Italian National Institute of Statistics (ISTAT)
DIRECTORATE FOR SOCIAL STATISTICS AND POPULATION CENSUS

Department for Statistical Production (DIPS)

Directorate for Social Statistics and Population Census  (DCSS)

Integrated System for Labour, Education and Training (SSE)

 Indices of Number of persons employed and Gross wages and salaries (STSIND, STSCONS, STSRTD, STSSERV).

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 (LES) used for large enterprises (LEs). Data from these two surveys allow the calculation of the wages and salaries indicators, while data on the number of employees, drawn from the two surveys, are integrated with National Accounts data, in order to get the estimate on the number of persons employed.

NACE Rev. 2.

Annex A: divisions 05-36;

Annex B: section F;

Annex C: division 47;

Annex D: divisions 45-46 and sections H, I, J, M_STS, N_STS

Number of persons employed and Gross wages and salaries are defined in coherence with Commission Regulation EC No 1503/2006.

In 2017 self-employed persons are about the 30% on the total number of persons employed.

Reporting and observation unit: Enterprise

Number of persons employed: all enterprises with at least one employed person which were active in the reference quarter in the STS economic activities. In 2017, these enterprises were, on average about 4.5 millions.

Gross wages and salaries: all enterprises with at least one employee which were active in the reference quarter in the STS economic activities. In 2017, these enterprises were, on average over the four quarters, about 1.2 millions.

Geographically the STS indicators on Number of persons employed and Wage and salaries cover the whole country. Activities outside the country are excluded.

The indicators classified by the NACE Rev. 2 are available since 2000.

The base year is 2015=100.

 Index

Quarter

Legal basis: Decreto Ministeriale 05.02.1969 and Decreto Ministeriale 24.02.1984. Obligation on units to provide data: The enterprises are obliged to pay the social contribution 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.

None. Data at this level are shared only internally at Istat.

Links to relevant acts on statistics are presented on the website of Sistan - National Statistical System – (http://www.sistan.it/index.php?id=203).

All the employment indicators are treated as confidential.

Gross wages data are free at all Nace levels.

At the moment data are not released.

No calendar because no release

Data are transmitted to Eurostat by teletransmission.While data on employment are transmitted in confidential form, data on gross wages are free. Data are not released at National level.

Quarterly (to Eurostat)

Not available.

At the moment data are not released.

Not available because data are not released

Not available because data are not released

Data are transmitted to Eurostat quarterly, within  60 days from regulation deadline for what concerns employment indicators and 90 days from deadline referring to gross wages, both in Gesmes format.

 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 http://siqual.istat.it/SIQual/visualizza.do?id=0026500 and

http://siqual.istat.it/SIQual/visualizza.do?id=5000065

Other sources and methodologies for the estimation of gross wages and salaries indicators and number of employees indicators are described in the following Istat methodological document: “Il sistema degli indicatori congiunturali sulla domanda di lavoro e le retribuzioni in Ateco 2007 e base 2005” (see Doc.1 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?” (see Doc.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 (see Doc.3).

At the moment no published documentation is available on the method used to estimate the total number of persons employed. Until March 2013 this indicator was approximated by the number of employees, available from the Oros+LES. In order to extend the definition to the total number, National Accounts Annual data have been used together with the Oros data. Internal documentation on the procedure followed is available at request.

Not available specifically on the STS indicators production process.4

Some 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. Link: https://www.istat.it/it/files//2018/07/doc_5_2010.pdf.

Congia M.C., Pacini S., Tuzi D. (2008a), “Quality Challenges in Processing Administrative Data to Produce Short-Term Labour Cost Statistics”, Proceedings of Q2008 European Conference on Quality in Official Statistics, Rome. Link: http://q2008.istat.it/sessions/paper/29Congia.pdf.

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. Link: http://www.unece.org/fileadmin/DAM/stats/documents/2008/04/sde/wp.8.e.pdf.

Piras M.G., Tuzi D. (2018), “Revision Policy and Revision Analysis in Short-term Statistics at Istat: Recent Developments in Standardization and Communication Innovations”, Proceedings of Q2018 European Conference on Quality in Official Statistics, Krakov. Link: https://www.q2018.pl/papers-presentations/Speed_Talk_Session05.

The data are produced in accordance with the European Statistics Code of Practice and with its Italian version

(http://www.sistan.it/fileadmin/Repository/Home/QUALITA_E_SVILUPPO/CODICE/Codice_italiano_delle_statistiche_ufficiali.pdf).

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. At the end of the process, a macro data validation is carried out which implies, among other aspects, time series analysis and macro-level comparisons with other statistical sources. 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. “Quality assessment and reporting in a short-term business survey based on administrative data” available at the link: http://q2008.istat.it/sessions/paper/22Congia.pdf and Congia M.C., Pacini S., Tuzi D. “The Editing Process in the Italian Short-Term Survey on Labour Cost based on Administrative Data” available at the link: http://www.unece.org/fileadmin/DAM/stats/documents/2008/04/sde/wp.8.e.pdf.

Eurostat is the main user. Data produced are coherent with the requests of the STS regulation.

The data are considered satisfying the STS regulation requests by Eurostat.

All STS requirements are fulfilled. However data on employment at all Nace levels and on wages at two digits level (and more) are still confidential.

Assessing accuracy in the Oros and LEs Surveys implies taking into account only non-sampling errors. Both the data sources refer to the census of the target units.

Estimations are not based on samples.

 Punctuality always achieved. 

Employment and wages indicators are defined in coherence with Commission Regulation (EC)  No 1503/2006. The data cover the entire national territory.

