Antonio R. Discenza: [email protected] [email protected] Silvia Loriga: [email protected]...

Antonio R. Discenza: [email protected]

Silvia Loriga: [email protected]

Alessandro Martini: [email protected]

ISTAT – Italian National Statistical Institute

Labour Force Survey Division

Rome, May 15-16, 2014Rome, May 15-16, 2014

8th Workshop on LFS Methodology

The Italian Labour Force SurveyThe Italian Labour Force Surveyconsistency frameworkconsistency framework

99thth Workshop on LFS Methodology Workshop on LFS Methodology

It is designed as a quarterly survey, all information obtained by interview, with no use of “Wave approach”.

Space-time allocation in order to produce direct monthly estimates of the main figures

Overview of the Italian LFS

Municipalities

Municipality 1

Municipality 2

Municipality 3

Municipality 4

Municipality 5

Municipality 6

Municipality 7

Municipality 8

Municipality 9

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Municipalities

Municipality 1

Municipality 2

Municipality 3

Municipality 4

Municipality 5

Municipality 6

Municipality 7

Municipality 8

Municipality 9

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01 02 03 04 05 06 07 08 09 10 11 12 13

FEBRUARY MARCH

QUARTER 1

JANUARY

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StrataSR / NSR

Stratum 1 SR

Stratum 2 SR

Stratum 3 SR

Stratum 4 SR

Stratum 5 SR

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Stratum 6 NSR

Monthly figures at national level (both SA and NSA series)

Quarterly figures up to the 21 NUTS_2 “ regions” (both SA and NSA series) and micro-data

Yearly figures up to the 110 NUTS_3 “provinces” and 13 larger Municipalities, as “direct estimates”, and micro-data

Yearly figures of employment and unemployment for the 686 Local Labour Market Areas, as small-area-estimates

Yearly figures by the households perspective

Quarter-on-quarter flow estimates and longitudinal micro-data

Year-on-year flows estimates and longitudinal micro-data

IT-LFS assures full consistency between figures and micro-data using calibration estimators and other benchmarking techniques.

Dissemination of results

External information on reference population

For the consistency framework of IT-LFS and its timeliness, a fundamental role is played by the auxiliary information updated on monthly bases, by the Demographic Division, for weighting purposes:

resident population in each Municipality

by sex, age and citizenship (Nationals/Non-Nationals).

13332211 mpsenmmpsenmmpsenm

psenq

zPzPzPP

psenmPA monthly population is used for monthly estimates

A weighted average of the monthly population is used for monthly estimates

Is the number of weeks in the month (4 or 5)mz

The quarterly weighting procedure

• A Generalized calibration estimator has been adopted in order to improve the accuracy of the estimates

• Final weights are obtained in three steps:

the base weights are obtained for all selected households as the inverse of the probability of inclusion in the sample;

the base weights are adjusted by a correction factor for total non-response worked out as the reciprocal of the response ratio for sub-groups of households;

final weights are obtained applying a calibration estimator that assures that the sample replies the same structure as the population, with regard to the several constraints.

Calibration to the reference population

Is obtained using constraints from several external sources

– population by sex and fourteen 5-year age groups (0-14, 15-19, …, 70-74, 75 and more years) at NUTS_2 level;

– non-national population (males, females, EU, Not EU) at NUTS_2 level;

– population by sex and five age groups (0-14, 15-29, 30-49, 50-64, 65 and more years) at NUTS_3 level

– population by sex and five age groups (0-14, 15-29, 30-49, 50-64, 65 and more years) for 13 large municipalities (> 250.000 inhabitants)

– number of households at NUTS_2 level for each rotation group;

– population by sex at NUTS_2 level each of the three months of the quarter (representing 4/13, 4/13, 5/13 of the whole quarter)

Monthly constraints and monthly weights

• The weighting procedure provides fully consistent monthly and quarterly weights.

• Monthly estimates could be directly obtained using the monthly sample and its monthly weights

mjmj zww

13,

Problems:

•These estimates are only available at the end of the quarter when all the interviews have been completed and quarterly weights have been computed;

•Time series showed a very high variability.

Monthly direct estimates were never published.

MONTHLY

ESTIMATES

• For few years Istat studied the possibility to improve timeliness and quality of the monthly estimates.

• It was found that a Regression Composite Estimator would have suited the purpose:– it is a design based estimator, purely based on LFS data, – and exploits the longitudinal dimension of the sample to

produce more robust estimate)

Provisional and final monthly estimates

Q1_Y1 Q2_Y1 Q3_Y1 Q4_Y1 Q1_Y2 Q2_Y2

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun

• After evaluating the results and tuning the model for a long period, monthly estimates where finally disseminated in 2009.

