Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Australia, between 2002 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector widened the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/search.json b/docs/search.json
index 02f7535..699ae72 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -158,7 +158,7 @@
"href": "tl3-cze.html#regional-inequality-trends",
"title": "Czech Republic",
"section": "Regional inequality trends",
- "text": "Regional inequality trends\nThe Czech Republic experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2008. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.054 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.036 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.873. For reference, the same value for OECD was 1.475. This gap increased by 0.138 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.313. For reference, the same value for OECD was 1.325. This gap increased by 0.122 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.313 in 2020 and increased by 0.122 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn Czech Republic, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 53%, 12 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Czech Republic, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\nThe Czech Republic experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2008. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.054 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.036 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.873. For reference, the same value for OECD was 1.475. This gap increased by 0.138 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.313. For reference, the same value for OECD was 1.325. This gap increased by 0.122 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.313 in 2020 and increased by 0.122 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn the Czech Republic, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 53%, 12 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Czech Republic, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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"href": "tl3-nld.html#regional-inequality-trends",
"title": "Netherlands",
"section": "Regional inequality trends",
- "text": "Regional inequality trends\nThe Netherlands experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2013. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.006 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.053 higher in the same period, indicating bottom convergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.33. For reference, the same value for OECD was 1.475. This gap increased by 0.044 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.238. For reference, the same value for OECD was 1.325. This gap decreased by 0.061 percentage points since 2000.\nThere is no data for the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants for 2000 and 2020.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn Netherlands, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 14%, 4 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Netherlands, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that used to be in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector widened the labour productivity gap between regions while the opposite was true for tradable services.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\nThe Netherlands experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2013. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.006 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.053 higher in the same period, indicating bottom convergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.33. For reference, the same value for OECD was 1.475. This gap increased by 0.044 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.238. For reference, the same value for OECD was 1.325. This gap decreased by 0.061 percentage points since 2000.\nThere is no data for the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants for 2000 and 2020.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn the Netherlands, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 14%, 4 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Netherlands, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that used to be in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector widened the labour productivity gap between regions while the opposite was true for tradable services.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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{
"objectID": "tl3-nld.html#recent-policy-developments",
@@ -613,7 +613,7 @@
"href": "tl3-svk.html#regional-inequality-trends",
"title": "Slovak Republic",
"section": "Regional inequality trends",
- "text": "Regional inequality trends\nThe Slovak Republic experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2009. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.074 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.022 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nThere is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.884. For reference, the same value for OECD was 1.325. This gap decreased by 0.016 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.813 in 2020 and increased by 0.067 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn Slovak Republic, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 68%, 7 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Slovak Republic, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022).\n\n \n\n\n\n\nTerritorial definitions\n\n\n\n\n\nThe data in this note reflect different sub-national geographic levels in OECD countries. In particular, regions are classified on two territorial levels reflecting the administrative organisation of countries: large regions (TL2) and small regions (TL3).\n\n\nSmall regions are classified according to their access to metropolitan areas (Fadic et al. 2019). The typology classifies small (TL3) regions into metropolitan and non-metropolitan regions according to the following criteria:\n\n\n\nMetropolitan regions, if more than half of the population live in a FUA. Metropolitan regions are further classified into: metropolitan large, if more than half of the population live in a (large) FUA of at least 1.5 million inhabitants; and metropolitan midsize, if more than half of the population live in a (midsize) FUA of at 250 000 to 1.5 million inhabitants.\n\n\nNon-metropolitan regions, if less than half of the population live in a midsize/large FUA. These regions are further classified according to their level of access to FUAs of different sizes: near a midsize/large FUA if more than half of the population live within a 60-minute drive from a midsize/large FUA (of more than 250 000 inhabitants) or if the TL3 region contains more than 80% of the area of a midsize/large FUA; near a small FUA if the region does not have access to a midsize/large FUA and at least half of its population have access to a small FUA (i.e. between 50 000 and 250 000 inhabitants) within a 60-minute drive, or contains 80% of the area of a small FUA; and remote, otherwise.\n\n\n\n\nDisclaimer: https://oecdcode.org/disclaimers/territories.html"
