A. R. Discenza, S. Loriga, A. Martini - The Italian Labour Force Survey consistency framework

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9th Workshop on Labour Force Survey Methodology 15-16 maggio

9th Workshop on Labour Force Survey Methodology 15-16 maggio

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  • Contiene il complesso degli stock di inizio e fine periodo , tutti i flussi naturali e mogratori , e i flussi tra condizioni per la popolazione longitudinale
  • Contiene il complesso degli stock di inizio e fine periodo , tutti i flussi naturali e mogratori , e i flussi tra condizioni per la popolazione longitudinale
  • Contiene il complesso degli stock di inizio e fine periodo , tutti i flussi naturali e mogratori , e i flussi tra condizioni per la popolazione longitudinale

Transcript

  • 1. Antonio R. Discenza: discenza@istat.it Silvia Loriga: siloriga@istat.it Alessandro Martini: alemartini@istat.it 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 Survey consistency frameworkconsistency framework 99thth Workshop on LFS MethodologyWorkshop on LFS Methodology
  • 2. 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 . . Municipalities Municipality 1 Municipality 2 Municipality 3 Municipality 4 Municipality 5 Municipality 6 Municipality 7 Municipality 8 Municipality 9 . . 01 02 03 04 05 06 07 08 09 10 11 12 13 FEBRUARY MARCH QUARTER 1 JANUARY . . SUPER-STRATUM1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Strata SR / NSR Stratum 1 SR Stratum 2 SR Stratum 3 SR Stratum 4 SR Stratum 5 SR . . . . Stratum 6 NSR
  • 3. 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
  • 4. 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). 13 332211 mpsenmmpsenmmpsenm psenq zPzPzP P ⋅+⋅+⋅ = psenm P A 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
  • 5. 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.
  • 6. 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)
  • 7. 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 m jmj z ww 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.
  • 8. MONTHLY ESTIMATES
  • 9. • 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
  • 10. 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
  • 11. mensili04/04/2011- 10/04/2011 11/04/2011- 17/04/2011 18/04/2011- 24/04/2011 25/04/2011- 01/05/2011 02/05/2011- 08/05/2011 09/05/2011- 15/05/2011 16/05/2011- 22/05/2011 23/05/2011- 29/05/2011 30/05/2011- 05/06/2011 06/06/2011- 12/06/2011 13/06/2011- 19/06/2011 20/06/2011- 26/06/2011 27/06/2011- 03/07/2011 11/07/2011 18/07/2011 25/07/2011 01/08/2011 08/08/2011 15/08/2011 22/08/2011 29/08/2011 05/09/2011 12/09/2011 19/09/2011 W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 Quarter 2, 2011 April 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 Preliminary check and weighting (2 days) Seasonal adjustments (2 days) Press release (3 days) and Eurostat file Preliminary check and weighting (2 days) Seasonal adjustments (2 days) Press release (3 days) and Eurostat file
  • 12. 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
  • 13. SA Reconciliation Procedure
  • 14. Source: Q2010 Conference, Assessing quality by means of temporal disaggregation. Riccardo Gatto, Silvia Loriga, Andrea Spizzichino and Alessio Guandalini Employment figures with three different estimators Another representation: irregular vs. seasonal
  • 15. 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
  • 16. CROSS SECTIONAL AND LONGITUDINAL ESTIMATES
  • 17. GROSS LABOUR MARKET FLOWS Quarterly and Yearly net changes are the final result of a high number of gross flows of different nature and different size
  • 18. 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.
  • 19. Reference longitudinal population and weights pseqpseqpseqpseqpseqpseq PincmP 22,12,12,12,11 =++−− pseqpseqpseqpsel cmPP 2,12,112,1 −−= 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
  • 20.  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
  • 21. GESIS – 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 the Municipalities 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 Inactive Longitudinal Population Employed Unemployed LabourStatusat 2001Q2 Employed Unemployed Inactive Total LabourStatusat 2001Q2 21.373 2.271 25.422 49.066 Population aged 15+ 2001Q2 Labour Status at 2002Q2 Employed Unemployed Inactive Total 28 7 521 556Children aged 15 21.757 2.209 25.246 49.213 Population aged 15+ 2002Q2 888 64 306 1.257 People Entering the Municipalities
