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titleBack to case studies

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 1. Broad description 


When creating a statistical register for research, data linking is used for two different purposes:

  • Different sources are combined to create an object set, the register population, with good coverage. 
  • Different sources are used to create the variables in the new register.

Different sources, such as administrative registers or pre-existing statistical registers, can consist of different object types. It may then be necessary to define the statistical units or objects in the new register in a suitable way so that data from sources with different kinds of units can be used together. Data linking can be used in the same way to combine microdata from sample surveys with data from administrative or statistical registers.

The case study is based on a report mentioned in section 8 below, which illustrates how different sources with agricultural data can be combined into a new kind of Farm Register with variables from many administrative and statistical registers. In the report data linking is discussed from a methodological point of view.

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 2. Why is it good practice?


The microdata at National Statistical Offices have not been created to meet the needs of academic research. To meet these needs, new sets of microdata should be created, where existing sets of microdata are combined so that data sets with richer content are created. Exact linking with identifying variables is used to create this kind of microdata for research.

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 3. Target audience


Persons at National Statistical Offices who prepare microdata for research and potential users of microdata for research.

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 4. Detailed description


The original aim of the case study was to investigate how data linking can be used to create a new Farm Register at Statistics Sweden based on administrative data instead of a census. A new Farm Register can be linked to the Business Register in two steps:

  • Integrating the census-based Farm Register and the administrative IACS Register with data from the Integrated Administrative and Control System, which is the system for agricultural subsidies used within the European Union. 
  • Linking the records in the integrated register with the records in the Business Register. After this linkage all variables in all statistical registers, which are linked to the Business Register, can be used to analyse the agricultural sector.

The role of the Business Register is to define the object set of all enterprises, including those belonging to the agricultural sector. To be able to create a set of microdata describing the agricultural sector, statistically interesting variables must be imported from other registers linked to the Business Register: 

  • Crop areas and subsidies of different kinds from the IACS Register. Persons employed by age and sex from the PAYE Register. The PAYE Register is based on the annual income verifications in which all employers provide information on wages paid to all persons employed.
  • A large number of economic variables from the Register of Standardised Accounts, which is based on annual income statements from all firms: data from profit and loss statements, balance sheets, investments and labour costs.
  • Turnover and other economic variables from the VAT Register, which is based on monthly or yearly VAT declarations from all firms.
  • A large number of variables describing different kinds of vehicles owned by the agricultural unit from the Vehicle Register, which contains data about vehicles owned by businesses and individuals.

The conclusions of the case study can be summarised as follows:

The matching process must be carefully planned, considering which linking variables should be used, and in which order the different sources should be combined. The quality of the linking variables is important, and editing of these variables is an important part of the work. Causes and extent of mismatch should be investigated, and it must be decided if the non-matching units should be excluded or included in the register population. If they are included, mismatch will result in units with missing values for some variables. Seemingly matching objects should also be checked, since false hits will otherwise give rise to gross errors in data.

The identifying variables should be edited before matching. Before editing of telephone numbers, only 47% of the farmers in the Farm Register could be matched to corresponding units in the IACS register. After corrections 64% could be matched. 

  • By combining two identifying variables (telephone number to the farm and the farm's tax identity number) the matching result is improved so that 96% of the units in the IACS register could be matched to units in the Farm Register.
  • By combining these two identifying variables the matching result is improved so that more relevant agricultural units can be defined. Matching with only the farm's tax identity number resulted in (almost) only one-to-one matches between objects in IACS and the Farm Register. However, matching with both the tax identity number and telephone number resulted in a number of one-to-many matches and many-to-many matches. After data linking, new units should be created in the following way:
    • In some cases husband and wife, relatives or companions on the same holding make separate IACS applications for different parts of the holding's activities. As the relationships between these persons are informal and can change over the years, it is appropriate to combine all IACS applications and all legal units in the Business Register connected with these applications.
    • In other cases a number of holdings and IACS applications refer to the same telephone number. This is an indication that all objects have the same administration. If all holdings, all IACS applications and all legal units in the Business Register connected to the same group are combined, we will get an agricultural unit, which can be described by all statistical variables in the register system.
  • Linkages must be checked. First the one-to-one linkages were checked. A match between identification variables is not sufficient proof that the IACS and Farm Register objects are identical. If the IACS object has a larger crop area than the FR object this can indicate that the IACS object should be linked with two FR objects and vice versa. The linkages were checked by comparing total arable area, reliable crop area and location described by parish. 
  • It was found that there was serious under-coverage in the agricultural part of the Business Register. By combining the Business Register with the PAYE Register, the Register of Standardised Accounts and the VAT Register, this undercoverage was reduced from 25% to 3%. These administrative sources are not used by the Business Register today.

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 5. Strengths


By combining microdata from different sources, the relevance or scientific value of the data can be increased to a great extent.

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 6. Weaknesses


Mismatch and/or false hits will give rise to quality problems.

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 7. References


This case study is based on the following report: 
Anders Wallgren and Britt Wallgren: Administrative Registers in an Efficient Statistical System - New Possibilities for Agricultural Statistics? How Can We Use Multiple Administrative Sources? Statistics Sweden and Eurostat 1999. The report is available at