1. Contact

1.1 Contact organisation

ISTAT

1.2 Contact organisation unit

Education, Training and Labour Division

1.3 Contact name

1.4 Contact person function

1.5 Contact mail address

1.6 Contact email address

1.7 Contact phone number

1.8 Contact fax number

2. Statistical presentation

2.1 Data description

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 or blank	Non-response: Member State doesn’t answer to Eurostat request for information
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.

Coverage
Coverage	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", that is the population recorded in the registry offices in the Italian municipalities. All the Italian regions are covered.	Only private households are included in the sample. Private households are made up either of persons living alone or of two or more persons, whether or not of the same family, usually living in the same dwelling and with family (marriage, relationships, adoption, guardianship) or affection ties. Non resident households, people not living in private households and household members emigrated abroad or absent from the selected household for more than 1 year are not covered.	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.

Sampling design & procedure
Sampling design	Base used for the sample (sampling frame)	Last update of the sampling frame	Primary sampling unit (PSU)	Final sampling unit (FSU)
The sample design is a two stage sampling with stratification of the primary units.	Population frame from registry offices of the Italian municipalities	The annual sample is composed by households which belong to 16 rotation groups (4 waves X 4 quarters = 16 rotation groups). A new sample of households is introduced every year starting from the first wave of the third quarter, This sample is selected from the most updated available frame (1st January 2014). This new sample is gradually introduced as the first wave rotation group.	Municipalities	Households

Sampling design & procedure
First (and intermediate) stage sampling method	Final stage sampling method	Stratification	Description of the rotation scheme
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.	The households are rotated according to a 2-(2)-2 rotation scheme. Households are interviewed during two consecutive quarters. After a two-quarters break, they are again interviewed twice in the corresponding two quarters of the following year. As a result, each household is included in four waves of the survey.

Sample size & Sampling rate
Overall theoretical yearly sampling rate (i.e. including non-response)	Size of the theoretical yearly sample (i.e. including non-response)
In 2014 the overall theoretical sampling rate has been 1.11%.	In 2014 the theoretical sample size has been 286,144 households.

Sample size & Sampling rate
Overall theoretical quarterly sampling rate (i.e. including non-response)	Size of the theoretical quarterly sample (i.e. including non-response)
In 2014 the overall theoretical quarterly sampling rate has been 0.28%.	In 2014 the theoretical quarterly sample size has has been on average 71,536.

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

Data collection methods: brief description

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.

Compulsory

Brief description of the method of calculating the weights

Is the sample population in private household expanded to the total population (including those in collective households)? (Y/N)

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.

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

Till 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.

2.2 Classification system

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.3 Coverage - sector

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.4 Statistical concepts and definitions

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.5 Statistical unit

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.6 Statistical population

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.7 Reference area

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.8 Coverage - Time

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

2.9 Base period

Not available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

3. Statistical processing

[not requested for the LFS quality report]

3.1 Source data

[not requested for the LFS quality report]

3.2 Frequency of data collection

[not requested for the LFS quality report]

3.3 Data collection

[not requested for the LFS quality report]

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 available.
New concept added with the migration to SIMS 2.0.
Information (content) will be available after the next collection.

4.2 Quality management - assessment

[not requested for the LFS quality report]

5. Relevance

[not requested for the LFS quality report]

5.1 Relevance - User Needs

5.2 Relevance - User Satisfaction

[not requested for the LFS quality report]

5.3 Completeness

5.3.1 Data completeness - rate

6. Accuracy and reliability

[not requested for the LFS quality report]

6.1 Accuracy - overall

[not requested for the LFS quality report]

6.2 Sampling error

6.2.1 Sampling error - indicators

Coefficient of variation (CV) Quarterly and annual estimates
For the calculation of the CV it is necessary to take into account the design effect.

