ISTAT - Istituto Nazionale di Statistica
ESS Standard for Quality Report Structure (ESQRS) V2 (ESQRS_MSD 3.0 ESTAT)
Employment and unemployment (Labour Force Survey)
2016 - A0
1. Contact
1.1 Contact organisation
Istat - Italian National Statistical Institute
1.2 Contact organisation unit
SSE - Division for integrated system for labour, education and training
1.3 Contact name
1.4 Contact person function
1.5 Contact mail address
Via Cesare Balbo, 39 00184 Rome Italy
1.6 Contact email address
1.7 Contact phone number
1.8 Contact fax number
2. Statistical presentation
Please specify 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
NC

No change from last report.

2.1 Data description
Coverage   
Coverage 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 reference population is the "resident population", living in private households of all the Italian regions.
Non resident households, people not living in private households are not covered.
 Private households are made up either of persons living alone or of two or more persons usually living in the same dwelling and linked by marriage, kinship, affinity, adoption, patronage and affection.
Households to be interviewed are selected from the frame of registered resident households. The household is included in the sample if in the dwelling there is at least the reference person or his/her partner or ex-partner (either married or in consensual union), according to the population register.
 

Household members are identified according to
1) the usual residence in the dwelling and
2) the marriage, kinship, affinity, adoption, patronage and affection ties.

People temporarily absent are included in the household if the duration of the absence is less than 12 months, otherwise they are excluded. Students in tertiary education, studying away from their parents’ home, are included in the household, unless they changed their registered residence; for them, the duration of the absence is irrelevant.
 15 and over. From the first quarter 2008 onwards Italian LFS data on people aged 15 years old don’t include neither employed nor unemployed (they are all classified as inactive people).

 

Reference week
 The sample is evenly spread over the 13 weeks of each quarter. The first week of the year or quarter is the week including the first Thursday of the year or quarter.
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
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 (continuosly updated or date of the last update) Primary sampling unit (PSU)   Final sampling unit (FSU)
 The sample design is a two stage sampling with stratification of the primary units.

Resident population and households register (LAC: liste anagrafiche comunali) managed by Italian municipalities

The sampling frame is continuously updated by the Italian municipalities, but it is delivered to Istat once per year, updated at the 1st January of the year. The sample selection is made once per year, during Spring.

 The yearly sample is composed by households which belong to 16 rotation groups (4 waves X 4 quarters = 16 rotation groups).

The sample selected from the last updated available frame is gradually introduced every year starting from the first wave of the third quarter, till the second quarter of the following year.

Municipalities Households
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..)
 In each NUTS 3 domain, large municipalities, with population over a given threshold (also called self-representative municipalities), are always included in the sample; smaller municipalities (not self-representative) are grouped in strata, then one municipality in each stratum is selected with probability proportional to the population  Households are randomly selected from the registry offices in all the municipalities drawn at the first stage.  Stratification of primary units is carried out in each NUTS-3 domain and it is based on the population of the municipalities. In 2012 a new stratification of the municipalities was made, to take into account updated information on their population. Consequently a new selection of the municipalities has been done, the new selected municipalities entered in the sample in the third quarter 2012. Due to rotation scheme, for 5 quarters until 2013Q3, old and new sampling designs have been  overlapped.  1121  2-2-2

 

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 2016 the overall theoretical sampling rate has been 1.11%.  In 2016 the theoretical sample size has been 286,135 households.

  

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)

 In 2016 the overall theoretical quarterly sampling rate has been 0.28%.  In 2016 the theoretical quarterly sample size has has been on average 71,534.

 

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)
 We don't use wave approach  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
 

The calibration estimator is used to obtain LFS estimates. Grossing weights are determined as follows:
1. Firstly, initial weights are obtained as the inverse of the inclusion probabilities of any household in the sample.
2. Then, correction factors for households non-response are worked out as the reciprocal of the response ratios (computed for specific kind of households and territorial domains). Intermediate weights corrected for non-response are then computed multiplying initial weights by these correction factors.
3. Then, starting from intermediate weights, final grossing weights are obtained solving a minimization problem under constraints. The function to be minimised is a distance between final and intermediate weights; the constraints regard the estimates of some auxiliary variables that have to be equal to the totals in the reference population derived by external sources (main constraints are population by gender and 14 5-year age groups at NUTS-2 level and population by gender and 5 age groups of different width at NUTS-3 level). Final weights ensure that all members of a given household have the same weight.
Through the calibration estimator, applying final grossing weights, the sample reproduces the same distribution of the population according to the chosen auxiliary variables.
Grossing weights are computed on quarterly basis, whereas annual estimates are calculated as averages of quarterly estimates.

