Regions and Cities at a Glance 2020 provides a comprehensive assessment of how regions and cities across the OECD are progressing in a number of aspects connected to economic development, health, well-being and net zero-carbon transition. In the light of the health crisis caused by the COVID-19 pandemic, the report analyses outcomes and drivers of social, economic and environmental resilience. Consult the full publication here.
OECD REGIONS AND CITIES AT A GLANCE - COUNTRY NOTE
CZECH REPUBLIC
A. Resilient regional societies
B. Regional economic disparities and trends in productivity
C. Well-being in regions
D. Industrial transition in regions
E. Transitioning to clean energy in regions
F. Metropolitan trends in growth and sustainability
The data in this note reflect different subnational geographic levels in OECD countries: • Regions are classified on two territorial levels reflecting the administrative organisation of countries: large regions (TL2) and small regions (TL3). Small regions are classified according to their access to metropolitan areas (see https://doi.org/10.1787/b902cc00-en). • Functional urban areas consists of cities – defined as densely populated local units with at least 50 000 inhabitants – and adjacent local units connected to the city (commuting zones) in terms of commuting flows (see https://doi.org/10.1787/d58cb34d-en). Metropolitan areas refer to functional urban areas above 250 000 inhabitants. Disclaimer: https://oecdcode.org/disclaimers/territories.html
Regions and Cities at a Glance 2020 Austria country note 2 A. Resilient regional societies
Prague has the highest potential for remote working in the country A1. Share of jobs amenable to remote working, 2018 Large regions (TL2, map)
LUX GBR AUS SWE CHE NLD ISL DNK FRA FIN NOR BEL LTU EST IRL GRC DEU AUT LVA SVN OECD30 PRT HRV POL ITA USA CZE HUN CAN ESP ROU SVK BGR TUR COL 0 10 20 30 40 50 % The share of jobs that can be performed remotely varies greatly across Czech regions, ranging from close to 50% in Prague to 26% in Northwest and Central Moravia (Figure A1). Such differences depend on the task content of the occupations in the regions, which can be amenable to remote working to different extents. Occupations available in the Prague regions tend to be more amenable to remote working than in other areas of the country.
Individuals in Prague has the highest internet usage across large regions in the Czech Republic with 95% of people being connected to internet (Figure A2).
A2- Internet usage % individuals who used the internet in the last three months, 2019
Prague Central Bohemian Region Southwest
Northeast Moravia-Silesia Central Moravia Southeast Northwest
50 60 70 80 90 100 % Low High
Figure [A1]: The lower percentage range (<25%) depicts the bottom quintile among 370 OECD and EU regions, the following ranges are based on increment of 5 percentage points. Further reading: OECD (2020), Capacity to remote working can affect lockdown costs differently across places, http://www.oecd.org/coronavirus/policy-responses/capacity-for-remote-working-can-affect-lockdown-costs-differently-across-places-0e85740e/ 3 Ageing was lower than OECD average in Czech regions up to 2019 The elderly dependency rate, defined as the ratio between the elderly population and the working age (15-64 years) population is evenly low across Czech regions, with an elderly dependency rate ranging between 28% and 33% in Central Bohemia and Hradec Králové (Northeast region), respectively (Figure A4). The elderly dependency rate is also similar across the different types of regions (Figure A3), and exceeded the OECD average in 2019.
