The French Business Register (SIRENE) · The French Business Register (SIRENE) France may be the...

Courrier des statistiques, English series no. 3, 1997 3

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E) France may be the only country to maintain a business

register designed for both statistical and administrativepurposes. This is a considerable advantage, as it is a way toensure high standards of information quality as well asprompt updates.

The first links in the management chain are the form-filingcenters (Centres de Formalités des Entreprises: CFEs) whereenterprises are required to report all «vital» events, notablystart-up, cessation, and status changes. The CFEs pass thisinformation on to the relevant legal, tax, social-insurance,or statistical bodies. One such organization is INSEE, theofficial statistics institute, which uses the inputs to updatethe national business register known as SIRENE.

The information collected by the CFEs does not alwaysdisplay the quality, accuracy, and detail needed to makea good statistical register. Moreover, the CFEs are not in aposition to gather certain statistically useful data. Lastly,some information either does not circulate via the CFEs, oris not reported by enterprises (when they go out ofbusiness). To make up for these deficiencies, INSEE iscontinuously improving the register, checking its contentand enhancing it with information on enterprises and localunits—for the benefit of all users.

Achieving «total quality» for the two facets of theregister—the administrative and the statistical—would betoo costly. Some compromises are therefore required.

In the administrative register, the information on each unitmust be of comparable quality and validity, whatever theunit’s size. In the statistical register, this criterion isconsiderably relaxed. Because of their potential impact onthe levels of the indicators compiled from the data, onlythe largest units need regular detailed checks. Informationon small units is verified only at an aggregate level.

As the body responsible for both the administrative and thestatistical management of the register, INSEE was obligedto strike a balance between the two standards. For theadministrative aspects, the principle of equal treatment forall units is enforced for the statutory data included in theregister. For the other kinds of information, «statistical» testsare performed. As it happens, because statistical analysesare being carried out at ever more detailed levels,small-unit groupings are converging in scope toward theindividual units. Administrative and statistical checks are

4

therefore tending to merge—but, for the same reason,management costs are rising as well.

The statistical information in the register is useful not only toprofess ional stat i s t ic ians, but to legal , tax, andsocial-insurance agencies as well. INSEE’s value added isrecognized by tax offices and units of the social-protectionsystem, which use the register identification numbers (SIRENfor enterprises and SIRET for local units) to track the unitsunder their supervision. They can thus make the mostefficient and cheapest use of the data in the register. Thiswell-established trend gathered momentum in the early1990s thanks to INSEE’s all-out drive to promote the register’sstatistical applications. The trend was further fueled by theso-called Madelin Law of 1993, which mandated the use ofa single identification number in dealings betweenenterprises and government departments.

Reciprocally, the use of a common identifier in alladministrative data bases makes it possible to search thosebases for information that will facilitate SIRENE checkingand enhancement, even for small units. This flexibility hashelped curb management costs and improve quality.

The value added by INSEE offers benefits to theadministrative system but, even more so, to statisticians fortheir sampling frames, the interlinking of administrative databases, and demographic studies on firms and local units.Naturally enough, the Institute is capitalizing on this valueadded by making the register available to the public inappropriate ways that comply with existing laws andregulations.

These various aspects of the SIRENE register and its uses arediscussed in the following pages. The opening article, by thepresent author, describes the register’s place in the Frenchadministrative system. Catherine Bernard details the dualmanagement of SIRENE, for administrative and statisticalpurposes. Dalila Vienne and Antoine Santolini deal with theissues involved in making selected information available tothe public. Jean-Baptiste Herbet concludes with adiscuss ion of ODISSEE, the future tool for publ icdissemination of the register data.

Hugues PicardHead of the Enterprise and Local-Unit

Register and Demography UnitINSEE

The inter-departmental system and SIRENE

With the decree of March 18, 1981, France established an innovative administrative systemto expedite paperwork for business firms. The legislation introduced units called Centres deFormalités des Entreprises (CFEs)—one-stop centers for reporting information on the «vitalstatistics» of enterprises and their local units (the official list of reportable events runs toseventy).

When a listed event occurs, the firm’s head or representative reports it to the relevant CFE,providing the basic information along with supporting documents. The Center forwards thematerial, directly or indirectly, to the appropriate units of the legal, tax, social-insurance, orstatistical system according to the needs and statutory requirements of each.

Typically, these inputs are sufficientto feed the recipients’ data bases.This minimizes paperwork, sincethere is no need to query the firmagain after it has reported the eventto the CFE. Some agencies,however, do ask for further details(a) when the information calls forspecial ized processing that isunavailable at the CFEs, whose staffare by definition generalists, or (b)when the information would be tooabundant for the initial report.

The choice of CFE is spelled out inthe legislation. Artisans (in France, acategory that includes certain typesof small entrepreneurs) have toreport to the CFE of their tradebodies (Chambres de Métiers).Indiv iduals and legal ent i t iesengaged in trade, as wel l asconsortia, have to file with a CFE at aChamber of Commerce andIndustry. Non-trading firms andsales agents have to report to aCommercial Court registrar.Professionals and employers whodo not f i t in any of the abovecategor ies must contact theagencies in charge of collectingemployers’ social-security andfami ly-benef i ts contr ibut ions(URSSAF). Local tax offices will takeforms from other reporting entitiesprovided they are registered for VATor subject to corporate income tax.

CFEs process the followinginformation on start-ups andchanges in business structure:

- for individuals: first name, lastname, address of registered office orlocal unit, home address, date ofbir th, mari tal status, marr iagecontract (setting out arrangementswith regard to asset ownership),activities of the enterprise, numberof employees, business name,characteristics (principal activity,address) of enterprise’s registeredoffice, and tax exposure (VAT,corporate income tax);

- for legal entities: name, addressof registered office, legal status,principal activities, business name,number of employees, capital,durat ion of company, accountclosure date and accounting period,names and t i t les of corporateoff icers and other personsaccountable for the company’sdebts without t ime l imitat ions,characteristics of registered office(for enterprises), and tax exposure(VAT, corporate income tax).

This informat ion must besupplemented, as appropriate, bysupporting documentation such astranscripts of vital records, a copy ofthe corporate charter, and othercertificates.

CFEs play a limited role. They aredeemed to have been contacted ifthe following information has beenreported:

- last and first name (individuals);organization name (legal entity)

- address of local unit (registeredoffice)- principal activit(ies)- number of employees.

A CFE cannot reject a report thatincludes the above data, even if itthinks they are mistaken. If any itemsare lacking, the Center can hold onto the report for five days to allow thereporting unit to make the necessarycorrect ions. After the f ive-daywaiting period, the CFE is required toforward the informat ion to i tsassociated organizations.

There are many direct and indirectrecipients of the data gathered bythe Center. The diagram p. 6 gives ahighly simplified view of them.

One of the recipients, INSEE, is incharge of coding certain informationi tems (legal status, pr incipaleconomic activity) and supplyingidentification numbers for newbornent i t ies (SIREN numbers forenterprises, SIRET numbers forlocal units). INSEE forwards thecoded informat ion, direct ly orindirectly, to the same recipients as


the CFEs, and to others as well. Thediagram p. 6 shows some of the«flow lines» from INSEE.

Issues concerning CFEs, theiroperations, and their relations andcommunicat ion with partnerorganizations are discussed by aspecial commit tee of theCommission for PaperworkReduction (COSIFORM).

Among other projects, theCommission is sponsoring thedevelopment of electronic data

interchange between CFEs and theirpartners. The goal is to reduce theuse of paper as far as possible, whilediminishing the risk of error. Theelimination of paper requires thelegalization of electronic signaturesand data encryption in France—astep that is unlikely to be taken forsome time.

As part of i ts plan to promotecomputer ized exchanges,COSIFORM has drafted operatingrules for CFEs that def ine theinformation to be gathered, its

proper arrangement, and therecipients of each data item. In fact,these rules enable Centers both toprepare a message for electronictransmission and to compile paperdocuments that can be sent toassociated organizations.

The inter-departmental system forenterprise «vital statistics» is nowalmost 15 years old. It has fulfilled itsinitial purpose, which was to lightenthe reporting burden for enterprises.It can be made even more reliableand eff ic ient by an ever moreintensive use of computer networks,and, eventually, by the elimination ofphysical signatures and supportingdocuments. Will it be possible to goeven farther down this road, forexample by enabl ing CFEs toreceive forms from any kind ofenterprise, or increasing the amountof informat ion col lected fromreporting units? This would requiremulti-specialist staff—a possiblesolution, but an expensive one intraining terms.

A comparison with practices in othercountries shows that the smoothfunctioning of the French system isdue to two factors: (1) the presence,at its core, of a high-quality registeravailable to all participants; (2) theuse of a single identification number(for enterprises and local units) in theregister and in user data bases. Ahigh-qual i ty register is thus aprerequisite for an efficient system.

Hugues PicardHead of the Enterprise and Lo-

cal-Unit Register and DemographyUnit

INSEE

H. Picard

6

Some of the CFE associated organizations and their links(simplified flow chart)

Note : for a complete identification of the acronyms, see the glossary p. 16.

The SIRENE directory

France’s national identification register of enterprises and their local units (RépertoireNational d’Identification des Entreprises et de Leurs Établissements : SIRENE for short) existsin its present form since 1973, but traces its origins back to a business register set up in 1942.SIRENE records the vital data of enterprises (such as activities, number of employees, andlegal status) for a twofold purpose: administrative and statistical. Each enterprise and localunit is assigned a single, mandatory identification number. This makes it easier to match allinformation sources.

In this article, Catherine Bernard describes SIRENE’s missions and data inputs, its role asinformation redistributor, and its main features (notably its coverage, the definition of unitsand variables observed, and updating procedures). She concludes with a discussion of its useas a sampling and analysis data base for a wide variety of organizations and purposes.

SIRENE is derived from a businessregister set up in 1942 and managedby employers’ trade associations. Itacquired its present form in the1970s.

A decree of March 14, 1973, createdthe Répertoire Nationald’Identification des Entreprises etde Leurs Établissements—SIRENEfor shor t . The purpose of theregister is to record the vital data onbusinesses: last name, first name,date and place of birth for soleproprietors, or company name forincorporated firms; also recordedare the acronym, legal status,reg is te red of f i ce , s ta r t -up orcessation date, and, for local units,the date o f , and reason for ,start-up.

The decree also requires SIRENE toassign to each unit a size indicatorbased on the number of employees,and an indicator denoting the unit’sactivit(ies).

Even more important is theassignment of a single number toeach unit handled by the register.The number assigned to anenterprise is called SIREN (box 1)and comprises nine non-significantdigits, the last being a control key.For each local unit (box 1), an extra

5-digit string called NIC is added, thelast digit being a check digit. Thetotal 14-digit sequence is called theSIRET number.

Registrations are made withoutdouble counting, since at each newaddition to the list the SIRENEoperator searches the vital data for asimilar unit. This characteristic is thestrength of the French system.

A dual mission:administrative tool

and statistical source

Each enterprise and local unit is thusassigned a single identifier, whoseuse is mandatory for all governmentdepartments and all enterprises.The number makes it easier tomatch administrative sources. Thismatching facility offers substantialbenefits for statistical work, since itgives access to a large amount ofindividual employment and tax datawithout having to perform surveys.

SIRENE also acts as a redistributor ofinformation across the administrativesystems. As a result, it institutes a defacto standardization of the objectshandled and of the informationvariables transmitted, such as namesand addresses.

Ini t ia l ly establ ished for inter-departmental purposes, SIRENEsoon found its scope enlarged toinclude new types of units and manyvariables for use in statistical anddemographic studies.

The legal units and local units (box 1)—"objects" common to al l theadministrative systems—do notnecessar i ly correspond to theobservation units that statisticianswould like to handle. For this reason,the register also manages two otherunits: the enterprise derived from thelegal unit and the local economic unitderived from the local legal unit.

SIRENE is the bridge betweenadministrative units and statisticaluni ts. Administrat ive sourcesprovide information on the existenceof legal units, avoiding the need for acensus—that is, a direct f ie ldobservation of all units at regularintervals.

From the type of economic activity,the legal status, and the listing oflocal units and employees, one candeduce the "economic consistency"of the legal unit, hence the existenceof the enterprise.

As a comprehensive register ofenterprises and local units, SIRENE



is an ideal reference frame foreconomic survey sampling, andserves as a major base for nationaland regional studies of a structuralnature.

The day-to-day recording ofenterprise and local-unit movementsenables statisticians to constructdemographic series and monitor unitlives.

The register ’s dual mission —statistical and administrative— has

given it a commercial role, which is topromote the disseminat ion ofSIRENE identification numbers inprivate information systems.

The administrative and statisticalmissions are not governed byt h e s a m e r e q u i r e m e n t s a sregards deadl ines, content,comprehensiveness, and quality.The fact that the two sets ofrequirements are interwoven forcesthe system to comply with the mostbinding constraints, in particular the

administrative obligation to recordevents as they occur.

The management needs of all thepartner government departmentsare addressed by a set of unified andinterlinked concepts. In principle,these are not the most relevant forstatistical purposes. However, it isthe administrative information-gathering units that act as datasuppl iers—part icular ly foremployment stat ist ics and theUnif ied System of Enterpr iseStatist ics (Système Unif ié desStatistiques d’Entreprise: SUSE).

The adoption of the singleidentification number is thereforethe keystone of the system ofenterprise statistics.

Diagram A (p. 10) outl ines thesystem for gathering statisticalinformation on the economy, andhighlights SIRENE’s crucial role asthe foundation of that system.

The European regulation of July 22,1993, required all member States tocompile a statistical register ofenterprises and local units, witheffect from January 1, 1996.

France, Sweden, and Denmarkmaintained such registers prior tothe regulation. The Netherlands also(and mainly) t racks uni ts of adifferent, statistical type, known asKind of Activity Units (KAUs). TheUni ted Kingdom has troubletracking local units and small units.Spain and I ta ly have veryincomplete registers. Belgium,Luxembourg, Germany, Greece,Ireland, and Portugal are currentlysetting up their registers.

An original approachto data input

The SIRENE decree of March 14,1973, and the accompanyingregulations designate organizationscalled "associates," to which all legalunits/enterprises must report as partof their administrative obligations.

C. Bernard

8

Basic principles and definitions

RegisterA register is a reference list for a population. It is therefore comprehensiveand identifies each unit without omissions or double counts.It contains few information items, but these are listed systematically for eachunit and updated regularly.By contrast, a data base contains much information, but on a limited numberof units, that is, a sub-population.

Enterprise (SIREN)The European Community regulation of March 15, 1993, defines theenterprise as "the smallest combination of legal units that is anorganizational unit producing goods or services, which benefits from acertain degree of autonomy in decision-making ..."An enterprise is an individual or legal entity engaged for his, her, or its ownaccount in a non-payroll activity using production factors such as buildings,labor, machinery, and customer bases. For this purpose, the enterpriserequires at least one legally recognized entity (the legal unit) in order to beable to own assets, sign contracts, and undertake legal proceedings.The legal unit recorded in SIRENE is often improperly called an enterprise.For a legal unit to qualify as an enterprise, it must have economicconsistency, in other words, it must implement production factors. SIRENEcontains 4.1 million legal units. If one excludes farmers, public and privatebodies, financial enterprises, and the 2.3 million enterprises ingoods-producing industries, trade, and services, one is left with more thanone million units that lack economic consistency. Registered for purelyadministrative purposes, these units are known as "singular units" (unitéssingulières).

Local legal unit (SIRET)A legal unit (enterprise) may engage in its activity in one or more locations(factory, office, store) that are topographically distinct and identified. Theseentities are called local units (établissements). There are 4.7 million of themin SIRENE. An enterprise may have several local units, but a local unitcannot belong to more than one enterprise. A local unit is called "ordinary"when it displays economic consistency, and "singular" otherwise.

Local economic unit (ETEC)The local economic unit (ETEC) denotes a set of local production factors(employees, machines, customers, etc.). It is used by an enterprise, not by alegal unit. It is recorded only when the local legal unit is of the "ordinary" type.The ETEC is distinguished from the local legal unit by a separate continuitytreatment. The ETEC is identified by a 9-character string, the first characterbeing the letter E and the last a key. Let us take, as an example, a productionunit that moves from number 1 to number 10 on the same street. Theregister will record a local-unit closure at number 1, followed by a local-unitopening at number 10. The SIRET number will thus change, while the ETECnumber will remain the same since the production factors have not changed.

1

Among these "associated"organizations, one network plays aleading role: the Centres deFormalités des Entreprises(CFEs), l i teral ly "centers forbusiness paperwork." Each year,INSEE regional offices receive 1.4million reports from this source.

The 1,500 CFEs have beenregulated since 1981 by a decreespelling out their role and operatingprocedures. They act as "one-stop"centers where enterprises arerequired to report when they start up,undergo changes, or go out ofbusiness. Before 1981, enterpriseshad to make the rounds of all thebodies that were empowered tomonitor their activities (the legalregister, the tax office, agenciescollecting employers’ contributions,statistical agencies, etc.). At eachvisit, the company had to fil l inredundant forms. The establishmentof CFEs was a major step towardsimplification. It has resulted in astandardization of reporting forms—which are now identical throughoutthe administrative system—and al ighter report ing burden forenterprises.

Each CFE has a well-defined area ofresponsibility that depends on itssupervisory ministry.

CFEs have thus been set up inchambers of trade for reporting byartisans, self-employed retailers,and craft enterprises; in chambers ofcommerce for reporting by traders,retail/wholesale companies, andindustrial firms; in commercial courtregistr ies for non-tradingpartnerships and consort ia; inURSSAFs (see glossary p. 16) forprofessionals, employers, and soleproprietors; and in tax offices fornon-profit groups and other units.

The latest arrivals among SIRENEassociates include Mutualité SocialeAgricole, a farmers’ mutualinsurance organization, which is incharge of forwarding data fromfarmers.

The reports gathered from these

networks are used to initialize theident i f icat ion data. The othervariables managed for statisticalpurposes are compi led fromsurveys, such as the annualbusiness surveys. They can also beobtained from statistical processingby the operator when entering thenew unit in the register. SIRENE isalso enhanced by inputs fromadministrative sources, notably thetax system and the social-insurancesystem.

An efficient redistributor ofinformation

SIRENE is not just a repository ofinformation. It is even more importantas a redistributor of information.Indeed, SIRENE is the key vehicle fordisseminating the single enterpriseidentification numbers.

By law, each system partner andeach enterprise must be informed ofany change in the vital data recordedin the register.

In compliance with this requirement,more than 12,000 pages, 2,800certificates, and 18,000 notices aresent daily from the Computer Centerin Nantes, not counting the 40monthly tapes and electronictransfers that account for the largestflows.

The register records more than twomillion operator-initiated transactionmovements a year and three timesas many in batch mode—that is,computer-programmed movementsfor such tasks as updat ingemployment or sales figures.

Al l SIRENE "associates" areprompt ly informed of newregistrations and changes in existingones. The associates include legalregisters, tax author i t ies,social-insurance agencies, locallabor-ministry offices, and evensubscribers to the commercialinformation service.

It should also be noted that someassociates pass the information on

to their own partners, thus feedingsecond- and third- leveldissemination subnetworks.

The key element is, of course, theSIREN registration number. It is anintegral part of the registrationnumber for business and traderegistries, and for the intra-EUidentification number for trade ingoods and services. It is also usedas a management key in manypublic and private data bases suchas IBM, SFAC, and Visa. Thereport ing uni t , meanwhi le, isrequired to use the number onletterheads, in advertising material,and in correspondence withgovernment agencies.

Comprehensive coverage

The register’s scope of coveragereflects the areas of responsibility ofthe associates. It thus comprises allenterprises regardless of their legalstatus or type of activity, as well aspublic and private bodies. Farmingenterprises, only a portion of whichwere registered until now, are beingexhaustively recorded thanks to thedata forwarded by MSA. Coverageof the farming sector should havecaught up with that of othereconomic sectors by 1997.

By 1997, therefore, the register willcover all economic agents in France.

SIRENE coverage of thepublic sector

In 1983, the register’s coverage wasextended to the public sector. Thedecree of February 20, 1983, liststhe government departments andbodies empowered to receivereports from (1) public legal entitiesexempt from registering in theRegistre du Commerce et desSociétés (these includeÉtablissements Publics à CaractèreIndustriel et Commercial [EPICs], aform of State-run enterprise, andRégies, a term applied to certain publiccorporations and to State monopolies),



C. Bernard

10

Diagram A - Dissemination of SIRENE identification numbersin statistical and administrative applications*

UNEDIC - ASSEDIC

SSP, CP, FINESS, RNE, ...

DDTE, CANAM, ORGANIC, CANCAVA, CRPL, IRCANTEC

CCI - CM - Court registrars

CNAVTS CRAM

URSSAF

DGI

S

IR

EN

E

DADS - TDS

Employmentstatistics

Income

Update of payroll statistics

O

C

E

A

N

BRIDGE

EAE

DARES

EB

SUSE

IPVI

CA3

IPI

Principal-activity (APE) enumeration

Other surveys/population census

Identification, MCA

Other users of commercially distributed data

National accounts

BRC

- national- regional

Labor-force surveyLocal surveys

(Credit-card companies, D & B, IBM, SFAC, water boards, etc.)

Economicsurveys

CA3 indexes

Statisticalapplications

Flows:

S

I

R

E

N

E

Administrativeapplications

statistics

*For a key to acronyms, see glossary p. 16.

and (2) central-governmentinstitutions and departments. TheSIRENE associates designated forthis purpose are the prefectures, theregional welfare offices (DRASSs),regional education authorit ies,government accounting bodies, andthe Defense Ministry.

The public-sector equivalent of the"enterprise" unit in SIREN is called a"body" (organisme). By definition, theterm applies to the legal entities ofcentral, regional, and local government,as well as public institutions. However,central-government institutions andunits, while not endowed with aseparate legal status, are identified as"bodies" (organismes) when they enjoya quasi-autonomous legal status. Thisis the case with constitutionalauthorities, independent governmentauthorities, ministries, centraladministrative directorates, and"external" (non-central) departments,whether or not they have local (orregional) jurisdiction.

The "local unit"-type SIRETdesignation is applied to any distinctgeographic location where an activityis carried out, or for which a distinctlyidentifiable budget is assigned. Inconsequence, unlike private-sectorunits, a public-sector SIREN entitycan have several SIRET numbers atthe same address.

