1. Quarterly Estimates of Jobs
based on Admin Data.
The Case of the Italian Oros Survey: Critical Aspects and Methodological Solutions.
Marco Lattanzio
15 March 2017, NTTS, Brussels

2. a) Late reporting (LR) in preliminary Admin dataID
VAR1
VAR2 …
VARn 1 2 3 4
OROS quarterly survey
OROS DATA ID
VAR1
VAR2 …
VARn 1
... 2 3 4 5
20%
LES DATA (>500 empl) ID
VAR1
VAR2 …
VARn 4 5
Issues with Admin data:
a) late reporting = incompleteness in preliminary data (accuracy)
b) admin data vs statistical definitions (definitional bias)
…at micro level.
a) Late reporting (LR) in preliminary Admin data
2.5% employment on quarterly average in the SMEs subpopulation.
Not persistent … BUT…
- Differently distributed within the quarter and by economic activity
Affected by unexpected but frequent administrative changes 1
Quarterly Estimates of Jobs based on Admin Data, Marco Lattanzio – NTTS Conference , 03/15/2017

3. Prediction of a list of monthly active LR units
Problems
Prediction of a list of monthly active LR units
not available from Admin data.
BR not updated for short term deadlines.
Impact of business demography on employment short term evolution.
Imputation of missing values
for active predicted units.
Solutions
Estimation of the current activity status with the past reporting status of the unit
Basic rule for LR units.
Use of past data (employment) of the single unit as auxiliary variables in a linear regression model.
estimated with all 2
Quarterly Estimates of Jobs based on Admin Data, Marco Lattanzio – NTTS Conference , 03/15/2017

4. b) Admin data vs statistical definitions.
Absence of employees not directly paid by the employers - NPE (due to sickness, parental leaves, short time working allowances).
Problems:
Finding out the subpopulation of elected units
Imputation of incomplete data
Solutions:
Use of information on presence of NPE at micro level in additional admin sources/data
Imputation of values
(ratio imputation by economic activity) 3
Quarterly Estimates of Jobs based on Admin Data, Marco Lattanzio – NTTS Conference , 03/15/2017

5. Revision error before adjustment Revision error after adjustment
Main results
a) Late reporting.
After 1 year from t the final population is available. Revision error = (F – P)/F %
% Mean revision error (SME + LES) over the period Q2:2012-Q4:2014.
Revision error before adjustment
Revision error after adjustment
Mean value
-1.98
0.10
b) Admin data vs statistical definitions - NPE component
Number of employees (in thousand - red line - right side axes) and
% correction for NPE (blue bars -leftside axes) 4
Quarterly Estimates of Jobs based on Admin Data, Marco Lattanzio – NTTS Conference , 03/15/2017