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 2026
2.2. Metadata last posted
12 June 2026
2.3. Metadata last update
12 June 2026
3.1. Data description
Indices of Number of employees and self-employed persons.
These indicators are produced by the Oros survey, through the integration of Social Security administrative data (collected by the Social Security Institute-INPS), used mainly for small and medium enterprises (SMEs) and the survey data on labour input and labour costs on the large enterprises used for large enterprises survey (LES). Data from these two surveys allow the calculation of the employee component. The number of self-employed persons is estimated using Annual National Accounts data, quarterly disaggregated using as indicators quarterly data on the number of self employees from the Labour Force Survey (LFS).
3.2. Classification system
NACE Rev.2
3.3. Coverage - sector
Divisions: B05-B09 ( B05 and B07 not sent, due to absences (B05) or an insignificant number (B07) of employees and self-employed persons), C10-C33, D35, E36, G45-G47, H49-H53, I55-I56, J58-J63 and G47 excluded 473
Sections: B, C, D, F, G, H, I, J, L, N and the M_STS (aggregation of 69, 70.2, 71, 73 and 74)
Migs: Intermediate goods (MIG_ING), Energy (MIG_NRG), Capital goods (MIG_CAG), Durable consumer goods (MIG_DCOG), Non-durable consumer goods (MIG_NDCOG).
Aggregate: B to E36 (BTE36) and H to N excluded K (HNXK).
Services (from H to N excluded K): 32% self-employed persons, 68% employees.
3.5. Statistical unit
Reporting and observation unit: Enterprise. For some large enterprises reporting and observation units are KAUs.
3.6. Statistical population
All enterprises with at least one employed or self-employed person, which were active at least one day in the reference quarter in the NACE economic activities of the coverage sectors.
In 2024, according to the last available Business Register version, these enterprises were, on average, about 3.9 millions:
371,808 for Industrial sector
544,886 for Construction sector
984,743 for Retail sector
1,964,439 for Services sector.
(source: BR-ASIA)
3.7. Reference area
Geographically the STS indicators on the Number of employees and self-employed persons cover the whole country. Activities outside the country are excluded.
3.8. Coverage - Time
The indicators, classified by NACE Rev.2, are available since 2000.
3.9. Base period
The base year is 2021=100.
Index.
Quarter.
6.1. Institutional Mandate - legal acts and other agreements
The number of employees and self-employed persons 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 the Number of persons employed, 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 the 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, other than with Eurostat. Data at this level 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.
Since the delivery of May 2019 indices on the number of employees and self-employed persons are delivered to Eurostat as free, at all Nace levels.
8.1. Release calendar
At the moment data on the number of employees and self-employed persons 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. Since the delivery of May 2019 (first quarter of 2019) data are sent as "free".
This kind of indices is not released at National level.
Quarterly (to Eurostat).
10.1. Dissemination format - News release
Not available.
10.2. Dissemination format - Publications
At the moment data are not released.
10.3. Dissemination format - online database
Not available because data are not released.
10.4. Dissemination format - microdata access
Not available because data are not released.
10.5. Dissemination format - other
Data are transmitted to Eurostat quarterly, within 60 days from the Regulation deadline 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: "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. “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., The system of short term business statistics on labour in Italy. The challenges of data integration (INE Baldi et al).
A document on the method used to estimate the self-employed component according to the new methodology, introduced since the delivery of May 2018 can be found in the following paper: M. Lattanzio “The new methodology for the STS indicator on the “number of persons employed” (link RIEDS – Vol. LXXIV-1 January-March 2020 – SIEDS).
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, n.5. (Istat files).
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, link ISTAT archive).
11.1. Quality assurance
At the basis of the production of the STS indicator, 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. Italy adopted in 2010 the Italian Code of Official Statistics 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. In addition, the compilation of indicators on the total number of employees requires the integration with auxiliary statistical sources to get the self-employed component. At this stage, only macro validation is possible implying, 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 see: Congia M.C. and Rapiti F.M. 2010 “Quality assessment and reporting in a short-term business survey based on administrative data” (Istat files).
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 STS requirements are fulfilled. Since May 2019 (first quarter of 2019) data on employment at all Nace levels are not more confidential but released as free.
13.1. Accuracy - overall
Assessing accuracy on the indicator on the Number of employees and self-employed persons, compiled using admin data integrated at micro level with survey data for the employee component and with macro data from the National Accounts, temporally disaggregated using LFS data, for the self-employee component, implies taking into account several aspects. First of all the indicator is compiled with a different methodology for the employee and self-employee component.
For the employees component, that is estimated using admin and survey data the following arguments can be considered. The massive quantity of administrative micro data (used for SMEs) covers almost the 93% in terms of number of jobs. By the way it requires, however, a very careful processing phase of Editing and Imputation, since if the non-response for the total economy is not relevant, it is not the same for the different economic sector. 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.
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 19% 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.
