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Regression composite estimation for the Finnish LFS from a practical perspective. Riku Salonen. Outline. Design of the FI-LFS The idea of RC-estimator Empirical results Conclusions and future work. The FI-LFS. Monthly survey on individuals of the age 15-74 - PowerPoint PPT Presentation
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Riku Salonen
Regression composite estimation for the Finnish LFS from a practical perspective
2LFS Workshop in Rome May 15 - 16, 2014
Outline
Design of the FI-LFS The idea of RC-estimator Empirical results Conclusions and future work
3LFS Workshop in Rome May 15 - 16, 2014
The FI-LFS
Monthly survey on individuals of the age 15-74Sample size is 12 500 divided into 5 waves
It provides monthly, quarterly and annual results Sampling design is stratified systematic sampling
The strata: Mainland Finland and Åland IslandsIn both stratum systematic random selection is applied
to the frame sorted according to the domicile code Implicit geographic stratification
4LFS Workshop in Rome May 15 - 16, 2014
Rotation panel design
Partially overlapping samples Each sample person is in sample 5 times during 15 months The monthly rotation pattern: 1-(2)-1-(2)-1-(5)-1-(2)-1
No month to month overlap60% quarter to quarter theoretical overlap40% year to year theoretical overlap
Independence: monthly samples in each three-month period quarterly sample consists of separate monthly samples
5LFS Workshop in Rome May 15 - 16, 2014
Sample allocation (1) The half-year sample is drawn two times a year
It is allocated into six equal part - one for the next six months
The half-year sample(e.g. Jan-June 2014)
Jan
MarAprMayJune
The monthly sample(e.g. Jan 2014)
Wave (1)Wave (2)Wave (3)Wave (4)Wave (5)
”Sample bank”
Earlier samples
Feb
6LFS Workshop in Rome May 15 - 16, 2014
Sample allocation (2)
The monthly sample is i) divided into five waves
wave (1) come from the half-year sample waves (2) to (5) come from ”sample bank”
ii) distributed uniformly across the weeks of the month (4 or 5 reference weeks)
The quarterly sample (usually 13 reference weeks) consist of three separate and independent monthly samples.
7LFS Workshop in Rome May 15 - 16, 2014
Weighting procedure The weighting procedure (GREG estimator) of the FI-LFS
on monthly level is whole based on quarterly ja annual weighting also.
For this purpose i) the monthly weights need to be divided by three to
create quarterly weights andii) the monthly weights need to be divided by twelve to
create annual weights. This automatically means that monthly, quarterly and
annual estimates are consistent.
8LFS Workshop in Rome May 15 - 16, 2014
The idea of RC-estimator
Extends the current GREG estimator used FI-LFS. To improve the estimate by incorporating information from
previous wave (or waves) of interview. Takes the advantage of correlations over time.
9LFS Workshop in Rome May 15 - 16, 2014
RC estimation procedure The technical details and formulas of the RC estimation
method with application to the FI-LFS are summarized in the workshop paper and in Salonen (2007).
RC estimator introduced by Singh et. al, Fuller et. al and Gambino et. al (2001).
Examined further by Bocci and Beaumont (2005).
10LFS Workshop in Rome May 15 - 16, 2014
RC estimation system implementation
The RC estimator can be implemented within the FI-LFS estimation system by adding control totals and auxiliary variables to the estimation program.
It can be performed by using, with minor modification, standard software for GREG estimation, such as ETOS.
It yields a single set of estimation weights.
11LFS Workshop in Rome May 15 - 16, 2014
Control totals of auxiliary variables
Population control totalsAssumed to be population values
Composite control totalsEstimated control totals
12LFS Workshop in Rome May 15 - 16, 2014
Population control totals Population totals taken from administrative registers
sex (2)age (12)region (20) employment status in Ministry of Labour's job-seeker
register (8) Obs! Weekly balancing of weights on monthly level is also
included in the calibration (4 or 5 reference weeks).
13LFS Workshop in Rome May 15 - 16, 2014
Composite control totals Composite control totals are estimates from the previous
wave of interviewEmployed and unemployed by age/sex groups (8)Employed and unemployed by NUTS2 (8)Employment by Standard Industrial Classification (7)
14LFS Workshop in Rome May 15 - 16, 2014
Table 1. Population and composite control totals for RC estimationvar N mar1 mar2 … mar20region 20 282 759 345 671 … 786 285sex 2 1 995 190 1 994 188 …age group 12 330 875 328 105 …reference week (4 or 5) 4 997 346 997 346 …register-based job-seeker status 8 68 429 115 171 …Z_emp1 0 161 128:Z_emp4 0 1 050 431Z_une1 0 17 846: COMPOSITEZ_une4 0 67 283 CONTROLZ_nace1 0 115 624 TOTALS:Z_nace7 0 804 126Z_nuts1 0 1 338 271:Z_nuts8 0 22 555
15LFS Workshop in Rome May 15 - 16, 2014
Composite auxiliary variables
Overlapping part of the sampleVariables are taken from the previous wave of interview
Non-overlapping part of the sampleThe values of variables are imputed
16LFS Workshop in Rome May 15 - 16, 2014
Example 1. Overlapping
January 2014Wave 1Wave 2Wave 3Wave 4Wave 5
Previous interviewnone
October 2013October 2013(July 2013)
October 2013
Dependence: Theoretical overlap wave-to-wave is 4/5 (80%)
17LFS Workshop in Rome May 15 - 16, 2014
Empirical results We have compared the RC estimator to the GREG
estimator in the FI-LFS real data (2006-2010) Here we have used the ETOS program for point and
variance estimation (Taylor linearisation method). Relative efficiency (RE) can be formulated as
A value of RE greater than 100 indicates that the RC estimator is more efficient than the GREG estimator.