• The framework: monthly estimates are disseminated as Provisional (timely and as Final at a later stage

Data are first disseminated on a provisional basis, about 30 days after each reference month, computed over a partial sample (the fieldwork is not completed yet).

• Press release on monthly unemployment the same day as Eurostat, focused on Seasonal Adjusted (SA) data;

• Simultaneously, monthly data (both SA and Not SA) are made available on Istat data warehouse (I.Stat)

The production process starts about 22 days after the end of the reference month.

Provisional monthly estimates

La produzione di stime mensili04

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W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13

Quarter 2, 2011April May June

Fieldwork of April

Fieldwork of May

Fieldwork of June

Provisional monthly data production timetable

Preliminary check and weighting (2 days)

Seasonal adjustments (2 days)

Press release (3 days) and Eurostat file







Step 1: a calibration to apply the regression composite estimator

Step 2: the seasonal adjustment of the estimates:

First a univariate seasonal adjustment;

Then a time series reconciliation procedure in two steps to ensure consistency between different aggregates and the total population, and between monthly and quarterly SA series.

Procedure based on a dual system of constraints:• contemporary constraints (monthly population by sex and

age groups)• inter-temporal constraints (quarterly SA figures of

Employment, Unemployment, Inactivity; quarterly population by sex and age groups).

The approach of benchmarking is based on the “movement preservation principle” in order to maintain the temporal profile of the original series.

Estimation procedure in two steps

SA Reconciliation Procedure

Source: Q2010 Conference, Assessing quality by means of temporal disaggregation. Riccardo Gatto, Silvia Loriga, Andrea Spizzichino and Alessio Guandalini

Employment figures with three different estimatorsAnother representation: irregular vs. seasonal

Final monthly data are then produced when the corresponding quarterly data are available, that is about 60 days after the reference quarter, for each of the three months

An additional step is added in the estimation procedure: this is a specific calibration step that assures that monthly data are consistent with the quarterly ones ( for the main aggregates, the weighted average of the three monthly figures, with weights equal to 4/13 or 5/13, is equal to the corresponding quarterly figures).

• the constraints are related to both single months (total population by sex and age groups at different levels of geographical detail);

• the quarterly estimates of the main aggregates: employed, unemployed and inactive, by gender and three age groups (15-24, 25-64, 65+)

Framework for dissemination of monthly estimates

CROSS SECTIONAL AND LONGITUDINAL

ESTIMATES

GROSS LABOUR MARKET FLOWSQuarterly and Yearly net changes are the final result of a high number of gross flows of different nature and different size

Definition of a reference longitudinal population

The longitudinal micro-data files constitute a “by-product” of the survey itself;

LFS is not a “real” panel survey (the longitudinal sample has no information on persons which move out of the selected households, or household which move out of the municipality)

Longitudinal estimates can refer only to a specific longitudinal reference population

Longitudinal Weights should:

reflect the longitudinal population,

account for the panel attrition (usually not at random),

ensure consistency with the other quarterly estimates.

Reference longitudinal population and weights

pseqpseqpseqpseqpseqpseq PincmP22,12,12,12,11

pseqpseqpseqpsel cmPP2,12,112,1

The longitudinal population in the IT-LFS is defined as: the population which is resident in the same municipality for the entire 3 or 12 months period, excluding • Deaths; those who have moved to other Italian municipalities

(change of residence); Migrants to other countries

It is fully consistent with the quarterly reference populations, given the general population equation

the longitudinal population is

A multi-step calibration procedure is used compute longitudinal weights, which produce results which are also consistent with quarterly cross-sectional populations and figures

This approach allow us to produce several kind of longitudinal estimates of gross flows and transition rates, assuring consistency of a large number of stock/flow results, by sex and age groups, and at NUTS2 and NUTS3 level.

It is straightforward to calculate:

quarterly flows: from one quarter to the subsequent one (3 months , quarter-on-quarter overlap);

yearly flows: from one quarter to the same quarter of the subsequent year (12 months, quarter-on-quarter overlap );

average yearly flows: as average of the 4 yearly flows, referring to the 4 quarters of the calendar year (12 months, year-on-year overlap)

append of the yearly longitudinal datasets and their weights divided by four.

flow estimates are consistent with yearly cross-sectional estimates (annual averages) for the 2 consecutive years.

more detailed analysis at regional level and for subgroups

longitudinal micro-data and transition matrices

GESIS – Mannheim, 5 – 6 march 2009

Complete Matrix with net and gross flows. Quarter 2 2001 –

Quarter 2 2002. (Thousands)

