The analysis utilizes EPU data from 16 countries, with comprehensive monthly data available from January 2013 to March 2024. However, to analyze the 2008-2009 global financial crisis and 20112012 Eurozone crisis periods, historical EPU data is incorporated for the same countries, ensuring consistency in our sample across all analyzed periods. This allows us to examine EPU patterns across different types of crises while maintaining a constant country set for comparability.

The selection of the 16 countries (Table 1) represents a balanced mix of developed and emerging economies, chosen based on data availability and economic significance. The sample includes major economies (USA, China, Japan), European nations significantly affected by various crises (Germany, France, UK), and emerging markets (Brazil, Mexico, Russia), providing a diverse perspective on global EPU patterns.

Table 1

1	Australia	9	Japan
2	Brazil	10	Mexico
3	Canada	11	Russia
4	Chile	12	South Korea
5	China	13	Spain
6	France	14	Sweden
7	Germany	15	UK
8	Italy	16	USA

Source: authors’ elaboration.

Figure 1 illustrates the EPU performance for each country. The graph compares EPU indices across the sample of countries from 2003 to early 2024. The visualization delineates five significant global economic and political periods.

2008-2009: Global economic downturn.

Figure 1 Source: authors’ elaboration.

In general, there has been a noticeable increase in EPU levels across most countries over time, with more frequent and intense spikes in recent years. Figure 1 shows significant synchronization of EPU spikes across countries during major global events, particularly during the 2008 financial crisis and the 2020 COVID-19 pandemic.

Among the highest EPU levels, China consistently ranks at the top, with significant spikes during the COVID-19 pandemic and the recent Ukraine invasion. Similarly, the UK experiences high volatility, with notable peaks during the Brexit period (2016-2019) and the COVID-19 pandemic. Germany also shows increasing EPU levels over time, with significant spikes during the European debt crisis and recent global events.

In contrast, Japan has the lowest EPU levels, with fewer fluctuations than other major economies. Australia also maintains relatively stable and lower EPU levels, with exceptions during global crises, while Sweden experiences low EPU levels with minor increases during international shocks. South Korea shows moderate EPU levels, with notable spikes during global crises, reflecting its sensitivity to international economic conditions.

In a period-specific analysis, during the global financial crisis (2008-2009), EPU sharply increased in most countries, with pronounced spikes in the US, UK, and Germany. Canada and Australia also had notable increases, emphasizing the global nature of the crisis. During the European debt crisis (2011-2012), European countries, particularly Spain, Italy, and France, exhibited elevated EPU levels, while non-European countries such as South Korea and Mexico showed less dramatic increases, underscoring the regional focus of this crisis.

In the Ukraine crisis (2014-2016), Russia’s EPU index displayed considerable volatility. European countries, particularly Germany and Sweden, also experienced heightened uncertainty. Brazil showed significant EPU fluctuations in Latin America, with spikes occurring between 2015 and 2016, coinciding with political instability and economic recession. Finally, concerns about trade relations with the US under the Trump administration in 2016-2017 also contributed to increased uncertainty.

During the COVID-19 pandemic (2019-2021), all countries experienced a dramatic and synchronized spike in early 2020. China rose earlier, reflecting its role as the pandemic initial epicen ter. The US, UK, South Korea, and several European countries maintained high EPU levels. In Mexico, the pandemic caused a significant but short-lived EPU spike; in contrast Brazil experienced historically high uncertainty levels.

In the Russian invasion of Ukraine (20222024), there was a marked increase in EPU for most countries, with Russia exhibiting extreme volatility. European countries, including Germany and Sweden, saw elevated uncertainty, while the US displayed a more moderate rise. Latin American economies generally maintain higher baseline uncertainty levels than Japan and South Korea. However, Brazil and Chile have experienced more pronounced EPU fluctuations in recent years than Mexico. During major global crises, such as the 2008 financial crisis or the COVID-19 pandemic, the gap in uncertainty levels between Latin American and developed economies narrows.

The clustering analysis of EPU patterns requires specific methodological considerations that address the unique characteristics of policy uncertainty data. The approach presented employs three complementary clustering techniques, each chosen to capture different aspects of EPU relationships: K-means, Agglomerative Hierarchical Clustering (AHC), and Density-Based Spatial Clustering of Applications with Noise (DBSCAN).