	CV of national quarterly aggregates (in %)
Quarter	Number of employed	Number of part-time employed	Number of unemployed	Rate of unemployment	Average number of hours actually worked per week
1	0.29	1.06	1.31	1.29	0.18
2	0.31	1.14	1.36	1.33	0.17
3	0.3	1.15	1.52	1.5	0.17
4	0.31	1.15	1.37	1.34	0.18
Annual	0.199	0.740	0.812	0.795	0.113

Reference on software used :	Reference on method of estimation:
Genesees: software produced by Istat	1) Metodi e Norme ISTAT vol. 32, 2006 - "La rilevazione sulle forze di lavoro: contenuti, metodologie, organizzazione"; 2) Tecniche e Strumenti ISTAT vol. 2, 2005 - "Genesees, v. 3.0. – Funzione riponderazione".

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	Number of part-time employed	Number of unemployed	Rate of unemployment	Average number of hours actually worked per week
ITC1	Piemonte	0.70	2.60	3.26	3.22	0.40
ITC2	Valle d'Aosta/Vallée d'Aoste	0.78	3.47	4.54	4.50	0.60
ITC3	Liguria	1.01	3.65	4.37	4.28	0.62
ITC4	Lombardia	0.43	1.91	2.69	2.67	0.28
ITF1	Abruzzo	1.32	4.87	5.10	4.98	0.70
ITF2	Molise	2.10	6.21	5.35	5.05	1.01
ITF3	Campania	1.01	3.00	2.57	2.45	0.48
ITF4	Puglia	1.04	3.21	2.63	2.49	0.49
ITF5	Basilicata	1.39	4.88	4.40	4.26	0.68
ITF6	Calabria	1.71	4.90	3.55	3.25	0.68
ITG1	Sicilia	0.87	2.55	2.01	1.88	0.40
ITG2	Sardegna	1.29	3.59	3.74	3.59	0.67
ITH1	Provincia Autonoma di Bolzano/ Bozen	0.90	2.61	6.68	6.63	0.56
ITH2	Provincia Autonoma di Trento	0.91	3.38	5.32	5.26	0.63
ITH3	Veneto	0.71	3.14	3.93	3.89	0.38
ITH4	Friuli-Venezia Giulia	0.76	2.88	4.33	4.28	0.59
ITH5	Emilia-Romagna	0.57	2.21	3.13	3.09	0.36
ITI1	Toscana	0.71	2.78	2.89	2.84	0.39
ITI2	Umbria	1.00	3.47	4.19	4.11	0.59
ITI3	Marche	0.80	3.42	3.94	3.88	0.63
ITI4	Lazio	0.63	2.41	2.79	2.74	0.42

