The Contribution of Regions and Cities to Inclusive Growth, presentation made at the 57th ERSA congress on Social Progress for Reslient Regions, held on 31 August 2017 in Groningen, Netherlands. Presentation by Joaquim Oliveira Martins, OECD Centre for Entrepreneurship, SMEs, Local Development and Tourism. More information: http://www.oecd.org/cfe/regional-policy/

1. THE CONTRIBUTION OF
REGIONS & CITIES TO
INCLUSIVE GROWTH
ERSA CONGRESS ‘SOCIAL PROGRESS FOR
RESILIENT REGIONS’
31 AUGUST 2017, GRONINGEN
Joaquim Oliveira Martins, OECD/CFE

2. Regional growth

3. Regional productivity is diverging in
the OECD
Notes: Average of top 10% and bottom 10% TL2 regions, selected for each year. Top and bottom regions are the aggregation of
regions with the highest and lowest GDP per worker and representing 10% of national employment. 19 countries with data included.
Averages
of top
10%
(frontier),
bottom
75%, and
bottom
10%
(lagging)
regional
GDP per
worker,
TL2
regions
50 000
60 000
70 000
80 000
90 000
100 000
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
USD PPP per employee
Frontier regions Lagging regions 75% of regions
1.6% per year
1.3% per year
1.3% per year
60% increase

4. A majority of regions have flat or declining
labour productivity catching-up
Type of regions Employment
share in
2000
GDP share in
2000
Annual avg.
GDP growth,
2000-13
GDP growth
contribution
Frontier 16.1% 20.1% 1.7% 21.9%
Catching up 20.3% 18.2% 2.2% 25.3%
Keeping pace 38.9% 39.1% 1.3% 30.4%
Diverging 24.6% 22.6% 1.6% 22.4%
OECD average 1.6%
Note: Frontier regions are fixed for the 2000-13 period. In four countries the values for 2000 or 2013
were extrapolated from growth rates over a shorter time period as data for 2000 or 2013 were not
available. The countries are FIN (2000-12), HUN (2000-12), NLD (2001-13) and KOR (2004-13).
 62% of OECD GDP is generated in regions were productivity is
Keeping pace or Diverging. They contributed to 53% of GDP growth

5. EU Regional Productivity dynamics:
two main types of countries
5
Source: Bachtler, Oliveira Martins, Wostner and Zuber(2017), “TOWARDS
COHESION POLICY 4.0”, Regional Studies Association.
Type-I: Austria, Germany, Czech Republic, Spain,
Italy, Poland, Portugal and Romania. Most of the
productivity performance of these countries is the result of
the catching-up of the lagging regions. The frontier regions
sustain high productivity levels, but productivity growth
dynamics occur elsewhere in the country.
Type-II: Bulgaria, Denmark, France, United
Kingdom, Finland, Greece, Hungary, Netherlands,
Slovak Republic and Sweden. In these countries, most of
the productivity dynamics is concentrated at the frontier
with limited effects from the catching-up process.

6. Region’s contributions to national labour
productivity growth 2000-2014: Type I countries
The contribution of a region is defined as the difference between the national annual average
labour productivity growth rate and the same rate excluding the indicated region, cf. OECD
Regional Outlook (2016).
GERMANY AUSTRIA

7. Region’s contributions to national labour
productivity growth 2000-2014: Type I countries
The contribution of a region is defined as the difference between the national annual average
labour productivity growth rate and the same rate excluding the indicated region, cf. OECD
Regional Outlook (2016).
SPAIN ITALY

8. Region’s contributions to national labour
productivity growth 2000-2014: Type I countries
The contribution of a region is defined as the difference between the national annual average
labour productivity growth rate and the same rate excluding the indicated region, cf. OECD
Regional Outlook (2016).
POLAND PORTUGAL

9. Region’s contributions to national labour
productivity growth 2000-2014: Type I countries
The contribution of a region is defined as the difference between the national annual average
labour productivity growth rate and the same rate excluding the indicated region, cf. OECD
Regional Outlook (2016).
CZECH REPUBLIC ROMANIA

10. Region’s contributions to national labour
productivity growth 2000-2014: Type II countries
FRANCE
The contribution of a region is defined as the difference between the national annual average
labour productivity growth rate and the same rate excluding the indicated region, cf. OECD
Regional Outlook (2016).
UK

11. Region’s contributions to national labour
productivity growth 2000-2014: Type II countries
SWEDEN
The contribution of a region is defined as the difference between the national annual average
labour productivity growth rate and the same rate excluding the indicated region, cf. OECD
Regional Outlook (2016).
DENMARK

12. Region’s contributions to national labour
productivity growth 2000-2014: Type II countries
GREECE
The contribution of a region is defined as the difference between the national annual average
labour productivity growth rate and the same rate excluding the indicated region, cf. OECD
Regional Outlook (2016).
HUNGARY

13. Region’s contributions to national labour
productivity growth 2000-2014: Type II countries
BULGARIA
The contribution of a region is defined as the difference between the national annual average
labour productivity growth rate and the same rate excluding the indicated region, cf. OECD
Regional Outlook (2016).
SLOVAK REPUBLIC

14. Region’s contributions to national labour
productivity growth 2000-2014: Type II countries
NETHERLANDS
The contribution of a region is defined as the difference between the national annual average
labour productivity growth rate and the same rate excluding the indicated region, cf. OECD
Regional Outlook (2016).

