SILC_ESQRS_A_SK_2011_0000 - Version 1

National Reference Metadata in ESS Standard for Quality Reports Structure (ESQRS)

Compiling agency: The Statistical Office of the Slovak Republic

Time Dimension: 2011-A0

Data Provider: SK1

Data Flow: SILC_ESQRS_A


For any question on data and metadata, please contact: EUROPEAN STATISTICAL DATA SUPPORT


1. ContactTop
1.1. Contact organisationThe Statistical Office of the Slovak Republic
1.2. Contact organisation unitSocial Statistics and Demography Department
1.5. Contact mail addressMileticova 3, 824 67 Bratislava, Slovak Republic


2. IntroductionTop
 

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.

 


3. Quality management - assessmentTop

Not requested by Reg. 28/2004


4. RelevanceTop
4.1. Relevance - User Needs

Not requested by Reg. 28/2004

4.2. Relevance - User Satisfaction

Not requested by Reg. 28/2004

4.3. Completeness

Not requested by Reg. 28/2004

4.3.1. Data completeness - rate

Not requested by Reg. 28/2004


5. Accuracy and reliabilityTop
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.

 
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 - We accepted Eurostat methodology for Slovakia.

 
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

20.6 0.69 19.3-22.0 13.0 0.62 11.8-14.2 10.6 0.56 9.5-11.7 7.6 0.52 6.6-8.7

Male

19.5 0.79 17.9-21.0 12.8 0.71 11.5-14.2 10.1 0.64 8.9-11.4 7.5 0.61 6.3-8.7

Female

21.7 0.70 20.3-23.1 13.1 0.61 11.9-14.3 11.0 0.56 9.9-12.1 7.8 0.53 6.8-8.8

Age0-17

26.0 1.53 23.0-29.0 21.2 1.46 18.3-24.0 12.4 1.34 9.8-15.0 7.3 1.07 5.2-9.3

Age18-64

20.6 0.71 19.2-21.9 12.4 0.60 11.2-13.5 10.3 0.56 9.2-11.4 7.8 0.47 6.8-8.7

Age 65+

14.5 0.91 12.7-16.3 6.3 0.62 5.1-7.5 9.7 0.76 8.2-11.2      
 
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
  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
 
5.3.1.1. Over-coverage - rate
 
 

Main problems

Size of error

Cross sectional

data

·Over-coverage

·Under-coverage

·Misclassification

unknown over-coverage (nearly no over-coverage: There is only a very small difference between the frame and the target population)

unknown under-coverage (nearly no under-coverage: There is only a very small difference between the frame and the target population)

there are no misclassifications

 
5.3.2. Measurement error
 

Cross sectional data

Source of measurement errors

Building process of questionnaire

Interview training

Quality control

 - the way of compiling the questionnaires, structure of questionnaires, ordering of questions in questionnaire, using of detailed structure of primary target variables,

- quality of interviewers´ training, individual skill of interviewer,

- interview in the case of households from previous wave or previous waves and contacted again in next year of the survey,

- searching of addresses of households or persons who moved to another residence compared to year 2010,

- logical checks of questionnaires received from interviewers.

In compiling of questionnaires EU SILC 2011 we resulted from until now proposed and applied questionnaires for the year 2010, where there were used and taken into account concrete knowledge from survey fieldwork and also changes made in some variables in accordance with Doc.065 for 2011 operation (e.g. taking into account changes in variable HY170 Value of goods produced for own consumption, new variable PL031 Self-defined current economic status,secondary target variables of 2011 module into questionnaire C). Some changes in questionnaires were made at national level and most of them were rising from effort to make better harmonization of core variables with other household surveys within Social statistics (e.g. national modalities of variable PE040 – The highest ISCED level attained, harmonisation of questions of PH variables with EHIS).

Since EU SILC 2010 all income components (including income intervals) were collected in EUR.

Questions in compiling of questionnaires were proposed in a way to cover all required variables.

The questions were grouped into particular modules by reason of better understanding, lucidity and securing more easily orientation of interviewers in questionnaires.

Compared to previous year of the survey we only took into account requirements and directions proposed in Doc. 065 (2011 operation) and also changes related to legislative on national level – i.e. adding some new income and tax components, e.g. employment bonus, parent contribution in family related allowances).

In EU SILC 2011 questionnaires were again printed in different colours shades. According to reactions from side of interviewers it made fieldwork much easier than in the previous year.

On the base of co-operation with the Ministry of Labour, Social Affairs and Family of the SR, questionnaire SILC 1-01/B was again completed by some questions related to national aspects of poverty proposed by Ministry. Data will serve only for internal purposes.

