1.1. Contact organisation
National Statistics Office (NSO)
1.2. Contact organisation unit
Labour Market Statistics and Information Society Statistics
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Malta Statistics Authority
c/o National Statistics Office
Lascaris
Valletta VLT 2000
Malta
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Data description
The LCS 2020 was based on Commission Regulation (EC) No 1737/2005 of the 21st October 2005 amending Regulation (EC) No 1726/1999 as regards the definition and transmission of information on labour costs. In this regard all compulsory variables were covered by the survey and the target population was enterprises engaged in NACE Rev 2 Sections B to S and employing 10 or more employees.
This report is intended to cover the following items as per Commission Regulation 698/2006 of 5th May 2006 implementing Council Regulation (EC) No 530/1999 as regards quality evaluation of structural statistics on labour costs and earnings:
- Relevance
- Accuracy
- Sampling errors
- Non-sampling errors
- Punctuality and Timeliness
- Accessibility and clarity
- Comparability
- Coherence
2.2. Classification system
2.3. Coverage - sector
National coverage of NACEs B to S
2.4. Statistical concepts and definitions
All the core concepts have been collected via a survey distributed amongst enterprises and via administrative sources. The core concepts being:-
- Base Salary
- Overtime payments
- Salary supplements
- Extraordinary payments
- Payments for incentives
- Compensation payments
- Payments in kind
- Training and work experience
2.5. Statistical unit
The Enterprise concept was used in this survey. This is defined as: ‘The enterprise is the smallest combination of legal units that is an organisational unit producing goods or services, which benefits from a certain degree of autonomy in decision-making, especially for the allocation of its current resources. An enterprise carries out one or more activities at one or more locations. An enterprise may be a sole legal unit.’
2.6. Statistical population
Target Population
Enterprises employing 10 or more persons and operating in NACE Sections B to S
Micro-enterprises were not surveyed.
2.7. Reference area
The survey had total geographical coverage of the Maltese Islands (NUTS 2 level)
2.8. Coverage - Time
LCS data is available for the following reference years 2004, 2008, 2012, 2016 and 2020
2.9. Base period
Not Applicable
3.1. Source data
Sampling methodology
The Sampling Frame
The sampling frame for this survey was the Business Register which is maintained by the Business Statistics Unit within NSO. This register contains 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 LCS could be chosen from this database.
The total number of enterprises operating in NACE Rev. 2 Sections B to S and employing 10 or more employees amounted to 3,454.
The statistical unit for this survey was the enterprise. Even though the definition of a local unit is different from that of an enterprise, one can safely say that in a local context this matter should not be an issue given the small size of our country. In addition, the sampling frame which was used for the extraction of the LCS sample is based on enterprises rather than local units and hence the LCS statistical unit had to be the enterprise as well.
According to Commission Regulation (EC) No 1737/2005, LCS data has to be collected for enterprises operating in NACE Rev. 2 Sections B to N and P to S split in the following size classes: 10 to 49, 50 to 249, 250 to 499, 500 to 999 and 1000+.
Sample selection was made using stratification by optimal allocation. The NACE sections and employment groups (as per Appendix 2 in Regulation No. 1737/2005) were used in order to create the strata.
After stratifying enterprises by NACE and size class a sample of 1,628 companies was chosen. Table A, Table B and Table C (in annex) illustrate the distribution of the population, net sample distribution and the probability of selection for each strata.
Annexes:
Population, Net Sample and Probability of Selection
3.2. Frequency of data collection
Every four years
3.3. Data collection
The Office instructed the IT unit to devise an application which could enable the automatic emailing of questionnaires to the different respondents along with pre-filled details of the enterprise. The 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 aspects of this survey. In cases when emails of enterprises were not available, the questionnaires were sent by post.
3.4. Data validation
To minimize processing errors, each incoming questionnaire was processed through the custom made software and validated for basic errors. Further checks were carried out at the analysis stage whereby data was compared on a per capita basis in order to determine any incorrect or inconsistent information.
3.5. Data compilation
Prior to the compilation of weights, checking relating to economic activity and size class of enterprise is carried out in order to ensure that sampled units are in line with the target population. Post stratification weights were based on the following variables: economic activity at section level, and size of enterprise.
3.6. Adjustment
Below is a description of how imputations for different variables were carried out.
