SILC_ESQRS_A_NL_2011_0000 - Version 2

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

Compiling agency: Statistics Netherlands

Time Dimension: 2011-A0

Data Provider: NL1

Data Flow: SILC_ESQRS_A


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


1. ContactTop
1.1. Contact organisationStatistics Netherlands
1.2. Contact organisation unitDivision of Social and Spatial Statistics Statistical Analysis department Heerlen
1.5. Contact mail address


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

.


4. RelevanceTop
4.1. Relevance - User Needs

.

4.2. Relevance - User Satisfaction

.

4.3. Completeness

.

4.3.1. Data completeness - rate

.


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 three 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.

 
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

 15.7  0.8  14.1 - 17.3  11.0 0.74   9.5-12.4  2.5 0.41  1.7-3.3  8.7  0.64  7.4-10.0

Male

 14.9  0.96  13.0-16.8  10.8  0.89 9.1-12.6   2.4  0.53  1.3-3.4  7.9 0.84   6.3-9.6

Female

 16.6  0.78  15.0-18.1  11.1  0.71  9.8-12.5  2.6  0.36  1.9-3.3  9.5  0.64  8.2-10.7

Age0-17

 18.0  1.44 15.2-20.8   15.5 1.46   12.7-18.4  2.9 0.72   1.5-4.3  6.3  0.95 4.5-8.2 

Age18-64

 17.0  0.89  15.3-18.8  10.5  0.78  9.0-12.0  2.8  0.44  1.9-3.7  9.6 0.65 8.3-10.9 

Age 65+

 6.9  0.74  5.4-8.3  6.5  0.73  5.1-7.9  0.4  0.17  0.1-0.7      
 
5.3. Non-sampling error
 

Non-sampling errors are basically of 4 types:

  1. Coverage errors: errors due to divergences existing between the target population and the sampling frame.
  2. 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
  3. Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting
  4. 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
    2. 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
 
 
5.3.1.1. Over-coverage - rate
 
 

Main problems

Size of error

Cross sectional

data

The sampling frame of addresses is constructed from the Population Register. First a complete list of addresses is made and then divided into 10 disjoint groups: A0, A1, A2 …, A9. Each of these subsets contains 10% of all the addresses in the Population Register. Subset A0 is used as an address sampling frame for the years 2000, 2010, 2020, …, subset A1 is used as an address sampling frame for the years 2001, 2011, and so on. With this kind of approach the sampling frames of ten subsequent years are disjoint and addresses that are contacted within one particular year will not be part of another address survey sample for the next nine years. This approach is in compliancy with the policy of Statistics Netherlands to reduce respondent burden in all surveys. Finally, additional information on the type of address and number of postal delivery points is added to the sampling frame. The result is a set of disjoint sampling frames (one for each year) with address information and personal information of all individuals that are registered in a Dutch municipality.

Each year in September the sampling frames for the next year are constructed. The sampling frame of addresses is updated monthly for changes related to births, deaths, migration, new addresses, and vacancies. Also taken into account are changes in municipality boundaries and postal codes. At the date of sample drawing the entries of the sampling frame are therefore practically equal to those in the Population Register (GBA). As the fieldwork period starts six weeks later, coverage errors may occur: during the six weeks between drawing and application of the sample new addresses will be established and some addresses have become vacant or have been demolished. 

Institutional addresses are removed after drawing the sample by comparing the sample addresses with entries in the register of institutional addresses. This register is updated once a year, so a small number of over-coverage errors are to be expected.

 
 
5.3.2. Measurement error
 

Cross sectional data

Source of measurement errors

Building process of questionnaire

Interview training

Quality control

  1. the questionnaire (effects of the design, content and wording)
  2. the data collection method (effects of the modes of interviewing)
  3. the interviewer (effects of the interviewer on the response to a question including errors of the interviewer)
  4. the respondent (effects of the respondent on the interpretation of items)
  1. Specialised expertise is used in developing questionnaire
  2. Questionnaire uses routing so only relevant questions are offered to respondents

 

  1. Extensive interviewer instructions
  2. Interviewer manual
  3. Regularly refreshing courses on basic interviewing skills and on EU-SILC
 

Questionnaires were programmed in Blaise with several data entry and coding controls to reduce processing errors.

