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MODELLING EQUITABLE AND
SUSTAINABLE WELL-BEING INDICATORS:
WHAT HAVE WE LEARNED?
HIGH-LEVEL CONFERENCE ON
WELL-BEING ANALYTICS FOR POLICY USE
MONDAY 30TH MAY 2022
FABRICE MURTIN
HEAD OF RESEARCH, MODELLING AND
ADVANCED ANALYTICS, OECD WISE CENTRE
© OECD |
2000
to
2005
2004
2002
2006
to
2010
2011
to
2015
2016
to
2021
2013
2014
2015
2009
2010
2012
2011
2021
2019
2018
2017
2016
More than half of OECD
countries have developed
multidimensional well-being
frameworks/ dashboards
Canada’s Quality of Life Framework
Australian Treasury’s
Well-being Framework
First Report on a Well-being Framework for Ireland
Measures of Australia’s
Progress
Finland’s Findicators
Latvia 2030
Australia’s Welfare
Israel Well-being, Sustainability and National
Resilience Indicators
Indicators of Well-being in Slovenia
Finland Strategic Government Programme
Indicators
France New Indicators of Wealth
New Zealand Living Standards Dashboard
United Kingdom Measures of National Well-
being
How’s Austria?
Italy Measures of Equitable and Sustainable
Well-being (full set)
Statistics Portugal Well-being Index
Quality of Life Indicators in Korea
Mexico Indicadores de bienestar
Belgium Complementary Indicators to GDP
Well-being in Germany
Italy Measures of Equitable and Sustainable
Well-being (short set)
Indicators Aotearoa New Zealand
Spain Quality of Life Indicators
Poland Responsible Development Index
Iceland Indicators of Well-being
Japan Cabinet Office Well-being Dashboard
Source: adapted from OECD (2021), COVID-19 and Well-being: Life in the Pandemic http://oe.cd/covid-19-and-well-being
Switzerland updated MONET 2030 Indicators
New Zealand Treasury Living Standards
Framework
Sweden New Measures of Well-being
Norway – How We Are Doing
Netherlands Monitor of Well-being
Slovenia National Development Strategy 2030
Luxembourg Index of Well-being
Chile Social Welfare Survey
© OECD |
• Shaping budgeting decisions
+ long-term fiscal strategies
• New legislation
• Planning and performance
frameworks
• New institutional structures
• Updated tools for policy
design + evaluation
Beyond measurement, a growing number of countries
have used various well-being policy tools
© OECD |
How does WISE support well-being policy application?
Gathering
knowledge
about countries’
experiences
Applying a
well-being lens
to specific policy issues
(e.g. mental health,
child well-being)
Developing
technical tools
for well-being
policy analysis
Directly supporting
countries
in building well-being
frameworks
and policy processes
© OECD |
The Italian context
• From the Italian budget law (n. 163/2016), the Ministry of Economy and Finance has to report on 12
headline indicators of Equitable and Sustainable Well-being (‘Benessere Equo e Sostenibile’) that are
now integrated into the economic and financial planning cycle :
1. a yearly Report presented to the Italian Parliament by February 15, in which the evolution of
the 12 ESW indicators is analysed in light of the policy measures that were adopted with the
recent Budget Law
2. an annex to the Economic and Financial Document (EFD) submitted to Parliament by April
10 that includes a forecast of the 12 ESW indicators over the following three years, both under a
no-policy change scenario as well as a policy scenario (considering the impact of the economic
policy that the Government intends to adopt over this period).
• In this context, WISE support consisted in:
• Building macro-econometric models to identify the economic and policy determinants of
healthy life expectancy, excess weight and obesity, rate of early leavers from school
 Forecast future ESW outcomes based on economic outlook and planned reforms, as requested
by the Italian Parliament
© OECD |
Equitable and Sustainable Well-being indicators
Healthy life expectancy in 2019 (years)
60 - 66
56 - 60
52 - 56
48 - 52
Healthy life expectancy Excess weight Early school leaving
© OECD |
Modelling healthy life expectancy: a multitude of drivers
A macro model (estimated from country
panel data) with high explanatory power
(R2=0.95)
A micro model (based on Aspects of Daily
Life survey) with reasonable explanatory
power (R2=0.30)
© OECD |
Living standards and lifestyle as key drivers of healthy life expectancy
Source Benchmark input
Relative
Variation of
Outcome
Absolute
Variation of
Outcome
Macroeconomic
Health expenditures per capita OECD-MEF Model +1% expenditure 0.03% 0.026
GDP per capita OECD-MEF Model +1% GDP per capita 0.04% 0.034
Higher Education OECD-MEF Model
+1ppt of people with at least upper secondary educ.
