1. A comparison of sample and register based survey: the case of labour market data De Gregorio C., Filipponi D., Martini A., Rocchetti I.

2. Contents Survey(LFS) – ADMIN Strategic issue Previous ESS research Long term innovation process Our purposes Answers and new questions Innovation leverage in several fields

3. Microdata LFS vs. ADMIN Integration: labour input measurement Definition of employment, Regular vs Irregular First: employment status comparison ADMIN wrt: LFS reference week, Employed and Self-employed.

4. Our purposes Managing inconsistencies between LFS and ADMIN Measuring Regular and Irregular employment Assessing Accuracy of LFS and ADMIN (Assumed error models, MSE’s derivation and computation, No considered benchmark ) Estimating ADMIN Over-coverage (precision) Estimating ADMIN Under-coverage (irregular) Estimating LFS Under-coverage (understatement)

5. Our model: LFS sample “True” status REGULAR IRREGULAR NOT EMPLOYED “ADMIN employed” status “LFS employed” status

6. Inconsistencies REGULARIRREGULAR

7. Our model Hypotheses (to simplify) –If LFS employed then employed –If True Regular then ADMIN employed –No LFS Non-response or substitution bias –ADMIN exhaustive and with no error –No problems with record linkage Key estimates –Probability of being truly employed if “ADMIN employed” –Rate & number of LFS false negatives –Probability of being truly employed if “LFS not employed” Assume it’s OK!

8. Compare LFS and ADMIN MSE Error model for LFS employment status (z) given the true employment status (y) Error model for ADMIN employment status (x) and ADMIN under-coverage (irregular employment) ADMIN over-coverage (false employment signal)

9. MSE by domain LFS ADMIN >95% of total MSE - given “true” employment, population and sample size Linear locus of “low impact” on MSE

10. LFS MSE: depends on the probability of under-coverage ADMIN MSE : balance of two opposite errors

11. LFS & ADMIN both have errors LFS has sampling and under-coverage errors Apparently ADMIN performs better, as the sources of errors tend to compensate ADMIN worsens in the domains with higher irregularity rates ADMIN produces higher errors at micro-level For analysis purposes, survey and ADMIN data should be integrated further An efficient usage of exhaustive ADMIN data should count on survey based estimates of actual employment status To conclude