Country

Indicator

Value type

Values present

Claims on central government (annual growth as % of broad money)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	2.9	-70.7%	40
Albania	0.943	-166%	57
United Arab Emirates	-0.737	-75.8%	74
Argentina	10.1	-97%	11
Armenia	3.49	-2.32%	36
Antigua & Barbuda	-2.74	+156%	91
Australia	-0.121	-85.5%	69
Azerbaijan	-9.23	-506%	101
Benin	5.15	-783%	24
Burkina Faso	3.53	+24.9%	35
Bangladesh	2.22	+4.54%	43
Bulgaria	2.86	+92.3%	41
Bosnia & Herzegovina	1.41	-25.9%	52
Belize	1.34	-56.2%	53
Brazil	3.67	-59.5%	34
Brunei	0.786	-93.3%	61
Bhutan	-2.74	-1,841%	92
Botswana	10.7	+29.5%	10
Chile	1.66	-980%	50
China	3.38	+21%	39
Côte d’Ivoire	4.97	+127%	26
Colombia	2.03	+41.1%	45
Costa Rica	0.913	-127%	58
Czechia	3.46	+5.72%	37
Djibouti	4.35	+20.1%	33
Dominica	5.18	-2,503%	23
Denmark	-2.83	+77.3%	93
Dominican Republic	12.6	+365%	7
Algeria	9.48	-477%	13
Ecuador	-1.24	-146%	83
Egypt	19.7	-6.52%	2
Fiji	-3.31	-359%	94
United Kingdom	-1.3	+33.8%	85
Georgia	4.56	+417%	30
Guinea-Bissau	6.19	-31.1%	20
Grenada	-9.36	+17.8%	102
Guatemala	-0.864	-33.2%	77
Guyana	15.9	+21.4%	4
Hong Kong SAR China	1.25	+55%	55
Honduras	-0.895	-145%	78
Haiti	-0.772	-182%	76
Hungary	4.82	-322%	27
Indonesia	-0.494	-72.3%	71
Iraq	8.96	+37.9%	14
Iceland	0.342	-86.4%	64
Jamaica	-0.667	+150%	73
Jordan	0.871	-24.3%	59
Japan	-1.36	-162%	86
Kazakhstan	4.37	-31.3%	32
Cambodia	-1.28	-513%	84
St. Kitts & Nevis	9.89	-3,942%	12
South Korea	1.03	-8.88%	56
Kuwait	0.836	-135%	60
St. Lucia	-5.2	-31.4%	98
Macao SAR China	-3.69	-159%	95
Morocco	1.25	-234%	54
Moldova	-0.767	-127%	75
Madagascar	6.1	-209%	21
Maldives	5.1	-43.3%	25
Mexico	6.76	+53%	19
North Macedonia	-0.662	-179%	72
Mali	0.05	-99.5%	68
Montenegro	-10.7	-1,086%	103
Mozambique	13.1	+113%	6
Mauritius	6.05	+41.1%	22
Malaysia	0.639	-72.8%	62
Niger	7.59	-50.8%	17
Norway	-0.953	+127%	80
Nepal	1.58	-67.8%	51
New Zealand	-1.98	+46.5%	90
Pakistan	16	-25.6%	3
Poland	1.94	-35.3%	46
Paraguay	-0.279	-123%	70
Palestinian Territories	2.43	+64.6%	42
Romania	4.57	-388%	29
Rwanda	-9.2	+27.8%	100
Senegal	1.76	-74%	48
Solomon Islands	0.232	+49.6%	67
Sierra Leone	24.3	-12.4%	1
El Salvador	-3.92	-198%	96
Serbia	0.638	-111%	63
Suriname	-1.9	-85.7%	88
Sweden	-0.914	-706%	79
Seychelles	-5.18	+1,085%	97
Togo	8.46	+599%	15
Thailand	1.82	-0.631%	47
Timor-Leste	8.13	-25.7%	16
Tonga	-0.999	-73.1%	81
Trinidad & Tobago	-1.88	-138%	87
Tunisia	7.07	+21.9%	18
Turkey	11.2	-8.56%	9
Tanzania	0.245	-91.9%	66
Uganda	11.2	+66.1%	8
Ukraine	3.43	+91.7%	38
Uruguay	0.272	-93.8%	65
United States	1.69	-564%	49
Uzbekistan	4.68	+28.1%	28
St. Vincent & Grenadines	14.5	+89.4%	5
Vanuatu	2.17	-1.79%	44
Samoa	-8.97	-13.1%	99
Kosovo	-1.23	+59.7%	82
South Africa	4.44	+40.2%	31
Zambia	-1.96	-116%	89

The indicator 'Claims on central government (annual growth as % of broad money)' serves as a vital measure of the financial obligations that a government incurs within the economy relative to the total money supply, often referred to as broad money. This metric is crucial as it provides insights into how much of the economy’s liquidity is tied up with the government, indicating the extent of dependence on fiscal operations. Growth in claims on the central government can be influenced by various factors, encompassing economic conditions, governmental policies, and international dynamics.