The registrat ion number forpublic-sector bodies is significant: italways begins with 1 or 2. The firsttwo digits denote the legal status; forbodies with local or regionaljurisdiction, the third and fourth formthe number of the départementwhere the head unit is located.

Geographic coverage

The register records units located inmetropolitan France, overseasdépartements (DOMs) (Reunion,Guadeloupe, Guiana, and Martinique),S a i n t - P i e r r e - e t - M i q u e l o n ,and—soon—Mayotte.

The register also includes foreignenterprises represented or operating

in France, as well as those withfinancial links to a French company.

SIRENE unitsand their dynamics

SIRENE manages four types of unit:the legal unit (SIREN), the enterprise(SIREN), the local unit (or local legalunit) (SIRET) and the local economicunit (ETEC). The hierarchic linksbetween these units are shown indiagram B.

To use these units correctly, it isimportant to understand theirdynamics, that is, the continuity ofthe numbers assigned to them.

The SIREN legal unit is linked tothe sole proprietor or, in the case ofan incorporated business, to thelegal status. A change of legal statuscan cause a change in the SIRENnumber. Examples include the shiftf rom a sole propr ietorship (anindiv idual) to an incorporatedbusiness form, or the shift from oneadministrative register to another.An individual, however, keeps his orher SIREN number for life. Likewise,the shift from one corporate form toanother, if both are recorded in theRegistre du Commerce et desSociétés (RCS) will not entail achange in the SIREN number. Fromthe statistician ’s viewpoint, thecontinuity rules for the legal entitygenerally ensure the continuity of theenterpr ise. Consequent ly, acompany set up as an SARL willkeep its SIREN number when itbecomes an SA.

For each SIREN unit, there alwaysexists a corresponding SIRETnumber for the unit’s registeredoffice.

A change in SIREN thusautomatically generates a change inSIRET. Moreover, the SIRET unit istied to a geographic location (anaddress). When the latter changes,the NIC number changes (seeearlier discussion in this article),hence the SIRET number changesas well.

This can be pose a problem forstatisticians when the new local unitkeeps the production factors of theold unit. The ETEC unit is thereforemanaged differently.

If the production factors—notably theworkforce and the activity—remainthe same, the ETEC number is keptregardless of any change in theSIREN or SIRET numbers.

At a given point in time, the ETEC unitand the SIRET unit are one and thesame, but they will later divergebecause their rules of continuity differ.

Variables, codes, and links

Box 2 describes some of thevariables and groups ofvariables managed in the register.The following details about some ofthe codes will be useful:

• the legal status (CJ) is codedaccording to an INSEE-managed listof about 200 categories. The list isbased on criteria such as capitalstructure, ownership of productionfactors, and business law.

• the principal activity (APE) iscoded according to the Frenchclassification of economic activities(NAF), derived from the EuropeanNACE. The NAF comprises 700headings. When an enterprise isengaged in several activities, themain one needs to be determined.This is done by applying a set of rulesaimed as establishing which activitygenerates the highest value added.

• the status variable is a key itemfor the statistician. While the mixedunit (legal unit/enterprise) may beei ther act ive or inact ive, forinter-departmental and statisticalpurposes the unit must be classifiedin both legal and economic terms. Atwo-digit code is thus used, as theuni t can be legal ly act ive yeteconomically inactive. Naturally,statisticians will use the second digit.

• the singularity code, managedat local-unit level, makes it possible



to determine whether an enterpriseexists or not. The unit is classified as"ordinary" if it produces goods andservices using production factors.Otherwise, it is called "singular." Forexample, an individual businesslessor is "singular" whereas thelessee running the business is"ordinary"; the ancillary legal unitsthat contribute to the enterprise’sactivity are "singular," while only theproducing legal unit/enterprise isdefined as "ordinary." In most of theirwork, statisticians focus on ordinaryunits.

The SIRENE register manages anumber of links between units:

• hierarchical links between thelegal unit, the enterprise, the localunit, and the local economic unit.

• l inks between legal unitsforming an enterprise.These linksare defined at local-unit level and arecalled singularity links, because theycan only join singular units to anordinary uni t . Here are someexamples:

- a joint venture known as société defait (de facto company). Legal unitsinvolved in the joint venture areregistered in SIRENE as singularunits. The register creates a linkbetween the SIRET number of theirlocal unit and the SIRET of the defacto company, which is an ordinaryunit.

- a complex enterprise with a mainlegal unit (whose registered office isan ordinary unit) and ancillary legalunits that assign staff to the locationwhere the activity takes place. Theregister creates a link between theSIRET numbers of these ancillaryunits and the SIRET number for theordinary unit registered at the sameaddress but under the main legal unit.

- situations where the ordinarymanaging ent i ty is thedecision-maker and other units takepart in operat ions, ei ther asassociates or by providingproduction factors—typically staff.

• in the public sector, SIRENEmanages:

- links documenting contributionsfrom other units over time, showingthe history of the body;

- hierarchical dependency linksbetween central-governmentbodies, in order to reconstruct theirorganization charts;

- l inks showing aff i l iat ions orsupervisory authorities of publicunits (in certain cases).

Updating the register

The vital data (i.e., identificationdata) are updated exclusively from

the administrative reports, known asl iasses (bundles), which areprocessed daily.

To update the economic andstatistical classification data, severalsources are used, includingadministrative reports—notably forthe unit’s start-up—and specialoperations undertaken by INSEE.The latter have covered the followingvariables:

• Number of employees (atDecember 31 of year n-1) of localunits. The figure is updated annuallyby means of the BRIDGE and DADSsources. This represents more than1.5 million updates a year. Theenterprise’s workforce equals thesum of the unit workforces.

• Principal activity (APE).Checked and updated continuouslyusing OCEAN, a coordinat ionresource for business surveys. Theannual enterprise surveys (EAEs)allow principal-activity checks on300,000 units a year, and on acomprehensive basis for the largestunits.

• Sales. Updated annually, with atwo-year lag, using the SUSE database.

• Status variable. Updated eitherfrom CFE reports, or from statisticalor administrative surveys based oncollations of tax records (FRP) or

C. Bernard

12

Diagram B - Ranking of the four types of units

SIREN 1 SIREN 1legal unit enterprise

(ordinary local unit)

local unit)

SIRET 1 A (ordinarylocal unit)

ETEC 1 A (local economicunit)

Change of

SIRET 1 B(ordinarylocal unit)

ETEC 1 A

legal unitSIREN 1 SIREN 1

enterpriseSIRET 1 B

ETEC 1 A

SIRET 1 C (singular

Takes overproduction factors

SIRET 1 A

Administrative

standpoint

Statistical

standpoint

Example :

local unitBirth of new local unitaddress of

Transfer to new

payroll-contribution records (CRAM,URSSAF).

• Presence variables included inthe register. Updated continuously.

They indicate— for a specif ieddate—the unit’s presence in, orabsence from, the administrativesources collated with SIRENE.Infra-annual sources such as the

monthly filings with URSSAF (BRCs)and monthly VAT returns (CA3)provide substantial, up-to-dateinformat ion for checking thepresence variables.

The SIRENE computer systemalso contains two interest ingsubsystems:

• the geographic subsystem, whichmanages the most detailed geographicitem for situating a local unit (TOPOcode). The item designates the streetin a locality (commune). Communes ofmore than 2,000 inhabitants possess aRivoli code for each street segment.The code is used by REPLIC (a table ofaddresses by building block). Thesubsystem can therefore assign abuilding-block code to each unit inSIRENE, making it possible togenerate maps. The register thus playsa central role as block-identifier for allINSEE applications using SIREN orSIRET numbers (such as EAE andSUSE). Consequently, SIRENE actsas a basis for producing researchmaps and commercial maps. TheRivoli code is updated monthly fromstreet data supplied by the official landregister (DGI). The subsystemautomatically updates unit addresses,for example when street are renamed,since the address is reconstructed atthe system output stage.

• the documentation subsystem,updated continuously.Incorporated into SIRENE, i tconsists of text f i les groupedtogether in series covering eachobject described. There are nowmore than 10,000 documentationfiles available on-line; they can beedited and distributed by anyoneworking in the statistical system.

Continuousimprovements

Start-ups of legal units are generallyreported as soon as they occur,because the legal unit often needsan identification number so that itsenterprise can actually undertake itsactivities. By contrast, many unitterminations are never reported,



Information managed by SIREN

� Legal unit/enterprise

• identification data:- registration number- personal name/company name, acronym- sex, date, place of birth for individuals- legal status.• economic classification data:- status, dates- principal activity (APE) (date and update source)- activity group- number of employees (date and update source) - number of employees at Dec.31 of year n-1- sales- economic consistency indicator (singularity code).The legal status is also a necessary complement to the activity indicator for definingthe types of enterprises included in economic surveys.• statistical data:- link group- date of start-up, termination, reactivation.For public-sector units (equity-stake and dependency links), singularity links- "economic summary" group- link with other statistical applications (OCEAN, BRIDGE, etc.)- presence in associated data bases.

� Local unit (SIRET)

• identification data:- registration number- registered office or not - "registered office [or not] + status" group- corporate name- address of activity location- mailing address.• economic classification data:- status- number of employees (date and update source)- principal activity of local unit- "activity" group- "economic summary" group- singularity code• statistical data:- date of birth, death- origin- affiliation codeThe singularity code indicates the unit’s economic consistency. The unit isclassified as "ordinary" if it produces goods and services using production factors;otherwise, it is classified as "singular."

� Local economic unit (ETEC)

The ETEC file contains:- a record of past and present unit operators (list of SIRET numbers for operatorsthat have successively used the production factors, with dates of start and end ofoperations, as well as reasons)- a record of activities successively carried out- a record of number of employees.

2

although they are officially supposedto be. A unit can therefore remainactive in the register even whenactually "dead": such a unit is knownas a false active unit.

To detect these false active units,about 200,000 administrativesurveys are conducted annually byselecting sub-populations on oneof three criteria:

• high-risk populations such asunits that have not reported anychanges in five years, units inactivefor more than a year, and so on;

• units not found in one or more taxor payroll-contribution data baseseven though they are theoreticallyl iable to a tax or contr ibut ion:examples include all employersactive in SIRENE and unrecorded inthe CRAMs;

• units open to multiplepresumptions of inactivity: if aunit ’s presence is "marked" inseveral data bases, the unit isdeemed to exist . Conversely,statisticians might regard multipleproof of non-presence as evidenceof a dormant state.

I t is important to note that thediscontinuances detected by theseoperations are economic, not legal innature. A monthly match againsttapes from the Bulletin Officiel(BODACC) allows the automaticinclusion in SIRENE of legalliquidations not reported by thecommercial-court registrars.

SIRENE: a toolfor statistical studies

SIRENE offers statisticians a wealthof opportunities that are all too oftenunfami l iar to users or evencompletely unknown.

While better communication isunquestionably needed to promoteSIRENE, statisticians’ interest in theregister will enhance its quality andpromote the development of newservices.

INSEE allocates fully 10 % of itsresources to SIRENE management,of which more than one-half forpurely statistical purposes.

Examples of applications include thefollowing:

• Sampling frame: SIRENE’scomprehensiveness and theeconomic-classification variablescovered make the register suitableas a reference base for surveysampling. It is used for this purposeby OCEAN and OCEAT.

• Reference system: applicationssuch as the population census, thelabor-force survey, SUSE, andDADSs use the register as a sourceof reference data such as principalactivity and unit status.

• Study resource: SIRENE dataprovide basic research material onspecific, localized populations. Themanagement of local-unit continuityand addresses makes it possible toanalyze business location andmigration patterns.

• Business-demography resource:the register is a key source fortracking the l ives of Frenchenterprises. The movementsrecorded are used to constructregional series for enterprisestart-ups and cessations according tovarious models. The data arepublished monthly in series J of theINSEE magazine InformationsRapides. They are also available onthe SIRENE videotext server.Supplementary infra-annual serieson employment, such as contributionrolls (BRCs), can be compared withstart-up figures to assess the impactof business start-ups on jobs.

Services offered

The offering of SIRENE-relatedservices includes:

• Survey-management resources:GESTEAR, the survey-managementsubsystem, was initially designed tosend out register-improvement

surveys and to track the replies.Today, GESTEAR is increasinglybeing used to carry out nationalsurveys (such as EPEI, SINE, andcensuses of local-communityfacilities) and regional surveys. Apopulation of enterprises or localunits can be selected by means ofappropriate tools and loaded intoGESTEAR. This allows automaticquestionnaire mailing, responsetracking, reminders, andprogress-indicator generation.Survey feedback can also beincorporated into the register itself.

• Data files: the management baseincludes many files, but their publicaccess is subject to restrictions:

. Once a year, in January, themanagement base is unloaded. Theresulting file, called REPER, forms abase for structural studies and forthe OCEAN sampl ing frame.OCEAN’s purpose is to coordinatesurveys and to help reduce thesurvey-response paperwork forsmall and mid-sized firms.

. Once a month, a f i le cal ledDOCMODIF is created byconcatenation to document thechanges in the register; this file is, inturn, reprocessed to produce thedemographic file, called DEMO.Both f i les are avai lable tostatisticians at all INSEE computercenters. Their structure is describedin the fact sheets of SYDOSI, theSIRENE documentation system.

Important points:

1 - These analytical files cannot bepassed on to third parties becausethey contain data protected byprivacy legislation applicable toadministrative records (tax offices,statistical bodies, computerizedregister of trades, etc.).

2 - The flow and stock files describedabove are used to generate thecommercial dissemination files. TheL1LEDA file, a stock file containingthe data on individual units, is notreleased to the public until the L1DIFfilters have been applied.

C. Bernard

14

3 - The many commercial-disseminationproducts based on the SIRENE registerare described elsewhere. They aregenerated from a panel data base fromwhich the following material has beendeleted: confidential or purely statisticaldata, units that have requested not to beincluded in commercially distributed files,and all Defense units.

• Access to individual data.A typical user might want to:- locate an individual unit, with littleinitial information- find out how many bakers there arein the Paris area, and how many jobsthey generate- identify a conglomerate’s parentcompany- determine the corporate structureof a group of firms

- obtain a company’s sales or exactworkforce- document the changes in an localeconomic unit since start-up.

For any of these searches, users canquery SIRENE either by dialing 3617+ SIRENE on a "Minitel" videotextterminal (in France), which suppliesthe identification number only, or viaa computer terminal that canconnect with the Nantes computercenter.

Only generic computer terminals canprovide access to al l data forgovernment stat ist ic ians. Thegeneral-public access via 3617 +SIRENE screens out confidentialdata and purely statistical data.Government statisticians can alsoaccess ETEC numbers (for localeconomic units), the individual filesthat generated changes in theregister, and the enterprise datarecorded in OCEAN.

The 3617 + SIRENE videotextaccess enables any user to obtain alegal unit’s identification number bymeans of simple or complex criteria.Navigation in the data base relies onl inks between the enterprise ’sregistered office and its local units.Several search criteria are availableto trace individuals (name and dateof birth, or name and geographiccriteria) and legal entities (companyname or trade name or acronym andgeographic cr i ter ia) . SIRENEidentification numbers can also beused.

• An automatic matching tool tomake customer files consistent withSIRENE is available at the Nantescomputer center.

This resource allows customers tocheck the accuracy of theidentification data or to enhancethem automatically with SIRENEnumbers, activity codes (APE), orother information. The facility is usedin all exchanges with administrativepartners.

The matching process uses eitherthe name and address, or theidentification number. The matchesbetween the customer l ist andSIRENE are recorded. At the end ofthe operation, the user obtains asingle response if all the data match,or a list of "close matches" that willneed to be examined depending onthe "match score" obtained.

Future developments

By the end of the 1990s, a modifiedSIRENE concept, called SIRENE III,will probably be developed. Themomentum for this change is comingfrom the telematic transfer of datafiles captured by the CFEs. INSEEwil l thus be receiv ing moreinformation, which will already havebeen structured, checked, andcoded.

Technological advances in transfermethods and computer systems willa lso impact on SIRENE ’sre-engineer ing. But the mostsweeping changes in SIRENEmanagement may well be driven bythe register’s ever-closer integrationinto the statistical system.

Significant developments under wayinclude: discussions on the use ofnew statistical units such as the"complex enterprise" and the "Kindof Activity Unit"; the designation ofreference sources; plans toel iminate the dupl icat ion ofprocessing operations such as theestabl ishment of economiccontinuity in BRIDGE, which closelyresembles the handling of ETECs inSIRENE. All these factors will bedecisive in shaping the new registerconcept.

Catherine BernardHead of SIRENE Register

Administration and EnterpriseDemography Division

INSEE



SIRENE leaflet (1995)"The only comprehensive data basefor all [French] enterprises and theirlocal units" - "SIRENE is the officialregister for 2.9 million enterprisesand 3.4 million local units"

SIRENE sectionAbbreviations and acronyms

APE: Activité Principale Exercée: principaleconomic activity of unitARDE: Division Administration du Répertoire etDémographie d’Entreprises et d’Établissements:INSEE division in charge of register anddemography of enterprises and local unitsASSEDIC: Association pour l’Emploi dansl’Industrie et le Commerce: sub-unit of UNEDICin charge of unemployment contributions andbenefitsBODACC: Bulletin Officiel d’Annonces Civiles etCommerciales: official publication of record(gazette) for civil and business noticesBRC: Bordereau Récapitulatif des Cotisations:summary of contributions paid into social-insurancesystem by employers (processed quarterly)BRIDGE: Base Relationnelle Inter-Régionale deDonnées sur les Grands Établissements:inter-regional data base on major local unitsCA3: Monthly value added tax (VAT) returnCAF : Caisse d ’Al locat ions Famil ia les:family-benefits agencyCANAM: Caisse Nationale d’Assurance Maladiedes Professions Indépendantes: nationalsickness-insurance fund for the self-employedCANCAVA: Caisse Autonome Nationale deCompensation de l ’Assurance Viei l lesseArtisanale: national old-age insurance fund forsmall crafts businessesCCI: Chambre de Commerce et d’Industrie:Chamber of Commerce and IndustryCDI: Centre Départemental des Impôts: taxoffice for département (territorial unit)CFE: Centre de Formalités des Entreprises:"one-stop" center where enterprises can fileofficial forms, returns, etc.CJ: Catégorie Juridique: legal statusCM: Chambre de Métiers: chamber of commercefor arts & crafts tradesCMR: Caisse Mutuelle Régionale: regionalmutual-insurance organizationCNAM: Caisse Nationale d’Assurance Maladie:national sickness-insurance fundCNAVPL: Caisse Nationale d’Assurance Vieillessedes Professions Libérales: national old-ageinsurance fund for self-employed professionalsCNAVTS : Caisse Nationale d ’AssuranceVieillesse des Travailleurs Salariés: nationalold-age insurance fund for payroll employeesCP: Comptabil i té Publique: public-sectoraccounting organizationCPAM: Caisse Primaire d’Assurance Maladie:local sickness-insurance fundCRAM: Caisse Régionale d’Assurance Maladie:regional sickness-insurance fundCRPL: Caisse Régionale des ProfessionsLibérales: regional sickness-insurance fund forself-employed professionalsD&B: Dun & Bradstreet (credit-rating andfinancial-information firm)DADS: Déclarations Annuelles de DonnéesSociales: annual statements of payroll data filedby private-sector employersDARES : Direct ion de l ’Animat ion de laRecherche, des Études et des Statistiques:Labor Ministry directorate for research, analysis,and statisticsDATAR: Dé légation à l ’Aménagement duTerritoire et à l’Action Régionale: French regionaldevelopment agency

DDTEFP: Direction Départementale du Travail,de l’Emploi et de la Formation Professionnelle:département-level public agency for labor,employment, and vocational trainingDGI: Direction Générale des Impôts: central taxdepartmentDOM: Départements d ’Outre-Mer: Frenchoverseas départementsDR: Direction Régionale: INSEE regional officeDRASS: Direction Régionale des AffairesSanitaires et Sociales: regional agency for healthand welfareDSE: Direction des Statistiques d’Entreprises:INSEE directorate for enterprise statisticsEAE: Enquête Annuelle d’Entreprises: annualenterprise surveyEB: Enquête de Branche: survey of specificindustryEPEI: Enquête auprès des Petites EntreprisesIndustrielles: survey of small manufacturing firmsETEC : É tabl issement Économique: localeconomic unitFINESS: Fichier National des ÉtablissementsSanitaires et Sociaux: national register of healthand social-services unitsFRP: Fichier des Redevables Permanents:national register of regular taxpayersGESTEAR : Gest ion des Enquê tesd’Amélioration du Répertoire: program formanaging surveys to improve SIRENE registerqualityGIE : Groupement d ’ Intérê t Économique:consortium (under French law)INPI: Institut National de la Propriété Industrielle:national industrial-property instituteIPI: Indice de la Production Industrielle: industrialproduction indexIPVI: Indice Prix de Vente Industriels: producerprice indexIRCANTEC: Institut de Retraite Complémentairedes Agents Non Titulaires de l’État et desCollectivités Publiques: supplementary pensionfund for non-tenured employees of central andlocal governmentMCA: Mise en Concordance Automatique:automated data-matching operationMDRS : Mise à Disposit ion du RépertoireSIRENE: INSEE divis ion in charge ofdisseminating SIRENE dataMSA: Mutualité Sociale Agricole: agriculturalmutual-insurance organizationNACE: Nomenclature Générale des ActivitésÉconomiques dans l ’Union Européenne:Classification of Economic Activities in theEuropean UnionNAF: Nomenclature d’Activités Française: newFrench classification of economic activities(1992), replaces NAPNAP: Nomenclature d’Activités et de Produits:old French classification of economic activitiesand products (1973), replaced by NAFNIC: Numéro d’Identification Complémentaire:supplementary identification number for localunitsOCEAN: Outil de Coordination des EnquêtesAnnuelles d’Entreprises: coordination tool forannual enterprise surveysOCEAT: Outil de Coordination des EnquêtesAnnuelles d’Établissements: coordination toolfor annual local-unit surveys

ODISSEE: Outil de Diffusion et d’InformationsSIRENE et de Statistiques sur les Entreprises etles Établissements: software application fordisseminating SIRENE information and statisticson enterprises and local unitsORGANIC: Organisation Autonome Nationalede l ’ Industr ie et du Commerce: nat ionalindependent federation of industrial and tradingfirmsRCS: Registre du Commerce et des Sociétés:national register of businessesREPLIC : Répertoires de Local isat ionInfra-Communaux: registers of locations on astreet-by-street and road-by-road basisRNE: Répertoire National des Établissementsd’Enseignement: national register of educationalinstitutionsRP: Recensement de la Population: populationcensusSA: Société Anonyme: a form of limited-liabilitycompany (see SARL)SAGE: Système Automatisé de Gestion desObservatoires: automated system for managinglocal data-gathering unitsSARL: Société à Responsabilité Limitée: a formof limited-liability company requiring less capitalthan an SASDIF: SIRENE dissemination softwareSEDDL: Système d’Études et de Diffusion deDonnées Locales: system for analyzing anddisseminating local dataSFAC: Société Française d’Assurance Crédit: aFrench credit-rating companySINE: Système d’Informations sur les NouvellesEntreprises: information system on recentbusiness start-upsSIREN: 9-digit SIRENE number (enterprise level)SIRENE: Système Informatisé du RépertoireNational des ENtreprises et des Établissements:computerized system handling the nationalregister of enterprises and local unitsSIRET: 14-digit SIRENE number (local-unitlevel) SSP: SIRENE Secteur Public: SIRENEsub-register of public-sector unitsSUSE : Système Unif ié des Stat ist iquesd ’Entreprise: unified system of enterprisestatisticsSYDOSI: Système Documentaire de SIRENE:SIRENE documentation systemSYNAPSE : SYstème de Nomenclaturesd’Activités et de Produits pour les Statistiquesd ’Entreprises: system of classif ication ofactivities and products for enterprise statistics

TDS : Transfert de Données Sociales:payroll-data transfer

TGF: Tabulation de Gros Fichiers: tabulation oflarge data files

UNEDIC: Union Nationale pour l’Emploi dansl’Industrie et le Commerce: agency in charge ofcollectingunemployment-insurancecontributions fromprivate-sector employers and disbursing benefits

URDEE: Unite Répertoire et Démographie desEntreprises et des Établissements: INSEE unit incharge of the SIRENE register and demographyof enterprises and local units

URSSAF: Union pour le Recouvrement descotisations de sécurité sociale et d’allocationsfamiliales: agency in charge of collectingcontributions to social-insurance and family-benefitsfunds from private-sector employers

C. Bernard

16

Making SIRENE-based productsavailable to users

In July 1994, INSEE’s Directorate General set up a new unit in its Enterprise StatisticsDirectorate (Direction des Statistiques d’Entreprises: DSE). The new entity is the "Unit incharge of the Register and Demography of Enterprises and Local Units" (Unité Répertoire etDémographie des Entreprises et Établissements: URDEE). Its duties include administering theSIRENE register, producing statistics and analysis from the register, and making registerinformation available to users.

