diff --git a/00_UPDATE_THIS_country_template_tl2.qmd b/00_UPDATE_THIS_country_template_tl2.qmd index 87b1e1f..4bb56b9 100644 --- a/00_UPDATE_THIS_country_template_tl2.qmd +++ b/00_UPDATE_THIS_country_template_tl2.qmd @@ -41,28 +41,6 @@ clrs5 <- tintin_colours$red_rackhams_treasure - - - - - - -
-

Territorial definitions

-

The data in this note reflect different sub-national geographic levels in OECD countries: -

    -
  • Regions: are classified on two territorial levels reflecting the administrative organisation of countries: large regions (TL2) and small regions (TL3). Small regions are classified according to their access to metropolitan areas (Fadic et al. 2019).
    • -
  • Functional urban areas consist of cities – defined as densely populated local units with at least 50 000 inhabitants – and adjacent local units connected to the city (commuting zones) in terms of commuting flows (Dijkstra, Poelman, and Veneri 2019). Metropolitan areas refer to functional urban areas above 250 000 inhabitants.

- -

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

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

- -

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

-
- ## Overview ```{r tbl} @@ -218,7 +196,7 @@ fig1 <- summary_wide %>% scale_colour_manual(values = clrs2) + scale_x_continuous(expand = c(0, 0)) + labs( - title = "Figure 1: Trends in GDP per capita inequality indicators,\nTL2 OECD regions", + title = "Figure 1: Trends in GDP per capita inequality indicators, TL2 OECD regions", x = "", y = sprintf("Statistic (%s=1)", min_y), linetype = "", @@ -330,11 +308,13 @@ ggplotly(fig1) %>% ```
-**Note**: Based on 1 586 TL3 regions in 27 countries with available data (no TL3 data (continuous time series for more than 1 region) for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Between Theil measures the dissimilarity of the national GDP per capita means with respect to the OECD average. Within Theil measures the dissimilarity between regional and national GDP per capita. - -Source: OECD Regional Database (2022). +**Note**: Based on 1 586 TL3 regions in 27 countries with available data (no TL3 data (continuous time series for more than 1 region) for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Between Theil measures the dissimilarity of the national GDP per capita means with respect to the OECD average. Within Theil measures the dissimilarity between regional and national GDP per capita.
+**Source**: OECD Regional Database (2022).
+
+
+ ```{r fig2} # read ---- @@ -403,7 +383,7 @@ fig2 <- dp11 %>% theme_oecd(base_size = 10) + scale_colour_manual(values = clrs3[1:2]) + labs( - x = "", y = "Productivity (2015 USD PPP)", colour = "Series", + x = "", y = "Productivity (2015 USD PPP)", colour = "", title = "Figure 2: Evolution of labour productivity, TL2 regions" ) + scale_x_continuous(labels = as.character(yrs), breaks = yrs) + @@ -437,11 +417,13 @@ ggplotly(fig2) %>% ```
-**Note**: Labour productivity for high productivity (low productivity) regions is equal to the sum of Gross Value Added across high productivity (low productivity) regions in a country/year divided by the sum of Employment across high productivity (low productivity) regions in a country/year. Gross Value Added is expressed in USD 2015 PPP. Regions are at the TL2 level for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States. A region is defined as high productivity if its productivity was equal or above the country median for at least two years out of the first four years of each region’ observation period, and low productivity otherwise. The starting year is the first available year for each country. - -Source: OECD Regional Database (2022). +**Note**: Labour productivity for high productivity (low productivity) regions is equal to the sum of Gross Value Added across high productivity (low productivity) regions in a country/year divided by the sum of Employment across high productivity (low productivity) regions in a country/year. Gross Value Added is expressed in USD 2015 PPP. Regions are at the TL2 level for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States. A region is defined as high productivity if its productivity was equal or above the country median for at least two years out of the first four years of each region’ observation period, and low productivity otherwise. The starting year is the first available year for each country.
+**Source**: OECD Regional Database (2022).
+
+
+ ```{r fig3} # read ---- @@ -606,7 +588,7 @@ fig3 <- dp21_2 %>% scale_fill_manual(values = clrs4[1:4]) + labs( x = "", y = "Employment share (%)", color = "", - title = "Figure 3: Evolution of sectoral specialisation in tradable sectors,\nTL2 regions" + title = "Figure 3: Evolution of sectoral specialisation in tradable sectors, TL2 regions" ) + facet_wrap(~category1) + # hide fill from legend @@ -626,9 +608,8 @@ ggplotly(fig3) %>% ```
-**Note**: The employment share for high productivity (low productivity) regions in a given sector is defined as total employment across high productivity (low productivity) regions in a sector/year divided by total employment across high productivity (low productivity) regions in a year. Regions are at the TL2 level for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States. A region is defined as high productivity if its productivity was equal or above the country median for at least two years out of the first four years of each region’ observation period, and low productivity otherwise. The tradable goods sector includes 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). The starting year is the first available year for each country. - -Source: OECD Regional Database (2022). +**Note**: The employment share for high productivity (low productivity) regions in a given sector is defined as total employment across high productivity (low productivity) regions in a sector/year divided by total employment across high productivity (low productivity) regions in a year. Regions are at the TL2 level for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States. A region is defined as high productivity if its productivity was equal or above the country median for at least two years out of the first four years of each region’ observation period, and low productivity otherwise. The tradable goods sector includes 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). The starting year is the first available year for each country.
+**Source**: OECD Regional Database (2022).
## Recent policy developments @@ -636,3 +617,33 @@ Source: OECD Regional Database (2022). ```{r txt} read_html_text(ctry) ``` + + + + + + + + +
+ Territorial definitions +
+

+ The 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). +

+

+ Small 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: +

+
    +
  • + Metropolitan 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. +
    • +
  • + Non-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. +
    • +
+

