Country

Indicator

Value type

Values present

Short-term debt (% of total external debt)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	12.4	+7.2%	45
Angola	8.65	+24.6%	60
Albania	11	-10.4%	54
Argentina	20.6	+11%	21
Armenia	19.6	-3.61%	23
Azerbaijan	4.21	+27.1%	85
Burundi	0.0281	+1.44%	114
Benin	15.1	+6.82%	32
Burkina Faso	0		118
Bangladesh	14	-26.6%	36
Bosnia & Herzegovina	20.6	+4.92%	22
Belarus	28.3	+11.1%	9
Belize	0.765	+14.3%	103
Bolivia	4.86	+22.8%	81
Brazil	13.3	+13.4%	41
Bhutan	0.0282	-83.8%	113
Botswana	14	+150%	37
Central African Republic	7.24	-28.2%	67
China	53.2	+2.93%	1
Côte d’Ivoire	8.16	+53.9%	62
Cameroon	5.6	-3.76%	77
Congo - Kinshasa	5.44	-10.1%	79
Congo - Brazzaville	2.54	+3.45%	91
Colombia	10.1	-3.95%	58
Comoros	0.119	-72.5%	112
Cape Verde	16.6	-0.297%	30
Costa Rica	13.9	-21.2%	39
Djibouti	14	-29.6%	38
Dominica	29.6	-10.6%	8
Dominican Republic	7.59	+15.6%	65
Algeria	26.1	+6.88%	12
Ecuador	2.87	-9.36%	90
Egypt	17.5	-5.41%	27
Eritrea	6.04	+8.77%	74
Ethiopia	0.646	-41.5%	105
Fiji	23.7	+39.3%	17
Gabon	1.63	-59.7%	98
Georgia	17	+7.23%	29
Ghana	8.59	-21.2%	61
Guinea	5.76	+14.5%	75
Gambia	6.89	+101%	69
Guinea-Bissau	0.131	+5.32%	110
Grenada	7.6	-17.7%	64
Guatemala	0.325	-72.8%	108
Guyana	2.04	-22.2%	94
Honduras	10.7	+36.8%	55
Haiti	0.0033	-2.94%	116
Indonesia	13.4	+10.9%	40
India	19.5	-5.99%	24
Iran	23.1	+15.6%	18
Iraq	4.78	-6.01%	83
Jamaica	17.1	+21.8%	28
Jordan	35.8	-6.47%	3
Kazakhstan	11.8	+16%	48
Kenya	6.56	+6.46%	73
Kyrgyzstan	11.1	+11.5%	53
Cambodia	19.4	-5.4%	25
Laos	0.689	-82.4%	104
Lebanon	25.9	+11.8%	13
Liberia	0.0038		115
St. Lucia	7.4	-31%	66
Sri Lanka	11.7	-19.7%	49
Lesotho	0.19	-76.3%	109
Morocco	14.5	-5.54%	33
Moldova	26.5	+5.4%	11
Madagascar	1.99	-34.2%	96
Maldives	2.46	-78.2%	93
Mexico	10.1	+7.88%	57
North Macedonia	25	-6.41%	15
Mali	1.01	+20%	101
Myanmar (Burma)	0.366	+44.3%	107
Montenegro	2.02	-17.1%	95
Mongolia	5.57	-2.74%	78
Mozambique	1.4	+14.5%	99
Mauritania	5.61	+24.6%	76
Mauritius	49.1	+3.84%	2
Malawi	0.382	-85.9%	106
Niger	0.774	-1.17%	102
Nigeria	18.5	-11.3%	26
Nicaragua	6.79	-11.5%	70
Nepal	4.19	+19.5%	86
Pakistan	6.79	-1.18%	71
Peru	14.2	+19.5%	34
Philippines	14.1	-5.75%	35
Papua New Guinea	0.122	-99.2%	111
Paraguay	10.6	+13.1%	56
Rwanda	3.89	+0.227%	87
Sudan	21.3	-0.431%	20
Senegal	12.2	+8.41%	46
Solomon Islands	7.73	-37.8%	63
Sierra Leone	6.59	-2.12%	72
El Salvador	12.8	+15.2%	42
Somalia	21.5	-22.2%	19
Serbia	1.98	-65.6%	97
São Tomé & Príncipe	4.86	-0.0391%	82
Suriname	5.08	-48.3%	80
Eswatini	4.57	+377%	84
Syria	12.8	+0.351%	43
Chad	1.08	+4.27%	100
Togo	2.97	-43.7%	89
Thailand	35	+3.02%	4
Tajikistan	9.86	+6.02%	59
Turkmenistan	0	-100%	118
Timor-Leste	3.04	+2,506%	88
Tonga	0		118
Tunisia	33.4	-0.0309%	7
Turkey	34.8	+7.29%	5
Tanzania	12.7	-10.8%	44
Uganda	2.5	-70.4%	92
Ukraine	11.9	+5.4%	47
Uzbekistan	11.5	-10.1%	51
St. Vincent & Grenadines	0.0001	0%	117
Vietnam	24.8	-4.88%	16
Vanuatu	11.2	+62.8%	52
Samoa	15.4	+1.39%	31
Kosovo	34.3	+0.253%	6
Yemen	7.11	+4.64%	68
South Africa	25.5	+1.82%	14
Zambia	11.6	+25%	50
Zimbabwe	27.7	-3.74%	10

