index.qmd

```{r dex_ctry}
ctry <- "AUS"
ctry2 <- "Australia"
```

# Australia

```{r dex_pkgs, warning=FALSE}
library(oecdcountryprofiles)
library(dplyr)
library(tidyr)
library(ggplot2)
library(patchwork)
library(oecdplot)
library(knitr)
library(readxl)
library(janitor)
library(stringr)
library(readr)
library(tintin)
library(glue)
library(scales)
library(plotly)

load_oecd_fonts()

missing_reg <- c("ITG2F")
excl_countries <- c("LUX", "IRL")

clrs <- tintin_colours$the_black_island
clrs2 <- tintin_colours$the_blue_lotus
clrs3 <- tintin_colours$the_red_sea_sharks
clrs4 <- tintin_colours$the_calculus_affair
clrs5 <- tintin_colours$red_rackhams_treasure
```

<!-- create a table with one row followed by a row with two columns -->
<table border="1" style="width:75%;margin-left:auto;margin-right:auto;">
    <tr>
      <th colspan="2" style="background:#fff9e6; padding:10px;font-size:9.0pt;" valign="center">
        OECD Regional Outlook
      </th>
    </tr>
    <tr>
      <td>
        <img style="padding:10px;" src="RO2023_cover.jpg" alt="OECD Regional Outlook 2023" height="200"/>
      </td>
      <td>
        <p style="padding:10px;font-size:9.0pt;">The OECD <a href="https://www.oecd-ilibrary.org/urban-rural-and-regional-development/oecd-regional-outlook_2dafc8cf-en" target="_blank"><i style="color:#0d6efd">Regional Outlook</i></a> reviews recent trends, policy developments, and prospects across OECD regions, including the underlying causes driving regional inequalities in performance and well-being. The report offers evidence, guidance and policy recommendations on how to improve competitiveness and productivity, promote inclusive growth, accelerate the net-zero transition and raise well-being standards through effective regional development policy and multi-level governance.</p>
      </td>
    </tr>
</table>

