Contents and use of register-based job files. Svein Gaasemyr and Steven Vale, Eurostat and Peter Struijs, Statistics Netherlands 1

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1 Working Paper No. 32 ENGLISH ONLY STATISTICAL COMMISSION and ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS STATISTICAL OFFICE OF THE EUROPEAN COMMUNITIES (EUROSTAT) Joint ECE/Eurostat Work Session on Registers and Administrative Records for Social and Demographic Statistics (Geneva, 1-3 march 1999) Session 2, supporting paper Contents and use of register-based job files By Svein Gaasemyr and Steven Vale, Eurostat and Peter Struijs, Statistics Netherlands 1 1. Introduction Eurostat arranged a seminar on use of administrative data for statistical purposes, in January One of the follow up measures of the seminar was the launching of an internal Task Force to co-ordinate the future work on use of administrative data. One of the projects initiated by the Task Force was the development of linked employeremployee data. Several Eurostat units participate in this work. Two papers were presented at an international symposium on linked employer-employee data, May 1998, [1] and [2]. In Eurostat Directorate E, social and regional statistics and geographical information systems, the work is part of the module: Integration of household surveys and registers. It is also related to the work on a set of harmonised core variables for persons and households, the census, labour accounts, and labour market statistics based on administrative sources and registers. In Eurostat Directorate D, business statistics, the work is related to a project on the use of administrative sources for structural business statistics and to the work on the use of administrative sources for updating business registers. The use of administrative sources in business statistics is of importance for statistics concerning small and medium size enterprises, (SMEs). The paper presents the concept of job, definition, related variables and data sources in sections 2, 3, and 4. A job file extended by data on other activities than work and other income sources than earnings is discussed in section 5. Development work in NSIs on register-based job files (RJF) is the theme of section 6; uses of RJFs are discussed in section 7. 1 This paper presents the viewpoints of the authors. They are not necessarily the same as those of Eurostat and Statistics Netherlands 1

2 The aim of the paper is to present the Eurostat work in this field and to encourage the NSIs to initiate and participate in European projects, see section The concept of job The definition of the job in the System of National Accounts (SNA) [3], and its European version the European System of Accounts (ESA) [4], is applied in this paper and the work of Eurostat, because National Accounts provide a conceptual framework in which employee and employer data are connected. The SNA/ESA define labour input variables in order to examine productivity. The main concept is job. As could be expected the recommendation is closely linked to the work on Labour Accounts. Definition of the job A job is defined in the SNA and the ESA as an explicit or implicit contract between a person and an institutional unit to perform work in return for compensation for a defined period or until further notice. The institutional unit may be the proprietor of an unincorporated enterprise; in this case that person is described as being selfemployed and earns a mixed income, see [3] para 17.8 and [4] para Employment does not enter as such into the SNA, but jobs do. A job is like a transaction in the SNA, while an employed person is not. Identification of jobs It follows from the definition that, in order to identify a job for a particular period, identification of two units are needed: the person who performs the work; the institutional unit for which the work is performed. The most efficient method for the identification of persons is to use officially assigned and unique personal identification numbers (PINs). In the absence of this, the name and address of the person can be used, possibly in combination with the date and address of birth. For the identification of the institutional unit a business identification number (BIN) would be the ideal. However, it may often be sufficient to identify the workplace of the person employed, since a workplace usually belongs to only one institutional unit. For countries that do not apply a BIN, this is a very practical alternative, because workplaces can be readily identified. Definitions of populations of jobs If the job is to be used as the unit that links employee and employer statistics, care has to be taken that populations of jobs are defined in a compatible way; this in turn depends on the definition of populations of persons employed and populations of institutional units. Two aspects are particularly important: the minimal conditions for employment and for the existence of an institutional unit; the implications of the existence of national boundaries. 2

