Version: September 2014globalurbancommons.org/.../Regional-Well-Being-User... · The OECD...

Version: September 2014

2

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 362 other OECD regions based on nine 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]

3

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 ........................................... 8

III. Defining Scores and Trends .............................................................................................................. 10

1. Regional well-being scores ............................................................................................................ 10

2. Trends ........................................................................................................................................... 10

3. Regions with similar well-being profiles in other countries ......................................................... 11

4. Regional disparities in a topic within a country ............................................................................ 11

IV. Defining Regions .............................................................................................................................. 12

V. Well-being Topics and Indicators...................................................................................................... 14

1. Income .......................................................................................................................................... 15

2. Jobs ............................................................................................................................................... 16

3. Health ............................................................................................................................................ 17

4. Education ...................................................................................................................................... 18

5. Environmental outcomes .............................................................................................................. 19

6. Safety ............................................................................................................................................ 20

7. Civic engagement .......................................................................................................................... 21

8. Accessibility to services ................................................................................................................. 22

9. Housing ......................................................................................................................................... 23

VI. Topics and indicators in the OECD Better Life Index and in the Regional well-being tool .............. 24

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

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

b. Statistics for Israel ......................................................................................................................... 26

c. Country ISO code ........................................................................................................................... 26

d. References .................................................................................................................................... 26

4

Introduction Where people live matters for their well-being. Quality of life is shaped by a

multitude of material and non-material dimensions, ranging from income and jobs to health and environment, among others. Outcomes in each dimension vary greatly not only from one country to another, 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 people’s level of well-being and its determinants is crucial to gear public policies towards better achieving society’s objectives. 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 and lessons from regions that

have being using well-being metrics to improve the delivery of policies, we encourage you to read the publication How’s life in your region? available from October 2014 at:

www.oecd.org/regional/how-is-life-in-your-region.htm

5

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 need to be addressed.

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.

6

Figure 1: Regional well-being conceptual framework

Nine well-being dimensions have been identified and a set of indicators developed for the 362 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 2011; OECD 2013a) and the Better Life Index web tool identify eleven dimensions that play a key role in individuals’ well-being and provide a set of indicators to measure them, allowing cross-countries comparison.

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

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.

7

disparities within and among regions in jobs, income, quality of life and sustainability still characterise most OECD countries (OECD, 2013b).

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.

8

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 nine 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 nine 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)

9

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 will appear. Users will also see its relative position 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 . The value of the well-being indicators expressed in its original unit (percentage, dollars, etc.) are at the bottom of the chart

.

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

10

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;

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 362 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 . Indicators that correspond to lower well-being outcomes (unemployment rate, mortality rate, air pollution and homicide rate) are inversely coded :

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 (mid 2000s for internet broadband access). It shows if the region has progressed in the topic, relatively to the other regions. The main

11

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% 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 range between the top and the bottom 20% regional values of a country to the range of the top and bottom 20% regional values in the OECD area. As for the well-being score, extreme values (here the highest and the lowest values) are eliminated for the identification of top and bottom OECD 20% regions. 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.

12

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. Estonia’s regions do not appear in the interactive web tool but the well-being scores are computed in the dataset available in the home page of the interactive web tool. The well-being topics and indicators are shown for the 362 TL2 OECD regions (Table 1).

The OECD report How’s life in your region? (OECD, 2014) also documents, when possible, well-being in smaller administrative regions (1 802 regions) and in the 275 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 Region (1) Finland Suuralueet (5) France Régions (22) Germany Länder (16) Greece Groups of development regions (4) Hungary Planning statistical regions (7) Iceland Regions (2) Ireland Groups regional authority regions (2) Israel Districts (6)

13

Italy Regioni (21) Japan Groups of prefectures (10) Korea Regions (7) Luxembourg State (1) Mexico Estados (32) Netherlands Landsdelen (4) New Zealand Groups of regional councils (2) 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)

14

V. Well-being Topics and Indicators

Overview

A set of indicators to measure the different topics of well-being has been developed for the 362 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 nine well-being topics: income, jobs, housing, education, health, environment, safety, civic engagement, and accessibility of services (Table 2).