A1 Number of employees
Mathematical imputation of this variable was not required since all item non-response concerning number of employees was collected using auxiliary information from other enterprise-based surveys held by NSO or from administrative sources. In addition, wherever administrative data was available along with information on wages and salaries, the number of employees was changed in order to align both variables and obtain them from the same source.
B1 Hours actually worked and C1 Total hours paid
The national questionnaire asked respondents to estimate the sum of hours actually worked (including overtime hours) as well as estimates for the following variables for the year 2020:
- Vacation leave and public holidays
- Sick leave and injury leave
- Maternity leave
- Other paid absence
- Unpaid absence
Respondents were free to provide the above informaiton in hours or days.
In addition, respondents were asked to provide the average number of hours worked per week for full time, part time and apprentice workers.
Figures on actual hours worked and on total hours paid were divided by 52 (to reflect the hours worked per week) and were split on a per capita basis. The answer was cross checked with information on the normal hours worked per employee per week. Whenever the sum of hours actually worked was equal to the normal hours worked, figures were tagged for subsequent imputation since the assumption was that respondents were not taking into account the number of hours which were paid and not worked in the actual hours worked computation.
Hot-deck imputation using mean actual hours worked per employee by economic activity or size class was used in order to cater for item non-response related to this variable.
The paid hours not worked were imputed using the mean figures for the economic activity or size class of the sampling unit. Imputations for actual hours worked for full time employees and apprentices were conducted by deducting the paid hours not worked from the estimated total normal hours worked. In the case of actual hours worked for part timers, the mean per capita value per economic activity or size class was used for imputing missing or inconsistent data.
D1 Labour costs
The national questionnaire contained a breakdown of labour costs. Respondents were asked to provide information on expenditure related to the following variables:
1.1 Wages and salaries
1.2 Overtime payments
1.3 A breakdown of bonuses not paid in each pay period
i. Statutory bonuses
ii. Production bonuses paid irregularly
iii. Performance bonuses
iv. Leaving and retirement bonuses
v. Other bonuses
1.4 A breakdown of bonuses paid in each pay period
i. Shift allowances
ii. Sales commissions
iii. Danger money
iv. Production bonuses paid regularly
1.5 Payments in kind
1.6 Statutory social security contributions
1.7 Other non-obligatory social contributions
1.8 Sick leave payments incurred by the employer
1.9 Maternity leave payments incurred by the employer
1.10 Injury leave payments incurred by the employer
1.11 Payments to employees leaving the enterprise
1.12 Payments to health insurance schemes
1.13 Payments to early retirement schemes
1.14 Payments for study grants
1.15 Other social contributions
1.16 Vocational training costs
1.17 Other expenditure
1.18 Subsidies received by the employer in connection with labour costs
1.19 Labour-cost-related subsidies received by the employer in connection with COVID
Imputations were therefore carried out at this level.
1.1 Wages and salaries
Missing information for this variable was imputed using the overall mean hourly rate per capita by economic activity. No other imputations were necessary for this variable since corrections made were the result of processing errors. Access to administrative information was also made in order to be in a position to obtain information at micro level.
1.2 Overtime payments
Figures for imputation were identified by comparing the annual basic wage per capita with the annual overtime pay per capita and by comparing the basic hourly rate with the overtime rate. In addition, cross checks between overtime hours worked and overtime payments were made in order to identify cases where overtime hours were given but no corresponding figure was provided for overtime payments. Imputations were subsequently based on the mean overtime hourly rate by economic activity.
1.3 Bonuses not paid in each pay period
Data for this variable was compared to data which was collected in the previous wave of the survey. Any large variations were identified for further checking. In addition checking with the information provided by companies in the initial part of the LCS was made so as to determine to what extent such types of payment were common at enterprise level. Bonuses per capita values were also analyzed. Where imputations were necessary, they were based on the mean bonuses not paid in each pay period per employee by NACE group.
For cases in which information for 2016 LCS was not available, figures where checked for their consistency, usually using per capita rates and imputations were based on overall mean bonuses not paid in each pay period per employee.
Statutory bonuses
Missing statutory bonuses were imputed based on the number of full time and part time workers since the amount of statutory bonuses incurred by an employer differs depending on the type of employment.
