Reference metadata describe statistical concepts and methodologies used for the collection and generation of data. They provide information on data quality and, since they are strongly content-oriented, assist users in interpreting the data. Reference metadata, unlike structural metadata, can be decoupled from the data.
Italian National Institute of Statistics (ISTAT) Directorate for Social Statistics and Population Census
1.2. Contact organisation unit
Department for Statistical Production (DIPS)
Social Statistics and Welfare Directorate (DCSW)
Integrated Labour, Education and Training Division (SWB)
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Istat - Italian National Institute of Statistics Via Cesare Balbo, 16 - 00184 Rome – Italy
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. Metadata last certified
12 June 2025
2.2. Metadata last posted
12 June 2025
2.3. Metadata last update
12 June 2025
3.1. Data description
Indices of Wages and Salaries.
These indicators are produced by the quarterly Oros survey, through the integration of monthly Social Security administrative data (collected by the Social Security Institute-INPS), used mainly for small and medium enterprises (SMEs) and the monthly survey data on labour input and labour costs on the large enterprises used for large enterprises (LES).
Reporting and observation unit: Enterprise. For some large enterprises reporting and observation units are KAUs.
Note: kind-of-activity unit.
3.6. Statistical population
All enterprises with at least one employee which were active in the reference quarter in the NACE economic activities of the coverage sectors.
In 2023 according to the last available Business Register version, these enterprises were, on average about 1,4 millions, distributed as follows:
222,119 for Industrial sector
212,597 for Construction sector
373,372 for Retail sector
555,615 for Services sector
(source: BR-ASIA)
3.7. Reference area
Geographically the STS indicators on Wages and Salaries cover the whole country. Activities outside the country are excluded.
3.8. Coverage - Time
The indicators are available since Q1:2000.
3.9. Base period
The base year is 2021=100.
Index.
Quarter.
6.1. Institutional Mandate - legal acts and other agreements
Wages and Salaries index is produced according to the requirements of the Commission Implementing Regulation 2020/1197 laying down technical specifications and arrangements pursuant to the European Business Statistics (EBS) Regulation (EU) 2019/2152 adopted by the European Parliament and of the Council on 27 November 2019.
Furthermore, all statistics produced and published by the National Statistical Institute of Italy are subjected to:
the Legislative Decree no. 322, of 6 September 1989 (and subsequent modifications and additions Decree of the President of the Republic (DPR) no. 166 of 7 september 2010), which is consistent with the U.N. Fundamental Principles of Official Statistics and places Istat at the center of the National Statistical System (SISTAN). Sistan is a network of public bodies and private agencies that provides official statistical information and covers the statistical offices of all levels of government, Chambers of commerce, industry, crafts industries, agriculture and other public bodies as well as private subjects having public functions;
the Decree of the President of the Council of Ministers (DPCM) which every year approves the National Statistical Programme;
moreover, the Committee for Directing and Coordinating Statistical Information (COMSTAT), over which Istat presides, defines and issues binding directives for executing the National Statistical Programme. Istat has a legal obligation to publish and disseminate data (Article 15, in particular paragraph 1[g] of the Legislative Decree no. 322, 6 September 1989).
The legal basis of the admin source used in the estimation process of Wages and salaries, is the Decreto Ministeriale 05 February 1969 and Decreto Ministeriale 24 February 1984 on the obligation of units to provide data: the enterprises are obliged to pay social contributions and to submit the monthly declaration (DM10 form until December 2009, UniEmens since January 2010) to INPS within 30 days after the end of the reference month. All firms which do not meet those obligations can be condemned to administrative and penal sanctions.
6.2. Institutional Mandate - data sharing
No data sharing. Indices calculated on totals are not officially released in Italy. They are shared only internally at Istat.
