version: june 2016 - oecd regional well-being// 5 i. framework to measure regional and local...

Version: June 2016

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How does your region perform when it comes to education, environment, safety and other topics

important to your well-being?

The interactive website allows you to measure well-being in your region and compare it with 395 other OECD regions based on eleven topics central to the quality of our lives.

It uses several indicators to rank regions, see trends over time and understand how large disparities are across regions.

Explore the visualisation

www.oecdregionalwellbeing.org

Give your feedback

[email protected]

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Table of Contents

Introduction ............................................................................................................................................ 4

I. Framework to measure regional and local well-being ........................................................................................... 5

II. The Interactive web tool at a glance www.oecdregionalwellbeing.org ........................................... 7

III. Defining Scores and Trends ................................................................................................................ 9

1. Regional well-being scores .............................................................................................................. 9

2. Trends ............................................................................................................................................. 9

3. Regions with similar well-being profiles in other countries ......................................................... 10

4. Regional disparities in a topic within a country ............................................................................ 10

IV. Defining Regions .............................................................................................................................. 11

V. Well-being Topics and Indicators...................................................................................................... 13

1. Income .......................................................................................................................................... 14

2. Jobs ............................................................................................................................................... 15

3. Health ............................................................................................................................................ 16

4. Education ...................................................................................................................................... 17

5. Environmental outcomes .............................................................................................................. 18

6. Safety ............................................................................................................................................ 19

7. Civic engagement and governance ............................................................................................... 20

8. Access to services .......................................................................................................................... 21

9. Housing ......................................................................................................................................... 22

10. Community .................................................................................................................................. 23

11. Life satisfaction ........................................................................................................................... 24

VI. Topics and indicators in the OECD Better Life Index and in the Regional well-being tool .............. 25

VII. Sources and References .................................................................................................................. 25

a. Data source and period ................................................................................................................. 25

b. Statistics for Israel ......................................................................................................................... 27

c. Country code ................................................................................................................................. 27

d. References .................................................................................................................................... 27

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Introduction

Where people live matters for their well-being. Quality of life is shaped by a multitude of factors - from income and jobs to health and environment, among others. Our results show that quality of life varies greatly, not only between countries, but also within countries.

The mix between different well-being dimensions is unique to each community where people live, study, work and connect. Improving people’s lives requires making where they live a better place.

Understanding personal well-being is crucial to gear public policies towards better societies. As many of the policies that bear most directly on people’s lives are local or regional, more fine-grained measures of well-being will help policy-makers to enhance the design and targeting of policies. They can also empower citizens to demand placed-based policy actions that respond to their specific expectations and, in turn, to restore people’s trust.

The OECD publication How’s life in your region? builds on the Better Life Initiative, that measures well-being at national level, as well as on the work carried out on regional inequalities through Regions at a Glance. How’s Life in your region? provides:

a conceptual framework for measuring well-being in regions and cities;

a common set of internationally comparable indicators of well-being and a critical assessment of the statistical agenda ahead;

guidance to policy-makers at all levels on the use of well-being metrics for improving policy results.

This Guide describes the general framework of How’s life in your region? and the methodology used to visualise the set of regional well-being indicators found in the interactive web tool.

For further analysis on well-being in regions, read the publication OECD Regions at a

Glance, available on June 16, 2016 at:

http://www.oecd.org/gov/regions-at-a-glance.htm

http://www.oecd.org/bookshop?9789264217416


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I. Framework to measure regional and local well-being

The framework for regional and local well-being starts with the consideration that making better policies for better lives means understanding what matters to people. What do people perceive and value about their local conditions? How do they behave when they are not satisfied with one aspect or more of their life? Do local inequalities in the accessibility of services matter in shaping citizens’ choices and do they have an impact on national well-being? How much does the place where we live predict our future well-being? These are some of the questions that are addressed in the OECD work on measuring regional well-being.

The OECD conceptual framework for measuring well-being in regions and cities has seven distinctive features (Figure 1):

It measures well-being where people experience it. It focuses both on individuals and on place-based characteristics, as the interaction between the two shapes people’s overall well-being.

It concentrates on well-being outcomes that provide direct information on people’s lives rather than on inputs or outputs.

It is multi-dimensional and includes both material and non-material dimensions.

It assesses well-being outcomes not only through averages but also by how they are distributed across regions and groups of people.

It is influenced by citizenship, governance and institutions.

It takes account of complementarities and trade-offs among the different well-being dimensions.

It looks at the dynamics of well-being over time, at its sustainability and at the resilience of different regions.

Eleven well-being dimensions are identified and a set of indicators developed for the 395 OECD regions.1 This set of indicators can also serve as a common reference for regions that aim to develop their own metrics of well-being. The availability of indicators comparable across regions and countries can be useful not only for benchmarking the relative position of a place, but also as a catalyst for policy-makers, to spur public support for action and to create a mechanism for prioritising resources.