15. Geography of
productivity
convergence
relative to
national
frontiers in
European
regions,
2000-14

16. Comparison of (weighted) productivity growth for
Type I and Type II countries
-4
-3
-2
-1
0
1
2
3
4
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Weighted TYPE I Weighted TYPE II
Average productivity growth Type I: 1.07%
Average productivity growth Type II: 1.12%

17. Comparison of (unweighted) productivity growth
for Type I and Type II countries
-4
-3
-2
-1
0
1
2
3
4
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Unweighted TYPE I Unweighted TYPE II
Average productivity growth Type I: 1.25%
Average productivity growth Type II: 1.34%

18. Inclusion of regions

19. Comparison of Productivity catching-up trends for
Type I and Type II countries (OECD TL2 regions)
55000
60000
65000
70000
75000
80000
85000
90000
95000
100000
105000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Frontier Top 10% Lagging Bottom 90%
in USD
1.4% per year
1.09% per year
55000
60000
65000
70000
75000
80000
85000
90000
95000
100000
105000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Frontier Top 10% Lagging Bottom 90%
in USD
0.95% per year
1.09% per year
Type I countries Type II countries
 There was less divergence/keeping pace for Type I countries,
mainly because of lower productivity performance of the Frontier
 Type II countries displayed productivity divergence

20. Comparison of Productivity catching-up trends for
Type I and Type II countries (OECD TL3 regions)
Type I countries Type II countries
 There was productivity convergence for Type I countries
 Type II countries displayed productivity divergence
55 000
65 000
75 000
85 000
95 000
105 000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Frontier Top 10% Lagging Bottom 90%
in USD
1% per year
1.1% per year
55 000
65 000
75 000
85 000
95 000
105 000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Frontier Top 10% Lagging Bottom 90%
in USD
1.6% per year
0.9% per year

21. Comparison of Regional inequalities for Type I
and Type II countries (OECD TL2 regions)
Gini of GDP per capita -
weighted averages TL2 regions
0.1
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
0.19
0.2
Type I Type II
Gini of GDP per capita –
simple averages TL2 regions
0.13
0.14
0.15
0.16
0.17
0.18
Type I Type II
 Type II countries displayed an increase of regional inequality,
especially before the crisis

22. Comparison of Regional inequalities for Type I
and Type II countries (OECD TL3 regions)
Gini of GDP per capita -
weighted averages TL3 regions
Gini of GDP per capita –
simple averages TL3 regions
0.14
0.15
0.16
0.17
0.18
0.19
0.2
0.21
0.22
0.23
Type I Type II
0.13
0.14
0.15
0.16
0.17
0.18
Type I Type II
 Type II countries displayed an increase of regional inequality,
especially before the crisis

23. The growth/inequality trade-offs also map at the
urban/metropolitan scale
23
Metropolitan population and income inequality, circa 2014
Metropolitan size and inequality (controlled for income levels and country effects)
Brussels
Antwerpen​
Liège
​
Red Deer
​
Calgary
​
​
Lethbridge
​
​
​
Thunder Bay
​
Québec
​
Trois Rivières
​
Montreal
​
Sherbrooke
​
​
Toronto
​
​
Brant
​
Windsor
​
Iquique
​
Antofagasta
​
​
Calera
​
​
San Antonio
​
Rancagua
​
​
Linares
​
​
Temuco
​
Osorno
​
Punta Arenas
Paris
​
Toulouse​
Saint-Etienne​
​
Rouen Roma
Milano
Napoli
​
​
​ Tijuana
​
Hermosillo
​
Reynosa
​
Torreón
​
​
León Guadalajara
​Pachuca de Soto ​
Mexico City
​ Toluca
​
​
​
​
Oslo
​
Malmö
​
Portland
​​
Buffalo
​
Albany
​
Boston
​
​
Cleveland
​
Omaha
New York
​
​
Philadelphia
​
Denver
​
​
Cincinnati
Washington
​
​ San Francisco
​
Fresno
Las Vegas
​
​
Albuquerque
Memphis
​
Los Angeles
​
Atlanta
​
Dallas
Houston
Miami
McAllen
0.05
0.1
0.15
0.2
0.25
10 11 12 13 14 15 16 17
Ginicoefficient(componentplusresiduals)
City population (natural logarithm)

24. Only around 20% of OECD metro areas have
grown inclusively
24
Change in GDP pc and in Gini coefficient of household disposable income, 2000-13
Source: OECD (2016), Making Cities Work for All, OECD Publishing, Paris.