A3. Elderly dependency rate A4. Elderly dependency rate, 2019 By type of small regions in Czech Republic, TL3 Small regions, TL3
Metropolitan regions Regions near a metropolitan area % Regions far from a metropolitan area 32
30
28
26 OECD 24 average of regions 22
20
18
16 2000 2010 2019
Czech regions have consistently more hospital beds per capita than OECD average
All regions in the Czech Republic have A5 - Hospital beds per 1000 inhabitants ‰ Large regions (TL2) more hospital beds per capita than the 10 OECD average, although this ratio has decreased in all regions since 2000 (Figure 8
A5). Regional disparities in hospital beds 6 are above OECD average, with Central
Bohemian Region having the lowest 4 Silesia
number of hospital beds per capita, almost 2 - Region
three beds less per 1 000 inhabitants than
Czech Republic Czech Northeast Southeast Northwest Southwest Moravia Moravia Central
OECD Prague
Central Bohemian Bohemian Central in the close by region of Prague in 2018. 0 2018 2000
Figure notes. [A3]: OECD (2019), Classification of small (TL3) regions based on metropolitan population, low density and remoteness https://doi.org/10.1787/b902cc00-en. Two-year moving averages. [A4]: Small TL3 regions contained in large TL2 regions. TL3 regions in Czech Republic are composed by 14 Kraje. Regions and Cities at a Glance 2020 Austria country note 4
B. Regional economic disparities and trends in productivity
Fast economic growth in the region of Prague drove an increase of regional economic disparities in recent years The regional gap in GDP per capita increased in the Czech Republic over the last eighteen years. Over the period 2000-18, GDP per capita in Prague increased by 68%, a rate twice as high as in Northwest, the poorest region of Czech Republic in 2018. Nevertheless, GDP per capita in Moravia-Silesia, the poorest Czech region in 2000, has grown at the same pace than Prague over the period (Figure B1). During the period 2000-18, the productivity gap between Northwest and Prague, the region with the lowest and highest productivity in the Czech Republic, respectively, has further increased. (Figure B2). After a period of relative stagnation of their productivity, regions far from a metropolitan area of at least 250,000 inhabitants have increased their gap to metropolitan regions since 2010 (Figure B3).
B1. Regional disparity in GDP per capita Top 20% richest over bottom 20% poorest regions Ratio Small regions Large regions
3
2
1
2018 2000 Country (number of regions considered) Note: A ratio with a value equal to 2 means that the GDP per capita of the richest regions accounting for 20% of the national population is twice as high as the GDP of the poorest regions accounting for 20% of the national population.
B2. Gap in regional productivity B3. Gap in productivity by type of region GDP per worker, large (TL2) regions Productivity level of metropolitan regions=100 USD Per cent 120 000 Prague (Highest productivity) 102 Central Moravia (Lowest productivity in 2000) 100 98 100 000 96 94 92 90 80 000 88 86 84 60 000 82 80 Metropolitan regions 78 Regions far from a metropolitan area 40 000 76
5 C. Well-being in regions
In the Czech Republic, regional disparities in people’s well-being are largest in the dimensions of community and access to services
C1 Well-being regional disparities, large regions (TL2)
Top region Bottom region Prague Regions (Oblasti)
Prague Prague
Prague
top top 20% Southeast Northeast Moravia-
(1 to 440) to (1 Prague Silesia
Northwest Moravia- Prague Silesia Prague Southwest
middle middle 60% Southwest Central Northwest Bohemian Northwest Region
Ranking Ranking OECD of regions Northwest Moravia- Central Silesia Bohemian Northwest Moravia- Region Northwest
bottom bottom 20% Silesia
Community Access to Health Income Civic Environment Education Jobs Life Safety Housing services Engagement Satisfaction
Note: Relative ranking of the regions with the best and worst outcomes in the 11 well-being dimensions, with respect to all 440 OECD regions. The eleven dimensions are ordered by decreasing regional disparities in the country. Each well-being dimension is measured by the indicators in the table below.
While Czech regions rank in the bottom 40% of OECD regions in the dimension of environment, most Czech regions – except for Northwest and Moravia-Silesia – rank among the top 20% of the OECD regions in educational attainment, with Prague being in the top 1%. In contrast, the country shows large regional disparities in community and access to services (Figure C1).
The top performing Czech regions fare better than the top 20% of OECD regions in terms of employment and unemployment rates, and in the share of population with at least upper secondary education (Figure C2).
C2. How do the top and bottom regions fare on the well-being indicators?