3 The choice to base the definition of the job on the SNA/ESA solves the first point in a direct way: only employment and institutional units contributing to GDP as defined in the SNA/ESA are to be taken into account. It should be noted that, as a consequence, both jobs of employees and jobs of the self-employed are in principle included in the definition of job populations. The complications arising from the existence of national boundaries are less easy to deal with. The key concept is residence. Let us first examine the resident population of persons. This is defined in the international recommendations for population censuses. The resident population of a certain year consists of resident persons at the first of January and the immigrants during the year. However, some of the resident persons have their workplace outside the country and some non-resident persons have the workplace in the country. For internationally comparable statistics it is essential that the definitions of populations be treated in a consistent way in all countries concerned. It should be ensured that at any given date, all resident persons are counted and a person is counted in one country only. The institutional units being resident in the economic territory of a country are defined in the SNA/ESA. The residence of an institutional unit is determined according to the location of its "centre of economic interest", which is defined as a location where a unit engages, and intends to continue to engage, in economic activities and transactions on a significant scale, for a period of at least one year. The residence of the job follows the residence of the unit where the job is carried out. International statistical co-operation should endeavour to ensure that all jobs are counted and that a job is counted in one country only. 3

4 3. Variables of linked employer - employee data The list of variables usually included in the file of linked employer-employee data should demonstrate the strength of this data file. The variables are grouped by the unit (person, job, or work place) to which the definition of the variable is related. Person Job Workplace Locality of residence Employment experience Type of household Unemployment experience Age Labour market qualification Sex Labour status Marital status Main activity (time use) Country background Main income source Educational attainment Socio-Economic group Year of fulfilled education Household income Status in employment Date of start of job Main or secondary job Occupation Hours paid for (part time/full time) Hours actually worked Absence from work Over time paid Over time not paid Wage sum Wage per hour Location Economic activity (industry) Type of ownership Age of workplace Employment size class Permanent or seasonal activity Enterprise identification Enterprise variables e.g. Institutional sector In the case of a register based job file the three units and the listed variables can be handled simultaneously. This creates the possibility to develop new statistics, see section 7. The variables of a register-based job file should be organised as longitudinal data. 4. Data sources for linked employer - employee data Information on jobs is collected by means of statistical surveys and from administrative sources. The statistical sources described below are the LFS, business surveys (BS), the business register (BR), the population census, and a job file based on administrative data, i.e. the register-based job file, RJF. The labour force survey The LFS identifies the main job of a respondent and one secondary job. The job is therefore the most disaggregated unit and the basic statistical unit of the LFS, not the 4

5 person or the household. If the job is identified by the name and address of the workplace, it is in principle possible to establish a link with the BR. The result of the LFS is a representative sample of main jobs for the resident population. Business statistics Data on employment is collected in a number of BS, the most important being the annual structural BS. The main functions of structural BS are to assess levels and to provide benchmarks for short-term BS. Structural BS tend to be fairly comprehensive and accurate, but perhaps their main drawback is that they are often a year or more out of date by the time they have been collected and processed. European short-term BS are under development, including integrated statistics on employment, hours and wage sum. Coverage of the business register It is clear that BRs will play a key role if BS data are to be linked to employee data via the RJF. For this purpose it is important that BRs are comprehensive. In theory BRs are comprehensive indeed, at least according to the BR Regulation which requires the registration of all businesses contributing to GDP for all economic activities except the primary sector and public administration. However, a threshold is usually applied for registration of a business. For the EU, the methodological recommendations on BRs specify that businesses should be recorded in BRs if their employment is at least half a man-year. Some countries apply a lower threshold. In addition, some MS have problems meeting the coverage requirements for small businesses above the threshold and for certain types of economic activities (some types of services) and the liberal professions. As a consequence, BRs generally cover a large proportion of domestic jobs but not all. If a RJF is to be part of the statistical system it is a precondition that all employers and all self-employed are covered. Units not covered by the BR have to be supplemented by administrative business registers. Wage statistics The job is the statistical unit of wage statistics. European statistics on the structure of earnings are based either on specific business surveys, or on administrative sources, or on simultaneous use of various sources. The NSIs decide which sources are to be used. Population census Both traditional questionnaire and register based population censuses cover all main employee and self-employed jobs and family work. Implicitly the census micro file creates a central person register, a central business register and a central register of main jobs. The register-based job file, RJF The accounts kept by employers on their staff are to a large extent controlled by the legislation on tax and social security. This in turn determines the employers reports to Social Security and Tax Agencies. The legislation that determines the design of the system of data on staff is rather severe and auditors of Tax Agencies visit the employers to check how it is operated. Apart from Social Security and Tax Agencies, job information is also kept in Employment Service records. 5

