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Metadata File NameSILC_ESQRS_A_FI_2012_0000 - Version 2
TimeDimension2012-A0
DataProviderFI1
DataFlowSILC_ESQRS_A
1 Contact
2 Introduction
3 Quality management - assessment
4 Relevance
5 Accuracy and reliability
6 Timeliness and punctuality
7 Accessibility and clarity
8 Comparability
9 Coherence
10 Cost and Burden
11 Confidentiality
12 Statistical processing
13 Comment


1 Contact
1 . 1 Contact organisation

Statistics Finland.

1 . 2 Contact organisation unit

Population and Social Statistics.

1 . 3 Contact name

Marie Reijo.

1 . 4 Contact person function

Data processing.

1 . 5 Contact mail address

FI-00022 Statistics Finland

Finland

1 . 6 Contact email address

marie.reijo@stat.fi

1 . 7 Contact phone number

+358-09-1734 2547

1 . 8 Contact fax number

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2 Introduction
 

The production of quality reports is part of the implementation of the EU-SILC instrument. In order to assess the quality of data at national level and to make a comparison among countries, the National Statistics Institutes are asked to report detailed information mainly on: the entire statistical process, sampling and non-sampling errors, and potential deviations from standard definition and concepts.

This document follows the ESS standard for quality reports structure (ESQRS), which is the main report structure for reference metadata related to data quality in the European Statistical System. It is a metadata template, based on 13 main concepts, which can be used across several statistical domains with the purpose of a better harmonisation of the quality reporting requirements in the ESS.

For that reason the template of this document differs from that one stated in the Commission Reg. 28/2004.

Finally it is the combination of the previous intermediate and final quality reports therefore it is worth mentioning that it refers to both the cross sectional and the longitudinal data from 2013 onwards.

 

This document refers to Finland's cross-sectional data of the EU-SILC 2012 operation.

 
3 Quality management - assessment

-

4 Relevance

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4 . 1 Relevance - User Needs

-

4 . 2 Relevance - User Satisfaction

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4 . 3 Completeness

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4 . 3 . 1 Data completeness - rate

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5 Accuracy and reliability
 

The concept of accuracy refers to the precision of estimates computed from a sample rather than from the entire population. Accuracy depends on sample size, sampling design effects and structure of the population under study. In addition to that, sampling errors and non sampling errors need to be taken into account. Sampling error refers to the variability that occurs at random because of the use of a sample rather than a census and non-sampling errors are errors that occur in all phases of the data collection and production process.

 

 
5 . 1 Accuracy - overall
 

In terms of precision requirements, the EU-SILC framework regulation as well the Commission Regulation on sampling and tracing rules refers respectively, to the effective sample size to be achieved and to representativeness of the sample. The effective sample size combines sample size and sampling design effect which depends on sampling design, population structure and non-response rate.

 

Considering the Finnish sample design described in Section 12 and data representativeness in general, it can be summarised that:

  • The Finnish SILC 2012 data is based on a nationally representative probability sample of the population residing in private households (non-institutionalised persons, two-phase stratified sampling design),
  • All private households and all persons aged 16 and over within the household are eligible for the operation (selection of persons, creation of household-dwelling units around persons and definition of households, i.e. housekeeping units, during the interviews),
  • Representative probability samples are achieved both for households, which are the basic units of sampling, data collection and data analysis, and for individual persons in the target population (selection of persons aged 16 and over from the register, creation of household-dwelling units around persons and definition of households, i.e. housekeeping units, during the interviews), and
  • The sampling frame and methods of sample selection ensure that every individual and household in the target population is assigned a known and non-zero probability of selection (for every non-institutionalised person the probability of selection is identified and greater than zero).

 

The precision requirements have been met by effective sampling design (Section 12.1.1), sampling rates and sizes (Section 12.1.3) and weighing methods of non-response corrections (Section 12.5.1).

 
5 . 2 Sampling error
 

EU-SILC is a complex survey involving different sampling design in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation. Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another .

The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries we have used different variables to specify strata and cluster information. In particular, countries have been split into four groups:

1)BE, BG, CZ, IE, EL, ES, FR, IT, LV, HU, NL, PL, PT, RO, SI, UK and HR whose sampling design could be assimilated to a two stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;

2) DE, EE, CY, LT, LU, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;

3) DK, MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata;

 

In case Eurostat methodology is not accepted by your country, please describe the methodology used at national level for computing the estimates.

 

The standard error and confidence limits estimates of the table below were computed by Eurostat's methododology (Taylor linearisation method).

The Annex on sampling errors provides standard error estimates to mean income components based on analytical variance estimation methods.

 
5 . 2 . 1 Sampling error - indicators
 
  AROPE At risk of poverty
(60%)
Severe
Material Deprivation
Very low
work intensity

Ind.

value

Stand. errors

Half

CI (95%)

Ind.

value

Stand. errors

Half

CI (95%)

Ind.

value

Stand. errors

Half

CI (95%)

Ind.

value

Stand. errors

Half

CI (95%)

Total

 17.2 0.426 16.4,18.1  13.2 0.390 12.5,14.0 2.9 0.211 2.5,3.4 9.1 0.355  8.4,9.8

Male

 17.0  0.510  16.0,18.0  12.9  0.464  12.0,13.8  3.0  0.251  2.5,3.5  10.0  0.448  9.1,10.9

Female

 17.4  0.519  16.4,18.5  13.6  0.478  12.6,14.5  2.9  0.247  2.4,3.4  8.2  0.433  7.3,9.0

Age0-17

 14.9  0.956  13.0,16.7  11.1  0.854  9.5,12.8  2.8  0.431  1.9,3.6  5.9  0.593  4.7,7.0

Age18-64

 17.3  0.461  16.4,18.2  12.4  0.413  11.6,13.2  3.4  0.248  2.9,3.9  10.3  0.373  9.6,11.1

Age 65+

 19.5  0.909  17.7,21.2  18.4  0.894  16.7,20.2  1.5  0.302  0.9,2.1  .. ..   ..
 
5 . 3 Non-sampling error
 

Non-sampling errors are basically of 4 types:

  • Coverage errors: errors due to divergences existing between the target population and the sampling frame.
  • Measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection
  • Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting
  • Non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
  1. – Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample. Unit non-response of the Finnish sample refers to the absence of information on the selected sample persons.
  1. – Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained
 
5 . 3 . 1 Coverage error
 

Coverage errors include over-coverage, under-coverage and misclassification:

  • Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice
  • Under-coverage: refers to units not included in the sampling frame
  • Misclassification: refers to incorrect classification of units that belong to the target population

Coverage errors of the Finnish SILC data are minor due to the exhaustive and up-to-date data source used for the sampling frame (section 12.1).