+ "text": "Regional inequality trends\nThe Slovak Republic experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2009. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.074 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.022 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nThere is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.884. For reference, the same value for OECD was 1.325. This gap decreased by 0.016 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.813 in 2020 and increased by 0.067 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn the Slovak Republic, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 68%, 7 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Slovak Republic, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022).\n\n \n\n\n\n\nTerritorial definitions\n\n\n\n\n\nThe data in this note reflect different sub-national geographic levels in OECD countries. In particular, regions are classified on two territorial levels reflecting the administrative organisation of countries: large regions (TL2) and small regions (TL3).\n\n\nSmall regions are classified according to their access to metropolitan areas (Fadic et al. 2019). The typology classifies small (TL3) regions into metropolitan and non-metropolitan regions according to the following criteria:\n\n\n\nMetropolitan regions, if more than half of the population live in a FUA. Metropolitan regions are further classified into: metropolitan large, if more than half of the population live in a (large) FUA of at least 1.5 million inhabitants; and metropolitan midsize, if more than half of the population live in a (midsize) FUA of at 250 000 to 1.5 million inhabitants.\n\n\nNon-metropolitan regions, if less than half of the population live in a midsize/large FUA. These regions are further classified according to their level of access to FUAs of different sizes: near a midsize/large FUA if more than half of the population live within a 60-minute drive from a midsize/large FUA (of more than 250 000 inhabitants) or if the TL3 region contains more than 80% of the area of a midsize/large FUA; near a small FUA if the region does not have access to a midsize/large FUA and at least half of its population have access to a small FUA (i.e. between 50 000 and 250 000 inhabitants) within a 60-minute drive, or contains 80% of the area of a small FUA; and remote, otherwise.\n\n\n\n\nDisclaimer: https://oecdcode.org/disclaimers/territories.html"
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"section": "Regional inequality trends",
- "text": "Regional inequality trends\nThe United Kingdom experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2015. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.061 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio did not change in the same period.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.078. For reference, the same value for OECD was 1.475. This gap increased by 0.035 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.111. For reference, the same value for OECD was 1.325. This gap increased by 0.069 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.16 in 2020 and decreased by 0.031 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn United Kingdom, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2004 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 10%, 3 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In United Kingdom, between 2004 and 2020, the share of workers in the industrial sector went down in all regions, approximately by the same amount. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that used to be in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector reduced the labour productivity gap between regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\nThe United Kingdom experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2015. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.061 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio did not change in the same period.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.078. For reference, the same value for OECD was 1.475. This gap increased by 0.035 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.111. For reference, the same value for OECD was 1.325. This gap increased by 0.069 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.16 in 2020 and decreased by 0.031 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn the United Kingdom, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2004 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 10%, 3 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In United Kingdom, between 2004 and 2020, the share of workers in the industrial sector went down in all regions, approximately by the same amount. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that used to be in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector reduced the labour productivity gap between regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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diff --git a/docs/tl0-col.html b/docs/tl0-col.html
index f12e4b7..75283ad 100644
--- a/docs/tl0-col.html
+++ b/docs/tl0-col.html
@@ -521,8 +521,8 @@
Regional inequa
@@ -532,8 +532,8 @@
Regional inequa
In Colombia, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2005 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 26%, 12 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
diff --git a/docs/tl0-irl.html b/docs/tl0-irl.html
index 3d12b86..088b7bd 100644
--- a/docs/tl0-irl.html
+++ b/docs/tl0-irl.html
@@ -561,8 +561,8 @@
Regional inequa
In Ireland, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 66%, 60 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -572,8 +572,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Ireland, between 2001 and 2020, the share of workers in the industrial sector went down in all regions, approximately by the same amount. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions.
diff --git a/docs/tl0-usa.html b/docs/tl0-usa.html
index db9cabb..faea6c9 100644
--- a/docs/tl0-usa.html
+++ b/docs/tl0-usa.html
@@ -493,8 +493,8 @@
Regional inequa
Polarisation, as measured by the Top 20%/Mean ratio was 0.063 higher in 2000 compared to 2020. Bottom divergence, as measured by the Bottom 20%/Mean ratio was 0.026 lower in the same period.