+

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

+
diff --git a/00_country_template_tl3.qmd b/00_country_template_tl3.qmd index 28c2ba6..ddb23b1 100644 --- a/00_country_template_tl3.qmd +++ b/00_country_template_tl3.qmd @@ -177,7 +177,7 @@ lev_3 <- c( "Mean GDP per capita" ) -fig1 <- summary_wide %>% +df_fig1 <- summary_wide %>% filter(iso3 == ctry, labels_index %in% lev_3, time >= min_y) %>% pivot_wider( names_from = index, @@ -185,7 +185,9 @@ fig1 <- summary_wide %>% ) %>% mutate( index_label = factor(labels_index, levels = lev_3) - ) %>% + ) + +fig1 <- df_fig1 %>% ggplot(aes(x = time)) + geom_line(aes( y = index_gdppc, @@ -203,10 +205,11 @@ fig1 <- summary_wide %>% y = index_theil, colour = index_label ), linewidth = 1.2) + + geom_hline(yintercept = 1, color = "lighgrey", linetype = "dashed") + scale_colour_manual(values = clrs2[c(1, 3, 4, 2)]) + # evil hack to match TL2 colours - scale_x_continuous(expand = c(0, 0)) + + scale_x_continuous(expand = c(0, 0), breaks = seq(from = min(df_fig1$time), to = max(df_fig1$time), by = 5)) + labs( - title = "Figure 1: Trends in GDP per capita inequality indicators,\nTL3 OECD regions", + title = "Figure 1: Trends in GDP per capita inequality indicators, TL3 OECD regions", x = "", y = "Statistic (2000=1)", linetype = "", @@ -297,9 +300,14 @@ ctry4 <- if (any(ctry %in% c("USA", "GBR", "CZE", "SVK", "NLD"))) { ctry2 } -paragraph_theil <- glue("{ ctry3 } experienced { theil_chg_txt } in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in { theil_max }. The figures are normalized, with values in the year {as.integer(min_y)} set to 1. +paragraph_theil <- if (any(ctry %in% c("GBR"))){ + glue("{ ctry3 } experienced { theil_chg_txt } in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in { theil_max }. The figures are normalized, with values in the year {as.integer(min_y)} set to 1. + +The Top 20%/Mean ratio was { paste(abs(polarization_pct), polarization_txt) } in { max(polarization_yrs) } compared to { min(polarization_yrs) }, indicating { polarization_txt2 } polarisation. The Bottom 20%/Mean ratio did not change in the same period.") +} else{ +glue("{ ctry3 } experienced { theil_chg_txt } in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in { theil_max }. The figures are normalized, with values in the year {as.integer(min_y)} set to 1. -The Top 20%/Mean ratio was { paste(abs(polarization_pct), polarization_txt) } in { max(polarization_yrs) } compared to { min(polarization_yrs) }, indicating { polarization_txt2 } polarisation. The Bottom 20%/Mean ratio was { paste(abs(polarization_2_pct), polarization_2_txt) } in the same period, indicating bottom { polarization_2_txt2 }.") +The Top 20%/Mean ratio was { paste(abs(polarization_pct), polarization_txt) } in { max(polarization_yrs) } compared to { min(polarization_yrs) }, indicating { polarization_txt2 } polarisation. The Bottom 20%/Mean ratio was { paste(abs(polarization_2_pct), polarization_2_txt) } in the same period, indicating bottom { polarization_2_txt2 }.")} ``` `r paragraph_theil` @@ -314,11 +322,13 @@ ggplotly(fig1) %>% ```
-**Note**: 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. - +**Note**: 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).
+
+
+ ```{r fig2} # Load file with top/bottom 20% data (provided by Eric using weights) top_bottom_c_oecd <- read_excel("data/top_bottom_gdppc_w_agg.xlsx") %>% @@ -415,8 +425,8 @@ fig2 <- country %>% position = position_dodge2(preserve = "single") ) + labs( - x = "Category", y = "Gap", - title = "Figure 2: GDP per capita gap by type of region\ncompared to the OECD average" + x = "", y = "Gap", + title = "Figure 2: GDP per capita gap by type of region compared to the OECD average" ) + # theme_oecd(base_size = 10) + # theme(plot.title = element_text(size = 13, hjust = 0, margin = margin(0, 0, 10, 0))) + @@ -507,18 +517,24 @@ gdp_3_gap_pct <- abs(round(gdp_5_gap - gdp_5_gap_lag, 3)) ``` ```{r next paragraph} -next_paragraph <- if (any(ctry %in% c("LTU"))) { - glue("There is no data for the gap in GDP percapita between large metropolitan and non-large metropolitan regions for 2000 and 2020.") +next_paragraph <- if (any(ctry %in% c("LTU", "EST", "FIN", "LVA", "NZL", "NOR", "SVK", "SVN", "CHE"))) { + glue("There is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.") } else { glue("In 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was { gdp_gap }. For reference, the same value for OECD was { gdp_2_gap }. This gap { gdp_gap_txt } by { gdp_gap_pct } percentage points between 2000 and 2020.") } + +last_paragraph <- if (any(ctry %in% c("KOR", "NLD"))) { + glue("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.") +} else { + glue("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 { gdp_5_gap } in 2020 and { gdp_3_gap_txt } by { gdp_3_gap_pct} percentage points since 2000.") +} ``` `r next_paragraph` Meanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was `r gdp_3_gap`. For reference, the same value for OECD was `r gdp_4_gap`. This gap `r paste(gdp_2_gap_txt, "by", gdp_2_gap_pct)` percentage points since 2000. -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 `r gdp_5_gap` in 2020 and `r paste(gdp_3_gap_txt, "by", gdp_3_gap_pct)` percentage points since 2000. +`r last_paragraph` ```{r fig2_3} # no interactivity @@ -530,26 +546,24 @@ ggplotly(fig2) %>% ```