 
5 . 3 . 1 . 1 Over-coverage - rate
 
 

Main problems

Size of error

Cross sectional

data

·Over-coverage

·Under-coverage

·Misclassification

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5 . 3 . 2 Measurement error
 

Cross sectional data

Source of measurement errors

Building process of questionnaire

Interview training

Quality control

Problems emerging in connection with integrating different data sources

 

The Finnish microdata are collected from administrative data and by computer-assisted telephone interviews (CATI, exceptionally CAPI). Administrative data are linked to the sample persons and co-residents by personal identification codes (ID codes). All person registers used for data compilation include ID codes. In the sampling phase, the codes are retrieved from the sampling frame. They are then used in all phases of linking different data sources. ID codes can be missing only from recently immigrated persons, members recently moved to the household or newly born co-residents.  

To help the interviewer in defining the household members of the selected sample person, the first survey year's questionnaires are pre-filled with the persons’ ID codes registered in the same dwelling with the sample person. The second and later waves'/replications' questionnaires are pre-filled using the information collected in the first year. The interviewer checks the co-resident’s position in the household deleting from the data base those who do not belong to it and adding the names, birthdays and ID codes of those who have moved into it. The ID codes to be added are either given by the respondent or searched later from the population register, using dates of birth, names and other information. If no ID code is found, the person is deleted from the data base. These cases are quite rare. In the 2012 gross sample operation, the ID code was missing for only 23 co-residents.

More importantly, reference periods in the administrative data sources may become a problem. In the Finnish SILC microdata, the variables on education attendance (PE010, PE020) are retrieved from registers referring to attendance in the educational institutes on 26 September pre-dating the survey year. Since most of the institutes start their terms in August or early September, and usually continue to May of the survey year, the coverage and reference period of this data source is reasonable.

  

Problems due to the need to keep up the national time series

 

According to the principles commonly agreed in the planning phase of the SILC survey, it was important to ensure coherence between the new instrument and the established national statistics. In Finland, the SILC survey was integrated into the national Income Distribution Survey (IDS), compiled yearly since 1976. A major problem in maintaining the national series was and still is in reconciling the variables on labour activity.  

In the IDS, the reference period for the labour information is the income reference year. In the SILC, labour information mainly refers to the current situation. Different reference periods in the IDS and SILC concern the variables PL031, PL040, PL050, PL111, PL130, PL140 and PL150. The SILC variables PL073 to PL090 are also in contradiction with similar IDS monthly activity variables: overlapping activities are permitted in the IDS, but in the SILC, one should define one's main activity for each month. Both reference periods cannot be collected; different reference periods would make it very hard for the interviewee and the interviewer to give accurate information, especially in cases where changes occurred during the income reference period (IRP) or currently. To make the fieldwork easier, from the very start of the EU-SILC, the reference periods were integrated. "Current" is operationalised as the December of the IRP.

 

Examples of labour information with different requirements in the IDS and EU-SILC

Concepts / Variables

Requirements

Solution

 

IDS

EU-SILC

Integrated

 

 

 

Current = December of the IRP

Main job

The longest period of employment during the year or the job with the highest income

Current

If the main job is different from the current job, information about both jobs are collected

Second job

The second longest period of employment during the year or the job with the second highest income

Current

If the second job is different from the current second job, information about both jobs are collected

PL020

---

Current - 4 weeks

December

PL025

---

Current + 2 weeks

December

PL031

---

Current

December

PL040

Status in the main job

Current

If the main job is different from the current job, information about both jobs are collected

PL050

Occupation in the main job

Current

If the main job is different from the current job, information about both jobs are collected

PL073, PL074, PL075, PL076, PL080, PL085, PL086, PL087, PL088, PL089, PL090

Number of months for each activity - 12 categories - overlaps allowed

Number of months for each main activity - no overlaps allowed

Number of months and calendar of activities collected for all members 16+

PL111

NACE in the main job

Current

If th emain job is different from the current job, information about both jobs are collected

PL140

Contract in the main job

Current

If the main job is different from the current job, information about both jobs are collected

 

Source of measurement error: selection of the informant

 

Finland’s EU-SILC uses the selected respondent -model. Typically, only one member of the household is interviewed. As a rule, this interviewee should be the selected sample person

In the EU-SILC, it is important to interview subjective questions from selected respondents. He/she gives all the information to the household questionnaire and all personal questionnaires.

Often a choice has to be made between the selected respondent and other member of his/her household. A significant part of the data collected from other members is about the household’s affairs. The selected respondent (especially the youngest selected respondents who still live with their parents, or very old respondents) may not be aware of the household economy, household debts, child care, housing items, the other household members' activities, or many other items.

A proxy respondent is defined as the respondent who is not the selected respondent. A proxy represented 11 per cent of the 10,307 selected respondents in 2012. In 89 per cent of the households, the selected respondent was interviewed. The proxy respondent rate has been slowly decreasing through years: from 25 per cent in 2004 to 11 per cent in 2012.

 

Use of proxy respondents, the 2012 survey 

Informant Information on:    
  Selected person Co-resident Total  
  N % N % N %
The person him/herself 9 184 89.1 1 993 19.6 11 177 54.6
Proxy 1 123 10.9 8 181 80.4 9 304 45.4
Total 10 307 100.0 10 174 100.0 20 481 100.0

 

 

Source of measurement error: General fieldwork problems

 

The main data collection mode is computed-assisted telephone interview (CATI). According to interviewers' estimate, about half of the interviews are made through mobile phones. In about six per cent of those cases, interviews take place outside the respondent’s home. Telephone interviews are afflicted by a sense of rush. In large households, the interview is too long for the telephone. The interviewers are allowed to change the mode into CAPI, in the cases the respondent has no phone or has an exceptionally large household. The CAPI mode was used in 206 households, that is, in two per cent of all households in the 2012 survey. 

Interview duration. According to the Interviewers' Feedback Survey 2012, 42 per cent of the interviewers felt that the duration of the interview was too long. 49 per cent of them assessed it had an effect on the refusal rate and 14 per cent thought that it weakened the quality of responses.

