Determinants of Foreign Aid to Ukraine

On Feb. 24, 2022, Russian military forces invaded Ukraine from Russia, Belarus and Crimea. The conflict has been catastrophic, with the U.N. estimating at least 14,534 civilian deaths in Ukraine as of November 2025 and other sources putting military deaths on both sides well into the hundreds of thousands. Since the onset of the war, many countries have sent aid to Ukraine, with the United States providing the most aid at $130.6 billion. In this blog post, we investigate why some countries give more than others as a proportion of their gross domestic product (GDP).

The Kiel Institute’s Ukraine Support Tracker provides our data on government aid to Ukraine from Jan. 24, 2022, the day after the U.S. started evacuating its embassy staff from Ukraine, through 2025. This source includes data on multiple measures of aid to Ukraine from 41 countries.The 27 countries of the European Union plus non-EU G7 countries (Canada, Great Britain, Japan and the U.S.), plus Australia, China, Iceland, India, New Zealand, Norway, South Korea, Switzerland, Taiwan and Turkey. In this post, we focus on total bilateral allocations, which include financial, humanitarian or military aid that is delivered or specified for delivery (PDF).

The figure below illustrates each country’s allocated aid, totaled over 2022 to 2025, to Ukraine as a percentage of 2021 GDP. All countries in the sample allocated at least some aid to Ukraine. European countries were the most generous donors in our sample, with Denmark giving about 3% of its GDP, the largest portion among the sample.Any change in the sample or the definition of aid will produce some changes in estimates of aid/GDP. While the U.S. has provided the most dollars to Ukraine, it has provided only 0.56% of its 2021 GDP to the war effort. The figure also suggests some geographic and political variables as predictors of aid to Ukraine.

First, the figure suggests a possible relation between aid as a percentage of GDP and distance from Russia-Ukraine. For example, the Baltic republics (Latvia, Lithuania and Estonia), Norway, Sweden, Finland and Poland are among the very largest donors in terms of their own GDP. These countries are all geographically very close to Russia and so would be most directly threatened by Russian military success in Ukraine. On average, the 10 non-European countries in our sample gave about 0.18% of their GDP to Ukraine, while the 31 European countries gave 0.77% of their GDP. This relation seemed to be better modeled by an indicator for European countries than by distance from each capital to Kyiv.

Second, the figure also suggests that NATO members and “major non-NATO” U.S. allies, such as Japan, were more likely to contribute to Ukraine than entities without such status, such as the People’s Republic of China, India and Taiwan. We use an indicator variable to identify NATO members and major non-NATO allies, as defined by the U.S. Department of State.

Third, we also considered whether a country’s GDP plays a role determining the aid-to-GDP ratio. For example, larger economies might donate proportionately more or less to Ukraine than smaller countries. Statistical tests show no strong systematic tendency in this direction, however. Countries tend to donate in proportion to their GDP, other things equal. So, we don’t include GDP in our model.

Modeling Determinants to Aid

The equation below describes our model.

We fit this equation to the data with standard statistical methods and find that the estimated coefficients make intuitive sense: A country being in Europe and a country’s ally status with the U.S. indicate that the logarithm of aid to GDP increases by 2.7 and 3.5, respectively.To find the impact of a country location in Europe and a country’s ally status on aid to GDP (not log of aid to GDP), we would need to transform the equation by taking the exponential function of both sides. If we do this, then the respective variables increase aid to GDP by approximately 0.018 and 0.045, respectively. These are small numbers compared with the average aid to GDP, which is 0.63, or 63/100 of 1% of GDP. Because the exponential relation is nonlinear, however, the combined effect of the two explanatory variables on aid to GDP is much larger, 0.66, than the sum of the two individual effects.* Statistical tests show that these relations are very unlikely to have occurred by chance.

In summary, our analysis reveals that proximity to Ukraine matters in determining aid, in the sense that being in Europe predicts more aid. Status as a NATO or major non-NATO ally of the United States also predicts higher aid to Ukraine. As discussed, the U.S. has been the largest donor to Ukraine in absolute terms, although not as a proportion of GDP.

* This sentence was revised on May 6, 2026, to correctly identify the increases as logarithmic values and to add an endnote with further explanation.  

Notes

1. The 27 countries of the European Union plus non-EU G7 countries (Canada, Great Britain, Japan and the U.S.), plus Australia, China, Iceland, India, New Zealand, Norway, South Korea, Switzerland, Taiwan and Turkey.
2. Any change in the sample or the definition of aid will produce some changes in estimates of aid/GDP.
3. To find the impact of a country location in Europe and a country’s ally status on aid to GDP (not log of aid to GDP), we would need to transform the equation by taking the exponential function of both sides. If we do this, then the respective variables increase aid to GDP by approximately 0.018 and 0.045, respectively. These are small numbers compared with the average aid to GDP, which is 0.63, or 63/100 of 1% of GDP. Because the exponential relation is nonlinear, however, the combined effect of the two explanatory variables on aid to GDP is much larger, 0.66, than the sum of the two individual effects.