## Overview

```{r dex_tbl}
read_html_table(ctry)
```

## Regional inequality trends

```{r dex_fig1}
iso3_g20 <- read_csv("data/ISO_3_G20.csv") %>%
    clean_names() %>%
    rename(iso3 = iso_3)

top_bottom_c <- read_excel("data/top_bottom_gdppc_w_agg.xlsx") %>%
    clean_names()

gdp_pop_00_20 <- read_csv("data/gdp_pop_00_20_imputed_agg_l.csv") %>%
    clean_names() %>%
    select(-x1) %>%
    left_join(iso3_g20, by = "iso3") %>%
    filter(!reg_id %in% missing_reg, !iso3 %in% excl_countries) %>%
    mutate(
        metro_large = ifelse(metro_large != "Metropolitan large", "Non-large", metro_large),
        metro_far = ifelse(metro_far != "Far from a FUA>250K", "Close", metro_far)
    ) %>%
    bind_rows(
        read_csv("data/gdp_pop_tl2_00_20_imputed_l_CHL_PACHA.csv") %>%
            clean_names() %>%
            select(time, reg_id, iso3, gdp, pop, gdp_pc, regional_name_eng = reg_name)
    )

summary_theil_tl2 <- read_csv("data/theil_gdppc_country_00_20_wCHE-CHL.csv") %>%
    clean_names() %>%
    filter(level == "gdppc_tl2") %>%
    select(-c(x1, level))

summary_stats <- gdp_pop_00_20 %>%
    group_by(iso3, time) %>%
    summarise(mean_gdp_pc = mean(gdp_pc, na.rm = TRUE)) %>%
    ungroup() %>%
    left_join(top_bottom_c, by = c("iso3", "time")) %>%
    mutate(
        top_bottom = top_20_gdppc / bottom_20_gdppc,
        top_mean = top_20_gdppc / mean_gdp_pc,
        bottom_mean = bottom_20_gdppc / mean_gdp_pc
    )

# no gdp data for CHL
# summary_stats %>%
#   filter(iso3 == ctry)

summary_stats <- summary_stats %>%
    full_join(summary_theil_tl2, by = c("iso3", "time"))

# summary_stats %>%
#   filter(iso3 == ctry) %>%
#   select(time, theil)

min_y <- summary_theil_tl2 %>%
    filter(iso3 == ctry) %>%
    pull(time) %>%
    min()

index_200x <- summary_stats %>%
    filter(time == min_y) %>%
    pivot_wider(
        id_cols = "iso3",
        names_from = "time",
        values_from = -c(time, iso3)
    )

# index_200x %>%
#   filter(iso3 == ctry) %>%
#   select(theil_2008)
# => rename cols

colnames(index_200x) <- gsub("_200[0-9]", "_200x", colnames(index_200x))

summary_stats_index <- summary_stats %>%
    full_join(index_200x, by = "iso3") %>%
    mutate(
        index_gdppc = mean_gdp_pc / mean_gdp_pc_200x,
        index_bottom = bottom_mean / bottom_mean_200x,
        index_top = top_mean / top_mean_200x,
        index_theil = theil / theil_200x,
        index_top_mean = top_mean / top_mean_200x,
        index_bottom_mean = bottom_mean / bottom_mean_200x,
        index_top_bottom = top_bottom / top_bottom_200x
    ) %>%
    select(
        iso3,
        time,
        index_gdppc,
        index_bottom,
        index_top_mean,
        index_top_bottom,
        index_bottom_mean,
        index_top,
        index_theil
    )

summary_wide <- summary_stats_index %>%
    pivot_longer(cols = -c(time, iso3), names_to = "index") %>%
    mutate(
        labels_index = case_when(
            index == "index_bottom" ~ "Bottom 20%",
            index == "index_top" ~ "Top 20%",
            index == "index_bottom_mean" ~ "Bottom 20%/Mean",
            index == "index_top_mean" ~ "Top 20%/Mean",
            index == "index_theil" ~ "Theil index",
            index == "index_gdppc" ~ "Mean GDP per capita",
            index == "index_top_bottom" ~ "Top 20%/Bottom 20%"
        )
    ) %>%
    left_join(iso3_g20, by = "iso3")

lev_2 <- c(
    "Theil index",
    "Top 20%/Mean",
    "Bottom 20%/Mean",
    "Mean GDP per capita"
)

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,
        colour = factor(labels_index, levels = lev_2)
    ), linetype = "dashed", linewidth = 1.2) +
    geom_line(aes(
        y = index_theil,
        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), 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",
        x = "",
        y = "Statistic (2000=1)",
        linetype = "",
        colour = ""
    ) +
    # theme_oecd(base_size = 10) +
    # theme(plot.title = element_text(size = 13, hjust = 0, margin = margin(0, 0, 8, 0)))
    theme_minimal()
```

```{r dex_fig1_summary}
# range(summary_wide$time) # = 2000-2021!!!
theil_chg <- summary_wide %>%
    # filter in 2 steps to avoid an out of range year
    filter(
        index == "index_theil",
        iso3 == ctry
    ) %>%
    filter(
        time %in% range(time)
    ) %>%
    arrange(time) %>%
    mutate(diff = value - lag(value)) %>%
    filter(time == max(time)) %>%
    pull(diff)

theil_chg_txt <- ifelse(theil_chg > 0, "an increase", "a decline")

theil_max <- summary_wide %>%
    filter(
        index == "index_theil",
        iso3 == ctry
    ) %>%
    filter(value == max(value)) %>%
    pull(time) %>%
    as.integer()

polarization_pct <- summary_wide %>%
    filter(
        index == "index_top_mean",
        iso3 == ctry
    ) %>%
    filter(time %in% range(time)) %>%
    arrange(time) %>%
    mutate(diff = lead(value) - value) %>%
    filter(time == min(time)) %>%
    pull(diff) %>%
    round(3)

polarization_txt <- ifelse(polarization_pct > 0, "higher", "lower")

polarization_yrs <- summary_wide %>%
    filter(
        index == "index_top_mean",
        iso3 == ctry
    ) %>%
    filter(time %in% range(time)) %>%
    pull(time) %>%
    as.integer()

polarization_2_pct <- summary_wide %>%
    filter(
        index == "index_bottom_mean",
        iso3 == ctry
    ) %>%
    filter(
        time %in% range(time)
    ) %>%
    arrange(time) %>%
    mutate(diff = lead(value) - value) %>%
    filter(time == min(time)) %>%
    pull(diff) %>%
    round(3)

polarization_2_txt <- ifelse(polarization_2_pct > 0, "higher", "lower")

polarization_yrs_txt <- summary_wide %>%
    filter(
        index == "index_bottom_mean",
        iso3 == ctry
    ) %>%
    filter(
        time %in% range(time)
    ) %>%
    arrange(time) %>%
    pull(time) %>%
    as.character()

# not all countries have polarization data
theil_paragraph <- glue("{ctry2} experienced {theil_chg_txt} in the Theil index of GDP per capita over {polarization_yrs_txt[1]}-{polarization_yrs_txt[2]}. Inequality reached its maximum in {theil_max}. The figures were normalized, with the values in the year {as.integer(min_y)} set to 1.")

theil_paragraph_2 <- if (is.na(polarization_2_pct)) {
    ""
} else {
    glue("Polarisation, as measured by the Top 20%/Mean ratio was {paste(abs(polarization_pct), polarization_txt)} in {min(polarization_yrs)} compared to {max(polarization_yrs)}. Bottom divergence, as measured by the Bottom 20%/Mean ratio was {paste(abs(polarization_2_pct), polarization_2_txt)} in the same period.")
}
```