yrc
ygr
tVtV
REˆˆ
100ˆˆ
18LFS Workshop in Rome May 15 - 16, 2014
Table 2. Distribution of calibrated weights for GREG and RC estimators (e.g 2nd quarter of 2006)
The calibrated weights are obtained by the ETOS program. The results show that the variation of the RC weights is smaller than that of the GREG weights.
GREG RCStatistics forcalibrated weights
Minimum 42.81 50.66Maximum 416.29 221.87Average 137.69 137.69Median 135.12 137.15
19LFS Workshop in Rome May 15 - 16, 2014
Table 3. Relative efficiency (RE, %) of estimates for the quarterly level of employment and unemployment by sex
Quarterly level estimatesLabour force status Sex RE (%)
Employed Male 184,8Female 178,6Both sexes 173,5
Unemployed Male 114,2Female 110,3Both sexes 106,4
20LFS Workshop in Rome May 15 - 16, 2014
Table 4. Relative efficiency (RE, %) of estimates for the monthly level of employment and unemployment by industrial classification
Monthly level estimatesNACE Sample size RE (%)
Agriculture 251 395,6Manufacturing 1 075 461,4Construction 375 373,6Wholesale and retail trade 899 318,3Transport, storage and communication 390 365,4Financial intermediation 802 398,3Public administration 1 865 361,1
21LFS Workshop in Rome May 15 - 16, 2014
Table 5. Relative efficiency (RE, %) of estimates for the quarterly level of employment and unemployment by industrial classification
Quarterly level estimatesNACE Sample size RE (%)
Agriculture 788 358,2Manufacturing 3 287 406,6Construction 1 098 375,7Wholesale and retail trade 2 698 375,6Transport, storage and communication 1 216 369,1Financial intermediation 2 403 370,8Public administration 5 951 361,3
22LFS Workshop in Rome May 15 - 16, 2014
Conclusions (1)
For the variables that were included as composite control totals, there are substantial gains in efficiency for estimates
For some variables it is future possible to publish monthly estimates where only quarterly estimates are published now?
Leading to internal consistency of estimatesEmployment + Unemployment = Labour ForceLabour Force + Not In Labour Force = Population 15 to 74
23LFS Workshop in Rome May 15 - 16, 2014
Conclusions (2)
It can be performed by using, with minor modification, standard software for GREG estimation, such as ETOS
It yields a single set of estimation weights The results are well comparable with results reported from
other countriesChen and Liu (2002): the Canadian LFSBell (2001): the Australian LFS
24LFS Workshop in Rome May 15 - 16, 2014
Future work
Analysis of potential imputation methods for the non-overlapping part of the sample?
Analysis of alternative variance estimators (Dever and Valliant, 2010)?
Incorporating information from all potential previous waves of interview
25LFS Workshop in Rome May 15 - 16, 2014
MAIN REFERENCESBEAUMONT, J.-F. and BOCCI, C. (2005). A Refinement of the Regression Composite
Estimator in the Labour Force Survey for Change Estimates. SSC Annual Meeting, Proceedings of the Survey Methods Section, June 2005.
CHEN, E.J. and LIU, T.P. (2002). Choices of Alpha Value in Regression Composite Estimation for the Canadian Labour Force Survey: Impacts and Evaluation. Methodology Branch Working Paper, HSMD-2002-005E, Statistics Canada.
DEVER, A.D., and VALLIANT, R. (2010). A Comparison of Variance Estimators for Poststratification to Estimated Control Totals. Survey Methodology, 36, 45-56.
FULLER, W.A., and RAO, J.N.K. (2001). A Regression Composite Estimator with Application to the Canadian Labour Force Survey. Survey Methodology, 27, 45-51.
GAMBINO, J., KENNEDY, B., and SINGH, M.P. (2001). Regression Composite Estimation for the Canadian Labour Force Survey: Evaluation ja Implementation. Survey Methodology, 27, 65-74.
SALONEN, R. (2007). Regression Composite Estimation with Application to the Finnish Labour Force Survey. Statistics in Transition, 8, 503-517.
SINGH, A.C., KENNEDY, B., and WU, S. (2001). Regression Composite Estimation for the Canadian Labour Force Survey with a Rotating Panel Design. Survey Methodology, 27, 33-44.