(from 59.8% to 60.8%) 0.03% 0.029
Lifestyle
Prevalence of smoking OECD-MEF Model -1ppt of smokers (from 19.8% to 18.8%) 0.15% 0.124
Alcohol consumption OECD-MEF Model -10% consumption (from 7.6L per capita to 6.8L) 0.14% 0.121
Total 0.40% 0.33 years
Macroeconomic
Employment rate OECD-MEF Model +1ppt employment rate 0.09% 0.057
Economic insecurity OECD-MEF Model -1ppt of highly insecure 0.11% 0.068
Foster Higher Education OECD-MEF Model -1ppt of people with lower or secondary educ. 0.12% 0.075
Lifestyle
Mental health OECD-MEF Model -1ppt of people with low mental health 0.36% 0.223
Diet OECD-MEF Model
+/-1ppt of people consumming more vegetables and
fruits, and less snacks and sweets 0.18% 0.111
Sport OECD-MEF Model +1ppt of people doing sport and fitness 0.28% 0.171
Obesity OECD-MEF Model -1ppt of overweight or obese people 0.01% 0.008
NCDs OECD-MEF Model -1ppt in the prevalence rate of 15 NCDs 3.13% 1.943
Total 3.96% 2.66 ppt
Life expectancy at birth (baseline is 83.6 years)
Good health status (% of population - baseline is 62% among 18+)
5 core determinants
with strong influence of lifestyle
(prevention policies)
Strong influence of NCDs (care
access & affordability), then
mental health and sport
© OECD |
Forecasts of healthy life expectancy
• Life expectancy was projected to decline by 1.1 year in 2020 (1.2 years for
males and 1.0 year for females), which compared to 1.2 years in reality. Life
expectancy should have recovered its 2019 level by 2022 (epidemiological
scenario) or 2023 (economic forecast).
• The share of people reporting being in good health was projected to drop by
3.5 percentage points in 2020. This forecast did not materialise, with self-
reported good health actually rising in 2020, from 70.4% to 74.3%.
• As a result, healthy life expectancy increased in the COVID-19 year of 2020,
underlining the challenges related to forecasting subjective indicators.
2018 2019 2020 2021 2022 2023 2024
Male 59.4 59.8 57.6 61.9 62.6 62.8 63.0 63.1
Female 57.6 57.6 55.4 60.1 60.8 61.4 62.0 62.5
Total 58.5 58.6 56.4 61.0 61.7 62.1 62.4 62.8
© OECD |
Education and lifestyle as key drivers of overweight and obesity
Source Benchmark input
Relative
Variation of
Outcome
Absolute
Variation of
Outcome
Overweight and obesity (% of population - baseline is 47.6%)
Macroeconomic
Economic insecurity OECD-MEF Model -1ppt of highly insecure people -0.04% -0.020
Foster Higher Education OECD-MEF Model
-1ppt of people with high school or middle high
school degree
-0.32% -0.153
Lifestyle
Healthy diet OECD-MEF Model
+1ppt of population having adequate breakfast,
awareness of salt intake, -1ppt of population having
large alcohol and carbonated drinks consumption
-0.43% -0.203
Sport practice OECD-MEF Model +1ppt of population doing sport -0.16% -0.075
Total (excl. Prevention) -0.94% -0.45 ppt
Prevention
Food labelling & media campaign SPHeP model
Broaden food choices and transport options SPHeP model
Tax & subsidies SPHeP model
Regulation of advertising, banning products SPHeP model
see OECD(2019) Table 6.1
see OECD(2019) Table 6.1
see OECD(2019) Table 6.1
see OECD(2019) Table 6.1
A micro model based on Aspects of Daily Life survey with relatively low explanatory power (R2=0.10)
Small changes as the
model has low R2
© OECD |
Forecasts of overweight and obesity
1. Forecasts went into the right
direction in 2021
2. There are many factors at play
3. The role of public health policies is
not factored in this simple micro model
Alcohol consumption
Vegetarian
Ordering food online
Salt intake awareness
Access to supermarkets
Sport Household income
Perceived economic insecurity
Education attainment
Migration
Region
-5.00% -4.00% -3.00% -2.00% -1.00% 0.00% 1.00%
Men
Women
Total
Adequate breakfast Alcohol consumption Vegetarian Ordering food online Salt intake awareness
Carbonated drinks consumption Access to supermarkets Access to food markets Sport Time use pressure
Reading books Household income Perceived economic insecurity Education attainment Migration
Living alone Region
Positive contribution (decline in probability of being overweight) Negative contribution (increase in probability of being overweight)
2019 2020 2021 2022 2023 2024
Male 55.0% 56.2% 55.7% 55.9% 55.6% 55.3% 55.0%
Female 38.4% 39.6% 38.4% 39.0% 38.4% 37.9% 37.4%
