Towards and OECD Localised Indicator Framework for SDGs - Paolo Veneri, OECD. OECD: Joint WPURB-WPTI workshop - May 14, 2019

1. Joint WPURB-WPTI workshop - May 14, 2019
Paolo Veneri
OECD/CFE
TOWARDS AN OECD LOCALISED
INDICATOR FRAMEWORK FOR SDGs

2. Measuring
• A tailored, consensual and localised indicator framework
• Harmonised and comparable OECD territorial statistics for SDGs
• Consistent definition of « city » and « region »
Learning
• Analyse & discuss local SDGs stories/evolving practices
• Draw lessons in terms of incentives, processes, outcomes
• Pilot-test the indicator framework in different contexts
Sharing
Policy Recommendations
OECD Programme
July 2018 – December 2019
• Peer Peer-to-peer dialogue between cities, regions and national government
• Twinning during field trips and missions
• Engagement of umbrella/city networks and stakeholder groups

3. • i) Mapping of all the work on local SDGs indicators by other
institutions (e.g. Bertelsmann, Eurostat, EC JRC, SDSN, WCCD)
OECD comparative advantages:
– International comparability (i.e. territorial definitions)
– Coverage
– Testing data sources and indicator definitions (dialogue with national statistical institutes)
• ii) Bottom-up inputs from cities and regions
– To integrate the framework and the data from national statistical institutes
• iii) Internal and external function in the work with the pilots
– External: benchmarking with peer cities and regions
– Internal: including additional context-specific indicators
Process for the development of the OECD framework

4. • i) Need to combine international comparable frameworks with context-specific indicators from the cities/regions
• ii) Better disaggregated data than a composite index (black box)
• iii) How to define end values?
• iv) Have a manageable and smart number of indicators (not too many)
• v) Indicators as an awareness raising/policy dialogue tool, not just to create sensitive rankings
• vi) Need to connect statistical persons in cities/regions with the national statistical institutes
• vii) Need to coordinate the various localised indicator frameworks, including ISO data
• viii) How to use data to measure interlinkages and interdependencies (matrix)
• ix) Data visualization should be different for citizens or policy makers: OECD will target local policy makers
• x) Current work of cities/regions on SDGs indicators is also inspiring the OECD framework
• xi) Associations of cities and regions can paly a key role in providing comparable sets of indicators (e.g. VVSG)
Outcomes workshop with the pilots (8 March)

5. SDGs require the participation of every level of government and
sector of society to be achieved
1. Local Governments need to know where they stand against the
2030 Agenda
2. Monitoring progress over time, relative to country and to other
peer regions and cities
3. Promoting an informed dialogue across levels of government and
sectors of society
4. Enhancing partnerships and cooperation
Why to measure the SDGs at the subnational level?

6. “SDG indicators should be disaggregated, where relevant, by income, sex, age, race,
ethnicity, migratory status, disability and geographic location.” - IAEG-SDG
• Which Regions and Cities?
– Regions: first administrative tier of subnational government or TL2 Large Regions.
– Coverage: 600+ TL2 Large Regions of 48 countries (36 OECD and 12 partners):
• 400 OECD TL2 large Regions
• + TL2 large Regions of 5 LAC countries, 5 EU28 countries, Tunisia and Russia
– Cities: Metropolitan areas or Functional Urban Areas of more than 250K people.
– Coverage: 600+ FUAs of 33 OECD countries and Colombia
• 628 OECD FUAs
• + FUAs of Colombia
At which subnational scale?

7. The concept of Functional Urban Area (FUA)
City of Paris
Core of FUA
City of Paris
Commuting zone of FUA
Core of FUA
City of Paris
A metropolitan area is a FUA of 250 000 population or more
Commuting zone
City (Core of FUA)

8. • i) What targets and indicators are
relevant at the subnational scale?
– Place-relevant vs. place-neutral
• ii) What targets and indicators are
relevant in OECD countries?
– Developed vs. developing country
Identifying SDG Targets and Indicators for OECD Regions
and Cities based on UN Global Indicator Framework
Place -
relevant
OECD
relevant
OECD Place-relevant

9. • Beyond Goal 11 on “Cities and human settlements” and Goal 10
looking at “Inequalities within Countries”; some Targets and
Indicators with an important subnational component are:
– For being a competence or jurisdiction of a Subnational government
• 1.5.4 Local government implementing a disaster risk reduction strategy
• 5.5.1 Proportion of seats held by women in local governments
– Due to potential important regional disparities within and/or across
countries
• 1.2.1 People living in relative poverty
• 8.5.2 Unemployment rates, by sex and age
• 16.1.1 Homicides per 100,000 people
• 8.1.1 Annual growth rate of real GDP per capita
• 11.6.2 Exposure to air pollution (PM2.5)
Relevant SDG Targets and Indicators for OECD
Regions and Cities