Performance bonuses
Data was checked at micro level and compared to information which companies forwarded in the previous LCS wave. Where imputation was necessary, this was carried out using per capita values by NACE.
1.4 Bonuses paid in each pay period
As above, amongst other checks and validations, data for this variable was compared to information which was collected in the previous wave. Any large variations were identified for further checking. Bonuses per capita values were also analyzed. Where imputations were necessary, they were based on the mean bonuses paid in each pay period per employee by NACE group. In addition checking with the information provided by companies in the initial part of the LCS was made so as to determine to what extent such types of payment were common at enterprise level.
1.5 Payments in kind
Per capita payments in kind were worked out on existing information and extremes in data were checked with the data provided in the questionnaire by respondents. Comparisons were also made with administrative data. The administrative source was used directly when data from respondent was nil, or where data provided by respondent was considereably different from the data available at the administrative source.
1.6 and 1.7 Statutory and non-obligatory social security payments
Checks for this variable concentrated on comparing the percentage of statutory social security payments out of total wages and salaries. This percentage was compared with 2016 figures so that any large variations were checked with other data which was available in the questionnaire. Large changes between 2016 and 2020 were first looked into by checking variations in employment in both years, since, such changes would result in differences in statutory social security contribution payments. Imputations were made by referring to the mean rate per capita (where ratio of total wages to social security contributions was between 5% and 15%) by economic activity. In addition data which was made available directly from administrative sources was used directly instead of the imputation.
1.8, 1.9 and 1.10 Sick, maternity and injury leave payments
Respondents were asked to provide information on the number of sick, maternity and injury hours, availed of by employees during the reference year 2020. This information helped in estimating missing information on the amounts of sick, maternity and injury payments. Imputations were based on the basic hourly rate (worked out by dividing the basic wages and salaries by the sum of hours excluding overtime). This rate was in turn used to impute maternity and injury payments by the total number of sick, maternity and injury leave hours.
1.11 Payments to employees leaving the enterprise
Checks were carried out for companies which recorded a decrease in employment between 2016 and 2020 and also during 2020 itself. Companies which were identified were checked at a micro level using information available from employment data from administrative records.
1.12 Payments to health insurance schemes
Imputations were based on the mean per capita value by economic activity multiplied by the number of full time employees.
1.15 Other social contributions
This variable included costs associated with contributions for the maternity fund. This fund came into force during 2015 and checks related to this variable were made possible through access to administrative data. In addition the variable was cross checked the mean rate that is to be paid per employee and any per capita rates which were below the mean were flagged for imputation.
1.16 Vocational training costs
Checks for this variable included a comparison with 2016 LCS data as well as with CVTS data for 2020. A cross check with a question contained in the LCS 2020 survey was also made whereby respondents were asked to identify whether they had training costs during 2020. Cases which had vocational training costs in either CVTS or LCS 2020 and no training costs in LCS 2020 were flagged for imputation. Imputations for vocational training costs were based on the mean per capita value by economic activity.
1.17 Other expenditure
Comparisions of other expenediture provided in 2016 were made along with cross checks with a question within the LCS 2020 survey whereby respondents were asked to identify if they have payments related to the provision of uniforms. Imputations for other expenditure were based on the mean per capita by economic activity.
1.18 Subsidies received by the employer
This check was mainly focused on NACE Rev 2 Sec P since at a national level schools run by the Church have their labour costs subsidized by Government. Other imputations were necessary for companies which were employing apprentices under specific schemes run by the national employment agency. In this regard use of administrative records helped to identify companies which were benefiting from any apprentice-related schemes. The Planning and Priorities Coordination Department also provided information on the payments which these companies were receiving as part of the scheme. This information was subsequently used to impute missing data.
For this round of LCS reference was made to administrative data related to schemes aimed at assisting companies in covering labour costs during the COVID pandemic. Data at micro level was available and figures were directly linked to the sampled enterprises.
4.1. Quality assurance
All key variables were checked with their respective time series in order to assure consistency over time. Other variables were compared with any administrative data avaialble.
4.2. Quality management - assessment
Not available.
5.1. Relevance - User Needs
No user survey to determine the needs of LCS users has been carried out. From information available regarding the actual use of this survey, one can say that LCS data is mainly used for the computation of the indicator on annual labour costs and to validate results obtained from other enterprise based surveys carried out by the NSO.
