1. Employer-Employee linked data in Italy
availability and usage by institusions
Conference of the Global Forum of Productivity
Sydney, June 2019
Session 6
Ottavio Ricchi

2. The ASIA-Employment LEED
The Italian Business Register to measure employment (ASIA-
Employment) has been realized in occasion of the first Economic
“virtual” Census (CIS2011)
Purpose
reproducing census data entirely from administrative sources by
revising and enlarging the informative statistical database on
enterprises and local units produced and disseminated every year
from the (ASIA) BR system (data on the structure of enterprises,
their local units and related employment)
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conceptual reference frameworks
 the “System of National Account –SNA 2008”
 the “International Classification of Status in Employment– ISCE 93”
 the “European System of Accounts - ESA2010

3. Administrative sources (20) and the system of registers
LEED
Insurance positions
workplace
Fiscal data
Social
security
data
Insurance
against
work
related
injuries
VAT register
Business Income
participation in
partnerships
Studi di settore
Tax register (Remun.-770-
form)
Employer decl. on each employee
Employer decl. on outworkers
Self-employed persons in agric.
handicraft and trade data
Links identification codes (fiscal code, br code)
enterprise ownership shares
partners and positions
shareholders
Chamber
of
Commerce
BR-ASIA
CIS
Frame for surveys
Longitudinal
analyses

4. LEED: Contents
 Enterprise characteristics
 Industry sector (NACE 4 digits)
 Size: number of employee, turnover
 Geographical location (local unit)
Each person involved in the business production process is identified
 Individual (worker) demographic characteristics
 "Sex ", "Age, " "Place of Birth", "Residence" …
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ASIA-Employment holds information from all workers and firms.
15 mln workers and 4 mln companies, 170mln records per year
 Different typologies of workers (Job positions) employed by firms:
 Employees
 Self-employed/Contributing family workers
 Outworkers- external component
 Temporary workers - external component

5. LEED: Contents
 jobs characteristics by typology of workers
 Job qualification
 Contractual working time (part time/full time)
 Type of contract (temporary or fixed time /permanent)
 maternity leave
 Illness
 Detailed classification of:
 Contractual forms aimed at young people
 Income support measures for vulnerable population groups
 Different forms of unemployed schemes
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6. 6
Further integration of data

7. LEED Measures
• LEED data assesses the presence of the worker–employer link
(job) at a point in time
• The structure of longitudinal LEED contains observations about
individuals and their employers linked by means of a work
history (job position).
• It allows to track the flows of workers across detailed industries,
across detailed demographic groups, and across regions and
provinces (workers are allocated to local productive units of
enterprises).
• The origin–destination information permits also measurement
of status changes or earnings changes of the workers
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8. 8
Information Content/Data
Analysis of different policies and their effects over time
Ex-Ante Analysis of Active Labour Market Policies
• Citizenship income (minimum income support scheme aimed
toward poor and at risk of poverty households). Assessment of the
cost of the measure as a function of the program beneficiaries.
• Minimum wage proposal. Assessment of the cost faced by
companies as a result of the wage increase with identification of
companies involved in terms of size, sector, region
Ex-post evaluation
• 80 euro measure: consumption behaviour of households that
received the monthly 80 euro tax rebate vis a vis those just above
the threshold who di not receive it. Difference in Difference anal.

9. Models of the Italian Ministry of Economy and
Finance based on LEED data
The AD-SILC dataset and T-DYMM
Structure of T-DYMM
Simulation Results
Future Implementations

10. The AD-SILC dataset
• AD-SILC is composed by matching the observations
contained in the survey SILC delivered by ISTAT with
administrative data from INPS
• Panel INPS: longitudinal data of individuals’ working
history since their entry in the LM: occupational status,
income evolution, contribution accumulation, etc.
• Panel SILC: longitudinal data of individual socio-
economic characteristics (up to 4 years): education,
marital status, number of children, etc.
• It comprises all SILC waves from 2004 to 2012

11. History
T-DYMM has been developed in 3 phases:
1° European Project (2010-2012): based on MIDAS and
EconLav, developed in Liam 1.0, covers 2005-2060
2° European Project (2014-2016): new and improved
AD-SILC, move to Liam 2.0, update of the legislation,
addition of private pension module, indexation of
pensions, unemployment benefits
Publication of ‘What are the consequences of the AWG
2018 projections and hypotheses on pension
adequacy?’ and aftermath: update of the legislation,
taxation module expanded and moved to Liam 2.0,
modelization of net migration

12. General features of T-DYMM
It is a Dynamic Model
It Works with annual data
It has a sequential structure
Socio-economic events occur according to
conditional transition probabilities (estimated on
data available)
It uses alignment procedures on demographic and
macroeconomic projections

13. AD-SILC uses:
Analyses, regressions and projections
Regressions are based on the entire dataset
Simulations are based on a single extract of AD-
SILC
For T-DYMM 2.0, 2011 is the starting point of the
simulation
The dataset is cross-sectional (SILC 2011),
integrated with retrospective information about
working conditions, acquired work experience, total
number of years of contribution, etc.

