ISTAT -Italian National Institute of Statistics

Directorate for Economic Statistics (DCSE)

Via Tuscolana 1788, 00173 Rome, Italy

The Community Innovation Survey (CIS) is a survey about innovation activities in enterprises. The survey is designed to collect the information on different types of innovation,  various aspects of the development of an innovation, objectives of innovation activities, sources of information, public funding or expenditure on innovation.  It is aim is to measure the innovativeness of sectors and enable the analysis of the factors of innovation.

 The CIS provides statistics by type of innovators, economic activities and size class of enterprises. The survey is currently carried out every two years across the EU Member States, EFTA countries and EU candidate countries.

In order to ensure comparability across countries, Eurostat together with the countries developed a Harmonised Data Collection (HDC) questionnaire accompanied by a set of definitions and methodological recommendations.

CIS 2018 concepts and its underlying methodology are based on the Oslo Manual (2018) 4th Edition. 

New review of the CIS2018  aims to meet several objectives :

1: Reduce subjectivity and biases in the main CIS indicators

2: Improve reporting about innovation activities and capabilities in the firm

3: Ensure international comparability (including compliance with the OM4)

4: Broaden the basis CIS information on enterprise management

5: Take better account the diversity of enterprises in the EU

6: Improve reporting about external drivers and enablers of innovation

7: Improve timeliness

8: Ensure the feasibility of data collection

9: Ensure continuity with the CIS 2016

10: Improve reporting about the output and impact of innovation

CIS2018 is conducted under Commission Regulation No 995/2012. This Regulation defines the mandatory target population of the survey referring to enterprises in the Core NACE economic sectors (see section 3.3.) with at least 10 employees. Further activities may be covered on a voluntary basis in national datasets. Most statistics are based on the 3-year reference period (t, t-1, t-2), but some use only one calendar year (t or t-2).

Please consider CIS t to be the survey that refers to the same year of the quality report and CIS t-2 to be the previous survey e.g.: CIS  2018= CIS t then, CIS t-2=CIS 2016

Indicators related to the enterprises are classified by country, economic activity (NACE Rev. 2), size class of enterprises and type of innovation.

The main typology of classification of enterprises in reference to innovation is the distinction between innovation-active enterprises (INN) and not innovation-active enterprises (NINN).

The enterprise is considered as innovative (INN) if during the reference period it successfully introduced a product or business process innovation, had ongoing innovation activities, abandoned innovation activities or was engaged in in-house R&D or R&D contracted out. Non-innovative (NINN) enterprises had no innovation activity mentioned above whatsoever during the reference period.

CIS covers main economic sectors according to NACE Rev.2 broken down by size class of enterprises and type of innovation activity.

In accordance with Commission Regulation 995/2012 on innovation statistics, the following industries and services are included in the core target population. Results are made available with these following breakdowns :

All NACE – Core NACE (NACE Rev. 2  sections & divisions B-C-D-E-46-H-J-K-71-72-73 )

CORE INDUSTRY (excluding construction) (NACE Rev. 2 SECTIONS B_C_D_E)

10-12: Manufacture of food products, beverages and tobacco

13-15: Manufacture of textiles, wearing apparel, leather and related products

16-18: Manufacture of wood, paper, printing and reproduction

20: Manufacture of chemicals and chemical products

21: Manufacture of basic pharmaceutical products and pharmaceutical preparations

19-22: Manufacture of petroleum, chemical, pharmaceutical, rubber and plastic products

23: Manufacture of other non-metallic mineral products

24: Manufacture of basic metals

25: Manufacture of fabricated metal products, except machinery and equipment

26: Manufacture of computer, electronic and optical products

25-30: Manufacture of fabricated metal products (except machinery and equipment), computer, electronic and optical products, electrical equipment, motor vehicles and other transport equipment

31-33: Manufacture of furniture; jewellery, musical instruments, toys; repair and installation of machinery and equipment

D: ELECTRICITY, GAS, STEAM AND AIR CONDITIONING SUPPLY

E: WATER SUPPLY; SEWERAGE, WASTE MANAGEMENT AND REMEDIATION ACTIVITIES

36: Water collection, treatment and supply

37-39: Sewerage, waste management, remediation activities

CORE SERVICES (NACE Rev. 2 sections & divisions 46-H-J-K-71-72-73)(NACE code in the tables = G46-M73_INN)