The conceptual framework to measure regional well-being builds on over ten years of OECD work focusing on measures of people's well-being and societal progress which led to the creation of the Better Life Initiative. The OECD Framework for Measuring Well-Being and Progress, developed as part of the Better Life Initiative, proposes to measure well-being through a multi-dimensional approach expanding on the work done by the Commission on the measurement of economic performance and social progress (Stiglitz et al., 2009). The publications How’s Life? (OECD 2015) and the Better Life Index web tool identify eleven

1 The OECD defines regions as the first tier of sub-national government (for example states in the United

States, provinces in Canada, or “régions” in France). See “IV. Defining Regions” to learn more.

http://www.oecd.org/statistics/betterlifeinitiativemeasuringwell-beingandprogress.htm

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dimensions that play a key role in individuals’ well-being and provide a set of indicators to measure them, allowing cross-countries comparison.

Figure 1: Regional well-being conceptual framework

A second important inspiration behind the conceptual framework for regional well-being is the OECD Regions at a Glance series. This work has shown that disparities within and among regions in jobs, income, quality of life and sustainability still characterise most OECD countries (OECD 2016).

Sub-national data offer a clearer picture of how life is lived than national averages do, allowing people to recognise their own experience more easily. A closer look at regional data shows that well-being in a region may differ widely according to the dimension considered. No country appears to have regions that enjoy simultaneously high or low levels of well-being in every dimension. For instance, a region may enjoy a satisfactory level of employment but suffer from poor environmental conditions; in another region, an increase in public transport may improve job outcomes, making it easier to commute to work, as well as improve air quality.

Data on disparities among and within regions might also capture the well-being of groups of people more accurately than national data do, especially when these groups are not distributed evenly across space. For example, health outcomes are likely to be influenced by the demographic characteristics of rural and urban populations.

Spatial analysis may also help to shed light on the impact of perceived distribution inequalities on subjective well-being. Evidence shows that individuals assign great importance to the inequalities they experience in their local living context when assessing their own well-being and forming expectations about returns of education and skills, and fairness and efficiency of service delivery.

Place characteristicsPeople’s well-being

Individuals’

characteristics

Including citizenship,

governance and institutions

People’s well-being is composed of many dimensions

Average outcomes and distribution across regions and

groups of people


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II. The Interactive web tool at a glance www.oecdregionalwellbeing.org

* Website design and production by Moritz Stefaner and Dominikus Baur with support from Raureif GmbH

The interactive website is a means to initiate a conversation on well-being on what people know best – their home region. The web tool localises the region where the user is and shows how the region fares on eleven well-being topics (for example Ile-de-France in the figure).

For each topic, a score on a scale from 0 to 10 is attributed to the region, based on one or more indicators. A higher score indicates better performance in a topic relative to all the other regions.

The regional well-being is assessed by looking at the different topics represented by the eleven branches of the illustration. The length of each branch reflects the performance (the score) of the region relatively to the other OECD regions.

The web tool does not include a regional composite well-being index. The trade-off between a composite index (which conveys a single unified view, but may dilute information) and a range of indicators (which offers detailed information, but is more difficult to communicate) is widely debated. As OECD (2014) underlines, translating a composite index into concrete policy messages and actions has proven to be a complex task in practice for regional policy makers. Therefore in the web tool we do not make a single statement about the overall well-being in a region. Instead, we present the information in such a way that users can consider the relative importance of each topic and bring their own personal evaluations to these questions.

“The user experience of the website is centred around the measurement of single regions in their

context. Reflecting your own region in context provides a natural starting point for further

explorations. For example, the option “regions with similar well-being” visualise other regions with

the same level of well-being all over the world. Who knew that Massachusetts and Hamburg are

actually not that far apart, when it comes to well-being? Or that Bavaria has a similar profile to

Northern Norway?” (M. Stefaner – Information designer for oecdregionalwellbeing.org)

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Below the findings for each region, users can also visualise regions from other countries with a similar combination of well-being outcomes.

Each region’s well-being can be compared with that of the other regions.

When selecting a specific topic of interest – for example health – the score for

the region is presented , as well as the relative position of the region compared to

the other regions in the same country , its relative position compared to all of the

OECD regions , and the trend, whether the region has increased or decreased its

relative ranking in the past decade . Values of well-being indicators expressed in their original units (percentage, dollars,

etc.) are at the bottom of the card , and you can share the card to your social

network .

You can also compare countries on the basis of their average score in

each topic2 and on the disparities of well-being outcomes across regions of

the same country . Regional disparities in a topic are measured by looking at the difference between the top and bottom 20% regional values in that specific topic compared to the other OECD countries.

