Employment and unemployment (Labour force survey) (employ)

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

Compiling agency: Hungarian Central Statistical Office


Eurostat metadata
Reference metadata
1. Contact
2. Statistical presentation
3. Statistical processing
4. Quality management
5. Relevance
6. Accuracy and reliability
7. Timeliness and punctuality
8. Coherence and comparability
9. Accessibility and clarity
10. Cost and Burden
11. Confidentiality
12. Comment
Related Metadata
Annexes (including footnotes)
 



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1. Contact Top
1.1. Contact organisation

Hungarian Central Statistical Office
1.2. Contact organisation unit

Quality of Life Statistics Department, Employment Statistics Section

Methodology Department, Sampling and Processing Methodology Section
1.5. Contact mail address

1024 Budapest, Keleti K. u. 5-7, Hungary


2. Statistical presentation Top
Please take note of the abbreviations used in the report 
Abbreviation Explanation
CV Coefficient of variation (or relative standard error)
Y/N Yes / No
H/P Households/Persons
M? Member State doesn’t know
NA Not applicable/ Not relevant
UNA Information unavailable
NR Non-response: Member State doesn’t answer to Eurostat request for information. Blank is allowed only in boxes with comments
LFS Labour Force Survey
NUTS Nomenclature of territorial units for statistics or corresponding statistical regions in the EFTA and candidates countries
2.1. Data description
Coverage   
Coverage Household concept Definition of household for the LFS Inclusion/exclusion criteria for members of the household Questions relating to employment status are put to all persons aged ...
The Hungarian LFS is a household survey, which provides some socio-demographic information on population  without age-limit and labour market information on population aged 15-74. The whole country is covered. Only private households are surveyed. Housekeeping Members living regularly together in the same dwelling, sharing income, household expenditures, food and other essentials for living. The inclusion or exclusion of the temporary absent persons – like persons living in student homes or worker homes – depends on their economic contribution to the household. For the person living abroad the same rule applies, they are surveyed only if they are contributing to the common income-consumption. Lodgers and domestic servants are surveyed and registered as separate households, since they live in the sampled dwelling.   15-74

 

Population concept  Specific population subgroups
Primary/secondary students Tertiary students People working out of family home for an extended period for the purpose of work People working away from family home but returning for weekends Children alternating two places of residence
Usual residence (12 months) Family home Family home Family home Family home Most of the time

 

Reference week
Fixed week (data collection refers to one reference week, to which the observation unit has been assigned prior to the fieldwork) Rolling week (data collection always refers to the week before the interview)                                  
   X
2.2. Classification system

[not requested for the LFS quality report]
2.3. Coverage - sector

[not requested for the LFS quality report]
2.4. Statistical concepts and definitions

[not requested for the LFS quality report]
2.5. Statistical unit

[not requested for the LFS quality report]
2.6. Statistical population

[not requested for the LFS quality report]
2.7. Reference area

[not requested for the LFS quality report]
2.8. Coverage - Time

[not requested for the LFS quality report]
2.9. Base period

[not requested for the LFS quality report]


3. Statistical processing Top
3.1. Source data
Sampling design & procedure
Sampling design (scheme; simple random sample, two stage stratified sample, etc.) Base used for the sample (sampling frame)  Last update of the sampling frame (continuously updated or date of the last update) Primary sampling unit (PSU)   Final sampling unit (FSU)
 The Labour Force Survey is based on a multi-stage stratified sample design. The sample design strata were defined in terms of geographic units, size categories of settlements and area types such as city centres, outskirts, etc.  Register of dwellings.  2020  In case of self-representing settlements dwellings are  PSU-s and in the other part of the sample settlements are PSU-s.  Dwellings are FSU-s.

 

Sampling design & procedure
First (and intermediate) stage sampling method   Final stage sampling method Stratification (variable used) Number of strata (if strata change quarterly, refer to Q4). Rotation scheme (2-2-2, 5, 6, etc.)
Sampling method: non-self-representing localities are selected with pps wr. Housing units are selected in sampled localities with systematic random sampling.

The quarterly sample is made up of three monthly sub-samples. The monthly sub-samples have no overlap.

At national level, self-representing localities are those which have at least 3 975 dwellings (i.e. approximately 5 000 inhabitants), while all other localities are non-self-representing. The former are all included in the sample with certainty, while a stratified (sub-)sample is selected from the latter with probability proportional to size (PPS). In the case of non-self-representing localities, design strata are defined as cross-classes of four size categories and 19 administrative units (counties). In such cases, the primary sampling units (PSUs) are localities, and the secondary (and ultimate) sampling units are dwellings. By contrast, the PSUs are dwellings in the case of self-representing localities, thus sampling has actually only one stage in this case.

The final sampling units are dwellings in each case. They are selected with systematic random sampling from lists of addresses belonging to the sampled localities. Prior to selection, the lists are properly sorted for the purpose of implicit stratification. As a result, the different parts of the localities (downtown areas, suburbs, etc.) will be properly represented. All households residing in the selected dwelling units are surveyed.

