A comparison of sample and register based survey:

the case of labour market data

De Gregorio C., Filipponi D., Martini A., Rocchetti I.

Contents

Survey(LFS) – ADMIN

Strategic issue

Previous ESS research

Long term innovation process

Our purposes

Answers and new questions

Innovation leverage in several fields

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.

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)

Our model: LFS sample

“True” status

REGULAR

IRREGULAR

NOT EMPLOYED

“ADMIN employed” status

“LFS employed” status

Inconsistencies

EMPLOYED Not employed TOTAL

EMPLOYED

Not employed

TOTAL POP >15

LFS

ADMIN

REGULAR IRREGULAR

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!

• Compare LFS and ADMIN MSE• Error model for LFS employment status (z) given

the true employment status (y)

kkkkkk yyyz )1(

)1()1( ,01,10 kkkkk yyx

• Error model for ADMIN employment status (x)

),1Pr( kk and

and

),1Pr( ,10,10 kk ADMIN under-coverage (irregular employment)

)1Pr( ,01,01 kk ADMIN over-coverage (false employment signal)

MSE by domain

N

Y

N

Y

N

N

n

fNY

n

NYtMSE S 1

1

1)1()ˆ( 222

LFS

ADMIN

210010101011010 )()1(1)ˆ( YNYNYtMSE A

>95% of total MSE - given “true” employment, population and sample size

Linear locus of “low impact” on MSE

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

DOMAIN λ θ01 θ10 LFS ADMIN

TOTAL 5.1 6.2 9.5 46.7 5.1 2.4

NW 3.6 3.9 6.8 51.3 3.6 3.1

NE 3.4 6.5 6.1 52.7 3.4 0.3

CE 4.1 5.3 10.3 49.3 4.1 4.8

SO 8.8 7.9 14.2 38.4 8.8 1.5

ITA 5.0 6.3 8.7 45.4 5.0 1.1

EU 4.8 2.8 21.1 70.3 4.9 19.9

XEU 6.6 4.3 15.6 63.9 6.7 13.2

M 4.5 8.8 9.2 57.5 4.5 2.7

F 6.1 4.6 9.9 36.8 6.1 2.0

NU

TS 1

CIT

IZ.

SEX

Empl. rate

CV

• 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