incorporating well-being indicators in the policy-making
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Incorporating well-being indicators in
the policy-making process
The 2019 Equitable and Sustainable Well-being Annex to the
Economic and Financial Document
Susan Battles, Eleonora Romano and Pietro Zoppoli
Department of the Treasury - Economic and Financial Analysis and Planning
Italy-Germany: Bilateral meeting on demographic change and equal living conditions
Rome, 25th June 2019
Outline
The well-being conceptual framework
Institutional framework at international level
Well-being measurement in Italy
Well-being into policy-making in Italy
Most recent data
Methodological aspects and further work
2
Incorporating well-being indicators in the policy-making process
The well-being conceptual framework
3
• Quality of life: from economic welfare to the wider concept of well-being
• Multidimensionality: economic, social, environmental
• Monetary vs. non-monetary dimensions: trade-offs or win-win situations
• Going beyond GDP: complementing traditional economic measures
Incorporating well-being indicators in the policy-making process
Institutional framework at international level
4
• OECD’s pioneering work (early 2000s)
• Report by Commission Stiglitz-Sen-Fitoussi (2009)
• European Commission’s Communication On GDP and beyond – Measuring progress in a changing world (2009)
• Eurostat Quality of life - Facts and views (2015); Final report of the expert group on
• quality of life indicators (2017)
• OECD
• Better Life Initiative-Measuring Well-Being and Progress: How's Life? Measuring well-being (2011; 2013; 2015; 2017)
• 2018: Beyond GDP-Measuring What Counts for Economic and Social Performance + For Good Measure: Advancing Research on Well-being Metrics Beyond GDP
• United Nations Sustainable Development Goals (2015) – 2030 Agenda
Incorporating well-being indicators in the policy-making process
5
• Equitable and Sustainable Well-being (ESW) indicators by Istat-CNEL
• Framework defined by a committee of experts and civil society (2010): 12 domains and about 130 indicators
• Annual report on Istat-ESW in Italy published by Istat since 2013
Well-being measurement in Italy
The 12 domains of Istat-ESW (2018)
1. Health 7. Security
2. Education and training 8. Subjective well-being
3. Work and life balance 9. Landscape and cultural heritage
4. Economic well-being 10. Environment
5. Social relations 11. Innovation, research and creativity
6. Politics and institutions 12. Quality of services
Source: Istat, Rapporto BES 2018 Incorporating well-being indicators in the policy-making process
Well-being into policy-making in Italy (1)
6
• ESW indicators systematically embedded in the budgetary process
• Monitoring of ESW indicators throughout the year (reform of the budget law - L. 163/2016)
April: ESW Annex to the Economic and Financial Document (EFD) presented by
the Ministry of Economy and Finance
o evolution of ESW indicators over the previous 3 years
o forecasts for the current year and over the budgetary process horizon (unchanged
legislation/policy scenario)
February: Report on ESW indicators by the Ministry of Economy and Finance
o evolution of ESW indicators in light of the budget law (up to year t+2)
• Data on the evolution of the 12 ESW indicators
Italian National Institute of Statistics (Istat): time series and update up to t0 (e.g. 2018)
Ministry of Economy and Finance: forecasts/impact evaluations t, t+1, t+2, t+3 (e.g. 2019-2022)
Incorporating well-being indicators in the policy-making process
Well-being into policy-making in Italy (2)
7
• Committee for the selection of ESW indicators to include in the policy making process: MEF, Istat, Bank of Italy + 2 academic experts
inspired by Istat-ESW methodological framework
Criteria for the selection of indicators: parsimony, data availability and timeliness, feasibility, sensitivity to policy changes