6 The quality and coverage of the records of Social Security and Tax Agencies is often good, due to the legal requirements and the administrative needs for high quality data. This has led several countries to develop RJFs. However, by definition black market employment is not covered in administrative sources. It should also be pointed out that tax and social security legislation is highly country specific. International standardisation is not well advanced, not even within the EU. As a consequence, RJFs have taken different shapes in different countries, see section An extended register based job file RJFs can easily be extended by including other administrative data in the system of linked files. A job is defined as an explicit or implicit contract between a person and an institutional unit to perform work in return for compensation for a defined period or until further notice The concept of job has two dimensions a) to perform work which is an activity and b) in return for compensation which is an income post. We find several activities other than job and income posts other than wage and salary which link persons and institutional units. The most interesting activities and income posts in addition to the unit of job are: periods in unemployment (job seeking is the activity and unemployment benefit is the income post) periods in education (study is the activity, the income post is the scholarship, student loan and support from parents) home work as main activity volunteer work (when this is a contribution to GDP) transfers to disabled (income post) transfers to old age pensioners (income post) other transfers Most of these concepts or units are available in administrative sources. Some MS have developed integrated files of all the units listed above. The integrated file also includes resident persons who are not registered with any of the mentioned activities or income posts, for example dependent persons. Statistics Sweden has presented this integrated file as the fourth basic register for official statistics. See the Swedish paper for session 3, [5] One important advantage of this integrated file of related activities and income posts is that the quality of the data improves simultaneously for all the included activities and income posts. The information based on an integrated file of the units of the extended job concept is needed for the variable: social status. This variable is used by most MS and the 6

7 grouping is more or less in accordance with the census recommendation: employed, unemployed, students, household work, disabled, retired, and other. It is evident from the list of social status categories that this variable is based on a combination of activities and income sources. The social status statistics are related to social accounting matrices, which are under development in the frame of the SNA/ESA. 6. Developments of register based job files in the MS The Eurostat work on linked employer-employee data is strongly related to the development of these data in the MS. 6.1 France The annual Social Data Statements (DADS) are administrative documents filled in by employers and reported to the Social Security and Tax Agencies. The NSI, INSEE, is the third official recipient of the DADS. The employee jobs of DADS are identified by the French PIN and BIN, but there are restrictions in linking the DADS information to other sources. INSEE excludes agricultural employees and government officials from its statistical processing, as the declarations on these groups are influenced by special Social Security schemes [6]. Some DADS information has been collected every year since 1950; the current system was first used for the declarations relating to With the DADS processing system the statisticians have a kind of ongoing census of wage earners. The results should be available 15 months after the end of the reporting year. Methodologies of linkage and imputation have to be elaborated. The best mix of micro and macro quality checks and editing has to be found; the Labour Accounts experience from recent years may prove to be very useful in this respect. Beside the statistical challenges also aspects like informed consent and privacy protection need further attention. The DADS statistics cover distribution of wages at the national level and statistics for local employment analysis. INSEE has carried out supplementary surveys to DADS including statistical sample surveys of employers and employees to compare the data of the three sources. The supplementary surveys had three goals: to measure the quality of DADS; the calculation of monthly wage distribution; to develop the Wage Structure Survey. 7