On January 2015 new population figures were available for the period 2002-2014, according to the results of the 2011 Population Census, Post Enumeration Survey of the 15th Italian population census and administrative register updates for the post censal period. Consequently LFS weights have been recalculated for the period 2002Q2-2014Q3.
 Y  NA   Y  14 5-year age groups (0-14, 15-19, ..., 70-74, 75+ ) at NUTS2 level and 5 age groups of different width at NUTS3 level NUTS3  Monthly population by gender (at NUTS2), number of households by wave (at NUTS2), foreigner population (Male, Female, EU and Not EU at NUTS2), population in metropolitan areas (municipalities with resident population over than 250 thousands units) by gender and 5 age groups.

 

Brief description of the method of calculating the yearly weights (please indicate if subsampling is applyed 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
  Average of the quarterly core weights. No subsampling is applyed to provide  yearly estimates  Y NUTS3 See above 

 

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)
The Quarterly core weights is applyed to produce households estimates. The adopted weighting scheme ensures the coherence between household and individual estimates. Resident population and households register.  Number of households   See quarterly core weights  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?
 The information is collected through computer assisted personal (CAPI) or telephonic (CATI) interviews, carried out by professional interviewers.
CAPI mode is usually used for the 1st wave, whereas CATI mode is usually used for later waves. Households without a telephone and non-Italian households are interviewed always by CAPI mode.
 Y  Compulsory

 

Sample unit collected by interviewing technique (%)
CAPI CATI PAPI CAWI POSTAL - OTHER
 46.6  53.4  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
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
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 main LFS statistics on employment (by professional status, temporary/permanent job, working time, economic activity sector, occupation, etc.), unemployment and inactivity represent the official estimates on the labour market supply, and they are considered very relevant by policy makers, other stakeholders, media and academic researchers.
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?
NUTS3 (stratification of primary units is at NUTS3 level and some constraints at NUTS3 level have been introduced in the final weights computation to allow the production on yearly basis of NUTS3 estimates). Starting from 2015 the new classification NUTS-2013 has been adopted. NUTS3 for annual estimates, NUTS2 for quarterly estimates, NUTS0 for monthly estimates  NUTS3 NUTS3 estimates are produced through the usual calibration estimator
5.3.1 Data completeness - rate

Assessment of errors (bias) in the registration of unemployment

Only for those countries using registered unemployment to produce NUTS-3 level data on unemployment or labour force.

 NA
6. Accuracy and reliability
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
 1500  2500  We don't use wave approach. NA  We don't use wave approach. 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 0.19 0.19 0.72 0.84 0.83 1.32 0.11
 SE 42100 0.12 29499 25301 0.10 0.50 0.04
 CI(**) (22075480-22240512) (61.33-61.79) (4039253-4154889) (2962447-3061627) (11.53-11.91) (36.79-38.75) (35.6-35.76)

 

Description of the assumption underlying the denominator for the calculation of the CV for the employment rate
The population figure at the denominator of the employment rate is not an estimate, but it is one of the totals introduced in the calibration as constraints (known from administrative source). thus its sampling error is equal to 0. The CV for the employment rate is the same as the CV for the employed people.

 

Reference on software used: Reference on method of estimation:
ReGenesees: software produced by Istat  http://www.istat.it/it/files/2014/03/ReGenesees.pdf

 

