1. Correcting for non-response bias using socio-economic register data
Liisa Larja & Riku Salonen
LAMAS WG, June, 2018, Luxembourg

2. The problem of the growing non-response
3 August 2019
Liisa Larja & Riku Salonen

3. Non-response is often correlated with the labour market status
3 August 2019
Liisa Larja & Riku Salonen

4. Effect of the bias: tertiary attainment in 30-34-years population
3 August 2019
Liisa Larja & Riku Salonen

5. Methodology:
Seven different weighting schemes were constructed as follows:
GREG_demo: sex, 5-year age group and region (20 areas based on NUTS3)
GREG0: demo + status in the unemployment register
GREG1: demo + level of education
GREG2: demo + origin
GREG3: demo + urban/rural
GREG7: demo + status in the unemployment register + level of education
GREG8: demo + status in the unemployment register + level of education + origins + urban/rural
3 August 2019
Liisa Larja & Riku Salonen

6. Calibration 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 %
3 August 2019
Liisa Larja & Riku Salonen

7. 3 August 2019
Liisa Larja & Riku Salonen

8. In addition to decreasing of bias, also precision is improved
Employed SE
Employment rate
GREG_demo (sex, age5, NUTS3)
18 538
70.9
0.52
GREG0_demo + unemployment register
16 506
70.0
0.46
GREG1 (demo + education)
18 652
70.1
GREG 2 (demo + origin)
18 662
70.5
GREG 3 (demo + urbanity)
70.8
GREG 7 (demo + unemployment register + education)
16 424
69.5
0.45
GREG 8 (demo + unempl register + education + origins + urbanity)
16 673
69.4
3 August 2019
Liisa Larja & Riku Salonen

9. 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.
At best, the auxiliary data should correlate with both non-response behaviour and labour market status
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.
Sharing of good practices in adjusting for non-response bias  increased quality and comparability of the figures between countries?
To increase the quality and comparabitliy of the figures, we would like to encourage also other countries to develop their estimation to better adjust for non-response bias.
3 August 2019
Liisa Larja & Riku Salonen