For the self-employees component, estimated using annual National Accounts data, distributed at quarterly level using LFS data through a model approach, problems of accuracy may emerge from the use of sampling estimates, that are the LFS ones, and from the estimation of the parameters of the disaggregation model used. This source of potential error is monitored, using appropriate statistics and controlling for revisions caused by the model's specification, and measuring its contribution on the quality of the overall indicator.
13.2. Sampling error
The only use of sampling estimates is in the calculation of the self-employee component of the indicator, in particular for the temporal disaggregation of the NA annual data and its sectorial disaggregation for which once more LFS data are used.
13.3. Non-sampling error
Measurement errors on the administrative data have affected very few units during the years and, during the last fifteen 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 99%; 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 2025 non-reporting enterprises were approximately 1.2% 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.
As far as the estimates of the self-employed component, as described in §3.1, the model identification is one of the source for possible revisions of already published data. It was calculated that the contribution of the model definition to the divergence between a first and a final estimate is negligible on the total, with small values in some quarters of the year. Highest values are recorded in the delivey of May, when revised NA annual data are included in the estimates. In this case it is not possible to isolate the impact of the NA data revisions in the model identification (parameters) from those of the annual data at the basis.
14.1. Timeliness
Preliminary: about 60 days after the end of 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
Number of employees and self-employed persons 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 total number of employees and self-employed persons 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 the Number of employees and self-employed persons indicators are constantly compared with figures drawn from the National Accounts and from the Labour Force Survey.
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 "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) for the employees component, National Accounts and LFS data for self-employees. None of the auxiliary Surveys used to calculate the STS indicators requires additional information.
17.1. Data revision - policy
Due to the revision policies of the sources used for the compilation of Number of employees and self-employed persons indicators, it is useful to distinguish revisions of the employees and self-employees components.
As far as it concerns the estimation of the employees’ part, 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 (May);
non-standard revisions (es. transition to a new base year).
For what concerns the estimation of the number of self-employed persons, as stated in §3.1, data from the Annual National Accounts and from the quarterly LFS are used:
National Accounts annual data on the last 3 years are included in the Oros estimates once a year, in occasion of May release. According to the Annual NA revision policy, in this occasion, the last 3 years are revised, implying a revision of the number of persons employed up to three years. In all the other releases, the self-employees quarterly estimates are not affected, even indirectly, by the NA data;
LFS normally are not submitted to revisions, but in the year 2021 the new Labor Force Survey adopted the updates introduced by the European Regulation (2019/1700). As regards the self-employed persons, the insertion of new LFS data affected insignificant and negligible revisions such as not to require the application of linking factors on the overlapping periods;
the inclusion of new NA data affects the parameters of the models used in the quarterly disaggregation, influencing the entire time series. For the scope of the number of persons employed indicators only the last three years of revisions due to the model's specification are considerend, frozening the previous when negligible.
As a synthesis indices on the number of employees and self-employed persons are revised according to the following schedule:
release of May (1rst quarter): last three years;
release of August, November, March (2nd-4th quarter): last four quarters.
The same revision policy, for each component, is applied both to data released nationally and to data transmitted to Eurostat.
An internal database of vintages exists and can be made available at request.
17.2. Data revision - practice
In the release of May 2025, with the first transmission of Q1:2025 the average revisions of the the year on year growth rates for the total, calculated with respect to the previous data transmission and considering 9 vintages (first vintage is the first release of the indices expressed in base 2021 occurred in May 2024), were the following:
at division level: MAR=0.9%; MR=0.4% RMAR=0.1%
at section level: MAR=0.4%; MR=0.1%; RMAR=0.3%
at MIG level: MAR=0.2%; MR=0.0%; RMAR=0.2%
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
As far as it concers the employees component, data drawn from the Oros and Large Enterprises surveys are used.
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.
The number of self-employees are estimated disaggregating at quarterly level Annual National Accounts data, using as reference indicators quarterly figures on the employees drawn from the LFS.
18.2. Frequency of data collection
For the employees component:
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.
For the self-employees component:
annual National Accounts data are acquired once a year and processed quarterly to get estimates on the total number of persons employed
LFS data, used to perform the quarterly disaggregatione of NA data, are acquired quarterly.
18.3. Data collection
Micro data are used only for the estimation of the employees component. For self-employees macro data are acquired at 2-digit Nace.
Employees 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 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
The production process that drives to the compilation of the number of employees and self-employed persons for STS consists of two main steps: the estimates of the employee component and the estimates of the self-employee ones.
Step1 - Employees component's estimate
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 employment: 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. 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.
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 (by collecting high detailed information on short-time working allowance, continuous contacts with the enterprises etc.) 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, 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.
