National Statistics Office (NSO)

Labour Market and Information Society Statistics

Labour Market Statistics

National Statistics Office

Lascaris

Valletta CMR 02

Malta

The Structure of Earnings Survey (SES) provides detailed and comparable information on relationships between the level of hourly, monthly, and annual remuneration, individual characteristics of employees (sex, age, occupation, length of service, highest educational level attained), and their employer (economic activity, size, and economic control of the enterprise).

The data collection is based on Council Regulation (EC) No. 530/1999 of 9 March 1999 concerning structural statistics on earnings and on labour costs and Commission Regulation (EC) No. 1738/2005 amending Regulation (EC) No. 1916/2000 as regards the definition and transmission of information on the structure of earnings.

The following classifications are used:

- NACE Rev. 2 for the economic activity;

- ISCO-08 for the occupation of the worker;

- ISCED11 for the highest successfully completed level of education and training;

The statistics cover all economic activities defined in NACE Rev. 2 sections B to S including Section O. 

The enterprises included employ at least 10 employees and the size classes (corresponding to the number of employees) available are 10 to 49, 50 to 249, 250 to 499, 500 to 999 and more than 1,000.

An employee is defined as a person who has a direct employment contract with the enterprise or local unit and receives remuneration, irrespective of the type of work performed, the number of hours worked (full or part-time) and the duration of the contract (fixed or indefinite).

The statistical unit is the enterprise and the employees working within the selected unit.

SES 2018 statistics refer to enterprises with at least 10 employees in the areas of economic activities defined by NACE Rev. 2 sections B to S including Section O.

Malta (NUTS 3)

2018

not applicable

SES 2018 data was compiled using a sample of enterprises operating in NACE Sections B to S.  Sampled enterprises received an electronic questionnaire. 

In addition, data for a number of units was compiled using administrative sources.  In this regard demographic information for the sampled employees was retrieved from a central register and data on education levels was provided from a database which the NSO is maintaining and updating on a regular basis.  Earnings information for the majority of public sector units was retrieved from a register which is maintained by a public entity in charge of human resources whereas tax data was used for a number of private units which did not submit their information via the electronic questionnaire on time.  Reference was also made to the list of employees kept at the national employment agency.

The sampling frame for this survey was the Business Register which is maintained by the Business Register Unit within NSO. This register includes data regarding legal units which are recognized as having autonomous management and an independent accounts system at NUTS 1 level. In this regard, the target population for SES could be chosen from this database.

The total number of enterprises operating in NACE Sections B to S employing 10 or more was 2903. According to Commission Regulation 1022/2009, SES data has to be collected for enterprises operating in NACE B to S split in the following size classes: 10 to 49, 50 to 249, 250 to 499, 500 to 999 and 1000+.

After stratifying enterprises by NACE and size class a sample of 1862 enterprises was chosen. 

The file attached below includes two tables which illustrate the distribution of the sample and the probability of selection for each strata.

Every four years.

A custom made application which enabled the automatic emailing of questionnaires to the different respondents along with pre-filled variables for the selected employees was used. 

The SES questionnaire was made available in excel version since this software is widely available and since it facilitates the copying and dragging of information for different employees. In cases when emails of enterprises were not available, the questionnaires were sent by post.  These units were given a deadline and following the elapse of the deadline further reminders were sent by our office. This was especially necessary for units which are key players in their respective sectors.  These units are bound to determine the developments taking place within the sector in which they operate and hence influence the representativity of the results.

The NSI also provided additional assistance to respondents mostly via telephone and email.

To minimize processing errors, questionnaires are checked by using a number of validations. These validations include consistency checks between information provided for the reference month and information given for annual earnings. 

In terms of processing errors emanating from data entry procedures for data which was provided by respondents in soft copy format, information was directly uploaded into the data entry programme, thus minimizing any data entry errors. Through this procedure a number of validations on the data could also be run.  For manually entered questionnaires inbuilt validations were applied to reduce data entry errors.

not applicable.

not applicable.

