Country

Indicator

Value type

Values present

Short-term debt (% of total reserves)

Source: worldbank.org, 01.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Angola	35.4	+15.2%	39
Albania	19.4	-20.3%	57
Argentina	238	+115%	2
Armenia	86.1	+9.23%	15
Azerbaijan	4.45	-0.268%	82
Burundi	0.325	+94.7%	94
Bangladesh	65.1	+18.6%	19
Bosnia & Herzegovina	31.4	+4.53%	43
Belarus	128	-0.117%	6
Belize	2.44	+19.6%	86
Bolivia	44.1	+162%	31
Brazil	22.7	+8.62%	52
Bhutan	0.141	-78.8%	95
Botswana	6.1	+133%	79
Central African Republic	15.4	-43.6%	67
China	37.3	-2.45%	37
Cameroon	17.6	+2.68%	62
Congo - Kinshasa	11.8	-12.2%	72
Congo - Brazzaville	27.6	+19.6%	48
Colombia	33.7	-1.2%	41
Comoros	0.14	-74.6%	96
Cape Verde	48.6	-11.7%	29
Costa Rica	41	-48.6%	32
Djibouti	95.3	-10.6%	12
Dominica	96.5	-0.603%	11
Dominican Republic	25.5	+16.9%	50
Algeria	2.36	-2.98%	87
Ecuador	39.2	+72.3%	33
Egypt	89.2	-5.25%	14
Ethiopia	10.6	-62.6%	74
Fiji	51.3	+56.9%	28
Gabon	8.57	-62.6%	75
Georgia	83.3	+6.86%	17
Ghana	104	+16%	10
Guinea	15.8	+34.8%	66
Gambia	15.8	+122%	65
Grenada	13.7	-21.4%	70
Guatemala	0.387	-73.9%	93
Guyana	6.73	+13.8%	78
Honduras	18.2	+54.7%	60
Haiti	0.0034	-16%	97
Indonesia	37.3	+6.62%	38
India	20.1	-10.7%	55
Iraq	0.865	-27.3%	89
Jamaica	53.8	+9.84%	24
Jordan	83.8	-3.37%	16
Kazakhstan	53.5	+14.6%	25
Kenya	38.3	+19.3%	35
Kyrgyzstan	34.8	+0.0167%	40
Cambodia	21.9	-15.5%	53
Laos	7.92	-83.4%	77
Lebanon	62.3	+30.6%	22
St. Lucia	18.9	-24.4%	58
Sri Lanka	165	-63.7%	3
Lesotho	0.394	-79%	92
Morocco	27.6	-10.4%	47
Moldova	51.8	-5.38%	27
Madagascar	4.88	-41.3%	81
Maldives	16.7	-69.2%	63
Mexico	28.1	+3.05%	45
North Macedonia	63	-16.7%	21
Myanmar (Burma)	0.476	+22.7%	91
Montenegro	11	+6.08%	73
Mongolia	38.9	-31%	34
Mozambique	25.7	-3.91%	49
Mauritius	130	+21.8%	5
Nigeria	59.1	-2.13%	23
Nicaragua	18.9	-27.2%	59
Nepal	3.35	-2.95%	84
Pakistan	64.7	-26.8%	20
Peru	17.9	+22.8%	61
Philippines	16.5	-4.77%	64
Papua New Guinea	0.478	-99.4%	90
Paraguay	28	+14.9%	46
Rwanda	24.2	+10.8%	51
Solomon Islands	5.93	-33.9%	80
Sierra Leone	31.7	+22.4%	42
El Salvador	94.6	+7.6%	13
Serbia	3.52	-71.6%	83
São Tomé & Príncipe	47.7	+50.2%	30
Suriname	15.3	-55.7%	68
Eswatini	11.8	+348%	71
Chad	3.3	-2.59%	85
Thailand	30.2	-4.31%	44
Tajikistan	20.5	+26.2%	54
Timor-Leste	1.19	+2,836%	88
Tonga	0		99
Tunisia	149	-11.8%	4
Turkey	124	+7.02%	7
Ukraine	51.9	-8.59%	26
Uzbekistan	19.7	+12.4%	56
St. Vincent & Grenadines	0.000323	+18.2%	98
Vietnam	38.1	-13.7%	36
Vanuatu	8.16	+64%	76
Samoa	14.9	-33.1%	69
Kosovo	117	+16.4%	8
South Africa	67.6	-4.75%	18
Zambia	106	+19.8%	9
Zimbabwe	3,410	+413%	1

The indicator "Short-term debt (% of total reserves)" is a critical metric for assessing a country's financial health and stability. It represents the proportion of a nation's short-term debt — obligations due within one year — relative to its total reserves, which primarily include foreign currency deposits and other liquid assets held by the central bank. This figure serves as a useful gauge of a country's capability to meet its short-term liabilities, especially in times of economic distress or currency volatility.