12 ESW indicators chosen (adopted by means of a Ministerial Decree - October 2017) covering 8 Istat-ESW domains
possible future revision of the set of indicators
• Gradual development of methodologies to forecast ESW indicators
at present forecasts/impact evaluations available for 5 ESW indicators
information exchange and collaboration with Istat and other institutions
Incorporating well-being indicators in the policy-making process
Well-being into policy-making in Italy (3)
8
The 12 ESW indicators by domain
ESW domain ESW indicator
Economic wellbeing 1. Per capita adjusted disposable income 2. Disposable income inequality 3. People living in absolute poverty
Health 4. Healthy life expectancy at birth 5. Excess weight (overweight & obese)
Education and training 6. Early leaving from education and training
Work and life balance
7. Non-participation rate, by gender 8. Ratio of employment rate for women aged 25-
49 years with preschool age children to the employment rate of women aged 25-49 without children
Security 9. Predatory crime rates (burglary rates, pick-
pocketing rates, robbery rates)
Politics and institutions 10. Efficiency of civil justice (length of civil
proceedings)
Environment 11. Emissions of CO2 and other greenhouse gases
(tons of CO2 equivalent per capita)
Landscape and cultural heritage 12. Illegal building rate
* In red the 5 indicators with available forecast/impact evaluation methodologies Incorporating well-being indicators in the policy-making process
1. Per capita adjusted disposable income (nominal values, euro)
Citizenship Income
Extension of the
favourable flat-rate
scheme (up to
65.000 euros)
Contract renewal
for public
employees
Hiring in the public
sector
9
Source: 2016-2018: Istat, National Accounts. 2018 data are provisional. 2019-2022:
MEF-DT forecasts.
21835 22234
22699
23150
23674
24157
24690
23151
23688
24177 24655
15000
16000
17000
18000
19000
20000
21000
22000
23000
24000
25000
2016 2017 2018 2019 2020 2021 2022
Adjusted disposable income per capita (nominal values)-unchanged legislationAdjusted disposable income per capita (nominal values)-policy scenario
Incorporating well-being indicators in the policy-making process
Adjusted disposable income, GDP per capita and deflators Nominal and real values; index number 2016=100; % change y/y for deflators)
10
Source: 2016-2018: Istat, National Accounts. 2018 data are provisional. 2019-2022: MEF-DT forecasts.
Incorporating well-being indicators in the policy-making process
0
1
2
3
4
5
6
7
8
9
10
90
95
100
105
110
115
120
2016 2017 2018 2019 2020 2021 2022
Consumption deflator (% change y/y; rhs)
GDP deflator (% change y/y; rhs)
Adjusted disposable income per capita
(nominal values)Adjusted disposable income per capita
(real values)Nominal GDP per capita
GDP per capita
2. Disposable income inequality (S80/S20)
Income support schemes
Citizenship Income
Minimum income («ReI» up to
2019)
’80-euro bonus’
Extension of the favourable
flat-rate scheme (up to 65,000
euros)
20% substitute tax regime for
individual entrepreneurs, artists
and professionals with
revenues between 65,000 and
100,000 euros
Higher income threshold (4000
euros) for non dependent
children (age < 24)
11
Source: 2016-2018: Istat, Eu-Silc; 2017 and 2018 are Istat flash estimates. 2019-
2022: MEF-DF forecasts.
5.9 6.0 6.0
5.7 5.6 5.6 5.6
4
5
6
7
2016 2017 2018 2019 2020 2021 2022
Incorporating well-being indicators in the policy-making process
3. Absolute poverty (% values) Poverty measure on
consumption
expenditures
(household-based)
o Many absolute
poverty thresholds
(household
composition,
geographical area,
municipality type)
o Istat consumption
expenditures
survey
12
Source: Istat, Indagine sulle spese delle famiglie; MEF (GDP per capita, 2010 chain-linked prices).
2018 data on absolute poverty are provisional.
