1. Correcting for non-response bias using socio-economic register data
Liisa Larja & Riku Salonen
LFS Workshop, May, 2018, Reykjavik

2. The problem of the growing non-response
18 November 2018
Liisa Larja & Riku Salonen

3. Non-response is often correlated with the labour market status
18 November 2018
Liisa Larja & Riku Salonen

4. Effect of the bias: tertiary attainment in 30-34-years population
18 November 2018
Liisa Larja & Riku Salonen

5. Methodology:
Seven different weighting schemes were constructed as follows:
GREG_base: sex, 5-year age group and region (20 areas based on NUTS3)
GREG_current: base + status in the unemployment register
GREG1: base + level of education
GREG2: base + origin
GREG3: base + urban/rural
GREG7: current + level of education
GREG8: current + level of education + origins + urban/rural
18 November 2018
Liisa Larja & Riku Salonen

6. Calibraton process Population frame  sample Age 15-24: 15 %
25-54: 50 %
55-74: 34 %
Status in the job-seeker register
Job-seeker: 7 %
ILOSTAT:
n/a
Response set  calibration to match the population frame
Age
15-24: 13 %
25-54: 49 %
55-74: 37 %
Status in the job-seeker register
Job-seeker: 6 %
ILOSTAT:
Employed: 59%
Unemployed 5 %
Not in the labour force 36 %
Estimates:
Age
15-24: 15 %
25-54: 50 %
55-74: 34 %
Status in the job-seeker register
Job-seeker: 7 %
ILOSTAT:
Employed: 60 %
Unemployed 6 %
Not in the labour force 34 %
18 November 2018
Liisa Larja & Riku Salonen

7. 18 November 2018
Liisa Larja & Riku Salonen

8. 18 November 2018
Liisa Larja & Riku Salonen

9. 18 November 2018
Liisa Larja & Riku Salonen

10. Discussion
Using appropriate socio-economic auxiliary data in the estimation process may significantly improve estimation by correcting the bias caused by non-response and by improving the precision of the estimates.
As compared to the models using only demographic auxiliary data, our results on estimates using auxiliary socio-economic data show bias as large as 1,5 percentage points in the employment rate.
Further tests remain to be done
income, student status, age of the youngest child?
other indicators: NEET, working time, etc.
subpopulations: men/women, youth, elderly, foreign-born, highly/least educated, etc
What kinds of experiences do you have on using other than demographic auxiliary data?
Ruotsalaisilta kannattaa pyytää ensin kommenttia, miksi he eivät ole ottaneet mukaan koulutusta kalibrointimuuttujaksi, vaikka ovat asiaa testanneet?
tanskalaisilta, millaisia kokemuksia heillä on sen käytöstä?
18 November 2018
Liisa Larja & Riku Salonen