Each country delivers a single dataset with core data and data for the modules.
For the reference year 2023, core and module data are requested for holdings in the main frame and are not requested for holdings in the frame extension. However, countries may send on voluntary basis core and module data on frame extension if they collect such data for national purposes.
The dataset includes the field HLD_FEF (Holding in frame extension flag) which flags holdings that belong to the frame extension. Eurostat is able to distinguish between holdings in the main frame and holdings in the frame extension in the dataset, in order to make possible the publication of data on the relevant population coverage as well as the meaningful analysis of trends (over 2020-2026) on the same population coverage.
The modules are to be collected for all holdings for which core data are collected or for a sub-sample of holdings of the core.
In order to ensure in Eurostat a clear understanding and a proper use of the correct fields for weighting the data and calculating the variance estimates for various variables belonging to core and modules, the dataset contains a separate set of sampling design and extrapolation factor fields for core and for each module. See the below table.
For core and each module:
- There are three foreseen extrapolation factor fields, but in many cases (census or one-stage stratified random sampling), only one extrapolation factor field should be filled in.
- There are many foreseen sampling design data fields, but in many cases either no data field should be filled in (census) or only the stratum identification number should be filled in (e.g. one-stage stratified random sampling).
Eurostat has foreseen all these fields in order to make possible variance estimation for complex sampling designs. In FSS 2013 and FSS 2016, Hungary and North Macedonia (for rural areas) used stratified one-stage cluster sampling, while North Macedonia (for urban areas) used stratified two-stage sampling. The present fields allow recording information for sampling designs up to three-stages.
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Extrapolation factors and sampling design fields
In the above table M=Mandatory i.e. the fields should always be filled in (they are always applicable). C=Conditional, the fields are mandatory only for holdings with module data and should be set to null if holdings do not have module data. V=Voluntary i.e. the fields can be either filled in (if applicable) or set to null (if not applicable). Sampling strategiesWhen the data for both core and a module are collected on samples, two strategies are identified to draw the samples: positive coordination and two-phase sampling. POSITIVE coordinationThe core and module samples are drawn with positive coordination from the same frame and at the same time using the Permanent Random Number technique to obtain maximum overlapping among the samples. All holdings in the module sample are included in the core sample. In case the core and module samples are drawn using one-stage stratified sampling, then for calculating the extrapolation factors and the variance estimates for core and module data, the usual procedures for one-stage stratified sampling are used for core and each module. The situation is analogous in the case of another sampling design. The positive coordination among samples does not change the procedures. TWO-PHASE SAMPLINGIn the case of two-phase sampling, the core sample is selected from the frame in the first phase and the module sub-sample is selected from the core sample in the second phase. There are at least two fundamental differences between multi-stage sampling and multi-phase sampling:
Assumption to simplify point and variance estimationTwo-phase sampling can be simplified to one-stage sampling i.e. the selection of a module sub-sample from a core sample can be considered a direct selection of the module sub-sample from the sampling frame. This simplification can be done if the independence condition is fulfilled. Independence basically means that the information collected for the core sample is not used in selecting or calibrating the module sub-sample. If the module sub-sample is selected at the same time as the core sample, the data for both the core sample and the module sub-sample are collected in parallel, and the collected core data are not used for calibrating the module data, the independence condition is met. In such a case, the theory is straightforward, since we are dealing with two independent sampling mechanisms and the inclusion probabilities for the module are products of two unconditional probabilities. Taking into consideration that in practice similar assumptions are already made:
we propose to assume the above-mentioned independence condition for the module data. Namely:
For example, suppose that the core sample is selected from the frame using one-stage stratified random sampling and that the module sub-sample is selected from the core sample, using random sub-selection of units in each stratum.