The external individually trained interviewers carried out the fieldwork. Mostly they were persons, who ensured interview in EU SILC 2010, possibly in previous years of the survey or persons who approved in previous national surveys realized in households (Population and Housing Census, Microcensus, etc.). The situation was again demanding, because the communication with households compared to the previous year again slightly got worse and it was more difficult to look for household willing to cooperate.

Also in the year 2011, the organisation of the survey in individual regions was ensured by regional coordinators of SO SR.  On each Regional Office there  was coordinator – expert for methodology who ensured personal contact (or contact by phone) with interviewers and solved occurred methodological unclearness on the base of consultation with SO SR. Training of interviewers preceded one day training of regional coordinators aimed at explanation of objectives, form, content of survey as well as methods and methodology. At the same time they were drew attention to mistakes determined during centralized processing. By reason of numbers of mistakes it was impossible to bring mistakes to concrete interviewer  attention, summary of mistakes by individual regions was made.

The Regional Offices of the SOSR in co-operation with the SOSR performed the training of interviewers with participation of experts. Nearly all trainings carried out one week before survey fieldwork and 403 interviewers were trained in total.  On the base of experience from previous surveys it was certified by Regional Offices the SOSR to carry out independent training for interviewers who realized interview in previous years and separate one for new less experienced interviewers. Apart from general methodological issues, this also allowed to deal with other specific problems in the survey according to needs a requirement of separated interviewers.

With respect to data collected during the previous waves of the survey,  interviewers were paying attention to quality of collected data, because in data processing there was underlined comparability of data in time.

Data processing was realized on two levels:

1. The following actions has been realized on the decentralized level:

a) taking questionnaires from interviewers, formal checking, preparation of questionnaires for data recording,

b) data recording and checking. The special software DCSILC2000 has been used for data recording, in which these types of controls were used: checks on the data integrity, identification of duplicity, frequency checks, checks to the permissible values, the logic checks within a questionnaire and between questionnaires, special conditions for data recording and non-responses. All the defined checks are included in the technical project (TP - part A/0463/6) to data processing EU SILC 2011. The checks are divided into two types: informative checks and necessary checks. System of the checks also comprised of certain chosen checks from the checking software of Eurostat.

c) on this level, also the errors caused by data recording have been eliminated. There were mainly errors created by a shift in editing codes yes/no/don’t know and by not realizing a visual check sufficiently. By monitoring errors in the phase of data recording, he errors were analysed and subsequently the situation was improved.

2. On the centralized level a final database was created. Logic controls, corrections, over weighting and imputations were realized using SW of system SAS.

 
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*

 95.41  100.00  89.64  95.53  100.00  100.00  14.47  4.47  0.00  0.00  14.47  5.15

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

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

 
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.

 
5.3.3.2.1. Item non-response rate by indicator
 
 

Total hh gross income
(HY010)

Total disposable hh income
(HY020)

Total disposable hh income before social transfers other than old-age and survivors benefits
(HY022)

Total disposable hh income before all social transfers
(HY023)

% of household having received an amount 100.00 100.00 99.67 98.48
% of household with missing values (before imputation) 0.00 0.00 0.00 0.00
% of household with partial information (before imputation) 0.00 0.00 0.00 0.00

 

 

Imputed rent
(HY030)

Income from rental of property or land
(HY040)
Family/ Children related allowances
(HY050)
Social exclusion payments not elsewhere classified
(HY060)
Housing allowances
(HY070)
Regular inter-hh cash transfers received
(HY080)
Interest, dividends, profit from capital investments in incorporated businesses
(HY090)

% of household having received an amount

92.13 3.42 40.27 4.12 0.29 6.58 17.17

% of household with missing values (before imputation)

0.00 0.56 0.00 12.62 0.00 4.09 0.00

% of household with partial information (before imputation)

100.00 0.00 0.00 0.47 0.00 0.00 0.00

 

  Cash or near-cash employee income
(PY010)
Other non-cash employee income
(PY020)
Income from private use of company car
(PY021)
Employers social insurance contributions
(PY030)
Cash profits or losses from self-employment
(PY050)
Value of goods produced for own consumption
(PY070)
Unemployment benefits
(PY090)
Old-age benefits
(PY100)
Survivors benefits
(PY110)
Sickness benefits
(PY120)
Disability benefits
(PY130)
Education-related allowances
(PY140

% of household having received an amount

51.94 37.44 0.59 50.29 5.54  - 2.37 23.99 7.85 2.22 5.34 0.60

% of household with missing values (before imputation)