MacQueen (1967) introduces K-means as a partitioning algorithm that divides observations into k clusters. The K-means algorithm partitions countries into k clusters based on their EPU patterns. In this analysis, the objective function J focuses on minimizing the sum of squared distances between each country’s EPU values and its cluster’s average EPU pattern:

J   =   ∑ j ∑ i ∥ E P U i j   -   u i ∥ 2   (1)

where EPU_ij represents the j - th country within the i - th cluster, ui denotes the average EPU pattern of the i - th cluster and ∥∙∥ is the Euclidean norm. The silhouette score (Rousseeuw, 1987) determines the optimal number of clusters, which measures how similar an object is to its cluster compared to other clusters. K-means is useful for identifying groups of countries with similar overall EPU levels during crisis periods. It is possible to resume K-means in four steps:

Random selection of K initial centroids.

In contrast, AHC constructs a hierarchy of clusters from the bottom up. For EPU analysis, the dissimilarity between clusters A and B is measured using Ward (1963) minimum variance criterion:

d A , B = ( n A * n B n A + n B ) = * ∥ E P U A   -   E P U B ∥ 2 (2)

where n_A and n_B are the number of points in clusters A and B, and EPU_A and EPU_B are their respective centroids (average EPU values). AHC helps to understand how countries’ EPU patterns merge into larger groups during crisis periods. The AHC process can be labelled as:

Initialization with individual clusters.

DBSCAN, proposed by Ester et al. (1996) identifies clusters based on the density of EPU patterns. The algorithm defines a neighborhood N_ε (p) for a point p:

N ε p = { q ∈ D   C o u n t r i e s     d ( E P U p ,   E P U q ) ≤ ε } (3)

where d(EPU_p,EPU_q) represents the Euclidean distance between the EPU patterns of countries p and q . DBSCAN is useful for identifying countries with consistently similar EPU responses during crisis periods, while also detecting outliers that may represent unique policy uncertainty patterns. In this sense, the key concepts of DBSCAN are:

Core points: Points that have at least min_p neighbors within a distance f which defines the neighborhood boundary between two samples.

The algorithm initiates with an arbitrary point and identifies all points reachable from that point given the ε and min_p parameters. If the point is a core point, it forms a cluster. If it is a border point, the algorithm moves to the next point. Points that are not part of any cluster are classified as noise. Each technique provides distinct advantages for EPU data analysis:

K-means: Efficient for large datasets (Hartigan & Wong, 1979).

For each crisis period analyzed (2008-2009, 2011-2012, 2014-2016, 2019-2021, and 20222024), there are implemented the following analytical steps:

Data preparation: monthly EPU values are standardized to ensure comparability across countries with different baseline uncertainty levels.

This methodological framework allows capturing both the static grouping of countries based on their EPU patterns and the dynamic evolution of these groups across different crisis periods. The use of multiple clustering techniques provides complementary perspectives on how countries group together during periods of elevated policy uncertainty.

The analysis of EPU across countries during the period 2008-2009, utilizing three distinct clustering methodologies: K-Means, AHC and DBSCAN yielded consistent results across all approaches. This uniformity in outcomes suggests a robust underlying structure in the data, which persists regardless of the clustering algorithm employed. The clustering analysis consistently identified two primary clusters; see, Figure 2.

Figure 2 Source: author’s elaboration.

The first cluster, designated as Cluster 0 in K-Means and AHC, and similarly in DBSCAN, encompasses a diverse group of nations including the United States, Australia, Brazil, Canada, China, Germany, and others. This cluster exhibited a mean EPU value of 140.738944, indicating a relatively high level of economic policy uncertainty among these countries during the analyzed period as can be seen in Table 2.

Table 2

Cluster type	Cluster ID	Countries	Mean EPU
K-Means Clusters	0	USA, Australia, Brazil, Canada, China, Germany, UK, France, Japan, South Korea	140.74
	1	Chile, Italy, Spain, Russia, Mexico, Sweden	90.65
AHC Clusters	0	USA, Australia, Brazil, Canada, China, Germany, UK, France, Japan, South Korea	140.74
	1	Chile, Italy, Spain, Russia, Mexico, Sweden	90.65
DBSCAN Clusters	0	USA, Australia, Brazil, Canada, China, Germany, UK, France, Japan, South Korea	140.74
	1	Chile, Italy, Spain, Russia, Mexico, Sweden	90.65

Source: authors’ elaboration.