 In sampled localities with systematic random sampling. The LFS sample is stratified by administrative units (i.e. the capital city and 19 counties) and by size categories of the localities.Total number of strata: 278, of which 175 are self-representing localities. The remaining 103 strata contain 513 non-self-representing sampled localities.  278 The sample has a simple rotation pattern: any household entering the sample at some time is expected to provide labour market information at six consecutive quarters, than leaves the sample for ever. This determines a simple and efficient rotation scheme splitting the sample at any time in six equal and statistically equivalent parts called rotation panels. At any time, there is a birth panel consisting of the new entrants, and the members of the other panels are in the second, third, fourth, fifth and sixth wave of their participation in the survey. The samples of two consecutive periods tend to be less than 5/6, which would be obtained at a 100% response rate.

Thanks to covid-19 in 2020 Q2 no new wave was introduced. At the same time, wave 6 in Q1 was not dropped but observed as wave 7 in Q2 (and similarly in later quarters).

 

Yearly sample size & Sampling rate
Overall theoretical yearly sampling rate Size of the theoretical yearly sample
(i.e. including non-response) (i.e. including non-response)
In the different strata of the LFS sample different sampling rates are used.
Overall sampling fraction: f = 3.68 %		 Total sample:  150 840 dwellings (with multiplicity)                                                                
Total of interviewed households: 87 260                                                   
Total of  interviewed persons aged 15-74: 149 903                                         
Remark: the annual sample is defined by pooling the quarterly samples and dividing the quarterly weights by four. All data on the sample reflect this concept which implies that, owing to rotation,  a specific unit can occur in the annual sample once, twice, three- or four times.

  

Quarterly sample size & Sampling rate
Overall theoretical quarterly sampling rate Size of the theoretical quarterly sample
(i.e. including non-response) (i.e. including non-response)
 0.92% Total sample:  37 710 dwellings
Total of interviewed households: 21 815      
Total of  interviewed persons aged 15-74: 37 476        
Remark: average of four quarters

  

Use of subsamples to survey structural variables (wave approach)
Only for countries using a subsample for yearly variables
 Wave(s) for the subsample  Are the 30 totals for ILO labour status (employment, unemployment and inactivity) by sex (males and females) and age groups (15-24, 25-34, 35-44, 45-54, 55+) between the annual average of quarterly estimates and the yearly estimates from the subsample all consistent? (Ref.: Commission Reg. 430/2005, Annex I) (Y/N) If not please list deviations List of yearly variables for which the wave approach is used (Ref.: Commission Reg. 377/2008, Annex II)
 NA NA  NA  NA

 

Brief description of the method of calculating the quarterly core weights Is the sample population in private households expanded to the reference population in private households? (Y/N) If No, please explain which population is used as reference population Gender is used in weighting (Y/N) Which age groups are used in the weighting (e.g., 0-14, 15-19, ..., 70-74, 75+)? Which regional breakdown is used in the weighting (e.g. NUTS 3)? Other weighting dimensions
At the first stage, design (or design based) weights are determined. For any stratum of the sample, the unique design weight is defined as the ratio of the total number of dwellings in the stratum to that in the sub-sample for that stratum. Some adjustments are also included at this stage.
At the second stage, calibrated weights are determined with the method of generalized raking. For each of the 20 geographical units (i.e. the capital city and the 19 counties), calibration variables for checking are the following:
- totals for age-sex groups (2x10 totals),
- total number of households,
- total resident population in cities with at least 50,000 inhabitants.
The calibration is organized so that the members in any sample households have the same calibrated weight as the household.
Method of deriving controls: demographic components method combined with census-based proportions.		  Y  NA Y  16 age groups:
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75+		  NUTS 3 Total number of households in  NUTS 3 regions (estimates based on updated population couns and average household size in these regions); percentages of the population living in major cities and in the rest of NUTS 3 regions.

 

Brief description of the method of calculating the yearly weights (please indicate if subsampling is applied to survey yearly variables) Gender is used in weighting (Y/N) Which age groups are used in the weighting (e.g., 0-14, 15-19, ..., 70-74, 75+)? Which regional breakdown is used in the weighting (e.g. NUTS 3)? Other weighting dimensions
The yearly weight is average of the four quarterly weights.  Y 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75+   NUTS3 Total number of households in  NUTS 3 regions (estimates based on updated population counts and average household size in these regions); percentages of the population living in major cities and in the rest of NUTS 3 regions.

 

Brief description of the method of calculating the weights for households External reference for number of households etc.? Which factors at household level are used in the weighting (number of households, household size, household composition, etc.) Which factors at individual level are used in the weighting (gender, age, regional breakdown etc.) Identical household weights for all household members? (Y/N)
Integrative calibration method ensures that weights are calculated at household level. A given household and all its members get the same weight. It is based on updated population counts and average household size. No factors are used but the overall number of households only (at NUTS3 level). gender, age, NUTS3  Y
3.2. Frequency of data collection

[not requested for the LFS quality report]
3.3. Data collection
Data collection methods: brief description Use of dependent interviewing (Y/N)? Participation is voluntary/compulsory?
Data are collected by laptops. First time the respondents are compulsory interviewed face-to-face; while during the subsequent time interviews could be conducted according to the situation – by telephone or face-to-face. Interviews are done during the week immediately following the reference week. Yes. In order to reduce respondent burden and to avoide temporal inconsistencies among waves, we use dependent interviewing. Those data which are rather stable in time, like demographic characteristics, are filled in the questionnaire from the previous period, but they are to be confirmed by the respondent.  voluntary

 

Final sampling unit collected by interviewing technique (%)
CAPI CATI PAPI CAWI POSTAL - OTHER
 33.6  66.4  NA  NA  NA
3.4. Data validation