Understanding the importance of this indicator lies in its implications for economic policies, investment attractiveness, and overall financial resilience. A low percentage of short-term debt compared to total reserves generally signifies that a country has a strong liquidity position, reducing the risk of default when facing unforeseen economic shocks. Conversely, a high percentage can indicate potential vulnerabilities, suggesting that a government may struggle to meet its obligations without resorting to solutions like refinancing or increasing debt levels. As such, policymakers, investors, and economic analysts closely monitor this ratio, using it to make informed decisions regarding economic strategies and investments.

This indicator is interrelated with various other economic metrics. For instance, it can be seen in conjunction with other debt indicators, such as total public debt to GDP or external debt metrics. A nation with high total debt levels may often face higher short-term debt ratios, indicating that much of its obligations are looming closely at hand. Moreover, strong economic indicators such as GDP growth rates can positively influence total reserves, potentially leading to a healthier short-term debt ratio. Furthermore, the balance of payments and trade balance also plays a vital role; any disruptions in trade can directly impact the reserves available to cover short-term debts.

Numerous factors affect the short-term debt ratio. A nation’s currency stability, inflation rates, and interest rates all contribute to the dynamics of liquidity and the ability to manage debt. For example, high inflation may erode the value of reserves while simultaneously increasing the cost of servicing debt, pushing the short-term debt ratio higher. Political stability and economic policies also play crucial roles, influencing investor confidence as well as a country's capacity to manage its debts. Nations with strong, transparent governance structures often find it easier to maintain lower short-term debt ratios, as they are seen as lower risk by foreign investors.

Addressing high short-term debt ratios requires targeted strategies. One effective solution is to bolster foreign reserves through increased exports, foreign investment, or remittances. Strengthening the domestic economy to improve GDP growth can also naturally increase reserves, thereby helping to lower the short-term debt ratio. Countries may also consider restructuring their debt, converting short-term obligations into longer-term commitments, which can alleviate immediate financial pressures. Additionally, it would be vital to adopt sound monetary policies that reduce inflationary pressures and improve overall economic stability, thereby fostering a robust environment for businesses to thrive.

However, there are inherent flaws and limitations in relying solely on the short-term debt to reserves indicator. For instance, it does not consider the broader context of external financial obligations or the nature of a country's debt. A country may have a low short-term debt ratio while struggling with high levels of long-term debt, which could lead to serious economic consequences over time. Moreover, the absolute level of reserves matters, as a high percentage of a small reserve could still signify vulnerability. Additionally, countries may also have access to other sources of financing, such as international assistance or central bank liquidity facilities, which can provide a cushion in case of financial distress.

As of 2023, the median value for short-term debt as a percentage of total reserves stands at 25.67%. This statistic provides a critical reference point for analyzing individual countries' performances against their peers. Among the notable nations, Zimbabwe reports an astonishing figure of 3409.7%, suggesting a profound financial liquidity crisis where short-term liabilities far exceed available reserves. Such a high percentage raises alarms regarding the risk of default and necessitates immediate attention to restore economic stability.

In contrast, Argentina exhibits a relatively high percentage as well, at 238.08%, indicating significant short-term challenges, though not as severe as Zimbabwe. This situation may reflect Argentina's ongoing economic struggles and efforts to stabilize its economy amid various economic reforms and external pressures.

Other countries like Tunisia, Mauritius, and Belarus also show levels above the median, with percentages of 149.05%, 130.29%, and 127.88%, respectively. These levels are considerable and highlight challenges related to short-term financing, compelling policymakers in these regions to devise strategies for improving reserve levels and managing debt more effectively.

Conversely, the bottom five areas demonstrate extraordinary fiscal management in this context. Tonga records a low of 0.0%, indicating that it does not rely on short-term debt against its reserves at all. Similarly, nations such as St. Vincent & Grenadines, Haiti, Comoros, and Bhutan showcase minimal percentages, suggesting robust reserve positions or strategies that allow these countries to navigate their debts without the pressure of looming short-term obligations.

In summary, the short-term debt (% of total reserves) indicator serves as a pivotal tool for understanding and assessing a nation's financial stability, shaping economic policy, and guiding investment decisions. Its complexities, interrelations with other economic indicators, and the various influencing factors highlight the need for a nuanced approach in analyzing and addressing the challenges posed by short-term debt commitments.

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.IR.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.IR.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 reserves)

Year: 2023

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