8 6.2 The Netherlands (This section is written by Wim P. Leunis, Statistics Netherlands) Since 1990 Statistics Netherlands has regularly published internally consistent Labour Accounts data on: Employment (employed persons, jobs, full-time equivalents); Hours of work, contractual as well as hours paid (totals and average per job); Earnings and labour cost (totals, per job and per hour). The construction of the Labour Accounts takes place at an intermediate level of aggregation, using data from: Social Security registration; Business statistics; LFS. All sources used are complemented and adjusted to reach the final Labour Accounts figures. In the analysis needed to decide on the adjustments, use is also made of data sets at the micro-level [7]. In deciding on the best estimates for the levels in the base year the Social Security files on gross earnings have been used as the cornerstone of the system. In subsequent years Social Security files have been used less extensively due to legal and administrative changes, which unfortunately led to a deterioration in the comparability over time. In the mid-nineties more register-based data became available. Until then individual data on earnings were only available from the Social Security files. With the receipt of earnings data for individual jobs, for the first time in 1996, new possibilities arose. This change was accompanied by a huge increase of individual records on earnings and hours of work received from enterprises by way of electronic data interchange. The Dutch part of the 1995 European survey on the structure of earnings was the first product of these new possibilities. By linking the individual data files from the Social Security system, the business records and the LFS (with imputations in case of partial non-response), it was possible to fulfil the requirements of Eurostat on the description of the earnings structure without raising the response burden of labour surveys through additional questionnaires. Recently a large research project has started to explore the possibilities of constructing a social statistical file with all available micro data, register-based 2 as well as coming from BS and household surveys. As a first goal this file has to produce 2 Micro-matching with taxation data is out of bounds for the time being for legal reasons. Aggregated data from the tax authorities will be used this year in the process of revising the National Accounts and Labour Accounts time series (also needed for the implementation of ESA 95). 8

9 data for the Dutch 2001 Census. In the future it is hoped that the data which are now part of Labour Accounts, Education Accounts and Socio-Economic Accounts will also be produced on the basis of this file. Statistics Netherlands faces a number of statistical challenges during this development period. The peculiarities of register data quality have to be mastered, including ways to monitor and improve quality. Methodologies of linkage and imputation have to be elaborated. The best mix of micro and macro quality checks and editing has to be found; the Labour Accounts experience from recent years may prove to be wvery useful in this respect. Beside the statistical challenges also aspects like informed consent and privacy protection need further attention. 6.3 Nordic countries The RJF, which is usually kept by an NSI, can be defined as a linked file of centralised administrative data systems of Social Security, Tax and Employment Service Agencies. These systems exist in one way or another in all MS. However, we find differences in national practice in degree of centralisation, identification systems, access to and use of administrative data, methods of linking sources, and updating strategy. A RJF can be constructed only if certain conditions are met. The infrastructure to develop statistical systems based on integration of administrative data and statistical surveys, legistation, basic registers, identification of statistical units, and sampling frames, is well developed in the Nordic countries. The RJF and the extended file including activities other than employment and income sources other than earnings, has an important function in the development of the register-based statistical system of the Nordic countries. The development of the RJF has been a key component in the development of register-based censuses, [8] and [5]. 7. Uses of register-based job files RJFs can be used to replace or supplement existing sources (usually surveys) in the production of existing statistics, to develop new types of statistics, and as a tool for the integration of social and economic statistics. Examples of each of these uses are given below. 7.1 Use in the production of existing statistics Population census The development of a RJF is a precondition to conduct a register-based population census. Register-based censuses are carried out in several Nordic countries, and the Dutch population census is based on the combined use of registers and statistical surveys. About half of the EU MS plan to use some register-based data as a source for the next census [9]. Combined use of RJF and LFS The idea of basing a statistics on a combined use of two or more sources is to utilise the strength of both sources. In the case of the RJF and the LFS the two sources complement each other. The RJF can be seen as a register-based census, i.e. there are 9