@@ -550,8 +550,8 @@
Regional inequa
As for the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 inhabitants, it was 1.115 in 2020, decrease of 0.06 percentage points since 2000.
@@ -561,8 +561,8 @@
Regional inequa
In the United States, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 24%, 1 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -572,8 +572,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In United States, between 2001 and 2020, the share of workers in the industrial sector remained approximately stable across all regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl2-can.html b/docs/tl2-can.html
index 16b0681..8342c59 100644
--- a/docs/tl2-can.html
+++ b/docs/tl2-can.html
@@ -490,8 +490,8 @@
Regional inequa
Canada experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2004. The figures are normalized, with values in the year 2000 set to 1.
@@ -501,8 +501,8 @@
Regional inequa
In Canada, the gap between the upper and the lower half of regions in terms of labour productivity remained stable between 2001 and 2019. Over this period labour productivity grew roughly by 14% in both groups of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -512,8 +512,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Canada, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl2-chl.html b/docs/tl2-chl.html
index 048de18..2c7a6eb 100644
--- a/docs/tl2-chl.html
+++ b/docs/tl2-chl.html
@@ -500,8 +500,8 @@
Regional inequa
Chile experienced a decline in the Theil index of GDP per capita over 2008-2020. Inequality reached its maximum in 2010. The figures are normalized, with values in the year 2008 set to 1.
@@ -511,8 +511,8 @@
Regional inequa
In Chile, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2013 and 2019. Over this period labour productivity in the upper half of regions declined roughly by 5%, while it increased by 15% in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -522,8 +522,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Chile, between 2013 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector remained approximately stable across all regions.
diff --git a/docs/tl2-mex.html b/docs/tl2-mex.html
index 5880a31..94a3c11 100644
--- a/docs/tl2-mex.html
+++ b/docs/tl2-mex.html
@@ -516,8 +516,8 @@
Regional inequa
Mexico experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2008. The figures are normalized, with values in the year 2000 set to 1.
@@ -527,8 +527,8 @@
Regional inequa
In Mexico, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2005 and 2019. Over this period labour productivity in the upper half of regions declined roughly by 3%, while it increased by 6% in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -538,8 +538,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Mexico, between 2005 and 2020, the share of workers in the industrial sector remained approximately stable across all regions. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions.
diff --git a/docs/tl3-aut.html b/docs/tl3-aut.html
index c48ee2d..6581ea9 100644
--- a/docs/tl3-aut.html
+++ b/docs/tl3-aut.html
@@ -487,8 +487,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.114 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.069 higher in the same period, indicating bottom convergence.
@@ -500,8 +500,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.143 in 2020 and decreased by 0.045 percentage points since 2000.
@@ -511,8 +511,8 @@
Regional inequa
In Austria, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 9%, 9 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -522,8 +522,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Austria, between 2001 and 2020, the share of workers in the industrial sector went down in regions that used to be located in the upper half of the labour productivity distribution while it remained stable in the rest. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that used to be in the lower half of the labour productivity distribution. Hence, the evolution of employment shares both in the industrial and in the tradable services sectors reduced the labour productivity gap between regions.
diff --git a/docs/tl3-bel.html b/docs/tl3-bel.html
index e68b0de..649da00 100644
--- a/docs/tl3-bel.html
+++ b/docs/tl3-bel.html
@@ -638,8 +638,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.032 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.023 lower in the same period, indicating bottom divergence.