These missions are allocated between two divisions, the "Enterprise and Local-UnitRegister-Administration and Demography Division" (Division Administration du Répertoireet Démographie des Entreprises et Établissements: ARDE) and the "Dissemination of SIRENEInformation Division" (Division Mise à Disposition du Répertoire SIRENE: MDRS).

To widen the availability of SIRENE data, INSEE has set up a National SIRENE DataDissemination Facility (Pôle National de Diffusion SIRENE) in the Institute’s Regional Office(Direction Régionale) for the Pays de la Loire. The Facility’s missions and ties to URDEE arespelled out in a formal agreement.

The MDRS Division missions maybe summarized as follows:

• To administer the data base fordisseminating information on acommercial basis. This involvesdefining the processes for creating thedistribution data bases, setting thefrequencies of the base updates, andestablishing procedures to revise theoriginal register codes as needed,enhance the data, correct anyanomalies, and continuously upgradethe dissemination application.• To design the processes andtools for disseminat ing thestatistical information derived fromSIRENE.• To monitor the dissemination ofSIRENE data among the variouscategories of users (box 1).• To act as "product manager"—i.e., to define and update theproducts and their accompanyingdocumentation, to set a price list,and to promote the products todistributors.• To manage the catalogue ofSIRENE-related products (box 2).

To perform these missionseff ic ient ly, the MDRS Divisiondef ines an annual work plan,approved by the Divis ion’ssupervisory officials. The tasks areapportioned between its own staffand the National Facility staff asspecified in the working agreement.Also, as an interface betweenproducers and distributors, theDivision works in partnership withother INSEE Directorates (engaged inrelevant missions or specializations)in the following areas:

• Billing, sales, and legal issuesrelating to distribution• Promotion• Coordination of regional initiatives;distribution and re-distribution• Maintenance, operat ion, anddevelopment of computerapplications.

Projects in progress

The ODISSEE project (for Outil deDiffusion d’Informations SIRENE etde Statistiques sur les Entreprises et

les Établissements, or "Tool forDisseminating SIRENE Informationand Statistics on Enterprises andLocal Units"). ODISSEE—whosefeatures are described in the articleby J. B. Herbet in the presentissue—is scheduled to replace thecurrent software and its satellites byearly 1997.

The revamping of the SIRENEvideotext service for businessusers (3617 SIRENE on the French"Minitel" system) is at the feasibilitystudy phase. The aim of theoperation is to offer the followingfeatures to non-INSEE usersquerying the SIRENE data base:

• ease of access in compliance withFrench Télétel standards and withthe Inst i tute’s own telemat icsstandards• development of additional salesfunctions such as placement oforders for selected productcategories, requests for salesliterature, etc.• a l ist ing of SIRENE-relatedproducts and services


• optional rerouting to other INSEEservers, notably 3617 NOMENCLA(for accessing classifications) andthe future fax server

• automatic reception by fax ofSIRENE ident i f icat ion f i les,tabulat ions, or classi f icat ionexcerpts

• connections, if possible, to Internetand file servers.

Handling large accounts. MajorSIRENE customers will be centrallyidentified. Profiles of their activitiesand needs will be established inorder to develop new standardproducts that will satisfy them more

fully and more quickly. Thanks to thistracking, other customers engagedin similar activities but who are notSIRENE users wi l l be offeredSIRENE-based services andproducts tailored to their specificneeds.

Facelifting the sales literature.The sales literature, previouslyundistinguishable from routineoff ic ia l documents, is beingrevamped. A new, attractive graphicdesign will be adopted, and thematerial will be subdivided intobrochures. These will focus onsingle product categories, and willtherefore be less bulky and bettersuited to our customers.

Improvement of SIRENE-matchingoperat ions (or automat ic l is tcollation) for non-INSEE customers.The goal is to simplify the structure ofthe files needed for the procedure.Results supplied to customers will bepresented in simpler form.Improvements in the system core(search algorithm) have also beenplanned to faci l i tate manualpost-processing of results.

Review of pricing structure. Thereview wi l l be conducted inaccordance with INSEE’s newgeneral pr ic ing guidel ines, incooperation with the DisseminationResources and MethodsDepartment.

The missions of theNational SIRENE DataDissemination Facility

The Nat ional SIRENE DataDissemination Facility was set up inAugust 1994 and reports to theInstitute’s Regional Office (DirectionRégionale) for the Pays de la Loire.As mentioned earlier, its missionsare defined in an agreement signedbetween the URDEE and theRegional Office. The Facility assiststhe MDRS Division in five areas:

• Assistance, training, andinformation for SIRENEdisseminators, i .e. , in Frenchregions, the staff of the analysis and

D. Vienne et A. Santolini

18

Customers

SIRENE customers are classified according to the type of licenses they have signed for datause:

Redistributors are allowed, under certain conditions, to redisseminate SIRENE data. Theyinclude:

• List rental agencies

• Business data redistributors, who supplement SIRENE data with other information of a legaland financial nature compiled from other sources.

Brokers sign a brokerage license with INSEE. They purchase SIRENE list excerpts on behalfof a particular customer and typically offer additional services to enhance the acquired file withother data. They can also transform the file by setting up a search program tailored to aspecific purpose such as market research or map-making software.

End-user customers sign an end-user license allowing them to exploit SIRENE data for theirown needs. These companies use SIRENE to enhance their customer, supplier, or prospectdata bases, to conduct direct advertising campaigns, or as a starting-point for their business,marketing, or promotional data bases. They are allowed to acquire usage rights for all or partof the register, to subscribe to updates on their selection, or to purchase an excerpted list for aone-shot promotional mailer. This category ranges from small trading, manufacturing, orservices firms to bank branches and major domestic and multinational corporations.

Research consultancies, which acquire tabulated data and statistics for a specificgeographic area or industry in connection with a study commissioned by a business firm orother organization.

1

Products and services

Excerpts. Enterprises (registered offices) and local units can be extracted from the SIRENEdissemination data bases according to several criteria:

• geographic location• legal status of enterprise• activity of enterprise or local unit (under new French standard classification of activities,NAF)• number of employees of enterprise or local unit (classified by standard size category).

The excerpts can be furnished on magnetic media (floppy disks, tapes, IBM cartridges). Theyare classified into four categories according to their information content:

• unit descriptions• simplified unit descriptions• addresses, which can also be supplied on self-adhesive or machine-compatible labels• lists in paper printout form.

Data bases on enterprise and local-unit start-ups and changes, sold by subscription for afixed period.

Tabulated data, in standard or customized format.

Statistical tables and series on enterprise demographics.

SIRENE-matching ("Sirénage"): collation of a customer data base with the SIRENE registerfor the purpose of updating, checking, or enhancing it with SIRENE data.

2

disseminat ion uni ts in InseeRegional Offices; in Paris, the staff ofthe SIRENE dissemination sectionof the INSEE "Info Service"Department.

In practical terms, this missioninvolves a telephone hot line, aninformation-exchange bulletin, aquarter ly progress report onSIRENE dissemination statistics,training programs, and more.

In 1996, the Facility developed amultimedia sales tool for SIRENEproduct distributors. The resourcewill enable them to make moreeffect ive and attract ivepresentations of SIRENE productsat conferences and exhibitions.

• Preparation of SIRENE productdocumentation, inc ludingcustomer support products such asa "help" floppy disk.

The a im here i s to make thepresentation of SIRENE productsmore user-friendly. The technicaldocumentation (fi le structures,instructions for use, etc.) has beenenhanced and expanded. The fileson floppy disks are supplied with a"help" floppy disk. The Windowsversion of the latter allows users toconvert files into several standardf o r m a t s c o m p a t i b l e w i t h t h eleading software applications onthe marke t . W i th the samediskette, users can also accessinformation on products and pointsof sale.

• Supply of selected products(dissemination over the Minitelvideotext network, list conversionsto SIRENE standards, andautomated list matching). In thiscapacity, the Facility fills orders forSIRENE files placed by customerson the 3617 SIRENE videotextserver, or helps customers who wantto make their own f i lesSIRENE-compliant by collating themwith the register.

• Provision of SIRENE-relatedservices for selected partners whohave signed working agreementswith INSEE. Because of their legalstatus (as statist ical off ices ofgovernment ministries or as publicagencies) or the type of their request,some customers benefit from specialagreements under which they cansubcontract some of their work toINSEE. Examples include the FrenchRegional Development Agency(DATAR), the unemploymentinsurance fund (UNEDIC), and thePostal Service (La Poste). For suchpartners, the Faci l i ty providescustomized SIRENE services.

• Upgrading SIRENE products andservices (participation in ODISSEEdevelopment, and renovation of the3617 SIRENE videotext and SIRENElist-matching services). Thanks to itsdaily contacts with Regional Officedistributors and key customers, theFacility offers valuable inputs toINSEE’s Directorate General for itslong-range planning of SIRENEproduct and service development.

Through its regular exchanges with theRegional Offices and its closeness toregional dissemination operations, theNational SIRENE Data DisseminationFacility is well placed to voicedistributors’ concerns in the workinggroups in which it participates.

It should be emphasized that theFacility is not a decentralized unit ofINSEE’s Directorate General, as it isnot directly involved in definingSIRENE disseminat ion pol icy.Neither is it a mere resource used bythe MDRS Division as and whenneeded. Indeed, the Faci l i ty ’sdistinctiveness resides in its closeties to both the Directorate Generaland the Regional Off ices—anintermediate status that benefits allits interlocutors.

Dalila Vienne and Antoine SantoliniINSEE

Dalila Vienne heads the National SIRENEData Dissemination Facility in Nantes.

Antoine Santolini heads the Disseminationof SIRENE Information Division at INSEE’s

Directorate General.

Making SIREN-based products available to users


Estimated demand for SIRENE data

Under the present system, there are no full, detailed statistics to allow cross-tabulatedbreakdowns by product and customer category. This function will be developed in theODISSEE application. However, the existing application, as well as the service-billingsystem (SAGE) used by the INSEE Regional Offices, yields some figures for 1995.

Requests for selections of fewer than 250,000 units are broken down as follows:

� by medium � by sizelistings 3,139 < 500 adresses 3,769labels 2,372 > 500 and < 3,000 4,035total paper media 5,511 > 3,000 and < 10,000 1,523

> 10,000 and < 50,000 697floppy disks 3,214 > 50,000 128tapes 403 Total, all size 10,152files 779multiples 252total magnetic media 4,648,

To these orders, one should add:- bulkier selection (too large to be treated by conventional means)- customized selections made from old data bases or supplied in special formats- selections on event-type data bases (for example, data base of business start-ups).

In 1995, there were 188 requests for SIRENE list-matching and about 3,300 telephoneconnections to the 3617 SIRENE videotext server, or an average 12,700 calls a day.

ODISSEE: a resource for disseminatingand publicizing SIRENE data

A major share of INSEE’s activities in statistics dissemination consists in managing access tothe SIRENE register for purposes of data extraction or tabulation. These operations arehandled by Regional Office staff and a specialized unit of the INSEE "Info Service"Department. As a result, the "network" of SIRENE disseminators is quite diverse. Somedisseminators may be just as busy managing sales of the Institute’s regional periodicals asmarketing data from the population census or SIRENE. Other disseminators are SIRENEadministrators with extensive knowledge of the significance of the register variables, but theytend to be less familiar with other sources of interest to the customer. A third category ofdistributors specialize in the dissemination of the register and are employed by an INSEEcommercial-distribution unit. Such diversity requires a multi-purpose dissemination tooltailored to each type of distributor as well as to customer needs.

SIRENE customers form adiversified group, in terms of sizeand activity, and this is a decisivefactor in their demand for INSEEdata.

Some customers want the entireSIRENE data base, complete withupdates. Most such requests comefrom large corporations in banking,insurance, or manufacturing—forexample, companies that leaseequipment to enterprises and localunits. Another category consists ofspecialized resellers of businessinformation.

Other customers, such as pollingorganizations, prefer a far moresophisticated selection based, forexample, on a sampling plan.

Whereas banks are pr imari lyinterested in "heavyweight"businesses, local governmentagencies may want information onnon-profit organizations and otherpublic bodies.

A final category of "customers" ismade up of the statistical units ofgovernment ministries and in-houseINSEE users who want to updatetheir data bases and conduct

economic studies on a particularindustry.

Today: a broad choice oftools

At present, informat ion in theSIRENE register can be accessed intwo ways: from the raw data files andvia generic search engines.

At regular intervals (yearly, monthly,daily), INSEE compiles "image-files"of the register in specific fields. Theydescribe the stock of enterprises andlocal units in SIRENE, or the list ofevents reported by these enterprisesand local units in a given period. Theimage-files are used for SIRENE’s"statistical" functions, namely, tosupply inputs to the major samplingframes on enterpr ises, and toprovide indicators on businessstart-ups.

SIRENE, however, also serves anadministrative purpose, which offersa twofold advantage: data arecontinuously being added to theregister, and the quality of those datacan be measured through their use.The single identification number is amajor strength here.

As regards the "general public"—thefocus of this article—it can accessSIRENE information through avariety of tools:

• Standard tables combine themain criteria for measuring the stockunits (activity, number of employees,legal status, and unit type) for eachdépartement and region.

Start-up tables show the number ofenterprise and local-unit births bytype of start-up and by month, foreach region, on an unadjusted basisand a seasonally-adjusted basis.

• The videotext service allowsusers to identify a unit from theday-earlier image-file of the register.The service is available by dialing3617 SIRENE on any 80-columnMinitel terminal. Users can alsoselect addresses on four criteria(geographic location, activity, legalstatus, and number of employees).The results are recorded on a floppydisk and mailed the following day.

• The central software program ofSIRENE Diffusion (SDIF) isavailable to INSEE personnel atregional offices. The program allowsthem to compile complex selections


on a register subset ( the"distributable" units) in order to tracklocal-unit stock or flows.

• An array of ancillary tools catersto other types of requests, forexample, searches from a set ofSIREN or SIRET numbers, thesupply of very large data bases, anddata outputs in special formats.

This complex set of tools evolvedfrom the central software designedabout a decade ago to meet theneeds of INSEE’s "Info Service"Department, which is responsible formarketing INSEE data in the greaterParis area. As a result, SIRENEdistr ibutors’ t ra in ing in theseresources is fragmentary—as is thepublic’s acquaintance with them.The substitution of the new NAFclassification for the older NAPclassification in early 1993 revealedthat these programs were hard toupgrade.

To remedy these deficiencies,INSEE, in 1993, launched a projectcalled ODISSEE, which stands forOutil de Diffusion d’InformationsSIRENE et de Statistiques sur lesEntreprises et les Établissements, or"Tool for Disseminating SIRENEInformat ion and Stat ist ics onEnterprises and Local Units."

Tomorrow:a true "workstation"

The ODISSEE project aims to supplySIRENEdistributorswitha resource to:

- serve customers through aneasy-to-use search and order-fillingsystem, complete with an on-linehelp function and capable of on-linechecking of request syntax;- facilitate the dissemination ofSIRENE-based stat ist ics ortabulations at various geographiclevels;- handle SIRENE customer trackingand management.

To meet these goal and offerdistributors a true "workstation," theproject developers haveconcentrated on three areas: coreconcepts, the use of"cross-functional" disseminationtools, and ergonomics.

ODISSEE uses SIRENE conceptsto def ine the uni t f ie lds thatcustomers are interested in.

To begin with, ODISSEE does notassign geographic locations toenterprises apart from the regionwhere it has the largest presence interms of units. An enterprise ischaracterized by information itemssuch as act iv i ty, legal status,company name, number ofemployees, and start-up date. Itcomprises one or more local units,only one of which—the registeredoff ice—has a dist inct ivecharacteristic. Local units, as well,exhibit individual characteristicssuch as their activities, number ofemployees, detailed geographiclocation, start-up date, and, whereappropriate, their "singularity" andtype.

ODISSEE enables users to querythree different data bases. Thefirst contains the stock of SIRENEunits at the start of the month inquestion. The second, constructedfrom the comparison of two stocks,allows customer file updates. Thethird contains events that haveaffected the units over a given period(week, month): in particular, it allowsa faster supply of data on enterpriseand local-unit start-ups.

The first two bases are designedwith an "enterprise structure." Inother words, each local unit belongsto an enterprise whose registeredoffice is described in the base. As aresult, for enterprises with at leastone local unit to be updated, INSEEhas had to reintroduce into the"comparison" data base theregistered off ices of thoseenterprises even though the officesthemselves are unaffected by theupdate.

Defining the field whenprocessing a request

To take these concepts into accountfor the request formulation, ODISSEEmanages three different fields.

Each field is defined as a set of unitswith the same character ist ics(economic and legal for enterprises,geographic and economic for localunits).

The complete f ie ld is thecombination of the enterprise field,the local-uni t f ie ld, and theregistered-office field.

An enterprise will therefore be se-lected if:● it qualifies for inclusion in the "en-terprise" fieldù its registered office qualifies forinclusion in the "registered office"field● at least one of its local units quali-fies for inclusion in the "local unit"field.

A local unit will be selected if:it belongs to a selected enterprise

J.B. Herbet

22

Defining a field and a type of search

The use of three complementary fields makes it possible, for example, to selectlocal units engaging in the "enterprise administration" activity (local-unit field),belonging to enterprises of the "SARL" type (enterprise field) with a registeredoffice in Paris (registered-office field).

The resulting data base will therefore contain the "SARL enterprise administration"local units of enterprises with Paris registered offices, together with thoseregistered offices themselves.

The search request will thus yield one of three sets of hits: (1) all local unitsengaging in the "enterprise administration" activity, (2) all the registered offices ofthe selected enterprises, or (3) the registered offices of these enterprises, followedby the local units engaging in the "enterprise administration" activity.

1

it qualifies for inclusion in the "localunit" field.

As explained above, the registeredoffices of selected enterprises areincorporated in order to provide theresulting data base with an enterprisestructure. By construction, the officesare included in the "registered office"field but not necessarily in the "localunit" field.

The end result is a data basecomprising the local units (from thelocal-unit field)and theregisteredofficesof enterprises (from the enterprise field)whose registered office is included inthe registered-office field and at leastone of whose local units belongs to thelocal-unit field.

Choosing the data mediumand the type of hit return

The next step consists in choosingthe type of hit return requested bythe customer. ODISSEE offers threetypes:

(1) The "all local units" option willreturn all the local units selected.

(2) The "registered office" option willreturn all the registered offices of theselected enterprises.

(3) The "enterprise" option combinesthe first two, and will return eachregistered office in (2) followed bythe local units in (1) linked to thatregistered office.

An example is shown in box 1.

At this point, the final selection isready to be transferred to thedata-storage medium requested bythe customer.

Several products are available forthis purpose. Customers can obtain allthe information, part of the information,or addresses only. INSEE also offerscomplementary services such assorting to specific criteria (for example,postal-code (zip-code) sorting, whichentitles users to preferential rates fromthe Postal Service). A broad range of

formats—such as FIS, SAS,EBCDIC, ASCII, and DBASE—will

also be available to satisfy as manycustomers as possible.

ODISSEE


Synergy between SEDDL and ODISSEE

As its French name indicates, SEDDL (Système d’Études et de Diffusion deDonnées Locales) is a system designed to allow access to, and supply of, INSEElocal data for purposes of analysis and dissemination. The software not only allowseasy access to these data and their documentation, but also enables users toprocess them in a PC environment and reproduce them in several forms—raw dataor enhanced (area portraits, profile data bases) and on a variety of paper orcomputer media (in "universal" formats).

