 employees, have not been accounted for. Special cases are included when the number of employees changed after the sampling.
• No combinations between survey data and register data have been done. That is, all data come from the survey results.

not applicable

not available

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a) key-data collection dates in the SES 2018

sampling – November 2018;

data were collected via an electronic questionnaire from 1st to 31st of March 2019.

b) key dates for the post-collection phase in the SES 2018

processing of data

compiled forms were checked and corrected- up to September 2019;

preliminary data before grossing were available in September 2019;

benchmark data after grossing were available in September 2019.

c) key publication dates in the SES 2018

dissemination of data

Quick news release on earnings structure by occupations of October 2018 was available in November 2019; it was the first publication of preliminary data, containing only the main information. The main publication from this survey was published at the end of February 2020.

see 7.1

see 7.1

Data were transmitted 18 months from the end of the reference period according to Commission Regulation No 1738/2005 and No 698/2006.

see 7.2

As for the geographical comparability, definitions of: statistical units, reference population and variables are based on EUROSTAT recommendation and that is why the results of the SES are comparable on international scale. It is worth to stress that SES 2018 refers only to employees who work the whole month October. The local units refer to units conducting activity in sections A-S of NACE Rev.2 (of which B-S that is the coverage of the priority for EUROSTAT) and employing more than 9 persons.

Applied classifications are in line with international classifications as follows:

• type of activity in line with NACE Rev.2;
• type of territorial units in line with NUTS 1;
• occupation classification in line with ISCO’08.

not applicable

As for the comparability over time, we changed the size of units covered by the SES namely:

• the SES for October 1999 covered units employing 6 and more persons;
• the SES for October 2001, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016 and 2018 covers units employing 10 and more persons.

Taking into account these circumstances we can state that changes in the size of units have the impact on the employees but they have not significant impact on the level of earnings by occupations and their structure. Thus, we can compare data for October 1999, 2001, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016 and 2018 with regard to level of earnings by occupations and earnings structure.

see 8.2

The comparison with National Accounts is not available.

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[Not requested]

Data are well documented in the form of:

- publications – the SES publication consists of 248 pages, it covers methodological note, characteristics of basic measures on earnings by occupations and earnings structure, information on sampling scheme; the SES publication is disseminated every 2 years;

The structure and level of average gross monthly and hourly wages and salaries and structure of wages and salaries by demographic and occupational characteristic.

Based on the results of the study, the breakdowns of employees according to the amount of remuneration are also prepared, as well as basic measures of wage differentiation.

The results for October of an even-numbered year are generally disseminated as part of:

1. Quicknews release containing basic preliminary data, indicators and breakdowns of gross and wages and salaries available in November of the year following the end of the reference year, where the end of the reference year takes place in February-March of the following year, i.e. after the payment of the thirteenth salary included in the amount of wages annual.

2. Analytical publication entitled "Structure of wages and salaries by occupations in October 2018" containing the general characteristics of the survey results and 16 tables and 29 charts presenting the result data in various sections and the calculated distributions of gross wage differentiation together with other selected measures illustrating the wage structure in Poland. It is available at https://stat.gov.pl/en/topics/labour-market/working-employed-wages-and-salaries-cost-of-labour/structure-of-wages-and-salaries-by-occupations-in-october-2018,4,6.html

The surveys for the years 2004, 2006, 2008, 2010, 2012, 2014 and 2016 can be found on the above website in the Archive tab.

The results of the survey are also published in the following collective publications of the Statistics Poland:

• Statistical Yearbook of the Republic of Poland,
• Concise Statistical Yearbook of Poland,
• Yearbook of Labour Statistics,
• Statistical Yearbook of Industry,

and thematic publications, e.g. Gender pay gap in Poland in 2016, Differences in wages and salaries of women and men in Poland in 2016 or Human Capital in Poland in 2014-2018 or Selected aspects of the labour market in 2018.

No Polish online database.

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Not available.

Methodological chapter included in publication "Structure of wages ..." described above. More on the methodological and organizational principles concerning the sample survey on the structure of earnings by occupations can be found in the Methodological report. Structure of wages and salaries by occupations. It is available https://stat.gov.pl/en/topics/labour-market/yearbook-of-labour/methodological-report-structure-of-wages-and-salaries-by-occupations,2,1.html 

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This report covers the main information on the data quality. It is worth stressing that Polish SES 2018 includes all mandatory variables, thus the quality of SES 2018 is accordant with EUROSTAT’s requirements.