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 labour force

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 

ITC1 Piemonte 0.67 0.67 2.67 3.6 3.56 5.74 0.38
ITC2 Valle d'Aosta/Vallée d'Aoste 0.74 0.74 3.22 4.4 4.35 8.35 0.58
ITC3 Liguria 0.86 0.86 3.67 4.46 4.39 7.91 0.61
ITC4 Lombardia 0.39 0.39 1.9 2.9 2.88 4.7 0.26
ITF1 Abruzzo 1.39 1.39 4.92 4.46 4.33 7.42 0.62
ITF2 Molise 1.7 1.7 6.12 6.98 6.84 7.32 0.73
ITF3 Campania 0.95 0.95 3.02 2.46 2.33 3.34 0.45
ITF4 Puglia 1 1 3.41 2.98 2.87 3.71 0.47
ITF5 Basilicata 1.18 1.18 5.04 4.27 4.16 7.93 0.7
ITF6 Calabria 1.65 1.65 5.31 4.52 4.31 4.77 0.68
ITG1 Sicilia 0.9 0.9 2.55 2.06 1.94 2.31 0.37
ITG2 Sardegna 1.06 1.06 3.57 3.23 3.08 3.29 0.56
ITH1 Provincia Autonoma di Bolzano/ 0.68 0.68 2.81 6.68 6.65 16.91 0.64
ITH2 Provincia Autonoma di Trento 0.89 0.89 3.11 5.42 5.36 9.56 0.53
ITH3 Veneto 0.61 0.61 2.5 4.22 4.19 8.82 0.4
ITH4 Friuli-Venezia Giulia 0.79 0.79 3.1 4.62 4.57 8.09 0.46
ITH5 Emilia-Romagna 0.53 0.53 2.39 3.13 3.09 6.99 0.32
ITI1 Toscana 0.65 0.65 2.66 3.11 3.07 5.79 0.37
ITI2 Umbria 0.97 0.97 3.75 4.49 4.41 7.81 0.57
ITI3 Marche 0.92 0.92 3.31 4.59 4.53 8.34 0.51
ITI4 Lazio 0.62 0.62 2.38 2.72 2.66 4.38 0.39

 (*) 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  2.09  1.61  Households are selected once a year from the municipalities’ registry; they cover the whole reference population. The data might contain errors as for information such as addresses (due for instance to recent change of the address), wrong inclusions (recent emigration) and missed inclusions (recent immigration).

As for the survey’s management strategies, Istat requires that each non-responding household be replaced with a household having similar characteristics of the first one, in order to maintain as much as possible the sample representativeness and to minimise the impact of unit non-response. No more than 3 replaces are admitted.

 N  N 

 

(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 2016 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)?
 Y  Only national variables  Y  Pilot on 1,000 households

 

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 contacting households, (Y/N) Monitoring directly listening 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  Y
Other / Comments  
6.3.3 Non response error
[not requested for the LFS quality report]
6.3.3.1 Unit non-response - rate
Methods used for adjustments for statistical unit non-response
Adjustment via weights (Y/N) Variables used for non-response adjustment Description of method
 Y  In the second step (see the “description of method” column), correction factors for household non-response are computed according to household typologies (number of family members and reference person's age) and territorial domains. In the third step calibration is applied: main constraints are population by gender and 14 5-year age groups at NUTS-2 level and population by gender and 5 age groups of different width at NUTS-3 level. Other constraints are: monthly population by gender (at NUTS2), number of househospanlds by wave (at NUTS2), foreigner population (Male, Female, EU and Not EU at NUTS2), population in metropolitan areas (municipalities with resident population over than 250 thousands units) by gender and 5 age groups.  Grossing weights are determined as follows:
1. First, initial weights are obtained as the inverse of the inclusion probabilities of any household in the sample.
2. Then, correction factors for households non-response are worked out as the reciprocal of the response ratios (computed for specific cells identified by household typologies and territorial domains). Intermediate weights corrected for non-response are then computed multiplying initial weights by these correction factors.
3. Finally, starting from intermediate weights, final grossing weights are obtained through calibration, solving a minimization problem under constraints. The function to be minimised is a distance between final and intermediate weights; the constraints regard the estimates of some auxiliary variables that have to be equal to the totals in the reference population derived by external sources (main constraints are population by gender and 14 5-year age groups at NUTS-2 level and population by gender and 5 age groups of different width at NUTS-3 level). Final weights ensure that all members of a given household have the same weight.
Through the calibration estimator, applying final grossing weights, the sample reproduces the same distribution of the population according to the chosen auxiliary variables. This estimator is able to reduce the bias effect of unit non-response.
Substitution of non-responding units (Y/N) Substitution rate Criteria for substitution
 Y 32.9% of the first selected households have been substituted: 22.6%  have been substituted with the first substitute unit, 6.7% with the second one, 3.6% with the third one. Non responding households (non-contacts, refusals, other non-respondents, ineligible units, etc.) are substituted only in the 1st wave or if they are assigned to CAPI mode. When the sample is drawn, for each selected household three additional households are chosen as substitute units, according to the same sampling design. That is for each household it is possible to do up to 3 substitutions. In order to correct for higher non response for non national people, households with foreigner reference person are substituted with households with the same characteristic.
Other methods (Y/N) Description of method
 N  NA