Regional measures, comparable across countries, are not currently available on three other well-being dimensions included in the OECD Better Life Initiative: social connections, life satisfaction (subjective assessment) and work-life balance. The OECD plans to include these indicators 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 report How’s life in your region? (OECD, 2014), including measures of income inequalities within regions.

Table 2: Well-Being topics selected for visualisation

Topics Indicators

Mat

eria

l co

nditi

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

Jobs Employment rate (%) Unemployment rate (%)

Housing Number of rooms per person (ratio)

Qua

lity

of li

fe

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, 2012

Safety Homicide rate (per 100 000 people)

Civic engagement Voter turnout (%)

Accessibility of services Share of households with broadband access (%)

Reference years: average over the 3 last years, trends over the last 10 years. See details in section VII. Source: OECD Regional Well-Being Database.

15

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 (PPP) at constant prices (year 2005).

Measuring outcome and trends

Inter-regional disparities in household income are large in many OECD countries. In the United States, Australia, Slovak Republic, Chile, Mexico, Poland, Israel and the United Kingdom, people in the top income region were more than 40% richer than the median citizen in 2011 (Figure 2). While the income gap has narrowed between urban and rural areas in most OECD countries, it remains a major concern in emerging economies and developing countries.

Figure 2: Regional disparities in disposable income Top and bottom values as a % of disposable income per capita in the country’s median region, 2011

Note: data for CHE, JPN, MEX and TUR (2010) and CHL (2012)

16

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.


On average, 40% of overall employment creation in OECD economies during 1999-2012 was generated in just 10% of their regions. With the economic crisis, also employment losses have become even more regionally concentrated. In fact, in Ireland, New Zealand, France, Estonia, the Netherlands, Canada, and the Slovak Republic, half or more of the total employment gap with respect to the 2007 values, could be filled if just one region returned its employment rate to its pre-crisis level (OECD 2013b).

Figure 3: Regional disparities in unemployment Top and bottom values as a % of unemployment in the country’s median region, 2013

17

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.


Differences in life expectancy at birth among American states can be as large as 6 years to 75 years in Mississippi and 81 years in Hawaii (US). Among all OECD countries, the difference in life expectancy is of 9 years (Mexico’s life expectancy is of 74.4 years and Switzerland’s 83 years) (Figure 4). Large differences also characterise the age-adjusted mortality rate within countries. In most countries, the richest regions tend to have a higher number of doctors and lower mortality rate.

Figure 4: Regional disparities in life expectancy at birth

Regions with the lowest and highest life expectancy at birth, 2012

Note: data for CAN, ISR, ISL, JPN, NZL, TUR and USA (2011)

18

4. Education Why does it matter for local well-being?

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 (Moretti, 2012).

Indicator

The labour force with at least 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.


In 2013, three-fourths of the OECD population had at least a secondary education. The one-fourth of population with only basic education is concentrated in a limited number of countries and regions. In most of the regions in Mexico, Portugal and Turkey, and in some regions in Spain, the proportion of the workforce with at least secondary education was less than 50% in 2013. These countries also show higher regional disparities in education (Figure 5).

Figure 5: Regional disparities in education

Regions with the lowest and highest percentage of workforce with at least secondary education, 2013

Note: data JPN, MEX (2010) and NZL, USA (2012)

19

5. Environmental outcomes Why does it matter for local well-being?

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, Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter, Environmental Health Perspectives, in press. doi: 10.1289/ehp.1408646.

Measuring outcome and trends Exploratory results find that local air pollution decreases self-reported life

satisfaction. The impact of decreasing average annual particulate matter concentrations by 1% is equivalent to increasing per capita income by 0.71% (Silva and Brown, 2013). In addition, the cumulative benefits of individual higher well-being in regions and cities point up to the need for policies at local level to preserve and improve environmental quality. Figure 6 shows that air quality – measured in terms of population exposure to air pollution – can be very different across regions, with some showing lower levels than national averages and others much higher levels.