2 The country average scores may differ from those obtained through the BLI since the underlying set of

indicators may be different. National comparisons ought to be done with the BLI rather than with the regional

well-being indicators as the BLI selection of indicators better reflects the national perspective

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2

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III. Defining Scores and Trends

1. Regional well-being scores

Well-being indicators are expressed in different units, for example the household disposable income per capita is expressed in USD whereas voter turnout is the percentage of registered voters who voted at the most recent national election. In order to compare indicators on a same scale, they have been normalised using the min-max method (OECD, 2008), a statistical formula that range values from 0 to 10. Three steps are followed to transform the regional value of an indicator into a well-being score:

1. Identify the regions with the minimum and the maximum values of the indicator across OECD regions;

2. Normalise each indicator with the min-max formula; and

3. Aggregate scores, when a topic contains more than one indicator.

First, for each indicator, the 395 regions have been sorted from the region with the lowest value to the region with the highest value. In order to reduce the skewness of the distribution, a threshold has been applied to eliminate the values that are below the 4th percentile and above the 96th percentile. In the case of the homicide rate, since only few regions have a very high value, the cut-offs are the 10th and the 90th percentile respectively. Imposing a threshold on extreme values allows to obtain well-being scores that are more evenly distributed and avoids cases where (as e.g. in the homicides rate) almost all regions would be comprised between 9 and 10. Secondly, the min-max formula is applied, the extreme values identified in the first step are assigned to the scores of 0 and 10, and other regions are assigned to a score x̂i. Indicators that correspond to lower well-being outcomes (unemployment rate, mortality rate, air pollution and homicide rate) are inversely coded x̌i:

𝑥𝑖 = (𝑥𝑖 −min(𝑥)

max(𝑥) − min(𝑥)) × 10 𝑥𝑖 = (

max(𝑥) − 𝑥𝑖

max(𝑥) − min(𝑥)) × 10

Finally, when a topic of well-being is measured by two indicators, like job which is composed by employment and unemployment rates, the score is defined by the arithmetic mean of the normalised value of the respective indicators.

2. Trends

Well-being trends compare the score of the region from the most recent year to its score in the early 2000s (2006 regarding internet broadband access). It shows if the region has progressed in the topic, relatively to the other regions. The main constraint to assess trends is related to the missing data in the earliest period, where some missing regions can jeopardise the comparability of the score across time. In order to overcome this issue, the indicators were normalised in the two periods using only the sample of regions for which values are available in the earliest period. Evolution of the score above +5% or below -5%

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over the period is considered respectively as an improvement (increasing arrow) or a decline (decreasing arrow), otherwise as a stable situation (horizontal arrow).

3. Regions with similar well-being profiles in other

countries

The interactive web tool presents regions from other countries that have a similar level of well-being outcomes as the selected region. The calculation to identify similar regions is based on the sum of the absolute differences in the topics scores, the so-called Manhattan distance. If one value in a topic is not available, the difference is set at 5 by default. The top four regions from different countries with the lowest distance to the selected region are displayed.

4. Regional disparities in a topic within a country

Low regional disparities (or regional similarities) within a country indicates the degree to which well-being outcomes are similar between regions belonging to the same country.

International comparability of regional disparities is limited by the fact that indexes are very sensitive to the size and number of regions. In fact, as the size of regions increases (or the number of regions decreases), territorial differences tend to be averaged out and disparities decrease. This effect can be reduced – but not totally be eliminated – by comparing the performance of top 20% regional values with the bottom 20% regional values.

An index to measure regional disparities in a country for each topic has been computed comparing the ratio between top and bottom 20% regional values of a country to the ratio of top and bottom 20% regional values in the OECD area. The index is then expressed in terms of similarity rather than disparities so that higher values of the index correspond to better territorial cohesion in the country: it ranges between 0 and 10, where 0 means the country has large regional disparities relatively to the other countries and 10 means that the country has small disparities relatively to the other countries.

�̿� = (1 −(

𝑡𝑜𝑝(𝑋)𝑏𝑜𝑡𝑡𝑜𝑚(𝑋)

) − bottom(𝑂𝐸𝐶𝐷)

top(𝑂𝐸𝐶𝐷) − bottom(𝑂𝐸𝐶𝐷))× 10

where top and bottom refer to the regional share in each indicator and corresponding to 20% of the national population.

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IV. Defining Regions

There are many ways to identify a region within a country: according to its administrative boundaries, whether it represents an electoral district, according to the space where people travel to work, according to the geographical features or instead economic functions, etc.

For analytical purposes, the OECD classifies regions as the first administrative tier of sub-national government (for example States in the United States, Provinces in Canada, or Régions in France). This classification is used by National Statistical Offices to collect information and it represents in many countries the framework for implementing regional policies.

While the number of regions (so called Territorial Level 2 or TL2 in the OECD classification) varies from country to country, the international comparability is ensured by the fact that these administrative regions are officially established in countries. No regions are defined in Luxembourg, while in Estonia only smaller than TL2 regions are defined and thus the 5 smaller regions (Territorial Level 3) are included in the interactive web tool. The well-being topics and indicators are shown for the 395 regions (Table 1).

The OECD publication Regions at a Glance? (OECD, 2016) also documents, when possible, well-being in smaller administrative regions (2 197 regions) and in the 281 metropolitan areas (functional urban areas with more than 500 000 population).

While the regional classification is being extended to non-OECD countries, the regional well-being indicators are currently available only for the 34 OECD countries.