 
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*

0.95 0.95  0.90 0.85   1  14.5  19.3  0  14.5 19.3 

* 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  100  100  100
% of household with missing values (before imputation)  0 0 0 0
% of household with partial information (before imputation)  1  3  3  3

 

 

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

 71  4  33  5  10  7  91

% of household with missing values (before imputation)

 0  0  0  0  0  0  0

% of household with partial information (before imputation)

 0  1  0  0  0  1  0

 

  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

 67  5 67  11 0  28  1  1  4  6

% of household with missing values (before imputation)

0 0  0  0  0 0  0  0  0  0  0  0

% of household with partial information (before imputation)

 0  0  0  0  0  0  0  0  0  0  0  0
 
5.3.4. Processing error
 
Data entry and coding Editing controls
 

Questionnaires were programmed in Blaise with several data entry and coding controls to reduce processing errors.

 
 
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 punctualityTop
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 clarityTop
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. ComparabilityTop
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
The reference population of EU-SILC is all private households and their current members residing in the Netherlands at the time of data collection. The West Frisian Islands with the exception of Texel were excluded from the target population. This is also true for persons living in collective households and in institutions. No difference to the common definition There are some minor differences in the treatment of special categories like lodgers or people temporarily away (e.g. students). These people are only included as a household member if they are registered at the households' address. According to the EU-definitions resident boarders, lodgers and tenants should be included if they share expenses, have no private address elsewhere or their actual/intended duration of stay must be six months or more. Statistics Netherlands does not apply this limit of six months.
 
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
Taxes on income and social contributions are based on the 'income received' in the income reference year (accruel basis) and do not refer to the amounts actually paid in the income reference year. The income data of EU-SILC 2011 refer to the calendar year 2010. The income data were mainly collected from registers. There are no taxes on wealth in the Netherlands. The EU-SILC fieldwork period started in June 2011 and ended in September 2011 . Therefore the lag is at minimum 5 months and at maximum 9 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)
L: The total household income (gross/disposable) has been computed without taking account the interest repayments on mortgage, the imputed rent, the contibutions to and benefits from individual private pension plans. Subsequently the payable tax on income and social insurance contributions have been corrected to get the fictitious amounts that should have been paid if these components were not received/paid. L: The total household income (gross/disposable) has been computed without taking account the interest repayments on mortgage, the imputed rent, the contibutions to and benefits from individual private pension plans. Subsequently the payable tax on income and social insurance contributions have been corrected to get the fictitious amounts that should have been paid if these components were not received/paid. L: In order to calculate HY022 Statistics Netherlands calculated the taxable income without the income components: PY090G + PY120G +PY130G + PY140G + HY050G +HY060G +HY070G. Subsequently the payable tax on income and social insurance contributions have been corrected and refer to the fictitious amounts that should have been paid if such social transfers were not received. L: Like HY022, but the income components PY100G and PY110G were also excluded.

 

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)

F

F

L: Maternity and parental leave benefits are not included in HY050 as those benefits cannot be separated from wages. These components are included in variable PY010.

 

F

F

L: Alimonies received from former spouse are available in the Tax Administration. Other transfers like payments received from parents living in a separate household (e.g. students) and child alimony are collected in the EU-SILC- interview.

 

F

F

F

F

L: Maintenance allowances to former spouse were collected form the Tax Administration. Other transfers like child alimony are collected in the EU-SILC interview.

 

 

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)

L: Allowances for transport to or from work are not included in PY010. Severance and termination payments to compensate employees and redundancy payments (including lump-sum payments) are also included in PY010G. They are not included in PY090G (unemployment benefits).