The second cluster, labeled Cluster 1 across all methodologies, comprises Chile, Italy, Spain, Russia, Mexico, and Sweden. This group demonstrated a lower mean EPU value of 90.65, suggesting these nations experienced comparatively less economic policy uncertainty during the same period.

The consistency in cluster composition and mean EPU values across the three distinct clustering techniques reinforces the validity of this grouping. It implies an apparent dichotomy in economic policy uncertainty levels between these two sets of countries during the 2008-2009 period, which coincides with the global economic downturn.

Finally, the higher EPU values in Cluster 0 may be attributed to the fact that it includes several major global economies (e.g., USA, China, Germany) at the epicenter of the financial crisis or significantly impacted by it. Conversely, the countries in Cluster 1, while not immune to global economic turbulence, appear to have experienced less policy uncertainty during this period.

The analysis of EPU for 2011-2012, encompassing the Eurozone sovereign debt crisis, reveals a more complex clustering pattern than the previous period. The K-Means and AHC methods produced identical results, identifying six distinct clusters, while DBSCAN yielded a different perspective with only two clusters. This divergence in results across methodologies suggests a more nuanced and heterogeneous landscape of economic policy uncertainty during this period as shown in Figure 3.

Figure 3 Source: author’s elaboration.

In this case, K-Means and AHC show that Cluster 0 (Chile, Sweden) exhibited the second-lowest mean EPU of 100.161, indicating relatively low policy uncertainty. For instance, cluster 1 (Canada, China) demonstrated a high mean EPU of 218.23, suggesting significant policy uncertainty in these diverse economies. Cluster 2 (Brazil, Italy, Spain, Russia, Japan) showed a moderate mean EPU of 136.31, representing a mix of emerging and developed economies affected by the crisis, as it can be seen in Table 3.

Table 3

Cluster	Cluster ID	Countries	Mean EPU
K-means Clusters	0	Chile, Sweden	100.16
	1	Canada, China	218.23
	2	Brazil, Italy, Spain, Russia, Japan	136.31
	3	UK, France	265.58
	4	USA, Australia, Germany, South Korea	172.44
	5	Mexico	60.29
Agglomerative Clusters	0	Canada, China	218.23
	1	USA, Australia, Germany, South Korea	172.44
	2	Chile, Sweden	100.16
	3	Brazil, Italy, Spain, Russia, Japan	136.31
	4	UK, France	265.58
	5	Mexico	60.29
DBSCAN Clusters	0	USA, Australia, Brazil, Canada, Chile, China, Germany, Italy, Spain, Russia, Japan, Sweden, South Korea	154.47
	1	UK, France	265.58

Source: authors’ elaboration.

Cluster 3 (UK and France) displayed the highest mean EPU of 265.577, reflecting the severe impact of the Eurozone crisis on these significant European economies. Cluster 4 (USA, Australia, Germany, and South Korea) exhibited a relatively high mean EPU of 172.44, indicating substantial policy uncertainty in these developed economies. Finally, cluster 5 (Mexico) showed the lowest mean EPU of 60.29, suggesting comparatively low policy uncertainty.

Notice that DBSCAN entails that cluster 0 is encompassed by most countries with a mean EPU of 154.47, suggesting a more homogeneous view of policy uncertainty across diverse economies. Cluster 1 (UK and France) isolated these two countries with the highest mean EPU of 265.577, consistent with the K-Means and Agglomerative results. The disparities in clustering outcomes between DBSCAN and the other methods highlight the complexity of economic policy uncertainty during this period. The Eurozone sovereign debt crisis appears to have had varying impacts across different economies, resulting in a more fragmented clustering pattern.

The high EPU values for the UK and France, consistently grouped across all methods, underscore the significant policy challenges that these major European economies faced during the crisis. Conversely, Mexico’s low EPU suggests it may have been relatively insulated from the Eurozone turmoil. The moderate to high EPU values for most other countries, particularly in the larger DBSCAN cluster, indicate widespread policy uncertainty, likely reflecting the global repercussions of the Eurozone crisis and ongoing recovery efforts from the earlier global financial crisis.

The period encompassing the Ukraine crisis (2014-2016) reveals intriguing patterns across the three clustering methodologies employed. This period shows a shift in global economic policy uncertainty, likely influenced by geopolitical tensions and their financial repercussions.