0.00 0.00 0.00 0.00 0.00  - 0.00 0.00 0.00 0.00 0.00 0.00

% of household with partial information (before imputation)

 0.00  0.00  100.00  100.00  0.00  -  0.00 0.00 0.00 0.00 0.00 0.00
 
5.3.4. Processing error
 
Data entry and coding Editing controls
The special software DCSILC2000 has been used for data recording, in which these types of controls were used: checks on the data integrity, identification of duplicity, frequency checks, checks to the permissible values, the logic checks within a questionnaire and between questionnaires, special conditions for data recording and non-responses. All the defined checks are included in the technical project (TP - part A/0463/6) to data processing EU SILC 2011. The checks are divided into two types: informative checks and necessary checks. System of the checks also comprised of certain chosen checks from the checking software of Eurostat.

The errors caused by data recording have been eliminated. There were mainly errors created by a shift in editing codes yes/no/don’t know and by not realizing a visual check sufficiently. By monitoring errors in the phase of data recording, he errors were analysed and subsequently the situation was improved.

On the centralized level a final database was created. Logic controls, corrections, over weighting and imputations were realized using SW of system SAS.

 
5.3.4.1. Imputation - rate

Not requested by Reg. 28/2004

5.3.4.2. Common units - proportion

Not requested by Reg. 28/2004

5.3.5. Model assumption error

Not requested by Reg. 28/2004

5.3.6. Data revision

Not requested by Reg. 28/2004

5.3.6.1. Data revision - policy

Not requested by Reg. 28/2004

5.3.6.2. Data revision - practice

Not requested by Reg. 28/2004

5.3.6.3. Data revision - average size

Not requested by Reg. 28/2004

5.3.7. Seasonal adjustment

Not requested by Reg. 28/2004


6. Timeliness and punctualityTop
6.1. Timeliness

Not requested by Reg. 28/2004

6.1.1. Time lag - first result

Not requested by Reg. 28/2004

6.1.2. Time lag - final result

Not requested by Reg. 28/2004

6.2. Punctuality

Not requested by Reg. 28/2004

6.2.1. Punctuality - delivery and publication

Not requested by Reg. 28/2004


7. Accessibility and clarityTop
7.1. Dissemination format - News release

Not requested by Reg. 28/2004

7.2. Dissemination format - Publications

Not requested by Reg. 28/2004

7.3. Dissemination format - online database

Not requested by Reg. 28/2004

7.3.1. Data tables - consultations

Not requested by Reg. 28/2004

7.4. Dissemination format - microdata access

Not requested by Reg. 28/2004

7.5. Documentation on methodology

Not requested by Reg. 28/2004

7.5.1. Metadata completeness - rate

Not requested by Reg. 28/2004

7.5.2. Metadata - consultations

Not requested by Reg. 28/2004

7.6. Quality management - documentation

Not requested by Reg. 28/2004

7.7. Dissemination format - other

Not requested by Reg. 28/2004


8. ComparabilityTop
8.1. Comparability - geographical

Not requested by Reg. 28/2004

8.1.1. Asymmetry for mirror flow statistics - coefficient

Not requested by Reg. 28/2004

8.1.2. Reference population
 

Reference population

Private household definition Household membership
 Fully comparable   Fully comparable   Fully comparable
 
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
Calendar year 2010 Calendar year 2010 Calendar year 2010 4 months
 
8.1.4. Statistical concepts and definitions
 
Total hh gross income
(HY010)
Total disposable hh income
(HY020)
Total disposable hh income before social transfers other than old-age and survivors' benefits
(HY022)
Total disposable hh income before all social transfers
(HY023)
F F F F

 

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

P

F

F

F

F

F

F

F

F

F

F

 

Cash or near-cash employee income
(PY010)
Other non-cash employee income
(PY020)
Income from private use of company car
(PY021)
Employers social insurance contributions
(PY030)
Cash profits or losses from self-employment
(PY050)
Value of goods produced for own consumption
(PY070)
Unemployment benefits
(PY090)
Old-age benefits
(PY100)
Survivors benefits
(PY110)
Sickness benefits
(PY120)
Disability benefits
(PY130)
Education-related allowances
(PY140)
Gross monthly earnings for employees
(PY200)

F

F

F

F

F

Variable PY070 is not used since 2010 operation

F

F

F

F

F

F

The variable was collected, but in terms of the fact that EU SILC 2011 is not a source for calculation of unadjusted gender pay gap, this variable was recorded only for national purposes

 

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
All the income variables are obtained by interview. The target income variables were divided to more subcomponents according to the Slovak benefit system. All income data was recorded as gross on component level.

Income variables on component level were collected on the base of personal interview in private households.