Table 1: Number of regions in OECD countries

Country Territorial level 2 (number of regions)

Australia States/territories (8)

Austria Bundesländer (9)

Belgium Régions (3)

Canada Provinces and territories (13)

Chile Regions (15)

Czech Republic Oblasti (8)

Denmark Regioner (5)

Estonia Groups of maakond (5, TL3)

Finland Suuralueet (5)

France Régions (22)

Germany Länder (16)

Greece Regions - Perifereies (13)

Hungary Planning statistical regions (7)

Iceland Regions (2)

Ireland Groups regional authority regions (2)

Israel Districts (6)

Italy Regioni (21)

Japan Groups of prefectures (10)

Korea Regions (7)

Luxembourg State (1)

http://www.oecd.org/gov/regional-policy/regionalstatisticsandindicators.htm

http://www.oecd.org/gov/regional-policy/regionalstatisticsandindicators.htm

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Mexico Estados (32)

Netherlands Provinces (12)

New Zealand Regional councils (14)

Norway Landsdeler (7)

Poland Vojewodztwa (16)

Portugal Comissaoes de coordenaçao e des. regional + regioes autonomas (7)

Slovak Republic Zoskupenia krajov (4)

Slovenia Kohezijske regije (2)

Spain Comunidades autonomas (19)

Sweden Riksomraden (8)

Switzerland Grandes regions (7)

Turkey Regions (26)

United Kingdom Regions and countries (12)

United States States and the District of Columbia (51)

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V. Well-being Topics and Indicators

Overview

A set of indicators to measure the different topics of well-being has been developed for the 395 OECD regions. These indicators, comparable across OECD countries, come from official sources in most of the cases and are available over different years. They are publicly available in the OECD Regional Well-Being Database. At present, regional measures are available for OECD countries in eleven well-being topics: income, jobs, housing, education, health, environment, safety, civic engagement and governance, access to services, community, and life satisfaction (Table 2).

Regional measures, comparable across countries, are not currently available on work-life balance, which is instead included in the OECD Better Life Initiative at the national level. The OECD plans to include this indicator in future releases.

For each topic, one or two indicators have been selected (Table 2). Improvements in the way we measure the well-being topics in regions are underway: for example, additional measures of access to services or indicators that measure other environmental performance are being developed. A larger set of indicators is available in the OECD publication Regions at a Glance (OECD, 2016), including measures of income inequalities within regions.

Table 2: Well-Being topics selected for visualisation

Topics Indicators

Ma

teri

al

co

nd

itio

ns Income Household disposable income per capita (in real USD PPP)

Jobs Employment rate (%)

Unemployment rate (%)

Housing Number of rooms per person (ratio)

Qu

ali

ty o

f life

Health Life expectancy at birth (years)

Age adjusted mortality rate (per 1 000 people)

Education Share of labour force with at least secondary education (%)

Environment Estimated average exposure to air pollution in PM2.5 (µg/m³), based on

satellite imagery data

Safety Homicide rate (per 100 000 people)

Civic engagement Voter turnout (%)

Accessibility of services

Share of households with broadband access (%)

Su

bje

ct

ive

we

ll-

be

ing

Community Percentage of people who have friends or relatives to rely on in case of need

Life satisfaction Average self-evaluation of life satisfaction on a scale from 0 to 10

Reference years: see details in section VII. Source: OECD Regional Well-Being Database.

http://stats.oecd.org/Index.aspx?DataSetCode=RWB

http://www.oecdbetterlifeindex.org/

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1. Income

Why does it matter for local well-being?

Income is an important component of individual well-being as it allows people to satisfy their basic needs and meet other purposes that are important for their lives. It is also associated to life satisfaction, perceived social status and social connections.

Indicator

The disposable income of private households per capita is derived from the balance of primary income by adding all current transfers from the government, except social transfers in kind, and subtracting current transfers from the households such as income taxes, regular taxes on wealth, regular inter-household cash transfers and social contributions. Regional disposable household income is expressed per capita (per person), in USD purchasing power parities (PPPs) at constant prices (year 2010).

Measuring outcome and trends

Inter-regional disparities in household income are large in many OECD countries. In Australia, Mexico, the United States, Slovak Republic and Turkey, people in the top income region were more than 40% richer than the average citizen in 2014.

Figure 2: Regional disparities in disposable income

Top and bottom values as a % of disposable income per capita in the country’s median region, 2014

Source: OECD Regional Well-being database. Note: available years are listed section VII. Sources and References

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2. Jobs

Why does it matter for regional well-being?

Employment represents another well-being dimension that can have a huge impact on the material conditions of people. In addition, having a job helps people maintain and develop their skills, and it affects other well-being dimensions, such as health, social connections and life satisfaction. Unequal access to employment is a major driver of inter-regional inequalities.

Indicators

The employment rate is calculated as the ratio between employed persons and working age population (aged 15-64 years).

The unemployment rate is defined as the ratio between unemployed persons and labour force, where the latter is composed of unemployed and employed persons.