 

Calculation of non-response. Annual average
Is the non-response on household level or person level? (H/P)
 H

 

Rates of non response by survey wave. Annual average. It should be calculated in all waves considering the theoretical initial sample.       
Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7 Wave 8
30.5 5.5 5.3   3.9  NA  NA NA NA

 

Rates of non response by survey mode. Annual average
Survey
CAPI CATI  PAPI  CAWI  POSTAL
24.7 4.4 NA NA NA

 

Divisions of non-response into categories. Quarterly data and annual average
Quarter  Non response rate (%)  Refusals (%)  Non-contacts (%)  Other reasons(%)
1

12.6

4

7.3

0.9

2

14.3

4.8

8.2

1.3

3

13.1

4.6

7.6

1.0

4

13.3

5.1

7.6

0.6

Annual

13.3

4.7

7.7

0.9

 

Rates of non response. Annual average
NUTS-2 region (code + name)  Non response rate (%)

ITC1-Piemonte

15.1

ITC2-Valle d'Aosta

12.6

ITC4-Lombardia

13.6

ITH1-Alto Adige

9.2

ITH2-Trentino

15.4

ITH3-Veneto

12.3

ITH4-Friuli-Venezia Giulia

12.6

ITC3-Liguria

12.4

ITH5-Emilia Romagna

11.7

ITI1-Toscana

12.0

ITI2-Umbria

12.4

ITI3-Marche

12.9

ITI4-Lazio

13.6

ITF1-Abruzzo

10.9

ITF2-Molise

10.8

ITF3-Campania

16.2

ITF4-Puglia

12.7

ITF5-Basilicata

10.9

ITF6-Calabria

13.9

ITG1-Sicilia

15.1

ITG2-Sardegna

13.4

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_073/74 HWWISH . . 16.1 .  
  Col_101 - Employed SEEKTYPE 21.6 19.1 19.4 18.1  
  Col_101 - Not employed SEEKTYPE 27.5 25.9 26 25.3  

 

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_150/151 COUNTR1Y 12.3 Item non-response is due to people aged less than 15 years, for which this information is not collected in the national questionnaire

 

Methods used for editing and imputation of statistical item non-response

Do you apply some data editing procedure to detect and correct errors? (Y/N)

Overall editing rate (Units with at least one item changed / Total Units)
 Y  8.53

 

Are all or part of the variables with item non response imputed? (Y/N) Overall imputation rate (Units with at least one item imputed / Total Units)
 Yes, all variables with item non response are imputed   2.13
 Main variables Imputation rate  Describe method used, mentioning which auxiliary information or stratification is used 

WSTATOR

INCDEC

0.01

13.84

After data captured contradictory information and item non-response are detected by using an automatic system based on Fellegi and Holt model of editing (1976). This operation is also performed during each quarter in order to notify in time possible errors due to the electronic questionnaire. The explicit edits relating to categorical variables (i.e. variables which are not subject to a meaningful metric) are implemented in SCIA (System for editing and automatic imputation), developed by ISTAT according to the Fellegi and Holt methodology (Barcaroli and Venturi, 1997). More exactly, the version of SCIA included in CONCORD (CONtrol and Data CORrection), a system designed for Windows environment, is used (Riccini Margarucci and Floris, 2000). The large number of edit rules and the complicated skip patterns of the questionnaire poses several computational problems that the entire set of edits need to be partitioned into two subsets. The problems arise since the Fellegi and Holt system runs to check the logical consistency of the entire edit system.
6.3.4 Processing error
[not requested for the LFS quality report]
6.3.4.1 Imputation - rate
[not requested for the LFS quality report]
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 reccomendation 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  N  Tramo/seats with Demetra 2.0
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
7.1 Timeliness
7.1.1 Time lag - first result
7.1.2 Time lag - final result
7.2 Punctuality
7.2.1 Punctuality - delivery and publication
8. Coherence and comparability
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  N  NA 
Unemployment  N  NA 

