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

LITHUANIA

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. Regions and Cities at a Glance 2020 Austria country note 2  A. Resilient regional societies

Vilnius region has the highest potential for remote working

A1. Share of jobs amenable to remote working, 2018 Large regions (TL2, map)

High (>40%) LUX 3540-50%-40% GBR AUS 3030-40%-35% SWE CHE 2520-30%-30% NLD ISL Low (<25%) 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 shares of jobs amenable to remote working in the Lithuanian regions range from 45% in the Vilnius region to 34% in Central and Western Lithuania. At the country level, Lithuania has a similar potential for remote-working to Estonia, placing it among the top 40% of OECD countries (Figure A1). Differences in the potential for remote-working depend on the task content of the occupations in regions, which vary in the extent to which they are amenable to remote working.

Lithuania has a relatively high coverage of fiber optic availability with more than 80% of the buildings connected to the network (Figure A2). A2- Internet infrastructure

Share of buildings connected, 2017

Latvia

Lithuania

Estonia

40 50 60 70 80 90 100 % Low coverage High coverage

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 challenges regions far from metropolitan areas more strongly The elderly dependency rate has been increasing in all types of regions in Lithuania since 2000. Regions far from metropolitan areas have higher elderly dependency rate (33%) compared to metropolitan regions (Figure A3). In Northeast Estonia, there are almost two elderly for every five persons in their working-age in 2019, making it the Estonian region that faces the greatest challenges in terms of ageing (Figure A4).

A3. Elderly dependency rate A4. Elderly dependency rate, 2019 By type of small regions in Lithuania (TL3) Small regions (TL3)

Metropolitan regions Regions far from a metropolitan area %

32

28

24 OECD average of regions

20

16 2000 2010 2019

Most Lithuanian regions have more hospital beds per capita than the OECD average A5 - Hospital beds per 1000 inhabitants ‰ Small TL3 regions The average availability of hospital beds 10 across Estonian regions is above the 8 OECD average. However, regional disparities in hospital beds also exceed 6 OECD average, with Telšiai having the 4 lowest number of hospital beds per capita in 2017, three times less than in Klaipeda. 2

(Figure A5).

Lithuania

OECD Alytus

​ ​ ​ Klaipeda Kaunas Vilnius Šiauliai Utena Taurage Marijampole Telšiai 0 2017 2007

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 regions. TL3 regions in Lithuania are composed by 10 counties. Regions and Cities at a Glance 2020 Austria country note 4 

B. Regional economic disparities and trends in productivity

Regional economic gaps have increased since 2000, due to higher growth of the richest regions The gap in GDP per capita between the richest (Vilnius) and the poorest (Taurage) Lithuanian region has been increasing since 2000, with GDP per capita in Taurage county being equivalent to 39% of GDP per capita in Vilnius in 2017. Lithuania remains close to the OECD median country in terms of regional economic disparities (Figure B1). With a productivity growth of 4.6% per year between 2000 and 2017, Taurage had a higher productivity growth than Vilnius (4.1%), the frontier region in Lithuania in terms of labour productivity (Figure B2). Regions far from a metropolitan area of at least 250,000 inhabitants have not been able to close the productivity gap to metropolitan regions since 2000 (Figure B3). Instead, this gap increased by 18% between 2000 and 2017. 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 of the most developed 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 in USD PPP USD Metro regions USD Vilnius (Highest productivity) 80 000 Regions far from a metro 100 000 Taurage (Lowest productivity) 70 000 80 000 60 000 60 000 50 000

40 000 40 000

20 000 30 000

0 20 000

 5

C. Well-being in regions

Lithuania has large regional disparities in 6 out of 11 well-being dimensions, with the largest disparities in the dimensions of jobs and community

C1 Well-being regional gap

Top region Bottom region Vilnius county Region

Alytus

top top 20% Vilnius (1 to 440) to (1

Taurage Utena Vilnius Marijampole

Alytus Utena middle middle 60%

Kaunas Šiauliai

Alytus Alytus Ranking Ranking OECD of regions Telšiai Marijampole Klaipeda Vilnius Marijampole Utena

bottom bottom 20% Utena Telšiai Telšiai Utena

Jobs Community Life Education Housing Income Environment Access to Civic Safety Health Satisfaction services Engagement

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 all Lithuanian regions are in the bottom 20% of OECD regions in the dimensions of civic engagement and health, 9 out of 10 Lithuanian regions are among the top 20% of OECD regions in educational outcomes. In contrast, results across regions are very unequal in the dimension of jobs. While Vilnius is in the top 25% of OECD regions in terms of jobs, Utena is in the bottom 20% of OECD regions (Figure C1). The average of the top performing Lithuanian regions is below the average of the top OECD regions in most well-being indicators, with the exception of employment rates and educational attainment (Figure C2).