Unemployment has soared in OECD countries in the years after the economic crisis and, although it partially recovered, the unemployment rate in 2014 was 7.3 in the OECD area, still 1.7 points higher than in 2007. In 2014, the difference in unemployment rates among all OECD regions was above 30 percentage points, almost 10 percentage points higher than the difference in unemployment among OECD countries. The largest regional disparities in unemployment rates were found in Turkey, Spain, Italy and Belgium.

Figure 3: Regional disparities in unemployment

Regions with the lowest and highest unemployment rate, 2014


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3. Health


There are also strong regional disparities in health outcomes, which are partly explained by unequal access to health services.

Indicators

Life expectancy at birth measures the number of years a new born can expect to live, if death rates in each age group were to stay the same during her or his lifetime.

Age-adjusted mortality rates eliminate the difference in mortality rates due to a population’s age profile and are comparable across countries and regions. Age-adjusted mortality rates are calculated by applying the age-specific death rates of one region to the age distribution of a standard population. In this case, the population by five years age class, averaged over all OECD regions.


In 55% of OECD regions life expectancy at birth now exceeds 80 years. The lowest levels of life expectancy, below 75 years, are found in 30 OECD regions. The difference in life expectancy among OECD countries is 8 years (between Spain or Japan and Mexico). Within countries, it is 11 years between British Columbia and Nunavut in Canada, and 6 years between the Capital Territory and the Northern Territory in Australia, or Hawaii and Mississippi in the United States.

Figure 4: Regional disparities in life expectancy at birth

Regions with the lowest and highest life expectancy at birth, 2013


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4. Education


Education can have many private returns, to skills, employment, health and civic engagement. Moreover, there is evidence that education also has important social returns, which affect the overall productivity of places, reduce crime rates and increase political participation. The industrial mix and a solid base of human capital make some regions competitive and attractive to employers. Evidence shows that the divergence in educational levels in regions is causing an equally large divergence in labour productivity and salaries for most of the workers, in particular for the highly-skilled but also for low-skills jobs.

Indicator

The labour force with at least upper secondary education is defined as the labour force, aged 15 and over, that has completed at least upper secondary educational programmes, defined as the ISCED level 3 by the international standard classification for education.


Large educational variations can be observed across regions. In seven OECD countries the difference between the region with the highest value and that with the lowest value in the share of the workforce with at least upper secondary education is higher than 20 percentage points. In Turkey and Mexico, the same indicator in the two capital regions, Ankara and the Federal District, is over 30 percentage points higher than that in North-Eastern Anatolia (Turkey) and the state of Chiapas (Mexico) respectively.

Figure 5: Regional disparities in education

Regions with the lowest and highest percentage of workforce with at least upper secondary education, 2014

Source: OECD Regional Well-being database. Note: available years are listed section VII. Sources and References.

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5. Environmental outcomes


The quality of the local environment has important effects on well-being of current and future generations. While at the moment only air pollution is included, various aspects of the environmental quality should be included – such as water, waste, amenities, etc. – as they might be very different in the same region.

Indicator

Population exposure to air pollution is calculated by taking the weighted average value of PM2.5 for the grid cells present in each region, with the weight given by the estimated population count in each cell. These estimates are made possible by the computation of satellite-based observations in OECD estimates from van Donkelaar, A., R.V. Martin, M. Brauer and B. L. Boys (2014) “Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter”, Environmental Health Perspectives, Vol. 123.


In 2014, in 52% of the OECD regions people were on average exposed to levels of air pollution higher than those recommended by the World Health Organisation (pollution concentration level of 10 µg/m³). The largest differences in regional disparity in air pollution are observed in Mexico, Italy and Chile. On the contrary, countries such as New Zealand, Iceland and Ireland present the smallest differences across regions. Italy and Korea were the countries where the highest concentrations of air pollution were observed. In the regions of Lombardy (Italy) and the Capital Region (Korea) pollution levels were above 25 of PM2.5 per person. People in all regions in Canada, Sweden, Portugal, Finland, Norway, Austria, New Zealand, Iceland, Ireland and Estonia were exposed to low levels of air pollution (below 10 µg/m³).

Figure 6: Regional disparities in Population exposure to PM2.5 air pollution

Regions with the lowest and highest population exposure levels (µg/m³), 2014


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6. Safety


Personal security is the extent to which people feel safe and rescued from personal harm or crime. Crime has of course a huge direct and often long-lasting effect on victims. However, it can also strongly affect the well-being of those who are not victims, but that live in the same community. While there is an increasing use of subjective measures of safety, data availability across OECD regions imposes the use of only objective indicators.

Indicator

Homicide is the unlawful killing of a human being with malice aforethought, more explicitly intentional murder. Reported homicides are the number of homicides reported to the police. The homicide rate is the number of reported homicides per 100 000 inhabitants.


This indicator shows relatively large disparities across OECD regions, especially in Northern and Southern American countries. The variability of crime rates across space has been known for many years and evidence shows that there is a clear link to other well-being dimensions related to spatial contexts. These are, among others, education, access to jobs and social connections.