[not requested for the LFS quality report]
3.5. Data compilation

[not requested for the LFS quality report]
3.6. Adjustment

[not requested for the LFS quality report]


4. Quality management Top
4.1. Quality assurance

[not requested for the LFS quality report]
4.2. Quality management - assessment

[not requested for the LFS quality report]


5. Relevance Top
5.1. Relevance - User Needs
Assessment of the relevance of the main LFS statistics at national level (e.g. for policy makers, other stakeholders, media and academic research)
 The LFS is a survey with a large variety of users, who at least in part have diverging user needs. 
Classification of users:
- Institutions:
National or regional level: Ministries of Economy or Finance, Other Ministries (for sectoral comparisons), NSIs, etc.
- Social actors: Employers associations, trade unions, lobbies, at national or regional level
- Media: national or regional specialised or for general public, interested both in figures and analyses/comments.
- Researchers, students
- Enterprises: for own market research activities or for consultancy services in the information sector.
We have no special survey on Labour Force Survey user satisfaction, however we try to know their opinion by using mail, phone and other communication channels. Our users are generally satisfied, the response time of data requests is very short (usually 5 weekdays) and the number of rejected requests (2-4 annually) are usually due to representative resons.
5.2. Relevance - User Satisfaction

[not requested for the LFS quality report]
5.3. Completeness
NUTS level of detail   
Regional level of an individual record (person) in the national data set Lowest regional level of the results published by NSI Lowest regional level of the results delivered to researchers by NSI Brief description of the method which is used to produce NUTS-3 unemployment and labour force data sent to Eurostat?
 – county level (NUTS-3) of the quarterly data  – county level (NUTS-3)  for the main figures of the quarterly data   – county level (NUTS-3) of the quarterly data

– monthly data: 3-month moving average  from the LFS dataset

– annual data:  4-quarter average from the LFS dataset
5.3.1. Data completeness - rate

[not requested for the LFS quality report]


6. Accuracy and reliability Top
6.1. Accuracy - overall

[not requested for the LFS quality report]
6.2. Sampling error
Publication thresholds   
Annual estimates Annual estimates - wave approach 
(if different from full sample thresholds) 
 Limit below which figures cannot be published  Limit below which figures must be published with warning  Limit below which figures cannot be published Limit below which figures must be published with warning
 If weighted totals are less than 2600, the corresponding CV's exceed 30 %, thus these data are suppressed.  If weighted totals are between 2600 and 4800, the corresponding CV's are in the range 20-30 %, thus  these data can be published only with a warning concerning their reliability.  NA  NA
6.2.1. Sampling error - indicators
Coefficient of variation (CV) Annual estimates
Sampling error - indicators - Coefficient of variation (CV). Standard Error (SE) and Confidence Interval (CI)       
 

Number of employed persons

Employment rate as a percentage of the population

Number of part-time employed persons

Number of unemployed persons

Unemployment rate as a percentage of labour force

Youth unemployment rate as a percentage of labour force

Average actual hours of work per week(*)
 

Age group: 20 - 64

Age group: 20 - 64

Age group: 20 - 64

Age group: 15 -74

Age group: 15 -74

Age group: 15 -24

Age group: 20 - 64
 CV  0.25  0.25  2.22  2.27  2.27  3.8  0.1
 SE  10726  0.18  4639  4493  0.1  0.48  0.04
 CI(**)  (4331381;4373426)  (74.67;75.39)  (199395;217578)  (189185;206798)  (4.06;4.44)  (11.81;13.71)  (38.2;38.35)

 

Description of the assumption underlying the denominator for the calculation of the CV for the employment rate
 The denominator of the employment rate (that is the total population) is control total in calibration and thus is like a constant in variance estimation.

 

Reference on software used: Reference on method of estimation:
 SAS software   Taylor linearization

 

Coefficient of variation (CV) Annual estimates at NUTS-2 Level        
NUTS-2  CV of regional (NUTS-2) annual aggregates (in %)     
Regional Code  Region Number of employed persons Employment rate as a percentage of the population Number of part-time employed persons Number of unemployed persons Unemployment rate as a percentage of labour force Youth unemployment rate as a percentage of labour force  Average actual hours of work per week(*)
    Age group: 20 - 64 Age group: 20 - 64 Age group: 20 - 64 Age group: 15 -74 Age group: 15 -74 Age group: 15 -24 Age group: 20 - 64 
HU1 Central-Hungary  0.49  0.49  4.67  4.90  4.92  8.82  0.22
HU11 Budapest  0.66  0.66  6.12  6.94  6.95  16.90  0.31
HU12 Pest  0.73  0.73  6.63  6.66  6.71  8.85  0.31
HU21 Central-Transdanubia  0.54  0.54  6.19  6.94  6.85  11.55  0.21
HU22 Western-Transdanubia  0.49  0.49   5.66  6.24  6.24  13.64  0.20
HU23 Southern-Transdanubia  0.82  0.82  6.17  5.73  5.73  8.96  0.28
HU31 Northern-Hungary  0.83  0.83  6.14  6.05  6.00  9.47  0.27
HU32 Northern-Great-Plain  0.69  0.69  5.44  5.18  5.17  7.12  0.22
HU33 Southern-Great-Plain  0.66  0.66  5.61  5.71  5.77  10.89  0.24

(*) The coefficient of variation for actual hours worked should be calculated for the sum of actual hours worked in 1st and 2nd jobs. and restricted to those who actually worked 1 hour or more in the reference week.