7.1. Confidentiality - policy
According to article n.9 of the Legislative Decree n.322 of 6 September 1989 data collected by statistical offices within the statistical surveys included in the National Statistical Programme may not be disclosed other than in aggregated form such that no reference to identifiable people can be extracted. Furthermore, they may be used only for statistical purposes. Data may not be communicated or disseminated neither to any external subject, public or private, nor to any department of the public administration other than in aggregate form and using modalities which prevent the identification of the people involved. In any case, data cannot be used to identify again the people involved. The Code of Conduct annexed to the Legislative Decree n.196 of 30 June 2003 (Personal Data Protection Code) provides special rules concerning the processing of personal data for statistical purposes within Sistan. In order to make statistical secrecy and protection of personal data effective, Istat has taken appropriate organizational, logistical, methodological and statistical measures in accordance with internationally established standards. In accordance with the Legislative Decree n.196 of 30 June 2003 (Personal Data Protection Code) and subsequent modifications and additions, respondents are informed of their rights and obligations with regard to the provision of information, and they are assured that the information they provide will be used for statistical purposes only.
Links to relevant acts on statistics are presented on the website of Sistan - National Statistical System – (sistan).
7.2. Confidentiality - data treatment
In general Istat has special aggregation rules which have been developed to ensure that indirect disclosure of individual data does not occur when aggregations of data are presented. For instance, access to individual data is restricted to staff who require the information in the performance of their duties. Provisions are in place to supervise analysts that require access to disaggregated data.
8.1. Release calendar
At the moment data on Wages and Salaries expressed as totals are only transmitted to Eurostat but not disseminated nationally at any level of detail.
8.2. Release calendar access
No calendar because no release.
8.3. Release policy - user access
Data are transmitted to Eurostat by teletransmission and flagged as free. This kind of indices is not released at National level.
Quarterly (to Eurostat).
10.1. Dissemination format - News release
Indices on Wages and Salaries required such as STS indicators, are not released at National level.
10.2. Dissemination format - Publications
At the moment data are not released at National level.
10.3. Dissemination format - online database
Not available because data are not released at National level.
10.4. Dissemination format - microdata access
Not available because data are not released at National level.
10.5. Dissemination format - other
Data are transmitted to Eurostat quarterly, within 90 days from the end of the reference quarter, in SDMX format.
10.6. Documentation on methodology
Details on the LES and Oros surveys are available at the Information System for Survey documentation and Quality Control (Siqual), on Istat’s Internet website, respectively at the link siqual_LEs and siqual_OROS .
Other sources and methodologies are described in the following Istat methodological document: Istat 2019: "La rilevazione trimestrale Oros su occupazione e costo del lavoro: indicatori e metodologie" (in english "The Oros quarterly survey on employment and labor costs: indicators and methodologies", Statistical readings – Methods, Istat, document link ISTAT archive, only in italian). Information on the administrative data used as main source in the Oros survey can be found in the following document: Rapiti F.M., Ceccato F., Congia M.C., Pacini S. and Tuzi D. 2010. “What have we learned in almost 10-years experience in dealing with administrative data for short term employment and wages indicators?”(INE Session 2 ).
An overview on the integration of sources and processes for the production of the main short term business indicators on labour market at Istat is described in the following document: Baldi C., Bellisai D., Ceccato F., Pacini S., Serbassi L., Sorrentino M., Tuzi D., 2011."The system of short term business statistics on labour in Italy. The challenges of data integration" (INE Baldi et al).
10.7. Quality management - documentation
Not available specifically on the STS indicators production process.
Information on the quality management in the survey are descibed in the documents available at the following links:
Congia M.C., Rapiti F.M (2010), "Quality assessment and reporting in a short-term business survey based on administrative data", Documenti Istat (Istat files)
Congia M.C., Pacini S., Tuzi D. (2008b), “The Editing Process in the Italian Short-Term Survey on Labour Cost based on Administrative Data”, paper presented to the UNECE - Conference of European Statisticians,Work Session on Statistical Data Editing, 21 – 23 April, Wien.