Distribution of total duration of interview in the 2012 survey by rotational group (DB075)

  1-25 26-35 36-60 61- Missing Total Mean
Cross-section, total,  n 4 926 3 098 2 055 222 0 10 301 28.4
% 47.8 30.1 19.9 2.2 0 100.0  
               
3  (Wave 1) n 1 077 1 115 966 103 0 3 261 32.0
% 33.0 34.2 29.6 3.2 0.0 100.0  
2,1,4 (Wave 2,3,4) n              3 849 1 983 1089 119 6 7 046 26.7
% 54.6 28.1 15.5 1.7 0.1 100.0  
               
3 (Wave 1) n 1 077 1 115 966 103 0 3 261 32.0
% 33.0 34.2 29.6 3.2 0.0 100.0  
2 (Wave 2) n 1 500 936 531 60 4 3 031 27.9
% 49.5 30.9 17.5 2.0 0.1 100.0  
1 (Wave 3) n 1564 708 381 40 1 2 694 26.0
% 58.1 26.3 14.1 1.5 0.0 100.0  
4    (Wave 4) n 785 339 177 19 1 1 321 25.5
% 59.4 25.7 13.4 1.4 0.1 100.0  

 

 

Source of measurement error: Variable-specific problems

 

HS130 Lowest monthly income to make ends meet. The difficulty of this question for the respondents is well illustrated by the large number of item non-responses in the cross-section data. Very low and very high figures were also answered.

PL060, PL100 Number of hours usually worked per week in main job, Total number of hours in the second, third…jobs. Item non-response rate is rather high, obviously, due to proxy respondents’ inability to report the hours accurately. Missing values were imputed by the hot-deck method using the known information by gender, employee- or self-employed status, broad occupational group, broad age group and, where possible, information by the self-reported part-time/full-time employment. There were 470 records with missing values in PL060 and/or PL100 in 2012, from which 235 were edited using the previous year's information. PL060 was imputed to 282 employees or self-employed persons based on 10,623 donators, PL100 was imputed to 45 cases based on 535 observations with known information about second jobs.

PE010–PE040, variables on education are collected from different registers. Variables PE010 and PE020 refer to the autumn term (15 September) preceding the operation year. Since the school year and academic year usually last from August-September to May-June, the current situation is sufficiently covered. PE030 (Year when the highest level of education was attained) includes a large number of missing values due to register imperfection, while the coverage of PE040 (highest ISCED level attained) is satisfactory. PE040 is known to be imperfect in the case of migrants.

RB031 (Year of immigration) is retrieved from the population register. The data coverage is deficient in cases of immigration that took place before 1990.

 

 

 

 

 

 

General description of the fieldwork tools

 

List of field work tools of EU-SILC 2012 (income reference period 2011)


1 Questionnaires for CATI/CAPI interviews

1 2012 for all panels, Finnish/Swedish

2 Interviewer's instructions

2A Instruction book for all panels, Finnish/Swedish

3 Contact letter

3A Contact letters to the selected persons,  first year, 3 different letters, Finnish/Swedish
3B Contact letters to the selected persons,  second wave, 3 different letters,  Finnish/Swedish
3C Contact letters to the selected persons,  third wave, 3 different letters, Finnish/Swedish

4 Brochures to present why and how the survey is performed, Finnish/Swedish

5 Pocket Statistics: a small collection of results from previous waves of the SILC survey, especially prepared for the respondents who wanted to know more about how the information is used, Finnish/Swedish

6 List of questions on housing costs; the interviewer may send the list to the respondent to let him/her find out about the costs in advance before the interview takes place, Finnish/Swedish.

7 List of questions on child day care payments in the income reference period (which are collected for national purposes)

The BLAISE-programmed questionnaire is divided into blocks of questions: a specific block for each household member aged 16+, blocks for child care, health, housing, household economics, household composition. The order of the blocks is optional to the interviewer. Only the household composition has to be fixed first, after that the interviewer is free to choose the blocks. In case he/she does not choose the blocks him/herself, the order is automatic. 

Questionnaire build-up has its starting point in the previous year's questionnaire, feedback from the field interviewers and feedback from the data editing process and users. The leading principle in the questionnaire build-up has been a gradual integration process of the SILC to the IDS, and avoidance of too many changes in the national IDS. Of course, another starting point of the questionnaire update is to check the changes in the SILC doc65. The changes were minimal in the 2012 survey.

During the process of BLAISE programming (autumn 2011) the questionnaire was table-tested several times by the team responsible for the IDS and EU-SILC. Six persons were involved. The focus was in the parts of the questionnaire undergoing some change. In the end, a group of professional interviewers checked the questionnaire against their experience. Finally, the technical functioning of the questionnaire was tested in the interviewer organisation before it was sent to the field.

The testing procedure makes use of the BLAISE-programmed questionnaire. The real field situation is simulated by a test sample being actual households from the preceding year's data base. Thus the test questionnaire is pre-filled with the information about the household composition and dates of birth. As in a real field situation, the second and consequent waves/replications have more information from the previous interview entered into the questionnaires. The testers fill in the questionnaire over again, trying all combinations of imagined situations, and likely errors (to disclose signalling), too. Testers are asked to pay attention to

- Spelling, language, formulations and conceptual correctness of the questions,

- Proper functioning of the routings and

- Adequacy of logical checks, signals and interviewing instructions on the screen.

Translations and formulations of certain questions on well-being were tested in the 2012 SILC questionnaire (collecting answers for five questions from nearly 10,000 selected respondents). The questions were useful in helping to choose the final formulations and the interviewer instructions for the next year's ad hoc -module on well-being.

 

Changes in the questionnaire

 

No major changes.

  

Building process of questionnaire: selection of the informant 

 

The contact letter is also sent to his/her parents or guardians, if the selected person is aged under 18. In the 2012 survey, 80 per cent of the selected respondents under the age of 18 have been represented by a proxy respondent.

 

Distribution of proxy interviews by their relationship to the selected person in age groups, the 2012 survey 

  Informant
  Selected respondent Proxy
Age of the selected respondent   Spouse Child Parent Sibling Other Proxies, All interviews
total
16-17 102 0 0 170 1 3 174 276
18-24 718 17 0 178 1 4 200 918
25-44 2 638 205 1 22 1 1 230 2 868
45-64 3 787 283 3 6 1 8 301 4 088
65+ 1 939 185 17 0 7 9 218 2 157
Total 9 184 690 21 376 11 25 1 123 10 307

  

The questionnaire is built to enable the change of respondent during the interview. The questionnaire is programmed to accommodate the mode of addressing the respondent depending on whether the selected person him/herself or another member of the household is responding (interviewing the selected respondent about him/herself: Did you…; interviewing through a proxy respondent: Did N.N. …). This helps the interviewer and respondent to keep control of the member-specific data collection. 