3.6 3.5 3.5 4 4
4 4.3
5.6
6.3 5.7
6.1 6.3 6.9
7.2
3.3 2.9 3.1 3.6 3.9
4.2 4.4
5.9
7.3 6.8
7.6 7.9
8.4 8.5
-8
-6
-4
-2
0
2
4
6
8
10
'05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18
GDP per capita (% change y/y)
Household absolute poverty (differences y/y)
Individual absolute poverty (differences y/y)
Household absolute poverty (head count ratio)
Individual absolute poverty (differences y/y)
Incorporating well-being indicators in the policy-making process
7.0
8.4
3. Absolute poverty (% values)
13
AN IMPACT EVALUATION OF THE CITIZENSHIP INCOME (CI) ON ABSOLUTE POVERTY
PRE CI (2017 microdata) POST CI
Absolute poverty
headcount ratio
(households)
Absolute poverty
headcount ratio
(individuals)
Poverty gap ratio
Absolute poverty
headcount ratio
(households)
Absolute poverty
headcount ratio
(individuals)
Poverty gap ratio
North 5,4 7,0 20,1 4,8 6,5 15,9
Centre 5,1 6,4 18,3 4,3 5,6 14,4
South & Islands 10,3 11,4 22,7 6,6 8,4 16,0
Italy 6,9 8,4 20,9 5,3 7,0 15,7
Source: PRE CI: Istat, Indagine sulle spese delle famiglie 2017; POST CI: MEF-DT impact evaluation
Incorporating well-being indicators in the policy-making process
4. Healthy life expectancy at birth (average number of years)
14
Source: Istat, Tavole di mortalità della popolazione italiana and Indagine Aspetti della vita quotidiana; MEF (PIL pro capite). 2018
data are provisional. Incorporating well-being indicators in the policy-making process
1.3
0.2
-3.3
-2.2
-0.1
1.0 1.3 1.7
1.0
2.3
0.8 0.6
-0.5
0.0 0.1 0.8
-0.1 -0.2
56.4 57.7 58.2 58.5 58.2 58.2 58.3 58.8 58.7 58.6
81.4 81.7 81.9 81.9 82.2 82.6 82.3 82.8 82.7 83.0
-5.0
-3.0
-1.0
1.0
3.0
5.0
7.0
9.0
11.0
13.0
15.0
30
35
40
45
50
55
60
65
70
75
80
85
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
GDP per capita (%change; rhs) Healthy life expectancy at birth (change; rhs)
Healthy life expectancy at birth Life expectancy at birth
5. Excess weight (overweight & obese) (% values)
15
Source: Istat, Aspetti della vita quotidiana (excess weight); MEF(GDP per capita).
Incorporating well-being indicators in the policy-making process
1.6
0.9
-1.8
-6.0
1.3
0.2
-3.3
-2.2
-0.1
1.0 1.3
1.7
1.0
0.2 0.3
-0.2
0.9
-0.5 -0.3 -0.1 -0.3
0.5
-1.4
0.7 0 0
45 45.2 45.5 45.3
46.2 45.7
45.4 45.3 45
45.5
44.1
44.8 44.8 44.8
-7.0
-5.0
-3.0
-1.0
1.0
3.0
5.0
7.0
35
37
39
41
43
45
47
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
GDP per capita (%change; rhs) Excess weight (change; rhs) Excess weight
6. Early leaving from education and training (%values)
16
Source: Istat, Labour Force early leaving from education and training), MEF (GDP per capita).
Incorporating well-being indicators in the policy-making process
0.3
1.6 0.9
-1.8
-6.0
1.3
0.2
-3.3
-2.2
-0.1
1.0 1.3 1.7
1.0
-1.0 -1.7
-0.9
0.1
-0.5 -0.5 -0.8 -0.5 -0.5
-1.8
-0.3 -0.9
0.2 0.5
22.1
20.4 19.5 19.6
19.1 18.6
17.8 17.3
16.8
15.0 14.7 13.8 14.0
14.5
-8
-6
-4
-2
0
2
4
6
8
10
12
14
16
0
2
4
6
8
10
12
14
16
18
20
22
24
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
GDP per capita (% change; rhs)
Early leaving from education and training (change; rhs)
Early leaving from education and training
7. Non-participation rate (%values)
Citizenship income
«Patto per il
lavoro» (labour
market active
policy)
Pension reform
Employment
incentives
Hiring in the public
sector
Childcare vouchers for
nursery fees (public
and private)
17
Source: 2016-2018: Istat, Labour Force Survey. 2019-2022: MEF-DT forecast.