`r theil_paragraph`

`r theil_paragraph_2`

```{r dex_fig1_1}
# no interactivity
# fig1

# interactivity
ggplotly(fig1) %>%
    config(displayModeBar = F)
```

<div class="oecdnote">
**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. <br>
**Source**: OECD Regional Database (2022).
</div>

<br>
<br>

```{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) %>%
#   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) <- c("time", "pw_lp_country", "pw_hp_country")

dp11 <- dp1 %>%
    select(time, matches("country")) %>%
    pivot_longer(-time) %>%
    rename(value_country = value)

dp11 <- dp11 %>%
    mutate(
        name = case_when(
            name == "pw_lp_country" ~ "Lower half",
            name == "pw_hp_country" ~ "Upper half"
        )
    )

# plot ----

yrs <- sort(unique(dp11$time))
yrs <- seq(min(yrs), max(yrs), 2)

hpgrew <- dp11 %>%
    filter(
        name == "Upper half",
        time %in% c(min(yrs), max(yrs))
    ) %>%
    summarise(grew = 100 * (value_country - lag(value_country)) / lag(value_country)) %>%
    drop_na() %>%
    pull() %>%
    round(1)

lpgrew <- dp11 %>%
    filter(
        name == "Lower half",
        time %in% c(min(yrs), max(yrs))
    ) %>%
    summarise(grew = 100 * (value_country - lag(value_country)) / lag(value_country)) %>%
    drop_na() %>%
    pull() %>%
    round(1)

hpdiff <- hpgrew - lpgrew

hpmore <- ifelse(hpdiff > 0, "more", "less")

fig2 <- dp11 %>%
    rename(
        value = value_country,
        series = name
    ) %>%
    ggplot() +
    geom_line(aes(x = time, y = value, color = series), linewidth = 1.2) +
    # theme_oecd(base_size = 10) +
    theme_minimal() +
    # scale_colour_manual(values = clrs3[1:2]) +
    scale_colour_manual(values = c("#177dc7", "#508551")) +
    labs(
        x = "", y = "Productivity (2015 USD PPP)", colour = "",
        title = "Figure 2: Evolution of labour productivity,\nTL2 regions"
    ) +
    scale_x_continuous(labels = as.character(yrs), breaks = yrs) +
    scale_y_continuous(labels = scales::number_format())
# theme(plot.title = element_text(size = 13, hjust = 0, margin = margin(0, 0, 10, 0)))

ctry3 <- if (any(ctry %in% c("USA", "GBR", "CZE", "SVK", "NLD"))) {
    paste("The", ctry2)
} else {
    ctry2
}

ctry4 <- if (any(ctry %in% c("USA", "GBR", "CZE", "SVK", "NLD"))) {
    paste("the", ctry2)
} else {
    ctry2
}
```

```{r aut_fig3_text}
fig3_text <- read_excel("data/fig3_text_FINAL.xlsx", sheet = "text") %>%
    clean_names()

fig3_text <- fig3_text %>%
    filter(iso3 == ctry) %>%
    pull(text_final)
```

`r fig3_text`

```{r dex_fig2_2}
# no interactivity
# fig2

# interactivity
ggplotly(fig2) %>%
    config(displayModeBar = F)
```

<div class="oecdnote">
**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.<br>
**Source**: OECD Regional Database (2022).
</div>