46: Wholesale trade, except of motor vehicles and motorcycles

H: TRANSPORTATION AND STORAGE

49-51: Land transport and transport via pipelines, water transport and air transport

52-53: Warehousing and support activities for transportation and postal and courier activities

J: INFORMATION AND COMMUNICATION

58: Publishing activities

61: Telecommunications

62: Computer programming, consultancy and related activities

63: Information service activities

K: FINANCIAL AND INSURANCE ACTIVITIES

64: Financial service activities, except insurance and pension funding

65: Insurance, reinsurance and pension funding, except compulsory social security

66: Activities auxiliary to financial services and insurance activities

M: PROFESSIONAL, SCIENTIFIC AND TECHNICAL ACTIVITIES

71: Architectural and engineering activities; technical testing and analysis

72: Scientific research and development

73: Advertising and market research

71-73: Architectural and engineering activities; technical testing and analysis; Scientific research and development; Advertising and market research

The Italian survey covered the following non Eu-core sectors: NACE Rev. 2 section F and NACE Rev. 2 divisions 45, 47, 70, 74.

In accordance with Commission Regulation 995/2012 on innovation statistics, the following size classes of enterprises according to number of employees are included in the core target population of the CIS:

• 10 - 49 employees
• 50 - 249 employees
• 250 or more employees

In the Italian Cis2018 size classes are defined by the number of persons employed and not by the number of employees.

The description of concepts, definitions and main statistical variables is available in CIS 2018 European metadata file (ESMS) Results of the community innovation survey 2018 (CIS2018) (inn_cis11) in Eurostat database.

Legal unit

Core target population are all enterprises in CORE NACE activities (see 3.3.1) with 10 or more persons employed.

NUTS level 2

Several rounds of Community Innovation Survey have been conducted so far at two-year interval since end of 90’s.

*two reference periods can be distinguished for CIS light: 2000-2002 and 2001-2003

Not relevant.

CIS indicators are available according to 3 units of measure:

NR: Number for number of enterprises and number of persons employed.

THS_EUR: Thousands of euros. All financial variables are provided in thousands of euros, i.e. Turnover or Innovation expenditure.

PC: Percentage. The percentage is the ratio between the selected combinations of indicators.

For CIS 2018, the time covered by the survey is the 3-year period from the beginning of 2016 to the end of 2018.

Some questions and indicators refer to one year — 2018.

The list of indicators covering the 3-year period and referring to one year according to the HDC is available in the Annex section of the European metadata (ESMS). 

CIS surveys are based on the Commission Regulation No 995/2012, implementing Decision No 1608/2003/EC of the European Parliament and of the Council on the production and development of Community statistics on science and technology.

This Regulation establishes innovation statistics on a statutory basis and makes the delivery of certain variables compulsory e.g. innovation activities, cooperation, development, expenditures and turnover (see the Regulation). Each survey wave may additionally include further variables. 

In addition, the Regulation defines the obligatory cross-coverage of economic sectors and size class of enterprises.

Decreto del Presidente della Repubblica 20 maggio 2019, di approvazione del Programma statistico nazionale 2017-2019– Aggiornamento 2018-2019 e dei collegati elenchi delle rilevazioni con obbligo di risposta per i soggetti privati e dei lavori per i quali la mancata fornitura dei dati configura violazione dell’obbligo di risposta sanzionata ai sensi degli artt. 7 e 11 del decreto legislativo 6 settembre 1989, n. 322 (S.O. n. 30 alla Gazzetta Ufficiale 16 luglio 2019 - serie generale - n.165)

Not requested.

CIS data are transmitted to Eurostat via EDAMIS using the secured transmission system.

- Decreto legislativo 6 settembre 1989, n. 322, “Norme sul Sistema statistico nazionale e sulla riorganizzazione dell'Istituto nazionale di statistica” – art. 6 (compiti degli uffici di statistica), art. 6-bis (trattamenti di dati personali), art. 7 (obbligo di fornire dati statistici), art. 8 (segreto d'ufficio degli addetti agli uffici di statistica), art. 9 (disposizioni per la tutela del segreto statistico), art. 11 (sanzioni amministrative), art. 13 (Programma statistico nazionale).

- Decreto legislativo 30 giugno 2003, n. 196 “Codice in materia di protezione dei dati personali”.