63

3

478

544

Deaths

565

101

456

1.123

People Leaving theMunicipalities

Employed 19.543 305 898 20.745

Unemployed 440 1.168 559 2.167

Inactive 858 666 22.963 24.488

Total 20.841 2.139 24.420 47.399

Total

Labour Status at 2002Q2

InactiveLongitudinal Population Employed Unemployed

La

bo

ur

Sta

tus

at

20

01

Q2

Employed

Unemployed

Inactive

TotalLa

bo

ur

Sta

tus

at

20

01

Q2

21.373

2.271

25.422

49.066

Population aged 15+ 2001Q2


Employed Unemployed Inactive Total

28 7 521 556 Children aged 15

21.757 2.209 25.246 49.213 Population aged 15+ 2002Q2

888 64 306 1.257 People Entering the Municipalities


Complete Matrix with net and gross flows. Quarter 2 2001 –

Quarter 2 2002. (Thousands) Deaths

63

3

478

544

Deaths


565

101

456

1.123


Population aged > 14 2001Q2

21.373

2.271

25.422

49.066

Population aged > 14 2001Q2



21.757 2.209 25.246 49.213 Population aged > 14 2002Q2

Net change in employment

+384



Employed 19.543 305 898 20.745

Unemployed 440 1.168 559 2.167

Inactive 858 666 22.963 24.488

Total 20.841 2.139 24.420 47.399


La

bo

ur

Sta

tus

at

20

01

Q2

Net change due toMigratory flows

+ 323

Net change due to Demographic flows

- 35

Net change due to Longitudinal Population

+ 96




Employed 19.543 305 898 20.745

Unemployed 440 1.168 559 2.167

Inactive 858 666 22.963 24.488

Total 20.841 2.139 24.420 47.399


La

bo

ur

Sta

tus

at

20

01

Q2

Net change+96

Leaving employment

1.203

Entering employment

1.298about 2.500 movements

Persistence in employment

Transition Matrix for longitudinal population. Quarter 2 2001 – Quarter 2 2002. (Thousands)

It is worth to have this consistency ?

The use of this methodological approach requires the availability of data on longitudinal population of good quality and details, and this is the case for Istat. It would be interesting to study the possibility to use it in other countries, or at European level.

What could be the limitations or the advantages of this method in countries with different survey design which sample dwellings, with area sample, etc.

Points of discussion

about consistency between stock and flows

A brief exercise

on

WAVE APPROACH

IT-LFS never used wave approach. All the variables are collected, in all quarters, on the whole sample.

We have the possibility to simulate a wave approach on past data and compare results with the annual averages already disseminated.

We assumed that some of the structural variables were observed only on the first waves of the 4 quarters

This exercises has been conducted to evaluate the impact of the introduction wave approach on:

estimation procedures

in terms of coherence/consistency between yearly estimates (from sub-sample) and annual averages (from the full-sample)

A brief exercise on Wave Approach

Quarter 3 2011 A3 D2 E1

Quarter 4 2011 A4 B3 E2 F1

Quarter 1 2012 B4 C3 F2 G1

Quarter 2 2012 C4 D3 G2 H1

Quarter 3 2012 D4 E3 H2 I1

Quarter 4 2012 E4 F3 I2 L1

Quarter 1 2013 F4 G3 L2

Quarter 2 2013 G4 H3

ROTATION GROUPREFERENCE

PERIOD SUB-SAMPLE STRUCTURAL VARIABLES

Rotational pattern, full and sub samples

the sub-sample has the same theoretical sample size of a quarterly sample.

We have reweighted the sub-sample benchmarking to the averages of the 4 quarters (from the full-samples) to get consistency with annual averages.

sets “conditions for the use of a sub-sample for the collection of data on structural variables”

It states that:

“Consistency between annual sub-sample totals and full-sample annual averages shall be ensured for employment, unemployment and inactive population by sex and for the following age groups: 15 to 24, 25 to 34, 35 to 44, 45 to 54, 55 +”

“The sample used to collect information on ad hoc modules shall also provide information on structural variables”.

Commission Regulation (EC) No 377/2008

Considering that:

-the sub-sample has to be used for the actual ad-hoc modules and future Supplementary Annual Modules (we want to possibility to analyse regional differences)

-It is important to take into account also the differences between the theoretical and the actual sample in terms of distribution over time and space (to compensate for a possible different total-non-response in different quarters and different regions).