10. Large disparities within countries in Cities’
Relative poverty rates
% of people with household income below half the national median household income, 2016
SDG Indicator 1.2.1: Proportion of population living below the
national poverty line

11. – For being a competence or jurisdiction of National government
• 1.5.3 Implementing a National disaster risk reduction strategy (Sendai
Framework)
• 8.8.2 National legislations for labour rights (ILO)
– Due to development, contextual or cultural characteristics of
OECD countries:
• 8.7 Ending exploitation of children as soldiers
• 7.1.1 Access to electricity (instead focus on % of renewable energy)
• 2.1.1 Undernourishment (instead focus on Obesity rate)
Less relevant SDG Targets and Indicators for OECD
Regions and Cities

12. 7+ datasets on DotStat:
- Demographics
- Economics
- Labour
- Social & Environmental
- Innovation
- Business Demography
- Migration
+ Well-being dataset
+ Regional Income distribution dataset
+ Subnational finance
Data collection:
- Annual excel questionnaire
- Eurostat and NSO’s web sites
- Cooperation with other OECD Directorates
- Own tabulations (e.g. microdata from surveys or
administrative sources)
- Modelled indicators (i.e. exposure to PM2.5)
Indicators collected
Resident Population by age and gender
Deaths by age and gender
Number of private households
Inter-regional migration
GDP ; GVA by industry (ISIC rev.4)
Primary Household Income ; Disposable Household Income
Deflators (regional accounts)
Employment ; Labour force ; Young labour force
Unemployment ; Long term unemployment ; Youth unemployement
Employment at place of work by industry
Part-time employment by gender
Labour force attainment by ISCED level
Students enrolment by ISCED level
R and D by sector (expenses and number of personnel)
Percentage of households with broadband access
Rate of young NEET ; Rate of early leavers from education
Number of physicians ; Number of hospital beds
Life expectancy at birth ; Infant mortality ; Transport-related mortality rate
Number of motor vehicles theft ; Number of homicides
Private vehicles
Voters
Municipal waste ; Recycled municipal waste
Air pollution (PM2.5 level) ; CO2 emissions by sector
CO2 emissions by sector
Share of land by type of coverage
ENVIRON
MENT
INNOVATION
EDUCATION
SOCIAL
DEMOGRAPH
IC
ECONOMI
C
LABOUR
The Regional Database
http://stats.oecd.org/Index.aspx?DataSetCode=REGION_DEMOGR

13. The unemployment gender gap has decreased in most OECD regions
from 2011 to 2017; however, there are still several exceptions in
southern European regions
SDG Indicator 8.5.2: Unemployment rate, by sex, age and
persons with disabilities
Tabasco
Antofagasta
BrusselsRegion
East
Podkarpacia
North
N.GreatPlain
Nevada
Hauts-de-France
East
Alberta
West
Moravia-Silesia
Border,Midland,W.
SeoulRegion
Ticino
Azores
Vienna
West
Groningen
GreaterLondon
South
S.-Kanto
EastMiddle
Saxony-Anhalt
Northland
AgderandRogaland
ReykjavikRegion
0
1
2
3
4
5
6
2017 Country average 2011 Highest regional gap 2017 Highest regional gap (name)
%-points %-points
S.E.AnatoliaE.
Campania
West
Extremadura
0
5
10
15
20
25
30
Difference between female unemployment rate and male unemployment rate
(in percentage points), 2011 and 2017

14. The OECD Regional and Metropolitan databases are
our main sources of data (but not only)
- Population (level and growth)
- Population density
- Population by age
-Total Area
-Built-up area (share and change)
-Polycentricity
-Concentration of population in core
areas
- Sprawl index
- Local units
- Local units in core area
- Territorial fragmentation
- GDP (level and growth)
- Disposable income per equivalent
household
- Income inequality (Gini index)
- Patents application
- Employment (level and change)
- Labour force (level and change)
- Unemployment (level and change)
- Income segregation (Entropy-
based index)
- Air pollution (PM 2.5)
Demographic Urban form Territorial organisation
Labour market/Social EnvironmentalEconomic and innovation
http://stats.oecd.org/Index.aspx?Datasetcode=CITIES
The Metropolitan Database

15. Exposure to air pollution in Australian cities (PM2.5), 2017
Indicators from Collaborations: Environment Database
SDG Indicator 11.6.2: Annual mean levels of fine particulate matter
(e.g. PM2.5 and PM10) in cities (population weighted)
End-value set by WHO

16. Bridging the data gaps
• OECD/CFE from other sources (in progress)
– GHS Grids: Goal 11, Built-up area growth over population growth
– Gallup World Poll: Goal 16, Feeling of Safety (%)
– World Database on Protected Areas: Goal 15, protected terrestrial zones
• WPTI: Questionnaire (collecting responses)
– Goal 3: Obesity rate
– Goal 4: Pupils and students enrolment
– Goal 5: Women victims of physical or sexual violence (%);
Women Mayors (%)
– Goal 6: Population connected to at least secondary wastewater treatment (%)
– Goal 7: Final energy consumption per capita
– Goal 12: Municipal waste that is recycled (%)