5.2. Relevance - User Satisfaction
One can point out that since this survey is carried out once every four years, users might not find it to be very useful, given that they usually require information on a more regular basis.
5.3. Completeness
All compulsory variables requested in Commission Regulation 1737/2005 were covered in the 2020 Labour Cost Survey.
5.3.1. Data completeness - rate
Not available.
6.1. Accuracy - overall
Respondents were encouraged to provide information using internal data from databases or registers. However, 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 company, any estimates are bound to be of a better quality if provided by respondents themselves.
6.2. Sampling error
Kindly refer to 6.2.1
6.2.1. Sampling error - indicators
The tables below provide the standard error and CV of annual labour costs by NACE section and size class.
Standard error and CV of annual labour costs by NACE section level
| Standard Error | CV | |
|---|---|---|
| XB | 0.00 | 0.00 |
| XC | 47347.49 | 3.31 |
| XD | 0.00 | 0.00 |
| XE | 0.00 | 0.00 |
| XF | 296302.68 | 18.93 |
| XG | 32519.09 | 5.49 |
| XH | 174959.24 | 10.63 |
| XI | 21043.73 | 5.00 |
| XJ | 94902.94 | 5.16 |
| XK | 143536.61 | 4.93 |
| XL | 0.00 | 0.00 |
| XM | 59508.82 | 5.15 |
| XN | 126467.34 | 7.30 |
| XO | 1005096.45 | 17.39 |
| XP | 1935427.12 | 53.62 |
| XQ | 1287488.05 | 32.18 |
| XR | 97697.79 | 4.99 |
| XS | 26945.02 | 6.38 |
| Total | 56786.17 | 3.54 |
Standard error and CV of annual labour costs by size class
| Standard Error | CV | |
|---|---|---|
| E10_49 | 38089.26 | 9.10 |
| E50_249 | 154765.45 | 6.46 |
| E250_499 | 1549839.83 | 16.53 |
| E500_999 | 0.00 | 0.00 |
| E1000 | 0.00 | 0.00 |
Standard error and CV for hourly labour costs by NACE section level
| Standard Error | CV | |
|---|---|---|
| XB | 0.00 | 0.00 |
| XC | 0.25 | 2.57 |
| XD | 0.00 | 0.00 |
| XE | 0.00 | 0.00 |
| XF | 0.51 | 4.59 |
| XG | 0.47 | 4.61 |
| XH | 0.49 | 4.19 |
| XI | 0.52 | 7.98 |
| XJ | 3.04 | 11.39 |
| XK | 1.05 | 4.93 |
| XL | 0.00 | 0.00 |
| XM | 1.35 | 6.65 |
| XN | 1.30 | 9.74 |
| XO | 0.86 | 3.47 |
| XP | 0.85 | 7.73 |
| XQ | 0.85 | 7.91 |
| XR | 1.37 | 7.78 |
| XS | 0.49 | 5.65 |
| Total | 0.56 | 4.16 |
Standard error and CV for hourly labour costs by size class
| Standard Error | CV | |
|---|---|---|
| E10_49 | 0.91 | 6.63 |
| E50_249 | 0.50 | 4.06 |
| E250_499 | 2.05 | 13.86 |
| E500_999 | 0.00 | 0.00 |
| E1000 | 0.00 | 0.00 |
6.3. Non-sampling error
Probability sampling has been used for LCS and therefore there are no non-probability sampling errors.
6.3.1. Coverage error
1. Misclassification errors
Coverage errors which have been identified in LCS include errors related to misclassification or to size of enterprise.
1.1 Misclassification of NACE
Misclassification errors refer to incorrect NACE classification to units present in the target population. In this regard, no enterprises have been misclassified.
1.2 Misclassification of size class
Another aspect of misclassification concerns size class. The reasons for such a misclassification were mainly two:
- enterprise employed less than 10 employees; or
- enterprise was no longer existent following a merger or acquisition in the reference year 2020.
As a result these companies had to be removed from the LCS 2020 sample since they were not part of the target population.
Units which had been assigned a different size class prior to the data collection were reclassified after the data collection phase was concluded and weights were worked out accordingly.
6.3.1.1. Over-coverage - rate
Over-coverage and under-coverage errors
Over-coverage errors found in LCS 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 the LCS.