 

F

F

F

F

F

L: PY090 includes the vocational training allowance, i.e. payment by social security funds or public agencies to targeted groups of persons in the labour force who take part in training schemes intended to develop their potential for employment. Statistics Netherlands has no information available on benefits (in-kind) related to vocational training.

 

F

F

F

F

F

F

 

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

The variables concerning income, wealth and taxes were almost entirely collected from registers. The most important source is the Tax Administration. Student grants were obtained from the student loan company. Some components were imputed on the basis of information given in the questionnaire. For example, child allowances were calculated on the basis of the information about the number and age of children in the household.

All income data derived from registers are recorded gross at component level. All income data are collected at the individual level (i.e. the person registered as the receiver of the income). This also concerns typically 'household' related incomes such as housing benefits and social assistance.

Not applicable

 
8.2. Comparability - over time

Household income components, EU-SILC 2009-2011

 

                       
 

EU-SILC 2009

 

   

EU-SILC 2010

 

   

EU-SILC 2011

 

   
 

count

 

sum

 

median

 

mean

 

count

 

sum

 

median

 

mean

 

count

 

sum

 

median

 

mean

 

                         
 

x 1000

 

mln euro

 

1000 euro

 

1000 euro

 

x 1000

 

mln euro

 

1000 euro

 

1000 euro

 

x 1000

 

mln euro

 

1000 euro

 

1000 euro

 

HY030G Imputed rent

 

4.072

 

9.999

 

2,2

 

2,5

 

4.168

 

10.579

 

2,2

 

2,5

 

4.219

 

10.417

 

2,2

 

2,5

 

HY040G Income from rental of a property or land 2)

 

248

 

2.051

 

4,2

 

8,3

 

271

 

1.584

 

4,3

 

5,9

 

300

 

2.102

 

3,9

 

7,0

 

HY050G Family/Children related allowances

 

1.975

 

4.021

 

1,9

 

2,0

 

1.976

 

4.197

 

1,9

 

2,1

 

1.973

 

4.169

 

1,9

 

2,1

 

HY060G Social exclusion not elsewhere classified

 

1.180

 

6.228

 

0,9

 

5,3

 

657

 

6.097

 

10,1

 

9,3

 

614

 

6.573

 

12,1

 

10,7

 

HY070G Housing allowances

 

1.167

 

2.047

 

1,8

 

1,8

 

1.348

 

2.439

 

1,9

 

1,8

 

1.244

 

2.376

 

2,0

 

1,9

 

HY080G Regular inter-household cash transfer received

 

648

 

2.551

 

2,5

 

3,9

 

692

 

2.606

 

2,5

 

3,8

 

761

 

2.830

 

2,0

 

3,7

 

HY090G Interest, dividends, profit from capita gain investments…

 

6.056

 

14.908

 

0,4

 

2,5

 

6.223

 

10.946

 

0,4

 

1,8

 

6.297

 

9.900

 

0,3

 

1,6

 

HY100G Interest repayments on mortgage

 

3.504

 

28.839

 

6,8

 

8,2

 

3.617

 

29.720

 

7,0

 

8,2

 

3.650

 

30.791

 

7,3

 

8,4

 

HY110G Income received by people under 16

 

113

 

92

 

0,3

 

0,8

 

86

 

65

 

0,4

 

0,8

 

104

 

64

 

0,3

 

0,6

 

HY120G Regular taxes on wealth

 

.

 

.

 

.

 

.

 

.

 

.

 

.

 

.

 

.

 

.

 

.

 

.