C2. How do the top and bottom regions fare on the well-being indicators?

Country OECD Top Lithuanian regions average 20% regions Top 20% Bottom 20% Jobs Employment rate 15 to 64 years old (%), 2019 73.0 76.0 76.7 63.6 Unemployment rate 15 to 64 years old (%), 2019 6.5 3.3 4.9 11.1 Community Perceived social netw ork support (%), 2014-18 86.3 94.1 89.4 82.2 Life Satisfaction Life satisfaction (scale from 0 to 10), 2014-18 6.1 7.3 6.3 5.8 Education Population w ith at least upper secondary education, 25-64 year-olds (%), 2019 95.0 90.3 97.4 90.7 Housing Rooms per person, 2018 1.5 2.3 1.7 1.4 Income Disposable income per capita (in USD PPP), 2018 18 130 26 617 20 848 14 526 Environment Level of air pollution in PM 2.5 (µg/m³), 2019 13.3 7.0 12.6 15.8 Access to services Households w ith broadband access (%), 2019 78.0 91.3 77.5 42.6 Civic engagement Voters in last national election (%), 2019 or latest year 49.4 84.2 52.9 41.0 Safety Homicide Rate (per 100 000 people), 2016-18 4.6 0.7 3.8 6.9 Health Life Expectancy at birth (years), 2018 75.8 82.6 76.5 75.0 Age adjusted mortality rate (per 1 000 people), 2018 11.0 6.6 10.9 12.0

Note: OECD regions refer to the first administrative tier of subnational government (large regions, Territorial Level 2). In the well-being figures for Lithuania, small regions (TL3) are represented. Lithuania is composed of ten small regions (Territorial Level 3). Visualisation: https://www.oecdregionalwellbeing.org.

Regions and Cities at a Glance 2020 Austria country note 6 

D. Industrial transition in regions

Employment in the manufacturing industry has fallen in Estonian regions, with Vilnius reporting a decline of 3-percentage points

D1. Manufacturing employment share, regional gap % Between 2005 and 2017, the two large regions 20 in Lithuania experienced a decline in the share of employment in manufacturing. With a reduction of 3-precentage points in the share Central and Western Lithuania of employment in manufacturing, Vilnius 15 recorded the largest decrease (Figure D1).

Lowest decline Vilnius Highest decline Region 10

In Central and Western Lithuania, decline in employment in manufacturing coincides with an increase in manufacturing gross value-added since 2000. In contrast, Vilnius region recorded simultaneous declines in both employment and gross value-added in the manufacturing sector (Figure D2).

D2. Manufacturing trends, 2000-18

Total jobs Jobs in GVA in R&D Electricity production, 2015 by region manufacturing manufacturing intensity Size of bubble represents Employment in Total regional GVA in Total electricity % of electricity the % value of the indicator manufacturing as Total R&D % of electricity employment as a manufacturing as a generated per produced from a share of expenses as a produced from Colours represent the share of national share of regional capita renewable Legend regional share of GDP coal 2000-17 change employment employment in kWh energy employment

3

n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. Central and Western n.a. n.a. n.a. 2 Lithuania n.a. n.a. n.a.

Vilnius Region 1 Central and Western Lithuania

Vilnius Region

0

Largest bubble size represents: 68% 17% 23% 2%

Note 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.