-the higher is the total non-response and the bias in the different waves or quarters, the higher is the risk of inconsistencies between the two kinds of annual averages

Conditions for weighting the sub-sample

- Some yearly variables in the sub-sample could be strictly correlated with those collected quarterly, not only with ILO status.

- If the sub-sample is biased with respect to those quarterly variable then the estimate of the yearly variable could be biased.

- For example, “income”, “second job” and “looking for another job” are probably correlated with STAPRO, FTPT, TEMP, NACE, ISCO.

Under these conditions, is the minimum set of requirements in the regulation 377/2008

sufficient to achieve coherent results, and to produce unbiased yearly estimates?

Weighting the sub-sample:

Conditions in the regulation do not seem sufficient to us

Several sets of Final Weights have been obtained:

Using calibrator estimators,

Starting from the quarterly weights,

with several different sets of constraints (SoC)

Annual distribution of the reference population by sex, age, region and citizenship (similar to quarterly weights)

Annual averages of several main variables correlated with the structural variables

For each SoC all constraints are contemporary

defined at NUTS 2 level.

Different sets of constraints (SoC)


SoC_1: Only the minimum set of constraints in the regulation 377/2008, but at NUTS2 level.

SoC_2: The same constraints on the populations as in the regular quarterly weights; not those in the 377/2008 regarding labour status

SoC_3: The same constraints on the populations as in the quarterly weights; plus WSTATOR by sex and broad age groups (the same traditionally used at NUTS3 level)

SoC_6: The same constraints on the populations as in the quarterly weights; plus WSTATOR by sex and broad age groups (the same traditionally used at NUTS3 level); plus STAPRO (employee/self-employed), FTPT, TEMP, NACE (3 groups), ISCO (3 groups).

SoC_7: The same constraints on the populations as in the quarterly weights; plus labour status by sex and age groups (the same traditionally used at NUTS3 level); plus STAPRO (employee/self-employed), FTPT, TEMP, NACE (3 groups), ISCO (3 groups); plus population 15 and over, by sex and labour status, by quarter.


SoC_1: SoC_2: SoC_3: SoC_6: SoC_7:01 10.1 11.0 11.2 11.1 11.3 11.302 9.9 9.8 9.9 9.9 10.0 10.003 11.4 11.8 11.9 11.9 11.9 11.804 8.8 8.8 8.8 8.8 8.7 8.705 11.2 11.5 11.4 11.4 11.4 11.406 9.8 9.6 9.5 9.6 9.5 9.507 8.9 8.5 8.5 8.5 8.5 8.508 9.5 8.9 8.9 8.9 8.8 8.809 10.5 10.0 9.9 9.9 9.8 9.810 10.1 10.1 10.1 10.1 10.0 10.0

INCDECIL Full-SampleSub-Sample

Table 1 – INCDECIL: Annual averages obtained from the full sample and the sub-sample using different sets of constraints. Year 2012. (Percentages)

For INCDECIL the sub-sample provides higher relative frequencies for lower monthly pay than the full-sample, especially for the first decile. The opposite happens for higher monthly pay. The differences became bigger in Soc_7 where constraints are put on the characteristics of the employment also.


Table 2 – MAINSTAT: Annual averages obtained from the full sample and the sub-sample using different sets of constraints. Year 2012. (Absolute values, Percentages)

For MAINSTAT (see Table 2), the sub-sample provides a lower number of employed (about 100 thousands) and a higher number of unemployed than the full-sample (100 thousands). The greater difference occur with Soc_2, where no constraints are put on labour statuses. No much difference between the other SoC’s.

SoC_1: SoC_2: SoC_3: SoC_6: SoC_7:Employed 22,455 22,345 22,273 22,343 22,331 22,329Unemployed 5,194 5,320 5,566 5,314 5,321 5,319Pupil, student 4,330 4,270 4,210 4,272 4,271 4,273In retirement 10,624 10,542 10,478 10,517 10,516 10,516Fulfilling domestic tasks 7,885 7,861 7,807 7,869 7,880 7,882Others 1,508 1,658 1,662 1,680 1,676 1,676% Employed 43.2 43.0 42.8 43.0 42.9 42.9% Unemployed 10.0 10.2 10.7 10.2 10.2 10.2% Pupil, student 8.3 8.2 8.1 8.2 8.2 8.2% In retirement 20.4 20.3 20.2 20.2 20.2 20.2% Fulfilling domestic tasks 15.2 15.1 15.0 15.1 15.2 15.2% Others 2.9 3.2 3.2 3.2 3.2 3.2

MAINSTAT Full-SampleSub-Sample

Different sets of constraints (SoC)Table 3 – EXIST2J-STAPRO2J-NACE2D2J-HWACTUA2: Annual averages obtained from the full sample and the sub-sample using different sets of constraints. Year 2012. (Absolute values, Percentages, averages)

