The Asymmetric Economic Effects of COVID-19 and the Russia–Ukraine War: Panel Evidence from G7 & BRICS+ (1970–2024)

The Asymmetric Economic Effects of COVID-19 and the Russia–Ukraine War: Panel Evidence from G7 & BRICS+ (1970–2024) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Asymmetric Economic Effects of COVID-19 and the Russia–Ukraine War: Panel Evidence from G7 & BRICS+ (1970–2024) ROLA, Cagay cCoskuner This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9133472/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This research looks at how COVID-19 and the Russia-Ukraine war differently impact the GDP growth rates of BRICS + and G7 countries, using a strong panel ECM model that accounts for connections between countries, from 1970 to 2024. Diagnostic tests show that the countries are linked and have significant errors in their data, which is why Diagnostic tests confirm cointegration and significant cross-country error correlation, hence the necessity for PCSEs. The results indicate a rapid error correction speed of 81.5% annually, underlining rapid adjustment to long-run equilibrium. We find strong evidence of conditional convergence, where poorer economies tend to grow faster in the longer run. The shock caused by the COVID-19 pandemic contributed a − 5.8 percentage point decline to annual growth, implying a sharp and simultaneous contraction of the global economy. In comparison to the above, the shock from the Russia-Ukraine conflict had a marginally positive and differential impact (insignificant), suggesting that it favoured export-oriented countries while harming import-dependent countries. The growth of investment becomes evident as the most significant short-term engine behind growth, while government spending growth shows crowding-out effects, indicating that increased investment is driving economic recovery more effectively than government spending in the current context. It is critical to note that trade openness becomes insignificant when accounting for cross-sectional dependency, refuting widely recognized paradigms on growth benefits from globalization. According to the study, sustainable growth, as well as the ability to overcome a crisis, is only achievable through strategic investment in education and green energy, the establishment of contingent fiscal buffers, support for a good governance environment (stable government), and improvement of global coordination regarding asymmetric shocks. Methodologically, we demonstrate that accounting for cross-country dependence has a significant impact on inference regarding growth determinants and provides a clearer foundation for making economic policies in a world economy in a future study. Macroeconomics GDP Growth COVID-19 Russia-Ukraine War Error Correction Model Cross-Sectional Dependence Panel Cointegration BRICS G7 Conditional Convergence Investment Fiscal Policy Figures Figure 1 Figure 2 Figure 3 Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9133472","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606617931,"identity":"3ac61df5-ad74-4087-8394-76ff6edf8803","order_by":0,"name":"ROLA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACfvbmAwcSDGzq++XPmEkw/vmT2CD//gFeLZI9xxIffKhIY5w5g69MgrHhWGIDM48BXi0GN3KUDWecOcy44Qb/N6CWw4S1MBzIYZPmbUtjNr7Bu404LYwNZ48Btdiwmd3vBWr5AdLCj98vzIx9aSBbeMwQtvA34NXCxsxjBtRyWMJ4Bo8ZzGEH8GrhYeMxBnnfwEACoSUBrxYJHjZwICdI3OAxNkhsOGbMRsj79vcfg6MygX/+GcMHH//8keMnGMgoAOQiNhLUj4JRMApGwSjAAQDY2E/xTKIBcQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-9882-6670","institution":"EMU UNIVERSITY","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"ROLA","suffix":""},{"id":606617932,"identity":"967834eb-368f-4ed3-b7be-6873706b5f0e","order_by":1,"name":"Cagay cCoskuner","email":"","orcid":"","institution":"EMU UNIVERSITY","correspondingAuthor":false,"prefix":"","firstName":"Cagay","middleName":"","lastName":"cCoskuner","suffix":""}],"badges":[],"createdAt":"2026-03-16 05:55:16","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":true,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9133472/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9133472/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104819895,"identity":"63759345-2a4b-4f22-af07-49b3cb208d6d","added_by":"auto","created_at":"2026-03-17 14:16:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76361,"visible":true,"origin":"","legend":"\u003cp\u003eTime-series plots for key variables exhibit marked heterogeneity and distinct trends across the BRICS+ and G7 economies from 1970 to 2024. Looking at the growth of GDP (GDP_GROWTH), emerging economies such as China, India, and Ethiopia reflect a higher volatility and wider cyclical swings—positive and negative—compared to advanced G7 countries like the United States, Germany, and Japan, which follow more stable and moderate growth paths. This visualizes the higher risk-return profile of developing markets. The level of GDP per capita (GDP_CONV) reveals a dramatic and increasing divergence: G7 countries and resource-rich countries (e.g., United Arab Emirates, Saudi Arabia) are prominent at the top with very high and rising levels, while the large emerging economies (India, Ethiopia) are at the bottom, very clearly illustrating the 'convergence club' hypothesis, whereby not all countries converge. Shares of investments (INVS) are highly dispersed: large investments are found in countries such as China and Saudi Arabia, while smaller investments are found in countries such as the United Kingdom. Finally, human capital (EDUC) has a strong positive global trend but shows a lot of stratification, ranging from the more advanced nations and transitioning nations such as Russia to the lower ones relative to progress regarding human development.