Figure 7: Regional disparities in homicide

Regions with the lowest and highest homicide rates, 2014


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7. Civic engagement and governance


Institutional conditions and governance matter for individual well-being, bearing in mind that many of the policies that affect people’s lives most directly are enacted at local level. Voter turnout is an indication of the degree of public trust in government and of citizens’ participation in the political process.

Indicator

Voter turnout is defined as the ratio between the number of voters to the number of persons with voting rights. The last national election is considered.


Voter turnout varies across OECD regions (Figure 8). Australia and Belgium (where voting is mandatory), Chile and Turkey, have regions with a turnout of over 90%. The largest regional disparities in electoral participation to national elections are presented in the United States, Canada, Spain, Mexico, Chile and Portugal (above 20 percentage points).

Figure 8: Regional disparities in turnout

Regions with the lowest and highest turnout in general election, 2014 or latest year


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8. Access to services


Accessibility of services is one of the key dimensions of well-being, affecting how people obtain what is necessary to satisfy their wants and needs. Measuring accessibility of services allows for a better understanding of inequality in communities. Significant disparities in the access to basic and advanced services, such as transport, water and sanitation, education, health and ICT, still persist across and within regions. These differences are relevant for policy makers because they reflect the opportunities available to people (Sen, 1993) and can help prioritise interventions in underserviced areas.

Indicator

Percent of households with internet broadband access


The largest regional disparities in broadband connection are observed in the countries where the average national level of access to services is relatively low, such as in Turkey, Mexico and Chile. In these three countries, the value in the region with the highest proportion of households with broadband connection is more than three times higher than the lowest value.

Figure 9: Regional disparities in broadband access

Regions with the lowest and highest share of household with broadband access, 2014 or latest year


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9. Housing


In measuring well-being, housing is an important dimension. Appropriate shelter is one of the most basic human needs, along with food and water. Furthermore, housing costs often represent the largest component of a household’s income. Housing is also strongly connected to other well-being dimensions, such as health, income and life satisfaction (OECD, 2011). At local and regional level, the characteristics of housing are also closely linked to the territorial/spatial configuration.

Indicator

Number of rooms per person in a dwelling. This indicator has some limitation, since it does not consider important elements such as housing prices, the overall cost of life in the region, the type of region (whether a city or a less densely populated region), or the potential benefits of trading space for location.


The number of rooms per person is a standard measure of whether people are living in crowded conditions; across OECD regions this number varies widely, from half a room in Eastern Anatolia (Turkey) to three in Vermont (United States), a difference almost twice as large as that observed across OECD countries. In 2013, regional differences in the number of rooms per person were the widest in Canada, the United States, Spain and Turkey.

Figure 10: Regional disparities in number of rooms per person

Regions with the lowest and highest number of rooms per person, 2013 or latest year


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10. Community


Good interpersonal relations, social network supports and general trust in others and institutions are considered important sources of individual well-being and social cohesion. Not only do they represent additional resources to the material and cultural ones, but they can also improve performance of institutions and reduce transaction costs.

Indicator

Perceived social network support is based on the survey question: “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”. The data shown here reflect the percentage of the regional sample responding “Yes”.


In most OECD regions, at least 80% of people report having someone to rely on in case of need. The exceptions are Korea where the values range between 73% to 79%, and Mexico, Chile, Turkey and Greece where regional differences are very large with some regions below 75%.

Figure 11: Regional disparities in perceived social network support

Percentage of people who report having relatives or friends they can count on, average 2006-14

Source: OECD Regional Well-being database.

24

11. Life satisfaction


Subjective well-being reflects the notion of measuring how people experience and evaluate their lives. It includes evaluation of life as a whole (generally referred as “life satisfaction”), evaluations of particular domains of life (for example, “satisfaction with time available for leisure”), feelings and emotions, as well as measures of “meaningfulness” or “purpose” in life. People’s evaluations of different domains and their expectations are useful information to guide policy making.

Indicator

Life satisfaction is expressed as the mean score on an 11 point scale (based on the Cantril ladder measure). It is measured using a survey question in which respondents are asked “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”.


This indicator shows relatively large disparities across OECD regions, especially in Northern and Southern American countries. The variability of crime rates across space has been known for many years and evidence shows that there is a clear link to other well-being dimensions related to spatial contexts. These are, among others, education, access to jobs and social connections.

Figure 12: Regional disparities in life satisfaction

Mean satisfaction with life; 0-10 points scale; average 2006-14

Source: OECD Regional Well-being database.

25

VI. Topics and indicators in the OECD Better Life Index and in the Regional well-being tool

The OECD regional well-being work makes uses of the same topics and similar indicators as in the Better Life Initiative at the national level, whenever data are available in a suitable format. Applying the framework used for the Better Life Initiative at the regional level has required some adjustments to bring in aspects that have special importance for regional policy-makers, for example the topic Access to services. For some topics of the Better Life Initiative, regional indicators are not currently available. More regional well-being indicators are available in the publication OECD Regions at a Glance. (OECD, 2016).

