IMF database
Highest level categories
For the purposes of distributing total government expenditures, the 04 Economic Affairs level is not used. Instead, its 9 subcategories are used.
Table 3.1 covers 194 different countries and a period of 53 years, ranging from 1972 to 2024.The areas of expenditure available are listed here:
| Expenditure |
|---|
| General public services |
| Defence |
| Public order and safety |
| Environmental protection |
| Housing and community amenities |
| Health |
| Recreation, culture and religion |
| Education |
| Social protection |
Reviewing the data availability further, one can observe that by country, availability differs significantly in the coverage of years and reported areas of expenditure: refer to Annex for a full table by all countries and all years; here, the table is revealed for countries with the coverage for the years from 2000 onward only: for the 25-year span, 0 countries have a full year coverage, 8 with exact 24 years and 32 with more than 22 years since 2000.
| Country of area | Distinct years | Distinct items |
|---|---|---|
| Afghanistan | 12 | 9 |
| Albania | 18 | 9 |
| Algeria | 0 | 0 |
| Andorra | 6 | 9 |
| Angola | 0 | 0 |
| Anguilla | 0 | 0 |
| Antigua and Barbuda | 0 | 0 |
| Argentina | 0 | 0 |
| Armenia | 8 | 9 |
| Aruba | 0 | 0 |
| Australia | 23 | 9 |
| Austria | 23 | 9 |
| Azerbaijan | 15 | 9 |
| Bahamas | 0 | 0 |
| Bahrain | 0 | 0 |
| Bangladesh | 0 | 0 |
| Barbados | 0 | 0 |
| Belarus | 21 | 9 |
| Belgium | 22 | 9 |
| Belize | 0 | 0 |
| Benin | 0 | 0 |
| Bhutan | 0 | 0 |
| Bolivia (Plurinational State of) | 6 | 9 |
| Bosnia and Herzegovina | 0 | 0 |
| Botswana | 0 | 0 |
| Brazil | 14 | 9 |
| Brunei Darussalam | 0 | 0 |
| Bulgaria | 23 | 9 |
| Burkina Faso | 0 | 0 |
| Burundi | 0 | 0 |
| Cabo Verde | 0 | 0 |
| Cambodia | 0 | 0 |
| Cameroon | 0 | 0 |
| Canada | 16 | 9 |
| Central African Republic | 0 | 0 |
| Chad | 0 | 0 |
| Chile | 0 | 0 |
| China | 19 | 9 |
| China, Hong Kong SAR | 22 | 9 |
| China, Macao SAR | 24 | 9 |
| Colombia | 0 | 0 |
| Comoros | 0 | 0 |
| Congo | 0 | 0 |
| Congo, Dem. Rep. of the | 0 | 0 |
| Cook Islands | 0 | 0 |
| Costa Rica | 5 | 8 |
| Croatia | 23 | 9 |
| Cyprus | 23 | 9 |
| Czechia | 22 | 9 |
| C�te d’Ivoire | 0 | 0 |
| Denmark | 23 | 9 |
| Djibouti | 0 | 0 |
| Dominica | 0 | 0 |
| Dominican Republic | 6 | 9 |
| Ecuador | 0 | 0 |
| Egypt | 14 | 9 |
| El Salvador | 20 | 9 |
| Equatorial Guinea | 0 | 0 |
| Eritrea | 0 | 0 |
| Estonia | 23 | 9 |
| Eswatini | 0 | 0 |