<br>
<br>

```{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) %>%
    clean_names()

# tidy ----

colnames(dp2) <- str_replace(colnames(dp2), tolower(ctry), "country")

dp21 <- dp2 %>%
    select(time, matches("country")) %>%
    pivot_longer(-time) %>%
    rename(value_country = value)

dp21 <- dp21 %>%
    mutate(
        name = case_when(
            name == "share_tgoods_lp_country" ~ paste(ctry, "TG LP"),
            name == "share_tgoods_hp_country" ~ paste(ctry, "TG HP"),
            name == "share_tserv_lp_country" ~ paste(ctry, "TS LP"),
            name == "share_tserv_hp_country" ~ paste(ctry, "TS HP")
        )
    )

# plot ----

yrs <- sort(unique(dp21$time))
yrs <- seq(min(yrs), max(yrs), 1)

tghpgrew <- dp21 %>%
    filter(
        name == paste(ctry, "TG HP"),
        time %in% c(min(yrs), max(yrs))
    ) %>%
    summarise(grew = value_country - lag(value_country)) %>%
    drop_na() %>%
    pull() %>%
    round(1)

tglpgrew <- dp21 %>%
    filter(
        name == paste(ctry, "TG LP"),
        time %in% c(min(yrs), max(yrs))
    ) %>%
    summarise(grew = value_country - lag(value_country)) %>%
    drop_na() %>%
    pull() %>%
    round(1)

tghpdiff <- tghpgrew - tglpgrew
tghpmore <- ifelse(tghpdiff > 0, "grew", "declined")

tglpdiff <- tglpgrew - tglpgrew
tglpmore <- ifelse(tglpdiff > 0, "grew", "declined")

tshpgrew <- dp21 %>%
    filter(
        name == paste(ctry, "TS HP"),
        time %in% c(min(yrs), max(yrs))
    ) %>%
    summarise(grew = value_country - lag(value_country)) %>%
    drop_na() %>%
    pull() %>%
    round(1)

tslpgrew <- dp21 %>%
    filter(
        name == paste(ctry, "TS LP"),
        time %in% c(min(yrs), max(yrs))
    ) %>%
    summarise(grew = value_country - lag(value_country)) %>%
    drop_na() %>%
    pull() %>%
    round(1)

tshpdiff <- tshpgrew - tslpgrew
tshpmore <- ifelse(tshpdiff > 0, "grew", "declined")

tslpdiff <- tslpgrew - tslpgrew
tslpmore <- ifelse(tslpdiff > 0, "grew", "declined")

dp21 <- dp21 %>%
    mutate(category = gsub(paste0("^OECD |^", ctry, " "), "", name))

dp21 <- dp21 %>%
    mutate(
        country = str_replace_all(name, " .*", ""),
        category1 = str_sub(category, 1, 2),
        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", "Lower half"),
        category2 = str_replace_all(category2, "HP", "Upper half")
    )

dp21_2 <- dp21 %>%
    select(country, time, category1, category2, value_country) %>%
    mutate(
        category2_2 = category2,
        category2 = str_replace_all(category2, as.character(min(yrs)), "minyr"),
        category2 = str_replace_all(category2, as.character(max(yrs)), "maxyr")
    ) %>%
    # remove all after productivy in category2
    # mutate(category2 = str_replace_all(category2, "productivity.*", "productivity")) %>%
    pivot_wider(names_from = category2, values_from = value_country) %>%
    clean_names()

# for plot_ly -----
dp21_2 <- dp21 %>%
    select(country, time, category1, category2, value_country) %>%
    mutate(type = ifelse(str_detect(category2, "Lower"), "Lower half", "Upper half"))

dp21_min_TG <- dp21 %>%
    select(country, time, category1, category2, value_country) %>%
    mutate(type = ifelse(str_detect(category2, "Lower"), "Lower half", "Upper half")) %>%
    filter(time == min(dp21_2$time) & category1 == "Tradable goods")

dp21_min_TS <- dp21 %>%
    select(country, time, category1, category2, value_country) %>%
    mutate(type = ifelse(str_detect(category2, "Lower"), "Lower half", "Upper half")) %>%
    filter(time == min(dp21_2$time) & category1 == "Tradable services")

dp21_max_TG <- dp21 %>%
    select(country, time, category1, category2, value_country) %>%
    mutate(type = ifelse(str_detect(category2, "Lower"), "Lower half", "Upper half")) %>%
    filter(time == max(dp21_2$time) & category1 == "Tradable goods")

dp21_max_TS <- dp21 %>%
    select(country, time, category1, category2, value_country) %>%
    mutate(type = ifelse(str_detect(category2, "Lower"), "Lower half", "Upper half")) %>%
    filter(time == max(dp21_2$time) & category1 == "Tradable services")

data_plotly <- tibble(
    "x" = c("Lower half", "Upper half"),
    "y" = dp21_min_TG$value_country,
    "y2" = dp21_min_TS$value_country,
    "y3" = dp21_max_TG$value_country,
    "y4" = dp21_max_TS$value_country,
    "name1" = dp21_min_TG$category2,
    "name2" = dp21_max_TG$category2
)

x <- c("Lower half", "Upper half")

y <- dp21_min_TG$value_country
y2 <- dp21_min_TS$value_country

y3 <- dp21_max_TG$value_country
y4 <- dp21_max_TS$value_country

name1 <- dp21_min_TG$category2
name2 <- dp21_max_TG$category2

p1 <- plot_ly() %>% add_trace(
    x = ~x, y = ~y3, color = ~x,
    type = "bar",
    name = ~name2,
    marker = list(
        color = c("#c8f075", "#6bc5f2")
    )
)

p1 <- p1 %>% add_markers(
    x = ~x, y = ~y, color = ~x,
    name = ~name1,
    mode = "markers",
    marker = list(
        color = clrs4[2:1],
        size = 12,
        symbol = "diamond-dot"
    )
)

p1 <- p1 %>% layout(
    title = "Tradable goods",
    xaxis = list(title = "", visible = FALSE),
    yaxis = list(title = "Employment share (%)")
)

p2 <- plot_ly() %>% add_trace(
    x = ~x, y = ~y4, color = ~x,
    type = "bar",
    marker = list(color = c("#c8f075", "#6bc5f2")),
    showlegend = FALSE
)

p2 <- p2 %>% add_markers(
    x = ~x, y = ~y2, color = ~x,
    mode = "markers",
    marker = list(
        color = clrs4[2:1],
        size = 12,
        symbol = "diamond-dot"
    ),
    showlegend = FALSE
)

p2 <- p2 %>% layout(
    title = "Tradable services",
    xaxis = list(title = "", visible = FALSE),
    yaxis = list(title = "Employment share (%)")
)

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),
        margin = list(
            l = 50, r = 50,
            b = 50, t = 120,
            pad = 4
        ),
        annotations = list(
            list(
                x = 0.25,
                y = 1,
                font = list(size = 14),
                text = "Industry",
                xref = "paper",
                yref = "paper",
                xanchor = "center",
                yanchor = "bottom",
                showarrow = FALSE
            ),
            list(
                x = 0.75,
                y = 1,
                font = list(size = 14),
                text = "Tradable services",
                xref = "paper",
                yref = "paper",
                xanchor = "center",
                yanchor = "bottom",
                showarrow = FALSE
            )
        )
    )
```


```{r can_fig3_text}
dp2 <- read_excel("data/countryprofile_fig4_alt.xlsx", sheet = ctry) %>%
    clean_names()

text_all <- dp2 %>%
    filter(time == 2020) %>%
    pull(text_all)
```