-**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). - +**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).
+
+
+ ```{r fig3} # read ---- - -finp <- if (any(ctry %in% c("AUS", "CAN", "COL", "CHE", "CHL", "IRL", "MEX"))) { - "data/countryprofile_option1_addon.xlsx" -} else { - "data/countryprofile_option1.xlsx" -} - -dp1 <- read_excel(finp, sheet = ctry) %>% +dp1 <- read_excel("data/countryprofile_fig3_alt.xlsx", sheet = ctry) %>% + select(time, pw_lp, pw_hp) %>% clean_names() # tidy ---- -colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") +# colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") + +colnames(dp1) <- c("time", "pw_lp_country", "pw_hp_country") dp11 <- dp1 %>% select(time, matches("country")) %>% @@ -594,9 +608,9 @@ hpdiff <- hpgrew - lpgrew hpmore <- ifelse(hpdiff > 0, "more", "less") fig3_title <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE"))) { - "Figure 3: Evolution of labour productivity,\nTL2 regions" + "Figure 3: Evolution of labour productivity, TL2 regions" } else { - "Figure 3: Evolution of labour productivity,\nTL3 regions" + "Figure 3: Evolution of labour productivity, TL3 regions" } fig3_1 <- dp11 %>% @@ -612,7 +626,7 @@ fig3_1 <- dp11 %>% # scale_colour_manual(values = clrs3[1:2]) + scale_colour_manual(values = c("#508551", "#177dc7")) + labs( - x = "", y = "Labour productivity (2015 USD PPP)", colour = "Series", + x = "", y = "Labour productivity (2015 USD PPP)", colour = "", title = fig3_title ) + scale_x_continuous(labels = as.character(yrs), breaks = yrs) + @@ -640,11 +654,13 @@ ggplotly(fig3_1) %>% ```
-**Note**: 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. - +**Note**: 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).
+
+
+ ```{r fig4} # read ---- @@ -849,7 +865,7 @@ fig4 <- subplot(p1, p2, nrows = 1, margin = 0.05, shareX = TRUE, shareY = TRUE) fig4 <- fig4 %>% layout( - title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0), + title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL3 regions", x = 0), margin = list( l = 50, r = 50, b = 50, t = 120, @@ -871,7 +887,7 @@ fig4 <- fig4 %>% x = 0.75, y = 1, font = list(size = 14), - text = "Services", + text = "Tradable services", xref = "paper", yref = "paper", xanchor = "center", @@ -894,7 +910,7 @@ text_all <- dp2 %>% # put fig4 title in black fig4 <- fig4 %>% layout( - title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0, font = list(color = "black")) + title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black")) ) # remove legend background @@ -908,11 +924,13 @@ ggplotly(fig4) %>% ```
-**Note**: 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). - +**Note**: 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).
+
+
+ ## Recent policy developments ```{r txt} @@ -931,14 +949,14 @@ read_html_text(ctry) The 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).

- Small 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: + Small 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:

diff --git a/data/fig4_text_FINAL.xlsx b/data/fig4_text_FINAL.xlsx index afbd552..c3e219a 100644 Binary files a/data/fig4_text_FINAL.xlsx and b/data/fig4_text_FINAL.xlsx differ diff --git a/index.qmd b/index.qmd index 9765afd..9f1e63b 100644 --- a/index.qmd +++ b/index.qmd @@ -177,13 +177,15 @@ lev_2 <- c( "Mean GDP per capita" ) -fig1 <- summary_wide %>% - filter(iso3 == ctry, labels_index %in% lev_2, time >= 2005) %>% +df_fig1 <- summary_wide %>% + filter(iso3 == ctry, labels_index %in% lev_2, time >= 2000) %>% drop_na() %>% pivot_wider( names_from = index, values_from = value - ) %>% + ) + +fig1 <- df_fig1 %>% ggplot(aes(x = time)) + geom_line(aes( y = index_gdppc, @@ -194,9 +196,9 @@ fig1 <- summary_wide %>% colour = factor(labels_index, levels = lev_2) ), linewidth = 1.2) + scale_colour_manual(values = clrs2[c(1, 2)]) + - scale_x_continuous(expand = c(0, 0)) + + scale_x_continuous(expand = c(0, 0), breaks = seq(from = min(df_fig1$time), to = max(df_fig1$time), by = 5)) + labs( - title = "Figure 1: Trends in GDP per capita inequality indicators,\nTL2 OECD regions", + title = "Figure 1: Trends in GDP per capita inequality indicators, TL2 OECD regions", x = "", y = "Statistic (2000=1)", linetype = "", @@ -204,7 +206,7 @@ fig1 <- summary_wide %>% ) + # theme_oecd(base_size = 10) + # theme(plot.title = element_text(size = 13, hjust = 0, margin = margin(0, 0, 8, 0))) - theme_minimal() + theme_minimal() ``` ```{r dex_fig1_summary} @@ -309,26 +311,34 @@ ggplotly(fig1) %>% ```
-**Note**: Based on 1 586 TL3 regions in 27 countries with available data (no TL3 data (continuous time series for more than 1 region) for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Between Theil measures the dissimilarity of the national GDP per capita means with respect to the OECD average. Within Theil measures the dissimilarity between regional and national GDP per capita. - -Source: OECD Regional Database (2022). +**Note**: Based on 1 586 TL3 regions in 27 countries with available data (no TL3 data (continuous time series for more than 1 region) for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Between Theil measures the dissimilarity of the national GDP per capita means with respect to the OECD average. Within Theil measures the dissimilarity between regional and national GDP per capita.