SEDDL caters to a broad audience of statisticians: research officers and assistantsat INSEE regional offices and Paris headquarters, disseminators at regional officesand INSEE Info Service, designers of dissemination products, and so on.

The system aims to facilitate both documentation- and dissemination-relatedapplications of INSEE local data, or data presentable in "local" form. Anotherconcern is to satisfy user requests for data of relevance to local and "territorial"issues such as decentralization and regional planning. With these aims in mind,SEDDL designers have focused on two points:

. Developing appropriate software resources for processing the local data, bycreating an all-purpose PC tool for analysis and dissemination that functions in auser-friendly Windows environment and integrates several data bases anddissemination tools.

. Data documentation, compiled by the data producers themselves.

SEDDL will host local data in the form of elementary variables or tables. The datawill pertain to a specific geographic unit: region, département, job-developmentzone, municipality, and—at a later stage—neighborhood and block. The data,produced at INSEE or elsewhere, will be tabulated. In other words, they will reflectpredefined options regarding cross-tabulation, aggregation, and classificationheadings. The access to "detailed" data will be possible when SEDDL interfaceswith the system known as TGF (Tabulation de Gros Fichiers: Large-FileTabulation).

Initially, SEDDL will use existing resources. It will interface with the disseminationtool for the 1990 population census. Later, it will be able to host other sources aswell.

The data are located in public data bases. They are fed into the system at a centralsite, but are available to users at all sites.

Soon, SEDDL will able to add "exclusive" data bases specific to individual sites.This will make it possible to make efficient use of sources currently exploited in asingle Regional Office, such as records of the CAF (Family Benefits Funds),URSSAF (the agency in charge of collecting contributions to social-insurance andfamily-benefits funds from private-sector employers), or a survey of local towns.

The data are documented: the characteristics, field, definition, and distributionconditions are given for each variable and table. The presentation and structure ofthis documentation will be harmonized.

Likewise, for each geographic area, the system lists the filiation (i.e., the area’s"nesting" in a higher-level geographic area), composition, and time record (that is,the changes reported by enterprises and local units in the area—mergers,start-ups, name changes, etc.—by type, with dates).

The potential benefits are crucial:

- An enhancement of INSEE data through faster access and high-quality outputtailored to user needs.

- A standardization of certain services and a synergy with other distributionsoftware such as ODISSEE will ensure prompter data dissemination andavailability.

2

A wide variety of media will also beoffered: cartridges or magnetictapes for mainframe systems, floppydisks or optical discs for customersusing personal computers, and evenpaper printouts or self-adhesivelabels for addresses. In-houseINSEE users will also be offered IBMdisks. Further developments willinclude remote downloading.

These media are among thoseavailable through the SERVICEprocedure, current ly underdevelopment. This procedure will beused to format and dispatch the datagenerated from ODISSEE.

Standard statistical tables andtabulations will also be available onseveral geographic scales.

The variety of geographic scales andthe search for synergy betweenODISSEE and the SEDDL software(box 2) have led to the decision touse SEDDL to distribute the stocktables generated from the SIRENEregister.

ODISSEE will supply SEDDL withthe files needed to compile thetables. At the same time, INSEE willdevelop a separate tool for

distributing tables too large fordissemination via SEDDL.

A consistent interface forthe distributor workstation

Disseminators will be able to performcustomer tracking andmanagement on their workstations,via access to selected functions ofthe SAGE software application (box3). Accordingly, ODISSEE will nothandle customer management oridentification, which are treated as"cross-functional" tasks in eachdistr ibut ion uni t . The SAGEfunct ions wi l l be used for thatpurpose, with the aid of gatewaysthat will allow information exchangesand ensure consistent procedures.

SAGE will also handle customermanagement and billing. Detailedinformation will be stored to analyzethe range of services sold and todefine a policy for SIRENE productupgrades. These choices wi l ldetermine the ODISSEE interface.

ODISSEE uses a Windows-typeinterface. Scrolling menus list thecross-functional features, while

windows and buttons offer access toODISSEE-specific features. Productrequests are formulated with the aidof objects similar or identical to thoseused in SEDDL.

For example, the "utilities" menuserves to manage users, the fieldsthey have defined, and the requeststhey have stored. Likewise, therequest in preparat ion can bevisualized, created by recalling apreviously stored request, or evenpart ly or total ly erased. Adocumentation function is used toobtain details of a geographic zoningunit (for example, the municipalitiesincluded in a selected" job-development zone") or auser-stored zoning unit (by zoningunit, we mean the combination ofbusiness, legal, and/or geographiccodes). A help feature is available oneach panel to guide users throughthe procedures.

SEDDL concepts are also used inthe geographic definition of the fieldrequested by the customer. Theselection criteria available includestandard administrative divisions(commune, arrondissement, canton,département, region) but alsodivisions used for study purposes("job-development zones," "urbanunits"). Planned SEDDL upgradeswill make it possible to define ageographic area from a geographicinformation system. Like ODISSEE,SEDDL wil l enable users, forexample, to select towns along aspecified river or road, or townssituated in a radius of severalkilometers around a central town.

This deliberate synergy betweenODISSEE and SEDDL shouldsatisfy researchers as much asthird-party customers who, oncethey have defined their area of study,are general ly keen to obtaininformation on individual economicagents.

The economic or legal definition ofthe f ie ld (act iv i ty, number ofemployees, legal status, etc.) relieson the same tools: the choice ismade from a closed list displaying

J.B. Herbet

24

The SAGE system

SAGE is the automated management system used by INSEE’s research anddissemination units. It is a nationwide application running in local-network modeunder Windows. SAGE manages the following business transactions:

. record-keeping for services rendered and customers; preparation of estimatesand invoices

. recording of payments received and issuance of reminders for unpaid bills

. management of partnership agreements.

Because the information is stored in data-base form, SAGE can conduct varioustypes of searches to produce listings of, for example, sales by product or customercategory, revenue totals, the number of estimates not followed by orders, or thevalue of services provided for free to partners under formal agreements.

SAGE also gives access to customer and prospect data bases, which can be usedfor targeted promotion campaigns.

Several types of users provide inputs to the SAGE system or query it, either locallyor nationally. SAGE functions cover sales, accounting, management, planning,marketing, and information-consultancy services.

All ODISSEE workstations will be able to connect to SAGE and extract informationfrom the system or feed information into it.

3

the selected variable’s code andcode label. Special tools allow usersto select all or part of the proposedlist, and the system offers a searchon the first eight characters of thecode label in order to speed theselection if the list is too long.

Working towardwidespread use

ODISSEE is therefore intended as auser-friendly tool for automatingstandard requests with maximumefficiency. It supports sufficientlycomplex definitions to meet the mostspecific demands without undue

delay. Disseminators will thus beable to spend more time on dialoguewith customers, which will enablethem to translate market demandinto INSEE concepts and satisfycustomer needs more effectively.

Inevitably, some requests will not beamenable to automatic processing.In such cases, ODISSEE will simplysupply the user with a file that islarger than the desired field butsmaller—and thus probably easierto handle—than the files generateddirectly from the register.

In sum, the concepts chosen shouldallow INSEE to serve an ever greater

number of users—both in-house andexternal—interested in SIRENEdata. Advanced ergonomics,ODISSEE’s abil i ty to integratecomplex fields, and the product’savailability on local networks shouldmake SIRENE data accessible to alarger number of users, includingdisseminators, researchers, andsurvey managers.

Jean-Baptiste HerbetDissemination of SIRENE

Information DivisionINSEE

ODISSEE


PRICES

FLOPPY DISK

ADDRESS

POSTALADDRESS

STANDARDS

CD-ROM

MARKETINGSTOCK

ESTIMATE

FORM-FILINGCENTERS

OFFICIALGEOGRAPHIC

CODE

GEOGRAPHICINFORMATION

SYSTEM


En

ter

pr

ise

Sta

tis

tic

sThe two gatherings of articles on “enterprise statistics” and“SIRENE” in this issue form a perfect complement, since theSIRENE business register is the backbone of the French systemof enterprise statistics.

France is just about the only country to maintain a businessregister that is both administrative and statistical. This dualcharacter offers considerable advantages in terms ofinformation quality and update speed.

The first links in the management chain are the Centres deFormalités des Entreprises (CFEs), one-stop reporting centerswhere all enterprises are required to report every event in theirlives. The CFEs forward the information to the relevant legal,tax, social-insurance, and statistical bodies. This enables INSEEto update the SIRENE register.

Achieving “total quality” for the two facets of theregister—the administrative and the statistical—would be toocostly. Some compromises are therefore needed.

For administrative purposes, the quality of the information oneach unit must be consistent, regardless of unit size. Forstatistical applications, that requirement is far looser: only thelargest units need routine checks.

INSEE’s contr ibut ion is recognized by the tax andsocial-insurance agencies involved in the system: they use theSIRENE register to identify the units under their supervision; in sodoing, they make the best, most economical use of theinformation available. This well-established practicegathered momentum in the early 1990 after an intensiveprogram by INSEE to enhance the statistical features of theregister. An additional incentive came in 1993 with theMadelin law, which instituted the mandatory use of a singleidentif ication number in communications betweenenterprises and government bodies.

Apart from its reliance on the inter-departmental SIRENEregister, the French system of enterprise statistics displays thefollowing features:

. a “semi-decentralized” organization comprising INSEE, thestatistical offices of several ministries, and trade organizations:this arrangement requires a tight coordination of methodsand programs;

. the intensive use of administrative sources (tax returns, formsfiled with social-protection agencies, payroll forms, etc.),made possible by the SIRENE identification number;

. a close tracking of the service sector, particularlynon-financial market services.

28

Today, the system is adjusting to the European Unionharmonization requirements. Several regulations to this endhave already been adopted since 1990. For example, Frenchclassifications were modified in 1993 to bring them into linewith European classifications, but their dissemination is slow.The regulation on statistical registers strengthened the role ofSIRENE. The regulation on statistical units led to the choice ofeight units. Among these, the enterprise and the establishment(or local unit) are by far the most widely used in France. Themost recently adopted is the regulation on structural statistics,with which the French fourth-generation Annual EnterpriseSurvey has been designed to comply. The regulation onshort-term indicators is still the focus of heated discussion. Therenovation-in-progress of French indicators should satisfy mostof the new requirements, but some indicators are still lacking.

Among his medium-term objectives, INSEE’s Director Generalhas listed closer ties between the statistical system and thecountry’s enterprises. Two types of initiative have beenlaunched to meet this goal:

- A substantial effort is being made to lighten the paperworkburden on small and medium-sized firms and to spread it morefairly. Some questions and even some surveys whoseusefulness has diminished over time have been eliminated.The aim is a more thorough use of administrative data bases.Survey samples are being coordinated. A system is beingdeveloped to rotate surveys from one industry to another.

- Statisticians are preparing several tools to meet the specificneeds of enterprises more effectively. Feedback torespondent firms will be increased. More user groups havebeen set up.

Naturally, enterprise statisticians are also working on theadjustments that will be needed in the next few years to makethe most efficient use of new technologies and to anticipatethe changes in enterprise structure and behavior. The issues tobe addressed here include:

• communication methods, the switch from printed toelectronic survey forms, electronic data interchange (EDI),etc.;

• the growing complexity of enterprises;

• the new geographic levels for which data will be needed.

Michel HébertDirector of Enterprise Statistics

INSEE

The system of enterprise statistics

The author, Jean-Pierre Grandjean, reviews the history and main characteristics of themethods used by the French public statistical system to observe enterprises and theiractivities. Topics discussed include structures, coordination, observation units, surveys,administrative sources, data compiled, and statistical summaries.

In the first section, the author describes the milestones in the gradual implementation of theobservation procedures. He emphasizes the growing influence of the construction of theEuropean Union, especially since the early 1990s. Before describing the system of enterprisestatistics itself, he outlines the methods and procedures introduced to ensure efficientcoordination of its operation and development.

Before World War II, the Frenchsystem for statistical observation ofbusiness activity was very limited,and lagged far behind that of otherdeveloped countries.

Historical overview

Wart ime shortages led to theestablishment of trade organizingcommittees to allocate the availableraw materials. The committeeseventual ly set up a system ofsurveys of industrial production,which yielded detailed statistics onphysical quantities. The committeeswere dissolved in 1946, but theirmissions were taken up byemployers’ organizations acting ongovernment approval.

As the shortages disappeared, theinterest in statistics faded. A series ofdiscussions led to the enactment ofthe law of June 7, 1951, on statisticalrequirements, coordination, andprivacy (see box A). This act, with itssubsequent amendments, st i l lprovides the basic guidelines forstatistical communications betweengovernment and enterprises. Itallows government approval oftrade organizations to serve asintermediaries in carrying out“compulsory surveys." Theseorganizations have extensively

benefited from the arrangement. Tothis day, they perform a majority ofthe surveys on industrial production.

In the 1950s, Frenchstat ist ic ians—at the urging ofnat ional accountants and thegovernment planning body, theCommissar iat Général duPlan—began compiling accountsby “activity sector . " Thecomparison between these accountswith the data on “industr ies"(branches) prepared fromindustr ia l -product ion surveyshighl ighted the inconsistencybetween the two series and the poorquality of statistical information onFrance’s production system.

In the early 1960s, with the steadymove toward free trade, thedevelopment of industry statisticsbecame crucial. An industrialcensus was carried out in 1962 in anattempt to reconcile “sectoral" dataand “industry" data. The censusserved as the starting-point for thebase-1962 system of nat ionalaccounts. However, it proved socumbersome to manage and so longto tabulate that it was not repeated.The census was replaced by AnnualEnterpr ise Surveys (EnquêtesAnnuelles d’Entreprise or EAEs:1see the article by P. Rivière on thefourth-generation EAEs, pp. 41-46).The first industrial survey was carried

out in 1966 on a narrow sample. Fullcoverage of the goods-producingindustries was achieved in 1970.

The 1960s also saw the decentralizationof enterprise-statistics offices.This basic policy choice, which hasnever been challenged since, aimedto provide a better match betweenstatistical instruments and the needsof the users most closely concerned.This would promote a momentum forgather ing the vi ta l resourcesneeded. Existing statistical officeswere revamped and new ones wereestabl ished—with sizableresources—in the ministries ofagriculture, industry, and publicinfrastructure. The transportationministry followed a few years later.

In the 1970s, the dominant concernswere of a structural nature: industrialconcentration, the rise of the servicesector, inflation, and the 1974 oilcrisis. All these developments fueleda growing interest in the analysis ofcorporate profitability and debt, aswell as in the competitive behavior off i rms. The f i rst stat ist ics onenterprise groups were compiled.The Annual Enterprise Survey wasintroduced in the retail/wholesale


1.For a full list of abbreviations and acronymsused in the “Enterprises" section, see p. 37.

trade between 1972 and 1974, intransportation in 1974, and in theservice industries in the early 1980s.A single, official classification ofactivities and products (NAP) wasestablished in 1973, and a detailedclassification of products (Nodep) in1983. The SIRENE register ofenterprises and establishments(local units) was introduced in 1970,along with FILE, an integrated database for launching surveys,replaced by the OCEAN system in1989. Advances in comput ingal lowed the f i rst systemat icmatchings of corporate tax returnsand Annual Enterprise Surveys, aspart of the SUSE operation (Frenchacronym for Unif ied System ofEnterprise Statistics). Computersalso made possible the creation ofENEIDE, a multi-source data baseon industrial firms.

At the same time, the 1970s saw thepreparat ion of new economicindicators to complement theindustrial production index, in usesince the 1950s. The new gaugestrack producer prices, sales, andchanges in inventory values.

By the early 1980s, the foundationsof the system of enterprise statistics

were in place. The 1980s werelargely a period of consolidation.New information needs emerged,however, spurring new statisticalinvestigations in areas such as theenvironment.

Since the start of the 1990s, themove toward European integrationhas exerted a more pronouncedinfluence. EU member States arefaced with an ever greater need forcomparable economic information.Regulations and directives havebeen adopted for this purpose atEuropean level. Some of thesemeasures have already had animpact in France. The Frenchversions of NACE Rev.1 andProdcom replaced NAP and Nodepin 1993. The Intrastat system fortracking intra-EU merchandise tradehas been in operation since January1, 1993 (see the article by M.F. Rivetin the French edition of Courrier desstatistiques, no. 70, June 1994).Other regulations—for example,concerning the use of enterpriseregisters for stat ist icalpurposes—wil l take longer toimplement. Last ly, a ser ies ofmeasures that will surely have animportant long-term impact on theFrench statistical system are still

under discussion: they concernstructural statistics on enterprisesand short- term indicators. Aregulation on the first of these topicswas adopted in late December 1996.

Alongside basic observations on astabi l ized, regular basis, thestatistical system also undertakesoccasional surveys on topics thatare less conventional but for whichthere is a strong informat iondemand, such as innovat ion,automation, and inter- industryrelations. The public statisticalservices enjoy solid comparativeadvantages here, notably samplingframes, technical expertise, andindependence.

In recent years, however, firms haveexpressed a growing concern overthe cost involved in responding to themany requests for information fromgovernment or para-governmentalagencies, and in part icular tostatistical surveys. The solutions willcome from a better coordination ofthese official bodies and, therefore,f rom an even greater use ofadministrative sources bystatisticians. Another likely positivefactor will be technical progress, inthe form of highly automatedenterprise reporting procedures.

System coordination

By comparison with thearrangements prevailing in othercountries, the French system ofenterprise statistics is organized in afair ly or ig inal manner. I t is“semi-decentralized"—half-way

J.P. Grandjean

30

Number of employees %

Fewer than 10 92

10-499 7

500 and over 1

Total 100

Source: INSEE, SIRENE register

Enterprise surveys

The law of June 7, 1951, established a National Council for StatisticalInformation (CNIS) alongside INSEE. One of the new body’s main tasks was tocoordinate statistical surveys carried out by public agencies. The CNIS preparesan annual program covering all the surveys planned for the year. The surveysmust be approved by INSEE’s supervising ministry and by the ministry withsupervisory authority over the survey organizers. Individuals and legal entitiesare required to respond in an accurate, timely manner to the compulsorysurveys.

Trade or inter-industry organizations may be officially authorized to act asintermediaries in carrying out statistical surveys. When an approved survey formis distributed by an authorized organization, respondents are offered the choiceof returning the forms to the organization or to the public agency responsible forthe survey.

The personal economic or financial information in the forms cannot, under anycircumstances, be used for tax-auditing or law-enforcement purposes. Theemployees of government agencies and of organizations acting asintermediaries in the surveys are bound by professional-secrecy rules.

INSEE or ministerial statistics offices are empowered to receive—for the solepurpose of preparing statistics—the information on individuals and legal entitiescompiled in the course of its assigned duties by a government department,public institution, local government authority, or private legal entity managing apublic service.

AB - Size breakdown of

French enterprises

between the decentralized systemsof the United States and Japan andthe centralized systems of Australia,Canada, and the Netherlands.

Many government departmentsgather information on aspects ofenterprise activity as part of theirlegal and regulatory duties. Forexample, the departmentsresponsible for economic andfinancial affairs levy taxes assessedlargely on the basis of accountingdata compi led by enterpr isesthemselves. For its part, the publicsocial-insurance system (Ministry ofLabor and social-protect ionagencies) gathers payrol l andemployment data from employers.

Also, many governmentdepartments undertake statisticalsurveys as defined in the 1951 law(box A). In the historical summaryabove, we have highlighted the dualdecentralization at work here.

The statistical offices of theministries concerned gather andtabulate the statistical information oneach broad economic sector: agriculture(SCEES); industry (SESSI);construction and public works; andtransportation (the two existingstatistical offices for the last two sectorsweremerged into thenewly-establishedSES in early 1996). One exception isworth noting: wholesale/retail trade andservices are handled directly byINSEE, as the resources allocated tothe ministries responsible for thesetwo sectors have never beensuff ic ient to meet stat ist ical-information requirements. Statisticaloffices for inter-industry monitoringhave also been set up in otherministries such as Labor (DARES),Environment (IFEN), and Research.

The second aspect of decentralizationis that the government has longdelegated extensive authority tomany trade organizations (about70) for conduct ing surveys onindustrial production.

In sum, much informat ion isgathered from French enterprises.However, it is often heterogeneous

and incomplete, and its qualitysometimes needs examining.Decentral izat ion cal ls forcoordination and synthesis: this dualtask has been assigned to INSEE.

Coordination is a many-facetedadministrative undertaking .INSEE maintains relationships withinternational organizations such asEurostat, the United Nations, OECD,and the IMF; i t preparesmedium-term statistical programs; itreviews budgets; it assigns its ownstatistical personnel to statisticaloffices in other government bodies(France is pract ical ly the onlycountry to do so); and it develops theannual program of public-sectorsurveys. All of this work is carried outin cooperation with the relevantministries. In addition, INSEE actsas a secretar iat and preparesworking documents for several of thecommit tees set up under theNational Council for StatisticalInformation (CNIS). The latter bodyis responsible for ensuring regularconsultations between users andproducers of statistical information.The committees are the following:

. the Approval Committee, whichexamines projects for new surveys;. the Litigation Committee, which

reviews proceedings broughtagainst enterprises that fai l torespond to compulsory surveys;. the Statist ical Confidential i tyCommittee, which reviews requestsfor access to data on individualenterprises.

Coordination is also achieved viatechnical resources. For example,INSEE is in charge of preparingofficial statistical classifications. TheFrench Classification of EconomicActivities (Nomenclature d’ActivitésFrançaise: NAF), in effect since1993, is an adaptation (in moredetailed form, for certain headings)of the European NACE Rev. 1classification, with which it is fullyconsistent. The French productclassification (CPF) is identical to theEuropean one (CPA). Themanagement of the detai ledclassification of products used inindustr ia l -product ion surveys(Prodfra, the French version ofProdcom) is largely delegated toSESSI and SCEES. INSEE is settingup a videotext service (36 17 + codeword NOMENCLA) documenting allthe existing statistical classificationsof activities and products (includingthe Combined Nomenclature andHarmonized System used in tradestatistics).