The importance of this indicator cannot be overstated. A high growth percentage suggests that more money is being channeled into government claims, which could signify increased government borrowing or fiscal expansions. This could reflect a government attempting to inject liquidity into the economy or to fund critical public programs, which can have both positive and negative repercussions depending on how the money is utilized. Conversely, low or negative growth in this area can indicate a contraction in government borrowing or a shift towards more prudent fiscal policies. Governments may be paying off debts or facing decreased revenue, influencing their ability to finance public services.

Analyzing 2023 data reveals a median value of 1.87% across various regions. This figure reflects a moderate client of claims on the central government relative to broad money in the economy. However, it is in stark contrast to notable extremes observed in specific areas. For instance, Argentina shows an astounding growth rate of 341.37%. Such an anomaly suggests severe monetary expansion due to potentially unsustainable government borrowing practices or desperate measures to handle economic crises. Similar situations can be seen in South Sudan (206.51%) and Zimbabwe (177.88%), where extreme inflationary pressures might prompt governments to heavily lean on local monetary sources to fund their obligations.

On the other end of the spectrum, certain territories exhibit negative values in claims on central government, with Suriname at -13.24% and Nicaragua at -12.62%. These declining figures may suggest that governments in these regions are either balancing their budgets by reducing borrowing, or they could indicate a lack of confidence in the economy, which benefits from lowering liabilities and curbing spending. Such negative growth in government claims can point to an unusual set of conditions wherein either the government has faced liquidity crises, or it has established measures to move away from reliance on broad money, possibly in an effort to stabilize the economy.

Relations between claims on central government and other economic indicators are multifaceted. This indicator often closely correlates with inflation rates and overall economic growth. High borrowing rates can lead to inflation if not managed properly, as increasing the money supply can devalue the currency. Accordingly, while high claims may initially provide liquidity, leading to growth, they can also trigger inflationary pressures if the economy does not grow in tandem, leading to diminished purchasing power for citizens. Additionally, interest rates can reflect the health of claims on government central, where increased government debt leads to higher borrowing costs as lenders become wary of default risk.

Factors that affect growth in claims on central government include political stability, economic conditions, spending and revenue policies, and external economic pressures. Countries facing political instability may incur higher claims due to a lack of trust from international leaders and potential increases in borrowing costs. Conversely, stable political environments with sound fiscal management tend to show lower and more stable growth figures. Global economic conditions, too, play a significant role, particularly in developing countries whose economic health can shift dramatically due to external trade relationships and foreign investment inflows. Efforts toward improving the efficiency of tax systems and managing public spending can also lead towards more sustainable national debt levels.

In terms of strategies to manage claims on central government sustainably, effective fiscal policies and governance are essential. Governments should strive to create balanced budgets and prioritize investment in economic growth initiatives while carefully monitoring and controlling government borrowing. Improved transparency and accountability can also foster the trust required from lenders and citizens alike. Additionally, diversifying funding sources through public-private partnerships or through innovative financing methods can reduce dependency on broad money and insulate against potential economic shocks.

However, it is essential to note the potential flaws inherent within the claims on central government as an economic indicator. It may not take into account all aspects of a nation’s economic health. For example, an economy can have a healthy level of claims while still grappling with severe inflation or deflation issues, indicating an imbalance. Moreover, the indicator alone does not reflect underlying socioeconomic conditions, such as income inequality and employment rates, which are crucial for a holistic understanding of an economy's health.

As we observe the ongoing developments globally, analyzing claims on central government not only equips policymakers with important fiscal insights but also guides citizens and investors in understanding the broader economic context. With the median hovering at 1.87%, the divergence among top and bottom regions elucidates the complex dynamics of fiscal health worldwide, underscoring the importance of targeted economic strategies in mitigating excessive claims on governments and ensuring stable growth moving forward.

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 = 'FM.AST.CGOV.ZG.M3'

# 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 <- 'FM.AST.CGOV.ZG.M3'

# 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

Claims on central government (annual growth as % of broad money)

Year: 2024

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