Dataset: IIF’s Sustainable Debt Monitor

The Institute of International Finance (IIF) produces a Sustainable Debt Monitor with useful information about the volume, sector, and geography of various types of sustainable debt products.

here("01_data_raw", "iif", "Sustainable_debt_monitor.xlsx") %>% excel_sheets()

##  [1] "Table of contents"               "Country coverage"               
##  [3] "1a_global markets - issuance"    "1b_global markets - outstanding"
##  [5] "2a_MM by country"                "2b_MM by sector"                
##  [7] "3a_EM&FM by country"             "3b_EM&FM by sector"             
##  [9] "4a_offshore centers by country"  "4b_offshore centers by sector"  
## [11] "5_supranationals by country"     "6_outstanding amt by country "  
## [13] "7_Share of ESG debt issuance"

Make the data on “1a_global markets - issuance” into a cohesive dataset:

here("01_data_raw", "iif", "Sustainable_debt_monitor.xlsx") %>% 
  #specify the sheet + the range of cells in excel
  read_excel(sheet = "1a_global markets - issuance",
             range = "B4:G41") %>%
  # rename the first column to "date"
  rename(date = `$ billion`) %>%
  # Tidy the dataset by making the column names into long format
  pivot_longer(cols = -date, names_to = "geography", values_to = "issuance_bn_usd") %>%
  # coerce the date column into a date object using lubridate::ymd() (year-month_date)
  mutate(date = ymd(date)) %>%
  # Add a column that includes the name of this specific data
  add_column(issuance_type = "Sustainable debt (bonds and loans)")

There are 11 different sub-datasets on this worksheet, so let’s save ourselves some time and make a function.

process_iif_gb_issuance <- function(range, issuance_type) {
  here("01_data_raw", "iif", "Sustainable_debt_monitor.xlsx") %>% 
    read_excel(sheet = "1a_global markets - issuance", range = range) %>%
    rename(date = `$ billion`) %>%
    pivot_longer(cols = -date, names_to = "geography", values_to = "issuance_bn_usd") %>%
    mutate(date = ymd(date)) %>%
    add_column(issuance_type = issuance_type)
}

Let’s use this functions to create a data object from the first set of data

bonds_and_loans <- process_iif_gb_issuance(range = "B4:G41", 
                                           issuance_type = "Sustainable debt (bonds and loans)")

bonds_and_loans

bonds <- process_iif_gb_issuance(range = "I4:N41", 
                                           issuance_type = "Sustainable bonds")

bonds

loans <- process_iif_gb_issuance(range = "P4:U41", 
                                           issuance_type = "Sustainable loans")

loans

greenabs <- process_iif_gb_issuance(range = "AD4:AI41", 
                                           issuance_type = "Green ABS")

greenabs

greenbonds <- process_iif_gb_issuance(range = "W4:AB41", 
                                           issuance_type = "Green bonds")

greenbonds

susbonds <- process_iif_gb_issuance(range = "AK4:AP41", 
                                           issuance_type = "Sustainability bonds")

susbonds

socialbonds <- process_iif_gb_issuance(range = "AR4:AW41", 
                                           issuance_type = "Social bonds")

socialbonds

socialbonds <- process_iif_gb_issuance(range = "AR4:AW41", 
                                           issuance_type = "Social bonds")

socialbonds

greenmunicipal <- process_iif_gb_issuance(range = "AY4:BD41", 
                                           issuance_type = "Green municipal bonds")

greenmunicipal

suslinkedbonds <- process_iif_gb_issuance(range = "BF4:BK41", 
                                           issuance_type = "Sustainability-linked bonds")

suslinkedbonds

greenloans <- process_iif_gb_issuance(range = "BM4:BR41", 
                                           issuance_type = "Green loans")

greenloans

suslinkedloans <- process_iif_gb_issuance(range = "BT4:BY41", 
                                           issuance_type = "Sustainability-linked loans")

suslinkedloans

Now we can bind them together into one dataset

iif_issuance_data <- bonds_and_loans %>%
  bind_rows(bonds)%>%
  bind_rows(loans)%>%
  bind_rows(greenbonds)%>%
  bind_rows(greenabs)%>%
  bind_rows(susbonds)%>%
  bind_rows(socialbonds)%>%
  bind_rows(greenmunicipal)%>%
  bind_rows(suslinkedbonds)%>%
  bind_rows(greenloans)%>%
  bind_rows(suslinkedloans)

iif_issuance_data

compsusdebt<- iif_issuance_data %>% 
  filter(issuance_type == "Sustainable debt (bonds and loans)")

ggplot(compsusdebt, aes (x = date, y = issuance_bn_usd, group = geography))+
  geom_line(aes(colour = geography))+
  geom_point(aes(x = date, y = issuance_bn_usd, colour = geography))+
  labs(
    title = "Sustainable debt (bonds and loans)",
    subtitle = "Geographical Comparison (Year 2013 - 2022)",
    caption = "Data from IIF",
    tag = "Figure 1",
    x = "Year",
    y = "issuance_bn_usd",
    colour = "Geography"
  )+
  theme_gray()

Tidying Dataset IIF - Lidan Zhang

Dataset: IIF’s Sustainable Debt Monitor