- Regole deontologiche per trattamenti a fini statistici o di ricerca scientifica effettuati nell’ambito del Sistema statistico nazionale - Delibera del Garante per la Protezione dei dati personali n. 514 del 19 dicembre 2018.

Confidentiality flags are when just one or two enterprise dominate the data.

The last statistical release was the 17th of December 2020: L'innovazione nelle imprese-Anni 2016-2018 (istat.it).

Not available.

Istat’s dissemination policy is oriented towards different target groups: citizens, who want data on the country, researchers, who require statistical classification and “ad hoc” analyses, students, surveys respondents and, of course, the media.

The Institute has set up different channels for each target, in order to better meet the requests for information.

Press releases are issued according to a fixed press release calendar. The press releases are simultaneously distributed by e-mail to institutions, all media and press members and research institutes, and are published on this website.

CIS is conducted and disseminated at two-year interval in pair years.

Accessibility and clarity refer to the simplicity and ease for users to access statistics using simple and user-friendly procedure, obtaining them in an expected form and within an acceptable time period, with the appropriate user information and assistance: a global context which finally enables them to make optimum use of the statistics.

Regular press releases linked to the data. The last one is available on the following website:  L'innovazione nelle imprese-Anni 2016-2018 (istat.it)

-              Online database (containing all/most results) : Istat Statistics

-              Analytical publication (referring to all/most results) : L'innovazione nelle imprese-Anni 2016-2018 (istat.it)

-              Analytical publication (referring to specific results, e.g. only for one sector or one specific aspect) : 

An online database is available on the following website: dati.istat.it

Not requested.

Microdata are disseminated through the Laboratory for Elementary Data Analysis (ADELE)- It is a “safe” environment in which researchers from universities or research institutions or bodies to which the Code of conduct and professional practice applying to processing of personal data for statistical and scientific purposes applies may conduct statistical analyses that require the use of elementary data, where information already available with other tools is not sufficient (I.Stat data warehouse). Within the Laboratory, data security and statistical confidentiality are guaranteed by the control of both the working methods and the results of the analyzes conducted by the users. Once the processing is complete, the output is evaluated in terms of statistical confidentiality by the experts of the ADELE Laboratory. Only results that positively comply with the Rules for the release of results can be issued. 

Microdata are also disseminated through microdata files, that are collections of elementary data. ISTAT releases microdata files free of charge.

No other data dissemination was done

Not requested.

Information on target population, sampling design, data collection and data treatment, weights calculation method, dissemination of the data is available in the Report published at every edition of the survey's edition. The last one is available at the following website: L'innovazione nelle imprese-Anni 2016-2018 (istat.it)

Not requested.

Clarity is difficult to assess and relates to the quality of statistical metadata which are disseminated alongside a statistical product. In effect, it refers to the extent to which the metadata satisfy users needs. Assessment requires information from both the producer for the description of the accompanying information and from the user, for assessing the adequacy and appropriateness of such information for future use.
Please comment on your users feedback on clarity, the available accompanying information to the data, the assistance available to users

A full technical and practical support is given to facilitate users understanding and usability of Italian CIS data. To our knowledge, users are quite satisfied with clarity of aggregated data and micro-data made available and their accompanying information.

Istat quality policy is consistent with the European quality framework developed by Eurostat, and transposes its main principles and definitions. The endorsement in 2005 of the European Statistics Code of Practice (last revised in 2017) established the principles to be applied in order to ensure and strengthen both the trust and the quality of the European Statistical System. Essential points of Istat quality policy are:

• Process quality: consisting in the production of accurate statistical information efficiently and effectively;
• Product quality: consisting in the dissemination of high-quality timely statistical data which are relevant for the users, also the potential ones;
• Documentation: consisting in the storage and availability of information necessary not only for a proper use of data but also to ensure transparency in all the production activities of statistical data;
• Respect for respondents: consisting in the reduction of response burden and in the respect of respondent’s privacy;
• Strengthening of statistical literacy: consisting in promoting a proper use of statistical information in policy-making to better support decisions and policies;
• Users’ orientation: consisting in making statistical information easily accessible and understandable and in satisfying user needs as much as possible.

This edition of Cis was fully conducted via Web through a on line questionnaire. In particular, we tested a new electronic data capture, a generalized system for aided development and monitoring of web surveys called GINO++ [much more than Gathering INformation Online] that allows the survey manager himself (that is without software developers) to perform three key phases of a survey: designing, capturing and monitoring.