D1. Manufacturing employment share, regional gap 2000=100 2000=100 130 130 120 120 110D1. Manufacturing employment share, regional gap 110 100 100 90 80 90 70 80 60 Saxony-Anhalt (highest gowth) Saxony-Anhalt (highest gowth) 70 50  7

E. Transitioning to clean energy in regions

While regions in Lithuania are coal-free in electricity generation, Vilnius – which accounts for 53% of country’s electricity – is lagging behind in the use of renewables

Lithuanian regions have achieved a full replacement of coal in electricity production. Vilnius and Kaunas contribute to 53% and 43% of the country’s total electricity production, respectively. Nevertheless, relative to Kaunas, Vilnius is still lagging behind in the transition to clean electricity production. In 2017, none of the electricity produced in Vilnius came from renewables sources – against 90% in Kaunas (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.)

Vilnius county 1 517 0% 0% 743 Vil. Kaunas county 1 207 90% 0% 85 Kau. Telšiai county 112 0% 0% 55 Tel.

Carbon efficiency in the production of electricity is very unequal across regions in Lithuania. While Vilnius emits around 490 tons of CO2 per gigawatt hour of electricity produced, Kaunas releases only 70 tons of CO2 per gigawatt hour. Kaunas produces 43% of electricity in the country, however, it emits less than 10% of total national

CO2 related to electricity generation (E2).

E2. Contribution to total CO2 emissions from electricity production, 2017

Share of electricity production

High carbonefficiency Share of CO2 emissions Low carbon efficiency % Contribution to total electricity production Contribution to total electricity production higher than contribution to CO2 emissions lower than contribution to CO2 emissions 80 70 60 50 40 30 20 10 0 Kaunas county Telšiai county Vilnius county

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. Renewable energy sources include hydropower, geothermal power, biomass, wind, solar, wave and tidal and waste. See here for more details. Regions and Cities at a Glance 2020 Austria country note 8  F. Metropolitan trends in growth and sustainability

Compared to the OECD average, Lithuania has less people living in functional urban areas

In Lithuania, 53% of the population lives in cities of more than 50 000 inhabitants and their respective commuting areas (functional urban areas, FUAs), which is lower than the OECD average of 75%. The share of population in FUAs with more than 500 000 people is 25%, one third the OECD average of 75%, due to the fact of the relatively small national population (Figure F1). F1. Distribution of population in cities by city size Functional urban areas, 2018 Lithuania,United percentage States of population in cities Population by city size, a global view

Above Between 250 000 Between 50 000 Other settlements above 500 000 pop. and 500 000 pop. and 250 000 pop. below 50 000 500 000 % other settlements 100 below 50 000

25% 80 2.8 million 60 47% people - 53%

live in cities 40 13% between 250 000 75% 64% 60% and 500 000 53% 20 15% 25% 25% between 50 000 0 and 250 000 Lithuania Europe OECD (29 countries) (37 countries)

Built-up area per capita slightly increased in the Vilnius metropolitan area Built-up area per capita has slightly risen in the Vilnius metropolitan area since 2000, with a higher growth of its urbanised area than the growth experienced in population. Vilnius was among the capital metropolitan area with the lowest built-up are per capita in 2014 across the OECD (Figure F2).

F2. Built-up area per capita Capital functional urban areas with more than 500 000 inhabitants m2 per capita 2014 2000 400

300

200

100

0

Oslo Riga

Paris

Berlin Seoul

Rome

Tokyo

Zurich

Dublin

Lisbon Tallinn Vilnius

Madrid

Vienna Athens

Ottawa

Bogota

Prague

Sydney London

Helsinki Geneva

Warsaw

Brussels

Budapest

Bratislava

Stockholm

Valparaiso

Amsterdam

Mexico City

Washington

Luxembourg Thessaloniki OECD average

Source: OECD Metropolitan Database. Number of metropolitan areas with a population of over 500 000: One in Lithuania compared to 349 in the OECD.  9

Among OECD metropolitan area of more than 500 000 inhabitants, Vilnius has recorded the second fastest growth in GDP per capita growth since 2000, only behind Warsaw (Poland). In Vilnius, GDP per capita growth is more than five times higher than the OECD median value, but Vilnius remains in the bottom 30% of OECD metropolitan areas in terms of GDP per capita. Similar GDP per capita levels are observed in Athens (Greece), Portsmouth (United Kingdom) and Liege (Belgium) and Vilnius ranks between Tallinn and Riga in the Baltic countries.

F3. Trends in GDP per capita in metropolitan areas Functional urban areas above 500 000 people