 

(*) See LABOUR FORCE SURVEY - EXPLANATORY NOTES (TO BE APPLIED FROM 2016Q1 ONWARDS)

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 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  NA  NA   NA   NA 
coverage (i.e. target population)  NA  NA   NA   NA 
legislation  NA  NA   NA   NA 
classifications  NA  NA   NA   NA 
geographical boundaries  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 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  NA  NA   NA   NA 
sample design  NA  NA   NA   NA 
rotation pattern  NA  NA   NA   NA 
questionnaire  NA  NA   NA   NA 
instruction to interviewers  NA  NA   NA   NA 
survey mode  NA  NA   NA   NA 
weighting scheme  NA  NA   NA   NA 
use of auxiliary information  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  LFS estimates the number of employed persons, both in regular or irregular jobs, whatever the number of jobs they have. Business statistics consider the number of regular jobs i.e. complying with tax and social security legislation and which can therefore be traced by business surveys, institutional and administrative sources.  UNA  Due to the number of different sources used by business statistics’ surveys and to the relevant differences in concepts and measures with those used in Italian LFS, no comparisons between LFS and business statistics’ data have been done up to now.  UNA
Total employment by NACE  Business data relating to Italian businesses in the fields of industry and services arise from two integrated sources: (i) a sample survey on medium and small enterprises and (ii) a census survey on enterprises having 100 workers and more. Further ISTAT’s surveys focus on businesses in the field of agriculture. In any case, both LFS and business surveys use the same classification of economic activity, referring to NACE  UNA   see above  UNA 
Number of hours worked  LFS collects individual information on the number of hours actually and usually worked. Business surveys collect information on firm's total amount of hours (thus considering only regular worked hours for employees)  UNA   see above   UNA 

 

Coherence of LFS data with registered unemployment  
Description of difference in concept Description of difference in measurement Give references to description of differences
Differences between Public Employment Services (in Italian "Servizi Pubblici per l'Impiego" SPI) and LFS data concern: (1) LFS excludes from unemployment all individuals who have any kind of job, whereas SPI include individuals who have temporary jobs or salaries below a given amount; (2) the immediate availability for a job required by LFS is based on self declarations. On the contrary, SPI exclude individuals who miss scheduled encounters or refuse a fair job offer; (3) in LFS registration at SPI in order to find a job is only one of the possible active search actions. At present, data from administrative sources are not available. The specification of the concept of unemployment adopted by the Public Service for Employment (SPI) is provided by the Legislative Decree no.181/2000 as amended by the Decree Law no.297/2002 containing provisions to facilitate the meet of supply and demand. LFS follows the Commission Regulation 1897/2000.

 

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)
 No estimation is possible  No estimation is possible  No estimation is possible  No estimation is possible  No estimation is possible  No estimation is possible
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  Differences in concepts between LFS and National Accounts (NA) are due to the different purposes of the two estimates: description of labour market conditions for the LFS; estimate of the amount of work as productive factor underlying GDP for NA. Employment in NA refers to “domestic employment” whereas LFS definition of employment is more similar to the idea of “national employment”. NA estimates are obtained starting from the LFS figures, adding: a) foreign workers present on the national territory for a period longer than one year, but not included in population registers; b) foreign seasonal workers (in the country for a period less than one year) not included in population registers; c) members of the country’s armed force who are abroad; d) staff of national embassies located abroad; e) resident workers permanently living in collective households, f) conscripted persons; g) non-resident frontier workers working in resident establishments; h) unpaid trainees working for national firms; i) employed individuals with an age of less than 15 years;  j) irregular workers not included in the LFS; and subtracting resident frontier workers working abroad ;k) since september  2014  the National Institute of Statistics releases the estimates of national accounts  in accordance with the definitions of the European System of Accounts (Esa 2010) and Council Regulation n. 549/2013, so the new national accounts include illegal activities and the relative employment. In addition to domestic employment, NA estimates full-time equivalent Labour Units (In Italian ULA: Unità di LAvoro) starting from domestic employment and considering part-time work and lay-off periods. This measure of labour input is used as an alternative to the total number of hours worked.  To produce their estimates NA collect and process information from different sources of various nature: household surveys, business surveys, administrative sources.  Figures refer to 2016:
LFS (employment): 22,758 thousands;                                        NA (employment): 24,814 thousands;                                           NA (ULA): 23,859 thousands
 

Details on the methodology used by the National Account to estimate employment have been provided in the report to Eurostat “Inventario delle fonti e dei metodi della contabilità nazionale interna” of December 2002, and in the technical report “I requisiti del Registro delle relazione di lavoro e dell’occupazione al fine di  garantire e supportare gli output di produzione dal lato domanda e offerta del lavoro”(2016).