25. Key issues
 There seems to be some trade-off between overall
productivity performance and regional inequalities.
Regional policies should help transform these
trade-offs into synergies
 Regional policy favouring the productivity
catching-up of lagging regions acts as an important
driver of a country-wide growth strategy
 Strong territorial asymmetries may signal that a
growth potential exists at the regional level that
could be further mobilised.

26. How can regional and
urban policies
contribute to inclusive
growth?

27. Rural-urban linkages favours rural productivity
catching-up
27
Rural remote regions present a higher variation in productivity growth rates than other types of regions
Annual average
labour productivity
growth, 2000-12
Standard deviation
Coefficient of
variation
Predominantly
urban
1.01% 1.02%
1.019
Intermediate 1.07% 1.09% 1.024
Predominantly
rural close to
cities
1.36% 1.32% 0.972
Predominantly
rural remote
0.70% 1.15%
1.641
Note: Labour productivity is defined as real GDP per employee. GDP is measured at PPP constant 2010 US Dollars, using SNA2008
classification; employment is measured at place of work. The coefficient of variation represents the ratio of the standard deviation
over the mean.
Source: OECD Regional Outlook 2016

28. Tradable sectors are important for regional
productivity catching-up
All tradable sectors, TL2 regions
Notes: Tradable sectors are defined by a selection of the 10 industries defined in the SNA 2008. They include: agriculture (A), industry
(BCDE), information and communication (J), financial and insurance activities (K), and other services (R to U). Non tradable sectors are
composed of construction, distributive trade, repairs, transport, accommodation, food services activities (GHI), real estate activities (L),
business services (MN), and public administration (OPQ).
20
25
30
35
40
45
50
Frontier Catching-up Diverging Frontier Catching-up Diverging
Tradable GVA share Tradable employment share
2013 2000
%

29. In countries with larger falls in manufacturing
jobs regional inequality has increased
Source: OECD Economic Outlook, 2016

30. Better Metro governance can improve both
agglomeration economies and reduce inequalities
30
-.05
0
.05
.1
.15
0 .2 .4 .6 .8 1
Administrative fragmentation
Higher administrative
fragmentation reduces city
productivity premia
Higher administrative
fragmentation increases
municipal inequality and
segregation

31. Make planning more flexible and better policy
coordination may improve housing sectors
How land is used
Public policies aimed at steering
land use
• Spatial planning
• Transport planning
• Land use planning
• Environmental regulations
• Building code regulations
Public policies not targeted at land use
• Tax policies
• Transport taxes and subsidies
• Fiscal systems and inter-governmental
transfers
• Agricultural policies
• Energy policies
How land is permitted to be used How individuals and businesses
want to use land

32.  Almost 60% of total public investment across the OECD (2014)
Source: OECD National Accounts
Public Investment in the OECD is mostly done
by subnational governments
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Rest of the public sector (central government and social security)
Sub-national governments (states, regions and local governments)
59%

33. • Invest using an integrated strategy tailored to different places
• Adopt effective co-ordination instruments across levels of
government
• Co-ordinate across SNGs to invest at the relevant scale
Pillar 1
Co-ordinate across
governments and
policy areas
• Assess upfront long term impacts and risks
• Encourage stakeholder involvement throughout investment cycle
• Mobilise private actors and financing institutions
• Reinforce the expertise of public officials & institutions
• Focus on results and promote learning
Pillar 2
Strengthen capacities
and promote policy
learning across levels of
government
• Develop a fiscal framework adapted to the objectives pursued
• Require sound, transparent financial management
• Promote transparency and strategic use of procurement
• Strive for quality and consistency in regulatory systems across
levels of government
Pillar 3
Ensure sound framework
conditions at all levels of
government
The OECD Recommendation on Effective Public
Investment across Levels of Government

34. References
Ahrend, R., Gamper C., Schumann A. (2014), The OECD Metropolitan governance survey. OECD
Regional Development Working Papers , 2014/04.
Boulant, J, Brezzi, M., Veneri, P. (2016), Income levels and inequality in OECD metropolitan areas. A
Comparative Approach in OECD Countries”, OECD Regional Development Working Papers, 2016/06,
OECD Publishing, Paris.
Bachtler, Oliveira Martins, Wostner and Zuber(2017), “TOWARDS COHESION POLICY 4.0”, Regional
Studies Association
OECD (2015), The Metropolitan Century. Understanding Urbanisation and its Consequences, OECD
Publishing, Paris.
OECD (2016), Regional Outlook 2016, OECD Publishing.
OECD (2016), Making Cities Work for All, OECD Publishing.
Veneri, P., Ruiz, V. (2016), “Rural-to-urban population growth linkages: evidence from OECD TL3
regions. Journal of Regional Science, Vol. 56(1), pp. 3-24.

35. Thank you!