The main reason which can be attributed to the over-coverage errors was the time lag between the reference period and the actual data collection. Business register data for 2020 provided information on the number of active units at the time and the economic activities and employment levels of the units. Data collection however took place 4 months after the end of the reference period and during this time units might have changed their activities, reduced the number of employees or ceased to operate altogether, thus making themselves ineligible for the survey.
Under-coverage problems were tied to the following reasons:
- enterprises for which the sampling frame registered an employment of 9 or less employees, whereas in actual fact they had 10 or more persons on their payroll, and
- enterprises which were established in 2020 but which were not yet included in the sampling frame
In order to account for problems of under and over coverage, the Labour Market Statistics unit had to ensure that the sampling frame was updated before the actual weighting procedure was carried out. This correction however resulted in changes in the original probability of selection for some strata.
6.3.1.2. Common units - proportion
Not available.
6.3.2. Measurement error
Bias is attributed to all forms of human error committed during the data collection and analysis stage, despite all the efforts that were made by the NSO in order to ensure that highest quality data is collected and compiled for this survey. These may include errors committed by the respondents during the filling of the questionnaire (e.g. under-reporting, respondents not following properly the instructions set in the questionnaire, etc.).
Unfortunately, this survey could not be conducted using face-to-face interviewing, due to lack of resources that were available. Given the ever increasing need for making data collection a more simple process, the Office instructed the IT unit to devise an application which could enable the automatic emailing of questionnaires to the different respondents along with pre-filled details of the enterprise. The 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 aspects of this survey. In cases when emails of enterprises were not available, the questionnaires were sent by post.
The effect of non-response cannot be ignored since significant non-response in particular strata might lead to misleading results.
NSO tried to minimize measurement errors during different stages of the data collection.
1. Accounting for measurement errors from the questionnaire
NSO’s initial but very important objective was to have a questionnaire which was self-explanatory to respondents without creating excessive response burden. In fact, NSO took on board feedback about the structure and content of the LCS questionnaire from past LCS waves. The questionnaire was modified where necessary, in order to have a clearer and user friendly version.
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.
2. Accounting for Respondent errors
Since LCS data had to be filled in by respondents themselves, NSO tried to make questions comprehensible so that even if the respondent refrained from reading supporting explanatory notes, s/he would still answer correctly.
In terms of errors resulting from lack of information on respondents’ registers or databases, respondents were asked to provide estimates. In this regard, feedback from respondents shows that the following variables were found to be difficult to retrieve from companies’ records on:
- actual hours/days worked
- paid hours/days not worked
- sick/maternity/injury leave payments incurred by the employer.
NSO tried to minimise respondent burden by trying to collect data in a way which was easier for the respondent to provide and understand. Terminologies which are being used at a national level where included in the questionnaire to facilitate the filling in of the survey. Respondents were given the option to provide data on hours worked in either hours or days in order to make it easier for them to provide an answer.
NSO asked additional questions in order to be in a position to check on the provided information. As done in the previous wave of the LCS, respondents were asked to provide the average number of weekly hours normally worked for full, part time employees and apprentices since this information was very useful for checking the answers provided on actual hours worked. Moreover, when LCS 2016 data was available for the same enterprise, information was crossed checked at a micro level. Internal records available within the Short Term Business Statistics unit were also referred to for checking the consistency of the information which was being provided. In the LCS 2020 version, respondents were asked a series of yes/no questions to determine if they incurred any of the following labour costs:
- training costs
- payments for uniforms
- payments related to dismissal of workers
- payments related to shift work, danger money, sales commissions, qualification allowances or production bonuses
- payments related to performance, production or leaving or retirement bonuses.
The main aim of these questions was to validate the answers with other questions relating to labour costs asked throughout the survey. For the first time a comments box was also made available with each question so that enterprises would be able to provide any relevant informaiton related to the specific question.
6.3.3. Non response error
Refer to 6.3.3.1 and 6.3.3.2.
6.3.3.1. Unit non-response - rate
The total number of units surveyed for this survey were 1,610, of which 1,536 replied to the survey.