Country OECD Top Czech regions Average 20% regions Top 20% Bottom 20% Jobs Employment rate 15 to 64 years old (%), 2019 75.1 76.0 78.5 72.5 Unemployment rate 15 to 64 years old (%), 2019 2.1 3.3 1.4 3.4 Civic engagement Voters in last national election (%), 2019 or latest year 60.8 84.2 65.6 54.0 Community Perceived social netw ork support (%), 2014-18 89.6 94.1 93.5 84.3 Education Population w ith at least upper secondary education, 25-64 year-olds (%), 2019 93.8 90.3 96.5 89.5 Access to services Households w ith broadband access (%), 3-year average 2017-19 85.3 91.3 90.0 81.1 Life Satisfaction Life satisfaction (scale from 0 to 10), 2014-18 6.7 7.3 7.0 6.5 Income Disposable income per capita (in USD PPP), 2018 16 494 26 617 20 023 14 804 Safety Homicide Rate (per 100 000 people), 2016-18 1.3 0.7 1.0 1.6 Environment Level of air pollution in PM 2.5 (µg/m³), 2019 19.8 7.0 14.4 23.0 Housing Rooms per person, 2018 1.4 2.3 1.6 1.4 Health Life Expectancy at birth (years), 2018 79.0 82.6 80.4 77.6 Age adjusted mortality rate (per 1 000 people), 2018 9.3 6.6 8.6 10.3
Note: OECD regions refer to the first administrative tier of subnational government (large regions, Territorial Level 2); Czech Republic is composed of eight large regions. Visualisation: https://www.oecdregionalwellbeing.org.
Regions and Cities at a Glance 2020 Austria country note 6 D. Industrial transition in regions
Differently from the rest of the country, manufacturing employment has grown in Northeast and Northwest regions between 2000 and 2017
D1. Manufacturing employment share, regional gap Large TL2 regions % Between 2000 and 2017, six out of eight large 30 regions in Czech Republic experienced a Northwest decline in the share of manufacturing 28 employment. With a reduction of
Southeast 2.8*percentage points in the share of 26 employment in manufacturing, the Southeast
24 region, the second most populous region, Highest growth recorded the largest decrease (Figure D1). Highest decline 22
Manufacturing gross value-added increased in all regions between 2000 and 2017, except Prague and Southeast. Both employment and gross value added in manufacturing increased in the same period only in the Northeast and Northwest regions (Figure D2).
D2. Manufacturing trends, 2000-17 Total jobs Jobs in GVA in by region manufacturing manufacturing
Size of bubble represents Total regional Employment in GVA in manufacturing the % value of the indicator employment as a manufacturing as as a share of regional Colours represent the share of national a share of regional
Legend GVA 2000-17 change employment employment
9
Prague 8
Southeast 7
Northeast 6
Central Bohemian Region 5
Central Moravia 4
Southwest 3
Moravia-Silesia 2
Northwest 1
0
Largest bubble size represents: 18% 36% 40%
Figure [D.2]: Regions are ordered by regional employment as a share of national employment. Colour of the bubbles represents the evolution of the share over the period 2000-17 in percentage points: red: below -2 pp; orange: between -2 pp and -1 pp; yellow: between -1 pp and 0; light blue: between 0 and +1 pp; medium blue: between +1 pp and +2 pp; dark blue: above +2 pp over the period. 7 E. Transitioning to clean energy in regions
Most of the electricity generation through renewable sources happens in Central Moravia Most Czech regions still rely on the use of coal in electricity production, with the exception of the Southwest and Southeast regions – which together account for 40% of the country’s electricity in 2017. In contrast, the Northwest region – the largest producer of electricity in the country, produced 86% of its electricity using coal. Central Moravia generated 82% of its electricity from renewable sources (Figure E1).