Dimensions Regional well-being indicators in

the interactive web tool National indicators in the

Better Life Initiative

Income Household disposable income

Household net adjusted disposable income

Household net financial wealth

Jobs

Employment rate

Unemployment rate

Employment rate

Long-term unemployment rate

Average annual earnings per employees

Job tenure

Housing Number of rooms per person

Number of rooms per person

Housing expenditure

Dwellings without basic facilities

Health status Life expectancy at birth

Age adjusted mortality rate

Life expectancy at birth

Self-reported health status

Education and skills

Educational attainment Educational attainment

Students cognitive skills (PISA)

Years in education

Environmental quality

Air quality Air quality

Satisfaction with water quality

Personal security Homicide rate Homicide rate

Self-reported victimization

Civic engagement and governance

Voter turnout Voter turnout

Consultation on rule making

Accessibility of services

Broadband connection N/A

Work-life balance N/A Employees working very long hours

Time devoted to leisure

Social connections

Social network support Social network support

Subjective well-being

Life satisfaction Life satisfaction

VII. Sources and References

a. Data source and period

Data source: OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en Data and detailed data sources are available in the excel file downloadable on the site.

http://www.oecdbetterlifeindex.org/

26

Table 3: Reference years for data: Last year (first year)

Disposable

income per

capita

Employment

rate

Unemploym

ent rate

Number of

rooms per

capita

Labour force

with at least

secondary

education

Life

expectancy

Mortality

rate

Air quality

(PM2.5)

Homicide

rate

Voter

turnout

Households

broadband

access

Perceived

social

network

support

Life

satisfaction

AUS 2013 (00) 2014 (00) 2014 (00) 2011 (06) 2014 (10) 2013 (01) 2012 (00) 2013 (03) 2013 (00) 2013 (01) 2013 (06) 2010 2010 AUS

AUT 2013 (00) 2014 (00) 2014 (00) 2013 (04) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (01) 2013 (02) 2014 (06) 2010 2010 AUT

BEL 2011 (00) 2014 (00) 2014 (00) 2012 (00) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (01) 2014 (03) 2014 (08) 2010 2010 BEL

CAN 2013 (00) 2014 (00) 2014 (00) 2011 (01) 2013 (00) 2011 (00) 2013 (00) 2013 (03) 2012 (00) 2015 (00) 2013 (06) 2010 2010 CAN

CHL 2012 (00) 2014 (00) 2014 (00) 2002 (..) 2014 (10) 2012 (00) 2012 (00) 2013 (03) 2012 (05) 2013 (01) 2012 (08) 2010 2010 CHL

CZE 2013 (00) 2014 (00) 2014 (00) 2013 (05) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (00) 2013 (02) 2014 (07) 2010 2010 CZE

DNK 2013 (00) 2014 (07) 2014 (07) 2014 (07) 2014 (07) 2013 (00) 2013 (06) 2013 (03) 2013 (00) 2015 (01) 2014 (08) 2010 2010 DNK

EST 2013 (08) 2014 (00) 2014 (00) 2011 (00) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (00) 2015 (03) 2014 (08) 2010 2010 EST

FIN 2012 (00) 2014 (00) 2014 (00) 2012 (05) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (00) 2012 (06) 2014 (06) 2010 2010 FIN

FRA 2011 (00) 2014 (00) 2014 (00) 2010 (00) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2012 (00) 2012 (02) 2014 (..) 2010 2010 FRA

DEU 2012 (00) 2014 (00) 2014 (00) 2013 (00) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2010 (08) 2013 (02) 2014 (06) 2010 2010 DEU

GRC 2013 (00) 2014 (00) 2014 (00) 2011 (01) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (00) 2012 (00) 2014 (08) 2010 2010 GRC

HUN 2012 (00) 2014 (00) 2014 (00) 2011 (01) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (00) 2014 (02) 2014 (08) 2010 2010 HUN

ISL 2012 (00) 2014 (00) 2014 (00) 2012 (05) 2012 (03) 2013 (00) 2013 (00) 2013 (06) 2012 (05) 2013 (03) 2012 (08) 2010 2010 ISL

IRL 2012 (00) 2014 (00) 2014 (00) 2012 (05) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (00) 2011 (02) 2014 (08) 2010 2010 IRL

ISR 2011 (00) 2014 (00) 2014 (00) 2013 (00) 2013 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (00) 2013 (09) 2013 (06) 2010 2010 ISR

ITA 2012 (00) 2014 (00) 2014 (00) 2011 (..) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (02) 2013 (01) 2014 (06) 2010 2010 ITA

JPN 2012 (01) 2014 (01) 2014 (00) 2013 (03) 2010 (00) 2010 (00) 2013 (01) 2013 (03) 2014 (00) 2014 (00) 2012 (06) 2010 2010 JPN

KOR 2013 (10) 2014 (00) 2014 (00) 2010 (..) 2014 (00) 2014 (05) 2013 (00) 2013 (03) 2013 (07) 2012 (00) 2014 (06) 2010 2010 KOR

LUX 2011 (06) 2014 (00) 2014 (00) 2012 (..) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2011 (00) 2013 (04) 2014 (08) 2010 2010 LUX