 

HY130G Regular inter-household cash transfer paid

 

812

 

3.562

 

2,4

 

4,4

 

921

 

3.609

 

2,0

 

3,9

 

938

 

3.905

 

2,0

 

4,2

 

HY140G

 

7.311

 

128.380

 

12,9

 

17,6

 

7.386

 

128.687

 

13,2

 

17,4

 

7.443

 

134.739

 

13,5

 

18,1

 

                         

Table 4.3 Personal income components, EU-SILC 2009-2011

 

                     
 

EU-SILC 2009

 

   

EU-SILC 2010

 

   

EU-SILC 2011

 

   
 

count

 

sum

 

median

 

mean

 

count

 

sum

 

median

 

mean

 

count

 

sum

 

median

 

mean

 

                         
 

x 1000

 

mln euro

 

1000 euro

 

1000 euro

 

x 1000

 

mln euro

 

1000 euro

 

1000 euro

 

x 1000

 

mln euro

 

1000 euro

 

1000 euro

 

PY010G Employee cah or near cash income

 

8.399

 

248.458

 

26,5

 

29,6

 

8.348

 

249.854

 

27,2

 

29,9

 

8.425

 

251.880

 

27,1

 

29,9

 

PY021G Company car

 

590

 

3.314

 

5,3

 

5,6

 

564

 

3.113

 

5,2

 

5,5

 

541

 

2.809

 

5,0

 

5,2

 

PY030G Employer's social insurance contribution

1)

8.674

 

42.852

 

3,8

 

4,9

 

8.536

 

44.377

 

4,1

 

5,2

 

8.628

 

45.710

 

4,0

 

5,3

 

PY035G Contributions to individual private pension plans

 

1.534

 

3.698

 

1,0

 

2,4

 

1.430

 

3.629

 

1,0

 

2,5

 

1.361

 

3.804

 

1,1

 

2,8

 

PY050G Cash benefits or losses from self-employment

 

1.408

 

27.703

 

6,3

 

19,7

 

1.501

 

26.231

 

5,6

 

17,5

 

1.490

 

27.813

 

6,6

 

18,7

 

PY080G Pension from individual private plans

 

67

 

670

 

5,1

 

10,0

 

98

 

1.037

 

4,9

 

10,5

 

70

 

836

 

6,4

 

11,9

 

PY090G Unemployment benefits

 

418

 

3.484

 

5,0

 

8,3

 

589

 

4.797

 

5,2

 

8,1

 

648

 

5.530

 

5,6

 

8,5

 

PY100G Old-age benefits

 

3.276

 

60.338

 

14,1

 

18,4

 

3.467

 

63.202

 

13,9

 

18,2

 

3.970

 

65.512

 

13,0

 

16,5

 

PY110G Survivor' benefits

 

88

 

954

 

14,2

 

10,9

 

76

 

786

 

12,6

 

10,3

 

92

 

895

 

11,0

 

9,7

 

PY120G Sickness benefits

 

207

 

744

 

0,7

 

3,6

 

221

 

1.153

 

2,6

 

5,2

 

222

 

1.375

 

2,9

 

6,2

 

PY130G Disability benefits

 

646

 

8.861

 

14,0

 

13,7

 

591

 

8.690

 

14,8

 

14,7

 

580

 

8.758

 

15,2

 

15,1

 

PY140G Education-related allowances

 

832

 

2.282

 

3,1

 

2,7

 

880

 

2.792

 

3,1

 

3,2

 

916

 

2.697

 

3,2

 

2,9

 

                         

1) mandatory from 2007

 

                       
8.2.1. Length of comparable time series

.

8.3. Comparability - domain

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9. CoherenceTop
9.1. Coherence - cross domain

Table 4.2 Personal income components, IPS 2010 - EU-SILC 2011

 

             
 

IPS 2010

 

     

EU-SILC 2011

 

   
 

count

 

sum

 

median

 

mean

 

count

 

sum

 

median

 

mean

 

                 
 

x 1000

 

mln euro

 

1000 euro

 

1000 euro

 

x 1000

 

mln euro

 

1000 euro

 

1000 euro

 

PY010G Employee cah or near cash income

 

8.248

 

249.557

 

26,5

 

30,3

 

8.425

 

251.880

 

27,1

 

29,9

 

PY021G Company car

 

600

 

3.559

 

5,6

 

5,9

 

