imf_weo_dataset

Since the following analysis is based on the cleaned dataset from IMF and IIF, lets start with an overview of the dataset.

glimpse(iif_issuance_data)

## Rows: 2,035
## Columns: 4
## $ date            <chr> "2013-03-31", "2013-03-31", "2013-03-31", "2013-03-31"…
## $ geography       <chr> "Mature markets", "Emerging markets", "Offshore center…
## $ issuance_bn_usd <dbl> 6.18958089, 0.45115568, 0.00000000, 1.15915118, 7.7998…
## $ issuance_type   <chr> "Sustainable debt (bonds and loans)", "Sustainable deb…

head(iif_issuance_data)

##         date        geography issuance_bn_usd
## 1 2013-03-31   Mature markets       6.1895809
## 2 2013-03-31 Emerging markets       0.4511557
## 3 2013-03-31 Offshore centers       0.0000000
## 4 2013-03-31   Supranationals       1.1591512
## 5 2013-03-31           Global       7.7998877
## 6 2013-06-30   Mature markets       4.0782106
##                        issuance_type
## 1 Sustainable debt (bonds and loans)
## 2 Sustainable debt (bonds and loans)
## 3 Sustainable debt (bonds and loans)
## 4 Sustainable debt (bonds and loans)
## 5 Sustainable debt (bonds and loans)
## 6 Sustainable debt (bonds and loans)

#Geographic Structure of the Market

Compare the market size based on geography and look into data in different years.

Issuance-type breakdown

issuance_2019_2022 <- iif_issuance_data %>%
  filter(date == c("2019-12-31","2022-03-31"))

#treemap:issuance_type as subgroup
treemap(issuance_2019_2022, 
        index = c("date","issuance_type"), 
        vSize = "issuance_bn_usd",
        fontsize.labels=c(15,12),
        fontcolor.labels=c("darkgreen","black"),
        fontface.labels=c(2,1),
        align.labels=list(c("left", "top"), c("center", "center")),
        overlap.labels=0.5,
        inflate.labels=F,
        palette = "Greens",
        title = "Issuance type: Comapre Pre and Post Pandemic Data"
        )

## Geographic breakdown

#treemap:geography the subgroup
treemap(issuance_2019_2022, 
        index = c("date","geography"), 
        vSize = "issuance_bn_usd",
        fontsize.labels=c(15,12),
        fontcolor.labels=c("darkgreen","black"),
        fontface.labels=c(2,1),
        align.labels=list(c("left", "top"), c("center", "center")),
        overlap.labels=0.5,
        inflate.labels=F,
        palette = "Greens",
        title = "Geography: Comapre Pre and Post Pandemic Data"
        )

#Make a multi-dimentional overview Generate an alluvial plot representation of the multi-dimensional categorical dataset.

iif_issuance_data %>%
  filter(date >= "2019-12-31") %>%
  filter(geography == c("Emerging markets","Mature markets")) %>%
  ggplot(aes(axis1 = date, axis2 = issuance_type, y=issuance_bn_usd))+
  scale_x_discrete(limits = c("date", "type"), expand = c(.2, .05)) +
  geom_alluvium(aes(fill = geography)) +
  geom_stratum() +
  labs(y = "Issuance in USD [Bn]")+
  geom_text(stat = "stratum", aes(label = after_stat(stratum)),size = 3) +
  theme_minimal() +
  ggtitle("Issuance Market Size after the Outbreak of the Pandemic",
          "stratified by date and type") 

Trend of growth

After the overview, let’s have a look at the trend.

iif_issuance_data %>%
  mutate(year = lubridate::year(date))%>%
  filter(geography == c("Mature markets","Emerging markets")) %>%
  group_by(year, geography, issuance_type) %>%
  summarize(total = sum(issuance_bn_usd)) %>%
  ggplot(aes(x = year, y = total)) + 
  geom_line(aes(color = issuance_type)) +
    labs(title = 'Growing Trend of Different Issuances',
         subtitle = "Emerging & Mature Market",
         x = "Time", 
         y = "Issuance in USD [Bn]") +
  theme_minimal()+
  facet_grid(geography ~.)

## `summarise()` has grouped output by 'year', 'geography'. You can override using
## the `.groups` argument.