6.3.3.2. Item non-response - rate
The item imputation rates are provided below:
| Variable | Imputation rate | Variable | Imputation rate |
|---|---|---|---|
| B1 | 21.4 |
D12 | 7.4 |
| B11 | 51.1 | D121 | 7.3 |
| B12 | 13.0 | D1211 | 7.6 |
| B13 | 0.0 | D1212 | 3.1 |
| C1 | 25.5 | D122 | 2.0 |
| D1 | 24.8 | D1221 | 29.6 |
| D11 | 29.1 | D1222 | 24.2 |
| D111 | 27.5 | D1223 | 15.1 |
| D1111 | 28.4 | D1224 | 0.0 |
| D11111 | 28.7 | D123 | 2.2 |
| D11112 | 28.5 |
D2 | 31.0 |
| D1112 | 0.0 | D3 | 18.5 |
| D1113 | 45.7 |
D4 | 0.0 |
| D1114 | 0.0 | D5 | 2.9 |
| D112 | 2.4 |
6.3.4. Processing error
To minimize processing errors, each incoming questionnaire was checked using a number of validations.
Any data inconsistencies were then verified directly with the sampling unit’s contact person either by phone or by email or through administrative records where possible. To minimise processing errors, the office asked for the details of two contact persons to ensure accountability for each and every figure reported in the survey.
6.3.4.1. Imputation - rate
The overall imputation rate resulted to be 14.4%. This rate is based on all the mandatory variables.
6.3.5. Model assumption error
I. Ensuring the good coverage of the target population
In order to ensure that the target population of companies was well covered, stratified random sampling was applied for those companies operating in NACE Rev 2 sections B to N and P to S employing 10 or more persons.
II. Combination of data from administrative and other sources
NSO made use of administrative data compiled by tax department, and structural business statistics data relating to the year 2020. In addition data on employment levels was also checked with information available from the Job Vacancy Survey and from the national Public Employment Office. Information relating to COVID subsidies was obtained from the entity administering such funds during the COVID pandemic.
6.4. Seasonal adjustment
Not applicable.
6.5. Data revision - policy
Not available.
6.6. Data revision - practice
Not available.
6.6.1. Data revision - average size
Not available.
7.1. Timeliness
LCS data was transmitted to Eurostat on the 12th July 2022.
7.1.1. Time lag - first result
Not available.
7.1.2. Time lag - final result
Not available.
7.2. Punctuality
The table in annex illustrates the various stages between data collection and analysis.
From this table, one can note that considerable time was taken to complete data collection. This was mainly due to the fact that key companies which had not forwarded their data and which had a probability of selection of one had to be chased numerous times until they submitted their questionnaire. The small size of the country necessitates the use of a census for most business surveys which are carried out amongst companies employing 50 or more employees. This fact therefore places a huge response burden on selected organizations which are constantly being contacted for various surveys which are held by various units within NSO and also by other entities in general. Since a number of these organizations are key players in their respective sectors one cannot afford to leave them out of any business survey since most often they determine the developments which are taking place within the sector in which they operate and consequently influence the representativity of the results.
The above issues also had a direct effect on the timings of data analysis and weighting procedures.
Annexes:
Time Table
7.2.1. Punctuality - delivery and publication
Not available.
8.1. Comparability - geographical
National concepts applied for LCS are in line with European concepts since the definitions outlined in Commission Regulation 1737/2005 were applied in the local context.
In terms of the statistical units which were covered for LCS, 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.
8.1.1. Asymmetry for mirror flow statistics - coefficient
Not available.
8.2. Comparability - over time
LCS 2020 results were compared with LCS 2016 both at macro-level and even at a micro-level when available. Between both periods there was the impact of Covid-19, this could have hindered the comparability between both years.
8.2.1. Length of comparable time series
Not avilable.
8.3. Coherence - cross domain
Coherence with Structure of Business Survey data
SBS data was customised for units employing 10 or more employees. Data on the Compensation of employees was worked out for SBS and LCS data and a comparison of results is presented in the table below.