@@ -651,8 +651,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.163 in 2020 and increased by 0.048 percentage points since 2000.
@@ -662,8 +662,8 @@
Regional inequa
In Belgium, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 15%, 6 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -673,8 +673,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Belgium, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-che.html b/docs/tl3-che.html
index 4f68737..6542d3f 100644
--- a/docs/tl3-che.html
+++ b/docs/tl3-che.html
@@ -484,8 +484,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.028 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.018 lower in the same period, indicating bottom divergence.
@@ -497,8 +497,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.183 in 2020 and decreased by 0.077 percentage points since 2000.
@@ -508,8 +508,8 @@
Regional inequa
In Switzerland, the gap between the upper and the lower half of regions in terms of labour productivity remained stable between 2011 and 2019. Over this period labour productivity grew roughly by 6% in both groups of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -519,8 +519,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Switzerland, between 2011 and 2020, the share of workers in the industrial sector went down in all regions, approximately by the same amount. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-cze.html b/docs/tl3-cze.html
index 911efd5..f46c0dd 100644
--- a/docs/tl3-cze.html
+++ b/docs/tl3-cze.html
@@ -522,8 +522,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.054 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.036 lower in the same period, indicating bottom divergence.
@@ -535,19 +535,19 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.313 in 2020 and increased by 0.122 percentage points since 2000.
Note: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland).
Source: OECD Regional Database (2022).
-
In Czech Republic, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 53%, 12 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
+
In the Czech Republic, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 53%, 12 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -557,8 +557,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Czech Republic, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-deu.html b/docs/tl3-deu.html
index 0afc34d..64018e8 100644
--- a/docs/tl3-deu.html
+++ b/docs/tl3-deu.html
@@ -519,8 +519,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.092 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.087 higher in the same period, indicating bottom convergence.
@@ -532,8 +532,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.033 in 2020 and decreased by 0.089 percentage points since 2000.
@@ -543,8 +543,8 @@
Regional inequa
In Germany, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 8%, 12 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -554,8 +554,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Germany, between 2001 and 2020, the share of workers in the industrial sector went down in regions that used to be located in the upper half of the labour productivity distribution while it remained stable in the rest. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-dnk.html b/docs/tl3-dnk.html
index 68b3f32..62c74a6 100644
--- a/docs/tl3-dnk.html
+++ b/docs/tl3-dnk.html
@@ -489,8 +489,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.063 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.033 lower in the same period, indicating bottom divergence.
@@ -502,8 +502,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.184 in 2020 and increased by 0.008 percentage points since 2000.
@@ -513,8 +513,8 @@
Regional inequa
In Denmark, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 18%, 6 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -524,8 +524,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Denmark, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector widened the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-esp.html b/docs/tl3-esp.html
index 51d0a23..afe6d77 100644
--- a/docs/tl3-esp.html
+++ b/docs/tl3-esp.html
@@ -505,8 +505,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.031 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.053 higher in the same period, indicating bottom convergence.
@@ -518,8 +518,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.113 in 2020 and increased by 0.028 percentage points since 2000.
@@ -529,8 +529,8 @@
Regional inequa
In Spain, the gap between the upper and the lower half of regions in terms of labour productivity remained stable between 2001 and 2019. Over this period labour productivity grew roughly by 11% in both groups of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -540,8 +540,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Spain, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-est.html b/docs/tl3-est.html
index f9fefcf..411e9e4 100644
--- a/docs/tl3-est.html
+++ b/docs/tl3-est.html
@@ -509,8 +509,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.052 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.039 lower in the same period, indicating bottom divergence.
@@ -522,8 +522,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 2.337 in 2020 and increased by 0.178 percentage points since 2000.
