Where are the highest living standards? Measuring well-being and inclusiveness in OECD regions

ABSTRACT

This paper assesses the levels and trends in living standards across regions in Organisation for Economic Co-operation and Development (OECD) countries from the early 2000s to 2012. It applies a measure of welfare called multidimensional living standards (MDLS) at the regional level. The MDLS builds on the equivalent-income approach by combining monetary income with non-monetary (health and jobs) outcomes and taking into account how such outcomes are distributed amongst individuals. Results highlight that regional disparities are amplified when observed through an MDLS lens, as opposed to purely income-based regional disparities. The paper also shows that metropolitan residents experience, on average, higher levels of the MDLS and income than those in other regions.

KEYWORDS:

• well-being
• growth
• inequality

JEL:

• R10
• R11
• R13

ACKNOWLEDGMENTS

The authors acknowledge the comments and suggestions provided by several OECD colleagues (Romina Boarini, Monica Brezzi, Luiz De Mello, Marco Mira D’Ercole and Joaquim Oliveira Martins).

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

ORCID

Paolo Veneri http://orcid.org/0000-0002-4640-5500

Fabrice Murtin http://orcid.org/0000-0002-5479-9191

Notes

1. Murtin et al. (2017) and Boarini et al. (2016) use the concept of ‘shadow prices’ to explain how non-income dimensions are monetized. While the framework presented here is qualitatively similar, we refrain from using the term ‘shadow price’ as it is misleading. In reality, the ‘exchange rates’ used to express longevity and unemployment in monetary terms are marginal rates of substitutions rather than relative prices.

2. This expression derives from the fact that equivalent income y*j,t yields, everything else being equal, the same life satisfaction at the reference level of life expectancy as the life satisfaction at the actual levels of income and life expectancy, hence:$\begin{array}{rl}life\mathrm{\_}Sa{t}_{j,t}& =a_j+b_t+\alpha \log y_{j,t}+{\beta }_{1}life\mathrm{\_}exp{t}_{j,t}+{\beta }_2{U}_{j,t}+\epsilon \\ & =a_j+b_t+\alpha \log y_{j,t}^{\ast }+{\beta }_{1}life\mathrm{\_}exp{t}_{j,t}^{\ast }+{\beta }_2{U}_{j,t}+\epsilon \end{array}$

3. The latter unitary compensating incomes are not only region specific but also quintile specific.

4. For details on the methodology to identify FUAs, see OECD (2012).

5. This finding was observed by considering the 15 OECD countries mentioned in the data section.

Additional information

Funding

The authors acknowledge the financial support from the Ford Foundation towards the work ‘Making Inclusive Growth Happen in Cities and Regions’.