Figure 6: Regional disparities in Population exposure to air pollution Regions with the lowest and highest population exposure levels (μg/m³), average 2010-12

20

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 (Figure 7). 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, 2012

21

7. Civic engagement


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 variability of turnout is very high in Northern and Southern American countries with a difference of turnout higher than 20 percentage points between the highest and the lowest regional value.

Figure 8: Regional disparities in turnout

Regions with the lowest and highest turnout in general election, 2013 or latest year

22

8. Accessibility 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

Share of households with internet broadband access (percentage).


Access to broadband connection can be highly differentiated in regions (Figure 9). Despite different average levels of accessibility by country, Turkey, Chile, and Mexico, show the highest regional disparities.

Figure 9: Regional disparities in broadband access

Regions with the lowest and highest share of household with broadband access, 2013 or latest year

23

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 limits, since it does not consider important elements such as housing prices, population density, the overall cost of life in the region or the potential benefits of trading space for location.


At a national level, the number of rooms per person starts at 0.9 in Turkey and goes up to 2.6 in Canada. When broken down to a regional level, however, a similar difference can be found in the same country. For example, Canada and the United States, the two first countries with the highest national averages also display the widest regional differences, highlighting disparities in living standards within their territories. Turkey, Poland and Hungary have regions where inhabitants live in a dwelling with less than 1 room per person as an average.

Figure 9: Regional disparities in number of rooms per person

Regions with the lowest and highest number of rooms per person, 2012 or latest year

24

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, 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 How’s life in your region? (OECD, 2014).

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

N/A Social network support

Subjective well-being

N/A Life satisfaction

25

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.