Coefficient of variation (CV) Annual estimates at NUTS-3 level
Region (NUTS-3)		Sample size (number of responding persons)	CV of regional (NUTS-3) annual aggregates (in %)
Regional Code	Region	Sample size (number of responding persons)	Number of persons in the labour force	Number of unemployed	Unemployment rate
ITC11	Torino	11372	0.83	4.98	4.91
ITC12	Vercelli	4058	1.40	10.16	10.06
ITC13	Biella	3309	1.51	7.68	7.53
ITC14	Verbano-Cusio-Ossola	3877	1.51	11.16	11.06
ITC15	Novara	2731	2.08	8.30	8.04
ITC16	Cuneo	6346	0.96	9.59	9.54
ITC17	Asti	3946	1.72	7.50	7.30
ITC18	Alessandria	3995	1.30	7.61	7.50
ITC2	Valle d'Aosta/Vallée d'Aoste	15844	0.63	4.54	4.50
ITC31	Imperia	2801	1.53	8.56	8.42
ITC32	Savona	4077	1.54	7.91	7.76
ITC33	Genova	5751	1.47	7.28	7.13
ITC34	La Spezia	3313	1.26	8.66	8.57
ITC41	Varese	4074	1.08	12.34	12.29
ITC42	Como	4581	0.90	5.44	5.37
ITC43	Lecco	5365	1.14	8.76	8.69
ITC44	Sondrio	3836	1.48	9.84	9.73
ITC46	Bergamo	5027	1.33	9.72	9.63
ITC47	Brescia	5162	1.24	7.74	7.64
ITC48	Pavia	3960	1.44	9.93	9.83
ITC49	Lodi	3804	1.03	8.40	8.34
ITC4A	Cremona	3782	1.24	9.63	9.55
ITC4B	Mantova	4601	1.34	8.37	8.26
ITC4C	Milano	14831	0.63	4.92	4.88
ITC4D	Monza e della Brianza	4286	1.43	8.32	8.20
ITF11	L'Aquila	2533	1.85	9.07	8.88
ITF12	Teramo	2983	2.76	10.48	10.11
ITF13	Pescara	2711	2.18	11.59	11.38
ITF14	Chieti	2851	1.98	8.17	7.93
ITF21	Isernia	1926	1.86	9.78	9.60
ITF22	Campobasso	5251	2.30	6.00	5.54
ITF31	Caserta	6043	2.64	8.51	8.09
ITF32	Benevento	2237	2.59	9.48	9.12
ITF33	Napoli	22980	1.12	3.20	3.00
ITF34	Avellino	2866	1.94	9.50	9.30
ITF35	Salerno	8293	1.44	5.49	5.30
ITF43	Taranto	3240	4.07	8.78	7.78
ITF44	Brindisi	2887	2.43	9.37	9.05
ITF45	Lecce	6001	1.50	5.75	5.55
ITF46	Foggia	4373	1.94	6.65	6.36
ITF47	Bari	9228	1.26	4.37	4.18
ITF48	Barletta-Andria-Trani	3137	1.76	6.31	6.06
ITF51	Potenza	10549	1.34	5.85	5.69
ITF52	Matera	4793	1.95	6.51	6.21
ITF61	Cosenza	5007	3.16	5.69	4.73
ITF62	Crotone	1783	3.30	6.88	6.04
ITF63	Catanzaro	2670	1.80	7.74	7.53
ITF64	Vibo Valentia	1731	3.74	12.60	12.03
ITF65	Reggio di Calabria	4285	2.17	8.39	8.10
ITG11	Trapani	3559	1.84	7.17	6.93
ITG12	Palermo	12216	1.32	3.89	3.66
ITG13	Messina	5990	1.75	4.90	4.58
ITG14	Agrigento	4296	1.80	7.28	7.05
ITG15	Caltanissetta	2566	3.74	7.56	6.57
ITG16	Enna	2528	2.25	9.19	8.91
ITG17	Catania	9920	1.77	4.90	4.57
ITG18	Ragusa	3376	3.64	8.13	7.27
ITG19	Siracusa	3745	2.12	6.50	6.14
ITG25	Sassari	2824	3.67	10.91	10.27
ITG26	Nuoro	1700	2.28	8.36	8.04
ITG27	Cagliari	3701	1.45	6.78	6.62
ITG28	Oristano	1522	2.24	5.78	5.33
ITG29	Olbia-Tempio	1981	2.18	11.61	11.40
ITG2A	Ogliastra	1988	3.39	8.10	7.36
ITG2B	Medio Campidano	1746	3.06	6.75	6.02
ITG2C	Carbonia-Iglesias	1512	2.68	10.13	9.77
ITH10	Bolzano/Bozen	13066	0.84	6.68	6.63
ITH20	Trento	13597	0.78	5.32	5.26
ITH31	Verona	3766	1.31	11.80	11.73
ITH32	Vicenza	3892	1.25	10.46	10.39
ITH33	Belluno	4778	1.31	11.38	11.30
ITH34	Treviso	3272	1.52	9.18	9.05
ITH35	Venezia	3500	1.71	8.21	8.03
ITH36	Padova	3874	1.12	8.88	8.81
ITH37	Rovigo	2714	2.22	10.77	10.54
ITH41	Pordenone	3694	1.31	8.63	8.53
ITH42	Udine	6551	1.15	6.77	6.67
ITH43	Gorizia	3676	1.53	9.43	9.31
ITH44	Trieste	3475	0.79	8.06	8.02
ITH51	Piacenza	5278	1.01	9.99	9.94
ITH52	Parma	4721	1.34	8.58	8.47
ITH53	Reggio nell'Emilia	5628	1.80	8.35	8.15
ITH54	Modena	4210	1.36	9.40	9.30
ITH55	Bologna	5254	0.95	8.10	8.04
ITH56	Ferrara	3717	1.52	6.71	6.54
ITH57	Ravenna	3597	1.47	8.64	8.51
ITH58	Forlì-Cesena	3494	1.52	13.96	13.88
ITH59	Rimini	2726	1.69	8.99	8.83
ITI11	Massa-Carrara	1737	2.44	13.88	13.66
ITI12	Lucca	2936	1.68	6.36	6.13
ITI13	Pistoia	2261	1.37	9.47	9.37
ITI14	Firenze	4438	1.29	7.12	7.00
ITI15	Prato	2156	1.20	4.15	3.97
ITI16	Livorno	3009	1.44	10.94	10.84
ITI17	Pisa	3309	1.44	9.11	9.00
ITI18	Arezzo	2944	1.58	9.52	9.39
ITI19	Siena	4270	1.34	9.33	9.23
ITI1A	Grosseto	3215	2.18	9.49	9.24
ITI21	Perugia	9670	0.94	4.91	4.82
ITI22	Terni	3114	1.53	8.18	8.04
ITI31	Pesaro e Urbino	4059	1.37	6.45	6.30
ITI32	Ancona	3654	1.14	8.36	8.28
ITI33	Macerata	3104	1.31	8.48	8.38
ITI34	Ascoli Piceno	2122	1.95	8.55	8.32
ITI35	Fermo	2594	1.62	8.55	8.40
ITI41	Viterbo	3015	1.27	6.89	6.77
ITI42	Rieti	3222	1.55	7.91	7.76
ITI43	Roma	24523	0.61	3.18	3.12
ITI44	<Latina	3963	1.66	8.34	8.17
<ITI45	Frosinone	3283	2.50	11.92	11.65