(**) The value is based on a CI of 95%. For the rates the CI should be given with 2 decimals.
6.3. Non-sampling error

 [not requested for the LFS quality report]
6.3.1. Coverage error
Frame quality (under-coverage, over-coverage and misclassifications(b))      
Under-coverage rate (%) Over-coverage rate (%) Misclassification rate (%)  Comments: specification and impact on estimates(a)   
 Undercoverage  Overcoverage  Misclassification(b)  Reference on frame errors

UNA

~19%

UNA

  UNA Unoccupied dwelling, not a dwelling unit, not existing address. Households with members over 75 only are considered in-scope.  UNA In Hungarian:  Ödön Éltető--Erzsébet Urbán: The Sample Enlargement of Labour Force Surveys, and the Reliability of Estimates. Statistical Review (HCSO), vol. 77, no 6, 1999. 

László Mihályffy--Elisabeth Lindner: The Methodology of the Labour Force Survey. Working Papers on Statistical Methodology, HCSO Budapest, 2006.  

In English: Ödön Éltető--László Mihályffy: Household Surveys in Hungary. Statistics in Transition, June 2002, vol. 5. no. 4, pp. 521-540

 

(a) Mention specifically which regions / population groups are not suitably represented in the sample.
(b) Misclassification refers to statistical units having an erroneous classification where both the wrong and the correct one are within the target population.
6.3.1.1. Over-coverage - rate

[Over-coverage rate, please see concept 6.3.1 Coverage error in the LFS quality report]
6.3.1.2. Common units - proportion

[not requested for the LFS quality report]
6.3.2. Measurement error
Errors due to the medium (questionnaire)   
Was the questionnaire updated for the 2019 LFS operation? (Y/N) Synthetic description of the update Was the questionnaire tested? (Y/N) If the questionnaire has been tested, which kind of tests has been applied (pilot, cognitive, internal check)?
 N

 NA

  NA  NA

 

Main methods of reducing measurement errors 
Error source  
Respondent  Letter introducing the survey (Y/N) Phone call for booking or introducing the survey (Y/N)
 Y  N
Interviewer  Periodical training (at least 1 time per year) (Y/N)  Feedbacks from interviewer (reports, debriefings, etc.) (Y/N)
 Y  Y
Fieldwork  Monitoring directly by contacting the respondents after the fieldwork (Y/N) Monitoring directly by listening the interviews (Y/N) Monitoring remotely through performance indicators (Y/N)
 Y  Y  Y
Questionnaire  Questionnaire in several languages (Y/N)  On-line checks (for computer assisted interviews (Y/N)
 Y  N
Other / Comments  
6.3.3. Non response error

[not requested for the LFS quality report]
6.3.3.1. Unit non-response - rate

IN THIS SECTION INFORMATION REFERS TO THE FINAL SAMPLING UNITS *

Methods used for adjustments for statistical unit non-response
Adjustment via weights (Y/N) Variables used for non-response adjustment Description of method
 Y  NR

 Calibration is used to compensate for unit non-response. The method used is the generalized iterative scaling or raking by Darroch and Ratcliff (1972). The auxiliary information in the calibration process is the following:

- totals of age – sex groups (according to the forecasted population with cohort-component method)

- total resident population in the major cities in the country (according to the forecasted population with cohort-component method)

- total numbers of households. 
Substitution of non-responding units (Y/N) Substitution rate Criteria for substitution
 Y UNA   If the dwelling is empty or not dwelling in the first and the second waves, new address will be selected in the next quarter from the register. 
Other methods (Y/N) Description of method
 N  NA

  

Non-response rates by survey mode. Annual average (% of the theoretical yearly sample by survey mode)
Survey
CAPI CATI  PAPI  CAWI  POSTAL
UNA  UNA  NA  NA  NA

 

Divisions of non-response into categories. Quarterly data and annual average
Quarter Non-response rate
Total (%)             of which:
 Refusals (%)     Non-contacts (including people who migrated (or moved) internally or abroad) (%)   of which people who migrated (or moved) internally or abroad (%)
1 33.29 8.97 22.76  0.63
2 47.49 2.17 44.79  0.66
3 29.47 9.84 18.11  0.91
4 30.27 12.3 15.48  0.87
Annual 35.29 8.26 25.79  0.77

 

Units who refused to participate in the survey  (Please indicate the number of the units concerned in the cells where the wave is mentioned)
Subsample Quarter1_2020 Quarter2_2020 Quarter3_2020 Quarter4_2020
Subsample_Q4_2018  138  61    
Subsample_Q1_2019  170  69  176  
Subsample_Q2_2019  270  92  244  287
Subsample_Q3_2019  386  101  370  398
Subsample_Q4_2019  1010  226  835  883
Subsample_Q1_2020  891  217   876  919
Subsample_Q2_2020        
Subsample_Q3_2020      750  852
Subsample_Q4_2020        915
Total in absolute numbers  2865  766  3251  4254
Total in % of theoretical quarterly sample  7.60  2.03  8.62  11.28
         