About the response rate, we reach a 70,1% considering the EU core target population. As a whole (considering the Nace section F and other non Eu-core service sectors), even if we didn't reach the 70% of respose rate, we had good results, after cleaning the initial sample from the frame and coverage errors, especially due to the out-of-scope and dead units. In particular, we got a total response rate of 65,7%.

About the accuracy of the data, the main perceived problems in CIS2018 are confirmed in the quantification of the turnover from innovative products (Question 3.3), the innovation expenditures (Question 3.10), other enterprise's enpenditures (Question 4.6). For what concerns the question with the highest non response rate (innovation expenditures), direct contacts with respondents confirmed, as in the previous editions, that some respondents consider the collection of such detailed information a very difficult and time-consuming activity.

Relevance is the degree to which statistics meet current and potential users needs. It includes the production of all needed statistics and the extent to which concepts used (definitions, classifications etc.) reflect user needs. The aim is to describe the extent to which the statistics are useful to, and used by, the broadest array of users. For this purpose, statisticians need to compile information, firstly about their users and their needs.

The CIS is based on a common questionnaire and a common survey methodology, as laid down in the 3rd edition of Oslo Manual (2005 edition), in order to achieve comparable, harmonised and high quality results for EU Member States, EFTA countries, Candidates and Associated countries.

Since one of our main purposes is to fulfil the needs defined at European level (by adopting a questionnaire as consistent as possible with the Eu one) and since one of the main priorities of our institute is to minimize as much as possible the statistical burden on respondents, we chose to be in line with the Eu questionnaire and to not introduce relevant changes if not really necessary for our various institutions or communities.

Italian National Institute of Statistics

No user satisfaction survey was undertaken.

The data are available for all the compulsory cells in the standard CIS 2018 output tabulation, except for the Nace 12 (included in the Nace 11). The voluntary cells that were missing refer to Non-Eu core Nace division that were not covered in the Italian survey (such as agriculture and some services).

Information on all the variables required by standard CIS 2018 output tabulation was provided.

Not requested.

Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).

That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.

The main indicator used to measure sampling errors for CIS data is the coefficient of variation (CV).

Coefficient of Variation= (Square root of the estimate of the sampling variance) / (Estimated value)

Formula:

where

The coefficient of variation is the ratio of the square root of the variance of the estimator to the expected value. It is estimated by the ratio of the square root of the estimation of the sampling variance to the estimated value. Sampling design, weighting and changes of strata of sampling units are taken into account in the estimation of the sampling variance.

Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.

Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.

Not requested.

Not requested.

Coverage errors arise mainly from over-coverage. The main discrepancies between target and frame population include: out-of-scope units, dead units, changes of strata, changes in address.

The estimation of the CVs have taken into account the sampling design and the changes of strata of sampling units.

Measurement errors occur during data collection and generate bias by recording values different than the true ones. The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.

No measure for reducing measurement errors.

Non response occurs when a survey fails to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.                                                                                                                                                                                              

There are two types of non-response:                                                                                                                                                                                      

1) Unit non-response, which occurs when no data (or so little as to be unusable) are collected about a population unit designated for data collection.                                                                                                                                                                      

a) Un-weighted unit non-response rate (%) = 100*(Number of units with no response or not usable response) / (Total number of in-scope (eligible) units in the sample)                                                                                                         

b) Weighted unit non-response rate (%) = 100*(Number of weighted units with no response or not usable response) / (Total number of in-scope (eligible) units in the sample)                                                                                                            

2) Item non-response, which occurs when only data on some, but not all survey data items are collected about a population unit designated for data collection.                                       

a) Un-weighted item non-response rate (%) = 100*(Number of units with no response at all for the item) / (Total number of eligible, for the item, units in the sample i.e. filters have to be taken into account) 

See below.

Un-weighted and weighted unit non-response rate by NACE categories and for enterprises with 10 or more persons employed

The number of eligible units is the number of sample units, which indeed belong to the target population.

Two reminders are undertaken before coding an enterprise as non-responding.

See below.