17. Indicators provided by WPTI delegates on new SDG
module of Questionnaire

18. Other sources: Gallup World Poll
% of people that feels safe by walking alone at night in the place where they live, 2008-2018
SDG Indicator 16.1.4: Proportion of population that feel safe
walking alone around the area they live

19. For which of the subnational Targets do we have
indicators? (in progress)
% of subnational Targets with at least one available indicator, by Goal
Around 100 indicators that allow monitoring progress in 59 out of the 100 targets
identified as very relevant for OECD regions and cities.
88
86
83
67
67
67
67
63
60
60
56
50
50
33
29
25
20
0
10
20
30
40
50
60
70
80
90
100
Goal8
Goal3
Goal16
Goal1
Goal7
Goal9
Goal15
Goal4
Goal2
Goal10
Goal11
Goal5
Goal6
Goal13
Goal12
Goal14
Goal17
%

20. • Defining “End-values” (UN, other experts, OECD)
– Some indicators have predefined end-values: Absolute values or relative to initial
levels.
– Certain indicators have not a clear “positive” sense, but still informative.
How do we define the end-values of indicators with a clear “positive” sense?
Our approach:
• Relative to peer’s performances: average of best performing regions, the “best region” of each
country.
• Specific end-values based on macro-regions: OECD vs. Non-OECD.
• How to communicate the results in a simple way?
Our approach:
A Composite Index by goal
• Normalise indicators from 0 to 100 where 100 is end-value.
• The composite index is the average of the normalised indicators.
Measuring the distance to the goals

21. OECD Visualisation tool
• Main objective
• Allowing policy-makers and other stakeholders to identify the distance
of their region and or city to the SDGs in an intuitive and easy way
• Other features
• Provides comparisons to country average and OECD peer regions or
cities
• Allows regions and cities to identify alike peers with similar or better
SDGs outcomes
• Coverage
• Around 1000+ OECD TL2 Regions and FUAs
• Around 90+ indicators for TL2 Regions; 20+ for Cities

22. Type your city or region to find out
Type your region or city
LOCALISING
IN OECD
REGIONS CITIES
Type your Region Type your City
Imagine you type
Brussels-Capital
Region

23. Goal 1
Goal 2
Goal 3
Goal 4
Goal 5
Goal 6
Goal 7
Goal 8
Goal 9Goal 10
Goal 11
Goal 12
Goal 13
Goal 14
Goal 15
Goal 16
Goal 17
End-value for 2030 Country average Regions Brussels-Capital Region
Performance in Goal 4
for Brussels-Capital Region
(Score from 0 to 100)
END-VALUE
(Normalised to 100)
Each Region and City can visualise its distance to an end-value
for 2030, compare to its peer regions and to country average
The tool suggests three
regions or cities with similar
characteristics than
Brussels-Capital R. but with
better outcomes
Suggested Region 2
Suggested Region 1
Link to SDG initiatives of Region 1
Link to SDG initiatives of Region 2
Click here to compare to all
OECD regions or cities

24. .
.
.
END-VALUE
Metadata and
methodologyBrussels-Capital Region
Brussels-Capital R.
Brussels-Capital R.
Brussels-Capital R.
Brussels-Capital R.
OECD
average
OECD
average
OECD
average
OECD
average
Each Region and City can visualise its distance to an
end-value relative to all OECD regions or cities
Each circle represents an OECD
region or city

25. END-VALUE
Metadata and
methodology
1.1 By 2030, eradicate extreme poverty for all
people everywhere, currently measured as
people living on less than $1.25 a day
1.1.1 Proportion of population below the international poverty
line, by sex, age, employment status and geographical
location (urban/rural)
1.2 By 2030, reduce at least by half the
proportion of men, women and children of all
ages living in poverty in all its dimensions
according to national definitions
1.2.1 Proportion of population living below the national poverty
line, by sex and age
1.2.2 Proportion of men, women and children of all ages living
in poverty in all its dimensions according to national
definitions
OECD
average
Texas is below
the target
Pennsylvania is
above target
Type Region/City name…
Brussels-
Capital R.
Brussels-Capital R.
Brussels-Capital R.
Brussels-Capital R.
OECD
average
OECD
average
OECD
average
Each Region and City can visualise its distance to an
end-value by Goal and by Indicators

26. 1. Among the most relevant Goals or Targets in your Region or
City, which are the ones with the lowest coverage in terms of
indicators and what actions are you taking to overcome this
issue?
2. For the indicators without a predetermined “End-value”, how
are you defining this value?
3. For a composite index by Goal, what is your preferred
methodology to normalise and aggregate indicators?
Questions for discussion?