The indicator 'Short-term debt (% of total external debt)' is a critical measure that reflects the financial vulnerability of a nation. It quantifies the proportion of a country's external debt that is due within a year. This metric is particularly important because it highlights the immediate repayment obligations of a country and provides insights into its liquidity risk, potential refinancing challenges, and overall financial health.

Understanding the significance of short-term debt is paramount for policymakers, investors, and economists. A high percentage of short-term debt in relation to total external debt can signal greater risk, as countries with more immediate obligations are more susceptible to external shocks, fluctuations in exchange rates, or changes in investor sentiment. Conversely, a low percentage indicates a more sustainable long-term debt structure, often associated with reduced refinancing risks.

In 2023, the median value for short-term debt as a proportion of total external debt stood at 8.62%. This figure provides a useful benchmark for assessing the financial position of nations worldwide. When analyzing the data, the countries with the highest levels of short-term debt relative to their total external obligations comprise a concerning list. Leading the pack is China with a staggering 53.19%. This figure raises certain questions regarding China’s short-term financing needs and may indicate a dependence on rolling over short-term debt which can be risky in periods of financial uncertainty.

Following China, Mauritius, Jordan, Thailand, and Turkey exhibit substantial short-term debt ratios, ranging from approximately 35% to 49%. These high percentages suggest that these nations may face significant refinancing risks, particularly if external conditions worsen or if interest rates rise. Factors such as exchange rate volatility and economic slowdown can exacerbate these challenges, potentially leading to financial distress.

On the flip side, the bottom five areas, which include Burkina Faso, Tonga, Turkmenistan, St. Vincent & Grenadines, and Haiti, all show minimal short-term debt proportions — some with figures as low as 0%. This could indicate either a strong reliance on long-term financing options or simply a lack of available external debt. While low levels of short-term debt can reflect stability, they may also raise concerns about a country's overall access to capital markets and financing options.

Despite its importance, measuring short-term debt alone could overlook broader financial indicators. It is crucial to relate this metric to other financial statistics, such as total external debt, debt service ratios, and the current account balance. These relationships can create a more nuanced picture of a nation’s financial health. For instance, a country with high short-term debt might appear vulnerable, but if it also has strong export performance, solid foreign reserves, and positive growth projections, its capacity to manage that debt may be enhanced.

Several factors influence the ratio of short-term debt to total external debt. Economic policies, interest rates, exchange rate stability, and overall fiscal health play critical roles. Nations experiencing rapid economic growth might lean towards short-term borrowing to capitalize on immediate investment opportunities, yet they must balance that against potential risks. Additionally, global financial conditions, such as interest rate increases by the Federal Reserve or fluctuations in oil prices, can heavily influence refinancing abilities and risk perceptions in these countries.

Strategically, countries can manage their short-term debt ratios by implementing prudent fiscal policies and ensuring a diversified borrowing strategy. This may involve refinancing short-term obligations before they mature or engaging in longer-term debt instruments where feasible. Countries could also focus on strengthening their foreign exchange reserves, which can serve as a buffer in times of financial stress.

One might consider solutions within the realm of international financial assistance. By working closely with multilateral development banks, countries can secure favorable terms for their existing debts. Moreover, improving economic fundamentals can enhance investor confidence, lower borrowing costs, and potentially decrease the reliance on short-term borrowing. Countries that enhance their financial transparency and policy predictability often attract foreign investment, which can lead to a more favorable debt composition.

However, the reliance on short-term debt can present flaws. For instance, when external conditions change unfavorably, countries with significant short-term liabilities may face immediate financial challenges that threaten economic stability. The balance between maintaining adequate levels of short-term debt for liquidity while ensuring long-term sustainability is delicate and requires constant monitoring, as economic environments can shift rapidly.

In conclusion, the 'Short-term debt (% of total external debt)' indicator remains a fundamental aspect of analyzing the financial health of countries. With a median value of 8.62% in 2023, the trends among countries underline essential lessons about risk management, the importance of robust economic fundamentals, and the need for strategic borrowing practices. By carefully evaluating and managing this metric in relation to broader economic indicators—while being mindful of the risks associated with high short-term debt—nations can enhance their resilience to financial shocks and cultivate a more stable economic environment.

Python
R

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'DT.DOD.DSTC.ZS'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'DT.DOD.DSTC.ZS'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))

Compare

Short-term debt (% of total external debt)

Year: 2023

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