Independently of the sampling strategy, calibration procedures are welcome. In case calibration is used, it is recommended to estimate variance by considering the effect of calibration on variance. For Eurostat this recommendation is not feasible. In order to correctly estimate the variance in the presence of calibration, Eurostat needs the residuals of the regression between the target variable and the calibration variables. This should be the case for each target variable. Countries use multitude of different set of variables and calibration methods. According to the conclusions for FSS in the Working Group meeting in October 2017 and for other domains (e.g. Labour Market Statistics Working Group in December 2015), calibration variability can be ignored when estimating variance in Eurostat. Still, the final calibrated weights are considered in Eurostat when estimating variance. Extrapolation factor fieldsThis section presents the principles for filling in the extrapolation factor fields. These principles allow Eurostat to calculate the extrapolated aggregate for any variable in the core or in a module by multiplying the value of the core or module variable with the product of the corresponding extrapolation factors EXTPOL_FACT1_* x EXTPOL_FACT2_* x EXTPOL_FACT3_* (once null values are replaced with 1). Extrapolation factors for the core (EXTPOL_FACT*_CORE)For the reference year 2023, the core data collection is required for the main frame and may be carried out as census or sample. Countries may send on voluntary basis core data on frame extension if they collect such data for national purposes. The first extrapolation factor field for the core (EXTPOL_FACT1_CORE) should be always filled in (is always mandatory), irrespective of the national coverage of the core data (main frame or main frame plus frame extension). In case of a census, it is completed in principle with 1; values different from 1 are accepted as non-response adjustment and calibration are done via the extrapolation factors. In case of a sample-based data collection, it is completed with values in principle higher than or equal to 1 (depending on whether the sampled holdings belong or not to take-all strata); where calibration is applied, some values can be lower than 1. The subsequent extrapolation factor fields for the core (EXTPOL_FACT2_CORE and EXTPOL_FACT3_CORE) should be completed only when applicable depending on the sampling design. Where not applicable, they should be set to null. The expected completion of the extrapolation factors depending on the sampling design is as follows:
See the examples in the section "Examples for IFS 2023", below. Extrapolation factors for the modulesAccording to art 7(2) of Regulation 2018/1091, module data collection is required for the main frame. Countries may send on voluntary basis module data on frame extension if they collect such data for national purposes. A module should be collected from all or from a sub-sample of holdings for which core data are collected. The following applies irrespective of the national coverage of the module data (main frame or main frame plus frame extension): 1. If a module is collected for all holdings for which core data are collected, then for all holdings:
See examples 1,4,5,7 and 10 from section "Examples for IFS 2023", below. 2. If a module is collected for a sub-sample of the core, then the following is valid only for the holdings in the sub-sample:
Suppose that the core is collected on census and the module sample is selected from the core population using one-stage stratified random sampling. Then the EXTPOL_FACT1_* of the module should record the extrapolation factors corresponding to the selection of the module sample from the frame population using one-stage stratified random sampling. Suppose that the core sample is selected from the frame using one-stage stratified random sampling and that the module sub-sample is selected from the core sample, using random sub-selection of holdings in each stratum. Then the EXTPOL_FACT1_* of the module should record the product of two extrapolation factors of the holdings (corresponding to the selection of the core sample from the frame using one-stage stratified random sampling and to the selection of the module sub-sample from the core sample, respectively). This is equivalent to the extrapolation factor calculated as if the module sub-sample is directly selected from the frame, as long as the same stratification is used in the core sample and module sub-sample.
See examples 2,3,6,8 and 9 from section "Examples for IFS 2023", below. Therefore the following rules are valid:
Sampling design fieldsThis section presents how to fill in the sampling design fields. The sampling design fields are of two types: data fields and flag fields. The data fields are: STRA_ID_*, PSU_*, SSU_* and OSU_S1_*. The flag fields are: STRA_IDF_*, PSUF_*, SSUF_* and OSU_SF1_*. "*" stands for "CORE", "LAFO, "RDEV", "MIRR", "MSMP", "MMEQ" and respectively "MORC" in IFS 2023. For example, STRA_ID_CORE (Stratum identification number (core)) is a data field, while STRA_IDF_CORE (Stratum identification number flag (core)) is a flag field. Depending on the national sampling design for a data collection, data are required in no field, one, some or all data fields. Therefore, the data fields can be either filled in (where applicable) or set to null (where not applicable). All flag fields should be filled in all cases. They should indicate the applicability of the data fields and where relevant the specific meaning of the data fields. See the examples in section "Examples for IFS 2023", below. Sampling design fields for core and modulesFor the reference year 2023, core and module data collection is required for the main frame and may be carried out as census or sample. Countries may send on voluntary basis core and module data on frame extension if they collect such data for national purposes. A module should be collected from all or from a sub-sample of holdings for which core data are collected. The following applies irrespective of the national coverage of the module data (main frame or main frame plus frame extension): 1. If a module is collected for all holdings for which core data is collected, then for all holdings:
See examples 1,4,5,7 and 10 from section "Examples for IFS 2023", below. 2. If a module is collected for a sub-sample of the core, then only for the holdings in the sub-sample:
Suppose that the core is collected on census and the module sample is selected from the core population using one-stage stratified random sampling. Then the STRA_ID_* of the module should record the strata used for the selection of the module sample from the frame population. Suppose that the core sample is selected from the frame using one-stage stratified random sampling and that the module sub-sample is selected from the core sample, using random sub-selection of units in each stratum. Then the STRA_ID_* of the module should record the same information as the STRA_ID_CORE.
