ERSA CONFERENCE, LISBON 27 AUG 2015, SESSION ON “MEASURING WELL-BEING TO IMPROVE THE DESIGN OF POLICY. HOW CAN WE DO IT?”

Monica Brezzi Joaquim Oliveira Martins

Paolo Veneri OECD Regional Development Policy Division

REGIONAL WELL-BEING: MEASUREMENT AND POLICY

Outline

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1. The OECD Regional Well-being framework • The balance across well-being dimensions

2. Moving towards a composite measure of Well-being

• Distributional issues across people

3. Regional policy and Well-being: what links? • Policy packages and complementarity

1. OECD work on regional well-being

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1. Overall objective: the OECD work on well-being aims to provide data, tools and policy analysis to help policy makers design and implement effective policy to improve people’s lives.

2. Going regional: measuring people’s well-being at regional level is a crucial because:

a) National average can be misleading (need to assess well-being close to where people live and WB dimensions may be disconnected across space) b) People’s well-being is affected by both individual and place’s characteristics c) Sub-national governments have a stake in policies that promote well-being.

Measuring well-being in regions OECD framework - How’s Life in Your Region? (2014)

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Main features : • Measures well-being where people

live • Focus on outcomes rather than

output • Multidimensionality (9

dimensions: material conditions and quality of life)

• Assess how well-being changes over time (resilience, sustainability)

• It considers that well-being can be manageable to change by citizens, governance and institutions

Consistent indicators can be used to compare 362 OECD regions in Income, Jobs, Housing, Health, Education, Environment, Access to services, Safety and Civic engagement. All indicators can be accessed through the OECD Regional Well-being databased and visualised in the web-tool: www.oecdregionalwell-being.org

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Regional Well-being in Lisbon

2. A composite multidimensional measure of living standards (MDLS) for OECD regions

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A useful extension of the OECD well-being framework consists in developing a overall measure of living standards (MDLS) which responds to the need to: • Account more explicitly for the inter-relationships between well-being

dimensions by using a common metric • Include the distribution of outcomes in the overall measure, not just averages • Nuance the traditional assessment of regional disparities • Understand the contribution of different dimensions to people’s well-being MDLS: Based on shadow prices, it adjusts income-based measure for risk of unemployment and life expectancy with the equivalent income method (Decancq, Fleurbaey and Schokkaert, 2015)

• Units of analysis: 209 TL2 regions • 15 countries covered over time: BE, CA, CL, CZ, EE, ES, FI, FR, UK, IT, KR, LU, ME, UK, US) • Time period covered: Around 2003-2011 • Indicators considered: 1) Household disposable income (by quintiles); 2) unemployment rate;

3) life expectancy at birth

How to compute a composite measure of well-being? (1/2)

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1) The well-being dimensions that matter the most for people - Income, jobs and

health – are selected based on life satisfaction regressions, Boarini et al. (2012). 𝐿𝐿𝑑=f(𝑦𝑑, 𝑈𝑐, 𝐿𝐿𝑐)

2) MDLS are expressed in monetary values by giving health and jobs outcomes a shadow price (p𝑈, p𝐿𝐿). Shadow prices are identified by running life satisfaction regressions at country level (panel) 𝐿𝐿𝐿𝐿_𝐿𝑆𝑆𝑗,𝑡 = 𝑆𝑗 + 𝑏𝑡 + 𝛼 𝑙𝑙𝑙(𝑦)𝑗,𝑡+𝛽1𝐿𝐿𝑗,𝑡 + 𝛽2𝑈𝑗,𝑡 + 𝜀

the shadow price of an additional year of life expectancy is the income necessary to maintain life satisfaction constant. Such shadow price is :

𝑝𝑗,𝑖𝐿𝐿 = 𝑦𝑗,𝑖 1 − exp −

𝛽1

𝛼

How to compute a composite measure of well-being? (2/2)

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3) Calculate living standards at the quintile level for each region (=actual income+

loss from unemployment + loss from longevity vs. benchmark)

𝐿𝐿𝑑=𝑦𝑑-p𝑈𝑈𝑐 − p𝐿𝐿∆𝐿𝐿𝑐

4) Aggregate living standards by quintile with a generalised mean = multidimensional living standards (combining average outcomes with distribution)

ττ −−

= ∑

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1*)(51

iiyW

y*i = equivalent income in the region based on income,

unemployment rate and life expectancy τ = aversion to inequality (set to focus on the median household) i = i-th income quintile

the value of τ has been estimated to approximate the income of the median household, thus it was set equal to 1.2. By increasing the coefficient one gives more weight to the lower part of the income distribution.

Well-being across OECD regions: country patterns and strong regional differences

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Levels of MDLS in 280 OECD regions, around 2011

This map shows data for 29 countries. For 15 countries only we have income distribution within regions for more than one point in time.

Higher disparities in MDLS than in income

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Coefficient of variation of MDLS and Household income, 2011

Regional disparities are generally higher when assessed in terms of MDLS rather than in terms of income only. This suggests that other well-being dimensions (i.e. jobs, health) amplify the difference in the conditions of people living in different places

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DNK NZL NLD NOR CHL SWE DEU CHE GRC JPN ITA FRA KOR FIN CAN GBR SVN AUS CZE USA ISR MEX BEL ESP SVK

Multidimensional living standards Income

Growth of GDP per capita and MDLS are positively correlated, but with large regional variations

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Growth in GDP per capita and in MDLS in OECD regions, 2003-11

Interpolating line

Bisector line

Interpolating line

Bisector line

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Aver

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-2 0 2 4 6Annual Per Capita GDP Growth (per cent)

Europe Canada and US Chile and Mexico Korea

y = 0.4987x + 0.4907 R² = 0.0305

Growth in MDLS during the crisis and along types of regions

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Growth in MDLS before and after the crisis by urban size

- Metropolitan regions have on average higher MDLS - The crisis was particularly strong in metropolitan regions, which experienced a

faster decline in MDLS during the crisis - Decline in MDLS was driven mainly by income and unemployment

*=statistically significant at 90%

3. Regional policy and well-being

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Life satisfaction and complementarities across well-being dimensions: the case of Mexico

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AguascalientesBaja California

Baja California Sur

Campeche

CoahuilaColima

Chiapas

Chihuahua

Federal District

Durango

Guanajuato

Guerrero

HidalgoJalisco

State of MexicoMichoacan

Morelos

Nayarit

Nuevo Leon

Oaxaca

Puebla Queretaro

Quintana Roo

San Luis Potosi

Sinaloa

SonoraTabasco

Tamaulipas

Tlaxcala Veracruz

Yucatan

Zacatecas

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0.5

1.0

1.5

2.0

2.5

3.0

3.5

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7.4 7.6 7.8 8.0 8.2 8.4 8.6

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life satisfaction

Conclusions and research agenda

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• Based on the OECD regional well-being framework it possible to compare internationally people’s lives in different dimensions of well-being

• MDLS allows an overall assessment of living standards which accounts for different dimensions and distributional issues.

Research agenda: • How to link the measurement of well-being to the design of better policy?

And what is the role of different levels of government? • What are the determinants of living standards that are more amenable to

change through policy making? • What are the policy channels for making progress to be shared across

groups of people?