Total 46.4% 47.6% 46.8% 47.1% 46.7% 46.3% 45.8%
© OECD |
Labour market, quality of general and vocational
education as key drivers of early leavers from school
Source Benchmark input
Relative
Variation of
Outcome
Absolute
Variation of
Outcome
Early Leavers from School (% of 18-24 population - baseline is 13.1%)
Macroeconomic
Education spending as a share of GDP OECD-MEF Model +10% -0.45% -0.059
GDP per capita OECD-MEF Model +1% -0.25% -0.033
Youth unemployment OECD-MEF Model +10% -1.21% -0.159
Share of jobs in industry OECD-MEF Model -5% -1.09% -0.143
Share of jobs in agriculture OECD-MEF Model -5% -1.20% -0.157
Share of jobs in construction OECD-MEF Model -5% -1.81% -0.237
Share of disadvantaged students OECD-MEF Model -1ppt of disadvantaged students -0.66% -0.087
Demographic
Share of young people OECD-MEF Model -1% -0.84% -0.111
Migrants rate OECD-MEF Model -5% -0.43% -0.057
Education
PISA reading score OECD-MEF Model +1% -0.86% -0.113
Pre-primary enrolment rate (lag) OECD-MEF Model +1ppt in pre-primary enrolment rate -0.67% -0.087
Share of Secondary students in vocational OECD-MEF Model +1ppt in share of students in vocational -0.30% -0.039
Professional development for teachers OECD-MEF Model +1% -0.89% -0.117
Accountability OECD-MEF Model +10% -0.88% -0.115
Autonomy in curriculum OECD-MEF Model +10% -0.51% -0.067
Autonomy in resource allocation OECD-MEF Model -10% -1.27% -0.167
Total -13.35% -1.75 ppt
A macro model estimated at regional level with strong explanatory power (R2=0.90)
Low youth unemployment and labour
market opportunities for low-skill pull
young people out of school
School resources, good management
and quality vocational education (e.g.
late and diversified tracking,
connection with business and
universities) help retain pupils at school
© OECD |
Forecasts of early leavers from school
Educational reforms would be necessary to decrease the share of early leavers by 2024
11
11.5
12
12.5
13
13.5
14
14.5
15
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
Observed Macro previsions Macro previsions + reforms
Key policy recommendations (OECD/WISE+OECD/EDU):
1. Support to learning basic competencies(reading,
writingand numeracy)
2. Financial support for learning (study materials, meals,
transportation)
3. Early detection mechanisms and solutions at school level
4. Reinforcement of alternative pathways at system level
5. Diversified curriculum to respond to different types of
learners
6. Ensure pedagogical support to deal with diversity in the
classroom
7. Coordination with ALMP (especially for the youth)
© OECD |
Wrapping-up
1. Policy-wise:
 There are a multitude of influential policy factors and hence no universal model:
qualitative evidence will always be much needed
 Key policy determinants of health outcomes are those supporting: i) higher living
standards; ii) healthier lifestyles; iii) accessible and affordable care
 Michele Cecchini (OECD/HD) is going to focus on prevention policies
 Key policy determinants of educational outcomes focus on quality education both
in the general and vocational systems
2. Methodology-wise:
 Policy impact elasticities are key information for fast-track policy analysis
 Macro models tend to have higher explanatory power (micro heterogeneity is
washed away) and to yield larger elasticities
 Inclusion of policy determinants can be difficult (lack of indicators) while complete
policy analysis often requires auxiliary information (e.g. behavioural response)
 Forecasting is a difficult exercise based on many assumptions, with sometimes
disappointing results (e.g. subjective variables)
© OECD |
Possible ways forward
1. Analytically: women outcomes on the labour market is a key well-being
issue in Italy (gender and territorial inequality) as discussed with Ministry’s
team, and refined modelling of policy drivers
2. Collective knowledge sharing: establish a well-being platform for peer
learning (cf: WISE PWB in 2023-24)
3. Country support: assisting countries with well-being modelling and
evaluation

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Well-being Analytics for Policy Use in Italy, Fabrice Murtin