541

 

2.809

 

5,0

 

5,2

 

PY030G Employer's social insurance contribution 1)

 

8.375

 

44.057

 

3,8

 

5,3

 

8.628

 

45.710

 

4,0

 

5,3

 

PY035G Contributions to individual private pension plans

 

1.215

 

3.590

 

1,2

 

3,0

 

1.361

 

3.804

 

1,1

 

2,8

 

PY050G Cash benefits or losses from self-employment

 

1.410

 

28.519

 

9,0

 

20,2

 

1.490

 

27.813

 

6,6

 

18,7

 

PY080G Pension from individual private plans

 

71

 

878

 

7,7

 

12,4

 

70

 

836

 

6,4

 

11,9

 

PY090G Unemployment benefits

 

625

 

5.140

 

5,2

 

8,2

 

648

 

5.530

 

5,6

 

8,5

 

PY100G Old-age benefits

 

3.867

 

62.987

 

13,3

 

16,3

 

3.970

 

65.512

 

13,0

 

16,5

 

PY110G Survivor' benefits

 

103

 

1.095

 

13,4

 

10,6

 

92

 

895

 

11,0

 

9,7

 

PY120G Sickness benefits

 

242

 

1.405

 

3,0

 

5,8

 

222

 

1.375

 

2,9

 

6,2

 

PY130G Disability benefits

 

568

 

8.382

 

15,1

 

14,7

 

580

 

8.758

 

15,2

 

15,1

 

PY140G Education-related allowances

 

898

 

2.511

 

3,0

 

2,8

 

916

 

2.697

 

3,2

 

2,9

 

                 

Household income components, IPS 2010 - EU-SILC 2011

 

               
 

IPS 2010

 

     

EU-SILC 2011

 

   
 

count

 

sum

 

median

 

mean

 

count

 

sum

 

median

 

mean

 

                 
 

x 1000

 

mln euro

 

1000 euro

 

1000 euro

 

x 1000

 

mln euro

 

1000 euro

 

1000 euro

 

HY030G Imputed rent

1)

4.214

 

10.509

 

2,2

 

2,5

 

4.219

 

10.417

 

2,2

 

2,5

 

HY040G Income from rental of a property or land

 

189

 

1.061

 

1,7

 

5,6

 

300

 

2.102

 

3,9

 

7,0

 

HY050G Family/Children related allowances

 

1.981

 

4.131

 

1,9

 

2,1

 

1.973

 

4.169

 

1,9

 

2,1

 

HY060G Social exclusion not elsewhere classified

 

595

 

6.261

 

12,3

 

10,5

 

614

 

6.573

 

12,1

 

10,7

 

HY070G Housing allowances

 

1.109

 

2.199

 

2,1

 

2,0

 

1.244

 

2.376

 

2,0

 

1,9

 

HY080G Regular inter-household cash transfer received

 

59

 

576

 

5,4

 

9,7

 

761

 

2.830

 

2,0

 

3,7

 

HY090G Interest, dividends, profit from capital gain investment

 

5.963

 

12.178

 

0,3

 

2,0

 

6.297

 

9.900

 

0,3

 

1,6

 

HY100G Interest repayments on mortgage

 

3.595

 

31.377

 

7,4

 

8,7

 

3.650

 

30.791

 

7,3

 

8,4

 

HY110G Income received by people under 16

 

92

 

77

 

0,4

 

0,8

 

104

 

64

 

0,3

 

0,6

 

HY120G Regular taxes on wealth

 

.

 

.

 

.

 

.

 

.

 

.

 

.

 

.