The variations between the two surveys are the result of the restricted details which emanate from SBS. The use of one variable in SBS to estimate annual wages and salaries is likely to give different results when compared to LCS data which is highly more focused on this element of labour costs. NACEs K,P,Q,R and S are not surveyed by the SBS Unit and hence no comparisons at this level can take place.
| LCS | SBS | |
|---|---|---|
| B | 1,742,890 | 2,063,773 |
| C | 395,289,851 | 467,053,391 |
| D | 5,763,426 | 7,813,191 |
| E | 36,472,622 | 35,776,011 |
| F | 298,731,698 | 209,312,582 |
| G | 379,307,103 | 480,114,732 |
| H | 232,829,968 | 282,649,707 |
| I | 185,646,195 | 245,203,745 |
| J | 271,748,663 | 321,546,126 |
| L | 24,853,154 | 39,334,544 |
| M | 348,518,158 | 415,657,592 |
| N | 404,369,914 | 383,355,846 |
8.4. Coherence - sub annual and annual statistics
The table below illustrates the growth rates between LCI and LCS for each NACE category. Variations between LCI and LCS figures, are mainly the result of the micro business effect (under 10 effect) which is being taken into account in the LCI averages but is missing in the LCS averages. This is the result of lower growth rates in case of the LCI when compared to those in the LCS.
| WAG (LCI) | WAG (LCS) | |
|---|---|---|
| NACE | 2020/2016 | 2020/2016 |
| B | 8.6 | 12.3 |
| C | 2 | -2.7 |
| D | 5.4 | -6.1 |
| E | 4.7 | -0.4 |
| F | 0.4 | 10.3 |
| G | 2.6 | -2.2 |
| H | 1.5 | -1.4 |
| I | -3 | -0.4 |
| J | 3.3 | -1.4 |
| K | 4 | -2.8 |
| L | 2.3 | -10.6 |
| M | 7.3 | -0.2 |
| N | 0.4 | -0.8 |
| P | 11.4 | 2.6 |
| Q | 2.3 | -4.9 |
| R | 9.9 | -1.1 |
| S | 6.6 | -6.4 |
8.5. Coherence - National Accounts
National Accounts data is being compared to LCS data. One is to note however that National Accounts information relates to all companies operating in the sector whereas LCS data refers to companies which employ 10 or more employees.
Variations between National Accounts and Labour Cost Survey figures, 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 LCS estimate.
| NACE |
Compensation of Employees per capita |
|
|---|---|---|
| Rev 2 |
National Accounts |
LCS |
| B to E |
24,407 |
22,613 |
| F |
19,815 |
29,987 |
| G to N |
26,305 |
23,834 |
| P to S |
33,461 |
24,107 |
| B to S |
28,167 |
24,588 |
8.6. Coherence - internal
Not available.
9.1. Dissemination format - News release
LCS statistics are not included in national news releases.
9.2. Dissemination format - Publications
LCS statistics are not included in national publications.
9.3. Dissemination format - online database
No online databases available
9.3.1. Data tables - consultations
Not available.
9.4. Dissemination format - microdata access
Not applicable.
9.5. Dissemination format - other
Aggregated tabular data on LCS statistics can be provided to users who request this type of information.
9.6. Documentation on methodology
Documentation of steps on LCS is available for internal purposes. In addition, this Office uses the implementation arrangements provided by Eurostat: EC 530/1999 and EC 1737/2005.
9.7. Quality management - documentation
Not available.
9.7.1. Metadata completeness - rate
Not available.
9.7.2. Metadata - consultations
Not available.
Not available.
11.1. Confidentiality - policy
At National level: Confidentiality is one of the major principles guiding the activities of the NSO.
Article 40 of the MSA Act stipulates the restrictions on the use of information and in Article 41, the prohibition of disclosure of information. Furthermore, Section IX of the Act (Offences and Penalties) lays down the measures to be taken in case of unlawful exercise of any officer of statistics regarding confidentiality of data. No cases of breaches in the law have been recorded to date.
Since its inception, the NSO has always operated within a culture of strict confidentiality to which it is also bound by the provisions of the Data Protection Act of 2000. This Act, which came fully into effect on July 15, 2003, seeks to protect individuals against the violation of their privacy by the processing of personal data.
Also further information on access to microdata is available on the NSO's website
During 2009, the NSO has set up a Statistical Disclosure Committee to ensure that statistical confidentiality is observed, especially when requests for microdata are received by the NSO.
Upon employment, staff is informed of the rules and duties pertaining to confidential information and its treatment. According to the MSA Act, before commencing work, every employee is required to take an oath of secrecy whose text is included in the Act.