Units who were not contacted (including people who migrated (or moved) internally or abroad) (Please indicate the number of units only in the cells where the wave is mentioned)
Subsample  Quarter1_2020  Quarter2_2020  Quarter3_2020  Quarter4_2020
Subsample_Q4_2018  482  1306    
Subsample_Q1_2019  567  1328  503  
Subsample_Q2_2019  779  1809  643  509
Subsample_Q3_2019  1030  2206  736  600
Subsample_Q4_2019  1660  4251  1161  877
Subsample_Q1_2020  2835  5002  1381  1022
Subsample_Q2_2020        
Subsample_Q3_2020      1616  1001
Subsample_Q4_2020        1762
Total in absolute numbers  7353  15902  6040  5771
Total in % of theoretical quarterly sample  19.50  42.17  16.02  15.31
         
of which people who migrated (or moved) internally or abroad) (Please indicate the number of units only in the cells where the wave is mentioned)
Subsample  Quarter1_2020  Quarter2_2020  Quarter3_2020  Quarter4_2020
Subsample_Q4_2018  62  52    
Subsample_Q1_2019  42  36  80  
Subsample_Q2_2019  48  38  73  77
Subsample_Q3_2019  35  48  69  80
Subsample_Q4_2019  38  44  78  101
Subsample_Q1_2020    17  42  54
Subsample_Q2_2020        
Subsample_Q3_2020        36
Subsample_Q4_2020        
Total in absolute numbers  225  235  342  348
Total in % of theoretical quarterly sample  0.60  0.62  0.91  0.92
         
Non-response rates. Annual averages (% of the theoretical yearly sample)      
NUTS-2 region (code + name) Non response rate (%)
HU1–Central–Hungary 50.20
HU11–Budapest 54.86
HU12–Pest  45.31
HU21–Central–Transdanubia 30.32
HU22–Western–Transdanubia  34.32
HU23–Southern–Transdanubia  31.23
HU31–Nothern–Hungary 28.30
HU32–Nothern–Great–Plain 27.78
HU33–Southern–Great–Plain  35.40

  * If the final sampling unit is the household it must be considered as responding unit even in case of some household members (not all) do not answer the interview
6.3.3.2. Item non-response - rate
Item non-response (*) - Quarterly data (Compared to the variables defined by the Commission Regulation (EC) No 377/2008)

Variable status

 Column Identifier Quarter 1 Quarter 2 Quarter 3 Quarter 4 Short comments on reasons for non-available statistics and prospects for future solutions
Compulsory / optional
compulsory Col_047/48 MSTARTWK . 12.6 18.8 26.3 EU-Filter: REFYEAR – YSTARTWK<=2    HUFilter: SUM (REFYEAR–YSTARTWK<2)  or  (SUM(REFYEAR–YSTARTWK=2)  and  (REFMONTH<=MSTARTWK)) + There is an upper-age limit (74 years) in HU-LFS for this variable.
compulsory Col_084 EXISTPR 24 22.8 23.8 23.7 There is an upper-age limit (74 years) in HU-LFS for this variable.  
compulsory Col_089/90 MONTHPR . . 18.3 21.7 EU-Filter: REFYEAR – YEARPR<=2        HU Questionnaire: The HU-LFS (in accordance with the EU-LFS) gives in some cases more detailed information than used in the Eurostat filter. This information is used by transcodification program of HU-LFS dataset. 
compulsory Col_110 - Employed METHODH C . C .  
compulsory Col_123 EDUCSTAT 12.6 12.1 12.4 12.4 There is an upper-age limit (74 years) in HU-LFS for this variable. 
compulsory Col_128 COURATT 12.6 12.1 12.4 12.4 There is an upper-age limit (74 years) in HU-LFS for this variable. 

 

Item non-response - Annual data (Compared to the variables defined by the Commission Regulation (EC) No 377/2008)    
Variable status Column Identifier This reference year Short comments on reasons for non-available statistics and prospects for future solutions
compulsory Col_146 WSTAT1Y 12.4 There is an upper-age limit (74 years) in HU-LFS for this variable 
optional Col_122 MAINSTAT 12.4 There is an upper-age limit (74 years) in HU-LFS for this variable 

(*) "C" means all the records have the same value different from missing.
6.3.4. Processing error
Editing of statistical item non-response
Do you apply some data editing procedure to detect and correct errors? (Y/N) Overall editing rate (Observations with at least one item changed / Total Observations )
 N NA 
6.3.4.1. Imputation - rate
Imputation of statistical item non-response
Are all or part of the variables with item non response imputed? (Y/N) Overall imputation rate (Observations with at least one item imputed / Total Observations )
 Y UNA 
 Main variables Imputation rate  Describe method used, mentioning which auxiliary information or stratification is used 
 INDECIL  58.9% Quality of income variable in HU-LFS datasets is very poor. The item non response of this information is very high and  the quality of the provided information compared with other earnings statistics  is very low.

Till 2014, for missing values of INCDECIL cold-deck donor imputation method was used, missing values were replaced with donor data coming from the previous/following periods of LFS data collections. Since it was proved to be not appropriate, data transmission to Eurostat for this variable was stopped.