Istat (2019), "La rilevazione trimestrale Oros su occupazione e costo del lavoro: indicatori e metodologie", Letture statistiche – Metodi, Istat. (in english "The Oros quarterly survey on employment and labor costs: indicators and methodologies", Statistical readings – Methods, Istat, (ISTAT archive)
11.1. Quality assurance
At the basis of the production of the STS indicators, the standard Istat systematic approach to quality following the International and European standards. Istat reference framework for quality policies relies on the European Statistics Code of Practice, adopted in 2005, revised in 2011 and, more recently, in November 2017 on Eurostat Quality Definition and on the recommendations of the LEG on Quality, approved by the Members States of the European Union in 2001. The Data Quality Assessment Framework, developed by the International Monetary Fund, also represents an important reference, especially for economic statistics and for National Accounts. With the Directive No 12/COMSTAT - Directive No 12/COMSTAT, only available in Italian language - Italy replaced and updated the Italian Code of Official Statistics announced in 2010, taking into account some important innovations: the indications contained in the European Code of Practice in its latest revision, that is based on the principle of the European Code and is statutory for the National Statistical System (Italian Statistic Code). The Quality Committee, set up in 2010, is a high level body in charge of quality monitoring and quality auditing of statistical processes and products. Quality auditing and self-assessment are aimed at verifying the compliance of statistical processes and outputs to the principles stated in the Quality Guidelines
11.2. Quality management - assessment
The Oros Survey is an innovative case of short-term statistics produced with the help of administrative sources (Social Security data) in order to cover all size enterprises in the private sectors. The use of administrative data in short-term statistics implies paying attention to unusual statistical quality aspects. Statisticians cannot prevent or reduce no-sampling errors in raw administrative data capturing, and some ex-post traditional editing techniques, like questionnaire revision and enterprise recalling, are not applicable. The complexity of the production process is also caused by the huge number of records and the highly disaggregated level of raw data. In fact, given the short-time constraint in the releases, the Italian NSO was obliged to capture data from the Italian Social Security Institute without any previous process of aggregation and checking. So, the retrieval and translation of the administrative data into statistical information is one of the most critical aspect to be faced at the beginning of the process; and its effectiveness has a significant impact on the quality of the final indicators. On the other hand, the availability of very disaggregated data allows for the exploitation of a very rich informative source for different statistical aims, and for a more direct control on the overall translation phase. When the statistical variables have been made available, a more traditional micro level check procedure is applied. Editing on outliers and anomalous values, and imputation of unit non responses may consequently be needed, with a particular attention to influential observations. Considerations linked to the quality of data suggest a micro-level integration between the administrative source and the Large Enterprises Survey data. This integration involves a record-linkage aspect and the computation of harmonized variables. When macro data are available, validation implies, among other aspects, time series analysis, macro-level comparisons with other statistical sources and analysis of revisions.
Finally, the standardization and documentation of the whole check and editing process is a fundamental target of the Oros quality procedure (for further details on the quality management in the Oros survey: Congia M.C. and Rapiti F.M. 2010 “Quality assessment and reporting in a short-term business survey based on administrative data” (Istat files)and Congia M.C., Pacini S., Tuzi D. 2008 “The Editing Process in the Italian Short-Term Survey on Labour Cost based on Administrative Data” (UNECE documents).
12.1. Relevance - User Needs
Eurostat is the main user. Data produced are coherent with the requests of the EBS Regulation.
12.2. Relevance - User Satisfaction
The data are considered satisfying the EBS Regulation requests by Eurostat.
12.3. Completeness
All requirements pursuant to the EBS Regulation are fulfilled.
13.1. Accuracy - overall
Assessing accuracy on the indicator of Wages and Salaries, compiled using admin data integrated at micro level with survey data implies taking into account only non-sampling errors. Infact, these data sources refer to the census of the target units.
The massive quantity of administrative micro data (used for SMEs) covers almost the 94% in terms of number of jobs. However, it requires a very careful processing phase of E&I, since if the non-response for the total economy is not relevant, it is not the same for the different economic sectors. At this stage a micro level model-based process of imputation is performed, so for an estimate of the accuracy the main components to take into account are the estimates of the parameters, and the residual bias for some sectors for which the model does not fit well, for which a macro adjustment based on the time series analysis is performed. Another source of bias comes from the definition of the statistical population. If from one hand the use of auxiliary information from the Business Register and from the more updated Tax Register helps defining the list of target units in the Social Security Register reducing problems of over-coverage, on the other hand the delay of the Business Register's updating date implies an additional bias due to the misclassification of the units. Survey data (used for LES) refer to enterprises with more than 500 employees at the base year 2021, this group adds up to about 1,630 enterprises covering about 20% of total employees in Italy in the STS sectors. Each one of these firms has a considerable influence on the estimates. Editing and imputation on this data are global (all units are checked) and performed by very expert personnel, assuring very high quality data and fast management of changes in units legal asset, non-responses, errors, etc. When considering accuracy, the micro level integration between the administrative records and the Large Enterprises Survey data must also be considered. Record linkage and computation of harmonized variables are the main processes to be taken into account.