In K-means, cluster 0 compresses Brazil, Canada, China, the UK, France and Russia, exhibiting a high mean of EPU of 234.82. The inclusion of Russia and significant Western economies in this high-uncertainty group likely reflects the direct and indirect impacts of the Ukraine crisis on these nations. Cluster 1 includes US, Australia, Chile, Germany, Italy, Spain, Mexico, Japan, Sweden, and South Korea. This larger cluster shows a considerably lower mean EPU of 115.71, suggesting that these countries experienced relatively less policy uncertainty during this period despite the global nature of the crisis, as it can be seen in Figure 4.

Figure 4 Source: author’s elaboration.

For AHC, cluster 0, like the K-means cluster 0, adds Germany. This group showed a high mean EPU of 225.73. Germany’s inclusion in this cluster might indicate its closer economic ties and policy alignment with other major European economies during the crisis. Cluster 1 compresses the remaining countries. This cluster demonstrated a lower mean EPU of 109.54, consistent with the K-means results but excluding Germany; see Table 4. It is noted that DBSCAN produced a single cluster encompassing all countries except the UK and Mexico, with a mean EPU of 158.57. This unified cluster suggests a more homogeneous view of global political uncertainty during the Ukraine crisis, potentially indicating widespread but varied impacts across different economies.

Table 4

Cluster type	Cluster ID	Countries	Mean EPU
K-means Clusters	0	Brazil, Canada, China, UK, France, Russia	234.82
	1	USA, Australia, Chile, Germany, Italy, Spain, Mexico, Japan, Sweden, South Korea	115.71
Agglomerative Clusters	0	Brazil, Canada, China, Germany, UK, France, Russia	225.73
	1	USA, Australia, Chile, Italy, Spain, Mexico, Japan, Sweden, South Korea	109.54
DBSCAN Clusters	0	USA, Australia, Brazil, Canada, Chile, China, Germany, Italy, France, Spain, Russia, Japan, Sweden, South Korea	158.57

Source: authors’ elaboration.

The K-means and AHC methods both identify an apparent dichotomy between high-uncertainty and low-uncertainty groups, with the high-uncertainty cluster consistently including major powers directly involved in or significantly affected by the Ukraine crisis (Russia, UK, France, and China). The emergence of Brazil and Canada in the high-uncertainty clusters across both K-means and AHC methods is noteworthy, possibly reflecting these countries’ economic ties to the involved nations or their roles in global commodity markets affected by the crisis.

On the other hand, the DBSCAN results, shows a single cluster suggesting that while there were variations in EPU levels, the Ukraine crisis had a broadly pervasive effect on global economic policy uncertainty, creating a more interconnected landscape across diverse economies. The consistent grouping of Russia with Western powers in high-uncertainty clusters underscores the complex economic interdependencies and policy challenges arising from the crisis despite geopolitical tensions. Finally, the lower EPU values for countries like the USA, Japan, and several European nations in the K-means and AHC results might indicate a degree of economic resilience or policy stability in these economies during the crisis period.

The COVID-19 pandemic period 2019-2021 reveals a striking pattern across all methodologies. This period is characterized by an unprecedented global health crisis with profound economic implications, reflected in the clustering results. For K-means and AHC, Cluster 0 (China), both methods isolate China in only one cluster, with an exceptionally high mean EPU of 682.9. This extreme value underscores the unique economic policy challenges faced by China, the initial epicenter of the pandemic. Cluster 1 (All other countries) comprising all other analyzed countries, including major economies like the USA, UK, Germany, and Japan, as well as emerging markets. This cluster shows a significantly lower, though still elevated, mean EPU of 210.2. DBSCAN produces a cluster that includes all countries except China, with the same mean EPU of 210.2 as observed in the other methods, as shown in Figure 5.

Figure 5 Source: author’s elaboration.

The extreme EPU value for China (682.9) across all methods highlights the country’s in tense economic policy uncertainty. This could be attributed to its role as the initial outbreak location, stringent lockdown measures, and the global scrutiny it faced. For all other countries, global homogeneity, regardless of their economic development status or geographical location, is remarkable, suggesting unprecedented shared economic policy uncertainty across diverse economies during the pandemic, as can be seen in Table 5.