  • 1. MODELLING EQUITABLE AND SUSTAINABLE WELL-BEING INDICATORS: WHAT HAVE WE LEARNED? HIGH-LEVEL CONFERENCE ON WELL-BEING ANALYTICS FOR POLICY USE MONDAY 30TH MAY 2022 FABRICE MURTIN HEAD OF RESEARCH, MODELLING AND ADVANCED ANALYTICS, OECD WISE CENTRE
  • 2. © OECD | 2000 to 2005 2004 2002 2006 to 2010 2011 to 2015 2016 to 2021 2013 2014 2015 2009 2010 2012 2011 2021 2019 2018 2017 2016 More than half of OECD countries have developed multidimensional well-being frameworks/ dashboards Canada’s Quality of Life Framework Australian Treasury’s Well-being Framework First Report on a Well-being Framework for Ireland Measures of Australia’s Progress Finland’s Findicators Latvia 2030 Australia’s Welfare Israel Well-being, Sustainability and National Resilience Indicators Indicators of Well-being in Slovenia Finland Strategic Government Programme Indicators France New Indicators of Wealth New Zealand Living Standards Dashboard United Kingdom Measures of National Well- being How’s Austria? Italy Measures of Equitable and Sustainable Well-being (full set) Statistics Portugal Well-being Index Quality of Life Indicators in Korea Mexico Indicadores de bienestar Belgium Complementary Indicators to GDP Well-being in Germany Italy Measures of Equitable and Sustainable Well-being (short set) Indicators Aotearoa New Zealand Spain Quality of Life Indicators Poland Responsible Development Index Iceland Indicators of Well-being Japan Cabinet Office Well-being Dashboard Source: adapted from OECD (2021), COVID-19 and Well-being: Life in the Pandemic http://oe.cd/covid-19-and-well-being Switzerland updated MONET 2030 Indicators New Zealand Treasury Living Standards Framework Sweden New Measures of Well-being Norway – How We Are Doing Netherlands Monitor of Well-being Slovenia National Development Strategy 2030 Luxembourg Index of Well-being Chile Social Welfare Survey
  • 3. © OECD | • Shaping budgeting decisions + long-term fiscal strategies • New legislation • Planning and performance frameworks • New institutional structures • Updated tools for policy design + evaluation Beyond measurement, a growing number of countries have used various well-being policy tools
  • 4. © OECD | How does WISE support well-being policy application? Gathering knowledge about countries’ experiences Applying a well-being lens to specific policy issues (e.g. mental health, child well-being) Developing technical tools for well-being policy analysis Directly supporting countries in building well-being frameworks and policy processes
  • 5. © OECD | The Italian context • From the Italian budget law (n. 163/2016), the Ministry of Economy and Finance has to report on 12 headline indicators of Equitable and Sustainable Well-being (‘Benessere Equo e Sostenibile’) that are now integrated into the economic and financial planning cycle : 1. a yearly Report presented to the Italian Parliament by February 15, in which the evolution of the 12 ESW indicators is analysed in light of the policy measures that were adopted with the recent Budget Law 2. an annex to the Economic and Financial Document (EFD) submitted to Parliament by April 10 that includes a forecast of the 12 ESW indicators over the following three years, both under a no-policy change scenario as well as a policy scenario (considering the impact of the economic policy that the Government intends to adopt over this period). • In this context, WISE support consisted in: • Building macro-econometric models to identify the economic and policy determinants of healthy life expectancy, excess weight and obesity, rate of early leavers from school  Forecast future ESW outcomes based on economic outlook and planned reforms, as requested by the Italian Parliament
  • 6. © OECD | Equitable and Sustainable Well-being indicators Healthy life expectancy in 2019 (years) 60 - 66 56 - 60 52 - 56 48 - 52 Healthy life expectancy Excess weight Early school leaving
  • 7. © OECD | Modelling healthy life expectancy: a multitude of drivers A macro model (estimated from country panel data) with high explanatory power (R2=0.95) A micro model (based on Aspects of Daily Life survey) with reasonable explanatory power (R2=0.30)