 

HY130G Regular inter-household cash transfer paid

 

91

 

731

 

4,5

 

8,1

 

938

 

3.905

 

2,0

 

4,2

 

                 
9.1.1. Coherence - sub annual and annual statistics

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9.1.2. Coherence - National Accounts

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9.2. Coherence - internal

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10. Cost and BurdenTop

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11. ConfidentialityTop
11.1. Confidentiality - policy

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11.2. Confidentiality - data treatment

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12. Statistical processingTop
12.1. Source data

A description of the sampling frame (reference period, updating actions, quality review actions)

12.1.1. Sampling design and procedure
 

Type of sampling design

The EU-SILC survey is an annual survey with a four-year rotational panel and has been carried out as an integrated survey, covering both cross-sectional and longitudinal primary target variables by a single operation. The cross-sectional sample of EU-SILC 2011, the seventh year of EU-SILC in the Netherlands, consists of four rotational groups. Group R3’ has entered the survey in 2008 and sample persons in group R4’ were interviewed for the first time in 2009. Group R1’ has entered the survey in 2010 and group R2’ consists of new sample persons who were drawn from the Labour Force Survey in 2011.

Sample persons in the new rotational group 1 (R2’) were partly drawn from the Labour Force Survey (LFS). The Labour Force Survey (LFS) has a panel structure with five rotational groups. In the first wave, interviews are conducted through face-to-face interviewing. Subsequent waves are conducted through telephone interviewing. The period between waves is three months. When the first wave of the LFS survey has been completed, addresses with all residents aged over 64 are removed from the sample. Households that have taken part in all five waves of the labour force survey are recruited for the EU-SILC survey. If a household is willing to participate, it is contacted in the month following the final LFS interview. As addresses with all residents aged over 64 are no longer present in the last wave of the LFS an extra sample for the EU-SILC survey is required. We therefore distinguish between two EU-SILC samples: the first sample (i.e. sample 1) represents the set of addresses with households that have participated in the LFS survey. At least one of the household members living on such an address is aged less than 65. The second sample (i.e. sample 2) is a set of addresses with all residents aged over 64.

The sampling frame for both sample 1 and sample 2 is the Dutch municipality administration (Gemeentelijke Basisadministratie or GBA). The sampling design can be classified as a stratified two-stage sampling design, with municipalities as primary sampling units and addresses as secondary sampling units. The sampling of first stage elements is with probability proportional to size (number of addresses per municipality). Municipalities with 7,300 addresses ore more are always in the sample. The second stage elements are selected with simple random sampling such that the total sampling design becomes self-weighting. From these addresses further sampling units are constructed: households. For the collection of detailed information on social variables one member of the household aged 16 or older is selected (the so-called selected respondent).

Stratification and sub stratification criteria

Stratification involves the division of the population into sub-groups, or strata, from which independent samples are taken. The stratification variables used in this design are the 40 COROP-regions (NUTS3). These are regional areas within the Netherlands and are used for analytical purposes by, among others, Statistics Netherlands. The Dutch abbreviation stands for Coördinatiecommissie Regionaal Onderzoeksprogramma, literally the Coordination Commission Regional Research Programme. Applying this type of stratification allows for representative samples on a regional level.

Sample selection schemes

The primary sampling units are selected by means of sampling with probability proportional to size. Therefore the ordering of these units in the strata is relevant: the primary sampling units in each of the strata are randomly ordered. The secondary sampling units are selected with simple random sampling in order that the total sampling design becomes self-weighting. Addresses corresponding to institutions and addresses in some small regions of the national territory (West Frisian Islands) are removed from the sample. These addresses are not part of the reference population. In the case of sample 1, a number of sampling units in each of the interviewer regions is randomly removed in order to fit the sample with the available face-to face interview capacity. The sampling design for this sample is therefore no longer strictly self-weighting.

Sample distribution over time

The following tables provide an overview of the cumulative sample development (all rotational groups) during the fieldwork period from 1June 2011 to 30 September 2011. Table 2.3 illustrates the sample development of sample 1, table 2.4 the cumulative sample size over time of sample 2

.