+**Source**: OECD Regional Database (2022).
+
+
+ ```{r dex_fig2} # read ---- -finp <- if (any(ctry %in% c("AUS", "CAN", "COL", "CHE", "CHL", "IRL", "MEX"))) { - "data/countryprofile_option1_addon.xlsx" -} else { - "data/countryprofile_option1.xlsx" -} - -dp1 <- read_excel(finp, sheet = ctry) %>% +# finp <- if (any(ctry %in% c("AUS", "CAN", "COL", "CHE", "CHL", "IRL", "MEX"))) { +# "data/countryprofile_option1_addon.xlsx" +# } else { +# "data/countryprofile_option1.xlsx" +# } +# +# dp1 <- read_excel(finp, sheet = ctry) %>% +# clean_names() + +dp1 <- read_excel("data/countryprofile_fig3_alt.xlsx", sheet = ctry) %>% + select(time, pw_lp, pw_hp) %>% clean_names() # tidy ---- -colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") +# colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") + +colnames(dp1) <- c("time", "pw_lp_country", "pw_hp_country") dp11 <- dp1 %>% select(time, matches("country")) %>% @@ -384,8 +394,8 @@ fig2 <- dp11 %>% # scale_colour_manual(values = clrs3[1:2]) + scale_colour_manual(values = c("#177dc7","#508551")) + labs( - x = "", y = "Productivity (2015 USD PPP)", colour = "Series", - title = "Figure 2: Evolution of labour productivity,\nTL2 regions" + x = "", y = "Productivity (2015 USD PPP)", colour = "", + title = "Figure 2: Evolution of labour productivity, TL2 regions" ) + scale_x_continuous(labels = as.character(yrs), breaks = yrs) + scale_y_continuous(labels = scales::number_format()) @@ -425,12 +435,14 @@ ggplotly(fig2) %>% ```
-**Note**: 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). +**Note**: 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).
-```{r dex_fig3} +
+
+ +```{r can_fig3} # read ---- finp <- if (any(ctry %in% c("AUS", "CAN", "COL", "CHE", "CHL", "IRL", "MEX"))) { @@ -528,8 +540,8 @@ dp21 <- dp21 %>% category1 = str_replace_all(category1, "TG", "Tradable goods"), category1 = str_replace_all(category1, "TS", "Tradable services"), category2 = paste(str_sub(category, 4, 5), time), - category2 = str_replace_all(category2, "LP", "Low productivity"), - category2 = str_replace_all(category2, "HP", "High productivity") + category2 = str_replace_all(category2, "LP", "Lower half"), + category2 = str_replace_all(category2, "HP", "Upper half") ) dp21_2 <- dp21 %>% @@ -591,23 +603,20 @@ name1 <- dp21_min_TG$category2 name2 <- dp21_max_TG$category2 p1 <- plot_ly() %>% add_trace( - x = ~x, y = ~y, color = ~x, + x = ~x, y = ~y3, color = ~x, type = "bar", - # text = y, - # textposition = 'auto', - name = ~name1, + name = ~name2, marker = list( - color = clrs4[1:2] # , - # line = list(color = 'rgb(8,48,107)', width = 1.5) + color = c("#c8f075","#6bc5f2") ) ) p1 <- p1 %>% add_markers( - x = ~x, y = ~y3, color = ~x, - name = ~name2, + x = ~x, y = ~y, color = ~x, + name = ~name1, mode = "markers", marker = list( - color = c("#6bc5f2", "#c8f075"), + color = clrs4[2:1], size = 12, symbol = "diamond-dot" ) @@ -616,22 +625,21 @@ p1 <- p1 %>% add_markers( p1 <- p1 %>% layout( title = "Tradable goods", xaxis = list(title = "", visible = FALSE), - yaxis = list(title = "Employment share (%)"), - legend = list(bgcolor = "rgb(242, 242, 242)") + yaxis = list(title = "Employment share (%)") ) p2 <- plot_ly() %>% add_trace( - x = ~x, y = ~y2, color = ~x, + x = ~x, y = ~y4, color = ~x, type = "bar", - marker = list(color = clrs4[1:2]), + marker = list(color = c("#c8f075","#6bc5f2")), showlegend = FALSE ) p2 <- p2 %>% add_markers( - x = ~x, y = ~y4, color = ~x, + x = ~x, y = ~y2, color = ~x, mode = "markers", marker = list( - color = c("#6bc5f2", "#c8f075"), + color = clrs4[2:1], size = 12, symbol = "diamond-dot" ), @@ -648,7 +656,7 @@ fig3 <- subplot(p1, p2, nrows = 1, margin = 0.05, shareX = TRUE, shareY = TRUE) fig3 <- fig3 %>% layout( - title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0), + title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0), margin = list( l = 50, r = 50, b = 50, t = 120, @@ -670,7 +678,7 @@ fig3 <- fig3 %>% x = 0.75, y = 1, font = list(size = 14), - text = "Services", + text = "Tradable services", xref = "paper", yref = "paper", xanchor = "center", @@ -681,6 +689,7 @@ fig3 <- fig3 %>% ) ``` + ```{r can_fig3_text} dp2 <- read_excel("data/countryprofile_fig4_alt.xlsx", sheet = ctry) %>% clean_names() @@ -692,16 +701,30 @@ text_all <- dp2 %>% `r text_all` -```{r dex_fig3_2} +```{r can_fig3_2} +# put fig3 title in black +fig3 <- fig3 %>% + layout( + title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black")) + ) + +# remove legend background +fig3 <- fig3 %>% + layout( + legend = list(bgcolor = "rgba(0,0,0,0)") + ) + fig3 ```
-**Note**: 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). +**Note**: 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).
+
+
+ ## Recent policy developments ```{r dex_txt} @@ -720,14 +743,14 @@ read_html_text(ctry) The 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). In Canada, TL2 corresponds to the provinces and territories.