For the calculation of the CV for NUTS-3 regions, the national design effect can be used as an approximation of the true regional design effect. Please indicate if this approximation is used (Y/N):
N

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)

Under-coverage rate (%)	Over-coverage rate (%)	Misclas-sification rate (%)	Comments: specification and impact on estimates (a)			Reference on frame errors
Under-coverage rate (%)	Over-coverage rate (%)	Misclas-sification rate (%)	Undercoverage	Overcoverage	Misclassification (b)	Reference on frame errors
	2.38	2.01	Households are selected once a year from the municipalities’ registry offices; 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.

6.3.1.1 Over-coverage - rate

[Over-coverage rate, please see chapter 5.3.1 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 reporting units and the interviewers

Give brief comments on the assessment of errors due to:
Reporting unit	Interviewers
UNA	UNA

Errors due to the medium (questionnaire)

Date of the last update of the questionnaire (before the end of the reference period for this report)	Date of the last pilot survey in order to test the questionnaire	Number of respondents to the pilot survey	Report from cognitive laboratory available (Y/N)
January 2014 for Q1, Q3 and Q4 - April 2013 for AHM in Q2	November 2013	1,005 households	N

Main methods of reducing measurement errors

Error source	Brief comments
Respondent	Advance letter to the household; translation of the advance letter and of the questionnaire in 11 languages, including English and German, in order to increase the participation for the non-nationals; telephone free line for the respondents; non-response analysis.
Interviewer	Briefing ad hoc, performance rates, news group, check-phone call on interviewed households
Questionnaire	On-line help, on-line check, personalised questions, test and analyses of output, test of different kinds of question
Other

Methodological notes (main references) on the measurement errors?

Main references

• F. della Ratta-Rinaldi, M. Tibaldi, M. E. Pontecorvo, “Strategie di Text Mining per il controllo e la correzione della codifica dell’attività economica nell’indagine Istat sulle forze di lavoro”, in E. Née, J.M. Daube, M. Valette, S. Fleury, (eds), Actes des 12es Journées internationales d’Analyse statistique des Données Textuelles. Paris: Sorbonne Nouvelle – Inalco, 2014
• 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
• C. Ceccarelli, S. Rosati Data editing for the Italian Labour Force survey, 2005 stical production and technical-scientific coordination Labour Force Survey, 2004
• Metodi e Norme ISTAT vol. 32, 2006 - "La rilevazione sulle forze di lavoro: contenuti, metodologie, organizzazione".