13.2. Sampling error
Estimations are not based on samples.
13.3. Non-sampling error
Measurement errors on the administrative data have affected very few units during the years and, during the last fourteen years, they have deeply decreased due to the greater attention that the Social Security Institute is paying on a very recent new system of data collection (Uniemens forms, since the beginning of 2010).
As far as it concerns non responses the administrative data framework (used for SMEs) must be distinguished by the survey data framework (used for LES).
In the preliminary estimate the administrative data coverage in term of units is about 98%; in the final (census) estimation the coverage in term of units is about 99.9%.
For what concerns Survey data, non-response of LES tends to increase gradually as the time span increase from the base year. During 2024 non-reporting enterprises were approximately 2% on the whole B to N aggregate. Monthly reminders (by e-mail and fax) and intensive follow-ups by phone are addressed to non-responding LE units. Two times a year a warning with penalty (registered letter with return receipt) is sent to firms that have not answered to LES in the previous three months.
14.1. Timeliness
Preliminary: before 90 days after the end of the reference quarter, usually 60 days anticipating the deadline (90 days after the reference quarter).
Final: about 1 year and 60 days after the end of the reference quarter.
14.2. Punctuality
Punctuality always achieved.
15.1. Comparability - geographical
Wages and Salaries indicators are defined in coherence with EBS Regulation. The data cover the entire national territory.
15.2. Comparability - over time
The methodology at the basis of the estimation of the Wages and Salaries has been subjected to several innovations over the time, implying discontinuities in the time series. The same effect occurs in the occasion of the transition to a new reference base, when the most relevant innovations are introduced, or when non-ordinary interventions are performed. In all these cases, to guarantee coherence of all the occurrences of the time series, linking factors are normally calculated on overlapping periods and applied to the quarters estimated in old methodology.
15.3. Coherence - cross domain
The quarterly and annual dynamic of Wages and Salaries indicators are constantly compared with figures drawn from the National Accounts and with National contractual (agreed) wages' figures.
Comparisons with Structural Business Data on annual basis are performed, too. Level of coherence is good. Differences can be attributed to the different methodologies used to estimate the considered aggregates and to the different levels of coverage, concepts, definitions and classifications.
15.4. Coherence - internal
Good coherence between indicators, known the differences in methodology, concepts, definitions etc.
3,5 persons work at Istat for the for the "Oros" survey, 3 persons work for the indicators on Large Enteprises.
To produce the STS indicators Istat did not increase at all the burden on enterprise because it has been used a pre-existent survey (Large Enterprises Survey) and administrative data (for SMEs). None of the auxiliary Surveys used to calculate the STS indicators requires additional information.
17.1. Data revision - policy
For total Wages and Salaries, the discrepancy between the preliminary estimate and later ones depends on the revisions of the Oros-LES indicators. In a standard practice of revisions, figures that contribute to this aggregate’s estimate are revised four times before they become final, that occurs after one year from their first publication. The main reasons of revision are:
the final version of the administrative micro data which are checked by INPS on reporting units, substitutes completely the preliminary version (which is checked and edited only by Istat);
non reporting units in the preliminary data are present in the final version;
the annual revision of the LES data referred to the previous year, included in the Oros-LES estimates yearly, in the delivery of the first quarter;
non-standard revisions (es. transition to a new base year).
As a synthesis, each quarter the last four quarters of the time series of Wages and Salaries indices are revised.
An internal database of vintages exists and can be made available at request.