 

 

Table 2.3:cumulative sample size over time, EU-SILC sample 1, at least one resident aged below 65

Fieldwork

 

Accepted interviews

 

01/06 – 30/06

 

1

 

,756

01/06 – 31/07

 

4,126

 

01/06 – 31/08

 

6,427

 

01/06 – 30/09

 

8,729

 

 

Table 2.4: cumulative sample size over time, EU-SILC sample 2, all residents at address are 65 or older

Fieldwork from .. to ..

 

Accepted interviews

 

01/06 – 30/06

 

430

 

01/06 – 31/07

 

864

 

01/06 – 31/08

 

1,298

 

01/06 – 30/09

 

1,763

 

 

 

 
12.1.2. Sampling unit

The sampling units are addresses that are registered in the sampling frame. All households on selected addresses are eligible for the survey, up to a maximum of three households per address.

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

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.

In 2011 approximately 9,000 households in the fifth wave of the LFS were recruited for the first wave of the EU-SILC survey (in rotational group R2'). Among them 3,428 were actually used for EU-SILC and 2,740 households completed the household questionnaire.  

Households in the LFS-sample which did not respond to the LFS-survey or which have not been used for recruiting EU-SILC respondents have not been registered in the EU-SILC household register (D-file). Only households which were actually used for the EU-SILC survey are registered in the household register.

Table 1: Sample size sample 1; at least one resident aged below 65

Addresses used by the institute for EU-SILC

 

3,428

 

addresses successfully contacted for EU-SILC

 

3,275

 

addresses not successfully contacted

 

153

 

   

Addresses successfully contacted for EU-SILC

 

3,275

 

household questionnaire EU-SILC completed

 

3,035

 

refusal to co-operate

 

73

 

household temporarily away for duration of fieldwork

 

 

unable to respond

 

7

 

other reasons

 

160

 

   

Household questionnaire completed

 

3,035

 

accepted for database

 

3,003

 

interview rejected

 

32

 

 

For the sample of addresses with all residents aged over 64, all of the issued 1,243 addresses were used and 1,098 households completed the EU-SILC questionnaire. Again a small number of interviews had to be rejected, 1,076 interviews were accepted for the database. Combining both samples, the number of new accepted household interviews in the new rotational group (R2’) is 3,720. 

Table 2.2. sample size sample 2; all residents at address are 65 or older.

Issued addresses

 

1,618

 

addresses used by the institute

 

1,618

 

addresses not used by the institute

 

0

 

   

Addresses used by the institute

 

1,618

 

addresses successfully contacted

 

1,501

 

addresses not successfully contacted

 

117

 

   

Addresses successfully contacted

 

1,501

 

household questionnaire EU-SILC completed

 

1,098

 

refusal to co-operate

 

218

 

household temporarily away for duration of fieldwork

 

 

unable to respond

 

110

 

other reasons

 

75

 

   

Household questionnaire completed

 

1,098

 

accepted for database

 

1,076

 

interview rejected

 

22

 

 

 
12.2. Frequency of data collection

Data were collected from the beginning of june 2011 until the end of september 2011.

In the Netherlands, 2005 was the initial year of EU-SILC. A new sample was constructed and divided into four rotational groups. Each rotational group is a subsample, each by itself representative of the whole population, and each constructed using the same sampling design. One of the subsamples was purely cross-sectional and was not followed up in 2006. Respondents in the second subsample participated two years, in the third subsample three years, and in the fourth subsample four years. Because accurate panel attrition rates were not available at the start of the EU-SILC survey, the subsample sizes are chosen to be of quite different sizes in order to guarantee a longitudinal sample of sufficient size. The longitudinal xxx sample consists of xxxx households (rotational groups R3’, R4’, and R1’).

Table 1: Size of rotational groups EU-SILC 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)
 0  0  100  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: 14 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

 1.24 Non-response adjustments are necessary because of the bias introduced by selective non-response on the household level. Selective non response affects the inclusion probabilities of the sampling units. Ideally the inclusion probability can be calculated by multiplying the inclusion probabilities of the sampling design with the exact response probabilities. Unfortunately, in practice these response probabilities are unknown and some kind of approximation has to be made.