Since January 1-st 2009 there is used common currency – EUR (converse rate 1 EUR = 30,1260 SKK) due to integration of Slovak Republic into Euro-zone.

For EU SILC 2011 the income reference period was previous calendar year 2010, so all income components were collected in common currency - EUR. Data for all income variables on household level (data file H) and personal level (data file P) are recorded in EUR.
 
8.2. Comparability - over time

It is not relevant.

8.2.1. Length of comparable time series

Not requested by Reg. 28/2004

8.3. Comparability - domain

Not requested by Reg. 28/2004


9. CoherenceTop
9.1. Coherence - cross domain

Achieved values were compared with information from external sources:

a) other surveys of the SO SR: LFS, HBS, Census 2001, Movement of the Population of the SO SR, Structure of Earnings Survey (SES),

b) administration sources (Social Insurance Agency, Ministry of Finance, Ministry of Labour, Social Affairs and Family).

Coherence of number of persons, who receive income from each “income component” with external sources:

 

  EU SILC  2011 Administrative source Ratio Source
Households sharing of expenditures  1 911 664 1 911 664 100,0 Demographic Research Centre, Infostat
Working (SILC - PX050) 2 325 802 2 317 500 100,4 LFS, SO SR, 2010
Unemployed (SILC - PX050) 375 077 389 000 96,4 LFS, SO SR, 2010
Pensioners (SILC - PX050) 1 036 947 954 661 108,6 Social Insurance, 2010

 

9.1.1. Coherence - sub annual and annual statistics

Not requested by Reg. 28/2004

9.1.2. Coherence - National Accounts

Income - comparison with national accounts - in million EUR

  EU SILC  2011 National accounts 2010 Ratio
Compensation of Employees 22 668,2 26 976,5 84.0
Social benefits 7 165,7 10 001,1 71.6
9.2. Coherence - internal

Not requested by Reg. 28/2004


10. Cost and BurdenTop

Not requested by Reg. 28/2004


11. ConfidentialityTop
11.1. Confidentiality - policy

Not requested by Reg. 28/2004

11.2. Confidentiality - data treatment

Not requested by Reg. 28/2004


12. Statistical processingTop
12.1. Source data

Data set is based only on a survey.

12.1.1. Sampling design and procedure
 

Type of sampling design

One -stage stratified sampling was used in EU SILC 2011. The proportional number of households was selected by simple random sampling in individual strata.

Households with rotation groups 3,4 and 1 in 2010 year were included into sample in EU SILC 2011 survey. Households included to 2-nd rotation group were excluded and substituted by new households for EU SILC 2011. Repeatedly stratified sampling was used for selection these new households and the proportional number of households was selected by simple random sampling in individual strata.

Stratification and sub stratification criteria

There are two criteria of area stratification in the sampling design:

- geographical stratification (8 standard administrative regions corresponding to the European NUTS 3 level.)

- degree of urbanization: 7 groups according to population size of municipalities and communes  (number of inhabitants in municipalities and communes)

48 final strata were created (variable DB050) by using of those two stratification criteria.

Sample selection schemes

 

The information about population, which was obtained from sampling frame, the information about updating of sampling frame and the rules for proportional stratified sampling was used in creating of sample selection scheme for new rotational group.

 

In selection of households for the new rotational group we proceeded by analogy as in the first year of survey, i.e. in EU SILC 2005:

 

- up-to date sampling frame (list of households sharing of expenditures) was created,

 

- strata were created (households sharing of expenditures from list were put in strata by region and level of urbanisation of municipalities),

 

- required number of selected households sharing of expenditures for new rotational group was approximately 1 500 households,

 

- probability of sampling for given number of households sharing of expenditures was appointed,

 

- random numbers from interval (0,1) were generated in each strata for each unit, which was not included in sampling in previous period,

- units with random number lower or equal than was probability of sampling were included into sampled population.

Sample distribution over time

Survey was carried out from 1 April to 30 April 2011.
 
12.1.2. Sampling unit

Households sharing of expenditures are the sampling units.

Households sharing of expenditures are private households comprised of persons in dwelling who live and manage together, including sharing in ensuring of the living needs. As manage together is considered: share in covering the basic household costs (catering, housing cost, costs of electricity, gas etc.). 

The fullest list of households sharing of expenditures and permanently occupied dwellings and houses is available on the base of data from the 2001 Population and Housing Census (acronym - SODB). Changes in the number of permanently occupied dwellings and houses within the period 2001-2004 and 2004-2010 were updated. The information on the number of allocation and reduction of dwellings and the announcement in regions of the Slovak Republic were used.