6.3.3 Non response error

Impact of non response: underestimation and overestimation bias of main characteristics

	Underestimation assessment		Overestimation assessment
	Quantitative	Descriptive	Quantitative	Descriptive
Total employment	M?	We have the idea the LFS underestimates irregular employment and the number of persons with second jobs, but we're not able to assess these effects.
Part-time employment	NA
Unemployment	NA
Numbers of hours actually worked	NA
Other characteristic	NA
Other characteristic	NA

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, correction factors for household non-response are computed according to household typologies (number of 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 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.	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. It is useful to reduce the bias effects of unit non-response.

Substitution of non-responding units (Y/N?)	Substitution rate	Criteria for substitution
Y	31% of the first selected households have been substituted: 21% have been substituted with the first substitute unit, 6.5% with the second one, 3.4% 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 technique. 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

Methods used for imputation of statistical item non-response

Characteristic	Imputation rate	Describe method used, mentioning which auxiliary information or stratification is used
The imputation rate expresses the percentage of records in which at least one variable has been imputed; for imputing we mean modification of original values (missing or not)	4% of individual record were imputed (1 variable or more)	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.

References to methodological notes on non response rates and their treatment
CECCARELLI, C., ROSATI, S. (2005). 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.

6.3.3.1 Unit non-response - rate

Calculation of non-response. Annual average
Is the non response rate weighted? (Y/N)	Is the non-response on household level or person level? (H/P)	If weighted, state the definition of the weights
N	H

Rates of non response by survey wave. Annual average
Waves
1	2	3	4	5	6	7	8
26.2	5.7	5.4	4.1	NA	NA	NA	NA

Rates of non response by survey mode. Annual average
Survey
CAPI	CATI	PAPI	CAWI	POSTAL
21.9	4.2	NA	NA	NA

Divisions of non-response into categories. Quarterly data and annual average
Quarter	Non response rate (%)	Refusals (%)	Non-contacts (%)	Other reasons(%)
1	9.88	1.13	7.77	0.98
2	12.39	3.37	8.06	0.95
3	12.86	4.38	7.55	0.93
4	11.96	4.63	6.69	0.65
Annual	11.78	3.39	7.51	0.88

Rates of non response. Annual average
NUTS-2 region (code + name)	Non response rate (%)
ITC1 Piemonte	13.3
ITC2 Valle d’Aosta/Vallée d’Aoste	12.2
ITC3 Liguria	11.0
ITC4 Lombardia	12.1
ITF1 Abruzzo	10.0
ITF2 Molise	9.0
ITF3 Campania	12.5
ITF4 Puglia	10.6
ITF5 Basilicata	9.3
ITF6 Calabria	12.3
ITG1 Sicilia	14.3
ITG2 Sardegna	11.4
ITH1 Provincia Autonoma di Bolzano/ Bozen	10.3
ITH2 Provincia Autonoma di Trento	12.8
ITH3 Veneto	11.0
ITH4 Friuli-Venezia Giulia	12.7
ITH5 Emilia-Romagna	10.8
ITI1 Toscana	9.7
ITI2 Umbria	11.5
ITI3 Marche	10.3
ITI4 Lazio	12.1