The method of logistic regression was adopted to approximate the response probabilities for the new rotational group. The response probabilities were modelled by the explanatory variables age, degree of urbanisation, type of household, and activity status. For the old rotational groups a proper model could not be fitted using logistic regression. Therefore the response probabilities were considered equal for all persons in the response.

Adjustments made by calibration schemes in general improve the accuracy of the data (mean square error). Three good reasons for using calibration schemes are: 1) the estimates of variables that are used in the calibration scheme are made consistent with those of more reliable sources. 2) the standard error of the estimates is reduced if the calibration variables correlate with target variables. 3) non-response bias is reduced if the calibration variables correlate with both target variables and response probabilities.

Two external data sources were used in the calibration procedure:

  • the Population Register (GBA), and 
  • the register on income data based on integral data from the tax authorities.

The adjustments were made on the basis of the base weights: the product of the design weights with the inverse of the response probabilities (non-response weights). The calibration was performed on household and personal level using linear consistent weighting, so that individuals within the household have identical weights equal to the household weight. The set of variables used for calibration includes the smaller subset suggested by Eurostat in document EU-SILC 065/04. Additional calibration variables that correlate strongly with the target variables were added: income data and data on tenure status from the income register. The following variables were included in the calibration scheme:

  • sex, 
  • age in years, 0,1,2,3,4…..85 and 85 years and over, 
  • household size: four categories (1, 2, 3, 4 or more household members), 
  • region: 12 categories, one for each of the provinces (nuts 2), 
  • tenure status, in two classifications (owner, tenant) 
  • equivalized disposable income (CBS-definition) in deciles 
  • main source of income (employee, self-employed, unemployed, social assistance, disabled, retired aged under 65, retired aged 65 years or older, student, no income). 
  • low income category, in three classifications (non target population, low income and other income). 
  • at-risk of poverty-rate based on the Income Panel Survey (IPS). 

Taking into account consistency requirements and the correlation of weighting terms with important target variables (Laeken indicators), the following weighting terms were constructed: 

weighting model terms at household level:

  • household size; 
  • region (nuts 2); 
  • tenure status; 
  • low income category.

weighting model terms at personal level:

  • sex x age; 
  • equivalized income (decile group); 
  • main source of income; 
  • At-risk of poverty-rate IPS.

 

The household cross-sectional weight DB090 and the personal cross-sectional weight RB050 are the direct result of the linear consistent weighting procedure that has been described. Children who were born in a sample household in the course of 2011 receive the weight of their household. 

The personal cross-sectional weight PB040 equals the weight PB050 for people of 16 years and older. For people younger than 16 years this weight equals 0. Finally, the cross-sectional weights for selected respondents are determined by adjusting the weight PB040 for the probability with which the respondent is chosen within the household. For the "old" rotational groups, these probabalities are equal to those in the initial year of the survey. Persons that are older than 16 years in the new households have the same probability of being selected as a sample person. This probability is four times as large for persons that are exactly 16 years

 
12.5.2. Estimation and imputation
 
Imputation procedure used Imputed rent Company car
Child allowances were calculated on the basis of the information about the number and age of children in the household. For estimating the equivalent market rents in EU-SILC, the parameter estimates have been calculated based on another survey, the Survey on Household Expenditures. A regression model was applied on the estimates of market rents of owner-occupiers by real estate agents. This model includes the market value of the dwelling, region, level of urbanisation and household type. The total market rent is calculated by the National Account Statistics. Next the distribution of the market rent over the households is based on the results of the regression model.

The estimation of the value of ‘company car’ has been specified by the amount of benefit for which the recipient is assessed for tax purposes. The calculation of the employee income component ‘company car’ follows the rules of the tax authorities. The additional wages or additional income is a percentage of the car’s (original) value. The percentage is in principle 25%, but can be lower for fuel efficient cars.

 
12.6. Adjustment

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13. CommentTop

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AnnexesTop