`r text_all`

```{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,\nTL2 regions", x = 0,
                 xanchor = "left",
                 xref = "paper",
                 font = list(color = "black", family = "Arial"))
    )

# remove legend background
fig3 <- fig3 %>%
    layout(
        legend = list(bgcolor = "rgba(0,0,0,0)")
    )

fig3
```

<div class="oecdnote">
**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).<br>
**Source**: OECD Regional Database (2022).
</div>

<br>
<br>

## Recent policy developments

```{r dex_txt}
read_html_text(ctry)
```

<table border="1" style="width:75%;margin-left:auto;margin-right:auto;">
  <tr>
    <th style="background:#d4edff; padding:10px;font-size:9.0pt;" valign="center">
      Territorial definitions
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    <td>
      <p style="padding-left:10px;padding-right:10px;padding-top:10px;font-size:9.0pt;">
        The data in this note reflect different sub-national geographic levels in OECD countries. In particular, <b>regions</b> 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.
      </p>
      <p style="padding-left:10px;padding-right:10px;font-size:9.0pt;">
        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:
      </p>
      <ul style="list-style-type:circle;font-size:9.0pt;padding-right:10px">
        <li>
          <b>Metropolitan regions</b>, if more than half of the population live in a FUA. Metropolitan regions are further classified into: <b>metropolitan large</b>, if more than half of the population live in a (large) FUA of at least 1.5 million inhabitants; and <b>metropolitan midsize</b>, if more than half of the population live in a (midsize) FUA of at 250 000 to 1.5 million inhabitants.
        </li>
        <li>
          <b>Non-metropolitan regions</b>, 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: <b>near a midsize/large FUA</b> 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; <b>near a small FUA</b> 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 <b>remote</b>, otherwise.
        </li>
      </ul>
      </p>
      <p style="padding-left:10px;padding-right:10px;font-size:9.0pt;">
        Disclaimer: <a href="https://oecdcode.org/disclaimers/territories.html" target="_blank"><i style="color:#0d6efd">https://oecdcode.org/disclaimers/territories.html</i></a>
      </p>
    </td>
  </tr>
</table>