Refer to concept 4.2.

The SES provides a unique opportunity by which users can be provided with data on labour costs, as reported by the employers themselves. Data on earnings derived from the employers themselves is generally deemed more reliable than that collected directly from employees. Information derived from the SES is based on a large sample of employees, and therefore high accuracy of results is expected.  

On the other hand, information relating to education levels is of less quality when sourced from enterprises since the individual would be more accurate on this matter.  Another limitation of this survey is that it does not cover micro-enterprises, which may have different earnings patterns when compared to larger units. 

Main users of this data include:  (1) International organisations (such as Eurostat, UNESCO, OECD, EU's Directorate General for Employment), (2) Public Entities (such as Ministries, Authorities) (3) Private entities (research organisations, unions, businesses), and (4) Research Institutes (5) Market Research Companies (6) Universities (7) Individuals.

No user survey to determine the needs of SES users has been carried out.

All requirements of the regulation are met. 

refer to 5.3

Every effort is made to reduce non-sampling errors, nevertheless a small element of these errors is inevitable in all variables. These include: 

- Recall Bias

- Data Entry Errors

- Response Error (definitional differences, misunderstanding... etc.)

Probability sampling - Bias

Section 3.1 Source data provides information on the sampling methodology used for this survey. 

The sample was determined into two stages. In the first stage NSO identified the enterprises which were to be included in the SES. In the second stage, the Office identified the names of employees on whom SES data for Part B was to be collected. This method was adopted for those companies where the Office managed to obtain a match between the Business Register references and the company references available at the National Public Employment Office (PES).

For those enterprises which were not provided with a list of employees to cover in the SES and thus had to choose the sample of employees themselves, a set of instructions for the selection process were provided in order to minimize any bias.  In addition, employers were encouraged to provide information for all the variables using internal data from their databases or registers.  Whenever such data was not available because it was not compiled, employers were encouraged to provide estimates. NSO believes that since employers have a better and more in depth knowledge of their enterprise, any estimates are bound to be of better quality if provided by the respondents themselves. 

For enterprises which had a sample of employees identified, NSO provided instructions that if an employee no longer worked for the company s/he had to be replaced, preferably, by another employee working in the same occupation.

Each completed questionnaire was analysed and various consistency checks were applied at a micro-level. Furthermore, if data provided was not deemed to be of sufficient quality or did not make sense when compared to other information available, employers were contacted by telephone or email in order for them to clarify estimates and figures provided.

The Office also tried to reduce respondent burden by obtaining a number of variables from administrative sources.  This was possible for those units where a match between BR references and PES references was possible. The variables obtained from administrative sources were the following:

• Form of economic and financial control (Variable 1.4)
• Sex (Variable 2.1),
• Date of Birth (used for Variable 2.2) and
• Date of Entry into Service with the enterprise (used for Variable 2.6) 

For this wave of SES, the Office also made use of other administrative sources, particularly for sampled public sector enterprises. In the case of these units all information was retrieved from three different sources:

• the national PES
• a national database for public sector employees which is administered by the People and Standards Division
• the tax department 

Since information for these public sector units was going to be obtained from administrative sources, a decision was taken to keep all employees working in these units and do away with a sub-sample selection. Hence in terms of grossing up procedures, each employee represented him or herself.

Non-Probability sampling

Probability sampling has been used for SES and therefore there are no non-probability errors.

The SES was carried by the 2-stage sampling design. In addition, sampling proportion of employees was not the same for all enterprises since the Office obtained full data pertaining to public sector employees. The following table illustrates the required employee sample size according to the enterprise size class:

Probability sampling

This section of the report presents a number of tables on the coefficient of variation for variables identified in EC Regulation No. 698/2006. One should note that Estimation of variance is not taking into account the sampling methodology since we are assuming Simple Random Sampling but it is taking into account the variance of weights.