For the period 2014 Q1-2019 Q4 we provided the INCDECIL data based on hot deck imputation. Data have been sent to Eurostat included into the relevant set of micro variables.
6.3.5. Model assumption error

[not requested for the LFS quality report]
6.4. Seasonal adjustment
Do you apply any seasonal adjustment to the LFS Series? (Y/N) If Yes, is your adopted methodology compliant with the ESS guidelines on seasonal adjustment? (ref. http://ec.europa.eu/eurostat/web/research-methodology/seasonal-adjustment) (Y/N) If Yes, are you compliant with the Eurostat/ECB recommendation on Jdemetra+ as software for conducting seasonal adjustment of official statistics. (ref. http://ec.europa.eu/eurostat/web/ess/-/jdemetra-officially-recommended-as-software-for-the-seasonal-adjustment-of-official-statistics) (Y/N) If Not, please provide a description of the used methods and tools
Y Y  Y  NA
6.5. Data revision - policy
Do you adopt a general data revision policy fully compliant with the ESS Code of Practice principles? (in particular see the 8th principle) (Y/N) Are you compliant with the ESS guidelines on revision policy for PEEIs? (ref. http://ec.europa.eu/eurostat/documents/3859598/5935517/KS-RA-13-016-EN.PDF) (Y/N)
Y   Y
6.6. Data revision - practice

[not requested for the LFS quality report]
6.6.1. Data revision - average size

[not requested for the LFS quality report]


7. Timeliness and punctuality Top
7.1. Timeliness
Restricted from publication
7.1.1. Time lag - first result
Restricted from publication
7.1.2. Time lag - final result
Restricted from publication
7.2. Punctuality
Restricted from publication
7.2.1. Punctuality - delivery and publication
Restricted from publication


8. Coherence and comparability Top
8.1. Comparability - geographical

Divergence of national concepts from European concepts

(European concept or National proxy concept used) List all concepts where any divergences can be found

    
Is there a divergence between the national and European concepts for the following characteristics? (Y/N) Give a description of difference and provide an assessment of the impact of the divergence on the statistics
Definition of resident population (*) N  NA
Identification of the main job (*) N  NA 
Employment Y See: Economic activity of persons receiving child care allowance or child care benefit during parental leave (1-2) 
Unemployment N  NA 
8.1.1. Asymmetry for mirror flow statistics - coefficient

[not requested for the LFS quality report]
8.2. Comparability - over time
Changes at CONCEPT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)
Changes in (Y/N) Description of the impact of the changes Statistics also revised backwards (if Y: year / N) Variables affected Break in series to be flagged (if Y: year and quarter/N)  
concepts and definition  N NA  NA  NA  NA
coverage (i.e. target population)  N  NA  NA  NA  NA
legislation  N  NA  NA  NA  NA
classifications  N  NA  NA  NA  NA
geographical boundaries  N  NA  NA  NA  NA

 

Changes at MEASUREMENT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)
Changes to (Y/N) Description of the impact of the changes Statistics also revised backwards (if Y: year / N) Variables affected Break in series to be flagged (if Y: year and quarter/N)
sampling frame  N  NA  NA  NA  NA
sample design  N  NA  NA  NA  NA
rotation pattern  Y  NA  NA  NA  NA
questionnaire  N  NA  NA  NA  NA
instruction to interviewers  N  NA  NA  NA  NA
survey mode  N  NA  NA  NA  NA
weighting scheme  N  NA  NA  NA  NA
use of auxiliary information  N  NA  NA  NA  NA
8.2.1. Length of comparable time series

[not requested for the LFS quality report]
8.3. Coherence - cross domain
Coherence of LFS data with Business statistics data    
  Description of difference in concept Description of difference in measurement Give an assessment of the effects of the differences Give references to description of differences
Total employment In BS  – in case of monthly surveys – enterprises with more than 4  employees, all budgetary institutions and some non profit institutions are surveyed. In 2018 the Hungarian Central Statistical Office introduced a methodological correction in earnings and employment statistics in BS. Due to this change the comparability of employment data between 2017 and 2018 is limited. From 2019 all enterprises with at least 5 employees and non-profit institutions – which are significant in respect of employment – are taken over from the social security reports provided by the National Tax and Customs Administration. Due to this change the comparability of the data of number of employees between 2018 and 2019 is limited.

In LFS according to the ILO recommendations employed are persons who worked one hour or more for pay or profit or had a job from which they were temporarily absent (sick-leave, holiday, etc.) during the reference week.

 Average of daily staff numbers of employees in BS.

Estimates from reference week stock number of employed persons in LFS.

 In comparison with the business data the LFS offers more integrated overview on labour market.

Number of employees in LFS for 2020 is 27.2% higher than that in BS. Annex „ Coherence with IS data 2020” includes comparison by industries.

 UNA
Total employment by NACE In BS employees are classified according to business register.
In LFS interviewed person is classified on the basis of the spontaneous answer given by the respondent.		  UNA  UNA   UNA 
Number of hours worked

In LFS usual, actual hours are also asked.

  UNA   UNA   UNA 

 

Coherence of LFS data with registered unemployment  
Description of difference in concept Description of difference in measurement Give references to description of differences
Registered unemployed: Persons are registered as jobseekers in the Public Employment Service (PES). It does not matter whether they receive or not jobseekers or any other allowance. Persons aren’t considered as registered unemployed although they satisfy the 3 criteria of the ILO, but did not enrol for registration in the PES due to specific reasons. The stock data of registered jobseekers in the PES concern the closing data of each month (20th of each month). The yearly average is the arithmetical mean of the 12 months data. https://www.ksh.hu/docs/eng/modsz/modsz21.html

In addition,HCSO's publications also include methodological descriptions.