Item non-response rate for new questions in CIS t (in Core NACE: B-C-D-E-46-H-J-K-71-72-73 enterprises with 10 or more persons employed)
 

Web techniques were used for CIS 2018 data capturing. Respondents - through their browsers - can access an electronic questionnaire, put on the Istat web site (https://imprese.istat.it) and fill in it online. The data capture technique used in this edition is a generalised system called GINO++ [much more than Gathering INformation Online] that allowed - without software developers - to design, capture and check on the data entered and monitor the progress of the survey in real-time. It allowed thus to insert some types of hard or soft rules associated with some variables to help in preventing from non sampling errors. With regard to the checking rules which were activated in the electronic questionnaire, we decided to contain them and to limit the hard checks (which prevent the respondent from going on without correcting his errors) so to minimise the risk that respondents give up to fill in the form. Further, we introduced interactive edits so to ask respondents for checking the entered data (and correcting them if necessary) in order to minimise the follow up from the operators. In particular, the system enabled/disabled the possibility of filling in some fields and activated some blocking checks on the questions of the Section 3 and some non-blocking consistency checks on Sections 2 and 4.

Not requested.

Timeliness and punctuality refer to time and dates, but in a different manner.

The timeliness of statistics reflects the length of time between data availability and the event or phenomenon they describe.

Timeliness of national data – date of first release of national level : 17 December 2020

Not requested.

Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.

Date of transmission of complete and validated data to Eurostat (Number of days between that date and 30 June 2020) : 0 

Comparability aims at measuring the impact of differences in applied statistical concepts and definitions on the comparison of statistics between geographical areas, non-geographical domains, or over time.

The coherence of statistical outputs refers to the degree to which the statistical processes by which they were generated used the same concepts (classifications, definitions, and target populations) and harmonised methods. Coherent statistical outputs have the potential to be validly combined and used jointly.

The Community Innovation Survey still suffers from some critical methodological drawbacks when it is used for regional analyses. One of the key question has to do with the adoption of the ‘enterprise’ used as statistical unit for data collection. The problem here is to what extent this methodological choice can represent ts the best one for measuring the regional dimension of innovation. The CIS gives the status of innovative to the enterprise as a whole and the criterion used for the regionalisation of the CIS data consists of assigning the overall innovation activity to the region where enterprises’ headquarters are located. This introduces a significant regional bias. Such a problem is especially found in multi-plant enterprises, whose innovation activities can be spread across different regions. More precisely, if regionalization is straightforward for enterprises with only one local unit, it becomes problematic for those with several local units in different regions. According to the traditional regionalisation approach, the enterprises with establishments in more than one region are considered as performing all their innovation activities in the region of the head office. It means that the local units involved in innovation activity and placed in other regions turn out undetectable with the consequence that the survey fails to take into account where innovation activities really take place. In other terms, the conventional CIS regional attribution of innovation activities thus might lead to biased results of the actual spatial distribution of innovation and, namely, lead to an underestimation of the innovation activities of those regions which host productive units of enterprises whose head offices are located elsewhere. A case in point is in out country, where many firms located in the Northern regions have production facilities in the South: this approach would not allow the innovation capabilities in the South to emerge.

Not requested.

Methodological deviations from the CIS Harmonised Data Collection (HDC)

Methodological deviations from the CIS Harmonised Data Collection (HDC)

Due to important methodological changes in CIS 2018 driven by Oslo Manual 2018, the data 2018 cannot be directly compared with previous CIS waves.

Not requested.

See the comparison between SBS and CIS data in the section 15.3.3 below.

Not requested.

Not requested.

This part compares key variables for aggregated CIS data with SBS data
Definition of relative difference between CIS and SBS data: DIFF = (SBS/CIS)*100

Comparison between SBS and CIS data (relative difference) by NACE categories and for enterprises with 10 or more persons employed

* Numbers are to be provided for the last year of the reference period (t)

Not requested.

Confidential information on the production cost of the CIS.

Not requested.

Not requested.

Not requested.

See below.

The sampling frame is the official statistical business register, called ASIA (Archivio Statistico delle Imprese Attive - statistical business register of active enterprises). ASIA provides both the key variables for the stratification (number of employees, NACE economic activity, NUTS geographical information) and the identification characters (enterprise name, address, etc.). Since the most updated version of Asia (reference year: 2018) was used, the CIS2018 survey universe consisted of all the profit enterprises and independent professional units active in 2018.