| Ethiopia | 0 | 0 |
| Fiji | 0 | 0 |
| Finland | 23 | 9 |
| France | 23 | 9 |
| Gabon | 0 | 0 |
| Gambia | 0 | 0 |
| Georgia | 24 | 9 |
| Germany | 23 | 9 |
| Ghana | 0 | 0 |
| Greece | 23 | 9 |
| Grenada | 0 | 0 |
| Guatemala | 10 | 9 |
| Guinea | 0 | 0 |
| Guinea-Bissau | 0 | 0 |
| Guyana | 0 | 0 |
| Haiti | 0 | 0 |
| Honduras | 0 | 0 |
| Hungary | 22 | 9 |
| Iceland | 24 | 9 |
| India | 0 | 0 |
| Indonesia | 16 | 9 |
| Iran (Islamic Republic of) | 9 | 9 |
| Iraq | 0 | 0 |
| Ireland | 22 | 9 |
| Israel | 24 | 9 |
| Italy | 23 | 9 |
| Jamaica | 0 | 0 |
| Japan | 18 | 9 |
| Jordan | 0 | 0 |
| Kazakhstan | 19 | 9 |
| Kenya | 2 | 9 |
| Kiribati | 13 | 9 |
| Korea, Republic of | 2 | 9 |
| Kosovo, Republic of | 8 | 9 |
| Kuwait | 6 | 8 |
| Kyrgyzstan | 10 | 9 |
| Lao People’s Dem. Rep. | 0 | 0 |
| Latvia | 23 | 9 |
| Lebanon | 0 | 0 |
| Lesotho | 0 | 0 |
| Liberia | 0 | 0 |
| Libya | 0 | 0 |
| Lithuania | 23 | 9 |
| Luxembourg | 23 | 9 |
| Madagascar | 0 | 0 |
| Malawi | 0 | 0 |
| Malaysia | 0 | 0 |
| Maldives | 10 | 9 |
| Mali | 0 | 0 |
| Malta | 23 | 9 |
| Marshall Islands | 0 | 0 |
| Mauritania | 0 | 0 |
| Mauritius | 22 | 9 |
| Mexico | 0 | 0 |
| Micronesia (Federated States of) | 0 | 0 |
| Moldova, Republic of | 22 | 9 |
| Monaco | 0 | 0 |
| Mongolia | 12 | 9 |
| Montenegro | 0 | 0 |
| Montserrat | 0 | 0 |
| Morocco | 0 | 0 |
| Mozambique | 0 | 0 |
| Myanmar | 8 | 9 |
| Namibia | 8 | 9 |
| Nauru | 5 | 9 |
| Nepal | 2 | 9 |
| Netherlands | 23 | 9 |
| New Zealand | 14 | 9 |
| Nicaragua | 0 | 0 |
| Niger | 0 | 0 |
| Nigeria | 0 | 0 |
| North Macedonia | 0 | 0 |
| Norway | 23 | 9 |
| Oman | 0 | 0 |
| Pakistan | 0 | 0 |
| Palau | 0 | 0 |
| Panama | 0 | 0 |
| Papua New Guinea | 0 | 0 |
| Paraguay | 0 | 0 |
| Peru | 0 | 0 |
| Philippines | 0 | 0 |
| Poland | 23 | 9 |
| Portugal | 23 | 9 |
| Qatar | 0 | 0 |
| Romania | 22 | 9 |
| Russian Federation | 20 | 9 |
| Rwanda | 0 | 0 |
| Saint Kitts and Nevis | 0 | 0 |
| Saint Lucia | 0 | 0 |
| Saint Vincent and the Grenadines | 0 | 0 |
| Samoa | 1 | 1 |
| San Marino | 8 | 9 |
| Sao Tome and Principe | 0 | 0 |
| Saudi Arabia | 0 | 0 |
| Senegal | 0 | 0 |
| Serbia | 6 | 9 |
| Seychelles | 4 | 9 |
| Sierra Leone | 0 | 0 |
| Singapore | 24 | 9 |
| Slovakia | 0 | 0 |
| Slovenia | 23 | 9 |
| Solomon Islands | 0 | 0 |
| Somalia | 6 | 9 |
| South Africa | 23 | 9 |
| South Sudan | 0 | 0 |
| Spain | 24 | 9 |
| Sri Lanka | 0 | 0 |
| Sudan | 0 | 0 |
| Suriname | 0 | 0 |
| Sweden | 22 | 9 |
| Switzerland, Liechtenstein | 24 | 9 |
