Knowledge Exchange Platform (KEP) Workshop 2 - Italy BES Indicators - Susan Battles.pdf

OECD Knowledge Exchange Platform on Well-being Metrics and Policy Practice (KEP): Virtual Workshop 2, 3 October 2024

Integrating multidimensional well-being evidence and principles in policy decision-making tools

1. TheItalianexperienceinincorporating
equitableandsustainablewell-being
indicatorsinthepolicy-makingprocess
SusanBattles
Department of the Treasury
EconomicandFinancialAnalysisandResearch
IntegratingMultidimensionalWell-BeingEvidenceand
PrinciplesinPolicyDecision-Makingtools
OECDKEPWorkshop
(3October2024)

2. The Well-Being Conceptual Framework
2
•Qualityoflife:fromeconomicwelfaretothewiderconceptofwell-being
•Multidimensionality:economic,social,environmental
•Monetaryvs.non-monetarydimensions:trade-offsorwin-winsituations
•GoingbeyondGDP:complementingtraditionaleconomicmeasures
Incorporating well-being indicators in the policy-making process:
The Italian experience

3. 3
•EquitableandSustainableWell-being(ESW)indicators-ISTAT-CNEL
➢Frameworkdefinedbyacommitteeofexpertsandcivilsociety(2010):12domainsand(today)
about153indicators
➢AnnualreportonIstat-ESWinItalypublishedbyIstatsince2013
•EquitableandSustainableWell-being–MinistryofEconomyandFinance(MEF)
➢CommitteefortheselectionofESWindicatorstoincludeinthepolicymakingprocess:
MEF,Istat,BankofItaly+2academicexperts
➢inspiredbyIstat-ESWmethodologicalframework→12ESWindicatorschosencovering8Istat-
ESWdomains
➢criteriafortheselectionofindicators:parsimony,dataavailabilityandtimeliness,feasibility,
sensitivitytopolicychanges
➢Includedintheannualpolicymakingprocessbylaw(L.196/2009)
Well-Being: from measurement to policy making
Incorporating well-being indicators in the policy-making process:
The Italian experience

4. Well-Being Indicators in the MEF-BES Analysis
Incorporating well-being indicators in the policy-making process: The Italian experience
4
Domain Indicator Source Analysis Forecast
Economic
well-being
1.
Adjusted gross disposable income per capita
(RDLC)
Istat - National Accounts (CN)
Incomes and services; nominal and real
values
Overall indicator;
nominal and real values
2.Disposable income inequality (S80/S20)
Istat –Statistics on Income and Living
conditions (Eu-Silc)
Components Overall indicator
3.Absolute poverty rate Istat - Household Budget Survey
Families and individuals, intensity of
poverty, geographical distribution, age,
family size
Overall indicator
Health
4.Healthy life expectancy at birth
Istat -Aspects of
Daily Life survey
(AVQ)
Istat - Life tables
Components, geographical distribution,
gender, fifths of equivalent income,
education
Overall indicator,
gender
5.Excess weight
Istat -Aspects of
Daily Life survey
(AVQ)
Non-standardized index, geographical
distribution, gender, education, fifhts of
equivalent income, age
Overall indicator,
gender
Education and
training
6.Early leaving from education and training
Istat - Labour Force Survey (RFL)
Geographical distribution, gender,
citizenship, education, employment,
family size
Overall indicator
Work and life
balance
7.Non-participation rate
Geographical distribution, gender, age,
education, employment
Overall indicator,
gender
8.
Ratio of employment rate for women aged 25-
49 with preschool age children to the
employment rate of women aged 25-49
without children
Age, geographical distribution, role in
the family
Security 9.Predatory crime
Data on crimes
reported to Police
Forces (Ministry of
the Interior)
Istat - Citizens' Safety
survey
Type of crime
Politics and
institutions
10.Efficiency of civil justice
Ministry of Justice, Directorate General for
Statistics and Organizational Analysis
Duration of pending procedures, type of
macro-subject
Disposition time
Environment
11.
Emissions of CO2 and other
greenhouse gases
Istat-Ispra -Inventory and emissions
accounts
Analysis of determinants Overall indicator
12.Land consumption Ispra Geographical distribution, components