Incorporating well-being indicators in the policy-making process
0 -0.1 -0.2 -0.2
18.2
17.3
16.6 17.1 17.3 17.2 17.2
25.9
24.5
23.6 23.4 23.1
22.8 22.3
21.6
20.5
19.7 19.9 19.9 19.7 19.5
-1
1
3
5
7
9
10
12
14
16
18
20
22
24
26
28
2016 2017 2018 2019 2020 2021 2022
Policy scenario - Unchanged legislation (rhs) Male Policy scenario - Unchanged legislation (rhs)
Female Policy scenario - Unchanged legislation (rhs) Male
Female Total
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
(% values)
18
Source: Istat, Labour Force Survey.
69.7
70.6 70.9
72.4
73.3
71.7 72.4
75.1 75.4
77.5 77.8
76.0 75.5
73.8
64
66
68
70
72
74
76
78
80
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Incorporating well-being indicators in the policy-making process
9. Predatory crime (burglary rate, pick-pocketing rate, robbery rate)
19
Source: Ministry of the Interior data and data from Istat (Indagine sulla Sicurezza dei cittadini). 2018 data are provisional.
8.5
10.1
11.8 10.5 10.2
11.1
13.6
15.2 16.0 16.3
15.0 13.6
12.4 11.8
5.0
6.3
6.5
5.0 4.5
4.5
5.3
5.7
6.4 6.9
6.6
6.2
6.1
5.6 1.3
1.4
1.5
1.3
0.8
0.9
1.1
1.7
1.8 1.6
1.4
1.4
1.3
1.1
14.8
17.8
19.8
16.8
15.5
16.5
20.0
22.6
24.2 24.8
23.1
21.1
19.8
18.6
0
2
4
6
8
10
12
14
16
18
20
22
24
26
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
burglary rate
pick-pocketing rate
robbery rate
Incorporating well-being indicators in the policy-making process
10. Efficiency of civil justice (average length of civil proceedings)
20
Source: Ministry of Justice, Dipartimento dell'organizzazione giudiziaria, del personale e dei servizi - Direzione Generale di
Statistica e Analisi Organizzativa. * Settore CIVILE - Area SICID al netto dell'attività del Giudice tutelare, dell'Accertamento
Tecnico Preventivo in materia di previdenza e dal 2017 della Verbalizzazione di dichiarazione giurata.
471 469
494
482
460
445
429
400
420
440
460
480
500
520
2012 2013 2014 2015 2016 2017 2018
Incorporating well-being indicators in the policy-making process
11. Emissions of CO2 and other greenhouse gases (tons of CO2 equivalent
per capita)
Renewal of
energy
efficiency
incentives
Bonus-malus
mechanism to
incentivise the
purchase of
low- and zero-
emission cars
21
Source: 2017: Istat provisional data; 2018: Istat estimates; 2019-2022 MEF-DT forecast.
7.2 7.2 7.1 7.1 7.0 6.9 6.9
0
2
4
6
8
10
2016 2017 2018 2019 2020 2021 2022
Incorporating well-being indicators in the policy-making process
12. Illegal building rate (number of illegal buildings/100 legal buildings)
22
Source: Cresme, Centro ricerche economiche sociali di mercato per l'edilizia e il territorio. 2018: Istat, provisional data.
11.9
9.9 9.0 9.4
10.5
12.2
13.9 14.2
15.2
17.6
19.9 19.6 19.8 19.0
5
7
9
11
13
15
17
19
21
23
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Incorporating well-being indicators in the policy-making process
Methodological aspects and further work
23
Innovative and challenging approaches (research activity)
Literature review (theoretical and empirical) for each ESW indicator
Development of adequate analytical tools
Indicators/phenomena heterogeniety
Data sources (microdata, national accounts)
Avalilability, timeliness of data (e.g. territorial disaggregation)
Underlying dynamics, sensitivity to public policies
Economic models
Micro approach: microsimulation, impact evaluation (e.g. inequality and poverty)
Macro approach: forecast (e.g. CO2 emissions)
Connection with Macroeconomic forecasts
Incorporating well-being indicators in the policy-making process
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