Hungary

Regions and Cities at a Glance

Regions and Cities at a Glance 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 the net zero-carbon transition. It presents indicators on individual regions and cities to assess disparities within countries and their evolution since the turn of the new millennium. Each indicator is illustrated by graphs and maps. The report covers all OECD countries and, where data is available, partner countries and economies.

The data in this note reflect different sub-national 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 (Fadic et al. 2019).

  • Functional urban areas consist 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 (Dijkstra, Poelman, and Veneri 2019). Metropolitan areas refer to functional urban areas above 250 000 inhabitants.

In addition, some indicators use the degree of urbanisation classification (OECD et al. 2021), which defines three types of areas:

  • Cities consist of contiguous grid cells that have a density of at least 1 500 inhabitants per km2 or are at least 50% built up, with a population of at least 50 000.
  • Towns and semi-dense areas consist of contiguous grid cells with a density of at least 300 inhabitants per km2 and are at least 3% built up, with a total population of at least 5 000.
  • Rural areas are cells that do not belong to a city or a town and semi-dense area. Most of these have a density below 300 inhabitants per km2.

Disclaimer: https://oecdcode.org/disclaimers/territories.html

Well-being, liveability and inclusion in regions

Regional well-being

Hungary faces stark regional disparities across seven well-being dimensions, with the starkest disparities in terms of community, jobs and education.

Figure 7: Regional gaps in well-being

Note: Regional indices provide a first comparative glance of well-being in OECD regions. The figure shows the relative ranking of the regions with the best and worst outcomes in the eleven well-being dimensions, relative to all 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.

Relative to other OECD regions, Hungary performs best in the community dimension, with 50% of of Hungarian regions lying in the top 20% of OECD regions.

The top 20% of Hungarian regions rank above the OECD median region in 9 out of 14 well-being indicators, performing best in terms of population with at least upper secondary education.

Figure 8: How do the top and bottom regions fare on the well-being indicators?

Note: Regional well-being indices are affected by the availability and comparability of regional data across OECD countries. The indicators used to create the indices can therefore vary across OECD publications as new information becomes available. For more visuals, visit https://www.oecdregionalwellbeing.org.

The digital divide

Fixed Internet connections in Hungarian cities and rural areas deliver speeds significantly faster than the OECD average (37% and 7%, respectively). This gap (30 percentage points) is larger than in most other OECD countries.

Figure 9: Speed of fixed Internet connections relative to the OECD average, by degree of urbanisation, 2020Q4

Note: Cities and rural areas are identified according to the degree of urbanisation (OECD et al. 2021). Internet speed measurements are based on speed tests performed by users around the globe via the Ookla Speedtest platform. As such, data may be subject to testing biases (e.g. fast connections being tested more frequently), or to strategic testing by ISPs in specific markets to boost averages. For a more comprehensive picture of Internet quality and connectivity across places, see OECD (2022), “Broadband networks of the future”.

Source: OECD calculations based on Speedtest by Ookla Global Fixed and Mobile Network Performance Maps for 2020Q4.

The average speed of fixed Internet connections is above the OECD average in 6 out of 8 Hungarian regions. Within the country, residents of Budapest, Pest and Northern Hungary experience the fastest connections.

Figure 10: Speed of fixed Internet connections relative to the OECD average, in large regions (2021Q4)

Relative poverty rates

In Hungary, relative poverty rates2 range from 7% to 20% across regions. This 13 percentage point difference is less pronounced than the average difference observed across the 29 OECD countries with available data (16 percentage points).

Figure 11: Relative poverty rates in 2020

Note: The OECD median gives the median relative poverty rate observed in a sample made of 326 large regions (from 28 countries), and 28 small regions (from Denmark, Lithuania and the Slovak Republic). Data corresponds to 2020 or the latest available year.

Environmental challenges in regions and cities

Greenhouse gas emissions in regions

Since 1990, production-based greenhouse gas emissions have decreased in all Hungarian regions. Pest (-7%) and Northern Hungary (-48%) experienced the lowest and largest decline in emissions, respectively.

On average, Hungarian regions decreased their emissions by 1.05% per year between 1990 and 2018. This is below the 1.93% yearly reduction rate needed to reach the EU target of a 55% reduction in emissions by 2030, with respect to 1990 levels.

Figure 18: Change in production-based emissions in large regions, 1990-2018

Note: Bubbles are proportional to per capita greenhouse gas emissions, not to the overall level of greenhouse gas emissions in the region.

Source: OECD calculations, based on the Emissions Database for Global Atmospheric Research (European Commission. Joint Research Centre. 2019).

In 2018, greenhouse gas emissions per capita in Hungary were largest in Northern Hungary, Central Transdanubia and Pest. Industry accounts for the largest share of greenhouse gas emissions in Central Transdanubia, while the power sector accounts for most emissions in Northern Hungary and Pest.

Figure 19: Production-based greenhouse gas emissions per capita in large regions, 2018

Note: Regions with low population counts may rank high in greenhouse gas emissions per capita while contributing relatively little to overall emissions in the country.

Urban heat island effect

In Hungarian cities, the difference in temperature between cities and their surrounding areas (i.e. urban heat island intensity) reaches 1.5 degrees Celsius (°C). The largest effect is observed in Eger and Zalaegerszeg, two cities that are, on average, 2.8°C and 2.9°C warmer than their surrounding areas, respectively.

Figure 20: Urban heat island intensity index, 2021

Note: The Urban Heat Island Intensity (UHI) index is defined as the difference in land surface temperature between built-up areas and non-built-up areas within functional urban areas. This index can be affected by the type of vegetation and climate in non-built-up areas.

Source: OECD calculations, based on land surface temperature data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) (Wan, Hook, and Hulley 2021a, 2021b)

References

Source of administrative boundaries: © OECD, © EuroGeographics, National Statistical Offices, © UN-FAO Global Administrative Unit Layers (GAUL)

Dijkstra, Lewis, Hugo Poelman, and Paolo Veneri. 2019. “The EU-OECD Definition of a Functional Urban Area.” https://doi.org/10.1787/d58cb34d-en.
European Commission. Joint Research Centre. 2019. Fossil CO2 and GHG emissions of all world countries: 2019 report. LU: Publications Office. https://doi.org/10.2760/687800.
Fadic, Milenko, José Enrique Garcilazo, Ana Moreno Monroy, and Paolo Veneri. 2019. “Classifying Small (Tl3) Regions Based on Metropolitan Population, Low Density and Remoteness.” https://doi.org/10.1787/b902cc00-en.
OECD. 2022. “Broadband Networks of the Future,” no. 327. https://doi.org/10.1787/755e2d0c-en.
———. 2022. “Regional and Metropolitan Databases.” http://dx.doi.org/10.1787/region-data-en.
OECD, The European Commission, Food, Agriculture Organization of the United Nations, United Nations Human Settlements Programme, International Labour Organization, and The World Bank. 2021. Applying the Degree of Urbanisation. https://doi.org/10.1787/4bc1c502-en.
Wan, Zhengming, Simon Hook, and Glynn Hulley. 2021a. “MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V061.” NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MYD11A1.061.
———. 2021b. “MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V061.” NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD11A1.061.

Footnotes

  1. International comparability in 2019 and 2020 is limited because of methodological differences in the calculation of employment counts during the height of the COVID-19 economic crisis.↩︎

  2. The relative poverty rate gives the share of people – as a % of the regional population – with an income below the relative poverty line (60% of the national median income).↩︎

  3. The elderly dependency rate compares the number of elderly people at an age when they are generally economically inactive (i.e. aged 65 and over), to the number of people of working age (i.e. 15-64 years old).↩︎