Number of registered jobseekers in 2020 – according to data provided by National Employment Services – is 59.6% higher than that in LFS. Annex „Coherence with RU data 2020” includes comparisons by sex, age-group and region in NUTS3 level.
In LFS persons are considered as unemployed by the 3 criteria of the ILO (he/she does not work, seeks a job actively and available for work). The yearly average of ILO unemployment in LFS is the average of the 4 quarters data classified by strict criteria (3 criteria of the ILO-unemployment).

 

Assessment of the effect of differences of LFS unemployment and registered unemployment     
Give an assessment of the effects of the differences          
Overall effect Men under 25 years Men 25 years and over Women under 25 years Women 25 years and over Regional distribution (NUTS-3)
159.63% 102.31% 156.93% 121.57% 188.02% See Annex: Coherence with RU data 2020


Annexes:
Coherence with IS data 2020
Coherence with RU data 2020
Coherence with JVS data 2020_JVS
Coherence_with JVS posts data 2020
8.4. Coherence - sub annual and annual statistics

[not requested for the LFS quality report]
8.5. Coherence - National Accounts
Coherence of LFS data with National Accounts data    
  Description of difference in concept Description of difference in measurement Give an assessment of the effects of the differences Give references to description of differences
Total employment NA: domestic concept

LFS: national concept 		 Annex „ Coherence with NA data 2019” includes both description of difference in measurement and comparison by industries. Source of data for National Accounts: Annual labour survey on employment of the enterprises with more than 20 (in case of monthly surveys with more than 5) employees and of all government institutions, labour force survey, census, tax records, social security records, company  Estimation for total employment of NAs in 2019 is 4.4% higher than that of LFS.

Estimation for total employees of NAs in 2019 is 3.8% higher than that of LFS. 

Estimation for selfemployed of NAs in 2019 is 12.8% higher than that of LFS. 

Annex „ Coherence with NA data 2019” includes comparison by industries.
Total employment by NACE See: total employment See: total employment See: total employment See: total employment
Number of hours worked UNA UNA UNA UNA

 

Which is the use of LFS data for National Account Data?   
Country uses LFS as the only source for employment in national accounts. Country uses mainly LFS, but replacing it in a few industries (or labour status), on a case-by-case basis Country not make use of LFS, or makes minimal use of it Country combines sources for labour supply and demand giving precedence to labour supply sources (i.e. LFS) Country combines sources for labour supply and demand not giving precedence to any labour side Country combines sources for labour supply and demand giving precedence to labour demand sources (i.e. employment registers and/or enterprise surveys)
N N N N Y N


Annexes:
Coherence with NA data 2019
8.6. Coherence - internal

[not requested for the LFS quality report]


9. Accessibility and clarity Top
9.1. Dissemination format - News release

[not requested for the LFS quality report]
9.2. Dissemination format - Publications
Please provide a list of type and frequency of publications
 

FIRST RELEASES - MONTHLY
Link to the national web page (Hungarian):
Employment: http://www.ksh.hu/foglalkoztatottsag
Unemployment: http://www.ksh.hu/munkanelkuliseg
Link to the national web page (English):
Employment: http://www.ksh.hu/employment_tn
Unemployment: http://www.ksh.hu/unemployment_tn

PUBLICATION REPERTORY – LABOUR MARKET, INFRA ANNUAL
Link to the national web page (Hungarian): http://www.ksh.hu/apps/shop.lista?p_lang=HU&p_temakor_kod=KSH&p_kapcsolodo=mpf
Link to the national web page (English): http://www.ksh.hu/apps/shop.lista?p_lang=EN&p_temakor_kod=KSH&p_kapcsolodo=mpf

 

WEEKLY MONITOR

Link to the national web page (Hungarian):

http://www.ksh.hu/heti-monitor/index.html?utm_source=kshhu&utm_medium=banner&utm_campaign=home

Link to the national web page (English):

http://www.ksh.hu/weekly-monitor/index.html?utm_source=kshhu&utm_medium=banner&utm_campaign=home
9.3. Dissemination format - online database
Documentation, explanations, quality limitations, graphics etc.    
Web link to national methodological publication Conditions of access to data Accompanying information to data Further assistance available to users

The website of the HCSO contains data according to the new methodology. The links provided are archive data with old methodology.

Link to the national web pnational language(s)):

1) STADAT TABLES (the electronic data store of the HCSO)
Society
Infra-annual data: http://www.ksh.hu/stadat_infra_2_1
Annual data: http://www.ksh.hu/stadat_annual_2_1
Long Time Series: http://www.ksh.hu/stadat_long

Regional Statistics– Society
Infra-annual data: http://www.ksh.hu/stadat_infra_6_2
Annual data: http://www.ksh.hu/stadat_annual_6_2
Long Time Series (Labour market tendencies in the regions of Hungary):
http://www.ksh.hu/5_1_long

Tables of the supplementary surveys of the Labour Force Survey: http://www.ksh.hu/stadat_infra_9_tart   

 


 		 Monthly results of the Hungarian LFS are published 3-4 weeks after the end of the reference month, quarterly results 5-6 weeks after the reference quarter and annual results 5-6 weeks after the end of the reference year.

The data are available simultaneously to all interested parties at 9:00 on the day of release by issuing a news release, "Employment and Unemployment". It is sent to all interested parties, including the media, at their request either by fax or by e-mail. At the same time, the news release is also published on the Internet website of the Hungarian Central Statistical Office
http://www.ksh.hu/

For the purpose of scientific research of statistical data, a Research room has been created where the access to anonymised microdata of HU-LFS is provided in strict compliance with the high-level protection of individual data and the rules of data protection.
Services of the Research room are available only after the Research room contract and the Confidentiality declaration were signed.