5. Methodological Aspects (1/4)
5
•Innovative and challenging approaches (research activity)
❑Literature review (theoretical and empirical) for each ESW indicator
❑Development of adequate analytical tools
❑Continuous refinement of theoretical and empirical frameworks, updating them to the frontier of
academic research
❑Cooperation with third parties (universities, research centers) to develop and establish the analytical
methodologies
•Indicators/phenomena heterogeneity
❑Heterogeneous data sources (microdata, national accounts, administrative data)
❑Availability, timeliness of data (e.g. territorial disaggregation)
❑Cooperation with other public institutions to use up-to-date information on the state of the Italian socio-
economic system from a multitude of perspectives (labour market, fairness, economic welfare, etc.)
❑Underlying dynamics, sensitivity to public policies
Incorporating well-being indicators in the policy-making process:
The Italian experience

6. Methodological Aspects (2/4)
6
•Economic models
▪Micro approach: microsimulation, impact evaluation (e.g. inequality and poverty)
▪Macro approach: forecast (e.g. CO
2 emissions)
▪Connection with Macroeconomic forecasts
▪Reliance on administrative data provision on actual individual data
•Gradual development of methodologies to forecast ESW indicators
▪Forecasts/impact evaluations available for 9 ESW indicators
▪Information exchange and collaboration with Istatand other national institutions (e.g.Ministry of Justice,
Ispra)
▪Research collaboration and ad hoc projects:
▪Research project on the labourmarket with Fondazione Giacomo Brodolini
▪Research project on poverty with the University of Rome La Sapienza
Incorporating well-being indicators in the policy-making process:
The Italian experience

7. Methodological Aspects (3/4) – Focus on Data
7
•The BES activities rely on many critical data management tasks, carried out on a multitude of
data sources
•Databases:
❖IT-SILC: Italian side of the Survey on Income and Living Conditions, provides data on socio-
demographic aspects as well as on households’ well-being
❖HBS: survey on households’ consumption and expenditure, useful to compute indicators of households’
economic welfare (e.g., poverty)
❖LFS: survey on labour market conditions and contracts
❖ADL: survey on health conditions among other aspects of daily life
❖Administrative registers (Istat, INPS, MEF, MJ, etc.)
❖Other sources(e.g., Eurostat, etc.)
•The Italian administrative system (Sistan) allows us to match different data sources in single
datasets (e.g., AD-HBS, AD-SILC)
•The matching of survey data with administrative data results in greater analytical possibilities
as well as in an increase in the explanatory power of our analyses → research potential
Incorporating well-being indicators in the policy-making process:
The Italian experience

8. Methodological Aspects (4/4) – Focus on Research Projects
8
•Routinary research activities and topic-specific research projects in cooperation with outside
entities
➢Research project with Sapienza University – Poverty:
❑Annually renewed project
❑Analysis on poverty and its determinants
❑Innovation of current state of the art literature
❑Research outputs (e.g. published material on the use of innovative dataset, impact of
energy prices, changes in income support schemes, energy poverty)
➢Research project with Fondazione Giacomo Brodolini – Labour market:
❑Recent collaboration
❑Analysis of labour market participation and its determinants
❑Focus on females participation and on territorial disaggregation
❑Focus on policy impact of local public policies
Incorporating well-being indicators in the policy-making process:
The Italian experience

9. Some Recent Evidence (1/5) – Poverty
9
Incorporating well-being indicators in the policy-making process:
The Italian experience
Source: 2019-2022,Istat,HouseholdBudgetSurvey;2023:Istat,preliminaryestimate.
2024-2027:MEF-DTestimate(absolutepovertymicro-simulationmodel).
▪Stable household
and individual
poverty (+0.2 and
+0.1 percentage
points) compared to
2022
▪Driven by increases
in expenditures but
hindered by inflation
▪Projections indicate
stability, in line with
S80/S20 previsions
▪This stems from
income support and
work inclusion
measures by the
government
6,2
6,4 6,5
7,2 7,3
6,7
7,8 7,7
8,3
8,5
6,9
7,4
7,8
8,3 8,3
7,6
9,1
9,0
9,7 9,8
-2
0
2
4
6
8
10
0
2
4
6
8
10
12
14
14 15 16 17 18 19 20 21 22 23
Absolute poverty of individuals (ab. an. var; right axis)
Absolute poverty of families (ab. an. var.; right axis)
Absolute poverty of families
Absolute poverty of individuals
-0,1
0,6
0,2
0,0
0,1 0,0 0,0
7,7
8,3
8,5 8,5 8,5 8,5 8,5
-2
0
2
4
6
8
10
0
1
2
3
4
5
6
7
8
9
10
11
12
2021 2022 2023 2024 2025 2026 2027
Absolute annual variation (right axis) Absolute poverty of families (left axis)

