This document discusses methods for integrating administrative data from different sources to produce statistics on earnings in the public education sector in Italy. It describes extracting data on employment and payrolls from tax registers and ministry sources, and linking this census data with survey data through record linkage to estimate characteristics of local education units. The process involves normalizing personal data to define a fiscal code to match records between sources, addressing challenges like schools belonging to multiple local units. The goal is to provide accurate, harmonized data on earnings as required by European legislation.

1. Archivi amministrativi per le statistiche
Eurostat
Stefania Cardinaleschi, Vincenzo Spinelli
Istat – Istituto Nazionale di Statistica
SIS VSP 19-20 Aprile 2012
Sessione: Utilizzo statistico di archivi amministrativi

2. Outline
The context
• Definition of Structural
Earning Survey (SES)
• Process flow for
Focus on the Education public sector
• Data integration by Record Linkage
• Estimation of local units
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3. The Context
LAbour MArket Statistics (LAMAS)
the Council Regulation (EC) No 530/1999
“…needs information on the level and composition of labour costs and
on the structure and distribution of earnings in order to assess the
economic development in the Member States…”
Labour Cost Survey (LCS) Structure of Earnings Survey(SES)
“The statistics on the level “The statistics on the structure and
and composition of labour distribution of earnings
costs
The objective of this legislation is to provide accurate and
harmonised data on earnings in EU Member States and other
countries for policy-making and research purposes (gender pay
gap, file MFR, ..
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4. SES - outline
Objective
• to provide accurate and harmonised microdata on earnings for
scientific purpose
• to monitor the structure and distribution of earnings, taking into
account job-related factors
• to provide information on several individual characteristics of
employees such as gender, age, occupation, education, length of
stay in service and others
Coverage
• reporting units consisted of enterprises with 10+ employees; results
related to local units
• C-K of Nace rev.1 plus M-O Nace rev.1 from 2006
• Private sector plus public sector from 2006
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5. Definition of SES by Eurostat
Structure of the Survey
SES 2010
Private sector Public sector
and S13 list (excluded P.A. estimates through administrative
and Education) data on all institutions
direct survey through a specifically
questionnaire
• Education
Firms chosen from the official
they cover the 11% of the total
list of firms (ASIA 2009)
employment
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6. Private sector and S13 List
Two stage sampling design: a sample of employees in a sample of
enterprises
enterprises
10-249 employees >249 employees
first stage: the enterprises
stratified sample census
by economic activity
dimension
geographical position
second stage: employees (october) belonging to the chosen
units
two chances by the enterprises:
1. simple random sample
2. they could be given a list of the VAT
code of employees to interview
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7. SES sample design – private sector
Sample design Second stage of sampling
Number of employees interviewed by dimension of enterprise to which
they belong
Enterprises Dimension Number of employees
10-19 all
20-49 20
50-99 25
100-249 35
250-499 40
500-999 50
1000-1999 60
2000-3999 65
4000-7499 75
7500-9999 100
>10000 200
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8. SES Education - public sector
Education - Public sector: Estimates based on data derived from
integration among administrative, fiscal and statistic sources
Administrative and fiscal data:
• 770 Form Tax Register by MEF (2010)
• Payroll dataset by MEF/Service Personale Tesoro List employement
teaching and not teaching by Ministero dell’Istruzione, dell’Università e
della Ricerca (2010-2011)
Statistical surveys (2010):
• Eu-Silc Panel Survey Statistics on Income and Living Conditions
• Labour force survey
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9. The Context
Process flow Education
Data acquisition School Employment
770 Payroll + List (MIUR)
Estimation of Census
Integration with survey data Eu-silc LFS
Next steps (sampling, checking, ....)
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10. The Context
Process flow Integration with survey data
sampling Census
eusilc fl
Eu-silc LFS
isco_2 isco_3
manag_2 manag_3
isced_2 isced_3
anz_3
Part-time full time _2 Part-time full time_3
tipo_2 tipo_3
cittad_2 cittad_3
ore_2 ore_3
orestra_3
bonus_2 bonus_3 SES - Education
RLM_2 RLM_3
checking
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11. Data integration in SES
The context:
Archives coming from heterogeneous sources.
Objective:
Assignment to the statistical units (employees)
in Census R85 of some features available in LFS
data.
Problem:
The two sources (SES and LFS) do not use the
same key fields to identify their statistical units.
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12. Data integration in SES
Warning:
Eu-silc can be considered as a “special case” of
LFS for the integration problem, and, for this
reason, it is not further mentioned in this
presentation.
Choice:
The key field in LFS is “personal code”, valid
only in this context. While census R85 is based
on “fiscal code” a well-defined key for physical
persons in administrative and fiscal archives.
This is why we want to define a mapping from
“personal code” to fiscal code and not vice versa.
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13. Data integration in SES
how can we integrate these archives?
(LFS) Personal code Birth date Sex Name & surname
Census (R85)
Personal code (LFS) and Fiscal
code (R85) and cannot be
compared directly!
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14. Data integration in SES
Hypothesis:
•Census is error free and must be considered as the benchmark for
LFS archives.
•In LFS archives the personal data are affected by random errors;
•the sistematic ones (or bias) must be corrected outside this
context;
•The errors are not uniformly distributed in all the fields of the
personal data of LFS. Errors in (Name, surname) fields are more
likely than in birth date/place or gender.
Consequence:
•We define and assign a “fiscal code” to each personal code
(statistical unit) in LFS.
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15. Data integration in SES
“Naïve” solution to matching problem
begin
•normalization step for personal data in LFS archive
•definition of fiscal code on normalized personal data
•<the archives from LFS and R85 can be joined by fiscal code>
end
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16. Data integration in SES
Results
LFS personal data (reference year 2010) : 92,129 records.
Before normalization step : Error rate: 27.8%
66,481 records can be matched in fiscal archives (i.e. Modello 770/2010).
After normalization step : Error rate: 18.5% (-9.3%)
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17. Estimation of local units
school 1
Local unit 1
school 2
MIUR
school 5
Local unit 2
school 4
school 3
But: what if there are multi-level local units?
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18. Estimation of local units
Local units structures
local unit
school addresses
A constraint must hold in this list: every school must be
“linked” to one and only one local unit
In other words, every school must belong to a cluster
having a unique “center of mass” (a school itself)
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19. Estimation of local units
From local units to graphs Local unit =
connected components
BIEE002018
BIEE002029
AGEE00101V AGEE001042 BIEE002007 BIEE00203A
AGEE00100T BIEE00206D BIEE00205C
This list can be seen as a graph G: the vertices are the
codes of the schools and the (oriented) edges are the
couples in each row of the list.
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20. Estimation of local units
Result
The local units in R85 are the connected components in G
such that they are (oriented) tree with one root.
Search of connected
components in G:
there are many algorithms to
compute the connected
components of a graph in
linear time using either
breadth-first search or
depth-first search.
local units
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21. Estimation of local units
Inputs
There are 36,923 schools (reference year 2010), i.e., vertices in G.
There are 24,031 (oriented) edges in G.
Before the clustering Algorithm….
We get 11,892 connected components, such that 3,126 are singletons.
The average size of these components is 3, while the largest
components has 21 vertices.
There are 512 components having less than 10 employees.
Results
We considered 11,380 local units (i.e. 35,152 schools) in SES 2010.
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