.
Total employment by NACE  As for differences in concept and measures of the total employment by NACE, it is firstly to note that National Account estimates refer to the concept of local level economic unit (UAEL), which is closely linked with that of economic activity unit (UAE). This concept traces the parties of a business that contribute to perform an economic activity at the class level (4 digits) in the NACE classification. UAEL is that part of the economic activity unit corresponding to one single local unit. On the other hand, LFS estimates the employment at local unit level (UL). In the production of their estimates, NA verify and amend the information on economic activity of employed people collected by households surveys following UAEL criterion.  UNA  UNA   UNA 
Number of hours worked  There are no conceptual differences between LFS and NA estimates of numbers of hours worked. Neverthless LFS data on hours worked refer to LFS employment whereas NA data on hours worked refer to NA employment  UNA  Negligible in terms of weekly hours  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   N   Y
8.6 Coherence - internal
[not requested for the LFS quality report]
9. Accessibility and clarity
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
 Every month/quarter new figures are disseminated by a press-release. Together with the quarterly press release a large number of indicators are made available on the Istat data warehouse http://dati.istat.it/. Annual averages are also disseminated in Istat data warehouse. Moreover for specific topics ad hoc releases or dedicated volumes are produced.
9.3 Dissemination format - online database
Documentation, explanation, quality limitations, graphics etc.    
Web link to national methodological publication Conditions of access to data Accompanying information to data Further assistance available to users


Link to the national web page (national language(s)):  http://www.istat.it/it/archivio/8263

Link to the national web page (English): http://siqual.istat.it/SIQual/visualizza.do?id=5000098

Access to macrodata: the main data distribution means is Internet, through the Istat website. As a matter of facts, all publications and a large set of already processed indicators are freely available on line. Specific users requests of indicators not already available are satisfied subject to a preliminary check on estimates reliability and against the payment of a fee which covers the cost related to time necessary for data processing. In this case data are provided in Excel format by e-mail. Access to microdata: Microdata are free of charge. Users are divided in three groups: Institutions belonging to the National Statistical System (SISTAN), such as Ministries, Municipalities; University or researcher, and other users. Two first users access to a greater level of detail in comparison with other users. .
A second difference concerns the list of blanked variables. For SISTAN users only variables with reliability problems are blanked, whereas for other users some variables are blanked also for anonymization reasons. Istat also gives external users the possibility to process microdata in his premise in a specific laboratory for the analysis of individual data (Laboratorio per l’Analisi dei Dati ELEmentari – ADELE ). In this case users can access the complete datasets, but Istat controls that the information produced by users respect privacy laws.
 Press releases include a short description of the main survey characterstics. Together with microdata files the list of columns and codes of the variables, the questionnaire and an Excel file useful to estimate sampling errors are provided. On every publication a reference person is indicated, with telephone number and e-mail adress, to whom it is possible to ask for any clarification.
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 are free of charge. Users are divided in three groups: Institutions belonging to the National Statistical System (SISTAN), such as Ministries and Municipalities; Universities or researchers;  other public users. The first two users groups access to a greater level of detail in comparison with other users. Micro data files for public users are available on the Istat website, to acquire them it is necessary to register at the dedicated area of the Istat website.

Another difference concerns the list of blanked variables. For SISTAN users only variables affected by reliability problems are blanked, whereas for other users some variables are blanked also because of anonymization reasons.

Istat also gives external users the possibility to process microdata in his premise in a dedicated laboratory for the analysis of individual data (Laboratorio per l’Analisi dei Dati ELEmentari – ADELE ). In this case users can access the complete datasets, but Istat controls that the information produced by users respects privacy laws.