10. Some Recent Evidence (2/5) – Life Expectancy
10
Incorporating well-being indicators in the policy-making process:
The Italian experience
Source: 2009-2022,Istat,LifetablesofItalianpopulationandAspectsofDailyLife
survey;2023:Istat,provisionaldata;2024--2027MEF-DTforecast.
▪HLE continues to
decline in 2023
towards the level in
2019, after the
increase in 2020
▪Changes in the
indicator are driven
by changes in good
health (subjective
component)
▪The change in 2024-
2027 compared to
2023 is expected to
be 0.7 years
▪The gender gap is
expected to increase
by 0.5 p.p. at the
end of the period
compared to 2023
1,3
0,5 0,3
-
0,2 -
0,1
0,1
-
0,2
0,1
2,4
-
0,5 -
0,4
-
0,9
0,4 0,3 0,3
-
0,3
0,5
-
0,2
0,3 0,3
-
1,1
0,4
0,1
0,5
56,4
57,758,258,558,358,258,358,858,758,558,6
61,060,560,1
59,2
81,481,882,082,082,382,682,382,882,682,983,2
82,182,582,683,1
-4
-2
0
2
4
6
8
10
12
14
16
30
35
40
45
50
55
60
65
70
75
80
85
90
95
200920102011201220132014201520162017201820192020202120222023
Healthy life expectancy (Absolute annual variation, right axis)
Life expectancy (Absolute annual variation, right axis)
Healthy life expectancy
0,0
-0,7 -0,7
0,2 0,2 0,3 0,3
-0,5 -0,4
-0,9
0,1 0,2 0,2 0,2
-0,8
-0,2
-1,2
0,1 0,1 0,1 0,2
61,9
61,2
60,5
60,7
60,9
61,2
61,5
59,3
59,1
57,9 58,0
58,1
58,2
58,4
60,5
60,1
59,2 59,3
59,5
59,7 59,9
-2
0
2
4
6
8
10
12
55
56
57
58
59
60
61
62
63
64
65
2021 2022 2023 2024 2025 2026 2027
Absolute annual variation - Males (right axis) Absolute annual variation - Total (right axis)
Absolute annual variation - Females (right axis) Healty life expectancy - Males
Healty life expectancy - Female Healty life expectancy

11. Some Recent Evidence (3/5) – Early Exit From Education
11
Incorporating well-being indicators in the policy-making process:
The Italian experience
Source: 2009-2022,Istat,LifetablesofItalianpopulationandAspectsofDailyLife
survey;2023:Istat,provisionaldata;2024--2027MEF-DTforecast.
▪Overall downward
trend interrupted
only in the first year
of the pandemic
▪In 2023 the gender
gap is 5.5
percentage points,
close to the record
in 2020
▪Improvement in 2027
(-0.3 percentage
points) compared to
2023
▪Affected by the
increase in income
and by fluctuations
in the sectoral
composition of the
labourmarket
4,2
3,9
5,6
4,3 4,5
5,5
14,3
13,3
14,2
12,7
11,5
10,5
12,1
11,3 11,3
10,5
9,1
7,6
16,3
15,2
16,9
14,8
13,6
13,1
0
5
10
15
20
25
0
5
10
15
20
25
2018 2019 2020 2021 2022 2023
Gap males females (right axis)
UPIF total
UPIF females
UPIF males
-1,5
-1,2
-1,0
0,2
-0,2 -0,2 -0,1
12,7
11,5
10,5
10,7
10,5
10,4
10,2
-3
-2
-1
0
1
2
3
4
5
6
7
8
6
7
8
9
10
11
12
13
14
15
16
2021 2022 2023 2024 2025 2026 2027
UPIF total (absolute annual variation, right axis)UPIF total (left axis)