- Small 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: + Small 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:

diff --git a/tl0-col.qmd b/tl0-col.qmd index 1d7c225..6fb3c47 100644 --- a/tl0-col.qmd +++ b/tl0-col.qmd @@ -165,13 +165,15 @@ lev_2 <- c( "Mean GDP per capita" ) -fig1 <- summary_wide %>% +df_fig1 <- summary_wide %>% filter(iso3 == ctry, labels_index %in% lev_2, time >= 2005) %>% drop_na() %>% pivot_wider( names_from = index, values_from = value - ) %>% + ) + +fig1 <- df_fig1 %>% ggplot(aes(x = time)) + geom_line(aes( y = index_gdppc, @@ -182,9 +184,9 @@ fig1 <- summary_wide %>% colour = factor(labels_index, levels = lev_2) ), linewidth = 1.2) + scale_colour_manual(values = clrs2[c(1, 2)]) + - scale_x_continuous(expand = c(0, 0)) + + scale_x_continuous(expand = c(0, 0), breaks = seq(from = min(df_fig1$time), to = max(df_fig1$time), by = 5)) + labs( - title = "Figure 1: Trends in GDP per capita inequality indicators,\nTL2 OECD regions", + title = "Figure 1: Trends in GDP per capita inequality indicators, TL2 OECD regions", x = "", y = "Statistic (2000=1)", linetype = "", @@ -269,21 +271,20 @@ ggplotly(fig1) %>% **Source**: OECD Regional Database (2022). +
+
+ ```{r col_fig3} # read ---- - -finp <- if (any(ctry %in% c("AUS", "CAN", "COL", "CHE", "CHL", "IRL", "MEX"))) { - "data/countryprofile_option1_addon.xlsx" -} else { - "data/countryprofile_option1.xlsx" -} - -dp1 <- read_excel(finp, sheet = ctry) %>% +dp1 <- read_excel("data/countryprofile_fig3_alt.xlsx", sheet = ctry) %>% + select(time, pw_lp, pw_hp) %>% clean_names() # tidy ---- -colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") +# colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") + +colnames(dp1) <- c("time", "pw_lp_country", "pw_hp_country") dp11 <- dp1 %>% select(time, matches("country")) %>% @@ -333,8 +334,8 @@ fig3_1 <- ggplot(dp11) + theme_minimal() + scale_colour_manual(values = c("#177dc7","#508551")) + labs( - x = "", y = "Labour productivity (2015 USD PPP)", colour = "Series", - title = "Figure 2: Evolution of labour productivity,\nTL2 regions" + x = "", y = "Labour productivity (2015 USD PPP)", colour = "", + title = "Figure 2: Evolution of labour productivity, TL2 regions" ) + scale_x_continuous(labels = as.character(yrs), breaks = yrs) + scale_y_continuous(labels = scales::number_format()) @@ -357,11 +358,13 @@ ggplotly(fig3_1) %>% ```
-**Note**: 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. - +**Note**: 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).
+
+
+ ## Recent policy developments ```{r col_txt} @@ -380,14 +383,14 @@ read_html_text(ctry) The 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).

- Small 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: + Small 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:

diff --git a/tl0-cri.qmd b/tl0-cri.qmd index 12f7141..2e55073 100644 --- a/tl0-cri.qmd +++ b/tl0-cri.qmd @@ -71,14 +71,14 @@ read_html_text(ctry) The 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).

- Small 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: + Small 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:

diff --git a/tl0-irl.qmd b/tl0-irl.qmd index 8376366..746069c 100644 --- a/tl0-irl.qmd +++ b/tl0-irl.qmd @@ -121,8 +121,8 @@ fig3_1 <- ggplot(dp11) + theme_minimal() + scale_colour_manual(values = c("#177dc7","#508551")) + labs( - x = "", y = "Labour productivity (2015 USD PPP)", colour = "Series", - title = "Figure 1: Evolution of labour productivity,\nTL2 regions" + x = "", y = "Labour productivity (2015 USD PPP)", colour = "", + title = "Figure 1: Evolution of labour productivity, TL2 regions" ) + scale_x_continuous(labels = as.character(yrs), breaks = yrs) + scale_y_continuous(labels = scales::number_format()) @@ -145,26 +145,24 @@ ggplotly(fig3_1) %>% ```
-**Note**: 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. - +**Note**: 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).
+
+
+ ```{r fig4} # read ---- - -finp <- if (any(ctry %in% c("AUS", "CAN", "COL", "CHE", "CHL", "IRL", "MEX"))) { - "data/countryprofile_option2_addon.xlsx" -} else { - "data/countryprofile_option2.xlsx" -} - -dp2 <- read_excel(finp, sheet = ctry) %>% +dp2 <- read_excel("data/countryprofile_fig3_alt.xlsx", sheet = ctry) %>% + select(time, pw_lp, pw_hp) %>% clean_names() # tidy ---- -colnames(dp2) <- str_replace(colnames(dp2), tolower(ctry), "country") +# colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") + +colnames(dp2) <- c("time", "pw_lp_country", "pw_hp_country") dp21 <- dp2 %>% select(time, matches("country")) %>% @@ -248,8 +246,8 @@ dp21 <- dp21 %>% category1 = str_replace_all(category1, "TG", "Tradable goods"), category1 = str_replace_all(category1, "TS", "Tradable services"), category2 = paste(str_sub(category, 4, 5), time), - category2 = str_replace_all(category2, "LP", "Low productivity"), - category2 = str_replace_all(category2, "HP", "High productivity") + category2 = str_replace_all(category2, "LP", "Lower half"), + category2 = str_replace_all(category2, "HP", "Upper half") ) dp21_2 <- dp21 %>% @@ -364,7 +362,7 @@ fig2 <- subplot(p1, p2, nrows = 1, margin = 0.05, shareX = TRUE, shareY = TRUE) fig2 <- fig2 %>% layout( - title = list(text = "Figure 2: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0), + title = list(text = "Figure 2: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0), margin = list( l = 50, r = 50, b = 50, t = 120, @@ -386,7 +384,7 @@ fig2 <- fig2 %>% x = 0.75, y = 1, font = list(size = 14), - text = "Services", + text = "Tradable services", xref = "paper", yref = "paper", xanchor = "center", @@ -414,11 +412,13 @@ ggplotly(fig2) %>% ```
-**Note**: 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). - +**Note**: 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).
+
+
+ ## Recent policy developments ```{r cri_txt} @@ -437,14 +437,14 @@ read_html_text(ctry) The 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).

- Small 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: + Small 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:

diff --git a/tl0-isl.qmd b/tl0-isl.qmd index 0cebbb2..e3cdb1f 100644 --- a/tl0-isl.qmd +++ b/tl0-isl.qmd @@ -72,14 +72,14 @@ read_html_text(ctry) The 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).

- Small 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: + Small 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:

diff --git a/tl0-isr.qmd b/tl0-isr.qmd index ba9cd70..967495e 100644 --- a/tl0-isr.qmd +++ b/tl0-isr.qmd @@ -71,14 +71,14 @@ read_html_text(ctry) The 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).