See examples 2,3,6,8 and 9 from section "Examples for IFS 2023", below. Therefore the following rules are generally valid:
Description of sampling design fieldsStratum identification number (STRA_ID_*)Identification code for the stratum of the holding Stratifying a population means dividing it into non-overlapping subpopulations, called strata. Independent samples are then selected in each stratum. The population is usually stratified before the units are selected in the first stage. The units selected in the first stage are either:
The code indicates the primary stratum each holding belongs to. The code should uniquely identify all primary strata in the dataset. The code refers to the original strata at the time of the selection, except for:
The code does not refer to the strata used for post-stratification or calibration. The code refers to explicit strata. Systematic sampling with implicit stratification will be accounted for through the use of field OSU_S1_* (see the explanation of this field, below). Stratum identification number flag (STRA_IDF_*)Flag indicating the applicability and the origin of the stratum
Self-representing PSUSelf-representing PSUs are PSUs selected with certainty (with a probability of 1). For example, a self-representing PSU is a municipality selected in the first sampling stage from a stratum with one municipality. For the purpose of estimating variance, self-representing PSUs should be treated as primary strata. Therefore, for a self-representing PSU, a separate, unique value is assigned to STRA_ID_* for its identification. STRA_IDF_* should receive the flag 2. See example 11 Collapsed stratum due to a single unit in the stratumIf a stratum consists of only one unit selected in the first stage (among a larger number of units in the stratum population), or if a stratum contains only one respondent unit selected in the first stage (among a larger number of selected units), primary strata have to be collapsed such that every stratum consists of at least two units. For doing so, strata should be grouped with strata that are most similar in terms of the main variables. The decision of which strata are collapsed should be based on information that is available in the sampling frame. Preferably, strata similar in terms of holding size or farm type are collapsed. The stratum code of the collapsed stratum is equal to the stratum code of the stratum that before collapsing already contained more than one unit. The holdings in the collapsed stratum receive STRA_IDF_* equal to 3.See example 12 Primary sampling unit (PSU_*)Code of the primary sampling unit A population is divided into clusters (i.e. disjoint sub-populations) in case direct-element sampling is either impossible (due to lack of sampling frame) or its implementation too expensive (the population is widely distributed geographically). A sample of clusters (PSUs) is then selected at the first stage of sampling. Primary sampling units (PSUs) refer to hierarchical clusters superior to agricultural holdings selected in the first stage of sampling. The code should uniquely identify all PSUs in the dataset, irrespective of the strata which they belong to. The field is applicable in case of one-stage cluster sampling or two- or more- stage (cluster) sampling. For example:
If PSUs are selected several times (i.e. they are sampled with replacement), at each selection the selected PSU should receive a separate unique code. This is due to the fact that if PSUs are drawn with replacement, the variance estimation procedure treats repeated instances of the same PSU as separate PSUs. The case of self-representing PSUs (see the definition of the self-representing PSUs, above) For the purpose of estimating variance, self-representing PSUs should be treated as primary strata and their secondary sampling units (SSUs) should be treated as PSUs. The field PSU_* is filled with the SSUs, as follows:
Primary sampling unit flag (PSUF_*)Flag indicating the applicability of primary sampling unit
Secondary sampling unit (SSU_*)Code for the secondary sampling unit Secondary sampling units (SSUs) are clusters which form the PSUs and which are hierarchically superior to agricultural holdings. SSUs are disjoint sub-populations independently drawn from each PSU. The field should uniquely identify all SSUs in the dataset, irrespective of the strata and PSUs which they belong to. The completion of this field is applicable only in case of two-stage cluster sampling or three- or more- stage (cluster) sampling. For example:
If SSUs are selected several times (i.e. they are sampled with replacement), at each selection the selected SSU should receive a separate code. This is due to the fact that if SSUs are drawn with replacement, the variance estimation procedure treats repeated instances of the same SSU as separate SSUs. The case of self-representing PSUs (see the definition of the self-representing PSUs, above) For the purpose of estimating variance, self-representing PSUs should be treated as primary strata, their SSUs should be treated as PSUs and their tertiary sampling units (TSUs) should be treated as SSUs. The field SSU_* is filled with the TSUs, as follows:
Secondary sampling unit flag (SSUF_*)Flag indicating the applicability of secondary sampling unit