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: 5 801

- the achieved sample size which is the number of observed sampling units (household or individual) with an accepted interview: 5 200

- 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: 4 250

Given that the effective sample size has been already treated in the section dealing with sampling errors, in this section the attention focuses mainly on the achieved sample size.

Achieved sample size:

- no. of household: 5 200

- no. of all persons: 15 335

- no. of persons 16+:13 439

 
12.2. Frequency of data collection

Data collection was carried out from 1 April to 30 April 2011.

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)
100.00 0.00 0.00 0.00

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 = 67.4 minutes

 
12.4. Data validation

Not requested by Reg. 28/2004

12.5. Data compilation

Not requested by Reg. 28/2004

12.5.1. Weighting procedure
 

Design factor

Non-response adjustments

Adjustment to external data

Final cross sectional weights

The sample was designed as a self-weighting sample. Design factor for all sampled dwellings is equal to 1.  

The reduction of weight deviation caused by households that had been contacted (DB120=11); however refused the interview (DB135=2), was solved by the correction of weights in relation to the response rate. The probability of response of each household is not known. We used dividing households into strata (region and rotational group) and we resulted from assumption that each household in stratum has the same probability of response.

Then the empirical value of the response rate within the stratum gives the estimate of the probability of response for each household in the stratum.
 

Calculation of the households cross-sectional weights DB090k0

- has been implemented by system of simple calibration of weights of the households by using of variables - number of households by number of membership in administration regions.

Calculation of the personal cross-sectional weights RB050ki0

- has been implemented by system of simple calibration of weights of household members by using of calibration variables - numbers of persons by aged groups and sex in the administration regions.

Final cross-sectional weight was result of Calmar calibration.

SUMMARY STATISTICS:            
                 
Variable N NMISS MIN MEAN MAX STD SUM CV
DB090 5200 0 118.8216 367.6277 1641.253 117.7576 1911664 32.0318
RB050 15335 0 118.8216 351.6430 1641.253 113.9274 5392446 32.3986
PB040 13439 0 118.8216 347.0405 1641.253 112.2585 4663877 32.3474
                 
                 
INTEGRATED CALIBRATION:            
Mean(RB050/DB090) = 1            
Std(RB050/DB090) = 0            
 
12.5.2. Estimation and imputation
 
Imputation procedure used Imputed rent Company car
Logical imputation; Historical imputation (from previous wave); Mean imputation; As results of researches in elaborated feasibility study (,,Testing of Methods of Imputed Rent Estimation for EU-SILC in the Slovak Republic”). and also Population and Housing Census 2001 show that the share of the privately-owned dwellings and houses rented at the market price is about 3 % of the total number of dwellings in the Slovak Republic, the conclusion recommended was to use user-cost method for estimation of imputed rent in the Slovak conditions.

In estimating the imputed rent by user-cost method there was computed net operating surplus from the imputed rent, which is estimated from the average net stock of the value of dwellings.

In estimating the net stock of the value of dwellings, there was used following approach:

1. The quantitative data on owner-occupied dwellings stratified by region, location (rural/urban area), dwelling type (own house/own dwelling), age (individual categories of age), and dwelling size (dwelling with one room, two rooms, …five rooms) are drawn.

Quantitative data was corrected on the basis of actual quantitative data from 2001 Census (data from Census 2001 - numbers of privately-owned houses and dwellings are updated according to the statistics of finished houses and dwellings)

2. To these data there were found out prices of dwelling/houses from administrative sources and there was determined price of dwelling/houses. Net operating surplus was determined through applying relevant percentage (2,5 %), which was used from data of National Accounts.

Benefit from using company car for personal purposes was estimated on the basis of depreciated price of company car for actual year and other cash benefits, which were provided by employer in connection with car for personal purposes – benefit paid for petrol, benefit related to compulsory car insurance and repair and maintenance benefits. As input components for estimation of depreciated price of car for the actual year was market price of new car, period of amortisation established by law (4 years) and age of car (on the basis of year of production). Market price of car for the year 2011 was updated according to available external sources.

¼ of price of new car is depreciated from price of new car every year. Theoretically depreciated price of 5-year car would equal 0. Practically older cars are used too and their actual depreciated price does not equal 0. Depreciated price of cars older than 4 years was calculated in such a way that ¼ of price of new car was divided by age of car overlapping 3 years (because for the period of 4 years, there is assigned ¼ of the price).

Total benefit from using company car represents the sum of estimated depreciated price of company car, benefit paid for petrol, benefit related to compulsory car insurance and repair and maintenance benefits.
 
12.6. Adjustment

Not requested by Reg. 28/2004


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