E1. Transition to renewable energy, 2017
Total electricity Regional share of Regional share of Greenhouse gas generation renewables in coal in emissions from (in GWh per year) electricity generation electricity generation electricity generated
(%) (%) (in Ktons of CO2 eq.) Northwest 24 565 1% 86% 18 885 Nor. Southwest 16 279 4% 7% 1 073 Sou. Southeast 16 123 8% 6% 972 Sou. Central Bohemian Region 8 816 11% 85% 6 355 Cen. Northeast 7 424 5% 95% 5 809 Nor. Moravia-Silesia 5 193 1% 99% 4 211 Mor. Central Moravia 1 911 82% 18% 319 Cen. Prague 867 0% 100% 711 Pra.
Carbon efficiency in the production of electricity is very unequal across Czech regions. While Northwest emits
770 tons of CO2 per gigawatt hour of electricity produced, Southeast releases 60 tons of CO2 per gigawatt hour. Relative to total national levels, although Northwest accounts for 30% of electricity produced in the country, it accounts for almost half the share of CO2 emissions related to electricity generation (E2).
E2. Contribution to total CO2 emissions from electricity production, 2017
Share of electricity production Low carbon efficiency High carbon efficiency Share of CO2 emissions % Contribution to total electricity production Contribution to total electricity production higher than contribution to CO emissions lower than contribution to CO2 emissions 50 2
40
30
20
10
0 Southeast Southwest Central Moravia Prague Moravia-Silesia Central Bohemian Northeast Northwest Region
Figure notes: Regions are arranged in Figure E1 by total generation, and in Figure E2 according to gap between share of electricity generation and share of CO2 emissions (most positive to most negative). These estimates refer to electricity production from the power plants connected to the national power grid, as registered in the Power Plants Database. As a result, small electricity generation facilities disconnected from the national power grid might not be captured. Only 93% of the total country's electricity production is covered. Electricity production from Biomass, Oil, Waste, Wind power plants is missing. See here for more details. Regions and Cities at a Glance 2020 Austria country note 8 F. Metropolitan trends in growth and sustainability
Metropolitan areas of at least half a million people account 35% of the national population, 10-percentage points more than in Europe, but below OECD average.
In Czech Republic, 52% of the population lives in cities of more than 50 000 inhabitants and their respective commuting areas (functional urban areas, FUAs). The share of the population in FUAs with more than 500 000 people is 35%, lower than the OECD average of 60% (Figure F1). F1. Distribution of population in cities by city size Functional urban areas, 2018 Czech Republic,United percentage States of population in cities Population by city size, a global view
Above Between 250 000 Between 50 000 Other settlements 500 000 pop. and 500 000 pop. and 250 000 pop. below 50 000 other settlements above % below 50 000 500 000 100
80 35% 10.6 million 60 48% people - 52% live in cities 40 75% 64% 60% 2% between 250 000 52% and 500 000 20 35% 15% 25% between 50 000 and 250 000 0 Czech Republic Europe OECD (29 countries) (37 countries)
Built-up area in Ostrava functional urban area has increased whereas its population decreased since 2000 Built-up area per capita levels in Czech metropolitan areas is in line with the OECD metropolitan average. The amount of built-up area per capita has increased significantly in Ostrava since 2000, mainly due to the increased in built-up area combined with a decline in population in the same period (Figure F2).
F2. Built-up area per capita Functional urban areas with more than 500 000 residents m2 per capita 2014 2000
500
400
300
200
100 0 Ostrava Prague Brno OECD average
Source: OECD Metropolitan Database. Number of metropolitan areas with a population of over 500 000: three in Czech Republic compared to 349 in the OECD. 9 GDP per capita in Czech metropolitan areas have grown faster than neighbouring metropolitan areas since 2000 GDP per capita in the three metropolitan areas with at least 500 000 inhabitants in the Czech Republic grew faster than metropolitan areas in Austria, Slovak Republic and Hungary over the period 2000-18 (Figure F3).
F3. Trends in GDP per capita in metropolitan areas Functional urban areas above 500 000 people