@@ -533,8 +533,8 @@
Regional inequa
In Estonia, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 57%, 38 percentage points less than in the lower half of regions. During 2020, the gap widened again. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -544,8 +544,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Estonia, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in regions already located in the upper half of the labour productivity distribution while it went down in regions located in the lower half. Hence, the evolution of employment shares both in the industrial and in the tradable services sectors widened the labour productivity gap between regions.
diff --git a/docs/tl3-fin.html b/docs/tl3-fin.html
index 303c430..2b97882 100644
--- a/docs/tl3-fin.html
+++ b/docs/tl3-fin.html
@@ -498,8 +498,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.052 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.134 higher in the same period, indicating bottom convergence.
@@ -511,8 +511,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.236 in 2020 and decreased by 0.091 percentage points since 2000.
@@ -522,8 +522,8 @@
Regional inequa
In Finland, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 5%, 10 percentage points less than in the lower half of regions. During 2020, the gap remained stable. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -533,8 +533,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Finland, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-fra.html b/docs/tl3-fra.html
index b5af153..25746cd 100644
--- a/docs/tl3-fra.html
+++ b/docs/tl3-fra.html
@@ -563,8 +563,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.078 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.004 lower in the same period, indicating bottom divergence.
@@ -576,8 +576,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.201 in 2020 and increased by 0.017 percentage points since 2000.
@@ -587,8 +587,8 @@
Regional inequa
In France, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 15%, 2 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -598,8 +598,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In France, between 2001 and 2020, the share of workers in the industrial sector went down in all regions, approximately by the same amount. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-gbr.html b/docs/tl3-gbr.html
index 5cb7cc5..bc8fc02 100644
--- a/docs/tl3-gbr.html
+++ b/docs/tl3-gbr.html
@@ -550,8 +550,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.061 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio did not change in the same period.
@@ -563,19 +563,19 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.16 in 2020 and decreased by 0.031 percentage points since 2000.
Note: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland).
Source: OECD Regional Database (2022).
-
In United Kingdom, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2004 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 10%, 3 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
+
In the United Kingdom, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2004 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 10%, 3 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -585,8 +585,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In United Kingdom, between 2004 and 2020, the share of workers in the industrial sector went down in all regions, approximately by the same amount. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that used to be in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector reduced the labour productivity gap between regions.
diff --git a/docs/tl3-grc.html b/docs/tl3-grc.html
index 5615e5a..a9d373b 100644
--- a/docs/tl3-grc.html
+++ b/docs/tl3-grc.html
@@ -511,8 +511,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.123 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.042 higher in the same period, indicating bottom convergence.
@@ -524,8 +524,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.155 in 2020 and increased by 0.001 percentage points since 2000.
@@ -535,8 +535,8 @@
Regional inequa
In Greece, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions declined roughly by 11%, while it declined only by 8% in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -546,8 +546,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Greece, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-hun.html b/docs/tl3-hun.html
index da02834..874aea8 100644
--- a/docs/tl3-hun.html
+++ b/docs/tl3-hun.html
@@ -525,8 +525,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.04 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.052 higher in the same period, indicating bottom convergence.
@@ -538,8 +538,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.274 in 2020 and increased by 0.095 percentage points since 2000.
@@ -549,8 +549,8 @@
Regional inequa
In Hungary, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 35%, 4 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -560,8 +560,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Hungary, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-ita.html b/docs/tl3-ita.html
index ef4c3e4..6aa956d 100644
--- a/docs/tl3-ita.html
+++ b/docs/tl3-ita.html
@@ -495,8 +495,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.044 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.006 higher in the same period, indicating bottom convergence.
@@ -508,8 +508,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.163 in 2020 and decreased by 0.043 percentage points since 2000.
@@ -519,8 +519,8 @@
Regional inequa
In Italy, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions declined roughly by 6%, while it declined only by 4% in the lower half of regions. During 2020, the gap remained unchanged. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -530,8 +530,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Italy, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-jpn.html b/docs/tl3-jpn.html
index 10bd7c1..55e01e2 100644
--- a/docs/tl3-jpn.html
+++ b/docs/tl3-jpn.html
@@ -465,8 +465,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.066 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.01 lower in the same period, indicating bottom divergence.