Use of a proxy is denied only in the self-reported health questions (PH010-PH030). A special procedure and a reminder function has been built in the questionnaire to help interviewers contact the selected respondent separately for health questions in those cases where a proxy has answered the rest of the questionnaire. Some ad hoc modules (e.g. the 2013 module on well-being) also require a separate contact with the selected respondent.

 

Building process of questionnaire: General fieldwork problems

 

To help communication by telephone and shorten the interview duration, a number of pre-fills using the data from the previous wave are served on the questionnaire. Each year new ideas on how to relieve the respondent burden are applied to the questionnaire. According to the feedback from the interviewers, it was easier to manage the questionnaire in 2012 than in the previous year. In the longer run, the questionnaire has improved. The percentage of interviewers who felt that the questionnaire functioned technically badly fell from 13 per cent in 2005 to four per cent in 2012. The percentage of those who felt that the questionnaire functioned badly as to the substance fell from 18 per cent in 2005 to zero per cent in 2012.

Letters describing the purpose and contents of the survey are sent to the selected respondents. A special brochure describes the data construction, how the results are published and data protection issues. Respondents also get a small booklet with some results from the earlier survey years. Respondents who have refused to take part in the survey receive a special letter trying to persuade them to participate.

It is also possible for the interviewer to change the mode of collection from CATI to CAPI. CAPI is allowed as an exception in cases where the respondent cannot or does not want to be interviewed by phone. Especially households with no phones or large households use this opportunity. Since the CAPI questionnaire is identical with the CATI questionnaire, the possible differences in outcomes must be due to interaction differences, or, the fact that the CAPI-interviewed households differ from the average in many respects, e.g. income and family composition. 

 

Type of interview (n, %), the 2012 survey

  1. wave 2. wave 3. wave 4. wave Total
  n % n % n % n % n %
CATI 3 161 96.9 2 979 98.3 2 654 98.5 1 307 98.9 10 101 98.0
CAPI 100 3.1 52 1.7 40 1.5 14 1.1 206 2.0
Total 3 261 100.0 3 031 100.0 2 694 100.0 1 321 100.0 10 307 100.0

 

 

Building process of questionnaire:  Variable-specific problems

 

HS130 The wording of the question is essential. The wording was reformulated at the Survey Laboratory of Statistics Finland for the 2006 operation but the high level of non-response prevails.

PL060, PL100 If the respondent could not give an exact answer, he/she was asked whether the hours exceed 30 hours per week.

General description of the interviewer training routines

 

Statistics Finland's interviewer services employs about 160 field interviewers on a permanent work contract. They work mostly part-time. They are given basic training on interviewing and questionnaire standards and codes of practices when they start working. They collect most of Statistics Finland's survey data, for the Labour Force Survey, Household Budget Survey, Time Use Survey and Adult Literacy Survey, for example. In other words, they are experienced. 

The questionnaire changes were introduced to the interviewers in a separate written report and, of course, in the instructions book. The instructions book is rewritten every year and it is also under constant development. 

Newly recruited interviewers were trained separately. They had two day's training about the SILC. The training programme included a lecture on the planning of the survey, including a description of Eurostat's process, legislation and future uses of the data, and Eurostat guidelines on data protection. Concern over international comparability was underlined. Instructions on the fundamental rules of central data collection were given and discussed, such as the definition of the target population, household definition and its implementation in practice, different concepts and classifications of activity, especially labour market activities, child care questions, housing costs and mortgages. A major part of the training time was used on going through the videoed BLAISE questionnaire with the aid of three lecturers. The panel design and the future modules were described. The last part of training consisted of data transferring, data protection and other practicalities.

During the whole fieldwork period, interviewers' information desk is open for them. They can ask for support from the IDS-SILC team. The interviewers, who are distributed all over the country, also have organised district meetings with each other to discuss professional matters.

 

Fieldwork management and data reception

 

The interviewers collect the data and transmit them to the central unit. At Statistics Finland, there is a separate organisation, the interviewers' services unit, to control, monitor and supervise the fieldwork. The central unit transmits the fieldwork tools to the field and organises interviewer training at the beginning of the project, follows the fieldwork progress, and receives the output from the field, checks that all the sampled units are adequately processed and transmits the data to the IDS-SILC team. It also collects feedback from the interviewers with a standardised questionnaire. All data contents are processed by the IDS-SILC team either using the BLAISE system or SAS. Mainly the IDS and SILC data processing is integrated.

 

 

Quality control: selection of the informant

 

Problems arising from the use of proxy respondents concentrate on the subjective questions: control in terms of which household member answers the questions involving subjective assessments, depends on the interviewer. The denial to use proxy respondents for the health questions produces item non-response. In 2012, the item non-response percentage was 5.9 (variable PH010_F = -1 weighted by PB060). This percentage reflects the share of the selected respondents who could not be reached at all in the survey due to denials or other reasons, but whose household respondents were contacted. 

The high percentage of proxy interviews guarantees a higher quality of the household information. Most of the proxy respondents are parents or spouses. Proxies are mostly (45 % in 2012) the 1st or 2nd persons responsible for the accommodation, which also indicates their competence regarding knowledge of the household affairs.

 

Quality control: General fieldwork problems

 

HB100, PB120 - Household and personal interview duration - In Finland's selected respondent model, the duration of the interview is measured as the duration for both household- and personal interview in variable HB100. Variable PB120 is empty.

In fewer than two per cent of the households, the duration of the interview has exceeded the 60 minutes maximum stipulated in the framework regulation.

 

Quality control: Variable-specific problems

 

HS130 None

PL060, PL100 Distributions check

PE010–PE040, missing values are checked and imputed. 

 

 

 
5 . 3 . 3 Non response error
 

Non-response errors are errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:

1) Unit non-response which refers to the absence of information of the whole units (households and/or persons) selected into the sample. According the Commission Regulation 28/2004:

  • Household non-response rates (NRh) is computed as follows:

NRh=(1-(Ra * Rh)) * 100

Where Ra is the address contact rate defined as:

Ra= Number of address successfully contacted/Number of valid addresses selected

and Rh is the proportion of complete household interviews accepted for the database

Rh=Number of household interviews completed and accepted for database/Number of eligible households at contacted addresses

  • Individual non-response rates (NRp) will be computed as follows:

NRp=(1-(Rp)) * 100

Where Rp is the proportion of complete personal interviews within the households accepted for the database

Rp= Number of personal interview completed/Number of eligible individuals in the households whose interviews were completed and accepted for the database

  • Overall individual non-response rates (*NRp) will be computed as follows:

*NRp=(1-(Ra * Rh * Rp)) * 100

For those Members States where a sample of persons rather than a sample of households (addresses) was selected, the individual non-response rates will be calculated for ‘the selected respondent’, for all individuals aged 16 years or older and for the non-selected respondent.

2) Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.

 
5 . 3 . 3 . 1 Unit non-response - rate
 

Cross sectional data

Address contact rate
(Ra)*

Complete household interviews
(Rh)*

Complete personal interviews
(Rp)*

Household Non-response rate
(NRh)*
Individual non-response rate
(NRp)*
Overall individual non-response rate
(NRp)*

A*

B*

A*

B*

A*

B*

A*

B*

A*

B*

A*

B*

 100.0  100.0  79.8 66.3   .. ..   20.2 33.7  .. .. 20.2  33.7

* All the formulas are defined in the Commission Regulation 28/2004, Annex II

A* = Total sample; B = * New sub-sample

..   = Not sample person.

Finland's data. All sample persons are enumerated due to the register sampling frame (Section 12.1). Therefore, only the sample persons no longer in-scope in the survey year have been dropped from the non-response rate calculations: address contact rate = 100.0 %.

 
5 . 3 . 3 . 2 Item non-response - rate
 

The computation of item non-response is essential to fulfil the precision requirements concerning publication as stated in the Commission Regulation No 1982/2003. Item non-response rate is provided for the main income variables both at household and personal level.

 

Item non-response rates of the income components in the cross-sectional data are provided in the Annex - Item non-response rate.

 

 

 
5 . 3 . 3 . 2 . 1 Item non-response rate by indicator
 

 

 

 

 

 

 

 

 

 

 

 

 
5 . 3 . 4 Processing error
 
Data entry and coding Editing controls
  • Fieldwork management and data reception
  • Missing identification information of sample persons
  • Incomplete interviews
  • Missing information, outliers, illogical and inconsistent information
  • Inconsistency between self-reported months of activity and registered income sources in particular
  • Database construction
  • Processing register data
  • Comparison of aggregates

Fieldwork management and data reception

 

The Interviewers Services Unit in the Data Collection Unit of Statistics Finland (unit has been valid since 2013) manages, supervises and monitors interviews’ fieldwork, provides interview tools to the field and organises interviewer training. This unit employs 160 trained interviewers operating in all parts of the country. Four field supervisors administer this network. The interviewers’ average period of service is 11 years. Communication between the Interviewers Services Unit and the interviewers is arranged through a job monitoring system. The system enables continual follow-up of the fieldwork by each interviewer. The accruing paradata include contacts, completed interviews, non-response, workload and costs. Interviewers collect the data and transmit them to the Data Collection Unit, which checks the interviewed data quality (e.g. sample units are adequately processed) and transfers them to the SILC team. Occasional controlling is made by re-interviewing. Feed-back from the central unit to the interviewers is regular. A standardised questionnaire on interviewers’ evaluations of the operation is in regular use.

 

Missing identification information of sample persons 

 

Missing identification information of sample persons has been checked and edited by using register information from the Population Information System. The process takes usually from mid-February to the end of the June, when the interviewed data have been received.

 

Incomplete interviews

 

After the fieldwork period, the SILC team looks through incomplete interviews and makes a decision on their acceptance. Some of the incomplete interviews are rejected. Since the register income data are nearly perfectly available, the acceptance decision is based on the sufficient completeness of the main activity and housing information. In the 2012 operation, 23 incomplete interviews were excluded from the received sample and treated as non-responses.

 

Missing information, outliers, illogical and inconsistent information 

 

The BLAISE programming system of the CATI and CAPI questionnaires primarily controls data quality. In addition, interviewed information and interviewers’ remarks on the questionnaires have been checked and edited against other interviewed and register data for the sample unit. Errors of objective type of information have been corrected, subjective and opinion questions are left without editing. Errors contain such as missing answers, denials and don't knows, clear mistakes, outliers, illogical and inconsistent answers. Special emphasis has been put on the questions of activity months, occupation, NACE, housing costs and childcare. They are checked against other information (including register information). Occupation and NACE are processed through automatic coding. Some of the cases remain open, which are processed manually.

 

Inconsistency between self-reported months of main activity and registered income sources in particular

 

The months of main activity have traditionally been heavily edited to comply with register data, especially with income data. As a result of comparisons between interviewed and registers, some activities of the respondents' answers are rejected and replaced with the corrected ones to be in coherence with the register information about salaries, entrepreneur income, pensions, unemployment and other benefits. Editing was started from the 2009 SILC operation. In the 2004 to 2008 operations, months of main activities (PL070, PL072, PL080, PL085, PL087, PL090) were collected and dealt as subjective responses given by respondents as defined in the EU-SILC document 065.  

 

Database construction

 

Simultaneously with the checking process, a database is opened and variable formation begins by SAS-programming. Variable construction based on interviews and registers is started. Interviewed variables are transferred from the questionnaires to the database. Variables that need constructing – i.e. combined interview- and register information and complex questionnaire items – are added into the database after all the checks and edits have been carried out. Imputations are done.

 

Processing register data

 

Register data - that have been subscribed from the register authorities with a special procedure - arrive in electronic form to the IT and Statistical Methods units. In 2012, use was made of eleven registers. The incoming data are checked technically and substantially. Possible defects are notified to the administrative authority in charge of the data source. The registers cover all units - population, dwelling units, income receivers, etc. The data are linked to the sample persons and transmitted into the database. The data are compared with available external data, i.e. those of the tax authority, pensions authority and other statistics.

 

Comparison of aggregates

 

Routines have been developed to compare the results on variable level with external sources such as the Labour Force Survey, National Accounts, wage statistics and statistics on different social transfers and taxation produced by the National Pensions Institute, National Board of Taxes and National Research and Development Centre for Welfare and Health. Standard comparisons are routinely made each year. These comparisons also have an effect on error detection.

 
5 . 3 . 4 . 1 Imputation - rate

-

5 . 3 . 4 . 2 Common units - proportion

-

5 . 3 . 5 Model assumption error

-

5 . 3 . 6 Data revision

-

5 . 3 . 6 . 1 Data revision - policy

-

5 . 3 . 6 . 2 Data revision - practice

-

5 . 3 . 6 . 3 Data revision - average size

-

5 . 3 . 7 Seasonal adjustment

-

6 Timeliness and punctuality

-

6 . 1 Timeliness

-

6 . 1 . 1 Time lag - first result

-

6 . 1 . 2 Time lag - final result

-

6 . 2 Punctuality

-

6 . 2 . 1 Punctuality - delivery and publication

-

7 Accessibility and clarity

-

7 . 1 Dissemination format - News release

-

7 . 2 Dissemination format - Publications

-

7 . 3 Dissemination format - online database

-

7 . 3 . 1 Data tables - consultations

-

7 . 4 Dissemination format - microdata access

-

7 . 5 Documentation on methodology

-

7 . 5 . 1 Metadata completeness - rate

-

7 . 5 . 2 Metadata - consultations

-

7 . 6 Quality management - documentation

-

7 . 7 Dissemination format - other

-

8 Comparability
 

According to the Regulation (EC) No 1177/2003 of the European Parliament and of the Council concerning EU-SILC: "Comparability of data between Member States shall be a fundamental objective and shall be pursued through the development of methodological studies from the outset of EU-SILC data collection, carried out in close collaboration between the Member States and Eurostat".