17.2. Data revision - practice
In the release of June 2024, with the first transmission of Q1:2024, the average revisions of the the year on year growth rates for the total, calculated with respect to the previous data transmission and considering 5 vintages (first vintage is the first release of the indices expressed in base 2021 occurred in June 2024), were the following:
at division level: MAR=1.3% RMAR=0.4% MR=0.2%
at section level: MAR=0.5% RMAR=0.1% MR=-0.3%
at MIG level: MAR=0.3% MR=0,1%; RMAR=-0.4%
Being the average revision statistics calculated as follows:
MAR= n-1 Σt=1,n|Lt- Pt| = n-1 Σt=1,n|Rt|
MR= n-1 Σt=1,n(Lt- Pt) = n-1 Σt=1,nRt
RMAR= (Σt=1,n|Rt| ) / (Σt=1,n|Lt|)
Where Lt and Pt are respectively the last estimate and the first one.
18.1. Source data
Data used to compile Wages and Salaries indices are drawn from the Oros and LES surveys.
The Oros Survey, based mainly on the administrative data on the Social Security contributions declarations (DM) collected by INPS (National Social Security Institute), is aimed at covering all size classes without increasing the statistical burden on respondents. The survey has been designed to satisfy the EU requirements on short-term statistics (EBS Regulation n.2019/2152 and LCI-Labour Cost Index Regulations n.450/2003). INPS data are integrated with the monthly Istat Survey on Labour input variables in large enterprises (LES-Large Enterprise Survey). The data from INPS cover the population of SMEs and the data of the LES cover the population of large enterprises. Some large enterprises not covered by LES are covered by the INPS data. The main source for the NACE code is the Business Register (BR) and, for residual units also the Tax Register (TR). Both the BR and the TR give also information on the legal characterization of the units, useful to restrict administrative data to the Oros target population.
18.2. Frequency of data collection
Administrative data used for SMEs are collected monthly by INPS and compiled quarterly by Istat. Data on LES are collected monthly by Istat and compiled quarterly by Oros.
18.3. Data collection
Wages and Salaries are estimated on the basis of the integration of administrative data, used for SMEs and survey data, for LES. Administrative data are stored by the Social Security Institute (INPS) in electronic format and delivered to Istat using inter-institutional electronic transmission. Data referring to the large enterprise survey are collected monthly by questionnaire, via website.
18.4. Data validation
Analysis on non responses and outliers. Corrections (imputation) at micro (based on selective criteria) and macro level. Checks are carried out via both automated procedures and experts’ analyses on data.
For the large enterprises sub population, reporting units may also be contacted again in order to validate or correct the data.
The files that are sent to Eurostat are produced from data stored in an Oracle database via a generalised Istat software. After their production, they are not checked with any further software or specialized tool.
18.5. Data compilation
Once admin data on DMs are acquired (about 10 million records per month) the monthly basic variables are calculated at unit level through a complex pre-treatment procedure based on a data base of metadata on rules, laws and regulations on social security contributions declaration. At the end of the process information on the main target variables referred to each unit identification is summarized in a single record (about 1.4 million records per month). Finally, quarterly variables are calculated as simple means of the related monthly statistical variables retrieved as above mentioned. Once the main quarterly variables have been derived from DMs, the survey target population has to be outlined, excluding the out-of-scope units (public sector or whose economic activity is not included in the target Nace). This operation is performed using the auxiliary information drawn from the Business Register (ASIA) and from the Tax Register.
At the scheduled time for the acquisition of the provisional population, it may happens that some DM are missing due to delays depending on firms liability or administrative system flaws. These missing units (late reporters) usually correspond to about 2-5% of the final population. That means that in the admin data an almost complete coverage of the target population occurs. Nevertheless, late reporting has a non-negligible impact in the estimates of variables expressed as totals, like Wages and salaries: even small differences from the final values could give misleading signals on the short dynamic of the target variables. In the Oros process, the jobs level estimate is massively adjusted to correct for the incompleteness of the preliminary data file due to the late reporters and Wages and salaries are adjusted proportionally under the hypothesis that the per-capita wages are not affected by late reporting. Micro imputation is the approach used in this phase of the process. The micro imputation procedure consists of two steps: a) the identification stage, that is the definition of a list of non-reporting units to impute; b) the imputation stage, that is the assignment of imputed values to the list of units above defined. Due to the absence of a theoretical list of active units for the admin data, a list of non-reporting active units has to be predicted. It’s derived on the basis of the structure of the preliminary admin data itself: based on the observation of the reporting patterns of the units. In the second imputation phase, the value of the units identified as active (expected late reporters) is imputed. The availability of a large quantity of micro data at longitudinal level on non-reporting units and considering the inertial trend of employment, a natural choice to reconstruct the missing values of jobs is to use a general regression model, where only the lagged values of the same variable are used as auxiliary variables, in detail, job values on the previous month and on the same month of the previous year. The adjusted estimates on jobs are applied to the per-capita wages calculated before imputation to get total wages.