\u003c/p\u003e\n\u003cp\u003ePanel unit root tests indeed confirm a sharp dichotomy between trending and stationary variables, which is an important fact for model specification. There are stochastic trends in the variables of series such as GDP per capita (GDP_CONV), trade (TRD), investment (INVS), government expenditure (GEXP), education (EDUC), and unemployment (UNEM). They are non-stationary in levels, I(1), meaning that they are governed by a random walk with drift and do not mean revert to a constant. Their upward or downward movements over time are persistent and contain a unit root. By contrast, GDP growth rate (GDP_GRWOTH) and inflation (INFL) are stationary (I(0)), which oscillates around a stable long-run mean without a persistent trend. This mixed integration necessitates modelling the I(1) variables in first-differenced form in order not to cause spurious regression when explaining the stationary GDP growth rate. The visual evidence above confirms findings in the growth and development literature with a fresh, longitudinal perspective. The volatility of growth rates in emerging markets is consistent with the traditional 'shocks and volatility' literature (Aguiar \u0026amp; Gopinath, 2007), which identifies this as a product of structural factors and term-of-trade volatility. The finding of persisting income divergence in the GDP per capita series above argues against strong unconditional convergence results but is instead consistent with models of conditional convergence (Barro, 1991) or multiple equilibria (Azariadis \u0026amp; Drazen, 1990). These diverse investment profiles are in line with studies that focus on savings rates, financial development, and political stability in explaining capital formation. The increasing but divergent educational profile corresponds to the global rise of schooling portrayed by Cohen and Soto (2007), but the enduring divergence indicates a rather sluggish percolation of knowledge frontiers-an important transmission channel according to technology-based growth models. Taken together, these figures indicate that while some of the more basic comparative statics of development hold over time, when adding China and India to an extended panel, only models incorporating these country-specific policy and growth regimes can be accurate.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9133472/v1/e71705e7e73ea2e84b597b30.png"},{"id":104835358,"identity":"b3d7d44f-8342-4ee7-b014-2cd3e8dff7fc","added_by":"auto","created_at":"2026-03-17 17:44:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":111691,"visible":true,"origin":"","legend":"\u003cp\u003eTime-series plots for key variables exhibit marked heterogeneity and distinct trends across the BRICS+ and G7 economies from 1970 to 2024. Looking at the growth of GDP (GDP_GROWTH), emerging economies such as China, India, and Ethiopia reflect a higher volatility and wider cyclical swings—positive and negative—compared to advanced G7 countries like the United States, Germany, and Japan, which follow more stable and moderate growth paths. This visualizes the higher risk-return profile of developing markets. The level of GDP per capita (GDP_CONV) reveals a dramatic and increasing divergence: G7 countries and resource-rich countries (e.g., United Arab Emirates, Saudi Arabia) are prominent at the top with very high and rising levels, while the large emerging economies (India, Ethiopia) are at the bottom, very clearly illustrating the 'convergence club' hypothesis, whereby not all countries converge. Shares of investments (INVS) are highly dispersed: large investments are found in countries such as China and Saudi Arabia, while smaller investments are found in countries such as the United Kingdom. Finally, human capital (EDUC) has a strong positive global trend but shows a lot of stratification, ranging from the more advanced nations and transitioning nations such as Russia to the lower ones relative to progress regarding human development.