MEX 2014 (08) 2014 (00) 2014 (00) 2010 (00) 2010 (00) 2014 (00) 2012 (00) 2013 (03) 2013 (00) 2012 (00) 2014 (10) 2010 2010 MEX

NLD 2011 (00) 2014 (00) 2014 (00) 2012 (..) 2014 (00) 2013 (01) 2013 (01) 2013 (03) 2009 (00) 2012 (00) 2014 (06) 2010 2010 NLD

NZL 2013 (00) 2014 (00) 2014 (00) 2013 (..) 2012 (00) 2013 (01) 2013 (00) 2013 (03) 2014 (00) 2011 (02) 2012 (06) 2010 2010 NZL

NOR 2012 (..) 2014 (00) 2014 (00) 2012 (05) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (02) 2013 (01) 2014 (06) 2010 2010 NOR

POL 2012 (10) 2014 (00) 2014 (00) 2012 (02) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2011 (00) 2015 (01) 2014 (..) 2010 2010 POL

PRT 2011 (00) 2014 (00) 2014 (00) 2011 (01) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (00) 2015 (02) 2014 (07) 2010 2010 PRT

SVK 2012 (00) 2014 (00) 2014 (00) 2012 (05) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (00) 2014 (02) 2014 (06) 2010 2010 SVK

SVN 2012 (00) 2014 (01) 2014 (01) 2012 (..) 2014 (01) 2013 (05) 2013 (00) 2013 (03) 2012 (00) 2014 (02) 2014 (..) 2010 2010 SVN

ESP 2011 (00) 2014 (00) 2014 (00) 2012 (01) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (00) 2015 (00) 2014 (07) 2010 2010 ESP

SWE 2012 (00) 2014 (00) 2014 (00) 2012 (00) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (00) 2014 (02) 2014 (09) 2010 2010 SWE

CHE 2010 (07) 2014 (00) 2014 (00) 2013 (00) 2014 (01) 2013 (00) 2013 (00) 2013 (03) 2013 (00) 2015 (03) 2014 (..) 2010 2010 CHE

TUR 2014 (..) 2014 (08) 2014 (04) 2012 (03) 2014 (06) 2013 (..) 2013 (09) 2013 (03) 2013 (05) 2011 (02) 2013 (..) 2010 2010 TUR

GBR 2013 (00) 2014 (00) 2014 (00) 2011 (01) 2014 (00) 2013 (00) 2013 (00) 2013 (03) 2013 (02) 2015 (01) 2014 (06) 2010 2010 GBR

USA 2013 (00) 2014 (00) 2014 (00) 2012 (05) 2013 (00) 2010 (00) 2013 (00) 2013 (03) 2013 (00) 2012 (00) 2013 (07) 2010 2010 USA

Note: last year (first year). For example "2014 (00)" means that 2014 is the reference year of the indicator used for the well-being score and 2000-2014 is the period used

for the trend. "2014 (..)" or "2014" means that the historical time series are not available for this indicator.

27

b. Statistics for Israel

The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

c. Country code

For all charts, the following codes for countries are used:

AUS Australia FRA France NLD Netherlands

AUT Austria GBR United Kingdom NOR Norway

BEL Belgium GRC Greece NZL New Zealand

CAN Canada HUN Hungary POL Poland

CHE Switzerland IRL Ireland PRT Portugal

CHL Chile ISL Iceland SVK Slovak Republic

CZE Czech Republic ISR Israel SVN Slovenia

DEU Germany ITA Italy SWE Sweden

DNK Denmark JPN Japan TUR Turkey

ESP Spain KOR Korea USA United States

EST Estonia LUX Luxembourg

FIN Finland MEX Mexico

d. References

OECD (2016), OECD Regions at a Glance 2016, OECD Publishing, Paris, http://dx.doi.org/10.1787/reg_glance-

2016-en.

OECD (2015), How's Life? 2015: Measuring Well-being, OECD Publishing, Paris, http://dx.doi.org/10.1787/how_life-2015-en

OECD (2014), , How's Life in Your Region?: Measuring Regional and Local Well-being for Policy Making, OECD Publishing, Paris. DOI: http://dx.doi.org/10.1787/9789264217416-en

OECD (2011), How's Life?: Measuring Well-being, OECD Publishing. doi: 10.1787/9789264121164-en

OECD/European Union/JRC (2008), Handbook on Constructing Composite Indicators: Methodology and User Guide, OECD Publishing. doi: 10.1787/9789264043466-en

Sen, A. (1993). Capability and Well-Being. In Nussbaum, M. & Sen, A. (eds), The Quality of Life (30-45).

Oxford: Oxford University Press.

Stiglitz, J. et al. (2009) Report by the Commission on the Measurement of Economic Performance and Social Progress

http://dx.doi.org/10.1787/how_life-2015-en

http://dx.doi.org/10.1787/9789264217416-en

http://dx.doi.org/10.1787/9789264121164-en

http://dx.doi.org/10.1787/9789264043466-en

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