regression composite estimation for the finnish lfs from a practical perspective

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


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


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


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


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.


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.


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.


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).


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.


Control totals of auxiliary variables

Population control totalsAssumed to be population values

Composite control totalsEstimated control totals


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).


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)


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


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


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%)


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


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


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


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


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


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


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


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


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.

regression composite estimation for the finnish lfs from a practical perspective

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