Presentation on Divided cities: understanding income segregation in metropolitan areas made at the European week of regions and cities on 11 October 2017. Presentation by Paolo Veneri, Terrtitorial Analysis and Statistics, OECD Centre for Entrepreneurship, SMEs, Local Development and Tourism. For more information: http://www.oecd.org/regional/regional-policy/regionalstatisticsandindicators.htm

1. Divided cities: understanding
income segregation in
metropolitan areas
Paolo Veneri
Territorial Analysis and Statistics
OECD

2. Definition and context
• Spatial segregation: the geographical over-concentration of
households with a similar income level
• The concentration of similar households in space is a natural
phenomenon.
– Spatial segregation can foster positive externalities, especially for those
living in affluent and high quality neighbourhoods (Morrison, 2015)
– Growing up in an area where disadvantages are concentrated can be a life-
long obstacle to opportunities available (Chetty & Hendren, 2015)

3. Research focus
• Comparison of spatial segregation of income groups
within metropolitan cities across different countries
– Why focus on income segregation? Income is the most
important driver of spatial segregation (Glaeser and Vigdor,
2012)
– As populations and economy grow, it is important to
understand how the benefits of life and work in cities are
distributed over inhabitants

4. Objectives of the project
•Provide a set of internationally comparable
indicators to advance in the measurement of
income segregation in OECD metropolitan areas.
•Assess the patterns of segregation across OECD
cities and countries.
•Document policy initiatives in different countries
to tackle the spatial concentration of disadvantage
Data
Evidence
Decision
making
Final Report expected by early 2018

5. Measurement issues

6. Indicator of income segregation
Spatial ordinal entropy index
• Measures the ratio (min = 0 and max = 1) between the proportion
of the population from each income group in a specific area to its
proportion in the city
• Uses information from gridded data (1 km x 1 km) on the
distribution of households by income group
• Distinct indexes capture, across each city’s surface, the level of
segregation for grid cells within specific radii
I.e., circular neighborhoods of 0.5km, 1km, 2km, 4km
The 1km radius is found to be optimal for the main analysis

7. Acapulco de Juárez
Dublin
Aarhus
Amsterdam
Brisbane
Auckland (AU)
Hamilton
Rotterdam
Tshwane
Ejsberg
Marseille
Gold Coast
Brasilia
Cariri
Emufuleni
Manchester
BradfordRennes
Monterrey
Vancouver
Memphis
Portland
0
0.05
0.1
0.15
0.2
0.25
BRAUSAZAFGBRCANMEXDNKFRANLDAUSNZLIRL
Entropy index
Levels of income segregation vary strongly across
OECD metropolitan cities – even within countries

8. Spatial segregation levels by income (tax bin)
Spatial entropy 1,000m measure of segregation

9. Do poorer or richer households drive segregation
levels in countries?

10. In most of the observed countries the rich are
more segregated than the poor
Ratios of segregation indicators between bottom and top income groups

11. Income segregation is positively associated with
the average household income in cities
0.05.1.15
Segregation(1,000mspatialentropy)
0 20000 40000 60000
Household disposable income
Fitted line City (FUA)
Segregation vs. household disposable income (N=141)

12. Cities in the US and France (N=83)
Segregation index vs. GINI
Positive relationship
..and positively associated with income inequality
0.05.1.15
.32 .34 .36 .38 .4 .42
GINI-coefficient
Fitted values Segregation (1,000m spatial entropy index)

13. Paris
Lyon
Toulouse
Strasbourg
Bordeaux
Nantes
Lille
Montpellier
St Etienne
Rennes
Grenoble
Toulon
Marseille
Nice
Rouen
0.06
0.07
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0.03 0.035 0.04 0.045 0.05 0.055 0.06 0.065
Gini of the HT block rate
Income segregation (1km spatial entropy index)
High positive correlation between local (house) tax heterogeneity and income
segregation in the largest 15 French cities
Possible role of tax organisation to explain different levels of segregation
- House Tax bloc rate includes municipal and inter-municipal federation (i.e. métropole,
communauté de communes, etc.) rates
- Gini of House Tax block rate increases as tax rates are more heterogeneous within a city
- Support to the idea that households are sensitive to the local taxation and sort into cities
according to tax rates
Source: based on Fourrey, K. (2017)

14. Income segregation within cities is made measureable in a way that
maximises comparability across countries
Levels of income segregation vary strongly across OECD metropolitan
cities – even within countries
Income segregation is generally higher in more affluent and more
unequal cities. Local tax heterogeneity is also positively associated to
segregation.
In most countries segregation is higher amongst the rich than amongst
the poor
Preliminary conclusions

15. Thanks!
Info: paolo.veneri@oecd.org