The anonymised Hungarian LFS microdatabase is also available for users from state and research institutions on CD/DVD for fee. For confidentiality reasons some information (name, address, name of the workplace, description of occupation, etc) is excluded from database. The special contract between Hungarian Central Statistical Office and data user stipulates the strict conditions of use. The users are ministries, banks, universities and other research institutes. In addition to anonymised database detailed documentation has been produced. This includes instructions about using the database, description of variables in the database, used classifications, questionnaires and interviewer’s instructions. In case of any problem users can contact collegues of Labour Statistics Section of HCSO for consultation.		 Publications of the Hungarian LFS include also a short description of the methodology with the following topics:
1) History of the LFS
2) Definitions;
3) Sample Design
4) Sampling Error of the Estimates (inc.tables:Number of Persons Belonging to the different Economic Activity Groups and their Sampling Error at 95% Confidence-level by sex, by Age-groups and by Counties.
5) Publications
6) Classification of Administrative Units
7) Questionnaire and interviewer’s manual

The methodology of the Hungarian Labour Force Survey is published in a separate publication ‘The Methodology of the Labour Force Survey’. Three such kind of reports has been published until now describing 1998, 2002 and 2006 methodology. The newest methodological report was published in 2006 describing methodological changes during last years. ‘The Methodology of the Labour Force Survey’ includes same topics as short overview in publications but it is more complete.

Additionally it includes:
1) Overview of changes in the questionnaire
2) Classifications used for coding (economic activities, occupations, administrative units, nationalities, countries, fields of education and training, highest level of education completed).
3) Information about non-response		 Information about publications of HCSO is available for users in the catalogue of ‘Statistical Publications’. It is published annually and is available also in Internet website (http://www.ksh.hu/). The request for information about the Hungarian Labour Force Survey sent to the Statistical Office are answered within 1-5 working days starting from the working day following the registration of the request for information. 
9.3.1. Data tables - consultations

[not requested for the LFS quality report]
9.4. Dissemination format - microdata access
Accessibility to LFS national microdata (Y/N) Who is entitled to the access (researchers, firms, institutions)? Conditions of access to data Accompanying information to data Further assistance available to users
Y Microdata files are available for scientific purposes only. Access to such data is granted only after an obligatory researcher accreditation procedure. The Hungarian Central Statistical Office offers 6 data access channels for users. In all these cases, the necessary legal instruments (contract, confidentiality commitment, terms of use) and methodological, IT safeguards are in place. Anonymised microdata sets are customised, and only the variables requested are provided to the researcher. Deidentified microdata files are available for scientific purposes also in safe environment (Safe Centre, remote access, remote execution). Documentation contains the core, activity and if needed, the requested ad-hoc questionnaires, also the changes and values of the variable list. Furthermore we give the users the description of the anonymization conditions (deleted variables, aggregated variables)

http://www.ksh.hu/data_requests_home		  N
9.5. Dissemination format - other

[not requested for the LFS quality report]
9.6. Documentation on methodology
References to methodological notes about the survey and its characteristics
 In Hungarian:
László Mihályffy – Elisabeth Lindner: The Methodology of the Labour Force Survey, in: Statistical Methodological Workbooks, Budapest, HCSO, 2006
László Mihályffy: Applying Up-to Date Methods for Representative Surveys Connected with the Census, in: Census at the Turn of the Millennium, HCSO, 2000
Ádám Marton: Non Response Study
In English:
Ödön Éltető – László Mihályffy: Household Surveys in Hungary, in: Statistics in Transition, June 2002, Vol. 5. No. 4, pp 521-540
Darroch, J.N. - Ratcliff, D.: Generalized iterative scaling for log-linear models. The Annals of Mathematical Statistics, 43, 1972
Ádám Marton: Non Response Study
9.7. Quality management - documentation

[not requested for the LFS quality report]
9.7.1. Metadata completeness - rate

[not requested for the LFS quality report]
9.7.2. Metadata - consultations

[not requested for the LFS quality report]


10. Cost and Burden Top
Restricted from publication


11. Confidentiality Top
11.1. Confidentiality - policy

[not requested for the LFS quality report]
11.2. Confidentiality - data treatment
Please provide information on the policy for anonymizing microdata in your country
Continuous serial number of the households since 1998Q1.
Among the non derived variables, the following variables are deleted that could identify the reference person: TERUL (code of the settlement) and SZLOK (code of the district).
Deletion of the variables required to determine the earnings.
Marital status: aggregated into two groups: 1. single (contains single, divorced and widowed) 2. married
Nationality, country of birth, country of residense one year ago, parent's country of birth: aggregation into three groups: 1. Hungary, 2. Not Hungary but an EU country, 3. Non-EU country
Local unit of the working place aggregated into six groups: 1. Hungary, 2. Austria, 3. Germany, 4. United Kingdom, 5. Other EU country, 6. Non-EU country
Economic activity aggregated at 2 digit level


12. Comment Top

[not requested for the LFS quality report]



Annexes:
Questionnaire_Soc_Dem_2020_HU
Questionnaire_Ec_Act_2020_HU


Related metadata Top


Annexes Top