- Small 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: + Small 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:

@@ -88,3 +88,4 @@ read_html_text(ctry) + diff --git a/tl0-lux.qmd b/tl0-lux.qmd index d6418ed..1f10c9a 100644 --- a/tl0-lux.qmd +++ b/tl0-lux.qmd @@ -71,14 +71,14 @@ read_html_text(ctry) The 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).

- Small 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: + Small 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:

diff --git a/tl0-usa.qmd b/tl0-usa.qmd index 4c01aba..bab8d73 100644 --- a/tl0-usa.qmd +++ b/tl0-usa.qmd @@ -176,7 +176,7 @@ lev_2 <- c( "Mean GDP per capita" ) -fig1 <- summary_wide %>% +df_fig1 <- summary_wide %>% filter(iso3 == ctry, labels_index %in% lev_2, time >= min_y) %>% pivot_wider( names_from = index, @@ -184,7 +184,9 @@ fig1 <- summary_wide %>% ) %>% mutate( index_label = factor(labels_index, levels = lev_2) - ) %>% + ) + +fig1 <- df_fig1 %>% ggplot(aes(x = time)) + geom_line(aes( y = index_gdppc, @@ -203,9 +205,9 @@ fig1 <- summary_wide %>% colour = index_label ), linewidth = 1.2) + scale_colour_manual(values = clrs2[c(1, 3, 4, 2)]) + # evil hack to match TL2 colours - scale_x_continuous(expand = c(0, 0)) + + scale_x_continuous(expand = c(0, 0), breaks = seq(from = min(df_fig1$time), to = max(df_fig1$time), by = 5)) + labs( - title = "Figure 1: Trends in GDP per capita inequality indicators,\nTL2 OECD regions", + title = "Figure 1: Trends in GDP per capita inequality indicators, TL2 OECD regions", x = "", y = "Statistic (2000=1)", linetype = "", @@ -335,6 +337,9 @@ ggplotly(fig1) %>% **Source**: OECD Regional Database (2022). +
+
+ ```{r usa_fig2} # Load file with top/bottom 20% data (provided by Eric using weights) top_bottom_c_oecd <- read_excel("data/top_bottom_gdppc_w_agg.xlsx") %>% @@ -431,11 +436,14 @@ fig2 <- country %>% position = position_dodge2(preserve = "single") ) + labs( - x = "Category", y = "Gap", - title = "Figure 2: Changes in the contribution of region types to regional income\ninequality based on TL3 GDP per capita" + x = "", y = "Gap", + title = "Figure 2: Changes in the contribution of region types to regional income inequality based on TL3 GDP per capita" ) + # theme_oecd(base_size = 10) + theme_minimal() + + theme( + axis.text.x = element_text(angle = 30, hjust = 1) + ) scale_fill_manual(values = clrs, name = "") ``` @@ -533,26 +541,24 @@ ggplotly(fig2) %>% ```
-**Note**: Far from a FUA>250K includes regions near/with a small FUA and remote regions. - +**Note**: Far from a FUA>250K includes regions near/with a small FUA and remote regions.
**Source**: OECD Regional Database (2022).
+
+
+ ```{r usa_fig3} # read ---- - -finp <- if (any(ctry %in% c("AUS", "CAN", "COL", "CHE", "CHL", "IRL", "MEX"))) { - "data/countryprofile_option1_addon.xlsx" -} else { - "data/countryprofile_option1.xlsx" -} - -dp1 <- read_excel(finp, sheet = ctry) %>% +dp1 <- read_excel("data/countryprofile_fig3_alt.xlsx", sheet = ctry) %>% + select(time, pw_lp, pw_hp) %>% clean_names() # tidy ---- -colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") +# colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") + +colnames(dp1) <- c("time", "pw_lp_country", "pw_hp_country") dp11 <- dp1 %>% select(time, matches("country")) %>% @@ -607,8 +613,8 @@ fig3 <- dp11 %>% theme_minimal() + scale_colour_manual(values = c("#177dc7","#508551")) + labs( - x = "", y = "Labour productivity (2015 USD PPP)", colour = "Series", - title = "Figure 3: Evolution of labour productivity,\nTL2 regions" + x = "", y = "Labour productivity (2015 USD PPP)", colour = "", + title = "Figure 3: Evolution of labour productivity, TL2 regions" ) + scale_x_continuous(labels = as.character(yrs), breaks = yrs) + scale_y_continuous(labels = scales::number_format()) @@ -643,11 +649,13 @@ ggplotly(fig3) %>% ```
-**Note**: 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. - +**Note**: 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).
+
+
+ ```{r usa_fig4} # read ---- @@ -862,7 +870,7 @@ fig4 <- subplot(p1, p2, nrows = 1, margin = 0.05, shareX = TRUE, shareY = TRUE) fig4 <- fig4 %>% layout( - title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0), + title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0), margin = list( l = 50, r = 50, b = 50, t = 120, @@ -884,7 +892,7 @@ fig4 <- fig4 %>% x = 0.75, y = 1, font = list(size = 14), - text = "Services", + text = "Tradable services", xref = "paper", yref = "paper", xanchor = "center", @@ -910,7 +918,7 @@ text_all <- dp2 %>% # put fig4 title in black fig4 <- fig4 %>% layout( - title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0, font = list(color = "black")) + title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black")) ) # remove legend background @@ -923,11 +931,13 @@ fig4 ```
-**Note**: 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). - +**Note**: 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).
+
+
+ ## Recent policy developments ```{r usa_txt} @@ -946,14 +956,14 @@ read_html_text(ctry) The 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).

- Small 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: + Small 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:

diff --git a/tl2-can.qmd b/tl2-can.qmd index e62d1a4..10afc95 100644 --- a/tl2-can.qmd +++ b/tl2-can.qmd @@ -181,7 +181,7 @@ lev_2 <- c( max_x <- max(summary_wide$time) min_x <- min(summary_wide$time) -fig1 <- summary_wide %>% +df_fig1 <- summary_wide %>% filter(iso3 == ctry, labels_index %in% lev_2) %>% pivot_wider( names_from = index, @@ -189,7 +189,9 @@ fig1 <- summary_wide %>% ) %>% mutate( index_label = factor(labels_index, levels = lev_2) - ) %>% + ) + +fig1 <- df_fig1 %>% ggplot(aes(x = time)) + geom_line(aes( y = index_gdppc, @@ -200,12 +202,9 @@ fig1 <- summary_wide %>% colour = index_label ), linewidth = 1.2) + scale_colour_manual(values = clrs2) + - scale_x_continuous( - expand = c(0, 0), - n.breaks = round((max_x - min_x) / 2, 0) - ) + + scale_x_continuous(expand = c(0, 0), breaks = seq(from = min(df_fig1$time), to = max(df_fig1$time), by = 5)) + labs( - title = "Figure 1: Trends in GDP per capita inequality indicators,\nTL2 OECD regions", + title = "Figure 1: Trends in GDP per capita inequality indicators, TL2 OECD regions", x = "", y = sprintf("Statistic (%s=1)", min_y), linetype = "", @@ -323,6 +322,9 @@ ggplotly(fig1) %>% **Source**: OECD Regional Database (2022). +
+
+ ```{r can_fig2} # read ---- @@ -392,8 +394,8 @@ fig2 <- dp11 %>% theme_minimal() + scale_colour_manual(values = c("#177dc7","#508551")) + labs( - x = "", y = "Labour productivity (2015 USD PPP)", colour = "Series", - title = "Figure 2: Evolution of labour productivity,\nTL2 regions" + x = "", y = "Labour productivity (2015 USD PPP)", colour = "", + title = "Figure 2: Evolution of labour productivity, TL2 regions" ) + scale_x_continuous(labels = as.character(yrs), breaks = yrs) + scale_y_continuous(labels = scales::number_format()) @@ -429,26 +431,24 @@ ggplotly(fig2) %>% ```
-**Note**: 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. - +**Note**: 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).
+
+
+ ```{r can_fig3} # read ---- - -finp <- if (any(ctry %in% c("AUS", "CAN", "COL", "CHE", "CHL", "IRL", "MEX"))) { - "data/countryprofile_option2_addon.xlsx" -} else { - "data/countryprofile_option2.xlsx" -} - -dp2 <- read_excel(finp, sheet = ctry) %>% +dp2 <- read_excel("data/countryprofile_fig3_alt.xlsx", sheet = ctry) %>% + select(time, pw_lp, pw_hp) %>% clean_names() # tidy ---- -colnames(dp2) <- str_replace(colnames(dp2), tolower(ctry), "country") +# colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") + +colnames(dp2) <- c("time", "pw_lp_country", "pw_hp_country") dp21 <- dp2 %>% select(time, matches("country")) %>% @@ -648,7 +648,7 @@ fig3 <- subplot(p1, p2, nrows = 1, margin = 0.05, shareX = TRUE, shareY = TRUE) fig3 <- fig3 %>% layout( - title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0), + title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0), margin = list( l = 50, r = 50, b = 50, t = 120, @@ -670,7 +670,7 @@ fig3 <- fig3 %>% x = 0.75, y = 1, font = list(size = 14), - text = "Services", + text = "Tradable services", xref = "paper", yref = "paper", xanchor = "center", @@ -696,7 +696,7 @@ text_all <- dp2 %>% # put fig3 title in black fig3 <- fig3 %>% layout( - title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0, font = list(color = "black")) + title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black")) ) # remove legend background @@ -709,11 +709,13 @@ fig3 ```
-**Note**: 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). - +**Note**: 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).
+
+
+ ## Recent policy developments ```{r can_txt} @@ -732,14 +734,14 @@ read_html_text(ctry) The 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). In Canada, TL2 corresponds to the provinces and territories.

- Small 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: + Small 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:

diff --git a/tl2-chl.qmd b/tl2-chl.qmd index b2e7657..c383886 100644 --- a/tl2-chl.qmd +++ b/tl2-chl.qmd @@ -205,7 +205,7 @@ fig1 <- summary_wide %>% n.breaks = round((max_x - min_x) / 2, 0) ) + labs( - title = "Figure 1: Trends in GDP per capita inequality indicators,\nTL2 OECD regions", + title = "Figure 1: Trends in GDP per capita inequality indicators, TL2 OECD regions", x = "", y = sprintf("Statistic (%s=1)", min_y), linetype = "", @@ -323,6 +323,9 @@ ggplotly(fig1) %>% **Source**: OECD Regional Database (2022). +
+
+ ```{r chl_fig2} # read ---- @@ -393,8 +396,8 @@ fig2 <- dp11 %>% theme_minimal() + scale_colour_manual(values = c("#177dc7","#508551")) + labs( - x = "", y = "Labour productivity (2015 USD PPP)", colour = "Series", - title = "Figure 2: Evolution of labour productivity,\nTL2 regions" + x = "", y = "Labour productivity (2015 USD PPP)", colour = "", + title = "Figure 2: Evolution of labour productivity, TL2 regions" ) + scale_x_continuous(labels = as.character(yrs), breaks = yrs) + scale_y_continuous(labels = scales::number_format()) @@ -432,26 +435,24 @@ ggplotly(fig2) %>% ```
-**Note**: 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. - +**Note**: 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).
+
+
+ ```{r chl_fig3} # read ---- - -finp <- if (any(ctry %in% c("AUS", "CAN", "COL", "CHE", "CHL", "IRL", "MEX"))) { - "data/countryprofile_option2_addon.xlsx" -} else { - "data/countryprofile_option2.xlsx" -} - -dp2 <- read_excel(finp, sheet = ctry) %>% +dp2 <- read_excel("data/countryprofile_fig3_alt.xlsx", sheet = ctry) %>% + select(time, pw_lp, pw_hp) %>% clean_names() # tidy ---- -colnames(dp2) <- str_replace(colnames(dp2), tolower(ctry), "country") +# colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") + +colnames(dp2) <- c("time", "pw_lp_country", "pw_hp_country") dp21 <- dp2 %>% select(time, matches("country")) %>% @@ -535,8 +536,8 @@ dp21 <- dp21 %>% category1 = str_replace_all(category1, "TG", "Tradable goods"), category1 = str_replace_all(category1, "TS", "Tradable services"), category2 = paste(str_sub(category, 4, 5), time), - category2 = str_replace_all(category2, "LP", "Low productivity"), - category2 = str_replace_all(category2, "HP", "High