Table 5

Cluster type	Cluster ID	Countries	Mean EPU
K-Means Clusters	0	China	682.9
	1	USA, Australia, Brazil, Canada, Chile, Germany, Italy, UK, France, Spain, Russia, Mexico, Japan, Sweden, South Korea	210.2
AHC Clusters	0	USA, Australia, Brazil, Canada, Chile, Germany, Italy, UK, France, Spain, Russia, Mexico, Japan, Sweden, South Korea	210.2
	1	China	682.9
DBSCAN Clusters	0	USA, Australia, Brazil, Canada, Chile, Germany, Italy, UK, France, Spain, Russia, Mexico, Japan, Sweden, South Korea	210.2

Source: authors’ elaboration.

While significantly lower than China’s EPU , the mean EPU of 210.2 for the rest of the world is still considerably high, reflecting the global nature of the crisis and its pervasive impact on economic policymaking worldwide. The near-identical results across K-means and AHC, with DBSCAN showing similar patterns, highlight the robustness of this clustering, suggesting an unambiguous division in EPU levels between China and the rest of the world.

Unlike previous periods in which regional or economic status-based clusters were observed, the COVID-19 period shows a striking lack of such distinctions, highlighting the pandemic role as a great equalizer regarding economic policy challenges. The high global EPU likely reflects shared challenges such as managing lockdowns, implementing fiscal stimulus measures, adapting monetary policies, and addressing supply chain disruptions. China’s extreme EPU might additionally encompass factors like international trade tensions and domestic policy responses to the initial outbreak.

The analysis of EPU for 2022-2024, marked by the Russian invasion of Ukraine and, subsequently, the Israeli-Palestinian conflict, reveals a complex and polarized global economic landscape. K-means and ACH reveal that Cluster 0 (China, Germany and Russia) has an exceptionally high mean EPU of 591.94. This clustering is particularly noteworthy given the diverse geopolitical positions of these nations about the conflict. Meanwhile, Cluster 1 (all other countries) is compressed by the remaining 13 countries, including major economies like the USA, UK, Japan, and various European and Asian nations; this cluster exhibits a significantly lower mean EPU of 182.58, as noted in Figure 6.

Figure 6 Source: author’s elaboration.

For the DBSCAN clustering results, cluster 0 is identical to cluster 1 in K-means and AHC methods, with the same mean EPU of 182.58. Cluster 1 (China and Germany), in this case, DBSCAN groups only China and Germany together, with an even higher mean EPU of 648.98. Notably, Russia is not included in this cluster; see, Table 6.

Table 6

Cluster type	Cluster ID	Countries	Mean EPU
K-Means Clusters	0	China, Germany, Russia	591.94
	1	USA, Australia, Brazil, Canada, Chile, Italy, UK, France, Spain, Mexico, Japan, Sweden, South Korea	182.58
AHC Clusters	0	USA, Australia, Brazil, Canada, Chile, Italy, UK, France, Spain, Mexico, Japan, Sweden, South Korea	182.58
	1	China, Germany, Russia	591.94
DBSCAN Clusters	0	USA, Australia, Brazil, Canada, Chile, Italy, UK, France, Spain, Mexico, Japan, Sweden, South Korea	182.58
	1	China, Germany	648.98

Source: author’s elaboration.

There is a high-uncertainty triad since the grouping of China, Germany and Russia in K-means and AHC exhibit an extremely high EPU (591.94), which is striking, indicating these countries faced unprecedented levels of economic policy uncertainty, albeit for potentially different reasons:

Russia: Direct involvement in the conflict and facing international sanctions.

Likewise, there is a DBSCAN divergence since the separation of Russia from China and Germany is intriguing. The even higher EPU for China and Germany (648.98) in this method suggests these two countries may have faced unique policy challenges distinct from Russia’s direct involvement in the conflict. The consistent clustering of most countries with a mean EPU of 182.58 across all methods indicates a significant but more moderate level of policy uncertainty for most of the world, recalling the global nature of the crisis’s economic impact, albeit to a lesser degree than for the directly involved or highly affected nations.

The difference between DBSCAN and the other methods in handling Russia’s position highlights the complexity of the situation. It suggests that while Russia’s policy uncertainty was high, its pattern might have been distinct from that of China and Germany. Finally, notice that Germany’s high EPU , grouped with China and Russia, points out the interdependencies in the global economy, particularly in energy markets and supply chains. Finally, it seems that the Palestinian-Israeli conflict did not contribute significantly in 2024 to increasing the EPU in the majority of the countries in the sample studied. Without a doubt, for Asian countries, particularly in the Middle East, EPU had significant increases in countries in the region for which there is unfortunately no data.