| Tajikistan | 3 | 9 |
| Tanzania, United Republic of | 0 | 0 |
| Thailand | 11 | 9 |
| Timor-Leste | 1 | 9 |
| Togo | 0 | 0 |
| Tonga | 0 | 0 |
| Trinidad and Tobago | 0 | 0 |
| Tunisia | 1 | 1 |
| Türkiye | 16 | 9 |
| Uganda | 0 | 0 |
| Ukraine | 23 | 9 |
| United Arab Emirates | 0 | 0 |
| United Kingdom | 24 | 9 |
| United States of America | 23 | 9 |
| Uruguay | 0 | 0 |
| Uzbekistan | 13 | 9 |
| Vanuatu | 0 | 0 |
| Viet Nam | 0 | 0 |
| West Bank and Gaza | 0 | 0 |
| Yemen | 11 | 9 |
| Zambia | 0 | 0 |
| Zimbabwe | 0 | 0 |
The following heatmaps illustrate the presence of missing data in government expenditure statistics across countries, years, and COFOG categories. These visualizations provide an overview of data completeness.
The first heatmap shows, for each country and year, how many COFOG categories (GF01_T to GF10_T) are missing from the dataset. Higher values indicate more extensive gaps in the government expenditure reporting across functional classifications.
The second heatmap displays how many years are missing for each COFOG function by country, revealing patterns of incomplete time coverage.
The final visualization consists of nine separate heatmaps, each representing a single COFOG category. For each category, the heatmap shows missing data across countries and years. This layout enables direct comparison of data completeness across functional areas of government expenditure.
This breakdown highlights which functional classifications tend to be more consistently reported and where reporting gaps are concentrated.
Groups of Economic affairs division
| Groups of Economic affairs division |
|---|
| GF041_T General economic, commercial and labour affairs |
| GF042_T Agriculture, forestry, fishing and hunting |
| GF043_T Fuel and energy |
| GF044_T Mining, manufacturing and construction |
| GF045_T Transport |
| GF0460_T Communication (CS) |
| GF047_T Other industries |
| GF048_T R&D Economic affairs |
| GF0490_T Economic affairs n.e.c. (CS) |
Table 3.2 covers consumption expenditure of subcategories of not used level 04 Economic Affairs. These they are:
| Expenditure |
|---|
| General economic, commercial and labour affairs |
| Agriculture, forestry, fishing and hunting |
| Fuel and energy |
| Mining, manufacturing and construction |
| Transport |
| Communication (CS) |
| Other industries |
| R&D Economic affairs |
| Economic affairs n.e.c. (CS) |
194 different countries are covered in this table for a period of 53 years, ranging from 1972 to 2024.
