The document discusses using a regression composite (RC) estimation method to improve estimates from Finland's Labor Force Survey (FI-LFS) by incorporating information from previous survey waves. Empirical results show the RC method provides substantial gains in efficiency over the current GREG estimator, with relative efficiencies over 100% for many estimates. The RC method can be implemented with standard software and yields consistent monthly, quarterly, and annual estimates while improving precision of some estimates. Future work may analyze imputation methods and alternative variance estimators to further enhance the RC approach for the FI-LFS.

1. Riku Salonen
Regression composite estimation for the
Finnish LFS from a practical perspective

2. May 15 - 16, 2014 2LFS Workshop in Rome
Outline
 Design of the FI-LFS
 The idea of RC-estimator
 Empirical results
 Conclusions and future work

3. May 15 - 16, 2014 3LFS Workshop in Rome
The FI-LFS
 Monthly survey on individuals of the age 15-74
 Sample 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 Islands
 In both stratum systematic random selection is applied
to the frame sorted according to the domicile code
Implicit geographic stratification

4. May 15 - 16, 2014 4LFS Workshop in Rome
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 overlap
 60% quarter to quarter theoretical overlap
 40% year to year theoretical overlap
 Independence: monthly samples in each three-month
period quarterly sample consists of separate monthly
samples

5. May 15 - 16, 2014 5LFS Workshop in Rome
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
Mar
Apr
May
June
The monthly sample
(e.g. Jan 2014)
Wave (1)
Wave (2)
Wave (3)
Wave (4)
Wave (5)
”Sample bank”
Earlier
samples
Feb

6. May 15 - 16, 2014 6LFS Workshop in Rome
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.

7. May 15 - 16, 2014 7LFS Workshop in Rome
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 and
ii) the monthly weights need to be divided by twelve to
create annual weights.
 This automatically means that monthly, quarterly and
annual estimates are consistent.

8. May 15 - 16, 2014 8LFS Workshop in Rome
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.

9. May 15 - 16, 2014 9LFS Workshop in Rome
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).

10. May 15 - 16, 2014 10LFS Workshop in Rome
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.

11. May 15 - 16, 2014 11LFS Workshop in Rome
Control totals of auxiliary variables
 Population control totals
 Assumed to be population values
 Composite control totals
 Estimated control totals

12. May 15 - 16, 2014 12LFS Workshop in Rome
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).

13. May 15 - 16, 2014 13LFS Workshop in Rome
Composite control totals
 Composite control totals are estimates from the previous
wave of interview
 Employed and unemployed by age/sex groups (8)
 Employed and unemployed by NUTS2 (8)
 Employment by Standard Industrial Classification (7)

14. May 15 - 16, 2014 14LFS Workshop in Rome
Table 1. Population and composite control totals for RC estimation
var N mar1 mar2 … mar20
region 20 282 759 345 671 … 786 285
sex 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 1 050 431
Z_une1 0 17 846
: COMPOSITE
Z_une4 0 67 283 CONTROL
Z_nace1 0 115 624 TOTALS
:
Z_nace7 0 804 126
Z_nuts1 0 1 338 271
:
Z_nuts8 0 22 555


15. May 15 - 16, 2014 15LFS Workshop in Rome
Composite auxiliary variables
 Overlapping part of the sample
 Variables are taken from the previous wave of interview
 Non-overlapping part of the sample
 The values of variables are imputed

16. May 15 - 16, 2014 16LFS Workshop in Rome
Example 1. Overlapping
January 2014
Wave 1
Wave 2
Wave 3
Wave 4
Wave 5
Previous interview
none
October 2013
October 2013
(July 2013)
October 2013
Dependence: Theoretical overlap wave-to-wave is 4/5 (80%)

17. May 15 - 16, 2014 17LFS Workshop in Rome
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
tV
tV
RE
ˆˆ
100ˆˆ 


18. May 15 - 16, 2014 18LFS Workshop in Rome
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 for
calibrated weights
Minimum 42.81 50.66
Maximum 416.29 221.87
Average 137.69 137.69
Median 135.12 137.15

19. May 15 - 16, 2014 19LFS Workshop in Rome
Table 3. Relative efficiency (RE, %) of estimates for the quarterly level of
employment and unemployment by sex
Quarterly level estimates
Labour force status Sex RE (%)
Employed Male 184,8
Female 178,6
Both sexes 173,5
Unemployed Male 114,2
Female 110,3
Both sexes 106,4

20. May 15 - 16, 2014 20LFS Workshop in Rome
Table 4. Relative efficiency (RE, %) of estimates for the monthly level of
employment and unemployment by industrial classification
Monthly level estimates
NACE Sample size RE (%)
Agriculture 251 395,6
Manufacturing 1 075 461,4
Construction 375 373,6
Wholesale and retail trade 899 318,3
Transport, storage and communication 390 365,4
Financial intermediation 802 398,3
Public administration 1 865 361,1

21. May 15 - 16, 2014 21LFS Workshop in Rome
Table 5. Relative efficiency (RE, %) of estimates for the quarterly level of
employment and unemployment by industrial classification
Quarterly level estimates
NACE Sample size RE (%)
Agriculture 788 358,2
Manufacturing 3 287 406,6
Construction 1 098 375,7
Wholesale and retail trade 2 698 375,6
Transport, storage and communication 1 216 369,1
Financial intermediation 2 403 370,8
Public administration 5 951 361,3

22. May 15 - 16, 2014 22LFS Workshop in Rome
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 estimates
 Employment + Unemployment = Labour Force
 Labour Force + Not In Labour Force = Population 15 to 74

23. May 15 - 16, 2014 23LFS Workshop in Rome
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 countries
 Chen and Liu (2002): the Canadian LFS
 Bell (2001): the Australian LFS

24. May 15 - 16, 2014 24LFS Workshop in Rome
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

25. May 15 - 16, 2014 25LFS Workshop in Rome
MAIN REFERENCES
BEAUMONT, 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.