productivity") + category2 = str_replace_all(category2, "LP", "Lower half"), + category2 = str_replace_all(category2, "HP", "Upper half") ) dp21_2 <- dp21 %>% @@ -651,7 +652,7 @@ fig3 <- subplot(p1, p2, nrows = 1, margin = 0.05, shareX = TRUE, shareY = TRUE) fig3 <- fig3 %>% layout( - title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0), + title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0), margin = list( l = 50, r = 50, b = 50, t = 120, @@ -673,7 +674,7 @@ fig3 <- fig3 %>% x = 0.75, y = 1, font = list(size = 14), - text = "Services", + text = "Tradable services", xref = "paper", yref = "paper", xanchor = "center", @@ -699,7 +700,7 @@ text_all <- dp2 %>% # put fig3 title in black fig3 <- fig3 %>% layout( - title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0, font = list(color = "black")) + title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black")) ) # remove legend background @@ -712,11 +713,13 @@ fig3 ```
-**Note**: 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). - +**Note**: 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).
+
+
+ ## Recent policy developments ```{r chl_txt} @@ -735,14 +738,14 @@ read_html_text(ctry) The 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).

- Small 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: + Small 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:

diff --git a/tl2-mex.qmd b/tl2-mex.qmd index 5558d47..a21d87d 100644 --- a/tl2-mex.qmd +++ b/tl2-mex.qmd @@ -181,7 +181,7 @@ lev_2 <- c( max_x <- max(summary_wide$time) min_x <- min(summary_wide$time) -fig1 <- summary_wide %>% +df_fig1 <- summary_wide %>% filter(iso3 == ctry, labels_index %in% lev_2, time >= 2003) %>% pivot_wider( names_from = index, @@ -189,7 +189,9 @@ fig1 <- summary_wide %>% ) %>% mutate( index_label = factor(labels_index, levels = lev_2) - ) %>% + ) + +fig1 <- df_fig1 %>% ggplot(aes(x = time)) + geom_line(aes( y = index_gdppc, @@ -200,12 +202,9 @@ fig1 <- summary_wide %>% colour = index_label ), linewidth = 1.2) + scale_colour_manual(values = clrs2) + - scale_x_continuous( - expand = c(0, 0), - n.breaks = round((max_x - min_x) / 2, 0) - ) + + scale_x_continuous(expand = c(0, 0), breaks = seq(from = min(df_fig1$time), to = max(df_fig1$time), by = 5)) + labs( - title = "Figure 1: Trends in GDP per capita inequality indicators,\nTL2 OECD regions", + title = "Figure 1: Trends in GDP per capita inequality indicators, TL2 OECD regions", x = "", y = sprintf("Statistic (%s=1)", min_y), linetype = "", @@ -323,21 +322,20 @@ ggplotly(fig1) %>% **Source**: OECD Regional Database (2022). +
+
+ ```{r mex_fig2} # read ---- - -finp <- if (any(ctry %in% c("AUS", "CAN", "COL", "CHE", "CHL", "IRL", "MEX"))) { - "data/countryprofile_option1_addon.xlsx" -} else { - "data/countryprofile_option1.xlsx" -} - -dp1 <- read_excel(finp, sheet = ctry) %>% +dp1 <- read_excel("data/countryprofile_fig3_alt.xlsx", sheet = ctry) %>% + select(time, pw_lp, pw_hp) %>% clean_names() # tidy ---- -colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") +# colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country") + +colnames(dp1) <- c("time", "pw_lp_country", "pw_hp_country") dp11 <- dp1 %>% select(time, matches("country")) %>% @@ -392,8 +390,8 @@ fig2 <- dp11 %>% theme_minimal() + scale_colour_manual(values = c("#177dc7","#508551")) + labs( - x = "", y = "Labour productivity (2015 USD PPP)", colour = "Series", - title = "Figure 2: Evolution of labour productivity,\nTL2 regions" + x = "", y = "Labour productivity (2015 USD PPP)", colour = "", + title = "Figure 2: Evolution of labour productivity, TL2 regions" ) + scale_x_continuous(labels = as.character(yrs), breaks = yrs) + scale_y_continuous(labels = scales::number_format()) @@ -432,11 +430,13 @@ ggplotly(fig2) %>% ```
-**Note**: 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. - +**Note**: 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).
+
+
+ ```{r mex_fig3} # read ---- @@ -535,8 +535,8 @@ dp21 <- dp21 %>% category1 = str_replace_all(category1, "TG", "Tradable goods"), category1 = str_replace_all(category1, "TS", "Tradable services"), category2 = paste(str_sub(category, 4, 5), time), - category2 = str_replace_all(category2, "LP", "Low productivity"), - category2 = str_replace_all(category2, "HP", "High productivity") + category2 = str_replace_all(category2, "LP", "Lower half"), + category2 = str_replace_all(category2, "HP", "Upper half") ) dp21_2 <- dp21 %>% @@ -651,7 +651,7 @@ fig3 <- subplot(p1, p2, nrows = 1, margin = 0.05, shareX = TRUE, shareY = TRUE) fig3 <- fig3 %>% layout( - title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0), + title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0), margin = list( l = 50, r = 50, b = 50, t = 120, @@ -673,7 +673,7 @@ fig3 <- fig3 %>% x = 0.75, y = 1, font = list(size = 14), - text = "Services", + text = "Tradable services", xref = "paper", yref = "paper", xanchor = "center", @@ -699,7 +699,7 @@ text_all <- dp2 %>% # put fig3 title in black fig3 <- fig3 %>% layout( - title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0, font = list(color = "black")) + title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black")) ) # remove legend background @@ -712,11 +712,13 @@ fig3 ```
-**Note**: 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). - +**Note**: 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).
+
+
+ ## Recent policy developments ```{r mex_txt} @@ -735,14 +737,14 @@ read_html_text(ctry) The 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).

- Small 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: + Small 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: