ISTAT - Istituto Nazionale di Statistica
ESS Standard for Quality Report Structure (ESQRS) V2 (ESQRS_MSD 3.0 ESTAT)
Labour costs survey 2008 and 2012 - NACE Rev. 2 activity
2012 - A0
1. Contact
1.1 Contact organisation

Instituto Nazionale di Statistica (ISTAT)

1.2 Contact organisation unit

Dipartimento per i Conti Nazionali e le Statistiche Economiche

Direzione centrale delle statistiche economiche congiunturali

Servizio Statistiche congiunturali su Occupazione e Redditi

U.O. Statistiche su retribuzioni, costo e domanda di lavoro

 

National Accounts and Economic Statistics Department

Division Directorate for Short Term Statistics

Short Term Employment and Income Statistics

Earnings, Labour Cost and Labour Demand Statistic Unit

1.3 Contact name
1.4 Contact person function
1.5 Contact mail address

Istat Istituto Nazionale Di Statistica

Via Cesare Balbo, 16, 00184 Roma

1.6 Contact email address
1.7 Contact phone number
1.8 Contact fax number
2. Statistical presentation
2.1 Data description

The LCS 2012 is based on the Council Regulation (EC) No 530/1999 concerning structural statistics on earnings and on labour costs and the Commission Regulation 1737/2005.

The target population is composed by all the enterprises and institutions belonging to the Private and Public sectors with at least 10 employees in the NACE Rev. 2 sections B to S. With the current edition, for the first time Italy provided also data related to section O (even if the extension to this section is still considered optional).

 

The innovations of LCS 2012 edition

 

Labour costs Survey 2012 has been completely re-vamped in terms of both methodological and technical aspects in order to increase data quality and to reduce the response burden.

Referring to the private sector part the main methodological innovation has been the use of the new wage register (RACLI) data. This register contains yearly information for individual employees on their qualification, types of contract, wages and paid time. The data on which it is based, the social security declarations, are the same used by the Business register to provide information on employment for each enterprise and local unit. Aggregated at the enterprise level, the RACLI data thus constitutes an extension of the Business register for the firms with employees on wages and paid time and have been used extensively to assist the production of LCS estimates in almost all the phases of the production process.

The questionnaire for the private sector has been redesigned, with the elimination of many questions used to collect extra-information now available through administrative data and the focusing on the variables requested by the regulation. The questionnaire has been built to be a bridge between the available RACLI data and the LCS standards and definitions. In doing this the questions were reformulated and the instructions refined.

Information on wages from RACLI  has entered in the sampling design allowing a better estimates of the variance and average parameters used in the allocation phase.

The editing and imputation procedures made the largest use of the RACLI data. The comparison between the figures provided by the enterprises in the survey questionnaire and those contained in the register have been used either to re-contact some influent respondents during the data collection to clarify and/or to edit the figures or, after the data collection, to substitute the values provided whenever implausible differences were found. In the same way RACLI data have been utilized to impute the main variables for unit non responses. Moreover the RACLI data has allowed to form more homogenous edit groups through the use of information on the national contract applied by the enterprises or the extent to which they have been involved in short time allowance programmes to manage the recent crisis.

Finally, in the calibration procedures, together with the number of employees, the social security wages data has been used as an auxiliary variable (in contrast with the previous procedure that used instead the number of enterprises) thus exploiting the high correlation with the target variables. For all these reasons the private sector survey can be defined an “administrative data assisted survey”.

From the technical point of view three innovations have been introduced in the data collection procedures. The first has been the adoption for the first time in Italy of the new Certified email (PEC) system to contact the enterprises instead of the traditional postal sending. Although it has implied some delay in the launch of the survey, it has reduced the costs of the sending of the cover letter and further communications and enhanced the flexibility of setting up reminders in response to the results of the data collection. The second innovation has been the web questionnaire, together with the auxiliary modes as explained above, which has simplified the of data collections operations. The third innovation has been the use of a dedicated contact service operated in the first stage of data collection to which the enterprises could direct their questions and receive information on the deadlines, explanations of the functioning of the web questionnaire including the checks, support for registration or other technical issues etc.. Together with the service of outbound telephone reminders, already in function for the LCS 2008, these three innovations have been the driver of a soar in the response rate with respect to the previous wave.    

For the public sector, the main innovation has been the extensive use of the Conto Annuale. While in the previous edition it had been used only to estimate non respondents to the direct survey, also thanks to improvements in its quality over the years, in this edition it has been the direct source of information for all the variable requested by the LCS regulation for all the institutions with the exception of the schools (for which it has provided only the breakdown of the wages and the rates of social contribution). An in depth study of the definitions of the many detailed variables contained in it has been necessary to aggregate them into the LCS variables. Whenever the source did not contain the necessary information adequate models and external information has been used. For instance the number of hours worked has been obtained as follows. First, for each category of full-time workers, the hours of absences contained in the source have been subtracted from the total number of contractual hours. These in turn are derived multiplying the number of employees by the standard number of contractual hours provided by information from national contracts. Second, the number of overtime hours has been added. These hours are estimated starting from the information, contained in the source, on overtime related wages and the premiums for an hour of overtime. A similar calculation has been performed for the part time workers taking into account the percentage of part time.

The use of the Conto Annuale has allowed to cover for the first time section O of the nace rev 2.  

2.2 Classification system

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.3 Coverage - sector

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.4 Statistical concepts and definitions

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.5 Statistical unit

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.6 Statistical population

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.7 Reference area

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.8 Coverage - Time

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.9 Base period

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

3. Statistical processing

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3.1 Source data

The surveys for the Private and Public sectors are entirely different. In what follows brief explanations of both is reported.

 

The survey for the private sector is a traditional sample survey. The frame list, derived from the Italian Business Register (ASIA) yearly updated by Istat, is composed by about 180,000 enterprises representing 8,350,000 employees.

The sampled enterprises had to provide data separated for each of the area (NUTS1) in which they were localized. Thus the sampling unit is the enterprise and the unit of analysis is the portion of enterprise comprised in one nuts area. The survey is a mixed mode one.

 

The public sector data  is mainly derived from available registers and data sources. Referring to public institutions the frame list is built combining the list of the Public Institution Census of 2011 and the latest available list of Public Institutions (S13 list). Within the Education sector a complete list of schools (and the personnel working for each) has been compiled starting from the archives of the Ministry of Public Education. The final list is composed by around 18,600 insitutions (every school is counted as a separate inistitution) for a number of about 3,300,000 employees.

The data necessary for computing the LCS variables for the public institutions excluding Education is mainly derived from the survey on the Cost of Personnel of Public Administration (Conto Annuale), a take all survey run yearly by the Ministry of Finance with detailed questions on number of employees. wages and its components. days of absences from works etc.. . Only a small fraction of institutions, not included in the Conto Annuale. was requested to fill in the questionnaire prepared for the private sector. Regarding the Education sector the LCS statistics are compiled combining the data from individual level tax declarations (770 forms), the individual monthly payslips and the Conto Annuale.

The information coming from these sources, combined with adequate models, allows to compile the LCS variables for almost all the institutions in the frame list and their NUTS portions. To take care of the very tiny fraction of the frame list for which no data is available a ratio estimator is used.

3.2 Frequency of data collection

[Not requested]

3.3 Data collection

For the first time, the LCS data has been collected mainly through a web based questionnaire (CAWI) with some built in checks (first level checks). In addition to CAWI, the groups of enterprises were offered to compile the questionnaire with an offline spreadsheet that could have been filled in for all the enterprises belonging to the group at once. Moreover, in the final stages of data collection. to overcome possible difficulties related to the use of a web instrument. smaller enterprises could compile the questionnaire with an offline pdf form. However the data collected through these secondary modes were passed through the web questionnaire and checked with the rules built in it and in case they were violated the respondents were re-contacted to clarify and correct the figures provided.

3.4 Data validation

[Not requested]

3.5 Data compilation

[Not requested]

3.6 Adjustment

[Not requested]

4. Quality management
4.1 Quality assurance

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

4.2 Quality management - assessment

[Not requested]

5. Relevance

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5.1 Relevance - User Needs

Information on LCS2012 has been used by employers’ associations and trade unions.

Other users are national and international organizations and academic researchers.

5.2 Relevance - User Satisfaction

No direct assessment of user needs and user satisfaction has been carried in recent times.

5.3 Completeness

[Not requested]

5.3.1 Data completeness - rate

[Not requested]

6. Accuracy and reliability

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6.1 Accuracy - overall

[Not requested]

6.2 Sampling error

As above mentioned data for private sectors are collected through a traditional sample survey. Sampling design of the LCS survey is a one stage stratified random sampling, with the strata defined by the combination of the modality of the characters Nace Rev.2 divisions, 5 size classes (10-49 employees, 50-249,250-499,500-999, 1000 +) and 5 Nuts 1 level areas. A fixed number of enterprises is selected in each stratum without replacement and with equal probabilities. The enterprises belonging to strata with over 250 employees or in strata with few units in the sample have been sampled with a probability equal to 1 for a total of 631 take all strata. As for the remaining 1375 strata, the number of units to be selected in each stratum is defined as a solution of a linear integer problem (Bethel, 1989). In particular, the minimum sample size is determined in order to ensure that the variance of sampling estimates of the variable of interest in each domain does not exceed a given threshold, in terms of coefficient of variation, which for the specific case has been fixed at 6.5%. The variables of interest considered for sample allocation are Number of employees, Wages bill, whose mean and variance are estimated in each strata by data from the frame. According to the final allocation, 20,177 enterprises (units) have been included in the sample from a target population of about 180,000. The sampling units have been drawn by applying JALES procedure (Ohlsson, 1995), in order to take under control the total statistical burden, by achieving a negative coordination among samples drawn from the same selection register, represented by the Italian Statistical Business Register.

 

It is worth notice that  a time lag exists between the reference year of the LCS survey and that of the available Business Register at the selection phase: namely, the most updated version of the Business Register was referred to the year 2011. When estimation is carried out the available Business Register is correctly updated for the reference year 2012. The problem of the BR lag may imply coverage errors (see also § 5.3.1) and have negative effects on the quality of the final estimates, so as the non-response (see also § 5.3.3) may have. A reduction of the bias effect due to these phenomena is achieved by applying the methodology based on calibration estimators. However in this context, unit non responses have been imputed with the auxiliary variables derived from the RACLI register while calibration has the main purpose of reduce coverage errors.  

The weight of every single enterprise is thus modified in order to match known population totals of selected auxiliary variables. The selected calibration variables are the number of employees on the Business Register and the wages bill available from the Social Security Register. Due to changes occurred in the population (and consequently in the updated BR), even enterprises belonging ex-ante to take-all strata might have received a calibrated weight different from 1.

As for the public sector, data for almost all the units belonging to the frame list (about 18600) is obtained through registers and existent data sources. A small quantity of units (259), for which no information was available have been requested to fill in the questionnaire. Due to non responses in this portion even the weights of the public institutions may have been changed to represent the initial list. A simple ratio based adjustment using available information on employment has been applied to them.

6.2.1 Sampling error - indicators

Table 1 reports the percentual coefficient of variation (CV) [1] of Annual labour costs (code D) and Hourly Labour Costs (code D/B1). For the country as a whole the CV is 0.4% for the first variable and 0.19% for the second. For the private sector  they are slightly higher (0.49% and 0.24%).

The CVs for Annual Labour Costs for the private sector are always far lower than the maximum percentage error prefixed, for wages and salaries in the allocation stage (6.5%). This results also holds for higher breakdowns like the 2 digits Nace rev 2 divisions. The CV for hourly labour costs are usually much lower.

 


[1] The coefficient of variation is expressed as cv(Ŷ)=[V̂(Ŷ)]1/2
. Derived estimates for the LCS parameters for Annual and Hourly Labour Cost are corrected for non-response and calibrated. The corresponding estimators are not linear functions. Variance estimates were obtained by analytic method through linearized statistics - Taylor first order approximations.

6.3 Non-sampling error

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6.3.1 Coverage error

The target population on which the survey has been designed is defined by the private businesses and institutions and public institutions belonging to the section B-S Nace Rev. 2. including the section O.

The private sector sample was drawn from the Business register (ASIA). Since at the time of sampling the last Business Register available was referred at 2011, some of the enterprises were no longer in the frame for the year 2012, because of deaths, downsizing and resulted under the threshold of 10 employees or because they changed economic activity. In other terms the sample is affected by a certain degree of over coverage.

 

Even if it is not measureable in terms of sampling units, the sample may also suffer of undercoverage of units born or risen over the threshold of 10 employees in 2012.

However, since ASIA 2012 was available by the time of the estimate procedure it has been taken into account to solve to coverage problem. In fact the sample is reweighted in the calibration method to satisfy the totals derived from the frame 2012. The use of two auxiliary variables very much correlated with the target variables, namely the number of employees and the social security wages, should minimize the risks of biased estimates due to coverage problems. In other terms, while the sample is affected by some coverage errors the estimates should not.

6.3.1.1 Over-coverage - rate

Table 2 shows that out of the 20,177 enterprises belonging to the original sample the 10.9% is not in the frame of 2012. In terms of employees the rate of overcoverage is 2.4%.

6.3.1.2 Common units - proportion

[Not requested]

6.3.2 Measurement error

Measurement errors are defined as those considered such by the Istat Editing and Imputation procedures. Three main E&I procedures have been set up for the private sector. In a first stage, during data collection, the enterprises reporting influent anomalous values have been re-contacted to check the data. Due to limited resources only a small amount of enterprises have been checked this way. A second procedure is consisted in comparing the values of the main variables (number of employees, wages, and hours paid) reported by the enterprises with those available in the RACLI register. In case the values reported were implausibly different from those in the RACLI register, the responses have been substituted and the secondary variables have been recalculated. The third procedure has checked for errors in the composition of the main variables and in the ratio between monetary values and hours and hours and number of employees.

In this last step the variable more affected by editing, among the subitems of the labour costs, was wages and salaries for days not worked. A large share of firms reported zero values or values implausibly low. While a part of these firms may have misunderstood the question, the main reason seems to be that this information is absent in their information systems, due to the fact that wages are fixed on a monthly basis and are not influenced by days not worked for holidays and leaves. When the value was zero, missing or implausibly low it was imputed with a value obtained by multiplying the reported number of hours not worked but paid for holidays and leaves for the value of a normal worked hour. This is in turn obtained by dividing the direct remuneration paid in each pay period (excluding wages for overtime) by the number of hours worked (minus the overtime).

The last step of E&I procedures is the imputation of unit non responses. Here, the main variables are supplied by the RACLI data and the secondary variables are obtained through a minimum distance donor imputation.

6.3.3 Non response error

The item non response rate for variable

6.3.3.1 Unit non-response - rate

Unit response rates are calculated, following the indication of regulation n. 698/2006 as follows.

r = 100x (number of in scope respondents)
(number of in scope sample units)

The units in the sample that are deemed in scope are those belonging to the frame 2012. In other terms, the sampling units that according to ASIA 2012 are not in the target population are considered out of scope. The results showed in Table 3 refer to effective response rates that is before any imputation of unit non responses. It has been considered a respondent an enterprise whose questionnaire has been successfully “sent” through the web application and has passed the formal check rules built in the electronic form[2].

 

The response rate in terms of enterprises is over the 69%[3]. while in terms of employees is about 85.5%.

 


[2] These includes those units who originally sent the data through one of the auxiliary modes (see part 1. §1) provided that they passed the web questionnaire built in checks either immediately or after recontacting the firm.

[3] In contrast the response rate calculated as the share of respondents over the intial sample, that is without taking into account the out of scope units, measures 64%.

6.3.3.2 Item non-response - rate

For the variable Annual Labour costs, code D in LCS classification, the item non response rate is 0.4%.

6.3.4 Processing error

Little information is gathered on processing errors.

6.3.4.1 Imputation - rate

Item Imputation rate

The Item imputation rate is calculated as the ratio of values imputed for a specific variable over the total number of values for that variable.

For the variable Annual Labour costs, code D in LCS classification, it is 41.5%. This total figure is composed by 22.9% changes of the reported figure from a positive value to another, 0.4% changes from a zero or blank value to a positive value and 18.2% deriving from the total non-responses which, as said before, have been imputed. The high value of changes (23.3%) depends on the two factors; on one hand that reported values have been compared with the benchmark values derived from the social security derived RACLI register and implausible differences have been considered errors in the reported data. In other terms the values substituted have been considered much closer to reality than reported values; on the other hand that changes in any components of the total labour costs may affect the number of changes in the total.  

 

Overall imputation rate

The overall imputation rate is calculated as the ratio of values imputed for any LCS mandatory variable over the total values for mandatory variables.

It is 26.9% of which 8.0% are changes of the figure provided from a positive value to another, 0.7% changes from a zero o blank value to a positive value and 18.2% deriving from the total non-responses.

6.3.5 Model assumption error

Model assumption errors may affect editing and imputation rules. For the imputation of unit non responses, the main variables (number of employees, number of hours paid -excpt overtime and total wages) have been drawn from the RACLI register. The remaining variables have been imputed according to a minimum distance donor imputation rule.

6.4 Seasonal adjustment

[Not requested]

6.5 Data revision - policy

[Not requested]

[Not requested]

6.6 Data revision - practice

[Not requested]

6.6.1 Data revision - average size

[Not requested]

7. Timeliness and punctuality

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7.1 Timeliness

The data appeared for the first time on Eurostat database at the end of October and have been published with a national press release on 22 December. Thus the length of time between the end of the reference year (2012) and the first publication of data is of 22 months.

7.1.1 Time lag - first result

[Not requested]

7.1.2 Time lag - final result

[Not requested]

7.2 Punctuality

The release of data was not announced in any release calendars for national purposes. As for the EU deadlines Italy delivered the data at the beginning of October 2014 that is with about three months delay with respect to the regulation deadline (30 June 2014). This is attributable to a delay in the launch of the survey due to the necessity of adopting the new electronic standard, set up by the legislation, for communicating with the enterprises (electronic certified email - PEC).

The cover letter informing the enterprises of the survey was sent on 15 November 2013. According to the letter the data should be delivered within 30 days. However, up to 3 official reminders have been necessary to obtain the desired responses. The data collection has been officially closed on 15 July 2014 resulting in a period of fieldwork of 8 months. The data processing period, including the time spent before the closure of data collection, has been of about 5 months.

7.2.1 Punctuality - delivery and publication

[Not requested]

8. Coherence and comparability

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8.1 Comparability - geographical

The LCS 2012 complies with the standard set up on the Council Regulation (EC) No 530/1999 concerning structural statistics on earnings and on labour costs and with the definitions of variables adopted in the Commission Regulation 1737/2005.

8.1.1 Asymmetry for mirror flow statistics - coefficient

[Not requested]

8.2 Comparability - over time

The LCS 2012 is broadly comparable with the previous edition of LCS (2008) for what concerns large breakdowns and the main aggregates. However since this edition introduced a brand new questionnaire and the procedures of E&I and estimation have been thoroughly modified from the previous edition some of estimates may have some problem of comparability. The change in the questionnaire and instruction have hopefully improved the estimates of some variables like wages and salaries in kind and other labour costs at the cost of some degree of incomparability with the past. The estimation procedures have introduced an important change in the measurement of employment. Following the changes of the Business register introduced in the reference year 2011, the number of employees is now measured as the average over the weeks of the year of the employees recorded in each of the weeks compared with the average over the months of the year of the previous editions. This change implies some degree of incomparability over time in the estimates of the number of employees and in every per capita variable (like hours worked per capita, labour costs per capita etc…).

 

The innovations  in the data collection and E&I procedures have improved the estimates for some sectors. For instance in the Nace Rev 2 Section R, the estimates now includes the labour costs of athletes and sports staff that before was missed. This has implied a steep increase in the labour costs due to remuneration of very high wage sportsmen, In nace rev 2 section N the estimates include a better measure of the labour costs of temporary staff hired by temporary work agencies.

 

The estimation method of the hours worked in the public part of the education sector (section P) has been completely revised, leading to a considerable reduction compared to the previous edition. It is to be mentioned however that the new method still fails to count the hours worked outside the workplace for the teachers.

8.2.1 Length of comparable time series

[Not requested]

8.3 Coherence - cross domain

Hours worked per employee: LFS vs LCS

Table 4 reports the difference between the average number of usual weekly hours of work in main job annualized estimated through the LFS and the average number of hours worked in the LCS (codes B1/A1). Although the concepts are very similar the difference in the levels is striking averaging 24.8% in the B-S sectors. Several factors may be at the root of these differences. A first main factor is that LCS produces figures on the worked hours paid by the enterprises, while LFS results are related to the number of hours actually worked by the individuals (workers) during the reference week which include all hours including extra hours, either paid or unpaid  (that is, this includes all hours worked including overtime, regardless of whether people were paid). In other terms while the LCS measurement, being a survey direct to employers, is unable to measures hours worked but not paid. LFS is likely to catch these irregular hours since the question is posed to the employees. On the other side it is known that LFS may underestimate the hours of absence from work (for holidays, leaves, sickness etc..) (see the work of the Task force).