Wholesale/retail642,180

Construction313,230

Food and beverages72,230

Manufacturing196,650

Household services430,660

Business services379,150

Hotels201,220

Transportation91,900

C - Breakdown of enterprises by industry(number of enterprises at Jan. 1,1996)

Source: INSEE, SIRENE register

INSEE takes part in work on thegeneral chart of accounts. Thisrequires French enterprises to followa uniform presentation of theiraccounts based on commonconcepts, def in i t ions, andclassifications. Statisticians andnat ional accountants wereextensively consulted for the draftingof the successive versions of thechart. The latest, dating from 1982,translates into French law the 4thand 7th European Communitydirect ives, which respect ivelyconcern parent-company andconsol idated accounts. Theexistence of a harmonizedaccount ing framework makesenterprise accounts a valuablesource of economic stat ist icalinformation.

INSEE manages the SIRENEregister of enterprises and localunits (see the first gathering ofarticles in this issue). It assigns toeach of the uni ts managed anidentification number used by manyother government bodies—apractice that greatly facil i tatessource matching. The Institute alsoassigns to each enterprise in theregister a principal economic activitycode (called the APE code), basedon the NAF classification. Thisoperation is crucial for sampleselection and for preparing statisticalresults.

Last ly, INSEE is developingOCEAN, a system for selectingand coordinating samples forenterprise surveys (see the articleby G. Laflamme and P. Rivière, pp.51-54).

Statistical observationunits and data gathered

The French stat ist ical systemfocuses on two statistical observationunits: the institutional unit and thelocal unit—to use the terms of the1993 European regulat ion on“statistical units for the observationand analysis of the productionsystem in the Community." Theregulation defines an enterprise as“the smallest combination of legalunits that is an organizational unitproducing goods or services, whichbenefits from a certain degree ofautonomy in decision-making,especially for the allocation of itscurrent resources." In practice,enterprises thus defined are treatedas institutional units. Local units arecommonly called “establishments"(établissements). Enterprise groupshave no legal status in France.Statisticians reconstruct them fromenterprise surveys; they do not usethe consolidated accounts preparedand published by the groupsthemselves. Trials are in progress toassess (1) the usefulness of greaterconvergence with the Europeandefinition of “enterprise," and (2) thepotential benefits of introducing theconcept of Kind of Activity Unit (KAU),which is also included in the 1993regulat ion, but is—for themoment—equated with aninstitutional unit.

As in other comparable countries,the French system of enterprisestatistics compiles data provided byenterpr ises at annual or morefrequent intervals, to producestatistical results and economic

indicators at di f ferent levels:quantitative structural data(annual) , quant i tat ive data onchanges (monthly and quarterly), andqualitative data on the businesscycle (monthly and quarterly).

In addi t ion to extensivedecentral izat ion, the Frenchstatistical system is characterized byits combined use of direct surveys ofenterprises and administrativesources. The latter sources havetheir drawbacks in terms of qualityand delays, but they also offer costadvantages to statisticians (sincethe data are collected and capturedby other government departments)and to enterprises (since the overallpaperwork burden is reduced as aresult). A change in the 1951 law,voted in 1986, allows INSEE andministerial statistical offices toreceive information of administrativeorigin on named entities, even whencovered by professional secrecyobligations. This communication is,however, subject to strict rules.

Administrative sources used

The system of enterprise statisticsrelies on four main sources of dataon individual entities.

Annual income statements are filedwith the central tax office (DirectionGénérale des Impôts: DGI) by sometwo million enterprises. The degree ofreporting detail depends on which ofthe two main taxation systems isapplied: the first is known as beneficeréel normal (standard assessment onactual profits); the second is thebénéfice réel simplifié (simplifiedassessment on actual profits) (seetable D). The first system has a fargreater information content. The600,000 enterprises taxed under thebénéfice réel normal systemeffectively include all business of anysignificant importance in France.Data available on them includeincome statements, completebalance sheets, fixed-asset listings,and depreciation schedules. In someindustries, smaller firms outnumberlarger ones and sometimescontribute the largest share of

J.P. Grandjean

32

Sales (in francs)

Tax treatmentIndustry andcommerce

Service providers

under 500,000 under 150,000 flat tax

500,000 - 3.5 million 150,000 - 1 million simplified assessmenton actual profits

over 3.5 million over 1 million standard assessmenton actual profits

D - Tax treatment of income of industrial and commercialenterprises

production. For such firms, theinformation supplied is less detailed.

Companies also file monthlyturnover (i.e., sales) statementswith the central tax office whenreturning their monthly value addedtax (VAT) forms. This series is usedto compile a monthly turnover indexby industry.

The annual statements of payroll data(Déclarations annuelles de donnéessociales: DADS) are fi led byprivate-sector employers with thecentral tax office and social-protectionagencies. These series contain about35 million recorded items, becausethey cover individual employees andemployment spells. Fully tabulatedsince 1993, they provide detailedinformation by local unit on thenumber of employees, compensationlevels and annual changes, andworker qualifications.

Monthly Intrastat declarations arecollected by the central customs andexcise office (Direction Générale desDouanes et Droits Indirects), whosestatistics department has been usingthem since 1993 to compile series onintra-European Union merchandisetrade, in compliance with the Intrastatregulation. This same departmentproduces all of France’s tradestatistics: data on trade with non-EUcountries are extracted from the“single administrative documents"(SADs) that accompany thesetransactions.

The statistical system draws onother administrat ive sources,particularly for employment andpayrol l data. Such documentsinclude the summary of contributionspaid into the social- insurancesystem by employers (BRCs), themonthly reports on changes inworkforce filed by private-sectoremployers (DMMOs), and thesurvey on employment structure byindustry (ESE). In addition, there aresources on foreign investment inFrance, French investment abroad,public contract tenders, patentfilings, etc.

The direct-surveys system

For special needs, or as acomplement to administrat ivesources, INSEE and ministerialstatistics offices conduct enterprisesurveys by direct mail. Most of thesesurveys are compulsory. A briefdescription of their methods is givenbelow.

The Annual Enterprise Surveys(Enquêtes Annuelles d’Entreprise:EAEs) cover about 200,000 firms inmost industries, with some notableexceptions such as finance andinsurance. They provide the basisfor static and dynamic analysis of theproduction system. Enterprises aresurveyed according to their principalact iv i ty code. SESSI coversmanufacturing, SCEES the food andbeverage industry, SESconstruction, public works, andtransportat ion, INSEEwholesaling/retailing and services.There are six broad types ofquestionnaire for these variouscategories.

The annual survey oninter-enterprise financial ties (LIFI),conducted by INSEE, analyzes thefinancial ties between companiesand serves to define enterprisegroups. These are identified by theparent company, which is groupedwith its subsidiaries (more than 50%controlled, directly or indirectly) andits minority shareholdings (stake ofless than 50%). By definition, aparent company is notmajority-controlled by any othercompany, French or foreign; asubsidiary can belong to only onegroup. A simi lar report ingprocedure—which, however, in legalterms, is an administrative filing andnot a statistical survey—is used tomonitor the scope of consolidation ofcompanies in which the State holdsa major i ty stake, as wel l aspublic-sector enterprise groups.

The industrial-production surveysare conducted by three parties: (1)government-approved tradeorganizations; (2) SESSI, for themanufacturing industry; (3) SCEES,

for the food and beverage industries.The list of reference products waschanged recently to enable Franceto supply Eurostat wi th theinformation required by Prodcom.This European regulat ion ofDecember 1991 provides for theestablishment of an EU survey onindustrial production (see article byD. Bresson in Courr ier desstatistiques, no. 78, August 1996).The surveys are designed to yieldseries on production volume andvalue by product. They are eithermonthly and “ l ight-weight" orquarterly (in some cases, annual)and detailed. The results are fed intoseveral gauges, notably INSEE’sindustrial production index (seearticle by A. Brizard and N. Ragachein Courrier des statistiques, no. 78,August 1996).

The INSEE quarterly survey ofinventor ies, revenues, andexpendi tures measuresstart-of-period and end-of-periodinventory values for 5,000enterprises. The findings are used tocalculate an inventory value index byindustry.

Questions on operating income anda breakdown of revenues andexpenditures were introduced intothe survey, and its sample is beingwidened. From 1998, these changesshould make it possible to obtainup-to-date information—currentlyunavai lable—on changes incorporate revenues and thebreakdown of value added.

INSEE conducts a direct survey onproducer prices among some 3,500industrial firms. The aim is to trackthe price changes on a selection ofrepresentative products chosen withthe approval of each companyconcerned. The surveys are monthlyor quarterly, but INSEE is seeking tomove quickly to a monthly frequencyfor all products tracked. Not allindustries are included, and thecoverage is expanding slowly. It isalmost complete for semi-finishedindustrial products. The presentef for ts are concentrat ing onproducer durables. INSEE hasconducted trials using a similar



methodology to calculate priceindexes for selected businessservices. The first indexes publishedconcern the secur i ty industry.Meanwhile, the economics andstatistics office of the French Ministryof Infrastructure, Housing,Transportation, and Tourism (SES)is using a comparable method tocompile quarterly price indexes forhome maintenance andimprovement.

INSEE carries out a survey, calledSINE, on new-born enterprisesand their changes over time. Italso uses the changes recorded inthe SIRENE register to preparemonthly indicators of enterprisebirths and deaths.

INSEE conducts some fi f teennon-compulsory qualitativebusiness surveys on a monthly orquarterly basis. Each survey is “slim"in terms of i ts sample andquestionnaire size. Executives areasked for their opinion on recentoutput and demand trends as well astheir outlook for the immediatefuture. The results are tabulated as abalance of pessimistic and optimisticreplies to each question, and biasesare corrected by comparison with,and benchmarking to, pastresponses.

DARES performs periodic surveyson labor act iv i ty andemployment status (Enquêtessur l ’Act ivi té et les Condit ionsd’Emploi de la Main d’Oeuvre:ACEMO) among loca l -un i tsamples. The aim is to obtainind ica tors on changes inemployment , hours worked,earnings, etc. The frequency isannual, half-yearly, or quarterly.Other surveys on narrower topicsare carried out at more infrequentintervals. This system will probablybe revamped in the near future.

The statistical office of the ResearchMinistry conducts an annual surveyon corporate R&D spending.SESSI and SCEES perform annualsurveys among local-unit samples

on energy consumption ande n v i r o n m e n t a l - p r o t e c t i o ninvestment by industr ial f i rms.INSEE carr ies out surveys onattendance in hotels and campingsites.

From this overview, we can concludethat the most glaring gaps in thesystem concern the within-yearmonitoring of order intake andinvestment. At present, there areno clearly identified and scheduledprojects to fill that gap.

Statistical summaries

Each type of sourceused—administrative records orstat ist ical surveys—providesmaterial for statistical processingand publications by the statisticaloffice responsible for that source,i .e. , a minister ia l stat ist icsdepartment or an INSEE unit.

Another set of products—designedfor statisticians, economists, ornational accountants—gather datafrom different sources and seek tocombine them into a consistentmicro-, meso-, or macroeconomicwhole.

These mult i -source datacompi lat ions serve two basicpurposes:

(1) They are intended to offerauthorized users an easy access toneeded data. Some of these bases,arranged according to INSEEidentification numbers, contain dataon enterprises and local units. Theyrecord the main characteristics ofthese entities: principal activity,geographical location, workforce,production, financial results, etc.The bases are also a source forpublicly disseminated statisticalproducts.

(2) Data col lat ion also revealsanomalies that require correction aspart of a cont inuous qual i ty-improvement drive. In this way,users specialized in other fields willhave a checked, reliable sourcematerial to work with.

As an example of data compilation,SESSI manages ENEIDE, a database on industrial enterprises withmore than 20 employees and theirlocal units. ENEIDE brings togetherdata from the Annual EnterpriseSurvey, industr ia l -product ionsurveys, and surveys oninter-company financial ties, R&D,and energy consumption.

The SUSE operation, conducted byINSEE, involves (1) amalgamatingthe data from the six AnnualEnterprise Surveys in a commonformat, and (2) matching the resultingfile against the annual income returnsto the tax authorities. After checks,corrections, comparisons withyear-earlier files, and reintroductionof absent data or units, we obtain asingle adjusted file. It offers themaximum reliability achievable giventhe resources applied and thedeadlines for data delivery, inparticular to the national accountants.

INSEE updates a base calledBRIDGE, containing time series onal l the major local units of theproduction system: all units withmore than f i f ty employees areincluded. The base provides on-lineaccess to the main data for the pastten years. It notably offers materialfor analyzing French industry atdetailed geographical levels.

On the macroeconomic level ,short-term information is used for thestandard computation of generaleconomic indicators such asindustrial-production indexes andproducer prices.

Annual information on enterpriseaccounts is a crucial input fornational accounting. The methodsused to process this source are notdiscussed here.

Two other projects are worthmentioning in conclusion, as theyseek to assemble data from multiplesources in a coherent manner at anintermediate level of detail.

The first is INSEE’s StructuralEnterprise Data Base (Banque de

J.P. Grandjean

34

Données Structurelles sur lesEntreprises: BDSE), described in thearticle by D. Bonnans in this issue. Thepurpose of BDSE is to gather in asingle place a set of structural dataorganized under the NAFclassification, with approximately 700headings. The data are produced by allthe statistical offices monitoringFrance’s production system.

The second project is Eurostat’s

Europroms, in which SESSI istaking part. Europroms will supplyinformat ion on markets bycombining industrial-productiondata and trade data under theProdcom classification.

Lastly, the preparation of satellite orindustry accounts in severaleconomic sectors, coupled with asummary on recent trends in annualeconomic act iv i ty, puts into

perspective many of the data seriescollected by INSEE and ministerialstatistics offices. These studies aresubmit ted to “account ingcommissions" composed of industryprofessionals, qualified individuals,and representat ives of thegovernment departmentsconcerned. The presentations offeran opportunity for fruitful discussionand give part ic ipants a betterawareness of each other’s concernsin regard to economic information.Accounting commissions have beenset up for transportat ion, thewholesale and retai l t rades,services, tourism, agricul ture,and—more recent ly—for thegoods-producing industries.

Jean-Pierre GrandjeanHead of the System of

Enterprise Statistics DepartmentINSEE



Suggested reading:

Les sources statistiques sur les entreprisesCollections de l’INSEE, E series, no. 117, September 1988

“Observer et représenter un monde de plus en plus complexe: un défi pourla statistique d’entreprise"INSEE Méthodes, no. 54

(proceedings of a seminar held by the Institute’sEnterprise Statistics Directorate, October 12, 1995)

J.P. Grandjean

36

Source : M. Quelennec, “Les statistiques d’entreprises,” July 1986, p.55, updated December 1996

Main links in Frenck system of enterprise statistics

BASIC FILES PRIMARY "ENTERPRISE"SOURCES

PRIMARY "LOCAL-UNIT"SOURCES

MICROECONOMICSUMMARIES

MACROECONOMICSUMMARIES

FOREIGN TRADE

CA3

FRP SIRENE

URSSAF

ASSEDIC

DMMO

ESEBRIDGE

EAEOCEANSUSE

EPEI

ENEIDE

ARISTIDE

RECME

Master file onenterprise

groups

Foreignlocations file

IPI

IPVI

ICA

SIE

Quarterlyaccounts

BDSE

DADS

BIC BRN BA

CITRUS

Str

uct

ura

lso

urc

esW

ith

in-y

ear

sou

rces

administrative source file merger project product

Industry surveys

Producer-pricessurvey

Qualitative businesssurvey

Inventories-revenues-expenditures survey

National

accounts

Energy consumptionsurvey

R&D survey

Key:

statistical survey

ACEMO survey

Financial-tiessurvey

“Entreprises” sectionAbbreviations and acronyms

ACEMO: Enquêtes sur l’activité et les conditionsd’emploi de la main d’oeuvre: surveys on laboractivity and employment status

AELE: Association Européenne de Libre-Échange:European Free-Trade Association (EFTA)

APE : Activité Principale Exercée: principaleconomic activity of unit

Assedic: Association pour l’Emploi dans l’Industrieet le Commerce: agency in charge of unemploymentcontributions and benefits

BA: Bénéfice Agricole: reported taxable incomefrom farming enterprises

BALO: Bulletin d’Annonces Légales Obligatoires:official publication of record (gazette) for specificcategories of corporate events (see also BODACC)

BDSE : Base de Données Structurel les etSectorielles d’Entreprises: structural and sectoralenterprise data base

BIC: Bénéfices Industriels et Commerciaux:reported taxable income of industr ia l andcommercial enterprises

BODACC: Bulletin Officiel d’Annonces Civiles etCommerciales: official publication of record (gazette)for civil and business notices (see also BALO)

BRC: Bordereau Récapitulatif des Cotisations:summary of employers’ contr ibut ions tosocial-insurance system (processed quarterly)

BRIDGE: Base Relationnelle Inter-Régionale deDonnées sur les Grands Établ issements:inter-regional data base on major local units

BRN : Bénéf ice Réel Normal: standard taxassessment based on actual profits of enterprise

BTP: Bâtiment et Travaux Publics: construction andpublic-works industry

CA3: Monthly value added tax (VAT) return

CECA: Communauté Européenne du Charbon et del’Acier: European Coal and Steel Community (ECSC)

CEPII : Centre d’Études Prospect ives etd’Informations Internationales: a Paris-basedresearch center on the international economy

CIC : Contrôle Interne Comptable:internal-accounting edit (IAE) in 4G AnnualEnterprise Survey

CITRUS: Coordination de l’Information et desTrai tements des Restructurat ions d’Uni tésStatistiques: system for coordination informationand processing operations on statistical-unitrestructurings

CIV : Contrôle Interne de Vraisemblance:internal-likelihood edit (ILE) in 4G Annual EnterpriseSurvey

CJO: Correction des Jours Ouvrables: adjustmentof data series for days actually worked in period

CNIS: Conseil National de l’Information Statistique:national council for statistical information

CPA: Classification de Produits par Activités:product classification by kind of economic activity

CPF: Classification des Produits Française: Frenchproduct classification

CPS: Comité du Programme Statistique: StatisticalProgramme Committee (of heads of nationalstatistical offices of EU Member States)

CQR: Contrôle Qualification Redressement: dataediting-qualification-imputation (DEQI) in 4G AnnualEnterprise Survey

CTV: Contrôle Temporel de Vraisemblance:time-likelihood edit (TLE) in 4G Annual EnterpriseSurvey

CVS: Correction des Variations Saisonnières:seasonal adjustment of time series

DADS: Déclaration annuelle de données sociales:annual statement of payrol l data f i led byprivate-sector employer

DARES: Direction de l’Animation de la Recherche,des Études et des Statistiques: French LaborMinistry directorate for research, analysis, andstatistics

DAU: Document Administratif Unique: SingleAdministrative Document

DGI: Direction Générale des Impôts: central taxoffice

DMMO : Déclaration de mouvement de maind’oeuvre: monthly report on changes in workforce(inflows/outflows) filed by private-sector employers

DREE: Direction des Relations ÉconomiquesExtérieures: foreign-trade directorate of FrenchFinance and Economics Ministry

EAE: Enquête Annuelle d’Entreprise: AnnualEnterprise Survey

EAE4G: Fourth-generation EAE

EDIFACT : Electronic Data Interchange ForAdministration, Commerce, and Transport

EFGI: Entreprise de Droit Français Appartenant à unGroupe International: enterprise incorporated underFrench law and controlled by an international group

EIG: Échanges Intra-Groupe: intra-group trade

EMI: Enquête Mondialisation Industrielle: IndustrialGlobalization Survey

EMMO: Enquête «mouvements de main d’oeuvre»:survey on changes in workforce among unitsemploying 10-50 people

ENEIDE: Ensemble Normalisé sur les EntreprisesIndustrielles pour le Developpement Économique:data base on industrial enterprises

EPEI: Enquête auprès des Petites EntreprisesIndustrielles: survey of small manufacturing firms

ESE: Enquête sur la structure des emplois: surveyon employment structure by industry

Europroms: European Products and MarketsStatistics (Eurostat project)

Eurostat: Statistical Office of the EuropeanCommunities

FEBI : Fédérat ion Européenne de BrancheIndustrielle: European federation of enterprises in aspecific industry

FILE: Fichier Intégré de Lancement d’Enquêtes:business-survey frame

FRP: Fichier des Redevables Permanents: nationalregister of regular taxpayers

Gesmes: General Statistical Message: EDIFACTmessage designed for transmission of statisticaltables

IAA: Industries Agro-Alimentaires: food andbeverage industry

IAE: see CIC

ICA: Indice de Chiffre d’Affaires: turnover (i.e.,sales) index

IFEN: Institut Français de l’Environnement:statistical office in French Environment Ministry

ILE: see CIV

Intrastat: Statistics on intra-European Union trade

IPI: Indice de la Production Industrielle: industrialproduction index

IPVI: Indice Prix de Vente Industriels: producerprices index

LIFI : L ia isons Financières: survey oninter-enterprise financial ties

NACE Rev1: Nomenclature Générale des activitésÉconomiques dans l ’Union Européenne:Classification of Economic Activities in the EuropeanUnion, Revision 1

NAF: Nomenclature d’activités française: newFrench classification of economic activities (1992),replaces NAP

NAP: Nomenclature d’activités et de produits: oldFrench classification of economic activities andproducts (1973), replaced by NAF (1992)

NC : Nomenclature Combinée: CombinedNomenclature (EU)

NES: Nomenclature Économique de Synthèse:economic classification system used for statisticalsummaries

Nodep: Nomenclature Détaillée de Produits:detailed product classification

OCEAN: Outil de Coordination des EnquêtesAnnuelles d’Entreprise: business-survey framereplacing FILE

OEST: Observatoire Économique et Statistique desTransports: stat ist ical of f ice in FrenchTransportation Ministry, replaced by SES in 1996

PAC: Politique Agricole Commune: CommonAgricultural Policy (European Union)

PECO: Pays d’Europe Centrale et Orientale:countries of central and eastern Europe

PME: Petites et Moyennes Entreprises: small andmedium-sized enterprises

Prodcom: Production Communautaire: statistics onEuropean classification of industial products

Prodfra: French version of Prodcom

RECME: Répertoire des Entreprises ContrôléesMajoritairement par l’État: register of enterprises inwhich French State is majority shareholder

SCEES: Service Central des Enquêtes et ÉtudesStatistiques: statistical office of French AgricultureMinistry

SDISC: Sous-Direction de l’Information Statistiquesur la Construction: statistical office of FrenchConstruction Ministry, replaced by SES in 1996

SES : Service Économique et Stat ist ique:economics and statistics office of Ministry ofInfrastructure, Housing, Transportation, andTourism

SESSI: Service des Statistiques Industrielles:statistical office of French Ministry of Industry, Posts,and Telecommunications

SIE : Système Intermédiaire Entrepr ises:intermediate system of enterprise statistics

SINE: Système d’Informations sur les NouvellesEntreprises: information system on recent businessstart-ups

SIREN: Enterprise identification number in SIRENEsystem

SIRENE: Système Informatisé du RépertoireNational des ENtreprises et des Établissements:computerized system handling the national registerof enterprises and local units

SIRET: Local-unit identification number in SIRENEsystem

SSE: Système Statistique d’Entreprises: system ofenterprise statistics (SES)

SSM: Service Statistique de Ministère: statisticalunit or office in a government ministry

SUSE : Système Unif ié des Stat ist iquesd’Entreprises: unified system of enterprise statistics

TLE: see CTV

TVA: Taxe sur la Valeur Ajoutée: value added tax(VAT)

URSSAF : Union pour le recouvrement descotisations de sécurité sociale et d’allocationsfami l ia les: agency in charge of col lect ingcontributions to social-insurance and family-benefitsfunds from private-sector employers

VABCF: Valeur Ajoutée Brute au Coût des Facteurs:gross value added at factor cost


BDSE : a multi-source data base forstructural enterprise statistics

In the complex field of enterprise statistics, public information is scattered and hard to find.Yet its accuracy, clarity, and timeliness are steadily increasing—thanks to a continuousreassessment of methods and concepts, as well as a collation and harmonization of sources.Adding value to this information and making it available are the goals of the BDSE. Theproject, described by Dominique Bonnans, consists in the compilation of a data base onstructural and sectoral enterprise data.