Order of selection of the unit in the first stage (OSU_S1_*)Rank of the selection of the units in the first stage The unit selected in the first stage is the:
This information is important for variance estimation purposes as a systematic drawing from a judiciously ordered sampling frame may substantially decrease sampling errors. The order of sampling units is relevant only when it is by a variable correlated with the main variables. This information makes possible to consider the effect of implicit stratification to the overall variance. For this purpose, Eurostat computes an additional field 'computational' strata in the dataset (see Calculation of weights, variance estimation and quality rating system - IFS-Integrated-Farm-Statistics - EC Extranet Wiki (europa.eu)). Order of selection of the unit in the first stage flag (OSU_SF1_*)Flag indicating the applicability of systematic sampling
Examples for IFS 2023For the reference year 2023, the core and module data are required only for the main frame and may be collected based on census or sample. Therefore, the examples from 1 to 10 consider various cases of census and sample of holdings covering the main frame. Core and module data on frame extension can be sent voluntarily to Eurostat. In this case, the completion of the fields for the holdings in the frame extension is the same as for the holdings in the main frame. As illustrated in the examples from 1 to 10, the following general rules apply for core and modules, irrespective of the population coverage (main frame or main frame plus frame extension):
Finally, example 11 shows how to fill in the dataset in case of self-representing PSUs while example 12 shows how to fill in the dataset in case of collapsed strata. Example 1 - Census for core and for modules, on main frame
Table 10 – Coverage and sampling strategy of the data collections for Example 1
The dataset should be filled in as presented below:
Example 2 - Census for core and stratified one-stage random sampling for modules, on main frame
Table 11 – Coverage and sampling strategy of the data collections for Example 2
The dataset should be filled in as presented below:
Example 3 – Census for core and stratified one-stage systematic sampling for the modules, on main frame
Table 12 – Coverage and sampling strategy of the data collections for Example 3
The dataset should be filled in as presented below:
Example 4 - Stratified one-stage random sampling for core and modules, on main frame
Table 13 – Coverage and sampling strategy of the data collections for Example 4
The dataset should be filled in as presented below:
Example 5 - Stratified one-stage cluster sampling for core and modules, on main frame
Table 14 – Coverage and sampling strategy of the data collections for Example 5
The dataset should be filled in as presented below:
Example 6 - Stratified one-stage cluster sampling for core and stratified two-stage sampling for modules, on main frame
Table 15 – Coverage and sampling strategy of the data collections for Example 6
The dataset should be filled in as presented below:
Example 7 – Stratified two-stage sampling for core and modules, on main frame
Table 16 – Coverage and sampling strategy of the data collections for Example 7
The dataset should be filled in as presented below:
Example 8 – Stratified one-stage sampling for core and sub-sample from the core sample for modules, on main frame
Table 17 – Coverage and sampling strategy of the data collections for Example 8
The dataset should be filled in as presented below:
Example 9 – Stratified two-stage sampling for core and sub-sample from the core sample for modules, on main frame
Table 18 – Coverage and sampling strategy of the data collections for Example 9
The dataset should be filled in as presented below:
Example 10 – Stratified three-stage sampling for core and modules, on main frame
Table 19 – Coverage and sampling strategy of the data collections for Example 10
The dataset should be filled in as presented below:
Let's consider example 7, where the core variables on main frame are collected using stratified two-stage sampling. In the first stage, from a stratum (STRA_ID_CORE =3000) with two municipalities (PSUs) in the population, both municipalities are selected with certainty. Their EXTPOL_FACT1_CORE is 1. They are self-representing PSUs. The codes of the PSUs (PSU_CORE) are 1 and 2. In the second stage, from each municipality, two holdings are selected with weights (EXTPOL_FACT2_CORE) equal to 3.00, respectively 4.00. There are therefore 4 holdings belonging to 2 PSUs in the dataset:
Now let's apply the rules on self-representing PSUs:
Therefore, the dataset will display the following information:
Let's consider example 7, where the core variables on main frame are collected using stratified two-stage sampling. To keep the table short, in the first stage,
1 holding belonging to PSU_CORE =1, 2 holdings belonging to PSU_CORE=2 and 2 holdings belonging to PSU_CORE=3 were selected and answered. The dataset includes 5 selected holdings belonging to 3 selected PSUs:
The stratum of PSU_CORE =1 is collapsed, by receiving the code 4000 (STRA_ID_CORE ) of another stratum, which let's say is the most similar in terms of holding size or farm type. The Stratum identification number flag becomes 3 for holdings belonging to the collapsed stratum.
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