Access to microdata file for national institutions belonging to the National Statistical System (SISTAN) is always granted

Access to microdata files for research (MFR) is allowed for research purposes only; projects are welcome from universities, research institutes or from bodies who can prove a recognized research attitude. Researchers from foreign universities and institutes are also allowed.

Micro data files for public users are available on the Istat website for registered users.

Microdata files are accompanied by the list of columns and codes of the variables, the questionnaire and an Excel file useful to estimate sampling errors are provided. On every publication or file disseminated a reference person is indicated, with telephone number and e-mail adress, to whom it is possible to ask for any clarification.
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

ISTAT- “La rilevazione sulle forze di lavoro: contenuti, metodologie, organizzazione” Metodi e norme n. 32 2006

C. Ceccarelli C., S. Rosati. Data Editing for the Italian Labour Force Survey, Conference of European Statisticians, Work Session on Statistical Data Editing, United Nations Statistical Commission and Economic Commission for Europe, Ottawa, Canada, 16-18 May 2005.

M. G. Grassia, F. Pintaldi, L. Quattrociocchi, Wage and Salary in the Labour Force Survey, 2006

F. Camillo, M. G. Grassia, F. Pintaldi, L. Quattrociocchi, How to estimate the effectiveness of on-line codify with searching engines -The Italian experience of Istat Labour Force Survey, Miami, 2006

G. Giuliani, M. G. Grassia, L. Quattrociocchi, R. Ranaldi, New methods for measuring quality indicators of ISTAT’s new CAPI/CATI Labour Force Survey, ISTAT - Department for statistical production and technical-scientific coordination Labour Force Survey ,2004

S. Bergamasco, S. Gazzelloni, L. Quattrociocchi, R. Ranaldi, A.Toma, V. Triolo New strategies to improve quality of ISTAT new CAPI/CATI Labour Force Survey, ISTAT - Department for statistical production and technical-scientific coordination Labour Force Survey, 2004

M. G. Grassia, F. Pintaldi, L. Quattrociocchi The electronic questionnaire in ISTAT’s new CAPI/CATI Labour Force Survey ISTAT - Department for stati• Istat 3 June 2004 S. Gazzelloni, M. Albisinni, L. Bagatta, C. Ceccarelli, L. Quattrociocchi, R. Ranaldi, A. Toma The new Labour Force Survey Contents methodology organisation, 2004

S. Bergamasco, G. Budano, L. Quattrociocchi, A.Toma, The new Istat network for capturing interview data: the technological architetture ISTAT - Department for statistical production and technical-scientific coordination Labour Force Survey, 2003

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
Number of staff involved in central and regional offices, excluding interviewers
Consider only staff directly employed by the NS  
Full-time equivalents
Total  34
- of which professional and managerial  10

 

Duration of the interview  Minutes
Total First wave Later waves 
Average time spent in the household

10.4

15.3

8.0

Core questionnaire (pr person)

3.9

5.1

3.4

Ad hoc Modules (pr person)

UNA

UNA

UNA

Note: This table should only show the burden on the respondents, not time spent in the field to contact the household or fill in adminstrative forms.

 

Number of units   Number
Total First wave Later waves
Households visited over the year

258,674

68,406

190,268

Persons interviewed over the year

584,571

157,056

427,515

Persons interviewed for the ad hoc module over the year

 25,160

7,256

17,904

11. Confidentiality
11.1 Confidentiality - policy
[not requested for the LFS quality report]
11.2 Confidentiality - data treatment
Please provide information on the policy for anonimizing microdata in your country
Different statistical disclosure control (SDC) techniques are applied in order to manage the disclosure risk of each statistical unit, for each type of microdata released.

In microdata files for National Statistical System no particular anonimizing technique is applied and the whole information is available (except for direct identifiers), since the Institution that access the data is in charge of compliance with the legal framework on privacy protection in Italy.

In Microdata for Reserach Use (MFR) variable suppression and global recoding are applied, to preserve as much as possible the analytical validity of microdata, only the units considered at risk of disclosure are generally modified.

An anonymisation report is included in the microdata release to give more details and information on the statistical disclosure limitation methods applied.

The Public use files (called mIcro.STAT)  are  derived from   the   corresponding   MFR   by   subsampling.   Both disclosure  risk  and  some  data  utility  requirements  are  taken  into  account  when determining  the  optimal  allocation.

12. Comment
[not requested for the LFS quality report]