Correcting for non-response bias using socio-economic register data Liisa Larja & Riku Salonen LAMAS WG, 13.-14. June, 2018, Luxembourg

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

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

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

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

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

3 August 2019 Liisa Larja & Riku Salonen

In addition to decreasing of bias, also precision is improved Employed SE Employment rate GREG_demo (sex, age5, NUTS3) 2 506 374 18 538 70.9 0.52 GREG0_demo + unemployment register 2 476 244 16 506 70.0 0.46 GREG1 (demo + education) 2 479 488 18 652 70.1 GREG 2 (demo + origin) 2 493 440 18 662 70.5 GREG 3 (demo + urbanity) 2 503 338 70.8 GREG 7 (demo + unemployment register + education) 2 459 777 16 424 69.5 0.45 GREG 8 (demo + unempl register + education + origins + urbanity) 2 453 760 16 673 69.4 3 August 2019 Liisa Larja & Riku Salonen

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