Please refer to the attached document Coefficient of variation.

refer to 6.3.1

Misclassification errors

Coverage errors which have been encountered in SES include errors relating to misclassification and over coverage.

Misclassification of NACE

Misclassification errors refer to incorrect NACE classifications which were assigned to units present in the target population. The table below provides the percentage distribution within each NACE division before and after the data collection process.  All misclassifications were corrected before grossing up the data to represent the whole population.

Misclassification of size class  

Another aspect of misclassification concerned size class. The following table indicates the extent of this misclassification. A comparative analysis is provided showing the sample classified by the six size classes prior to data collection and after information was returned to the Office.

 
A number of units resulted to be ineligible for SES since they employed less than 10 employees.  These units were excluded from the sample, whereas the rest of the units which had been assigned a different size class prior to the data collection were reclassified and weights were worked out accordingly.

Over-coverage errors found in SES mainly related to misclassified units which were not within the scope of the survey or units which were no longer active during the reference period identified for SES.  In this regard, these units were excluded from the initial population because they had less than 10 employees or because they ceased to operate before October 2018 which was the reference month for the SES.

To correct for this error, NSO reclassified units in the categories in which they were actually operating in the reference period, excluded ineligible units and applied grossing up factors accordingly.

not applicable

NSO tried to minimise measurement errors during different stages of the data collection.

Accounting of measurement errors from the questionnaire 

NSO’s initial objective was to have a questionnaire which was easy to understand without creating excessive respondent burden.  For SES variables the Office opted for a combined questionnaire for all sampled employees.  This was deemed to be a more suitable option for the local context since employers could fill in data on various employees simultaneously.

Respondents were also provided with additional assistance by staff working within the Labour Market Statistics Unit.  Such assistance was mostly provided via telephone and email.  In a number of cases on-site meetings were also held with enterprises in order to explain the method in which data had to be collected and to assist in the compilation of information.  

Accounting for Respondent errors 

Use of administrative data was made in order to minimise response burden.  Data for public sector employees which were included in the SES sample, was totally derived from a combination of administrative sources.  The following variables were found to be difficult to retrieve from enterprises’ records:

• education of employees
• annual days of absence
• annual bonuses and allowances 

Additional efforts intended to reduce respondent errors concerned the variable Length of Service in the enterprise.  Since this variable was bound to produce biases in information provided, respondents were asked to provide the Office with the Date of Entry into service with the enterprise and the Date of Termination (if applicable). The difference between these two dates was in turn used to work out the variable Length of Service in the enterprise.

Unit response rate and non response

The unit response rate for the Structure of Earnings Survey 2018 stood at 94.5%.

For ths survey wave there was an increase in the response rate since this office managed to access more administrative sources and as a result the collection of data directly from respondents was minimised.

refer to 6.3.3

refer to 6.3.3

Processing errors were minimised through the use of automatic validations.  Any errors which were identified through this procedure were checked by the Labour market personnel.  Since the survey was highly based on electronic submissions, the chances of processing errors were minimised.

Item imputation rate

The item imputation rate for the variable Gross earnings in the reference month resulted to be 3.2%.  This rate is based on information which was directly obtained from responding units and excludes data obrained from administrative records.

For the SES 2018, October was selected as the representative month.  A number of considerations for choosing this month:

• October is not considered a vacation period and therefore is not likely to be characterized by a lot of absences (example the Education Sector is not affected in October and no public holidays happen to be during this month)
• The month is also not characterized by any irregular payments (for instance no statutory bonuses are paid in October.

In order to ensure that the target population of enterprises was well covered, stratified random sampling was applied for those enterprises operating in NACE sections B to S and employing 10 persons or more. Sample selection was at NACE two digit level and size class in order to ensure adequate representatively at all stages.  

not applicable

not applicable

not applicable.

not applicable

refer to 7.2

refer to 7.2

refer to 7.2

The following table illustrates the various stages between data collection and analysis.