At European level: Regulation (EC) No 223/2009 on European statistics (recital 24 and Article 20(4) of 11 March 2009 (OJ L 87, p. 164), stipulates the need to establish common principles and guidelines ensuring the confidentiality of data used for the production of European statistics and the access to those confidential data with due account for technical developments and the requirements of users in a democratic society.
11.2. Confidentiality - data treatment
For the LCS 2020, primary and secondary confidentiality are taken into consideration. Primary confidentiality is flagged on too few enterprises, where the number of units is less than 3 and the dominance share of the largest units is 75%. Secondary confidentiality data is flagged in order to protect primary confidential data, which are suppressed so that sensitive information is not revealed. They are identified and flagged by NSO using a common methodology applied by other statistical agencies.
No further comments.
The LCS 2020 was based on Commission Regulation (EC) No 1737/2005 of the 21st October 2005 amending Regulation (EC) No 1726/1999 as regards the definition and transmission of information on labour costs. In this regard all compulsory variables were covered by the survey and the target population was enterprises engaged in NACE Rev 2 Sections B to S and employing 10 or more employees.
This report is intended to cover the following items as per Commission Regulation 698/2006 of 5th May 2006 implementing Council Regulation (EC) No 530/1999 as regards quality evaluation of structural statistics on labour costs and earnings:
- Relevance
- Accuracy
- Sampling errors
- Non-sampling errors
- Punctuality and Timeliness
- Accessibility and clarity
- Comparability
- Coherence
All the core concepts have been collected via a survey distributed amongst enterprises and via administrative sources. The core concepts being:-
- Base Salary
- Overtime payments
- Salary supplements
- Extraordinary payments
- Payments for incentives
- Compensation payments
- Payments in kind
- Training and work experience
The Enterprise concept was used in this survey. This is defined as: ‘The enterprise is the smallest combination of legal units that is an organisational unit producing goods or services, which benefits from a certain degree of autonomy in decision-making, especially for the allocation of its current resources. An enterprise carries out one or more activities at one or more locations. An enterprise may be a sole legal unit.’
Target Population
Enterprises employing 10 or more persons and operating in NACE Sections B to S
Micro-enterprises were not surveyed.
The survey had total geographical coverage of the Maltese Islands (NUTS 2 level)
Respondents were encouraged to provide information using internal data from databases or registers. However, 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 company, any estimates are bound to be of a better quality if provided by respondents themselves.
Prior to the compilation of weights, checking relating to economic activity and size class of enterprise is carried out in order to ensure that sampled units are in line with the target population. Post stratification weights were based on the following variables: economic activity at section level, and size of enterprise.
Sampling methodology
The Sampling Frame
The sampling frame for this survey was the Business Register which is maintained by the Business Statistics Unit within NSO. This register contains 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 LCS could be chosen from this database.
The total number of enterprises operating in NACE Rev. 2 Sections B to S and employing 10 or more employees amounted to 3,454.
The statistical unit for this survey was the enterprise. Even though the definition of a local unit is different from that of an enterprise, one can safely say that in a local context this matter should not be an issue given the small size of our country. In addition, the sampling frame which was used for the extraction of the LCS sample is based on enterprises rather than local units and hence the LCS statistical unit had to be the enterprise as well.
According to Commission Regulation (EC) No 1737/2005, LCS data has to be collected for enterprises operating in NACE Rev. 2 Sections B to N and P to S split in the following size classes: 10 to 49, 50 to 249, 250 to 499, 500 to 999 and 1000+.
Sample selection was made using stratification by optimal allocation. The NACE sections and employment groups (as per Appendix 2 in Regulation No. 1737/2005) were used in order to create the strata.
After stratifying enterprises by NACE and size class a sample of 1,628 companies was chosen. Table A, Table B and Table C (in annex) illustrate the distribution of the population, net sample distribution and the probability of selection for each strata.
Annexes:
Population, Net Sample and Probability of Selection
LCS data was transmitted to Eurostat on the 12th July 2022.
National concepts applied for LCS are in line with European concepts since the definitions outlined in Commission Regulation 1737/2005 were applied in the local context.
In terms of the statistical units which were covered for LCS, 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.
LCS 2020 results were compared with LCS 2016 both at macro-level and even at a micro-level when available. Between both periods there was the impact of Covid-19, this could have hindered the comparability between both years.