Similarly as for Table 3.1, for this table 3.2, since 2000, the data availability is as follows (for a full range of
years, see Annex):
| Country of area | Distinct years | Distinct items |
|---|---|---|
| Afghanistan | 9 | 9 |
| Albania | 15 | 9 |
| Algeria | 0 | 0 |
| Andorra | 6 | 9 |
| Angola | 0 | 0 |
| Anguilla | 0 | 0 |
| Antigua and Barbuda | 0 | 0 |
| Argentina | 0 | 0 |
| Armenia | 8 | 9 |
| Aruba | 0 | 0 |
| Australia | 23 | 9 |
| Austria | 23 | 9 |
| Azerbaijan | 15 | 9 |
| Bahamas | 0 | 0 |
| Bahrain | 0 | 0 |
| Bangladesh | 0 | 0 |
| Barbados | 0 | 0 |
| Belarus | 21 | 9 |
| Belgium | 21 | 9 |
| Belize | 0 | 0 |
| Benin | 0 | 0 |
| Bhutan | 0 | 0 |
| Bolivia (Plurinational State of) | 6 | 5 |
| Bosnia and Herzegovina | 0 | 0 |
| Botswana | 0 | 0 |
| Brazil | 14 | 9 |
| Brunei Darussalam | 0 | 0 |
| Bulgaria | 23 | 9 |
| Burkina Faso | 0 | 0 |
| Burundi | 0 | 0 |
| Cabo Verde | 0 | 0 |
| Cambodia | 0 | 0 |
| Cameroon | 0 | 0 |
| Canada | 16 | 6 |
| Central African Republic | 0 | 0 |
| Chad | 0 | 0 |
| Chile | 0 | 0 |
| China | 19 | 9 |
| China, Hong Kong SAR | 22 | 9 |
| China, Macao SAR | 0 | 0 |
| Colombia | 0 | 0 |
| Comoros | 0 | 0 |
| Congo | 0 | 0 |
| Congo, Dem. Rep. of the | 0 | 0 |
| Cook Islands | 0 | 0 |
| Costa Rica | 5 | 9 |
| Croatia | 23 | 9 |
| Cyprus | 23 | 9 |
| Czechia | 22 | 9 |
| C�te d’Ivoire | 0 | 0 |
| Denmark | 23 | 9 |
| Djibouti | 0 | 0 |
| Dominica | 0 | 0 |
| Dominican Republic | 6 | 9 |
| Ecuador | 0 | 0 |
| Egypt | 6 | 5 |
| El Salvador | 19 | 9 |
| Equatorial Guinea | 0 | 0 |
| Eritrea | 0 | 0 |
| Estonia | 23 | 9 |
| Eswatini | 0 | 0 |
| Ethiopia | 0 | 0 |
| Fiji | 0 | 0 |
| Finland | 22 | 9 |
| France | 23 | 9 |
| Gabon | 0 | 0 |
| Gambia | 0 | 0 |
| Georgia | 24 | 9 |
| Germany | 23 | 9 |
| Ghana | 0 | 0 |
| Greece | 22 | 9 |
| Grenada | 0 | 0 |
| Guatemala | 10 | 9 |
| Guinea | 0 | 0 |
| Guinea-Bissau | 0 | 0 |
| Guyana | 0 | 0 |
| Haiti | 0 | 0 |
| Honduras | 0 | 0 |
| Hungary | 22 | 9 |
| Iceland | 24 | 9 |
| India | 0 | 0 |
| Indonesia | 0 | 0 |
| Iran (Islamic Republic of) | 0 | 0 |
| Iraq | 0 | 0 |
| Ireland | 22 | 9 |
| Israel | 11 | 9 |
| Italy | 22 | 9 |
| Jamaica | 0 | 0 |
| Japan | 18 | 9 |
| Jordan | 0 | 0 |
| Kazakhstan | 19 | 9 |
| Kenya | 2 | 9 |
| Kiribati | 0 | 0 |
| Korea, Republic of | 2 | 4 |
| Kosovo, Republic of | 8 | 9 |
| Kuwait | 5 | 5 |
| Kyrgyzstan | 10 | 9 |
| Lao People’s Dem. Rep. | 0 | 0 |
| Latvia | 22 | 9 |
| Lebanon | 0 | 0 |
| Lesotho | 0 | 0 |
| Liberia | 0 | 0 |
| Libya | 0 | 0 |
| Lithuania | 23 | 9 |
| Luxembourg | 23 | 9 |
| Madagascar | 0 | 0 |
| Malawi | 0 | 0 |
| Malaysia | 0 | 0 |
| Maldives | 10 | 5 |
| Mali | 0 | 0 |
| Malta | 22 | 9 |
| Marshall Islands | 0 | 0 |
| Mauritania | 0 | 0 |
| Mauritius | 19 | 9 |
| Mexico | 0 | 0 |
| Micronesia (Federated States of) | 0 | 0 |
| Moldova, Republic of | 22 | 9 |
| Monaco | 0 | 0 |
