CFE Trento 23rd February 2018

Regional and Metropolitan data

and tools

Economic Analysis, Statistics and Multi-level Governance

OECD Centre for Entrepreneurship, SMEs, Local Development

and Tourism

Territorial Analysis and Statistics Unit

2

1) What are the main databases/indicators that we

collect/produce?

2) What is the scale at which indicators are available?

4) How are the indicators collected and their timeliness in each

database?

3) What are the tools available to visualise/download the data?

5) What are the main challenges in having comparable

indicators at regional level and at city level?

6) What are your major data needs or challenges you

encounter in using the Regional and/or Metropolitan

database?

•

QUESTIONS ADDRESSED

Mandate of the WPTI (2015-2019)

The objective of the Working Party on Territorial Indicators

(WPTI) is to contribute an evidence based analysis of the

regional characteristics, resources, drivers and potential for

development and to improve the understanding of sub-

national patterns and dynamics of structural change in all

types of regions

Databases at subnational level serve the WPTI

Regional Development Policy

Committee (RDPC)

WP on Territorial Policy in Urban Areas

WP on Territorial Policy in Rural Areas

WP on Territorial Indicators (WPTI)

i. Updating, improving, and broadening the OECD

Regional and Metropolitan databases

ii. Broadening the work on identifying and measuring

functional regions

iii. Deepening the work on measuring people’s well-being in

regions and cities

iv. Measuring business demography and entrepreneurship

v. Supporting evidence for policy decisions and evaluation

vi. Engaging in promoting and sharing innovative methods

to integrate geographical and statistical information

(…)

Intermediary objectives of the Working Party

include:

at subnational level:

– Regional database

– Metropolitan database

at country level:

– Subnational Government Structure

and Finance database

– Tourism database

What are the main databases that we

produce?

6

REGIONAL DATABASE

What is the scale at which indicators are

available?

7

National territory

Large regions (TL2)

Small regions (TL3)

Intermediate

Predominantly rural

Predominantly urban

Rural close to a city

Rural remote

Territorial grid Territorial typology

Based on density Based on accessibility

Territorial grid

Regions in each member country have been classified based on two

administrative territorial levels (TLs):

TL2 large regions are defined as the first administrative tier of subnational

government and consists of 398 OCDE large regions. For EU countries TL2

are equivalent to NUTS2, with the exception of Belgium, Germany and the

United Kingdom for which TL2=NUTS1

TL3 small regions are composed of 2 241 small regions , TL3 = NUTS3 for

EU countries

Regions are subject to change over time, especially for EU countries with a

change of classification every 3 years: e.g. as from the 1st of January 2018, data

are submitted following the new NUTS2016 classification, implying split, mergers,

shifts and change in codes. Historical data for the new breakdowns to be sent by

1 January 2020. (http://ec.europa.eu/eurostat/fr/web/nuts/history)

We keep the longest time series when possible

Territorial grid (pdf) is available in the metadata displayed on Dotstat

The most recent changes in the boundaries of

European regions

Country NUTS 2 NUTS 3

Germany

Cochem-Zell recoded from DEB16 into DEB1C and Rhein-

Hunsrück-Kreis recoded from DEB19 into DEB1D due to boundary

change; merge of DE915 Göttingen and DE919 Osterode am Harz

into DE91C Göttingen

Ireland structure revised from 2 into 3 regions regions reassigned and partially relabelled

France many regions reassigned due to revised NUTS 1

structure

Lithuania

Lietuva split into two: Sostinės regionas and Vidurio ir

vakarų Lietuvos regionas (Capital Region and Central

and Western Lithuania Region)

Vilniaus apskritis (NUTS 3 region) reassigned to new NUTS 2

region LT01 Sostinės regionas (Capital Region); recodings due to

establishment of new regions at NUTS 2

Hungary Közép-Magyarország (HU10) split into two: Budapest

(HU11) and Pest (HU12)

NUTS 3 regions of Budapest (previously HU10, now Budapest

HU11) and Pest (previously HU10, now Pest HU12) reassigned

The Netherlands NL121, 122, 123, 338, 339, 322 and 326 recoded into NL124, 125,

126, 33B, 33C, 328 and 329 due to boundary changes

Poland 2 NUTS 2 regions reassigned, 1 new created (capital

region)

several NUTS 3 regions reassigned due to changes at NUTS 1 / 2

level, 1 new created

Finland Kainuu recoded from FI1D4 to FI1D8 and Pohjois-Pohjanmaa

recoded from FI1D6 into FI1D9 due to boundary change

UK

new region Southern Scotland created from parts of

Eastern Scotland and South Western Scotland (now

Western Scotland)

in Scotland, several regions reassigned due to changes at NUTS 2

level; UKN (Northern Ireland) restructured the NUTS 3 level from 5

into 11 regions

Territorial typology

• TL3 regions are defined following 3 categories of typology:

Predominantly Urban , Intermediate, Predominantly Rural

• and 2 sub-categories: rural remote/close to a city

• This typology was initially based upon municipality density. The

European Union updated its typology which relies on the

classification of grid-cells according to pre-established density

and size thresholds

• The OECD has updated its classification of European TL3

regions following these changes. Non-EU countries could also

be updated after formal approval from countries.