Let us imagine, for a moment, that amarket ing manager of a largeoffice-automation firm wants tooptimize the latter’s medium-termsales strategy by identifying thestrengths and weaknesses of itscorporate customers.

The manager may well want toqualify these market segments interms of business buoyancy,demand prospects, workforcemanagement, and financial health.Let us assume that the managerwi l l turn for help to the publ icstatistical system, which guaranteesrecognized standards of informationquality and is also a high-volumesupplier of quantitative data.

Alas, we can be almost certain thatthis at tempt to access publ icstatistics will swiftly be discouraged:our marketing manager will have tofall back on incomplete sources thatare less reliable but far more readilyavailable.

A coherent stock ofaggregated data

The first deterrent is the fact thatinformation about enterprises isscattered across administrativerecords (forms filed by enterprises withgovernment agencies) and statisticalsurveys. To make matters worse, thestatistical observations are conductedby several different survey units.

The diagram of the main links in theSystem of Enterprise Statistics (seeJ.P. Grandjean’s article, pp. 29-36)illustrates this fragmentation.

To remedy the problem, INSEE hasdeveloped the Structural andSectoral Enterprise Data Baseproject (Base de DonnéesStructurel les et Sector ie l lesd’Entreprise: BDSE). The prime goalis to compile a coherent stock ofaggregated data on enterpriseindustries (sectors) at level 700 ofthe French classi f icat ion ofeconomic activities (NAF). Data onenterpr ise demographics, jobcharacteristics, restructurings,industry prof i ts, and industryf inancing wil l be gathered in asingle repository. This will providea fu l ler p ic ture of the var iousfacets of each industry. A Europeansection—whose inputs will in alll ikel ihood be suppl ied byEurostat—should provide the itemsneeded for internat ionalcomparisons.

The data series will be annual, andre leased in th ree s tages :preliminary in ca. June/July N + 1,semi-revised toward DecemberN + 1, and final in June/July N + 2.One of the project’s aims is tooffer estimates from within-yearsources on those industries andvar iab les fo r wh ich t ime lyin fo rma t ion i s deemed to becrucial.

The first step toward consistency isto establish a minimal harmonizationthat will yield a large set of variablescommon to all the observedfields. One can do this, for example,by defining common observationfields, such as enterprises with morethan 20 employees. Some variantswill, however, be maintained in orderto take into account the specificity ofcertain industries and to construct afull description of them.

The second step is to decide on theproper tradeoff between speed andquality, in order to release a singlefigure per aggregate at each date.This means checking for anyexcessive discrepancy betweensources.

The third step is a uniformorganization of the data within thebase , which wi l l minimize the“admission cost” for external usersentering the complex universe ofenterprise statistics.

Intelligible information

The above-mentioned complexity is asecond obstacle to the disseminationof information. Users need to grasp theconcepts used, such as the active orinactive status of an enterprise, theindustry or activity sector, and actualinvestment. Users must also be awareof the events—such as enterprise

BDSE: a multi-source data base ...


bir ths, deaths, and restructur ings—that can disrupt theinterpretation of results.

The first prerequisite for intelligibility isto restrict the size of the data stockand turn it into a gateway for accessingenterprise statistics. Choosing theinformation that is likely to offer a gooddescription of the industry is a vitalproject task—even if this meanssteering expert users towardcomprehensive data bases, such assingle-industry data bases. To guidethis selection, we prepared ascenario of potential demands andtested a prototype on a user group inorder to assess the relevance of theinformation included in the base.

Work currently in progress aims toprovide fuller monitoring of changesover time (fourth-generation AnnualEnterprise Surveys), restructurings(CITRUS project) and enterprisedemographics (estimated deaths,etc.). All these undertakings maygreatly enhance the information onindustries by helping to identify theexplanatory factors and making theresults easier to read. The sameconcern motivated the search for thebest breakdown criterion (workforcesize bracket, region, financial tie toan enterprise group, etc.) and the

supply of dispersion indicators. Also,we should not over look theuser-education aspect, which iscrucial. This calls, in particular, for ahigh-quality documentation on thebase structure and content.

Suitable disseminationproducts

The above-mentioned complexity ofenterprise statistics largely explainswhy they are so under-used, except“in house” by INSEE as sourcematerial to prepare the nationalaccounts or economic indicators, forexample. As a result, few usersrealize the abundance of informationavailable and its effectiveness indocumenting many issues.

After compiling the data and definingthe outlines of the base to make itconsistent and intel l igible, theproject will need to map the bestpaths to access the information. Thismeans identifying user groupsand developing disseminationproducts that will best meet theirdemands in terms of form andcontent. It may prove worthwhile totap other data bases in order tocomplement the industry informationand to supply reference points at a

more aggregated or less aggregatedlevel. The main effort, however,must bear on developing an efficient“key” for entering the base. Thisrequires an immersion in thecorporate world and anunderstanding of its various aspects.

The preceding outline of the projectrequirements underscores the needfor a collective examination of theissues, with the participation of allplayers in the French system ofenterprise statistics.

The chosen organizational structuredesignates the system’s internalworking groups as the prime forumsfor project development work. A“user needs” group submitted itsreport in July 1995, which spelledout a f ramework for theproceedings of the “data baseconten t ” g roup. Es t imat ionmethods will be addressed by amore technically-focused group. Insecond-quarter 1996, the “BDSEdisseminat ion” group beganexamining ways of making the datastock available to users.

Dominique BonnansInformation and Statistical Work forEnterprises DivisionINSEE

D. Bonnans

40

BDSE project: organization and timetable

Within-SES groups

SES Group"User needs"

Report: July 1995

Mixed groups

CNIS sub-group

Report: November 1995

"Base content"

Outside users’ group

SES Group"BDSE dissemination"

Group

January 1995

January 1996

January 1997

"Identifying corporateneeds for statisticalinformation"

CONTENT

STAGE

DISSEMI-

NATION

STAGE

SES Group

"Estimates". Testing the basecontent witha prototype

Analyzing demandto develop

a project framework

. Fullest possible listingof stated needs

. Define content

. Define sources

. Suggestdocumentation

Estimatingstructuralvariablesfromwithin-yearinformation

. Define range ofdissemination products

. Specify dissemination rules

Steering

committee

Flanked by an outside

users’ group?

The New Annual EnterpriseSurveys in France

In 1997, most of the French Annual Enterprise Surveys (EAEs) will switch from the oldthird-generation survey structures to what looks to be a promising “fourth generation.” Thismarks the final stage of a long and demanding process that begun in 1990 and has centeredon the “fourth generation” project launched in 1992. Everything will change: the computerenvironment, the tools, the methods (from data editing to the coding of activities and trendcalculations), specifications drafting, the links with OCEAN, and the treatment of corporaterestructuring. In addition, the content of the survey itself is being revamped with the move toa common core of variables. Pascal Rivière gives an overview of the characteristics of thisnew generation of surveys, focusing in particular on the statistical methods.1

The Annual Enterprise Surveys arecurrently managed by six surveydepartments, each working on one ofsix broad economic sectors:goods-producing industries, food andagriculture, wholesaling/retailing,services, construct ion, andtransportation. In practice, eachdepartment has its own system andits own methodology to manage “its”survey. Yet, for a number of yearsnow, these surveys have beenconstant ly encounter ing newproblems and requirements:lightening the response burden forenterprises, reducing delays, takinglocal units into account, making thestatistics more homogenous andmutually complementary, and so on.These factors made all participantsaware of the need for a completeoverhaul. The new project was theoutcome of this collective review,and therefore reflects a joint effort bythe six survey departments.

The “fourth generation”principles

The main proposals for the newgeneration of surveys were detailedin an article by Emmanuel Raulin in1993 (Courrier des statistiques,no64) just before the end of theproject pi lot study. This study

identified a set of options as project“guidelines,” broadly outlined below.

As regards survey content, therewas a clear preference forshortening the employment sectionof the questionnaire. Conversely,the Annual Enterprise Surveysneeded to take account of newinformation requirements, especiallyfor intangible i tems such assoftware-program investments andbusiness expenses. The statisticalunit is still the legal unit, but localuni ts are included in thequest ionnaire on their parententerprise. The questionnairesneeded (1) to differ according to thesize of the enterprise and (2) tocontain a common core, i.e., a set ofvariables common to the six surveys.The plan was to develop specificstrategies for small enterprises(using tax data) and very largeenterprises (more “individualized”information collection).

It was decided to adopt commonstatistical methods for all the AnnualEnterprise Surveys. In particular, adata editing software program and asectoral-trend calculation programwould be developed, since theresul ts of the new stat ist icalprocessing had to be published byDecember of the survey year. The

entire system was to be supportedby a computer architecture commonto all the survey departments. Theproject would be operational in 1997to cover the 1996 financial year(which, in France, general lycoincides with the calendar year).

In retrospect, we can see that the“actual” surveys are more or less inline with the “planned” surveys. It isworth noting, however, that the “largeenterprises” strategy is not on theagenda. The idea has been dropped,but will be reconsidered in due course.

The common core for the AnnualEnterprise Surveys was developedfollowing extensive consultationswith nat ional accountants andregional economists in addition tothe ministerial statistics offices. Thisworking method was applied to thesimplified questionnaires (smallenterpr ises), the generalquestionnaire, and the local-unitquestionnaires. Contrary to thecommon core for INSEE household


1. On the general principles and innovations ofthe fourth-generation annual enterprisesurveys, see also the article by EmmanuelRaulin, “Pour une nouvelle générationd’enquêtes annuelles d’entreprise,” Courrierdes statistiques, no. 64, December 1992.

surveys, the common core for theAnnual Enterpr ise Surveysconsists of variables and not ofquestionnaires. No part of thequestionnaires is common to alleconomic sectors. Each surveydepartment is therefore free to adoptits own observation strategy tocollect the common-core variables.This entails some heterogeneityamong the questionnaires. Eachdepartment also needs to collect thevariables specific to its sector ofstudy. These are called sectoralvariables.

The common-core variables havenow been defined in a dictionary ofvariables,2 which is available inhypertext as well as in standardformat. The dictionary distinguishes

the account ing, stat ist ical ,macroeconomic, and tax-relateddimensions of each variable.

The new data-editingmethods

Data editing is an important step instatistical production. It is particularlyimportant for the Annual EnterpriseSurveys. The wide diversity ofsituations often leads to data beingwrongly considered “abnormal” andconsequently being returned to thesurvey clerk for no valid reason. Thislarge number of apparent lyavoidable manual interventionsobviously lengthens delays.3

An edit is an operation to detectwhether the value of a variable isplausible or not, on the basis ofmultiple criteria. There are threetypes of data edit in the new tool:

• Internal-likelihood edit (ILE):each ILE calculates the relationbetween two unit variables (e.g.sales/number of employees) and

determines whether this ratio isacceptable.

• Time-likelihood edit (TLE): agiven variable of study is comparednot with other variables for the unit inthe same year, but with the samevariable in the previous year bycalculating its growth rate. Thisgrowth is then checked forplausibility.

• Internal-accounting edit (IAE):consists simply in determiningwhether a given accounting equationis true or not (to the nearest tolerance).

Each variable is checked by severalILEs, TLEs, and IAEs.

The apparent l ikel ihood orunlikelihood of a ratio dependsessentially on the type of enterpriseconsidered, as the enterprise set isextremely heterogeneous. Twocharacteristics logically come intoplay to separate this set into moreconsistent groups: the principalactivity and a size criterion such asthe number of employees. Strata arethus def ined by crossing theprincipal activity (at group level) withthe number-of-employees range.Prior to the survey, a parameter setis defined for each stratum and eachratio using the previous year’s data.

In particular, the quartiles arecalculated for the distribution of theratio in the stratum. These are thenused to construct two validity intervals:a “small” interval and a “large” intervalcontaining the small one. In the case ofnormal distributions, the small intervalcorresponds to the 10th and 90thpercentiles and the large to the 5th and95th percentiles. A calculation is alsomade to find mean ratios in thestratum, which will be used for theimputation.

We begin by determining the stratumto which a unit belongs beforeediting the data on that unit. Weestablish the principal activity usinga specific model developed by thefourth-generat ion project. I t is

P. Rivière

42

Likelihood data edit logic(basic-variables group)

.

2. Prepared by A. Benedetti3. See D. Bonnans and E. Raulin, “Premièresréflexions et analyses sur les méthodes detraitement des données dans les enquêtesannuelles d’entreprises,” in “Actes desjournées de méthodologie statistiques,”December 15-16, 1993, INSEE Méthodes,no. 56-57-58.

calculated from a breakdown ofsales by sector using a methodbased on the decision-makingcr i ter ia def ined by theclassi f icat ion-makers. Thenumber-of-employees range posesno special problems. Once we havefound the stratum with this method,the sui table data-edi t anddata-imputation parameters areautomatically deduced for the unitin question. These parameters arethen used throughout the rest of theprocess.

For each edit, we thus have ourintervals indicating whether a ratio islikely, fairly likely or not very likely.We must then decide how to use thisinformation, i.e., how to establishwhether the value of the variableitself, used in several ratios, isconsistent or not.

Consistency assumptionand editing hierarchy

One possible and commontechnique is to consider a valueabnormal when at least one edit on itproves negative (i.e., the ratio’svalue lies outside the interval). Thenew methodology uses a very

different principle. Rather thanseeking out the anomaly, we lookfor the “good” edits. Any edityielding a positive result is regardedas a “consistency assumption” infavor of the variable involved. To dothis, we assign three marks to eachedi t , based on three possiblescenarios: (1) the ratio lies in thesmall interval; (2) the ratio lies in thelarge interval and not in the small; (3)the ratio lies outside both. The markin (3) is always zero. The other twomarks depend on the variable andthe number of edits made on it(generally from three to seven). Thevar iable is then given aqualification, i.e., the marks of allthe edits in which it appears arecombined to provide an overallresult, called the variable’s qualitycode.4

At this stage, there are twopossibilities: (1) the quality code liesabove a certain threshold (10 inpractice), in which case the variableis considered “likely”; (2) it lies belowthis threshold, in which case thevariable needs to be imputed. Avalue can thus be deemedplausible even when certain editsare negative. Provided there are

enough “positive” edits, we canreasonably assume the value ’sconsistency.

We now have the criteria needed toseparate the plausible from theimplausible. Yet this is not the end ofthe story. A value can be deemedunlikely because the edits made on ituse erroneous variables. If thesevariables had been imputed, theresult would have been different.This raises the obvious question:which variables should be editedfirst?

The approach chosen is to rank thevariables into “groups of variables.”At the top of the hierarchy is the mostimportant group of “basic variables”:number of employees at year-end,average number of employees,payroll, production, inputs, totalrevenues, and total expenses. Thisgroup of variables is therefore editedand imputed first, with no furtherchanges. Below this group are thegroups of variables that require oneof the basic variables to be imputed.The “production” group, for example,includes: sales, in-houseproduction, increase in finishedgoods and in-process inventory, andtotal production. This last variablealready appears in the basic group,but serves here as a guiding variablefor the other three, i.e., it provides areference for their edi ts andimputation by featuring, for example,in certain ILEs and in the accountingequation linking the four variables.The groups directly linked to thebasic group can therefore containvariables that are themselves guidesin lower-ranking groups (forexample, the “sales” variable, whichappears in the “sales breakdown”group). We therefore obtain a grouphierarchy that creates a processinghierarchy.

All in all, data editing in the newprocess is not an operation that can

The New Annual Entreprise Surveys in France


Links between groups

var4

var2 var3

var10

var8var3

var7var2

var2

4. The way in which the different marks arecombined depends on the variable and moreparticularly on the number of edits made on it.The principle is always to add up the bestmarks or all the marks.

easily be isolated, as it is too closelyl inked with qual i f icat ion andimputation. The operation carriedout on the data is, in our jargon,called “data editing-qualification-imputation” or DEQI (in French:Contrôle Qualification Redressementor CQR). This is done group bygroup in the order of these groups.

Data imputation

As mentioned above, a variable isimputed only if its quality code is under10. This is the case, for example,when the variable is missing.

There are three main types ofimputation methods: deterministic,by subsidiary trend, and by stratummean. The second and third arebased on estimations and will notentail a change in the quality code.

A deterministic imputation isperformed on a variable before itsqualification. Typically, the methodconsists in reconstruct ing thevariable from other variables. Sales,for example, are imputed from thesum of the sectoral breakdown. Inpractice, several determinist icimputations are performed, and ifone of them allows us to “qualify” thevariable, the imputed value is kept.

A subsidiary-trend imputation ofvariable Y using the subsidiaryvariable X assumes that the Y/X ratiohas not changed since the previousAnnual Enterprise Surveys. Y in yearn is therefore calculated simply bywriting: Y (n) /X (n) = Y (n-1) /X (n-1).This imputat ion method isconsidered to be the most reliable,but it requires variable X to have asuitable quality in year n and X and Yto be known in n-1.

A stratum-mean imputation alsouses a subsidiary variable X, but thistime the Y/X ratio is assumed to beequal to the average ratio of thesetwo variables in the stratum. It istherefore no longer necessary tohave the n-1 data, but theassumption here is much stronger.

The subsidiary variable may be ofinsufficient quality for an imputationand may therefore prevent theimputation. In practice, data editingtherefore involves trying out anumber of subsidiary variables andimputations until one is found towork. Should none of them satisfythe conditions, the operation iscalled an editing failure. This wouldobviously be the case if the DEQIwere appl ied to a totalnon-response.

Making an expertevaluation

The data-editing operation providesthe information needed to decidewhether a given variable has an“abnormal” value or not. There aretwo ways of handling an anomaly:

• automatically, with one of the threeimputation methods;• manually: the questionnaire isreturned to the survey clerk, whogenerally recontacts the enterprise.

A new decision criterion is needed todetermine whether an enterprise’sanswers are abnormal enough tomerit an expert evaluation. Whereasthe validity intervals referred toratios and the quality code to avariable, we look here for a criterionrelating to the statistical unit, i.e., toall the variables. The post-DEQIstatistical processing is called the“expert evaluation.” It consists incalculating a unit quality code,called an EXPERT code, whichguides subsequent processing.

To simplify matters, let us distinguishbetween three scenarios:

• The unit is acceptable as it stands:no additional operation is needed.• The imputations made are notdisputed, but one or more problemsnevertheless require action by theclerk: headings that remain to becoded, the APE code to be checkedwhen it has changed since theprevious year, etc. In this case, theunit data are taken as they stand fortabulation.

• The unit cannot be used as it standsto produce statistical results.

Situation (3) arises when the unit’sstatus is a total non-response. Yet italso arises with a “editing failure”: asimputed values are not available,they cannot be aggregated withothers (the editing failure should,however, be seen as a rareconfiguration). Last but not least, theunit is judged unusable when theimpact of imputation on at leastone target variable is too strong:after the imputation, we calculate thedifference between the gross valueand the imputed value, measure therat io of that di f ference to theaggregate of the variable, andcompare the di f ference with apreviously defined threshold.

This highly original principle is the maincriterion for rejecting or accepting theunit as a whole on the basis ofstatistical considerations. Thecombination of this technique andthe variable-editing method (consistencyassumption, not a search foranomalies) should significantly limitthe number of questionnaires sentback to the clerk and consequentlyreduce delays while ensuring quality.The aim is to attain overall statisticalquality rather than individual quality:some errors can remain as long asthey do not impair the overall result.

The “fourth generation” AnnualEnterprise Surveys also use theaggregate data editing technique. Thisis a fairly classic method in othercountries,but isnewtoFrenchbusinesssurveys. It consists in determiningwhether each unit has too strong aninfluence on certain target aggregatesor target ratios. The underlying notion isa fairly natural one: if one enterprisealone substantially changes thestatistics, it is preferable to checkwhether these data contain errors, evenif they have successfully passed theprevious stages in the process (DEQI inparticular). Therefore, if a targetaggregate is “appreciably” affected bythe unit, the unit is examined by theclerk. This again guards against the riskof impairing the overall results.

P. Rivière

44

What happens after the expertevaluation module? If the unit is notacceptable, it goes back to the clerk,who wi l l makes a number ofchanges, the most important ofwhich are:

• changing the value of a variableonce new information has beencollected;5• “controlling” the edits: acceptingthe value of a variable after it hasbeen analyzed, even if the edit on itsuggested an anomaly.