12. Some Recent Evidence (4/5) – Non-Participation Rate
12
Incorporating well-being indicators in the policy-making process:
The Italian experience
Source: 2019-2023,Istat,LabourForceSurvey;2024-2027,MEF-DTforecast.
▪Territorial and
gender gaps are very
wide, despite
progressive
improvement.
▪Non-participation
rate (TMP) is in all
cases higher in the
South compared to
other geographical
areas.
▪Reductions in TMP
are foreseen for
2024
▪Despite subsequent
increases, at the end
of the period the
indicator is
estimated to be
below 2023 levels,
for all components
0
5
10
15
20
25
30
35
40
45
North
Center
Mezzogiorno
North
Center
Mezzogiorno
North
Center
Mezzogiorno
North
Center
Mezzogiorno
North
Center
Mezzogiorno
North
Center
Mezzogiorno
2018 2019 2020 2021 2022 2023
Males Females
-0,3
-3,2
-1,4
-0,6
0,2 0,1 0,2
-0,2
-3,0
-1,2
-0,5
0,2 0,1 0,2
-0,6
-3,5
-1,6
-0,8
0,2
0,0
0,1
19,4
16,2
14,8
14,2 14,5 14,5 14,7
16,5
13,5
12,3
11,8 12,0 12,1 12,3
23,0
19,6
18,0
17,2 17,4 17,4 17,5
-6
-4
-2
0
2
4
6
8
10
12
14
3
6
9
12
15
18
21
24
27
2021 2022 2023 2024 2025 2026 2027
TMP - total (absolute annual variation, right axis) TMP - males (absolute annual variation, right axis)
TMP - females (absolute annual variation, right axis) TMP - total
TMP - males TMP - females

13. Some Recent Evidence (5/5) – Land Consumption
13
Incorporating well-being indicators in the policy-making process:
The Italian experience
Source: ISPRA processing of ISPRA-SNPA data
▪Overall land
consumption has
increasedover the
years.
▪Major consumption
in the Islands in 2022
with respect to
2021. Similar trends
in the North and
South.
▪Large part of the
transformations are
reversible (79 per
cent in 2022) - often
because of the
initiation of
construction sites
(reversible
consumption), which
in most cases are
not yet completed.
0
0,05
0,1
0,15
0,2
0,25
0,3
0,35
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Permanent soil consumption
Reversible soil consumption
Total soil consumption
0,00
0,05
0,10
0,15
0,20
0,25
0,30
0,35
0,40
0,45
0,50
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
North Center South Islands Italy

14. 14
Incorporating well-being indicators in the policy-making process:
The Italian experience
•What training or skills development are needed for different types of well-being-
informed policy tool? → In the case of quantitative impact assessments, statistical,
econometrical as well as data management skills are needed. A basic policy-oriented
research aptitude is also necessary.
•What type of data and evidence are needed to underpin well-being-informed policy
tools? →Administrative data and survey are particularly suitable to perform well-
being analysis and inform policy makers on such topics. The matching of survey and
administrative data is particularly promising as a tool for analysis and to feed into
micro-simulation models.
•Which areas of government decision making could benefit from a more widespread
use of well-being-informed policy tools? →The adoption of well-being- informed
policy tools would surely benefit the policy programming of line ministries as well as
to ensure a whole-of government approach to policy making when adopting
budgetary decisions and making policy changes, both in the short- and medium-
term.
•What is the need for/role of co-appraisal/co-evaluation that engages non-
government actors?→The importance of non-government actors as stakeholders in
policy making and potential allies in several fields: auditing, collaboration, research,
divulgation.
Cross-Cutting Questions

15. THANK YOU!
Our latest official ESW documents areavailable in English on the following
web-site
https://www.dt.mef.gov.it/export/sites/sitodt/modules/documenti_it/analisi_pr
ogammazione/documenti_programmatici/def_2024/DEF_2024_ALLEGATO_
BES_finale_EN.pdf
https://www.dt.mef.gov.it/export/sites/sitodt/modules/documenti_en/analisi_p
rogammazione/documenti_programmatici/Relazione-BES-
2024_finale_EN.pdf