Chinese Development Finance Commitments by Country — gcdf_country

A dataset containing Chinese development finance commitments and related country characteristics derived from the Global Chinese Development Finance (GCDF) Dataset 3.0, World Bank World Development Indicators, and IMF World Economic Outlook data. The dataset includes total commitments, weighted average interest rates, and key country characteristics like GDP, population, region and income group.

Usage

gcdf_country_commitments

Format

A data frame with approximately 150 rows and 8 variables:

country_name: Character. Country name
iso3c: Character. ISO 3-letter country code
total_commitments_bn: Numeric. Total Chinese development finance commitments in billions of constant 2021 USD
weighted_interest_rate: Numeric. Weighted average interest rate across all loans, weighted by commitment amount
region_name: Character. World Bank geographic region
income_level_name: Character. World Bank income group classification
gdp_usd_bn: Numeric. Nominal GDP in billions of USD (2021)
population_mn: Numeric. Population in millions (2021)

Source

GCDF 3.0: Commitment and interest rate data from AidData's Global Chinese Development Finance Dataset, Version 3.0
WDI: Region and income group classifications from World Bank World Development Indicators
WEO: GDP and population data from IMF World Economic Outlook (Fall 2024)

Examples

# Get top 10 recipients by commitment amount
gcdf_country_commitments |>
  dplyr::arrange(desc(total_commitments_bn)) |>
  head(10)
#> # A tibble: 10 × 8
#>    country_name iso3c total_commitments_bn weighted_interest_rate region_name   
#>    <chr>        <chr>                <dbl>                  <dbl> <chr>         
#>  1 Russia       RUS                  170.                    3.86 Europe & Cent…
#>  2 Argentina    ARG                  139.                    7.17 Latin America…
#>  3 Venezuela    VEN                  113.                    5.02 Latin America…
#>  4 Pakistan     PAK                  103.                    3.63 South Asia    
#>  5 Angola       AGO                   65.1                   4.34 Sub-Saharan A…
#>  6 Kazakhstan   KAZ                   64.1                   4.60 Europe & Cent…
#>  7 Indonesia    IDN                   55.2                   4.87 East Asia & P…
#>  8 Brazil       BRA                   54.3                   3.82 Latin America…
#>  9 Vietnam      VNM                   28.9                   3.73 East Asia & P…
#> 10 Turkey       TUR                   28.3                   3.82 Europe & Cent…
#> # ℹ 3 more variables: income_level_name <chr>, gdp_usd_bn <dbl>,
#> #   population_mn <dbl>

# Calculate average commitment size by region
gcdf_country_commitments |>
  dplyr::group_by(region_name) |>
  dplyr::summarize(
    avg_commitment_bn = mean(total_commitments_bn, na.rm = TRUE)
  )
#> # A tibble: 7 × 2
#>   region_name                avg_commitment_bn
#>   <chr>                                  <dbl>
#> 1 East Asia & Pacific                   9.45  
#> 2 Europe & Central Asia                16.7   
#> 3 Latin America & Caribbean            12.5   
#> 4 Middle East & North Africa            6.47  
#> 5 South Asia                           22.8   
#> 6 Sub-Saharan Africa                    6.38  
#> 7 NA                                    0.0599

# Create basic visualization of commitments by income group
if (requireNamespace("ggplot2", quietly = TRUE)) {
  ggplot2::ggplot(
    gcdf_country_commitments,
    ggplot2::aes(x = income_level_name, y = total_commitments_bn)
  ) +
    ggplot2::geom_boxplot()
}