Rates of non response. Annual average
NUTS-3 region (code + name)	Non response rate (%)
ITC11 Torino	14.0
ITC12 Vercelli	9.1
ITC13 Biella	12.7
ITC14 Verbano-Cusio-Ossola	13.9
ITC15 Novara	16.5
ITC16 Cuneo	13.8
ITC17 Asti	12.0
ITC18 Alessandria	14.0
ITC20 Valle d’Aosta/Vallée d’Aoste	12.2
ITC31 Imperia	12.3
ITC32 Savona	8.7
ITC33 Genova	13.6
ITC34 La Spezia	7.8
ITC41 Varese	9.4
ITC42 Como	11.0
ITC43 Lecco	8.5
ITC44 Sondrio	10.3
ITC4C Milano	14.8
ITC4D Monza e della Brianza	14.3
ITC46 Bergamo	12.1
ITC47 Brescia	14.4
ITC48 Pavia	13.5
ITC49 Lodi	8.6
ITC4A Cremona	8.0
ITC4B Mantova	11.2
ITF11 L’Aquila	14.4
ITF12 Teramo	7.6
ITF13 Pescara	9.9
ITF14 Chieti	7.9
ITF21 Isernia	12.6
ITF22 Campobasso	7.5
ITF31 Caserta	14.1
ITF32 Benevento	12.5
ITF33 Napoli	11.3
ITF34 Avellino	12.8
ITF35 Salerno	14.1
ITF46 Foggia	7.8
ITF47 Bari	11.7
ITF48 Barletta-Andria-Trani	7.6
ITF43 Taranto	11.3
ITF44 Brindisi	11.7
ITF45 Lecce	11.2
ITF51 Potenza	9.0
ITF52 Matera	10.2
ITF61 Cosenza	12.5
ITF62 Crotone	11.0
ITF63 Catanzaro	10.1
ITF64 Vibo Valentia	10.7
ITF65 Reggio di Calabria	14.2
ITG11 Trapani	10.9
ITG12 Palermo	15.6
ITG13 Messina	16.4
ITG14 Agrigento	17.3
ITG15 Caltanissetta	13.6
ITG16 Enna	9.1
ITG17 Catania	14.6
ITG18 Ragusa	10.6
ITG19 Siracusa	11.9
ITG25 Sassari	9.0
ITG26 Nuoro	12.5
ITG27 Cagliari	12.0
ITG28 Oristano	12.2
ITG29 Olbia-Tempio	13.6
ITG2A Ogliastra	9.2
ITG2B Medio Campidano	11.8
ITG2C Carbonia-Iglesias	12.2
ITH10 Bolzano-Bozen	10.3
ITH20 Trento	12.8
ITH31 Verona	10.4
ITH32 Vicenza	12.9
ITH33 Belluno	10.4
ITH34 Treviso	10.2
ITH35 Venezia	10.9
ITH36 Padova	11.1
ITH37 Rovigo	10.5
ITH41 Pordenone	10.3
ITH42 Udine	15.6
ITH43 Gorizia	13.2
ITH44 Trieste	9.5
ITH51 Piacenza	9.7
ITH52 Parma	11.2
ITH53 Reggio nell’Emilia	8.4
ITH54 Modena	14.9
ITH55 Bologna	11.0
ITH56 Ferrara	7.6
ITH57 Ravenna	14.7
ITH58 Forlì-Cesena	14.1
ITH59 Rimini	5.3
ITI11 Massa-Carrara	13.9
ITI12 Lucca	8.7
ITI13 Pistoia	10.8
ITI14 Firenze	10.6
ITI15 Prato	9.6
ITI16 Livorno	12.5
ITI17 Pisa	11.7
ITI18 Arezzo	9.0
ITI19 Siena	7.7
ITI1A Grosseto	4.2
ITI21 Perugia	10.9
ITI22 Terni	13.0
ITI31 Pesaro e Urbino	9.7
ITI32 Ancona	9.4
ITI33 Macerata	11.4
ITI34 Ascoli Piceno	11.2
ITI35 Fermo	10.3
ITI41 Viterbo	12.2
ITI42 Rieti	8.3
ITI43 Roma	13.1
ITI44 Latina	11.6
ITI45 Frosinone	8.4

Availability and calculation of non-response at NUTS-3 level
Is non response rate available (Y/N)	Is the non response rate weighted? (Y/N)	If weighted, state the definition of the weights
Y	N