10 no non-response and sampling variances. The LFS is a sample survey, and can use RJF data as auxiliary information, e.g. for sample stratification, for use in the procedures to correct for total and partial non-response and for post-stratification. Employment and wage statistics RJF-based statistics on employment can provide valuable information, e.g. detailed regional data, information on educational attainment, household income etc. Other uses in social statistics When the RJF is linked to other sources containing information on areas such as time use, income, wealth, and demographic data, the result is a source for important background variables in social statistics. Background variables based on this file could be main and secondary activity (time use), main income source, main social status (a combination of activity and income source), and socio-economic groupings. These register-based variables can then be used in every household survey. The interplay of BRs and RJFs BRs are the link between RJFs and BS. RJFs can make use of information from the BRs in two ways, as a source for business identity numbers (BINs), and as a source for workplace address information. BRs can also make use of information from RJFs in several ways, e.g. to improve coverage, to derive the employment size-class of businesses, and to establish the continuity of businesses. Business statistics Information from RJFs can be used in statistics based on the BRs, e.g. employment and wage sum are variables can be taken from the RJF directly rather than be collected by means of a survey. In practice this is done only to a limited extent, because the employment variable is often collected in BS to ensure that the unit surveyed is delineated properly. Most BS cover all large businesses, whereas small and medium enterprises (SMEs) are usually surveyed by means of a sample. RJFs can be very useful in providing data for businesses that do not belong to the sample. As a consequence the sample design can be more efficient. Quality checking By combining employer and employee data it becomes possible to check the coverage of both types of data. For example, an enterprise may have 100 employees according to the sources used for business statistics purposes, but the employee sources show no employees for that enterprise. This could indicate many things, e.g. lags in the administrative systems, under-coverage, duplication, errors, fraudulent declarations etc.. By investigating the reasons for this type of discrepancy it should be possible to get a better understanding of the quality of the different sources, and to consider where and how improvements can be made. 7.2 Use for the development of new statistics By linking employer and employee data in RJFs, it becomes possible to look at how the performance of enterprises is related to certain characteristics of their employees. Examples of possible statistics that can be developed are; 10

11 Correlation of enterprise profitability with education levels, age structure, wages etc. of employees Comparison of the characteristics of people attracted to various types of enterprises and various sectors of economic activity Profiles of the types of enterprise most likely to recruit graduates, women, longterm unemployed, ethnic minorities etc. Investigation of the characteristics of enterprises with high staff turnover rates, and the characteristics of the employees of such enterprises Profiles of the social backgrounds of owners/managers/directors of different categories of enterprise The development of demographic and panel studies to look at trends over time, e.g. to follow the employment patterns of individuals, or the changes in the characteristics of employees of certain enterprises. These are only a few examples of the sort of analyses that become possible when data on employers and employees is combined. Many other types of analysis could be developed, which could produce statistics of great interest to those responsible for economic, employment, social and education policy. 7.3 RJFs as a tool for the integration of social and economic statistics Through the use of RJFs it is possible to integrate certain aspects of social and business statistics, particularly those relating to wage and employment variables. This can lead to a greater coherency of statistics in this area, and can increase pressure for harmonisation of concepts and definitions. Examples of actual and possible links are; Labour Accounts Like the National Accounts, the Labour Accounts are based on an integration of all available sources in labour statistics, i.e. BS, the LFS and the RJF. The idea is to utilise the strength of each of the sources and to have consistency of statistics on employment, production and turnover with National Accounts. Some MS have developed Labour Accounts. The NSIs of Denmark, the Netherlands, and Switzerland contribute to a Labour Accounts project organised by Eurostat. The aim is to develop a handbook on Labour Accounts. Use of information from BS in social statistics If the RJF provides a micro-level link between BS and social statistics, it is possible to compile data on the businesses in which the persons of interest work. In practice a number of problems may arise if such attempts are made. The main obstacle might be the fact that most BS are based on samples, at least for SMEs. This quickly entails accuracy problems for the analysis envisaged. Use of information from social statistics in BS Likewise, if the RJF provides the micro-level link between BS and social statistics, it is possible to compile data on the people working in the businesses of interest. Again, in practice a number of problems may arise if such attempts are made. However, the fact that most BS are based on samples may not be as big a problem as in the previous case, because the business population studied can be chosen in such a way that the BS variables in question are sufficiently accurate. Now the problem tends to lie on the 11