When the clerk has finished makingchanges, a new DEQI isautomatically launched, followed bythe expert evaluation module. Thislatter stage again judges whetherthe unit is acceptable or not. Theinteraction with the clerk thereforetakes the form of a cyclical process,which is broken once the unit isacceptable.

Extrapolating totalnon-responses

When publishing aggregate results,it is vital to take all units—especiallytotal non-responses—into account.The proport ion of totalnon-responses is never negligibledespite all the reminders sent. Theseunits cannot be processed by DEQIas they stand. So another methodhas to be found to extrapolate them.

There are three basic ways of doingthis: the use of n-1 data, reweighting,and hot-deck. Extrapolation usingn-1 data has been chosen whendata of sufficient quality from theprevious year are available for theunit. What is the best method here?Copying the n-1 data would ignorethe changes from one year to thenext. Alternatively, we could apply amedian growth rate to al l thevariables, but this is easier said thandone: among other things, we haveto ensure that the data are internallyconsistent—which is anything butcertain. The method finally adoptedconsists in using this mediangrowth rate to calculate a smallnumber of variables. The other

variables are then considered to bepart ia l non-responses. Al l thevariables are then put through theDEQI “mill,” which is relaxed slightlyso as to prevent any editing failures.

If there are no n-1 data (because theenterprise was not in the sample) orthese data are of poor quality, thehot-deck method is used. For eachextrapolat ion stratum, we puttogether a set of enterprises called“donors,” which are both acceptableand non-atypical. We then select aunit at random from this set and copyi ts data. We do this for al l theenterprises that are known to beactive. If this information is notavailable, we extend the donorcoverage to dormant enterprises inthe stratum. This means that thehot-deck may furnish a dormantenterpr ise, which cannot betabulated.

It should be noted that extrapolationappl ies not only to totalnon-responses, but also to otherunits deemed to be unusable as theystand, i.e., the above-mentionedediting failures and excessive edits.

Calculating growth fromone year to the next

Results dissemination is not limitedto the publication of aggregates forthe reference year. We also need todetermine growth in theseaggregates from one year to thenext. The fourth-generation surveycalculates it sector by sector. Thismay not seem too difficult at firstglance: to calculate the growth ofaggregate X from n-1 to n, a formulaof the type X (n) – X (n-1) /X (n) isapplied. However, this approachraises some problems.

From a theoretical point of view,insofar as the sample changes, theestimators are not optimal. We canhave a very good estimator of X (n)and X (n-1) without having anexcellent growth estimator, preciselybecause the sample changes fromone year to the next.

The New Annual Entreprise Surveys in France


Check-edit flowchart

Surveydatabase

manager

questionnairecorrection

D E Q I

5. A special instance: finding more informationfor a partial non-response.

More importantly, however, growthcalculated in this way conceals anumber of trends that would beuseful in analyzing the data and areerased by this simplistic calculation.For example, let s be a sector of 100enterprises each with FF100 millionin sales for year n-1. Suppose thatthis figure remains constant for allthe enterprises, but that fifty of themleave the sector. The aggregatetherefore decreases from FF10billion to FF5 billion—a 50% fall. Thegrowth obtained would have beenexactly the same if the sales for allthe enterprises in s had been cut inhalf and none had left the sector. Yetthe underlying growth is clearly notthe same.

The fourth-generation survey doesnot reject this calculation. It simplybreaks i t down into separatecomponents to accommodate thechange in samples. It divides thisapparent growth into six parts : (1)the growth of sustainableenterprises remaining in the sector,(2) births, (3) deaths, (4) entries intothe sector in n, (5) exits from thesector in n-1 and n, and (6) units thatare being restructured.

We also calculate economicgrowth in order to eliminate theeffects due to shifts (sector changesand restructuring) between n-1 andn. A real variation is thus calculatedfor the enterprises arriving in sector sin n, taking into account their valuesin n-1 even though they were not in sat that time. Conversely, the unitsleaving sector s between n-1 and n arenot factored into economic growth. Onthe other hand, the growth of enterprisesthat restructure their activities betweenn-1 and n is calculated here withrestructuring clusters,6 each ofwhich will receive an activity code. Allthe enterprises in the cluster are thusplaced in the sector corresponding tothe cluster code.

This process prevents restructuring from“polluting” the growth calculations byintroducing aberrant variations.

Moving to the fourthgeneration

The fourth-generation project hasinvolved the creation of a number ofcomputer modules, as detailedabove: DEQI, extrapolation, growthcalculat ion, pr incipal-act iv i tycalculation, etc. However, theseprograms in no way form a singleand comprehensive managementprogram delivered to each surveydepartment for immediate use in allAnnual Enterprise Surveys. Such aprogram would not be feasible, inparticular because each sector hasits own “sectoral” variables with theirspeci f ic stat ist ical processingmethods.

The project has therefore producedbasic “building-blocks”: it is up toeach survey department toincorporate them, build them intoits processing chain, and create itsown building-blocks such assectoral var iable-data edi t ingmodules. In part icular, thesurvey-clerk interface is put togetherseparately survey by survey, eventhough it is very closely linked to the“ fourth generat ion” generalprograms.

The transi t ion to the fourthgenerat ion therefore requiresextensive changes in each surveydepartment. In terms of informationtechnology, the use of common-coremodules has meant programmingan entirely new chain in an unfamiliarcomputer environment. From astatistical point of view, the newmethodology has also changedworking habi ts. Al l stat ist icalspecifications will now be written in adedicated language called SSL(Survey Specification Language) inl ine with the above-descr ibedmethodology. The questionnairesare no exception. They have to takeinto account the common core ofvariables that applies to them. Theclerk ’s work may change

considerably as well. The lowernumber of returned questionnaireswill probably lead to a decrease inrepetit ive tasks, but also to anincreased expert-evaluation role.

Nineteen-ninety-seven will thereforesee the launch of a new generationof Annual Enterprise Surveys infour survey departments:goods-producing industr ies,wholesaling/retailing, services, andthe agricultural and food industries.A number of computer and statisticaltests were carried out in 1996 inpreparation for this transition. Yet atest can never really replace thefull-scale use of a system. The firsttrials with the new software may welllead to some marginal adjustments.

In the longer term, the actualimprovements introduced by the“fourth generation” include twoparticular points of note:

• the fourth generation of surveys willdefinitely improve the “visibility” ofthe processing chain and, in sodoing, the control of the entireprocedure. In this regard, thecommon specification languagecompat ib le with the newmethodology is a major advance.

• the business-survey coordinationunit (today, the steering committee;tomorrow, the maintenancecommittee or an equivalent body),the common tools, and the commonstatistical techniques all make iteasier to run the system ofenterprise statistics as a wholeand make it more efficient. Thisopens the door for other rewardingcooperation between ministerialstatistics offices and consequently thegradual establishment of a range ofnew structures, methods, and tools.

Pascal RivièreHead of Enterprise Survey

Harmonization DivisionINSEE

P. Rivière

46

6. The restructuring clusters are calculated byanother system, called CITRUS, whichtransmis them to the Annual EnterpriseSurveys (see the article by P. Corbelpp. 47-50).

CITRUS, a new tool for enterprise statistics

Citrus is the French acronym for “Coordination of Information and Processing Operations onStatistical-Unit Restructurings,” a tool now being implemented at INSEE. As Patrick Corbelshows in this article, CITRUS evolved out of the preliminary studies for the fourth-generationAnnual Enterprise Surveys (EAE4G). Its prime purpose is to harmonize the treatment ofcorporate restructuring in different statistical applications. The server’s main data sources arethe official publications of record (gazettes) of business notices. By year-end 1996, the serverallowed a pre-printing of the annual surveys. In 1997, the first EAE4G change indicators willthus be able to incorporate consistently processed data on the main restructurings. The serverwill be available to all partners in the System of Enterprise Statistics by year-end 1997.

The project to set up a newcoordination tool arose from therecommendations of a workinggroup of the System of EnterpriseStatistics Committee.1 In December1992, the Committee wound up itsproceedings with a proposal toestablish “a centralized tool, readilyaccessible as a SIRENE satellite (aswas done with OCEAN or, if feasible,via OCEAN itself).”

Five years later, we can say that theprogram outlined above has beenfleshed out with more detai ledspecifications, but that its spiritremains broadly faithful to the initialproject, which aimed to centralizeinformat ion and harmonizeprocessing operations.

Keeping track ofrestructurings

For enterprise statisticians, thedef in i t ion of “restructur ing” isultimately a matter of contingency:restructurings are “the events thatcause disturbances in the statistics.”As specifically regards enterprises,and to define these “disturbances”more accurately, we can speak ofthem as “activity transfers.”2

When the French conglomerateThomson CSF, for example, spinsoff some its operat ions tosubsidiaries, the statistician will

observe a break in the series onactivity levels of legal units, andt h e r e f o r e a b r e a k i n t h e“enterprise statistics,” at least att h e i n d u s t r y l e v e l , i f t h etransferred activit ies belong todi f ferent headings of the NAFclassification.

Such events would not be of greatconsequence if full information wereimmediately available, if all the unitsinvolved and the transferredactivities were well documented, ifthe impact on the variables trackedin the stat ist ics were easi lymeasurable, and so on.

It is not hard to see that thesedifficulties, in practice, create manyinconsistencies (between sources,and between annual and within-yearstat ist ics), raise many issues,generate partial responses fromfirms, and complicate the updating ofstatistical tools such as SIRENE andOCEAN.

To address these difficulties, fourgoals were assigned to CITRUS:

• To collect the key information onsignificant restructurings and makeit available in a timely manner to allpartners in the System of EnterpriseStatistics.

• To supply on a simultaneous basis

the data and methods—changeindicators, within-year and annualindicators, data summaries—thatwill make it possible to harmonizethe treatment of restructuring indifferent statistical applications.

• To eliminate redundant querying.

• To manage the information in asingle place , so as to avoiddivergences and allow statisticiansto prepare an author i tat ivesummary.

Courrier des statistiques, English series no 3, 1997 47

1. In French: “EAE4G comité SSE.” See thearticles by J.P. Grandjean and Pascal Rivièrein this issue.2. This article does not directly address theissue of how to define “restructuring.” Inkeeping with the proposals of the SES workinggroup, the term as used here actually denotestransactions that (1) influence the continuingproduction activity of economic units (theproduction account and generation-of-incomeaccount in the Intermediate System ofEnterprise Accounts) and (2) are materializedby simultaneous transfers of productive fixedassets and of employees. This definitionencompasses the following categories: (1)restructurings" in the narrow sense (fullmergers, splits partial mergers) to whichspecial legal and tax arrangements apply(French law of 1996 on “business companies”and later amendments); (2) equivalenttransactions, known as “cash transactions,”that are not eligible for tax relief; (3) franchisingagreements.

Systematic collection andharmonized treatment

To gather vital information in theshortest possible time, the first stepis a systematic processing of datafrom legal notices. CITRUS will usetwo sources for this: (1) the BALO,3which announces plannedrestructuring operations one monthbefore shareholders’ meetings; (2)the BODACC,4 which publishesannouncements of sales,divestments, and other changes incorporate structure.

In 1996, CITRUS began byprocessing BALO notices. Theinformation obtained and enteredinto the data base concerned some 150restructurings and 400 enterprises.In 1997, the server input is beingwidened to: (1) regular electronic“feeds” from BODACC on“demographic events” recorded in theSIRENE register under theDOCMODIF program and (2)information sent by SES partners, suchas the Inventories, Revenues, andExpenditures Survey, industrysurveys, VAT-form data, businessconfidence surveys, etc.

Once the server starts receivingregular inputs from the sourceslisted above, its first function will beto conduct a thorough examinationof this set of events in order toensure that the transactions are fullydescribed and to isolate the most

important transactions for a moreexhaustive treatment.

All the statistical uses of enterprisedata for compiling change indicatorsinvolve the calculat ion ofvar iable-f ie ld resul ts andconstant- f ie ld resul ts. Theconstant-f ield results adjust orcomplement the var iable-f ie ldstatist ics by measuring (1) theimpact of movements into and out ofthe field, (2) unit births and deathsunrelated to restructuring, and (3)restructurings themselves.

The operation that has come to beknown as the “ inclusion ofrestructurings” thus consists indetermining the “restructurings”component o f constant- f ie ldstat ist ics. This involves (1)identifying the units to exclude themfrom other stages of thechange-indicator calculation; (2)assigning different weights to eachof these units or to the set of unitsincluded in a single restructuring:this is to make sure they will appearin the change calculation only to theextent of a comparable activity for agiven transaction.

The basic method we propose forsolvingthisproblemcomprisestwosteps:

• Al l the uni ts involved in arestructuring are classified in asingle economic-activity category forthe purposes of the changecalculation (on a constant-field basis).

• Any non-additive variable in the setof units included in the restructuringwill be benchmarked on the knownor estimated value added of all theunits.5

To implement this proposal, CITRUSwill provide the following items foreach identi f ied and processedtransaction:

• list of units concerned (legal unitsidentified by a SIREN number);

• activity classification relevant to thecalculation of the constant-fieldchange indicator;

• the “aggregation” coefficient thatshould be applied to these units in orderto determine a “comparable-activity”change.

Complementary surveyof businesses

Some restructur ings involvesubstantial activity transfers andhave a powerful impact on industrystatistics. In these cases, it is best toobtain as soon as possible:

• a detailed, accurate description ofthe activities concerned;

• the most accurate possibleassessment of the impact on themain act iv i ty var iables: sales,number of employees, value added.

These cases will be dealt with bygathering data from enterprises bymeans of written forms or—when theimpact on the industry is thought tobe greater—by interviews. Thequestionnaire will likely concernsome fifty restructurings a year andwi l l be sent to the “ leadingenterprise” in the transaction.

This “supplementary” inquiry, even ifit applies to a small number of firms,should actual ly reduce thepaperwork burden for enterprises.This requires closer coordinationamong partners.6

A coordinated system

The proposed system comprises acentral tool managed by a smallteam, and a communicat ionsmodule to provide consistency withother SES tools (SIRENE, OCEAN,SUSE) and links with partners whowill also be the system users.

The central team not only managesCITRUS but also serves as expert,primarily for analyzing the mostcomplex or most signi f icantrestructurings. The managers alsoperform the following tasks with thesupport of the central-tool functions:

P. Corbel

48

3. Bulletin d’Annonces Légales Obligatoires, apublication of record (gazette) in which listedcompanies and their subsidiaries are requiredto advertise their intended full mergers, splits,or partial mergers.4. Bulletin Officiel d’Annonces Civiles etCommerciales, a companion publication to theBALO.5. This is the basic proposal for harmonizingthe change-indicator calculations. Theproposal will be applied for the first time to thetreatment of restructurings in the initial resultsof the EAE4G. This principle needs to beelaborated in greater detail, notably in thecase of within-year statistics (specifyingprocedures for register ing dates ofimplementation and decision; taking intoaccount whether the restructured units belongto the samples or not).6. As is already the case with SIRENE and theInventories, Revenues, and ExpendituresSurvey.

• selecting the most significantcases;

• ensuring the time-consistency ofinformation;

• settling or identifying any difficultiesthat may occur, such asinconsistencies in industryclassifications.

SES partners supply the server withevents or informat ion onrestructurings; in exchange, theyuse the server in two ways: to accessthe ent i re set of informat ioncollected, and to exploit its valueadded—such as informat ion-checking, industry classification,and data needed for statisticalapplications.

Two-way communicat ionprocedures have been set upbetween CITRUS and the othersystem tools: SIRENE, OCEAN, andSUSE, to which one should add the4G Annual Enterprise Surveys as“common-core tool.”

Within the project f rameworkestablished at year-end 1994 byINSEE and non-INSEE partners, thefirst steps completed were thedefinition and validation of userneeds and the drafting and approvalof the project feasibility study.

The main purpose of the feasibilitystudy was to define the data-base“objects” and the methodologicalprinciples to be applied. In addition,the study lay the groundwork for the

choice of architecture. This was acrucial decision, since the futuresystem involves not only a centralteam but many applications andmanagement departments, both inand outside INSEE.

The future system will therefore relyon:

• the PARADOX data base, availableto central-team managers throughan interface that already exists inprototype form;

• the ADABAS data base on thecentral site (a PARADOX replica),available to all partners or users via3270 terminals or PCs with 3270emulation; the central site will thusserve as a br idge for two-way

Citrus, a new tool for enterprise statistics

Courrier des statistiques, English series no 3, 1997 49

Partners

CITRUS partners and users jointly defined the project’s framework and functions. The steering committee and the usercommittee provided the forums for this cooperation.

CITRUS partners manage the following surveys and operations:

• At INSEE

- processing of VAT returns (CA3 forms)

- Inventories, Revenues, and Expenditures Survey

- business confidence surveys

- Annual Enterprise Surveys in retailing and services

SIRENE, OCEAN, and SUSE

• In ministerial statistics offices

- Annual Enterprise Survey

- in manufacturing (SESSI)

- in construction (SDISC)

- in transportation (OEST)

- in food and agriculture industries (SCEES)

- other surveys, notably SESSI surveys (industry surveys and other)

- DARES surveys.

While not directly associated with the project, other INSEE users in the following areas are also concerned: national ac-counts, industry monitoring, goods and services statistics, economic studies.

informat ion f lows betweencentral-team managers and users orpartners.

Goals for 1996 and 1997

The implementation stages wereplanned to meet the constraints ofthe EAE4G project schedule: theCITRUS server was ready atyear-end 1996 for the launch of theAnnual Enterprise Surveys, whichinvolved an OCEAN update and thepreparation of data for pre-printingthe survey forms. The server mustalso be capable of receiving surveydata, and then supplying data for thechange-indicator calculations of thenew-generation survey in Q2 1997.

As a first step in compil ing theinformat ion needed for theseapplications of the first 4G surveys,data from the BALO gazette havebeen fed into the CITRUS data base.

The detai led study and theconstruction were completed in timeto open the server input facility to allsources in early 1997. The projectwill be finished in the second half of1997.

Patrick CorbelEnterprise Survey

Harmonization DivisionINSEE

P. Corbel

50

TCP-IP

OCEAN: toward a general system forsample selection and coordination

Sampling procedures are one of the most crucial operations in business surveys: samplecoordination is a prerequisite for limiting the “response burden”—the target of recurrentcomplaints by enterprises. Hence the need to establish a common, proven methodology forensuring a consistent, homogeneous survey sample. Such a methodology—described in thefollowing pages by Guy Laflamme and Pascal Rivière—was developed for the AnnualEnterprise Surveys (EAEs), which provide one of the most important sources of information onthe French production system. Since 1989, the EAEs have been using the annual-surveycoordination tool called OCEAN (for Outil de Coordination des Enquêtes Annuelles), whichenables the response burden to be more evenly distributed—or actually reduced—whileensuring greater consistency in the System of Enterprise Statistics.

In an article published in Courrierdes statistiques in 1989,1 F. Cottongave a detailed description of theOCEAN mechanism used by theAnnual Enterprise Surveys. Theart ic le was wri t ten before theOCEAN came into use. This seconddiscussion of the subject will give abr ief assessment of i ts actualuse—past, present, and future.

Two functions: sampleselection and facilitating

the information flow

OCEAN serves two main purposes:to select a sample and to facilitatethe flow of information betweensystem partners. OCEAN, therefore,is neither a survey managementsoftware package nor a register ofstatistical units. It must be usedjoint ly with other tools for thesuccessful completion of a survey.

Unl ike the sample-select ionfunction, the application to handleinformation flows between partnerswas specifically developed for theAnnual Enterprise Surveys. It cannotbe used “as is” for another survey.Apply ing this methodologyelsewhere would thus require aninvestment that would only be

justified for a major survey with alarge sample. These conditionshave been met only once, when anenvironment resembling OCEANwas set up for several Labor Ministrysurveys. However, while based onthe same concepts, the twoapplications remain distinct, for theyuse different sample-selectionmethods.

The sample-selection functioncomprises two stages: theconstruction of a sampling frame and,from that frame, the actual selection.

Constructing the samplingframe

The sampling frame gives access totarget-population units for which wewant to produce estimates. Inbusiness surveys, the sampling frameis a list of target-population units fromwhich the sample is drawn.2

To identify the units in the population,we need to define the survey fieldbased on the known characteristicsof the units contained in the initial list.One could describe this as a filterapplied to an initial list in order toextract the sampling frame. InOCEAN, the characteristics that can

be used for this purpose are: principalactivity, legal status, and number ofemployees in the unit at December 31.

To make sure that the samplingframe created by OCEAN will workfor several different surveys, wedefine a very wide field. The fieldcovers all active and non-singularunits3 engaged in a market activity.This so-called “overall” samplingframe is created once a year andcontains about 3.7 million units.

No survey makes direct use of theoverall sampling frame. In practice,the field of a given survey is definedby means of a file containing thethree variables mentioned above. Asthe overall sampling frame includesboth enterprises and local units, it


1. F Cotton, “OCEAN”, outil de coordinationdes enquêtes annuelles," Courrier desstatistiques no. 52, Dec. 1989.2. In household surveys, it is often impossibleto compile the list of individuals to becontacted; in such cases, other techniques areused.3. The “active/non-singular” code, managedby the SIRENE register, makes it possible toidentify the local units engaged in an economicactivity of their own. By eliminating suchcategories as units that lease facilities,non-operating units, etc., the code preventsdouble counting.

can be exploited for a wide range ofsurveys.

The main qualities of an effectivelist-type sampling frame are:

• exhaustiveness: the list mustcontain all the units belonging to thetarget population;

• exclusiveness: the list containsonly the units belonging to the targetpopulation;

• lack of duplicates: each unit islisted only once;

• high information quality, inparticular its recency.

Comparing informationsources

OCEAN uses three informationsources to create the overall samplingframe: the SIRENE register, a portionof the Annual Enterprise Surveyresults, and the previous year’s overallOCEAN frame. The initial list of alllocal units and enterprises is extractedfrom SIRENE. The overall samplingframe thus meets the first threerequirements listed earlier, as all localunits active in France must registerwith SIRENE. Moreover, SIRENE isthe only entity responsible for issuingthe single identification number(SIRET) for each local unit. All thisgreatly reduces the risk of duplication.