  • 8. © OECD | Living standards and lifestyle as key drivers of healthy life expectancy Source Benchmark input Relative Variation of Outcome Absolute Variation of Outcome Macroeconomic Health expenditures per capita OECD-MEF Model +1% expenditure 0.03% 0.026 GDP per capita OECD-MEF Model +1% GDP per capita 0.04% 0.034 Higher Education OECD-MEF Model +1ppt of people with at least upper secondary educ. (from 59.8% to 60.8%) 0.03% 0.029 Lifestyle Prevalence of smoking OECD-MEF Model -1ppt of smokers (from 19.8% to 18.8%) 0.15% 0.124 Alcohol consumption OECD-MEF Model -10% consumption (from 7.6L per capita to 6.8L) 0.14% 0.121 Total 0.40% 0.33 years Macroeconomic Employment rate OECD-MEF Model +1ppt employment rate 0.09% 0.057 Economic insecurity OECD-MEF Model -1ppt of highly insecure 0.11% 0.068 Foster Higher Education OECD-MEF Model -1ppt of people with lower or secondary educ. 0.12% 0.075 Lifestyle Mental health OECD-MEF Model -1ppt of people with low mental health 0.36% 0.223 Diet OECD-MEF Model +/-1ppt of people consumming more vegetables and fruits, and less snacks and sweets 0.18% 0.111 Sport OECD-MEF Model +1ppt of people doing sport and fitness 0.28% 0.171 Obesity OECD-MEF Model -1ppt of overweight or obese people 0.01% 0.008 NCDs OECD-MEF Model -1ppt in the prevalence rate of 15 NCDs 3.13% 1.943 Total 3.96% 2.66 ppt Life expectancy at birth (baseline is 83.6 years) Good health status (% of population - baseline is 62% among 18+) 5 core determinants with strong influence of lifestyle (prevention policies) Strong influence of NCDs (care access & affordability), then mental health and sport
  • 9. © OECD | Forecasts of healthy life expectancy • Life expectancy was projected to decline by 1.1 year in 2020 (1.2 years for males and 1.0 year for females), which compared to 1.2 years in reality. Life expectancy should have recovered its 2019 level by 2022 (epidemiological scenario) or 2023 (economic forecast). • The share of people reporting being in good health was projected to drop by 3.5 percentage points in 2020. This forecast did not materialise, with self- reported good health actually rising in 2020, from 70.4% to 74.3%. • As a result, healthy life expectancy increased in the COVID-19 year of 2020, underlining the challenges related to forecasting subjective indicators. 2018 2019 2020 2021 2022 2023 2024 Male 59.4 59.8 57.6 61.9 62.6 62.8 63.0 63.1 Female 57.6 57.6 55.4 60.1 60.8 61.4 62.0 62.5 Total 58.5 58.6 56.4 61.0 61.7 62.1 62.4 62.8
  • 10. © OECD | Education and lifestyle as key drivers of overweight and obesity Source Benchmark input Relative Variation of Outcome Absolute Variation of Outcome Overweight and obesity (% of population - baseline is 47.6%) Macroeconomic Economic insecurity OECD-MEF Model -1ppt of highly insecure people -0.04% -0.020 Foster Higher Education OECD-MEF Model -1ppt of people with high school or middle high school degree -0.32% -0.153 Lifestyle Healthy diet OECD-MEF Model +1ppt of population having adequate breakfast, awareness of salt intake, -1ppt of population having large alcohol and carbonated drinks consumption -0.43% -0.203 Sport practice OECD-MEF Model +1ppt of population doing sport -0.16% -0.075 Total (excl. Prevention) -0.94% -0.45 ppt Prevention Food labelling & media campaign SPHeP model Broaden food choices and transport options SPHeP model Tax & subsidies SPHeP model Regulation of advertising, banning products SPHeP model see OECD(2019) Table 6.1 see OECD(2019) Table 6.1 see OECD(2019) Table 6.1 see OECD(2019) Table 6.1 A micro model based on Aspects of Daily Life survey with relatively low explanatory power (R2=0.10) Small changes as the model has low R2
  • 11. © OECD | Forecasts of overweight and obesity 1. Forecasts went into the right direction in 2021 2. There are many factors at play 3. The role of public health policies is not factored in this simple micro model Alcohol consumption Vegetarian Ordering food online Salt intake awareness Access to supermarkets Sport Household income Perceived economic insecurity Education attainment Migration Region -5.00% -4.00% -3.00% -2.00% -1.00% 0.00% 1.00% Men Women Total Adequate breakfast Alcohol consumption Vegetarian Ordering food online Salt intake awareness Carbonated drinks consumption Access to supermarkets Access to food markets Sport Time use pressure Reading books Household income Perceived economic insecurity Education attainment Migration Living alone Region Positive contribution (decline in probability of being overweight) Negative contribution (increase in probability of being overweight) 2019 2020 2021 2022 2023 2024 Male 55.0% 56.2% 55.7% 55.9% 55.6% 55.3% 55.0% Female 38.4% 39.6% 38.4% 39.0% 38.4% 37.9% 37.4% Total 46.4% 47.6% 46.8% 47.1% 46.7% 46.3% 45.8%