SoC_1: SoC_2: SoC_3: SoC_6: SoC_7:Employed with a second job 331 434 433 436 435 435- Employees 142 172 177 177 177 177- Self employed 189 262 257 259 258 258-- Agricolture 16 24 23 23 24 23-- Industry 21 30 30 30 30 29-- Services 295 380 381 383 382 383Number of hours worked 7,781 8,016 8,059 8,116 8,133 8,045% of Employed with a second job 1.4 1.9 1.9 1.9 1.9 1.9% Employees 42.8 39.7 40.8 40.6 40.7 40.7% Self employed 57.2 60.3 59.2 59.4 59.3 59.3% Agricolture 4.7 5.5 5.3 5.3 5.4 5.4% Industry 6.3 7.0 6.9 6.8 6.8 6.7% Services 89.0 87.5 87.9 87.9 87.8 87.9Average Number of hours worked per employee 23.5 18.5 18.6 18.6 18.7 18.5

EXIST2J - STAPRO2J - NACE2J2D - HWACTUA2 Full-SampleSub-Sample

Table 3 shows the results for some of the variables related to the SECOND JOB. The sub-sample provides a much higher number of employed with a second job (+30%), and a much higher incidence (from 1.4% to 1.9%). As consequence, the number of total hours worked is higher (about 20%) providing a much smaller number of hours worked per employees (from 23.5 to 18.6). The estimates are higher for both employees and self-employed, and in all the main NACE sectors. However, the sub-sample tends to reduce the incidence of employees and of the employed in the Service sector, and increase the incidence of self-employed and of the employed in Agriculture and industry.

It is indubitable that a panel attrition exist and that quarterly estimates could be biased. Thus their annual averages could also be biased but have higher precision.

On the other hand, it seems also reasonable that estimates from the sub-sample should be “in principle” less biased than those from the full-sample, but with a lower precision.

An important questions arises:

Is it methodologically correct to benchmark

the sub-sample estimates to the full sample ones if we suspect that

the latter are more biased than the former ?

Points of discussion about the wave approach

Are we sure that the benefits

• of a reduction in respondents burden

are so high that they compensate, or exceed, the much bigger effort needed for

• the continuous management of questionnaires and micro-data,

• the implementation of a more complex methodology?Time series for the structural variables could have breaks

when we introduce wave approach. How to manage this?

What would be the dissemination strategy? (given the new limitations due to the consistency problem)

What kind of yearly indicators can be produced: levels or percentage distributions?

Points of discussion about the wave approach

and VERY MUCH INDEEDfor your

PATIENCE,TOLERANCE,

TENACITY,mental alertness,

physical resistance,great capacity to remain calm .... although ..

THANK YOU FOR YOUR ATTENTION!

European Conference on Quality in Official Statistics – Q2010 3 - 6 May 2010 - Helsinki

49

2

495

547

Deaths

Employed 20.346 353 1.281 21.980

Unemployed 489 449 514 1.452

Inactive 1.260 757 23.131 25.149

Total 22.095 1.559 24.926 48.581

Total


InactiveLongitudinal Population Employed Unemployed

Lab

our

Sta

tus

at

200

7Q1

Net change due to Longitudinal Population flows

+ 115

817

102

377

1.296


22.846

1.556

26.021

50.424

Population aged 15+ 2007Q1



23.170 1.761 25.870 50.801 Population aged 15+ 20087Q1

Net change in cross-sectional employment

+324

Net change due toMigratory flows

+ 258

Net change due to Demographic flows

- 49

Complete Matrix with net and gross flows. Quarter 1 2007 – Quarter 1 2008. (Thousands)

European Conference on Quality in Official Statistics – Q2010 3 - 6 May 2010 - Helsinki

Transition Matrix for longitudinal population. Quarter 1 2007 – Quarter 1 2008. (Thousands)

Employed 20.346 353 1.281 21.980

Unemployed 489 449 514 1.452

Inactive 1.260 757 23.131 25.149

Total 22.095 1.559 24.926 48.581

Total


Inactive

Longitudinal

Population Employed Unemployed

Labo

ur S

tatu

s at

20

07Q

1

Net change+105

Leaving employment

1.634

Entering employment

1.749

Persistence in employment

almost 3.400 movements

Antonio R. Discenza: [email protected] [email protected] Silvia Loriga: [email protected]...

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Transcript of Antonio R. Discenza: [email protected] [email protected] Silvia Loriga: [email protected]...