The use of administrative sources placed additional challenges for this wave of SES.  Despite the fact that a unique identification number is applied across the different agencies which forwarded the NSO with administrative information, the person number on its own was not enough to retrieve the necessary variables since a link had also to be established between the person and his/ her employer.  When a person had more than one job during the reference period, and hence had a link with more than one employer, the NSO had to determine which employer was to be used to retrieve the information which was required for the SES in order to avoid matching earnings data with the wrong employer.

This task proved to be difficult and time consuming since there is no unique employer number which is being used by the different administrative sources.

The issues above also had a direct effect on the timings of data analysis and weighting procedures.

refer to 7.2

National concepts applied for SES were in line with European concepts since the definitions outlined in Commission Regulation 1022/2009 were applied in the local context. 

The target population for SES was units which operated in NACE Sections B to S and employed 10 or more persons. 

In terms of the statistical units which were covered for SES, data was collected from legal units which are recognized as having autonomous management and an independent accounts system.  At NUTS 1 level, the whole country is represented; therefore information could be collected from enterprises which were recognized to be legal units by the Business Register.

A number of classifications had to be applied for this survey.  These included NACE and ISCO, both of which are applied for other enquiries.  In terms of education data, this was collected using national breakdowns and subsequently information was reclassified to be in line with ISCED levels.

not applicable.

All the variables for SES 2018 did not deviate from the Community legislation.

[Not requested]

Coherence with National Accounts data 

National Accounts data is being compared to SES data. One is to note however that National Accounts information relates to all enterprises operating in the sector whereas SES data refers to enterprises which employ 10 or more employees.

Gross Annual Earnings per employee (€)

Variations between National Accounts and Structure of Earnings Survey estimates are the result of the micro business effect (under 10 effect) which is taken into account in the National Accounts averages but is missing in the SES estimate. The largest difference in earnings relates to NACE K,R and S.  One is to note that National Accounts data includes a benchmark revision exercise which took place in September 2020.  This exercise usually brings about a number of revisions to data.

[1] Source: National Accounts data as at November 2020.  Data is subject to revisions.

not applicable.

refer to 8.3

[Not requested]

At national level, results are intended to be published in 2021. These results will be published in the form of a news release and will be disseminated to the media and via the office’s website.

not applicable.

not applicable.

not applicable.

Micro data may be provided upon request. NSO has a specific set of regulations on the issue and data goes through a process of annonymisation before it is disseminated. (https://nso.gov.mt/en/Services/Microdata/Pages/Access-to-Microdata.aspx)

Results have not been published at a national level yet. 

Methodological notes will be accompanying the news release.

The methodological manual provided by Eurostat was consulted to ensure the full conformity to Eurostat definitions. 

NSO recognises that the production of high quality statistics from this survey is paramount for policy making purposes. Many efforts were made during the data collection and data analysis stages in order to ensure accuracy of results. Great importance is also given to the production of harmonised results, so as to ensure comparability of results with those produced by other NSIs. 

The following is a list of concrete measures that were taken in order to ensure high quality of results:

> Checking of all paper questionnaires prior to data entry

>  Uploading and validation of data in a custom made application 

>  Further checks at aggregate level were made in order to ensure consistency of results

> Aggregate statistics were compared with auxiliary sources  available at the time of the analysis in order to ensure consistency of results.

not applicable

not applicable.

not available.

Micro data is collected in terms of the Malta Statistics Authority, in which Part VIII - Use of Records of Public Authorities and protection of collected information stipulates that:
• All information furnished by any person, undertaking or public authority under this Act shall be used only for the purpose of statistical compilation and analysis.
• No information obtained in any way under this Act which can be related to an identifiable person or undertaking shall, except with the written consent of that person or undertaking or the personal representative or next-of-kin of that person, if he be deceased, be disseminated, shown or communicated to any person or body except -
     • for the purposes of a prosecution for an offence under this Act, or
     • to officers of statistics in the course of their duties under this Act.

Statistics based on less than 3 counts is not published. On the other hand, micro-data is fully anonymised before being disseminated to researchers.

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