 

Wages and salaries per employee: SBS vs LCS

Table 5 compares the wages and salaries per employee as measured in SBS statistics and the one measured in LCS (codes D11/A1). To perform a more meaningful analysis the comparison is restricted to the target population in common between the two statistics: enterprises in the private sector with at least 10 employees with the exception of sections K and O. The two sources measurement systems are very different in some aspects but have also similarities. LCS, for the private sector, is a direct sample survey, although edited and integrated with social security data. The estimates of employees are calibrated to the business register values, which in turn are basically those derived by the same social security data, SBS, on the other hand in its recent evolutions in Italy, is more based, for what concerns the variable at hands, on administrative data. In particular the sources providing the wages and salaries for the enterprises with less than 100 employees are the Company accounts and other fiscal sources, while for the larger enterprises the source is a direct. take-all, survey. For SBS, the employees at the denominator are taken from the Business register.

With the exception of section B the differences between the two sources are quite limited, averaging -1.3% in the total. This is due to the fact that the concepts of wages and salaries in the two statistics should be quite close. It is not easy to say how much close since, while the official definition of wages and salaries in LCS is very detailed, the one in SBS is more uncertain. The prevalence of negative signs, indicating higher wages and salaries in LCS, may suggest that in the latter some items are included in the variable which are not in SBS or at least that their weight is larger than items not included in LCS and included in SBS.

 

Hourly labour costs growth rates: LCI vs LCS

Table 6 shows the difference in the average annual growth rates between 2008 and 2012 of hourly labour costs between the unadjusted LCI and LCS. Empirically in the total the difference amount to 0.44 percentage points. However this average hides quite relevant differences between single sections. Here the difference in definitions should not be relevant. In fact the circumstance that labour costs (code D=D1+D2+D3+D4-D5) in LCS includes items that are not included in the labour costs for LCI, where the total labour costs is equal to D1+D4-D5, should play a minor role in the difference of the growth rate due to the tiny share of vocational training costs. D2, and other expenditure, D3, on the total labour costs in Italy. Many factors here can be at the base of he differences. In some sectors, as noted in the paragraph on comparability over time, the methodological changes between LCS 2008 and 2012 introduce a difference in the levels of the variables that simply make the change between the two years meaningless. It is especially the case of section P and section R. A relevant impact on the differences may be due to the different coverage. LCI cover enterprises and institutions of  every size, while lcs is restricted to those over 10 employees. The impact can be very relevant in sectors where the share of small units is large, like in Construction (section F). Hotel and restaurants (section I) and transports (section H).  

 

[Not requested]

8.4 Coherence - sub annual and annual statistics

[Not requested]

8.5 Coherence - National Accounts

The definitions of compensation of employees in LCS and NA (code D1 in both statistics) are quite similar. As a matter of fact it is not clear whether the tiny differences that can be spotted between the two regulations must be interpreted as a result of an intention of the legislator or whether they can be ascribable only to a different way of writing down the definitions.

The empirical comparison of LCS and NA data (table 7) shows quite pronounced differences.  This is probably due to the differences in coverage (all the enterprises in NA; enterprises with 10 and more employees in LCS). Being the labour cost increasing with enterprise size, LCS’ labour costs appears higher in almost all NACE sectors. A further factor explaining the difference in favour of LCS is the fact that the NA statistics include also irregular employees which have labour costs much lower than regular employees.

8.6 Coherence - internal

[Not requested]

9. Accessibility and clarity

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9.1 Dissemination format - News release

A press release with synthetic results in the series Statistica report was released on 22 december 2014. Together with the report a number of tables in spreadsheet format was released. The link at which they are available is: http://www.istat.it/it/archivio/143789.

9.2 Dissemination format - Publications

No publications are planned for this statics.

9.3 Dissemination format - online database

More detailed data are going to be released through Istat data warehouse I.Stat. The link is: dati.istat.it under Labour-Labour Costs- (section: Labour Costs Survey).

9.3.1 Data tables - consultations

[Not requested]

9.4 Dissemination format - microdata access

[Not requested]

9.5 Dissemination format - other

No results will be  sent back to responding units included in the sample.

9.6 Documentation on methodology

The Statistica report includes a glossary illustrating the main items definitions. Moreover it is accompanied by a methodological note describing the main aspects of the survey process and methodology applied. These are available at the link above.Information on data quality will be available on the Istat Information system on quality (SIQual) at http://www.istat.it/it/strumenti/qualit%C3%A0-dei-dati/siqual

9.7 Quality management - documentation

[Not requested]

9.7.1 Metadata completeness - rate

[Not requested]

9.7.2 Metadata - consultations

[Not requested]

10. Cost and Burden

[Not requested]

11. Confidentiality

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11.1 Confidentiality - policy

[Not requested]

11.2 Confidentiality - data treatment

[Not requested]

12. Comment

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