OROS quarterly indicators derives from the integration of administrative and Large Enterprises survey (LES) data. The integration process aims at producing quarterly micro data by replacing the admin source with the large firms survey data, for the overlapping enterprises. This integration improves the estimates’ quality given both the higher quality of survey data and their adherence to statistical contents and of the overall admin-LES (Oros) estimates in case of missing response (MR), considered the relevant influence of large enterprises especially in some economic activity sectors. Producing quarterly integrated admin-LES microdata implies identifying and excluding from the admin source the enterprises belonging to the LES survey and estimating coherent variables from data collected for different purposes. This process implies taking into account linkage and variables harmonization issues. The main phase of the integration process is the definition of quarterly lists of common units in both admin source and LES. To identify correctly this sub-population avoiding double counting, record linkage and micro-integration are implemented. For units belonging to the defined quarterly list, Oros economic variables are replaced with the LES ones. The higher detail of LES variables on employment allows to perform coherent estimates from the two sources at the basis of Oros. To this scope an harmonization procedure according to the statistical requirements is carried out. Before the aggregation of the main variables, units not belonging to LES (already including E&I operations from Les survey) undergo an editing and imputation procedure. This operation is aimed at identifying and correcting missing response and/or outliers that could generate bias in the estimates both in preliminary and final data. It is based on selective criteria: influent units (less than 50 units a quarter) are automatically detected through functional relations on information on the same quarter of previous year (t-4), according to established cut-off thresholds. Specifically, the quarter-on-quarter variations are the target variables of the selective editing procedure, to better detect outlier incorporating seasonality, contributing to reduce the number of influent units. The most anomalous values or missing response are interactively analyzed and, if necessary, imputed by an automatic deterministic procedure.
For furter details on the methodology at the basis of the Oros process see:
Istat, 2019. La Rilevazione trimestrale Oros su occupazione e costo del lavoro: indicatori e metodologie. Letture Statistiche - Metodi (in english: "The Oros quarterly survey on employment and labor costs: indicators and methodologies", Statistical readings – Methods - Istat (ISTAT archive).
Rapiti F.M., F. Ceccato, M.C. Congia, S. Pacini e D. Tuzi. 2010. What have we learned in almost 10-years experience in dealing with administrative data for short term employment and wages indicators? Paper presented at the seminar: Using administrative data in the production of business statistics”. Roma, 18-19 marzo (Ine session2).
18.6. Adjustment
The indices on Wages and Salaries are available in unadjusted form and, for the first time from the first quarter of 2021, also adjusted for working days, according to new requirements pursuant to the last Regulation (EU) 2019/2152.
The raw indices are trading days adjusted by TRAMO-SEATS procedure, version 942 for Linux. Working days adjustment series are estimated down to 2 digit level.
A Reg-Arima approach is used for working days adjustment by performing a country specific calendar which also take into account national holidays. At the release of first quarter of each year, the adjustment models will be completely reviewed, while the parameters are re-estimated every quarter, so every quarterly release the adjusted series are revised from January 2000. No calendar adjustment is performed where no significant effect is found. Leap year and moving holidays like Easter are adjusted.
These indicators are produced by the quarterly Oros survey, through the integration of monthly Social Security administrative data (collected by the Social Security Institute-INPS), used mainly for small and medium enterprises (SMEs) and the monthly survey data on labour input and labour costs on the large enterprises used for large enterprises (LES).
Reporting and observation unit: Enterprise. For some large enterprises reporting and observation units are KAUs.