STEP 1:

Identification

of urban

clusters

Regional typology

- For OECD non-EU countries, the data source is the

LandScan - High resolution Global Population Dataset

(Census circa 2011)

- For EU countries (including Switwerland,and Norway), the

data source is Eurostat, JRC and European Commision

Directorate-General for regional Policy

Minimum population density threshold:

- For Japan and Korea, 600 inhabitants per km2

- For other countries, 300 inhabitants per km2

Minimum population threshold:

- For Japan and Korea, 10'000 inhabitants

- For other countries, 5'000 inhabitants

If the share of the regional population in urban clusters is:

- higher than 80%, the region is predominantly urban (PU)

- between 80% and 50%, the region is intermediate (IN)

- less or equal to 50%, the region is predominantly rural

(PR)

Note: the typology is defined for small regions (TL3) , except

for Israel (TL2 large regions) STEP 2:

Define

typology - An intermediate region becomes predominantly urban if at

least 25% of its population lives in urban clusters of at least

500 000 inhabitants

- A predominantly rural region becomes intermediate if at

least 25% of its population lives in urban clusters of at least

200 000 inhabitants

- Predominantly Urban (PU)

Regions are classified as: - Intermediate (IN)

- Predominantly Rural (PR)

5. Adjust the

classification to take in

account the presence of

cities (large urban

clusters)

4. Share of urban

population by region:

Attribute a typology to

regions depending on their

share of population in

urban clusters

3. Urban clusters:

Apply a minimum

population threshold to

contiguous densed cells to

identify urban clusters

2. Density threshold:

Apply a minimum density

threshold to 1km2 grid cells

to identify densed

populated cells

1. Data input:

Population grid density of

1km2

Population share by typology

On-going work: refinement of the terminology with distance criteria to split intermediate and rural categories into Remote / Close to a city with the new typology based on grid cells

0

10

20

30

40

50

60

70

80

90

100

Predominantly Urban Intermediate Predominantly Rural

What are the main indicators that we

collect/produce?

6+ datasets on DotStat:

- Demographics

- Economics

- Labour

- Social & Environmental

- Innovation

- Business Demography (as from 2017)

+ Migration (forthcoming)

+ Well-being dataset

+ Regional Income distribution dataset

+ Subnational finance (as from 2014)

Data collection:

- Annual excel questionnaire

- Eurostat and NSO’s web sites

- Cooperation with STI/STD/ENV

- Own tabulation (e.g. Gallup)

Indicators collected:

- Regular pool of indicators but adapted

to your need.

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 coverageEN

VIR

ON

ME

NT

INN

OV

AT

ION

ED

UC

AT

ION

SO

CIA

LD

EM

OG

RA

PH

IC

EC

ON

OM

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CLA

BO

UR

How are the indicators collected and their

timeliness in each database?

Data main update Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun

Excel Questionnaire to

all countries

sent to

countries

received

processed

processed

+ NSO

web

DotStat

GDP/GVA by industry

(eurostat) Dotstat

Employment by

industry (eurostat) Dotstat

Labour LFS (eurostat) Dotstat

Business Demography Dotstat

Patents (STD) Dotstat

Well-being Dotstat

Subnational accounts Dotstat

Income distribution

dataset (every 2 years)

The OECD Regional Databases

4 – Data visualisation

• DotStat: Regional Database http://stats.oecd.org/Index.aspx?DataSetCode=REGION_DEMOGR

How to access DotStat (STATA/Eviews/SAS/Python):

http://oecdshare.oecd.org/itn/kb/as/AS%20Help/DotStatGet_DirectAccess.pdf

• Data Visualisation: Regional explorer http://stats.oecd.org/OECDregionalstatistics/#story=0

Regional well-being: http://www.oecdregionalwellbeing.org

• Regions at a Glance: http://www.oecd.org/regional/oecd-regions-at-a-glance-19990057.htm

Well-being in OECD regions

Province of Trento

AostaValley

Province of Bolzano-Bozen

Sardinia

Liguria

Province of Bolzano-Bozen Province of

Bolzano-Bozen

Province of Bolzano-Bozen

Province of Bolzano-Bozen

AostaValley

Liguria

Lazio

Sicily Calabria Lombardy

Molise

CalabriaCampania

Basilicata

Campania

Calabria

Campania

Sardinia

Safety Jobs Environment Community CivicEngagement

Income Access toservices

Health LifeSatisfaction

Housing Education

Top region Bottom region

Ra

nkin

g o

f O

EC

D r

eg

ion

s(1

to

39

5)

top

20

%b

otto

m 2

0%

mid

dle

60

%

Province of Trento

Provinces

17

METROPOLITAN DATABASE

OECD-EU definition of Functional Urban

Areas (cities)

Why an harmonised definition of cities?