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 compulsory / optional	Column	Identifier	Quarter 1	Quarter 2	Quarter 3	Quarter 4	Short comments on reasons for non-available statistics and prospects for future solutions
compulsory	Col_073/74	HWWISH	.	.	16.7	.	The questions on WISHMORE-HWWISH in the IT questionnaire are referred to the wish of working more than the actual number of hours. Most of the item non-responses are due to persons that did not want to work at all in the reference week (code "0" is not available); they are mainly concentrated in the 3rd quarter in which there are summer holidays. A little percentage of item non-responses is due to "don't know" answers.
	Col_101 - Employed	SEEKTYPE	21.9	23.3	18.8	21.9	Item non-responses are due to persons that do not have preferences about an employment as self-employed or employee.
	Col_101 - Not employed	SEEKTYPE	25.8	26.7	28.1	28.2	Item non-responses are due to persons that do not have preferences about an employment as self-employed or employee.

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

6.3.4 Processing error

Information available about data capture errors and the error rates

Info.on data capture errors(Y/N/NA)	Error ratein%	Comments
N

Information available about codification errors and the error rates

Info. on data codification errors (Y/N)	Error rate in %	Comments
N

Information available about editing errors and the error rates

Info. on errors during the editing phase (Y/N)	Error rate in %	Comments
N

Information available about other processing errors and the error rates

Info. on other process errors (Y/N)	Error rate in %	Comments
N

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

[not requested for the LFS quality report]

6.5 Data revision - policy

[not requested for the LFS quality report]

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

[not requested for the LFS quality report]

7.1 Timeliness

7.1.1 Time lag - first result

[not requested for the LFS quality report]

7.1.2 Time lag - final result

[not requested for the LFS quality report]

7.2 Punctuality

7.2.1 Punctuality - delivery and publication

[not requested for the LFS quality report]

8. Coherence and comparability

[not requested for the LFS quality report]

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
Identification of the main job (*)	N
Employment	N
Unemployment	N

Improvements in THIS reference year that have been made on the questionnaire so that it complies with the Twelve Principles
(See Annex 2 of Commission Regulation (EC) No 1897/2000)

Principle	Description of improvement
NA

Improvements in THIS reference year that have been made on the questionnaire so that it accurately transcodes to the EU list of variables.
(See Commission Regulation (EC) No 377/2008)

Variable	Description of improvement
NA

Improvements in THIS reference year that have been made on the questionnaire so that the transmitted data comply with the EU definition of unemployment.
(See Annex 1 of Commission Regulation (EC) No 1897/2000)

Concept	Description of improvement
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	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
coverage (i.e. target population)	NA
legislation	NA
classifications	NA
geographical boundaries	NA

Changes at MEASUREMENT 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)
sampling frame	NA
sample design	NA
rotation pattern	NA
questionnaire	NA
instruction to interviewers	NA
survey mode	NA
weighting scheme	NA
use of auxiliary information	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 counts the number of persons employed, both in regular or irregular jobs, whatever the number of jobs they have. Business statistics’ surveys consider the number of regular jobs i.e. complying with tax and social security legislation and which can therefore be traced by business, institutional and administrative sources.		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.
Total employment by NACE	Economic 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 with 100 workers and more. Further ISTAT’s surveys focus on businesses in the field of farm. In any case, both LFS and business surveys use the same classification of economic activity, referring to NACE
Number of hours worked	LFS collects individual information on the number of hours worked. Business surveys collect information on firm's total amount of hours

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

[not requested for the LFS quality report]

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 2014: LFS (employment): 22,279 thousands; NA (employment): 24,343 thousands; NA (ULA): 23,454 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.
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.
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		Negligible in terms of weekly hours

8.6 Coherence - internal

[not requested for the LFS quality report]

9. Accessibility and clarity

[not requested for the LFS quality report]

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

Possible improvements, compared to the previous situation.

(Y/N):Y

Link to the national web page (national language(s)):http://www.istat.it/it/archivio/8263
http://dati.istat.it/?lang=it
http://siqual.istat.it/SIQual/visualizza.do?id=5000098

Link to the national web page (English):http://dati.istat.it/?lang=en
http://siqual.istat.it/SIQual/lang.do?language=UK

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.