The use of Annual Enterprise Surveydata improves the quality of OCEANinformat ion. A change in uni tcharacter ist ics may not beimmediately recorded in SIRENE,whereas the survey contacts manyenterprises each year. It is entirelypossible, therefore, that a morerecent item of information may bepresent in the survey but not inSIRENE. That is why, when settingup the overall sampling frame, wecompare the SIRENE and surveyinformation on units included in bothfiles. The comparison rules can behighly elaborate for some variables,but, as a rule, the more recentinformation takes precedence. Theyear-earlier sampling frame is usedto check replacements in the surveysample.

The overall sampling frame obtainedin this manner contains fewvariables, so as to avoid storinginformation already present in otherfiles. However, it does centralize theinformation needed to coordinatethe samples for two surveys. At theend of the processing sequence, theoverall sampling frame containsidentification variables, stratificationvariables, a brief statistical history ofthe unit, and a random numberbetween 0 and 1. The samples areselected and coordinated on thebasis of this unit’s random number.

Thanks to i ts sol id theoret icalfoundations, OCEAN is capable ofselect ing samples strat i f iedaccording to three variables. Theuser must specify the survey fieldand the sampling plan. The selectionis performed stratum by stratum, inkeeping with the sampling plansuppl ied. The basic select ionprinciple is very simple: to select nunits in a stratum, the user simplychooses the units corresponding tothe n smallest random numbers.4

G. Laflamme, P. Rivière

52

INDU

UU

STRY

CON

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O

T

NSP

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RETAIL

SERVICES

SMALL

EAE SURVEY UNITS

SIRENE

SAMPLINGFRAME

OCEAN

OCEAN

DIFFUSIONBatch

updates

Sampling

Construction,update

EAR

TRANSFERS

Samplingplans

OCEAN: files partnerspartners

VADERETRO

AVISO

procedures

Construction,update

Real-timeupdates

Food&agri

c.

STRUCT

BUSINESSES

4. In reality, the OCEAN sample selection ismore complex, for i t includes arandom-number management procedure anda method for controlled rounding of samplesize in each stratum.

OCEAN in practice

Many OCEAN funct ions weredeveloped to meet the specificrequirements of the AnnualEnterpr ise Survey, which wi l ldoubtless remain the principal user.The survey therefore provides aconvenient illustration of OCEAN inactual use.

The Annual Enterprise Survey isthe main source of structuralstatistics; it supplies many estimatesneeded for preparing the nationalaccounts. The est imates areproduced from data gathered from asample of enterprises concerningthe financial year just ended. Thesurvey covers six broad sectors ofeconomic activity: industry (i.e.,manufacturing and related); foodand agriculture; construction andpubl ic works; t ransportat ion;wholesale/retail trade; services. Thesurvey field is defined on the basis ofpr incipal act iv i ty, number ofemployees, and legal status. Thefields are coordinated to make surethat all the economic activities arecovered and that each enterprise isincluded in just one survey field,without omissions or double-counts.

Each sector is handled by a separatesurvey unit. Four are managed bythe survey department of a ministrywith authority in that sector; thesurveys of the wholesale/retail tradeand services are managed byINSEE.

The survey sample is selectedf rom a sub-set of the overal lsampling frame every December.The survey universe contains about1.8 million enterprises; the sample,about 190,000 enterprises. Thesample comprises two groups: (1) an“exhaust ive” group of 80,000enterprises that are surveyed eachyear without sampling, and (2) asampled group. Each year, one-halfof the sampled group is replaced.

The entire survey sample selectiontakes about two weeks. There arethree main reasons for theDecember timing:

• to take advantage of the massupdates of the SIRENE population,which occur in the fourth quarter;

• to obtain a maximum amount ofinformat ion on enterpr isescontacted by the survey;

• to meet the schedul ingrequirements of the surveying units,which want to be able to start theirsurvey in early January.

The list of enterprises to contact isforwarded to each surveying unitand also loaded into a data basecontaining al l the enterpr isessurveyed in EAEs since 1989. Thisdata base is the central node for thecirculation of information betweenEAE partners, i.e., the surveyingunits and SIRENE. The base itself isinappropr iately referred to asOCEAN: in fact, it only stores theinformation needed to manage thesample, along with a series ofkey-event dates.

At every stage of the surveyprocessing, the OCEAN data base isupdated by the surveying units (forexample, to record the death of anenterpr ise l isted as l iv ing inSIRENE). The information receivedfrom respondents may entail anupdate of the principal activity code.In some cases, this update shifts theenterprise to the field of anothersurveying unit. When this happens,OCEAN sends a transfer-alert signalto the surveying unit concerned.

The updates can creatediscrepancies between SIRENE andOCEAN information on a givenenterprise. An OCEAN procedureidentifies these discrepancies andreports them to SIRENE. TheSIRENE staff can then conduct aninvest igat ion to resolve theinconsistency between the twosources. While this procedure canbe applied to several variables, onlythe discrepancies on the principalactivity have been handled so far.

Updating the OCEAN data baserequires a considerable effort by thesurveying units. This investment is

ful ly just i f ied by the resul t ingbenefits, as the surveying units gaina simple, ef fect ive access toSIRENE resources. In exchange,SIRENE gains a complementaryinformation source enabling it toidentify hitherto undocumentedchanges. Lastly, the OCEAN database is the channel for the EAE dataused to construct the overal lsampling frame, whose quality isimproved as a result.

Sample coordination

Thanks to a suitable “management”of the random numbers,5 OCEANal lows the coordinat ion of (1)consecutive samples of a singlesurvey; (2) samples of differentsurveys in a given year; and even (3)local-unit samples and enterprisesamples. This “management” issufficiently flexible to meet the needsof most enterprise surveys. Thethree types of coordination aredescribed more fully below.

(1) The time coordination ofsamples of a single survey isdesigned to str ike a balancebetween the need to lighten theresponse burden and the need forrobust est imates of change.Obtaining good est imates ofchanges between two periodsrequires a large overlap between thesamples. However, when theoverlap rate rises, so does theresponse burden, al though i tbecomes more concentrated. Hencethe need for a compromise that willminimize the burden while making itpossible to calculate reasonablyaccurate estimates of change. Asmentioned earlier, one-half of eachsurvey sample is replaced everyyear. In fact, this does not mean thatonly 50% of the enterprises aresurveyed again. The selection in thereplaced part is not linked to theprevious year ’s hal f -sample.

OCEAN


5. For details of this method, see F. Cottonand C. Hesse, “Tirages coordonnésd’échantillons,” INSEE working paper E9206.

Consequently, some of the units arere-selected.

(2) The number of surveys that arespondent must answer in a givenper iod is the respondent ’s“response burden” at that date. Thecoordinat ion of samples ofdifferent surveys is a imed atfinding the right tradeoff betweenreducing the burden and obtainingfu l l in fo rmat ion on eachrespondent . By combin inginformation from two surveys, wewill obtain a fuller, more detailedpicture. This gain does, however,inc rease the burden forrespondents. If the combination ofthe surveys is shown to benon-essential, every effort will bemade to avoid sample overlaps.This is done by renumbering theunits: as the selected units carrythe lowest random numbers, wechange the numbers so as to placethe uni ts just surveyed at thebottom of the list, without violatingthe ind ispensab le s tochas t icproperties.

(3) Some parameters also enable usto coordinate a local-unit samplewith an existing enterprisesample.6 If the coordination ispositive, the local units of a selectedenterprise are themselves morelikely to be selected. By combining

the data on the enterprise with thedata from its local units, the user willobtain a broad range of information.But there is the usual downside: aheavier response burden for units ofa single enterprise.

Two points are worth making beforewe conclude this section:

First, the OCEAN methodologyexhibits a particularly valuablefeature: it allows the coordinationof samples of surveys withdifferent fields and differentstratifications.

Second, one should always bearin mind the unavoidablel imitat ions of coordinationmethods —whatever the methodused. F o r e x a m p l e , o n e c a nnever s e p a r a t e t w o s a m p l e ss e l e c t e d f r o m t h e s a m ep o p u l a t i o n i f t h e s u m o f t h esampl ing rates exceeds uni ty.Speci f ical ly, there is no point incoordinat ing the “exhaust ive”po r t i ons o f samp les . Bu t thep r e s e n c e o f a n e x h a u s t i v ep o r t i o n i s v i t a l t o o b t a i n i n geconomic statistics of acceptableq u a l i t y . L a s t l y , t h e p r o p e rtreatment of deaths and births ofs t a t i s t i c a l u n i t s — a l w a y s adelicate operation in enterprisesurveys—can reduce the user’sroom for maneuver.

Toward a general system?

OCEAN is not yet a truly all-purposetool capable of selecting coordinatedsamples for any type of enterprisesurvey. Indeed, the selection ofsamples for certain DARES surveyssuch as ACEMO and EMMO requiredthe development of a new tool, calledOCEAN-DARES, based on ratherdifferent theoretical principles.

In view of the growing pressure fromenterprises—particularly from smalland medium-sized businesses—tolighten the response burden, it willbe necessary to take account of the“overal l ” burden. To meet thischallenge, OCEAN needs to beremodeled into a general tool forsample selection. The first step willbe to reassess the methodology usedto select and coordinate samples.Reducing the burden will also requirethe development of a tool thatenables users to prepare efficientsampling plans. Ultimately, OCEANwould become a “toolbox” containingall the resources needed to selectsamples for most enterprise surveys.

As this article has tried to show, thegoals are ambitious, and it wil lcertainly take much time to reach thedesired degree of generality. Weinvite the reader to a fresh progressreport in seven years’ time.

Guy Laflamme andPascal Rivière

Enterprise SurveyHarmonization Division

INSEE

G. Laflamme, P. Rivière

54

6. Conversely, OCEAN al lows thecoordination of an enterprise sample with anexisting local-unit sample.

France’s first Industrial GlobalizationSurvey

The Arthuis Report on the economic impact of cross-border business relocations, publishedin June 1993, noted the lack of awareness in France of the extent of globalization in thenation’s production system. As Michel Hannoun explains in this article, no statistics had everbeen gathered before in France to identify and quantify intra-group trade (IGT) —yet thesedata are of considerable strategic value to policy-makers and business management. In May1993, SESSI1 issued a blueprint for a survey on this topic, developed by a steering committeecomposed of representatives from a wide variety of organizations.

The survey, in conjunction with the Annual Enterprise Survey (EAE), tapped many sources todetermine the degree of involvement of French enterprises in the globalization process. Thesurvey covered 6,800 enterprises; the IGT data obtained on industrial groups located inFrance would be worth supplementing with a harmonized European survey initiated byEurostat.

Market globalization and integrationhave highlighted the need for twoimportant tasks: the revamping of theclassic indicators used to track industrialeconomies, and the reappraisal of thetheory of international trade. Theglobalization of industrial productionremains a major component of thispresent phenomenon. Its two mainconsequences are:

• The growth of intra-group trade(IGT) within multinational firms, bothlarge and small.

• A geographic extension ofsubcontracting and manufacturingpartnerships beyond purely nationalborders. Business units belonging toenterprise groups—but alsoindependent companies—areincreasingly turning to independentforeign partners. These practices arebeing adopted not only bygoods-producing enterprises but alsoby mass merchandisers and formermanufacturing companies. After closingdown their domestic productionfacilities, manufacturers have taken tomarketing—under their exclusive brandnames—goods produced abroad bysubcontractors.

Intra-group trade: aglobalization indicator

These flows of merchandise importsand exports are fairly easy to identifyand quantify. They form the basis of arobust statistical approach to a complexand as yet under-documentedphenomenon: the degree o fglobalization of a national productionsystem and of i ts const i tuentindustries.Astatisticalgaugeof thiskindisof strategic value both for policy-makersand corporate management.

In the short and medium term,exports and imports betweenenterprises belonging to the samegroup are relatively unaffected by thestandard trade determinants: tariffsand exchange rates. By measuringthese flows, we can define a freshapproach to competit ivenessindicators such as market share,penetration rate, and trade balance.

The cross-flows between French firmslocated abroad and foreign firms inFrance have a quantitative andqualitative influence on job locations.The same is true of partnershiparrangements with foreign

subcontractors—a cheaper, moreflexible type of globalization thandirect investment. Both trends lie atthe heart of the debate on themedium–and long-term effects ofindustrial globalization on domesticjobs.

No statistics had ever been gatheredbefore in France to identify andquantify intra-group trade (IGT). Bycontrast, surveys on the topic havebeen conducted for a number of yearsin the United States (five-year surveyof foreign subsidiaries in the U.S., andof U.S. subsidiaries abroad, by theCommerce Department’s Bureau ofEconomic Analysis) and in Japan(MITI annual survey of Japanesesubsidiaries overseas). The debateson the economic impact ofcross-border business relocationsunderscored this statistical vacuum(see the Arthuis Report published inJune 1993). For this reason, SESSIvolunteered, in April 1993, to preparea specific investigation into thesubject.


1. Industrial statistics office of the Ministry ofIndustry, Posts, and Telecommunications.

A project elaboratedin 1993

A draft survey was submitted to theindustry unit of the National Council onStatistical Information (CNIS)onMay12,1993. The Council approved the idea ofa survey, subject to the establishment ofa steering committee composed ofrepresentatives of enterprise and tradeorganizations, as well as trade expertsfrom various French institutions (CEPII,DREE, Bank of France, universities,

INSEE, etc.). The committee was setup in June 1993 and worked on thesurvey goals, coverage, andquestionnaire. SESSI’s initial projectwas modified to take the committee’scriticisms and proposals into account.A semi-final version of thequestionnaire was ready bymid-October 1993. The document’suser-friendliness and the responsecapability of enterprises were testedthrough visits to a sample of twentycompanies. The steering committeeconcluded that a full coverage of the

phenomenon would also require asurvey of the sales subsidiaries ofFrench and foreign industrial groupsand of the leading independentimporters. This finding led INSEE, asthe inst i tut ion responsible forstatistical surveys of the serviceindustries, to take charge of a jointsurvey in this field. CNIS approvedthe project at year-end 1993 andadded it to the off ic ia l l is t ofcompulsory surveys.

Some characteristicsof the survey deserve emphasis:

Linkage to the AnnualEnterprise Survey

This investigation was conducted aspart of the surveys linked to the AnnualEnterprise Survey (EAE), which coversthe 24,000 French industrial enterprisesemploying more than 20 people. Fromtime to time, SESSI complements theEAE with one-of-a-kind surveys ontopical statistical issues such asinnovation (1991) and the environment(1993). This combination makes itpossible to focus the questions on thespecial topic without re-examining allthe economic and financial variablescovered in the regular annual survey.The arrangement has proved efficient,but for practical reasons the mailingsand questionnaire-processing for theIndustrial Globalization Survey (EMI93)and EAE were conducted separately.

A well-focusedinvestigation thanks to

multiple sources

One cannot readily identify theenterprises involved in globalizationand, more precisely, in intra-groupinternational trade.

An exhaustive survey of the 24,000French industr ia l enterpr isesemploying more than 20 people andof commercial enterprises wouldhave been too bulky. We thereforehad to develop a robust method fordetecting the enterprises most likelyto be involved in the phenomenon.For this, we asked three questions:

M. Hannoun

56

Industrial groups in French foreign trade

Source: SESSI industrial globalization survey

Interpretation: French industrial exports break down as follows:. 34% from an enterprise incorporated under French law and controlled by an internationalgroup* (by a French group: 21%; by a foreign group: 13%) to an enterprise abroad belongingto the same group;. 34% from an enterprise incorporated under French law and controlled by an internationalgroup to a non-group enterprise;. 32% in sales abroad by French enterprises that do not belong to an international group.The “imports” pie chart shows flows in the opposite direction between the same categories.* referred to in French statistics as EFGIs (see abbreviation table p. 37).

• Does the enterprise belong to agroup? In other words, is i t asubsidiary (str ict ly more than50%-owned) or an aff i l iate(ownership strictly greater than 10%and less than or exactly 50%)? Thisis a necessary but not sufficientcondi t ion for the existence ofintra-group flows.

• If the answer is yes, does the groupcontrol at least one enterpriseabroad? This is a necessary but notsufficient condition for the existenceof intra-group import and/or exportflows. The answer is easy for Frenchsubsidiaries of foreign groups, butmore complex for French groups.

• If the enterprise is independent,does i t outsource to foreignsubcontractors?

For industr ia l enterpr ises, theinvestigative method draws onancillary statistical sources for threesub-populations:

• French subsidiaries andaffiliates of a foreign group: theseenterpr ises can be accuratelyidentified by the register of foreigninvestment in France, kept by theFrench Treasury.

• Foreign subsidiaries andaffiliates of a French group: atwo-stage procedure is used: (1) Weidentify the French enterprises thatcontrol or hold a minority interest inat least one enterprise abroad. Thesource used is the register of Frenchinvestment abroad, managed by theFinance and Economics Ministry’sforeign-trade directorate (DREE). (2)These French enterprises are thenidentified in the INSEE-managedregister of financial ties (LIFI). Thissource provides identification of theparent group, as well as all thegroup’s French subsidiaries andaffiliates located abroad—whichqualifies them for inclusion in thesurvey field.

• Independent French enterprisesoutsourcing to foreignsubcontractors : the lack ofdedicated sources makes this group

hard to ident i fy. The fol lowingempirical criterion is used: anyindependent enterprise whose 1992ratio of outsourced production tototal production is equal to or greaterthan 10% (according to its responseto the Annual Enterprise Survey) isregarded as a major potent ia loutsourcer to foreignsubcontractors.

INSEE has used a similar method todeal with wholesale firms.

In al l , 6,800 enterpr ises weresurveyed. The total comprised 6,000industr ial enterpr ises and 800commercial enterprises.

A slim questionnaire

The four-page quest ionnaireconsists of six boxes:

• Box I: enterprise and respondentidentification.

• Box II : veri f icat ion of type offinancial links. The aim is to establishwhether the enterprise effectivelyl ies within the f ie ld of theglobalization survey. If the answer isyes, we check whether theenterprise belongs to one of thesub-populations described above.

• Box II I : quant i f icat ion of theproduct ion and workforcebreakdown between France andabroad, for the enterprise itself and,in some cases, the parent company.

• Box IV: quantification of intra-groupexports of industrial products. Theexamination method is based on amatrix of product-country pairs.Product coding is based on theFrench classification of products at3-digi t level . Real- l i fe tests inenterprises showed that this level ofdetai l of fers an acceptablecompromise between companypractices and the requirements ofstatistical analysis. Countries arecoded with the French customsclassi f icat ion. For each

France's first Industrial Globalization Survey


Intra-group trade as share oftotal cross-border trade by enterprise groups

Source: SESSI industrial globalization survey

Interpretation. Sales by enterprises incorporated under French law and controlled by aninternational group (EFGIs) to enterprises of the same group located in the EU accounted for61% of total EFGI exports to the EU; the corresponding share of exports to Japan was 36%.Purchases by EFGIs from enterprises of the same group located in the EU accounted for 49%of total EFGI imports from the EU; the corresponding share of imports from the United Statesor Canada was 30%.

product-country flow, the enterpriseis asked to supply the transactionamount in thousands of Frenchfrancs and to assign a code to theproduct-country pair from a simple,seven-category classification thatidentifies the product’s use.

• Box V documents intra-group importsusing the same method as Box IV.

• Box VI covers subcontracting andindustrial partnerships betweenFrench outsourcers and foreignsubcontractors where no financiallink exists between the two parties.The product-country breakdown ofsubcontracting work performed bymanufacturing partners is handledwith the same method used toclassify intra-group exports. Theenterprise is asked to specify thetype of partnership using afour-category classification basedon the al locat ion between thepartners of six stages in theproduct ion process: design,sourcing, manufactur ing ofcomponent elements, assembly,finishing work, and commercialdistribution.

In the field

The survey management clearlybenef i ted from the SESSIinfrastructure in many areas:compilation of the “start-up” file frommult ip le sources, customizedprintout of forms on a non-impactpr inter, etc. Speci f iccomputer-management applicationswere developed from the BLAISEsoftware package (produced by theNetherlands Statistical Office).BLAISE proved to be an efficientmanagement tool for a survey of this

kind. Statistical operations—qualitycontrol, editing, estimation, andanalysis—were performed with theaid of SAS software.

The questionnaire was sent to thesampled enterprises in early April1994. Two reminder letters and atelephone fol low-up campaignyielded fairly satisfactory responserates: 80% in terms of the number ofenterprises and 95% in terms of thevariables of study, i.e., imports andexports.

While the response rates are broadlyadequate, the same cannot be saidof the spontaneity of the repliesthemselves. As in any “first-time”survey, some unexpected difficultiesarose. For example, by construction,the detai led responses byproduct-country pair should havematched the information in customsforms. But a col lat ion of theresponses with the customs datashowed frequent discrepancies thatcould only be settled by recontactingthe enterprises concerned. Toensure an acceptable responsequality in a future survey, it wouldcertainly be a good idea to make thereference to customs data moreexpl ic i t . In sum, responseprocessing proved morecumbersome and hence moret ime-consuming than ini t ia l lyexpected. Almost two-thirds of therespondent enterprises had to becontacted by telephone forreminders or additional information.

We relied on data from fully-checkedenterprises to correct any residualdiscrepancies with customs dataand to estimate non-responses.

The early results of the survey weresubmitted to the steering committee,

which had met in 1993 to prepare thesurvey. The findings offer highlyoriginal insights into the size ofintra-group trade by industr ialgroups located in France. Suchtransactions account for 34% ofFrench industrial exports. This rate iscomparable to that observed in theUnited States and Japan (see thefour-page SESSI bulletin no. 49,June 1995). The results are beingfurther analyzed, notably with theuse of other available statistics onglobalization. The conclusions are tobe publ ished in the “SESSIStatistiques” series and a volume ofcollected studies.

The Industrial Globalization Surveywas thus completed in twoyears—from the launch decision tothe release of initial results. The timespan may seem long, but we aredealing with a first-ever operation.The lead times on a subsequentsurvey may be considerablyshortened by taking steps to copewith the two main problemsencountered: the exactdetermination of the survey field,and the checking of responsesagainst customs data. We hope this“first” will serve as a model for otherstatistical offices and that the nextsurvey wi l l be conducted on aharmonized basis by otherEuropean countr ies act ing onEurostat’s initiative.

Michel HannounSESSI

Ministry of Industry, Posts,and Telecommunications

M. Hannoun

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