  • 12. © OECD | Labour market, quality of general and vocational education as key drivers of early leavers from school Source Benchmark input Relative Variation of Outcome Absolute Variation of Outcome Early Leavers from School (% of 18-24 population - baseline is 13.1%) Macroeconomic Education spending as a share of GDP OECD-MEF Model +10% -0.45% -0.059 GDP per capita OECD-MEF Model +1% -0.25% -0.033 Youth unemployment OECD-MEF Model +10% -1.21% -0.159 Share of jobs in industry OECD-MEF Model -5% -1.09% -0.143 Share of jobs in agriculture OECD-MEF Model -5% -1.20% -0.157 Share of jobs in construction OECD-MEF Model -5% -1.81% -0.237 Share of disadvantaged students OECD-MEF Model -1ppt of disadvantaged students -0.66% -0.087 Demographic Share of young people OECD-MEF Model -1% -0.84% -0.111 Migrants rate OECD-MEF Model -5% -0.43% -0.057 Education PISA reading score OECD-MEF Model +1% -0.86% -0.113 Pre-primary enrolment rate (lag) OECD-MEF Model +1ppt in pre-primary enrolment rate -0.67% -0.087 Share of Secondary students in vocational OECD-MEF Model +1ppt in share of students in vocational -0.30% -0.039 Professional development for teachers OECD-MEF Model +1% -0.89% -0.117 Accountability OECD-MEF Model +10% -0.88% -0.115 Autonomy in curriculum OECD-MEF Model +10% -0.51% -0.067 Autonomy in resource allocation OECD-MEF Model -10% -1.27% -0.167 Total -13.35% -1.75 ppt A macro model estimated at regional level with strong explanatory power (R2=0.90) Low youth unemployment and labour market opportunities for low-skill pull young people out of school School resources, good management and quality vocational education (e.g. late and diversified tracking, connection with business and universities) help retain pupils at school
  • 13. © OECD | Forecasts of early leavers from school Educational reforms would be necessary to decrease the share of early leavers by 2024 11 11.5 12 12.5 13 13.5 14 14.5 15 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Observed Macro previsions Macro previsions + reforms Key policy recommendations (OECD/WISE+OECD/EDU): 1. Support to learning basic competencies(reading, writingand numeracy) 2. Financial support for learning (study materials, meals, transportation) 3. Early detection mechanisms and solutions at school level 4. Reinforcement of alternative pathways at system level 5. Diversified curriculum to respond to different types of learners 6. Ensure pedagogical support to deal with diversity in the classroom 7. Coordination with ALMP (especially for the youth)
  • 14. © OECD | Wrapping-up 1. Policy-wise:  There are a multitude of influential policy factors and hence no universal model: qualitative evidence will always be much needed  Key policy determinants of health outcomes are those supporting: i) higher living standards; ii) healthier lifestyles; iii) accessible and affordable care  Michele Cecchini (OECD/HD) is going to focus on prevention policies  Key policy determinants of educational outcomes focus on quality education both in the general and vocational systems 2. Methodology-wise:  Policy impact elasticities are key information for fast-track policy analysis  Macro models tend to have higher explanatory power (micro heterogeneity is washed away) and to yield larger elasticities  Inclusion of policy determinants can be difficult (lack of indicators) while complete policy analysis often requires auxiliary information (e.g. behavioural response)  Forecasting is a difficult exercise based on many assumptions, with sometimes disappointing results (e.g. subjective variables)
  • 15. © OECD | Possible ways forward 1. Analytically: women outcomes on the labour market is a key well-being issue in Italy (gender and territorial inequality) as discussed with Ministry’s team, and refined modelling of policy drivers 2. Collective knowledge sharing: establish a well-being platform for peer learning (cf: WISE PWB in 2023-24) 3. Country support: assisting countries with well-being modelling and evaluation