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
0.1
.2.3
.4C
oeffi
cien
t of v
aria
tion
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
-10
-50
510
Aver
age
annu
al g
row
th o
f liv
ing
stan
dard
s (p
er c
ent)
-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
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
7.4 7.6 7.8 8.0 8.2 8.4 8.6
varia
bilit
y ac
ross
wel
l-bei
ng d
imen
sion
s
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?