| Mongolia | 12 | 9 |
| Montenegro | 0 | 0 |
| Montserrat | 0 | 0 |
| Morocco | 0 | 0 |
| Mozambique | 0 | 0 |
| Myanmar | 8 | 9 |
| Namibia | 8 | 9 |
| Nauru | 5 | 9 |
| Nepal | 2 | 9 |
| Netherlands | 23 | 9 |
| New Zealand | 0 | 0 |
| Nicaragua | 0 | 0 |
| Niger | 0 | 0 |
| Nigeria | 0 | 0 |
| North Macedonia | 0 | 0 |
| Norway | 23 | 9 |
| Oman | 0 | 0 |
| Pakistan | 0 | 0 |
| Palau | 0 | 0 |
| Panama | 0 | 0 |
| Papua New Guinea | 0 | 0 |
| Paraguay | 0 | 0 |
| Peru | 0 | 0 |
| Philippines | 0 | 0 |
| Poland | 22 | 9 |
| Portugal | 23 | 8 |
| Qatar | 0 | 0 |
| Romania | 21 | 9 |
| Russian Federation | 20 | 9 |
| Rwanda | 0 | 0 |
| Saint Kitts and Nevis | 0 | 0 |
| Saint Lucia | 0 | 0 |
| Saint Vincent and the Grenadines | 0 | 0 |
| Samoa | 0 | 0 |
| San Marino | 8 | 9 |
| Sao Tome and Principe | 0 | 0 |
| Saudi Arabia | 0 | 0 |
| Senegal | 0 | 0 |
| Serbia | 2 | 5 |
| Seychelles | 4 | 9 |
| Sierra Leone | 0 | 0 |
| Singapore | 24 | 9 |
| Slovakia | 0 | 0 |
| Slovenia | 23 | 9 |
| Solomon Islands | 0 | 0 |
| Somalia | 6 | 9 |
| South Africa | 22 | 9 |
| South Sudan | 0 | 0 |
| Spain | 24 | 9 |
| Sri Lanka | 0 | 0 |
| Sudan | 0 | 0 |
| Suriname | 0 | 0 |
| Sweden | 21 | 9 |
| Switzerland, Liechtenstein | 24 | 9 |
| Tajikistan | 3 | 9 |
| Tanzania, United Republic of | 0 | 0 |
| Thailand | 11 | 9 |
| Timor-Leste | 0 | 0 |
| Togo | 0 | 0 |
| Tonga | 0 | 0 |
| Trinidad and Tobago | 0 | 0 |
| Tunisia | 0 | 0 |
| Türkiye | 16 | 9 |
| Uganda | 0 | 0 |
| Ukraine | 21 | 9 |
| United Arab Emirates | 0 | 0 |
| United Kingdom | 24 | 9 |
| United States of America | 0 | 0 |
| Uruguay | 0 | 0 |
| Uzbekistan | 13 | 9 |
| Vanuatu | 0 | 0 |
| Viet Nam | 0 | 0 |
| West Bank and Gaza | 0 | 0 |
| Yemen | 0 | 0 |
| Zambia | 0 | 0 |
| Zimbabwe | 0 | 0 |
As in the previous case, the following heatmaps illustrate the presence of missing data in government expenditure statistics across countries, years, and COFOG categories. They provide a visual overview of data completeness.
The first heatmap shows, for each country and year, how many COFOG categories (GF041_T to GF0490_T) are missing from the dataset. Higher values indicate more extensive gaps in the government expenditure reporting across functional classifications.
The second heatmap shows how many years of data are missing for each COFOG subcategory under the 04 Economic Affairs level of function, by country, revealing patterns of incomplete time coverage.
The final visualization consists of nine separate heatmaps, each representing a single COFOG subcategory under the 04 Economic Affairs level of function. Each heatmap displays missing data across countries and years, allowing for direct comparison of data completeness across functional areas of government expenditure.
This breakdown highlights which functional classifications are more consistently reported and where reporting gaps are concentrated.