Step 2 - Self-employees component's estimates
The Oros and the LES sources allow the estimation of the number of employees. In order to get estimates on the remaining part of the number of persons employed, i.e. the number of self-employed persons, Annual National Accounts data and quarterly LFS data are also used. Until February 2018 NA data were quarterly disaggregated using as indicators the quarterly employment estimates by the Oros+LES data. Since the delivery of May 2018 a new disaggregation methodology has been introduced. The new methodology includes two major improvements: 1) the exploitation of a new source for the estimation of the quarterly dynamic of self-employees, that is the number of self-employed persons produced by the Istat Labour Force Survey (LFS), used to temporally disaggregate the NA annual number of self-employees; 2) a new method for the temporal disaggregation, based on a mixed use of the Chow-Lin regression model, applied at an aggregated level, and a Preserving Quarterly Structure method, for the NACE disaggregation (2-digit NACE level).
For further 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. Rome. (in english "The Oros quarterly survey on employment and labor costs: indicators and methodologies", Statistical readings – Methods, (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 Number of employees and self-employed persons indices are available only in unadjusted form.
Not requested.
Indices of Number of employees and self-employed persons.
These indicators are produced by the Oros survey, through the integration of Social Security administrative data (collected by the Social Security Institute-INPS), used mainly for small and medium enterprises (SMEs) and the survey data on labour input and labour costs on the large enterprises used for large enterprises survey (LES). Data from these two surveys allow the calculation of the employee component. The number of self-employed persons is estimated using Annual National Accounts data, quarterly disaggregated using as indicators quarterly data on the number of self employees from the Labour Force Survey (LFS).
Services (from H to N excluded K): 32% self-employed persons, 68% employees.
Reporting and observation unit: Enterprise. For some large enterprises reporting and observation units are KAUs.
All enterprises with at least one employed or self-employed person, which were active at least one day in the reference quarter in the NACE economic activities of the coverage sectors.
In 2024, according to the last available Business Register version, these enterprises were, on average, about 3.9 millions:
371,808 for Industrial sector
544,886 for Construction sector
984,743 for Retail sector
1,964,439 for Services sector.
(source: BR-ASIA)
Geographically the STS indicators on the Number of employees and self-employed persons cover the whole country. Activities outside the country are excluded.
Quarter.
Assessing accuracy on the indicator on the Number of employees and self-employed persons, compiled using admin data integrated at micro level with survey data for the employee component and with macro data from the National Accounts, temporally disaggregated using LFS data, for the self-employee component, implies taking into account several aspects. First of all the indicator is compiled with a different methodology for the employee and self-employee component.
For the employees component, that is estimated using admin and survey data the following arguments can be considered. The massive quantity of administrative micro data (used for SMEs) covers almost the 93% in terms of number of jobs. By the way it requires, however, a very careful processing phase of Editing and Imputation, since if the non-response for the total economy is not relevant, it is not the same for the different economic sector. 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.
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 19% 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.
For the self-employees component, estimated using annual National Accounts data, distributed at quarterly level using LFS data through a model approach, problems of accuracy may emerge from the use of sampling estimates, that are the LFS ones, and from the estimation of the parameters of the disaggregation model used. This source of potential error is monitored, using appropriate statistics and controlling for revisions caused by the model's specification, and measuring its contribution on the quality of the overall indicator.
Index.
The production process that drives to the compilation of the number of employees and self-employed persons for STS consists of two main steps: the estimates of the employee component and the estimates of the self-employee ones.
Step1 - Employees component's estimate
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 employment: 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. 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.
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 (by collecting high detailed information on short-time working allowance, continuous contacts with the enterprises etc.) 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, 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.
Step 2 - Self-employees component's estimates
The Oros and the LES sources allow the estimation of the number of employees. In order to get estimates on the remaining part of the number of persons employed, i.e. the number of self-employed persons, Annual National Accounts data and quarterly LFS data are also used. Until February 2018 NA data were quarterly disaggregated using as indicators the quarterly employment estimates by the Oros+LES data. Since the delivery of May 2018 a new disaggregation methodology has been introduced. The new methodology includes two major improvements: 1) the exploitation of a new source for the estimation of the quarterly dynamic of self-employees, that is the number of self-employed persons produced by the Istat Labour Force Survey (LFS), used to temporally disaggregate the NA annual number of self-employees; 2) a new method for the temporal disaggregation, based on a mixed use of the Chow-Lin regression model, applied at an aggregated level, and a Preserving Quarterly Structure method, for the NACE disaggregation (2-digit NACE level).
For further 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. Rome. (in english "The Oros quarterly survey on employment and labor costs: indicators and methodologies", Statistical readings – Methods, (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).
As far as it concers the employees component, data drawn from the Oros and Large Enterprises surveys are used.
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.
The number of self-employees are estimated disaggregating at quarterly level Annual National Accounts data, using as reference indicators quarterly figures on the employees drawn from the LFS.
Quarterly (to Eurostat).
Preliminary: about 60 days after the end of the reference quarter. Final: about 1 year and 60 days after the end of the reference quarter.
Number of employees and self-employed persons 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 total number of employees and self-employed persons 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.