12 other side: the persons employed may only be covered in social statistics on a sampling basis. 8. Further work in NSIs and Eurostat There are various projects either planned or on-going in Eurostat related to the development of linked employer-employee data files. They can be summarised under five broad headings; Harmonisation of concepts and definitions Harmonised concepts and definitions are essential to the efficient linking of data from different sources. Activity is taking place on two levels within Eurostat. There are projects to harmonise concepts and definitions within social statistics, e.g. the action programme on the harmonisation of a set of core variables on persons and households, and within business statistics, e.g. the development of harmonised variables for structural and short-term business statistics. There are also plans to develop a central concepts and definitions database (CODED) covering all statistical activities carried out by Eurostat. This database already contains concepts and definitions from business and transport statistics, and is gradually being expanded to cover other areas. Methodology for data linkage Efficient and accurate linking of data from different sources is vital to the future of this work. This can either be at the micro level, or at aggregate levels using synthetic methods. There is an ongoing project on data fusion under the SUP-COM research programme, which aims to evaluate existing research and software tools, and to propose a future strategy. The subject of data linkage is also being considered in respect to work on BRs. It is proposed to add a chapter on linking BRs and administrative registers to the Manual of Recommendations on BRs. Co-ordination of work on the use of administrative sources Administrative sources are a major input to linked employer-employee files. There are several projects underway in Eurostat looking at the possibility of using more administrative data for statistical purposes. Work is also starting on ways to assess the quality and cost of data from administrative sources. All of these projects are being co-ordinated by the Methodological Co-ordination Section of Unit D1. A manual documenting national practices in the use of administrative sources for structural business statistics is due to be published shortly. The creation of a network of experts To continue work on the use of linked employer-employee files it will be useful to create an international network of experts in this field. The aim will be to share research and experiences, and to facilitate the dissemination of information. Proposal for a project under the Fifth Framework Programme 12

13 The Fifth Framework Programme is a major programme of research funded by the European Commission. It covers many areas, including statistics, and would be a useful source of funding for future work on linked employer-employee files. Eurostat Directorates D and E would appreciate projects in this area. It will be necessary to draw-up detailed proposals during 1999, and propose a suitable international consortium consisting of statisticians and academics. This is therefore strongly linked to the creation of an international network of experts. References [1] Eurostat, Gaasemyr and Struijs, 1998, The Role of International Standards in Developing Register-Based Job Files [2] Eurostat, Gaasemyr and Struijs, 1998, The Role of International Standards in Using Register-Based Job Files [3] UN, Eurostat, et al, 1993, The System of National Accounts (SNA) [4] Eurostat, 1995, European System of Accounts (ESA 95) [5] Blom and Carlson, 1999, Registers in Official Statistics: A Swedish Perspective [6] Faure Jean-Louis, 1996, A French Experiment Combining a Survey with an administrative File: The Annual Wage Declaration Supplementary Survey [7] Altena and Leunis, 1996, Labour Accounts in the Netherlands, ; How to Cope with Fragmented Macro Data in Official Statistics, in International Statistical Review (1996) 64, [8] Eurostat, 1995, Statistics on Persons in Denmark- A register-based statistical system [9] Eurostat, Laihonen, 1999, Development of the use of administrative data in population and housing censuses in Europe