Although the best way for keeping the comparability of data is to apply the same methods and definitions of variables, small departures of the definitions given by Eurostat are allowed in EU-SILC. In this way, the mentioned Regulation in its article 16th says: "Small departures from common definitions, such as those relating to private household definition and income reference period, shall be allowed, provided they affect comparability only marginally. The impact of comparability shall be reported in the quality reports."

 
8 . 1 Comparability - geographical

-

8 . 1 . 1 Asymmetry for mirror flow statistics - coefficient

-

8 . 1 . 2 Reference population
 

Reference population

Private household definition Household membership
Members of the private households permanently resident (the census definition) in Finland on 31 December 2011. For migrants in particular, permanent residency means that the persons have resided or intend to reside for at least 12 months and they have not a permanent recidence abroad. Persons living in institutions, in collective households or in residential homes have been excluded.

The private household includes a person residing alone, or all the persons, related or not, who reside and have their meals together or otherwise use their income together. The definition equals with the obligatory EU-SILC definition on "shares in household expenses", but uses other words "use income together" in the interview.

See the private household definition.

For persons who were temporarily absent from the household’s main dwelling and from home no specific time duration (6 months) was set for the absence in the interview provided that the criteria of household formation and membership (shares in household expenses) were fulfilled. Such persons have close family ties to the household and they do not form a household of their own. Therefore, the following persons are also counted in household members:

- Persons conducting military service or conscript service
- Persons residing and working in another locality or abroad if they are involved in the acquisition and use of household income
- Persons residing and studying in another locality if they use income received mostly from their parents
- Persons temporarily in institutions, on holiday or travelling.

 

The following persons form a household of their own:
- Subtenants
- Domestic staff
- Students living on their own if they live mostly on their own income or on a student loan
- Students residing in dormitories, unless they are married or officially cohabiting.

 
8 . 1 . 3 Reference Period
 
Period for taxes on income and social insurance contributions Income reference periods used Reference period for taxes on wealth Lag between the income ref period and current variables
2011  2011  2011  4-5 months
 
8 . 1 . 4 Statistical concepts and definitions
 

  Cross and longitudinal data

 

Total hh gross income Total disposable hh income Total disposable hh income before social transfers other than old-age and survivors' benefits Total disposable hh income before all social transfers
(HY010) (HY020) (HY022) (HY023)
F F F F

 

 

Imputed rent Income from rental of property or land Family/ Children related allowances Social exclusion payments not elsewhere classified Housing allowances Regular inter-hh cash transfers received Interest, dividends, profit from capital investments in incorporated businesses Interest paid on mortgage Income received by people aged under 16 Regular taxes on wealth Regular inter-hh transfers paid Tax on income and social contributions Value of goods produced for own consumption
(HY030) (HY040) (HY050) (HY060) (HY070) (HY080) (HY090) (HY100) (HY110) (HY120) (HY130) (HY140) (HY170)
F F F F F F F F F F NC

 

 

Cash or near-cash employee income Other non-cash employee income Income from private use of company car Employers social insurance contributions Cash profits or losses from self-employment Pension from individual private plans Unemployment benefits Old-age benefits Survivors benefits Sickness benefits Disability benefits Education-related allowances Gross monthly earnings for employees
(PY010) (PY020) (PY021) (PY030) (PY050) (PY080) (PY090) (PY100) (PY110) (PY120) (PY130) (PY140) (PY200)
F F F L F F F F F F F F NC

 

 

The source or procedure used for the collection of income variables The form in which income variables at component level have been obtained The method used for obtaining target variables in the required form
 Gross  Gross Register (99.0 % of all gross income and 97.8 % of all paid transfers, weighed income) / Interview (1.0 % of all gross income and 2.2 % of all paid transfers, weighted income) 
 
8 . 2 Comparability - over time

The income data are comparable over the survey years 2004—2012.

8 . 2 . 1 Length of comparable time series

-

8 . 3 Comparability - domain

-

9 Coherence
 

The coherence of two or more statistical outputs refers to the degree to which the statistical processes, by which they were generated, used the same concepts and harmonised methods. A comparison with external sources for all income target variables and the number of persons who receive income from each ‘income component’ will be provided, where the Member States concerned consider such external data to be sufficiently reliable.

 
9 . 1 Coherence - cross domain

Information about coherence of the cross-sectional data with external data sources (incl. National Accounts) is provided in the Annex - Coherence. 

9 . 1 . 1 Coherence - sub annual and annual statistics

-

9 . 1 . 2 Coherence - National Accounts

-

9 . 2 Coherence - internal

-

10 Cost and Burden

-

11 Confidentiality

-

11 . 1 Confidentiality - policy

-

11 . 2 Confidentiality - data treatment

-

12 Statistical processing
 

Detailed information concerning sampling frame, sampling design, sampling units, sampling size, weightings and mode of data collection can be found in this section. Such information is mainly used for the computation of the accuracy measures. 

 
12 . 1 Source data

Description of the sampling frame 

 

The Population Information System maintained by the Population Register Centre of Finland is the source data used for the sampling frame (see below). The Population Information System is a compilation of local registers kept up by population register districts. It covers basic data on all Finnish citizens and aliens permanently resident in Finland. Persons living in private households, institutions, persons living temporarily abroad and homeless persons are also included in the Population Information System. Because these persons do not belong to the target population, they are excluded from the sampling frame. 

Finland uses a unified identification code system across register sources, which means that every person residing in Finland has a unique identification code and each dwelling has a domicile code. Each person has been registered in the municipality where he/she has a permanent place of residence. The domicile code is the link between a person and his/her permanent dwelling. Persons without an address are registered in municipal registers as homeless persons. A person with a permanent address may also have a registered temporary address. 

A register data copy of the population information system is used as the sampling frame for the selection of the master sample, i.e. selected sample persons. Data refer to the end of the year preceding the survey year. Persons aged under 16 and persons placed in institutions and homeless people are excluded from the frame. The order of the frame is based on the domicile code indicating the location of a person's dwelling.