14. The 4 Modules of T-DYMM
AD-SILC 2011
Demographic
module
Labor Market
Module
Pension Module
Taxation Module
2012
-
-
-
2070
END

15. • Modelling demographic dynamics:
Ageing, Births, Immigration, Education, Leaving household,
Coupling / marriage and divorce
• Analyses of dynamics within the labor market:
Transition matrixes >
Earnings trends (monthly gross wages and months worked)
Unemployment benefits; disability
• Pension module:
Public scheme (fully fledged modelization on first pillar)
Private scheme (2nd and 3rd pillar)
• Taxation module:
From gross income to net income
The 4 Modules of T-DYMM

16. The Labor Market Module <
Are you in ‘active age’?
• In education
• Retired
• Other inactive
Are you employed?
(probabilistic + alignment*)
Are you an atypical worker?
(‘P.IVA’ + ’parasubordinati’,
probabilistic)
Private or public
(probabilistic)
NO
NO • Disable (only 100% disability)
• On unemployment benefits
• Other unemployed
* AWG 2018 Projections
YES
YES
NO
Are you an employee?
(probabilistic)
NO
You are an independent
worker (residual category)
YES Temporary or permanent
(probabilistic)
Part-time or full-time
(probabilistic)

17. The Pension Module
Public scheme
Contribution payment
(according to employment category)
Benefit computation
(according to pertinent legislation)
Check for eligibility
(age, seniority, pension amount)
Retirement choice
(deterministic)
Indexation
(operates as discount)
NO
NO
Old-age / Seniority Pensions
YES
YES
• “Quattordicesima”
• “Integrazione al minimo”
• “Assegno sociale”
• Disability Pensions
• Survivor Pensions

18. Simulation Results
• T-DYMM has generally been used to assess the
adequacy of the Italian pension system.
• Published results inculde:
• Average retirement age;
• Average duration of retirement at death;
• Replacement rate at retirement;
• Gini index;
• Income quintile share ratio;
• At-Risk-of-Poverty Rate.
• Results have generally been proposed on a number of
sensitivity and policy scenarios

19. Future Implementations
• Development of a ‘Wealth Module’ (financial, real-estate
wealth, TFR)
• Development of a ‘Migration Module’ (immigration and
emigration, characterized patterns for migrant workers)
• Inclusion of working pensioners (retirement is now an
‘absorbment state’)
• Expansion of welfare legislation
• Expansion of ‘Disability Module’ (probabilistic, improve
alignment procedures)
• Expansion of the simulation sample (weighting)

20. Tax-Benefit Microsimulation Model
Dataset: matching survey and administrative data
 The dataset is based on an exact matching among the representative sample
survey of the Italian income and living conditions (EU-SILC, provided by ISTAT,
Italian Institute of Statistics) and the corresponding personal income tax returns,
as well as cadastral data of the real estate properties of each individual (provided
by the Department of Finance of the Italian Ministry of Economy and Finance) on
the same tax years.
Aim: evaluating revenue and distributional effects of tax reforms
 The static (non-behavioural) tax-benefit microsimulation model allows to
evaluate revenue and redistributive effects of personal income tax reforms
(marginal tax rate, tax incidence, Gini index, Reynold-Smolenski index, etc.), with
details on tax deductions.
 The model should also allow more reliable and detailed results compared to
those based only on survey data; furthermore, it is an useful tool to estimate tax
evasion, according to the so-called bottom up approach (by taking into account
the incomes declared by self-employers to interviewers).
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21. VAT-Excise Microsimulation Model
Dataset: matching survey and administrative data
 The dataset is based on a statistical matching among the representative sample
survey of the Italian income and living conditions (EU-SILC, provided by ISTAT,
Italian Institute of Statistics) and the Italian Household Budget Survey (HBS,
provided by ISTAT).
 The former includes income data for each household, but not consumption data;
the latter includes consumption data for households, but not income data. The
integrated dataset makes it possible to analyse income and consumption together.
Aim: evaluating revenue and distributional effects of VAT/excise reforms
 The static (non-behavioral) tax-benefit microsimulation model allows to evaluate
revenue and redistributive effects of VAT/excise reforms.
 The main theoretical and empirical reference of the VAT-Excise behavioral
microsimulation model is the AIDS (Almost Ideal Demand System) model, developed
by Deaton e Muellbauer in 1980. The model provides an estimation of the income
and own-price elasticity of demand (marshallian and hicksian demand) following an
increase in VAT rates.
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22. New Project: Employer-Employee Data
Dataset:
 The aim of the new project is to set up, with reference to the three years 2014-
2016, an integrated tax-contribution database, including micro-data on employers' and
employees' taxes and social security contributions.
Tax data are derived from tax returns (“Unico” model for individuals, partnerships and
corporations); social contributions are derived from the 770 model and the
“Certificazione Unica”.
 Further steps will be devolved to the integration of tax files with data provided by
INPS (including all the information on employees).
Aim: assessing the effectiveness of tax and social contribution reforms
 The dataset will allow to perform an assessment of the effectiveness of the new
measures aiming at reducing the burden of tax and contribution, breaking down the
overall effect by geographical area, sector of economic activity and size.
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23. Thank you for your attention