@@ -478,8 +478,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.045 in 2020 and increased by 0.026 percentage points since 2000.
diff --git a/docs/tl3-kor.html b/docs/tl3-kor.html
index 3c38138..2eadbf3 100644
--- a/docs/tl3-kor.html
+++ b/docs/tl3-kor.html
@@ -508,8 +508,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.007 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.055 higher in the same period, indicating bottom convergence.
@@ -521,8 +521,8 @@
Regional inequa
There is no data for the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants for 2000 and 2020.
@@ -532,8 +532,8 @@
Regional inequa
In Korea, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2008 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 24%, 3 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
diff --git a/docs/tl3-ltu.html b/docs/tl3-ltu.html
index 3ab0bf7..750b930 100644
--- a/docs/tl3-ltu.html
+++ b/docs/tl3-ltu.html
@@ -515,8 +515,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.177 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.064 lower in the same period, indicating bottom divergence.
@@ -528,8 +528,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.804 in 2020 and increased by 0.414 percentage points since 2000.
@@ -539,8 +539,8 @@
Regional inequa
In Lithuania, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 89%, 12 percentage points less than in the lower half of regions. During 2020, the gap remained unchanged. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -550,8 +550,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Lithuania, between 2001 and 2020, the share of workers in the industrial sector went down in regions that used to be located in the upper half of the labour productivity distribution while it went up in regions located in the lower half. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-lva.html b/docs/tl3-lva.html
index a2daabb..1851c8c 100644
--- a/docs/tl3-lva.html
+++ b/docs/tl3-lva.html
@@ -484,8 +484,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.018 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.083 higher in the same period, indicating bottom convergence.
@@ -497,8 +497,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 2.065 in 2020 and decreased by 0.057 percentage points since 2000.
@@ -508,8 +508,8 @@
Regional inequa
In Latvia, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 79%, 42 percentage points less than in the lower half of regions. During 2020, the gap remained unchanged. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -519,8 +519,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Latvia, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-nld.html b/docs/tl3-nld.html
index 5036758..772576e 100644
--- a/docs/tl3-nld.html
+++ b/docs/tl3-nld.html
@@ -465,8 +465,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.006 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.053 higher in the same period, indicating bottom convergence.
@@ -478,19 +478,19 @@
Regional inequa
There is no data for the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants for 2000 and 2020.
Note: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland).
Source: OECD Regional Database (2022).
-
In Netherlands, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 14%, 4 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
+
In the Netherlands, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 14%, 4 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -500,8 +500,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Netherlands, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that used to be in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector widened the labour productivity gap between regions while the opposite was true for tradable services.
diff --git a/docs/tl3-nor.html b/docs/tl3-nor.html
index ff741e9..18228d5 100644
--- a/docs/tl3-nor.html
+++ b/docs/tl3-nor.html
@@ -488,8 +488,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.076 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.006 lower in the same period, indicating bottom divergence.
@@ -501,8 +501,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.325 in 2020 and decreased by 0.151 percentage points since 2000.
@@ -512,8 +512,8 @@
Regional inequa
In Norway, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2008 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 18%, 7 percentage points less than in the lower half of regions. During 2020, the gap widened again. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -523,8 +523,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Norway, between 2008 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-nzl.html b/docs/tl3-nzl.html
index 6dcf45f..18c9a64 100644
--- a/docs/tl3-nzl.html
+++ b/docs/tl3-nzl.html
@@ -536,8 +536,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.072 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.04 higher in the same period, indicating bottom convergence.
@@ -549,8 +549,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.231 in 2020 and increased by 0.016 percentage points since 2000.
@@ -560,8 +560,8 @@
Regional inequa
In New Zealand, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 12%, 10 percentage points less than in the lower half of regions.
@@ -570,8 +570,8 @@
Regional inequa
diff --git a/docs/tl3-pol.html b/docs/tl3-pol.html
index 53d9787..219a062 100644
--- a/docs/tl3-pol.html
+++ b/docs/tl3-pol.html
@@ -520,8 +520,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.074 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.045 lower in the same period, indicating bottom divergence.
@@ -533,8 +533,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.306 in 2020 and increased by 0.016 percentage points since 2000.
@@ -544,8 +544,8 @@
Regional inequa
In Poland, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 66%, 17 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -555,8 +555,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Poland, between 2001 and 2020, the share of workers in the industrial sector went down in regions that used to be located in the upper half of the labour productivity distribution while it went up in regions located in the lower half. At the same time, the share of workers in the tradable services sector went up in regions that used to be located in the lower half of the labour productivity distribution while it remained stable in the rest. Hence, the evolution of employment shares both in the industrial and in the tradable services sectors reduced the labour productivity gap between regions.
diff --git a/docs/tl3-prt.html b/docs/tl3-prt.html
index 0b2ea87..411898b 100644
--- a/docs/tl3-prt.html
+++ b/docs/tl3-prt.html
@@ -497,8 +497,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.148 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.15 higher in the same period, indicating bottom convergence.
@@ -510,8 +510,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.062 in 2020 and decreased by 0.005 percentage points since 2000.
@@ -521,8 +521,8 @@
Regional inequa
In Portugal, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 12%, 22 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -532,8 +532,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Portugal, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-svk.html b/docs/tl3-svk.html
index 5f37bf4..b716fe4 100644
--- a/docs/tl3-svk.html
+++ b/docs/tl3-svk.html
@@ -367,8 +367,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.074 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.022 lower in the same period, indicating bottom divergence.
@@ -380,19 +380,19 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.813 in 2020 and increased by 0.067 percentage points since 2000.
Note: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland).
Source: OECD Regional Database (2022).
-
In Slovak Republic, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 68%, 7 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
+
In the Slovak Republic, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 68%, 7 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -402,8 +402,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Slovak Republic, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-svn.html b/docs/tl3-svn.html
index 9fe1833..b73f753 100644
--- a/docs/tl3-svn.html
+++ b/docs/tl3-svn.html
@@ -486,8 +486,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.079 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.009 lower in the same period, indicating bottom divergence.
@@ -499,8 +499,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.001 in 2020 and increased by 0.068 percentage points since 2000.
@@ -510,8 +510,8 @@
Regional inequa
In Slovenia, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 31%, 12 percentage points less than in the lower half of regions. During 2020, the gap widened again. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -521,8 +521,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Slovenia, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-swe.html b/docs/tl3-swe.html
index fd3e527..1bc644b 100644
--- a/docs/tl3-swe.html
+++ b/docs/tl3-swe.html
@@ -528,8 +528,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.011 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.036 lower in the same period, indicating bottom divergence.
@@ -541,8 +541,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.263 in 2020 and increased by 0.014 percentage points since 2000.
@@ -552,8 +552,8 @@
Regional inequa
In Sweden, the gap between the upper and the lower half of regions in terms of labour productivity remained stable between 2001 and 2019. Over this period labour productivity grew roughly by 27% in both groups of regions. During 2020, the gap remained stable. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -563,8 +563,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Sweden, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares both in the industrial and in the tradable services sectors widened the labour productivity gap between regions.
diff --git a/docs/tl3-tur.html b/docs/tl3-tur.html
index 8a7943e..9bb9cb7 100644
--- a/docs/tl3-tur.html
+++ b/docs/tl3-tur.html
@@ -512,8 +512,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.075 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.056 higher in the same period, indicating bottom convergence.
@@ -525,8 +525,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.183 in 2020 and decreased by 0.104 percentage points since 2000.
@@ -536,8 +536,8 @@
Regional inequa
In Turkey, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2009 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 25%, 23 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -547,8 +547,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Turkey, between 2009 and 2020, the share of workers in the industrial sector went up in regions that used to be located in the lower half of the labour productivity distribution while it remained stable in the rest. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.