The sample survey is based on a stratified random sample with equal inclusion probabilities for all population units. The target population with less than 250 persons employed was broken down into strata. The stratification was made taking mainly into account the study-domains for the output tabulation defined at European level.
The stratification variables to be used are:
1. the economic activity in accordance with NACE Rev. 2. Stratification by NACE was done at two-digit (division) level, except for section F;
2. the enterprise size according to the number of persons employed. The size-classes used were the following ones: between 10 and 49; between 50 and 249; 250 and more;
3. the regional variable. The breakdown of national territory into regions was performed on the basis of the NUTS level 2.
A multi-variable and multi-domain sample allocation was used. The adopted procedure was an application of the Bethel algorithm [Bethel, J. (1989) Sample allocation in multivariate surveys. Survey Methodology, 15: 47 -57]. It was an optimum allocation since it aimed at minimizing survey costs under the constraint that sampling errors of estimates of each variable of interest didnt exceed the given upper bounds assigned to each of them. For the pursuit of the best allocation, three auxiliary variables were used: number of persons employed, turnover and total innovation expenditure. In particular, previous CIS 2016 results were used, all referring to the year 2016.
To keep the response burden down, a coordinated selection technique (Jales sampling) was adopted in order to avoid the inclusion of the same enterprises in the sample over time [Ohlsson, E., B. G. Cox, D. A. Binder, B. N. Chinnappa, A. Christianson, M. J. Kott e P. S. Colledge (eds.). Coordination of samples using permanent random numbers. In Business Survey Methods. Wiley, New York, 1995].

Variables and indicators filled or prefilled from other sources.

According to the Commission Regulation (UE) 995/2012, the innovation statistics shall be provided to Eurostat every two years in each even year. The data collection takes place every second year in year t-2 preceding the data provision.

See below:

It is a mandatory survey included in the National Statistical Programme that regulates the production of official statistical information.

It is a combination of sample and census survey.

The census refers to the enterprises with 250 + persons employed. For the rest of population, a stratified random sample has been built.

The census refers to the enterprises with 250 + persons employed. For the rest of population, a stratified random sample has been built.

Data collection method

Not requested.

Operations performed on data to derive new information according to a given set of rules.

Imputation is the method of creating plausible (but artificial) substitute values for all those missing.

Definition of imputation rate:

Imputation rate (for the variable x) (%) = 100*(Number of replaced values) / (Total number of values for a given variable)

Definition of weighted imputation rate:

Weighted imputation rate= 100*(Number of total weighted replaced values) / (Total number of weighted values for a given variable)

Imputation rate for metric variables by NACE categories and for enterprises with 10 or more persons employed:

(1) = Total turnover in the last year of the reference period (t) (TUR)

(2) = Share of the turnover in the last year of the reference period (t) due to new or improved product new to the market in the total turnover for product innovative enterprises TUR_PRD_NEW_MKT/TUR(INNO_PRD)

(3) = R&D expenditure performed in-house (EXP_INNO_RND_IH)

Weights calculation method for sample surveys

Calibration estimators methodology, currently applied at Istat, was used for the estimation process [Deville, J.C. and Srndal, C.E. (1992) Calibration estimators in survey sampling, Journal of the American Statistical Association 87, 367.382]. It can be applied to the extent that the known totals of some auxiliary variables, strictly correlated to the variables of interest, are available. These calibration estimators have the following properties: they are more efficient than the direct estimators because of the auxiliary information used; they reduce the bias effect due to the non-response and the under coverage; they produce estimates of auxiliary variables that equal the known totals of such variables.
The final weights are obtained by adopting the following procedure: an initial weight is assigned to each sampled unit with reference to the sampling plan as the reciprocal of the inclusion probability. Two correction factors for initial weights are then calculated: a first one is the unit non response factor; a second one is to satisfy equality between estimation of auxiliary variables and known totals from the Register. The final weights are thus obtained as the result of the product between initial weights and correction factors. For CIS, as well as for most of the business surveys, number of enterprises and number of persons employed were used as auxiliary variables, according to the information provided by the Italian Official Business Register ASIA.
The software used was GENESEES, a generalised software implemented in SAS language by ISTAT researchers, available for all users from ISTAT website: http://www.istat.it/strumenti/metodi/software/produzione_stime/genesees/index.html .
For detailed information, please contact: Mariagrazia Rinaldi: mrmarina@istat.it.

Not requested.