OROS-LES have been subjected to several methodological innovations over the time. In order to guarantee coherence in the time series, linking factors have been calculated on overlapping periods and applied to the quarters estimated in old methodology.

Quarterly National Accounts data, Quarterly Labour Force Survey data. Comparisons with Structural Business Data on annual basis. 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.

Good coherence between indicators, known the differences in methodology, concepts, definitions etc.

Employment and wages indicators for STS do not follow the same revision policy: even if the estimates are calculated in a unique process, the addition of NA data in the estimates of the number of persons employed, in particular the number of self-employed persons, implies some differences in the replacing time of the final data to the preliminary ones..

In the estimates of Gross wages and salaries and on the Employees’ part of the total number of persons employed 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).

For what concerns the estimation of the number of self-employed persons, as stated in §3.1, data from the Annual National Accounts are used. Data on the last 3 years are included in the Oros estimates once a year, with the release of June. 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 others releases, only data on the current year are submitted to revisions.

An internal vintage database exists and can be made available at request. 

In the release of June 2017, with the first transmission of Q1:2017 and according to the standard Oros revision policy, all the quarters of 2016 both on employment and wages figures have been revised. Only for the number of persons employed, via the estimates of the self-employed part (see §17.1) the last three years have been revised:

The average revisions of the indices on the number of persons employed with respect to the previous data transmission (March 2017) were:

• at division level: 1.9;
• at section level: 3.5;
• at MIG level: 1.9.

The main cause of these high revisions are the self-employed estimates’ revisions that, in the release of June, incorporate the revised NA data.

Wages and gross salaries indices were revised, with respect to March 2017 transmission:

• at division level: 0.1;
• at section level: 0.;
• at MIG level: 0.1.

Administrative data are used for SMEs and a questionnaire for large enterprises.

The Social Security Institute (INPS) stores administrative data in electronic format and delivers them to ISTAT using inter-institutional electronic transmission. Data referring to the large enterprise survey are collected monthly by questionnaire via website, e-mail or fax

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.

Once administrative data are captured an extensive and complex check and editing procedure covers the whole Oros production process (for more details see: Congia M.C., Pacini S., Tuzi D. “The Editing Process in the Italian Short-Term Survey on Labour Cost based on Administrative Data”  http://www.unece.org/fileadmin/DAM/stats/documents/2008/04/sde/wp.8.e.pdf. The translation of administrative data into statistical information is the first step: check and imputation is necessary not only to correct administrative micro data but also to modify translation procedures and metadata errors to follow frequent changes in administrative concept and definitions. Once the statistical variables have been made available a more traditional micro level check procedure becomes necessary. Outliers correction may consequently be needed, particularly in the case of influential observations. Check and imputation of the outliers in the LES data follow a traditional interactive process, in which there can be a direct feedback with the firms. For the administrative INPS data extensive edit procedures check for accuracy and consistency. Given the enormous number of records to check, the editing procedures are very selective. The automatic editing is utilised in very few cases. The micro editing process includes the comparison of the values of all variables available (e.g. number of employees, wages per employee, other labour costs, etc.) reported for two consecutive months by the same unit to detect large changes or errors in reporting. Whenever there is a very large change or the data appear inconsistent in any of the variables, the records are flagged. Subsequently, the records flagged at least in one variable are ordered for size of wages and other labour costs changes. The record are displayed and checked on the screen, which shows a very complete dashboard with all information about the unit under control helping the editor to understand if the anomalies are errors or just outliers. They can be acknowledged and values are further treated as correct. Then the errors are manually corrected if necessary. In principle this process ought to allow to make reliable statistics, however some errors in small size units remain undetected, and some could show up only after micro-data aggregation. The preliminary population is extremely large but the number of units which are singled out as relevant outliers is very little. Furthermore, measurement errors in the administrative data have gradually disappearing over the time.

At the scheduled time for the acquisition of the provisional population, it may happen that some INPS data are missing for the preliminary estimates due to delays depending on firms liability or administrative system flaws. These late reporters are considered unit non-responses. Imputation of non-reporting units at unit level is, at the moment, performed only on a sub group of units of the Oros target population, the temporary employment agencies, because of their strong relevance in terms of employment: the absence of even few of these units in the provisional population may impact even on the target per capita variables estimates. Because no alternative sources are available on these units, actually they are not included in the target population of the LES, imputation is the way as unit non-responses are adjusted. Imputation of these units is highly based on longitudinal criteria, using all the available information on the units to be imputed themselves over the time. Correction for non-reporting on the rest of the population of the administrative data is performed through a macro level approach that exploits the reporting and non-reporting behaviours over the time.

For what concerns LES, the methodology of estimating missing data (unit and item non responses) works on a deterministic basis. It uses both information of clusters defined in terms of economic activity and time series data of the enterprise itself.

The integration between the administrative data on SMEs and the survey data on LEs is one of the final step in the process and is justified by the fact that large firms were underrepresented in the early reporters  used for the Oros preliminary estimates. The integration procedure aims at replacing admin with survey data for the overlapping enterprises. This implies record linkage, singling out of corporate events, that must be monitored quarterly, harmonization of variables. For more details on the integration of the two sources see: 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”http://www.ine.es/e/essnetdi_ws2011/ppts/Baldi_et_al.pdf.

The Oros and the LES sources allow the estimation of wages and salaries number of employees indicators. In order to get estimates on the remaining part of the number of persons employed, Annual National Accounts data are also used. These data are quarterly disaggregated using as indicators the quarterly employment estimates by the Oros+LES data.

The indices of employment are available only in unadjusted form.