The frame is also used for the construction of the household-dwelling units for the master sample, and for the sample stratification. After various checks and combinations (e.g. excluding collective households) household-dwelling units are compiled by adding all the persons sharing the same domicile code (occupying the same dwelling) with the selected person (target person) to the master sample. Before the interview fieldwork begins, the information for the second, third and fourth waves/replications of the EU-SILC and changes occurred for the first wave after the sample selection are updated on the basis of the register data.

The master sample of household-dwelling units is used for different sampling purposes, one of them is the Finnish EU-SILC survey.

 

Information about the frame: reference period, updating actions, quality review actions

 

In general, the Population Information System of the Population Register Centre of Finland is exhaustive and up-to-date as regards persons.  Information on population changes: births, deaths, migration, immigration and emigration, marriages, divorces, adoptions and changes of names are updated regularly. The Population Register Centre updates the official population figures on the 5th to 8th day of every month in all municipalities in Finland. 

The system is maintained by notifications of changes made by population districts authorities. The inhabitants themselves are responsible only for the notification of changes of residence. Those who move or immigrate are expected to report the new residence address to their local register office within one week of the move, specifying all the family or household members involved in the move. Those emigrating should supply a notice of the change of address in the country of entry. According to an agreement between the Nordic countries, the population register authorities of the country of entry inform the population register authorities about the country of exit. In the years when municipal elections are arranged (every 4th year), the population is corrected by around 1,000 persons, when emigrants whose emigration has been left unnoticed return notifications of voting. 

A reliability survey on the Population Information System is conducted yearly by means of a sample interview (CATI) survey of approximately 10,000 persons.  From the EU-SILC point of view, reliability of its address information has special relevance. In the quality surveys, the final proportion of the correct addresses in the total sample has always been high, 98 to 99 per cent.

The EU-SILC collects the variables PB130, PB140, PB150, PB190, PB210, PB220A and PB220B directly from the Population Information System. None of these, however, have been checked in the reliability survey. 

The Population Information System has no under-coverage in any population groups. Asylum seekers and refugees are not included in the resident population until their permit of residence has been processed. The small over-coverage exists as a consequence of the necessity to draw the sample in time before the reference time point of the sample households (31. Dec.). The sample data has further been updated by final register information (including tax information to be connected to the master sample in order to create the strata, for example) available after it’s selection. Various checks are conducted. At this point those who have died, moved permanently abroad or placed into an institution between the sample selection time point and the end of the year are excluded from the master sample. With this processing the frame imperfection (not describing the reference time point) is corrected in the sample. 

A household-dwelling unit may consist of several households, or all the dwelling occupants are not sharing in household expenses - a housekeeping unit is not a register concept – that is why the household composition is checked in the interview (referring to 31. Dec). The household members' presence vs. absence either temporarily or permanently is checked in the interview as well. Persons who recently changed a place of residence and/or household, new-borns, recently moved to institutions or died are the usual sources of non-correct register-based pre-entries in the EU-SILC questionnaire.

12 . 1 . 1 Sampling design and procedure
 

Type of sampling design

Two-phase stratified sampling design.

Stratification and sub-stratification criteria

Strata contain socio-economic groups grouped by income class (13 strata in the new sample). The register information used for strata construction is about all members of the selected sample person's household-dwelling unit available at the time of sample selection (updated by the final population register data later). Strata are created only for those who do not belong to the over-coverage. The stratification normally takes the highest earning person as a categorising person for a stratum, but an exception to the rule is an entrepreneur who does not have to be the highest earning one in the category of entrepreneurs. The income class division allocates the sample more to high-earners.

 

The strata of the new sample/rotational group (DB075=3)

       
Socio-economic categorisation of the household-dwelling unit Income Class

Stratum

DB050

 
Wage-earners Lowest  1  
  2nd lowest  2  
  3rd lowest 3  
  Highest  4  
Entrepreneurs Lower  5  
  Higher  6  
Farmers Lower  7  
  Higher  8  
Pensioners Lower  9  
  Higher  10  
Others Lower  11  
  Higher  12  
No tax information - 13  
       

 

The strata of the older rotational groups in the data refer to the equivalent groups of the new sample as follows: 2nd replication (DB075=2), DB050-12; 3rd replication (DB075=1), DB050-24; 4th replication (DB075=4), DB050-36.

Sample selection schemes

The new sample is selected with a two-phase stratified sampling design. In the first phase, a master sample of persons (50,000) is selected with systematic sampling from the population register data. In the second phase, a sample of persons (5,000) in the household-dwelling units is selected from the stratified master sample with simple random sampling without replacement within every stratum and using non-proportional allocation. The new sample selection scheme is consistent with the first years' sample selection of the old rotational groups. 

The old rotational groups (3 groups) are included in the set of responded households (DB135=1, including initial sample person) from the previous survey year to be interviewed (gross sample). Those households in that the initial sample person is no longer in-scope (moved to a collective household or institution in the country, moved outside the country or dead) are omitted for the final net sample of the survey year as over-coverage. The final household composition is defined during the interview. In the survey year, all old rotational groups are supplemented by an extra sample of persons aged 16 (selected from the stratified master sample with simple random sampling without replacement within every stratum and using non-proportional allocation, Section 12.1.3).

The sample does not contain substitutions.

Sample distribution over time

The reference population is defined as the population registered as permanently resident in Finland on 31 December 2011. Household composition is also dated to 31 December 2011. The income reference period is constant for all households and persons: the calendar year 2011.

In the SILC 2012 operation, the fieldwork period stretched over five months; it started at the beginning of January 2012 and ended in May 2012. The cross-sectional sample of the EU-SILC consists of four rotational groups from which old groups (from the 2nd to 4th replications) are also for the longitudinal component. The fieldworks of old rotational groups were started at the beginning of January and were completed mostly by the end of April. Only a few households were interviewed after April. The new sample households were interviewed in February to May.  

  New sample        
         
1st wave,    DB075=3 2nd wave, DB075=2 3rd wave,  DB075=1 4th wave,   DB075=4 Total
Month of interview N % N % N % N % N %
December, 2011                    
January, 2012 . . 1 285 42.4 126 4.7 106 8.0 1 517 14.7
February 242 7.4 1 185 39.1 955 35.4 385 29.1 2 767 26.8
March 811 24.9 535 17.7 1 094 40.6 467 35.4 2 907 28.2
April 1 215 37.3 13 0.4 508 18.9 356 26.9 2 092 20.3
May, 2012 993 30.5 13 0.4 11 0.4 7 0.5 1 024 9.9
Total 3 261 100.0 3 031 100.0 2 694 100.0 1 321 100.0 10 307 100.0

 

 
12 . 1 . 2 Sampling unit

The sampling unit is a person.