  • 22. GESIS – Complete Matrix with net and gross flows. Quarter 2 2001 – Quarter 2 2002. (Thousands)Deaths 63 3 478 544 Deaths People Leaving the Municipalities 565 101 456 1.123 People Leaving the Municipalities Population aged > 14 2001Q2 21.373 2.271 25.422 49.066 Population aged > 14 2001Q2 28 7 521 556Children aged 15 888 64 306 1.257 People Entering the Municipalities 21.757 2.209 25.246 49.213 Population aged > 14 2002Q2 Net change in employment +384 Labour Status at 2002Q2 Employed Unemployed Inactive Total 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 Employed Unemployed Inactive Total LabourStatusat 2001Q2 Net change due to Migratory flows + 323 Net change due to Demographic flows - 35 Net change due to Longitudinal Population + 96
  • 23. GESIS – Labour Status at 2002Q2 Employed Unemployed Inactive Total 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 Employed Unemployed Inactive Total LabourStatusat 2001Q2 Net change +96 Leaving employment 1.203 Entering employment 1.298 about 2.500 movements Persistence in employment Transition Matrix for longitudinal population. Quarter 2 2001 – Quarter 2 2002. (Thousands)
  • 24. 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
  • 25. A brief exercise on WAVE APPROACH
  • 26. 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
  • 27. 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 GROUP REFERENCE PERIOD SUB-SAMPLE STRUCTURA L 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.
  • 28. 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
  • 29. 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
  • 30. - 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:
  • 31. 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)
  • 32. 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.
  • 33. Different sets of constraints (SoC) SoC_1: SoC_2: SoC_3: SoC_6: SoC_7: 01 10.1 11.0 11.2 11.1 11.3 11.3 02 9.9 9.8 9.9 9.9 10.0 10.0 03 11.4 11.8 11.9 11.9 11.9 11.8 04 8.8 8.8 8.8 8.8 8.7 8.7 05 11.2 11.5 11.4 11.4 11.4 11.4 06 9.8 9.6 9.5 9.6 9.5 9.5 07 8.9 8.5 8.5 8.5 8.5 8.5 08 9.5 8.9 8.9 8.9 8.8 8.8 09 10.5 10.0 9.9 9.9 9.8 9.8 10 10.1 10.1 10.1 10.1 10.0 10.0 INCDECIL Full-Sample Sub-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.
  • 34. Different sets of constraints (SoC) 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 22,329 Unemployed 5,194 5,320 5,566 5,314 5,321 5,319 Pupil, student 4,330 4,270 4,210 4,272 4,271 4,273 In retirement 10,624 10,542 10,478 10,517 10,516 10,516 Fulfilling domestic tasks 7,885 7,861 7,807 7,869 7,880 7,882 Others 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 domestictasks 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-Sample Sub-Sample
  • 35. 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 295 380 381 383 382 383 Number 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 87.9 Average Number of hours worked per employee 23.5 18.5 18.6 18.6 18.7 18.5 EXIST2J - STAPRO2J - NACE2J2D - HWACTUA2 Full-Sample Sub-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
  • 36. 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
  • 37. 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
  • 38. and VERY MUCH INDEED for your PATIENCE, TOLERANCE, TENACITY, mental alertness, physical resistance, great capacity to remain calm .... although .. THANK YOU FOR YOUR ATTENTION!
  • 39. and VERY MUCH INDEED for your PATIENCE, TOLERANCE, TENACITY, mental alertness, physical resistance, great capacity to remain calm .... although .. THANK YOU FOR YOUR ATTENTION!
  • 40. European 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 Labour Status at 2008Q1 Inactive Longitudinal Population Employed Unemployed LabourStatusat 2007Q1 Net change due to Longitudinal Population flows + 115 817 102 377 1.296 People Leaving the Municipalities 22.846 1.556 26.021 50.424 Population aged 15+ 2007Q1 0 0 584 584Children aged 15 1075 202 359 1.636 People Entering the Municipalities 23.170 1.761 25.870 50.801 Population aged 15+ 20087Q1 Net change in cross-sectional employment +324 Net change due to Migratory flows + 258 Net change due to Demographic flows - 49 Complete Matrix with net and gross flows. Quarter 1 2007 – Quarter 1 2008. (Thousands)
  • 41. European 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 Labour Status at 2008Q1 Inactive Longitudinal Population Employed Unemployed LabourStatusat 2007Q1 Net change +105 Leaving employment 1.634 Entering employment 1.749 Persistence in employment almost 3.400 movements