Note: kind-of-activity unit.
All enterprises with at least one employee which were active in the reference quarter in the NACE economic activities of the coverage sectors.
In 2023 according to the last available Business Register version, these enterprises were, on average about 1,4 millions, distributed as follows:
222,119 for Industrial sector
212,597 for Construction sector
373,372 for Retail sector
555,615 for Services sector
(source: BR-ASIA)
Geographically the STS indicators on Wages and Salaries cover the whole country. Activities outside the country are excluded.
Quarter.
Assessing accuracy on the indicator of Wages and Salaries, compiled using admin data integrated at micro level with survey data implies taking into account only non-sampling errors. Infact, these data sources refer to the census of the target units.
The massive quantity of administrative micro data (used for SMEs) covers almost the 94% in terms of number of jobs. However, it requires a very careful processing phase of E&I, since if the non-response for the total economy is not relevant, it is not the same for the different economic sectors. At this stage a micro level model-based process of imputation is performed, so for an estimate of the accuracy the main components to take into account are the estimates of the parameters, and the residual bias for some sectors for which the model does not fit well, for which a macro adjustment based on the time series analysis is performed. Another source of bias comes from the definition of the statistical population. If from one hand the use of auxiliary information from the Business Register and from the more updated Tax Register helps defining the list of target units in the Social Security Register reducing problems of over-coverage, on the other hand the delay of the Business Register's updating date implies an additional bias due to the misclassification of the units. Survey data (used for LES) refer to enterprises with more than 500 employees at the base year 2021, this group adds up to about 1,630 enterprises covering about 20% of total employees in Italy in the STS sectors. Each one of these firms has a considerable influence on the estimates. Editing and imputation on this data are global (all units are checked) and performed by very expert personnel, assuring very high quality data and fast management of changes in units legal asset, non-responses, errors, etc. When considering accuracy, the micro level integration between the administrative records and the Large Enterprises Survey data must also be considered. Record linkage and computation of harmonized variables are the main processes to be taken into account.
Index.
Once admin data on DMs are acquired (about 10 million records per month) the monthly basic variables are calculated at unit level through a complex pre-treatment procedure based on a data base of metadata on rules, laws and regulations on social security contributions declaration. At the end of the process information on the main target variables referred to each unit identification is summarized in a single record (about 1.4 million records per month). Finally, quarterly variables are calculated as simple means of the related monthly statistical variables retrieved as above mentioned. Once the main quarterly variables have been derived from DMs, the survey target population has to be outlined, excluding the out-of-scope units (public sector or whose economic activity is not included in the target Nace). This operation is performed using the auxiliary information drawn from the Business Register (ASIA) and from the Tax Register.
At the scheduled time for the acquisition of the provisional population, it may happens that some DM are missing due to delays depending on firms liability or administrative system flaws. These missing units (late reporters) usually correspond to about 2-5% of the final population. That means that in the admin data an almost complete coverage of the target population occurs. Nevertheless, late reporting has a non-negligible impact in the estimates of variables expressed as totals, like Wages and salaries: even small differences from the final values could give misleading signals on the short dynamic of the target variables. In the Oros process, the jobs level estimate is massively adjusted to correct for the incompleteness of the preliminary data file due to the late reporters and Wages and salaries are adjusted proportionally under the hypothesis that the per-capita wages are not affected by late reporting. Micro imputation is the approach used in this phase of the process. The micro imputation procedure consists of two steps: a) the identification stage, that is the definition of a list of non-reporting units to impute; b) the imputation stage, that is the assignment of imputed values to the list of units above defined. Due to the absence of a theoretical list of active units for the admin data, a list of non-reporting active units has to be predicted. It’s derived on the basis of the structure of the preliminary admin data itself: based on the observation of the reporting patterns of the units. In the second imputation phase, the value of the units identified as active (expected late reporters) is imputed. The availability of a large quantity of micro data at longitudinal level on non-reporting units and considering the inertial trend of employment, a natural choice to reconstruct the missing values of jobs is to use a general regression model, where only the lagged values of the same variable are used as auxiliary variables, in detail, job values on the previous month and on the same month of the previous year. The adjusted estimates on jobs are applied to the per-capita wages calculated before imputation to get total wages.
