When administrative data are used for statistical purposes, the first problem to be faced is that the information acquired is not exactly that which is needed, since questionnaires are designed for specific administrative purposes. Statistical and administrative purposes require different kinds of data to be collected and different acquisition methods (which strongly influence the quality of data). Strict interaction between statisticians and administrative departments is essential, although it does not guaranty that a good compromise solution can be found. Statistics Sweden began long ago to take advantage of administrative data, though as Wallgren and Wallgren observe in Chapter 2 of this book: 'The Business Register and the Farm Register at Statistics Sweden are not harmonized today.'
The list of codes adopted for administrative and for statistical purposes should be harmonized, but this is not an easy task. For example, the legend used in the IACS is much more detailed for some land use types than that adopted by the Italian Ministry of Policies for Agriculture, Food and Forest (MIPAAF) for producing crop statistics (AGRIT project; for a description of the AGRIT survey, see Chapters 13 and 22 of this book) and is less detailed for others, mainly due to the different aims of the data collection. Extracting the data from the IACS system in order to obtain the number of hectares of main crops is not an easy task. Moreover, the acquisition date of the IACS data does not allow information to be collected on yields; thus, an alternative data source is needed in order to estimate these important parameters.
Administrative data are not collected for purely statistical purposes, with the guarantee of confidentiality and of no use for other purposes (unless aggregated); they are collected for specific purposes which are very relevant for the respondent such as subsidies or taxation. On the one hand, this relevance should guarantee accurate answers and high quality of data; on the other, specific interests of respondents can generate biased answers. For example, the IACS declarations have a clear aim; thus the units that apply for an administrative procedure devote much attention to the records concerning crops with subsides based on area under cultivation, due to the checks that are carried out, and less attention to the areas of other crops. In Sweden (see Selander et al., 1998; Wallgren and Wallgren, 1999), for crops with subsidies based on area and for other crops which are generally cultivated by the same farms, the bias is low, but for other crops the downward bias can be about 20%. Moreover, for very long and complicated questionnaires the risk of collecting poor-quality data is high.
Unclear dynamics can be generated by checks carried out on the IACS data, since some farmers may decide not to apply for subsides even if they are available, others may tend to underestimate the areas to avoid the risks and the consequences of the checks, and still others may inflate their declarations, hoping to escape the checks.
The most critical point, when using administrative data for statistical purposes, is that their characteristics can change from one year to the next depending on their aims and without regard to statistical issues. For example, a simplification of Swedish tax legislation made farm incomes virtually indistinguishable from other forms of income in tax returns; thus tax returns could no longer be used to produce statistics on farmers' income. Another very important example is given by the change in common rules for direct support schemes under the Common Agricultural Policy applied from 2005, which strongly simplified aid applications.1
A premium per hectare now applies only in isolated cases, such as the specific quality premium for durum wheat, the protein crop premium, the crop-specific payment for rice and the aid for energy crops. The law does not require information on other crops. Some Italian regions still request information on the area covered by each crop in the farm, but the farmers know that subsidies are not linked to this information and they tend to give less accurate answers; moreover, the response burden is very high. For these reasons, at present the IACS data cannot be the main source for crops statistics.
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