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

Labour Force

education

Employment rate

Unemploy-ment rate

Household disposable

income

Homicide rate

Mortality rate

Life expectancy

Air pollution

Voter turnout

Broadband access

Number of rooms

AUS 2013 (01) 2013 (00) 2013 (00) 2011 (00) 2012 (00) 2012 (01) 2012 (00) 2012 (03) 2013 (01) 2013 (07) 2011 (..) AUT 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2012 (01) 2012 (01) 2012 (00) 2012 (03) 2013 (02) 2013 (07) 2013 (..) BEL 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2012 (01) 2012 (01) 2012 (00) 2012 (03) 2010 (00) 2013 (07) 2012 (..) CAN 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2012 (00) 2012 (01) 2011 (00) 2012 (03) 2011 (00) 2012 (07) 2006 (..) CHE 2013 (00) 2013 (00) 2013 (00) 2010 (..) 2012 (00) 2012 (01) 2012 (00) 2012 (03) 2007 (03) 2011 (07) 2012 (..) CHL 2013 (..) 2013 (00) 2013 (00) 2012 (00) 2012 (05) 2011 (01) 2012 (00) 2012 (03) 2010 (00) 2012 (08) 2002 (..) CZE 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2011 (00) 2012 (01) 2012 (00) 2012 (03) 2013 (00) 2013 (07) 2012 (..) DEU 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2011 (..) 2012 (01) 2012 (00) 2012 (03) 2013 (02) 2013 (07) 2011 (..) DNK 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2012 (00) 2012 (01) 2012 (00) 2012 (03) 2011 (01) 2013 (07) 2012 (..) ESP 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2011 (00) 2012 (01) 2012 (00) 2012 (03) 2011 (00) 2013 (07) 2011 (..) EST 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2012 (00) 2012 (01) 2012 (00) 2012 (03) 2011 (03) 2013 (07) 2011 (..) FIN 2013 (00) 2013 (00) 2013 (00) 2011 (01) 2012 (00) 2012 (01) 2012 (00) 2012 (03) 2012 (06) 2011 (07) 2012 (..) FRA 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2012 (00) 2012 (01) 2012 (00) 2012 (03) 2012 (02) 2013 (07) 2012 (..) GBR 2013 (00) 2012 (00) 2013 (00) 2011 (00) 2012 (00) 2012 (01) 2012 (00) 2012 (03) 2010 (01) 2013 (07) 2012 (..) GRC 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2009 (00) 2009 (01) 2012 (00) 2012 (03) 2009 (00) 2013 (07) 2013 (..) HUN 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2011 (00) 2012 (01) 2012 (00) 2012 (03) 2010 (02) 2013 (07) 2011 (..) IRL 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2012 (00) 2011 (01) 2011 (00) 2012 (03) 2011 (02) 2013 (07) 2012 (..) ISL 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2012 (05) 2012 (01) .. (..) 2012 (03) 2012 (04) 2012 (07) .. (..) ISR 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2012 (00) 2012 (01) 2012 (00) 2012 (03) 2013 (..) 2012 (07) 2011 (..) ITA 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2012 (02) 2012 (01) 2012 (00) 2012 (03) 2013 (01) 2013 (07) 2012 (..) JPN 2010 (00) 2013 (00) 2013 (00) 2010 (00) 2012 (00) 2012 (01) 2011 (00) 2012 (03) 2012 (00) 2012 (07) 2008 (..) KOR 2013 (00) 2013 (00) 2013 (00) 2011 (01) 2012 (..) 2012 (01) 2012 (00) 2012 (03) 2012 (02) 2008 (..) 2010 (..) LUX 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2011 (00) 2012 (01) 2012 (00) 2012 (03) 2009 (04) 2013 (07) 2012 (..) MEX 2010 (00) 2013 (00) 2013 (00) 2010 (..) 2012 (00) 2012 (01) 2012 (..) 2012 (03) 2009 (00) 2013 (10) 2010 (..) NLD 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2012 (00) 2012 (01) 2012 (00) 2012 (03) 2010 (02) 2013 (07) 2012 (..) NOR 2013 (00) 2013 (00) 2013 (00) 2011 (04) 2012 (00) 2012 (01) 2012 (00) 2012 (03) 2013 (01) 2013 (07) 2012 (..) NZL 2012 (00) 2012 (01) 2013 (00) 2011 (00) 2012 (00) 2008 (01) 2011 (00) 2012 (03) 2011 (02) 2012 (06) 2001 (..) POL 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2011 (00) 2012 (01) 2012 (00) 2012 (03) 2011 (01) 2013 (07) 2012 (..) PRT 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2011 (00) 2012 (01) 2012 (00) 2012 (03) 2011 (02) 2013 (07) 2012 (..) SVK 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2011 (00) 2012 (01) 2012 (00) 2012 (03) 2012 (02) 2013 (07) 2012 (..) SVN 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2012 (00) 2012 (01) 2012 (00) 2012 (03) 2012 (02) 2012 (07) 2012 (..) SWE 2013 (00) 2013 (00) 2013 (00) 2011 (00) 2011 (00) 2012 (01) 2012 (00) 2012 (03) 2010 (02) 2012 (07) 2012 (..) TUR 2013 (04) 2013 (04) 2013 (04) 2010 (..) 2012 (05) 2012 (01) 2011 (..) 2012 (03) 2011 (02) 2013 (..) 2011 (..) USA 2012 (00) 2012 (00) 2013 (00) 2011 (00) 2012 (00) 2010 (01) 2011 (00) 2012 (03) 2012 (00) 2012 (07) 2012 (..)

Note for Table 3: last year (first year). For example 2013 (00) means that 2013 is the reference year of the indicator used for the well-being score and 2000-2013 is the period used for the trend. 2013 (..) means that

the historical time series is not available for this indicator.

26

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 ISO code For all charts, ISO 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 Moretti, E. (2012), The New Geography of Jobs, Houghton Mifflin Harcourt. OECD (2014), How’s life in your region? Measuring regional and local well-being for policy

making, OECD Publishing. OECD (2013a), How's Life? 2013: Measuring Well-being, OECD Publishing.

doi: 10.1787/9789264201392-en OECD (2013b), OECD Regions at a Glance 2013, OECD Publishing.

doi: 10.1787/reg_glance-2013-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

Version: September 2014globalurbancommons.org/.../Regional-Well-Being-User... · The OECD...

Documents

Transcript of Version: September 2014globalurbancommons.org/.../Regional-Well-Being-User... · The OECD...