– Policies need to reflect the reality of where people live and

work

– The connections between cities and with surrounding areas

can lead to important changes in how and where economic

production takes place

– Individual cities are interested in comparing their performance

The approach

– It identifies urban areas beyond city boundaries, as integrated

labour market areas

– It identifies urban areas of different size (small urban,

medium-sized urban, metropolitan and large metropolitan)

– It allows comparisons among the different forms that

urbanisation takes

What is the method for FUA?

• The method uses commuting data and population density

calculated for grid spatial units of 1 km ²

• The functional urban areas are defined as densely populated

municipalities (city cores) and adjacent municipalities with high

levels of commuting towards the densely populated urban cores

(commuting zone).

• A minimum threshold for the population size of the functional urban

areas is set at 50^000 population

• It is applied to 30 OECD countries and identifies 1 198 urban areas

For more details on the methodology: “Redefining urban: a new way to

measure metropolitan areas” , OECD Publishing, 2012

A map of Italian FUAs

• In Italy our method

allows us to

identify 74 FUAs

• Total population in

2014 ranges from

52,000 to 4.2

million (Milan)

• 51% of Italian

population live in

FUAs (Milan

represents 13%)

What is the scale at which indicators are

available?

For the metropolitan areas (FUAs > 500,000 inhabitants)

• OECD-EU definition is applied to 30 OECD countries 1 198 FUAs

covering two-thirds of OECD population + 53 in Colombia.

• Shapefiles of the metropolitan areas available on the website:

http://www.oecd.org/gov/regional-

policy/functionalurbanareasbycountry.htm

• List of municipalities by FUA:

http://www.oecd.org/gov/regional-policy/List-municipalities.xls

- Population (level and growth)

- Population density

- Population by age

- Total Area

- Urbanised 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

- CO2 emissions per capita

- CO2 emissions from transport

and energy sector

Demographic Urban form Territorial organisation

Labour market/Social Environmental Economic and innovation

What are the main indicators that we

collect/produce?

Variable Years available Method

Population (total, core and commuting

zone), population density and by age

2000-2014 Two census data points were collected at municipal level. Inter-census years were interpolated.

GDP (current and constant prices) 2000-2013

Update will be based on the new regional

data

Municipal population data and GDP data at TL3 level were used to downscale GDP at metro

politan level (with the exceptions of Mexico, Canada and Chile where GDP at TL2 level were

used and US for which GDP data at metropolitan level were provided by the Bureau of Economic

Analysis).

Labour (Employment, Unemployment,

Labour force)

2000-2014

Update will be based on the new regional

data

Municipal population data and Labour data at TL3 level were used to downscale Labour data at

metropolitan level (with the exceptions of Portugal, Mexico, Canada and Chile where Labour

data at TL2 level were used and US data for which it was collected from the Bureau of Labour

Statistics).

Labour productivity 2000-2013 Ratio between GDP and total employment in a metropolitan area.

CO2

2005 and 2008

Possibility to compute 2000 data

PM 2.5 estimates 2002, 2005, 2008, 2011 and 2013

Data refer to three-year average

The satellite-based data of air pollution at 1km2 are multiplied by the population living in that

area (using a 1km2 resolution population grid). The exposure to air pollution in a city is given by

the sum of the population weighted values of PM2.5 in the 1km2 grid cells falling within the

boundaries of the city. Finally, the average exposure to PM2.5 concentration is given by dividing

this aggregated value by the total population.

Patents 2000-2008

Possibility to update the data with the

new REGPAT database

Data on patent activity in metropolitan areas are available only for 16 OECD countries .

Extension of countries require correspondence tables between zip –municipalities FUAs.

Housing 5 countries: Canada (2006), US (2012),

Norway (2001), Chile (2002) and Mexico

(2010)

New data would need to be collected for other OECD countries before including it in the

Metropolitan database.

Urban land 2000 and 2010 The finest Global Land Cover dataset: resolution 30m2.

Income

How are the indicators collected and what

are their timeliness?

• OECD.Stat http://stats.oecd.org/Index.aspx?Datasetcode=CITIES

• http://measuringurban.oecd.org/

What are the tools available to

visualize/download the data?

• To improve our capacity to provide useful

information for Trento

Thank you!

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