Sample persons refer to persons selected in the household-dwelling units in the second phase of the two-phase stratified sampling (selected respondents). 

12 . 1 . 3 Sampling rate and sampling size
 

Concerning the SILC instrument, three different sample size definitions can be applied:

- the actual sample size which is the number of sampling units selected in the sample

- the achieved sample size which is the number of observed sampling units (household or individual) with an accepted interview

- the effective sample size which is defined as the achieved sample size divided by the design effect with regards to the at-risk-of poverty rate indicator

 

Cross-sectional data

 

The Finnish data provide  a greater size of sample than the minimum sample required to be achieved in the EU-SILC. Because Finland uses a sample of persons, the minimum effective sample size is 5,063, and when taking account of the design effect term for the Finnish sample, deff2= 1.25 , the minimum sample to be achieved is 6,239 for the cross-sectional data. The minimum size of sample to be selected is 8,328, The figure includes non-response (overall response rate R is approximately 0.76 in the Finnish survey).

In the survey year 2012, the size of the actual sample was 12,913 in the cross-sectional component. When excluding the presumed non-response (25% for the first panel, and 8.5% for the following panels), the number of respondents was expected to be 11,004. The realised number of accepted respondents was smaller, 10,307, due to higher non-response rate especially in the new rotational group. Also the non-response rate of the second rotational group (DB075=2) was relatively high in the survey year 2012. However, the minimum sample to be selected for cross-sectional data was clearly reached (6,239). 

 

Achieved sample size according to the rotational groups (DB075) in the 2012 survey year

  Accepted households Number of persons aged 16 or older,  members of the accepted households (DB135=1) and for whom interview was completed (RB250=11 to 13). Number of selected respondents, members of the accepted households (DB135=1) and who completed a personal interview (RB250=11 to 13).
Cross-sectional, total: 10 307 20 481 10 307
New sample (DB075= 3) 3 261 6 564 3 261
Old rotational groups (DB075= 2,1,4) 7 046 13 917 7 046
DB075 Wave      
2 2 3 031 5 994 3 031
1 3 2 694 5 290 2 694
4 4 1 321 2 633 1 321

 

Information about the new sample (DB075=3) by primary strata (DB050) in the 2012 survey year

Socio-economic categorisation of the household-dwelling unit Income Class Stratum Master sample (1st phase) 2nd phase sample 2nd phase sample excluding over-coverage 2nd phase sample, accepted respondents 
Wage-earners Lowest  1 10 346 821 814 500
  2nd lowest  2 8 707 650 646 418
  3rd lowest 3 7 614 567 563 402
  Highest  4 3 602 500 494 332
Entrepreneurs Lower  5 1 875 400 396 261
  Higher  6 905 299 298 212
Farmers Lower  7 850 200 198 155
  Higher  8 671 183 183 140
Pensioners Lower  9 6 558 500 462 297
  Higher  10 5 116 400 387 294
Others Lower  11 2 321 299 297 149
  Higher  12 332 134 133 88
No tax information - 13 251 47 46 13
    All 49 148 5 000 4 917 3 261

 

Information about the old rotationals groups' initial strata. Strata refer to the 2009-2011 survey years depending on the wave/replication.

Socio-economic categorisation of the household-dwelling unit Income Class Stratum of the initial sampling (1st wave) 2nd phase sample incl. over-coverage 2nd phase sample excl. over-coverage Accepted respondents
DB075     2 1 4 2 1 4 2 1 4
Wave     2 3 4 2 3 4 2 3 4
Wage-earners Lowest  1 820 821 1 230 813 813 1 222 547 526 829
  2nd lowest  2 650 650 975 646 649 968 470 445 709
  3rd lowest 3 567 567 849 562 564 840 411 410 615
  Highest  4 500 500 750 497 490 737 380 351 514
Entrepreneurs Lower  5 400 400 600 394 397 589 271 275 421
  Higher  6 300 300 450 297 299 445 205 219 311
Farmers Lower  7 199 200 300 198 198 298 163 155 250
  Higher  8 184 183 276 183 180 273 154 156 235
Pensioners Lower  9 500 501 750 469 471 708 353 330 515
  Higher  10 400 400 600 387 386 578 304 310 459
Others Lower  11 299 300 453 298 298 444 184 162 260
  Higher  12 133 131 195 132 128 193 90 87 139
No tax information - 13 48 47 72 46 44 69 13 19 23
    All 5 000 5 000 7 500 4 922 4 917 7 364 3 545 3 445 5 280

 

 

 

 

 

 

 

 
12 . 2 Frequency of data collection

Annually.

12 . 3 Data collection
 

Mode of data collection

 

A description of the mode of data collection used in your country. Please mention if you use mixed mode of data collection.

1-PAPI
(% of total)
2-CAPI
(% of total)
3-CATI
(% of total)
4-Self administrated
(% of total)
 0.0  2.0  98.0  0.0

 

The mean interview duration

 

The mean interview duration per household is calculated as the sum of the duration of all household interviews plus the sum of the duration of all personal interviews, divided by the number of household questionnaires completed. Only households accepted for the database have to be considered. Average interview duration = 28.4 minutes.

 
12 . 4 Data validation

-

12 . 5 Data compilation

-

12 . 5 . 1 Weighting procedure
 

Design factor

Non-response adjustments

Adjustment to external data

Final cross sectional weights

Information about weighing procedure (design factor, non-response adjustments, adjustment to external data, final cross-sectional weights) is delivered in the Annex - Weighing procedure.      
 
12 . 5 . 2 Estimation and imputation
 
Imputation procedure used Imputed rent Company car
Information about estimation and imputation is provided in the Annex - Estimation and Imputation.    
 
12 . 6 Adjustment

-

13 Comment

National questionnaire is available in Circa BC at: https://circabc.europa.eu/

Please select EU-SILC section and then select the folder called "06 National Questionnaire" in the library list.

 

Annexes:

Coherence

Data collection

Estimation and imputation

Item non-response rates

National Questionnaire in Finnish

Sampling errors

Weighting procedure

Related metadata
Annexes Description
Annex Sampling errors .xls
Annex - Item non-response rate.xls
Annex - Estimation and Imputation.pdf
Annex - Coherence.pdf
Annex - Data collection.pdf
Annex - Weighting procedure.pdf
Annex - National Questionnaire Finnish.pdf