OROS quarterly indicators derives from the integration of administrative and Large Enterprises survey (LES) data. The integration process aims at producing quarterly micro data by replacing the admin source with the large firms survey data, for the overlapping enterprises. This integration improves the estimates’ quality given both the higher quality of survey data and their adherence to statistical contents and of the overall admin-LES (Oros) estimates in case of missing response (MR), considered the relevant influence of large enterprises especially in some economic activity sectors. Producing quarterly integrated admin-LES microdata implies identifying and excluding from the admin source the enterprises belonging to the LES survey and estimating coherent variables from data collected for different purposes. This process implies taking into account linkage and variables harmonization issues. The main phase of the integration process is the definition of quarterly lists of common units in both admin source and LES. To identify correctly this sub-population avoiding double counting, record linkage and micro-integration are implemented. For units belonging to the defined quarterly list, Oros economic variables are replaced with the LES ones. The higher detail of LES variables on employment allows to perform coherent estimates from the two sources at the basis of Oros. To this scope an harmonization procedure according to the statistical requirements is carried out. Before the aggregation of the main variables, units not belonging to LES (already including E&I operations from Les survey) undergo an editing and imputation procedure. This operation is aimed at identifying and correcting missing response and/or outliers that could generate bias in the estimates both in preliminary and final data. It is based on selective criteria: influent units (less than 50 units a quarter) are automatically detected through functional relations on information on the same quarter of previous year (t-4), according to established cut-off thresholds. Specifically, the quarter-on-quarter variations are the target variables of the selective editing procedure, to better detect outlier incorporating seasonality, contributing to reduce the number of influent units. The most anomalous values or missing response are interactively analyzed and, if necessary, imputed by an automatic deterministic procedure.
For furter details on the methodology at the basis of the Oros process see:
Istat, 2019. La Rilevazione trimestrale Oros su occupazione e costo del lavoro: indicatori e metodologie. Letture Statistiche - Metodi (in english: "The Oros quarterly survey on employment and labor costs: indicators and methodologies", Statistical readings – Methods - Istat (ISTAT archive).
Rapiti F.M., F. Ceccato, M.C. Congia, S. Pacini e D. Tuzi. 2010. What have we learned in almost 10-years experience in dealing with administrative data for short term employment and wages indicators? Paper presented at the seminar: Using administrative data in the production of business statistics”. Roma, 18-19 marzo (Ine session2).
Data used to compile Wages and Salaries indices are drawn from the Oros and LES surveys.
The Oros Survey, based mainly on the administrative data on the Social Security contributions declarations (DM) collected by INPS (National Social Security Institute), is aimed at covering all size classes without increasing the statistical burden on respondents. The survey has been designed to satisfy the EU requirements on short-term statistics (EBS Regulation n.2019/2152 and LCI-Labour Cost Index Regulations n.450/2003). INPS data are integrated with the monthly Istat Survey on Labour input variables in large enterprises (LES-Large Enterprise Survey). The data from INPS cover the population of SMEs and the data of the LES cover the population of large enterprises. Some large enterprises not covered by LES are covered by the INPS data. The main source for the NACE code is the Business Register (BR) and, for residual units also the Tax Register (TR). Both the BR and the TR give also information on the legal characterization of the units, useful to restrict administrative data to the Oros target population.
Quarterly (to Eurostat).
Preliminary: before 90 days after the end of the reference quarter, usually 60 days anticipating the deadline (90 days after the reference quarter).
Final: about 1 year and 60 days after the end of the reference quarter.
Wages and Salaries indicators are defined in coherence with EBS Regulation. The data cover the entire national territory.
The methodology at the basis of the estimation of the Wages and Salaries has been subjected to several innovations over the time, implying discontinuities in the time series. The same effect occurs in the occasion of the transition to a new reference base, when the most relevant innovations are introduced, or when non-ordinary interventions are performed. In all these cases, to guarantee coherence of all the occurrences of the time series, linking factors are normally calculated on overlapping periods and applied to the quarters estimated in old methodology.