\u003c/p\u003e\n\u003cp\u003ePanel unit root tests indeed confirm a sharp dichotomy between trending and stationary variables, which is an important fact for model specification. There are stochastic trends in the variables of series such as GDP per capita (GDP_CONV), trade (TRD), investment (INVS), government expenditure (GEXP), education (EDUC), and unemployment (UNEM). They are non-stationary in levels, I(1), meaning that they are governed by a random walk with drift and do not mean revert to a constant. Their upward or downward movements over time are persistent and contain a unit root. By contrast, GDP growth rate (GDP_GRWOTH) and inflation (INFL) are stationary (I(0)), which oscillates around a stable long-run mean without a persistent trend. This mixed integration necessitates modelling the I(1) variables in first-differenced form in order not to cause spurious regression when explaining the stationary GDP growth rate. The visual evidence above confirms findings in the growth and development literature with a fresh, longitudinal perspective. The volatility of growth rates in emerging markets is consistent with the traditional 'shocks and volatility' literature (Aguiar \u0026amp; Gopinath, 2007), which identifies this as a product of structural factors and term-of-trade volatility. The finding of persisting income divergence in the GDP per capita series above argues against strong unconditional convergence results but is instead consistent with models of conditional convergence (Barro, 1991) or multiple equilibria (Azariadis \u0026amp; Drazen, 1990). These diverse investment profiles are in line with studies that focus on savings rates, financial development, and political stability in explaining capital formation. The increasing but divergent educational profile corresponds to the global rise of schooling portrayed by Cohen and Soto (2007), but the enduring divergence indicates a rather sluggish percolation of knowledge frontiers-an important transmission channel according to technology-based growth models. Taken together, these figures indicate that while some of the more basic comparative statics of development hold over time, when adding China and India to an extended panel, only models incorporating these country-specific policy and growth regimes can be accurate.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9133472/v1/d763d5500339a654c0867e87.png"},{"id":104819896,"identity":"9cc54a4e-6cf4-4fce-a510-48b02055a9a7","added_by":"auto","created_at":"2026-03-17 14:16:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67997,"visible":true,"origin":"","legend":"\u003cp\u003eTime-series plots for key variables exhibit marked heterogeneity and distinct trends across the BRICS+ and G7 economies from 1970 to 2024. Looking at the growth of GDP (GDP_GROWTH), emerging economies such as China, India, and Ethiopia reflect a higher volatility and wider cyclical swings—positive and negative—compared to advanced G7 countries like the United States, Germany, and Japan, which follow more stable and moderate growth paths. This visualizes the higher risk-return profile of developing markets. The level of GDP per capita (GDP_CONV) reveals a dramatic and increasing divergence: G7 countries and resource-rich countries (e.g., United Arab Emirates, Saudi Arabia) are prominent at the top with very high and rising levels, while the large emerging economies (India, Ethiopia) are at the bottom, very clearly illustrating the 'convergence club' hypothesis, whereby not all countries converge. Shares of investments (INVS) are highly dispersed: large investments are found in countries such as China and Saudi Arabia, while smaller investments are found in countries such as the United Kingdom. Finally, human capital (EDUC) has a strong positive global trend but shows a lot of stratification, ranging from the more advanced nations and transitioning nations such as Russia to the lower ones relative to progress regarding human development.\u003c/p\u003e\n\u003cp\u003ePanel unit root tests indeed confirm a sharp dichotomy between trending and stationary variables, which is an important fact for model specification. There are stochastic trends in the variables of series such as GDP per capita (GDP_CONV), trade (TRD), investment (INVS), government expenditure (GEXP), education (EDUC), and unemployment (UNEM). They are non-stationary in levels, I(1), meaning that they are governed by a random walk with drift and do not mean revert to a constant. Their upward or downward movements over time are persistent and contain a unit root. By contrast, GDP growth rate (GDP_GRWOTH) and inflation (INFL) are stationary (I(0)), which oscillates around a stable long-run mean without a persistent trend. This mixed integration necessitates modelling the I(1) variables in first-differenced form in order not to cause spurious regression when explaining the stationary GDP growth rate. The visual evidence above confirms findings in the growth and development literature with a fresh, longitudinal perspective. The volatility of growth rates in emerging markets is consistent with the traditional 'shocks and volatility' literature (Aguiar \u0026amp; Gopinath, 2007), which identifies this as a product of structural factors and term-of-trade volatility. The finding of persisting income divergence in the GDP per capita series above argues against strong unconditional convergence results but is instead consistent with models of conditional convergence (Barro, 1991) or multiple equilibria (Azariadis \u0026amp; Drazen, 1990). These diverse investment profiles are in line with studies that focus on savings rates, financial development, and political stability in explaining capital formation. The increasing but divergent educational profile corresponds to the global rise of schooling portrayed by Cohen and Soto (2007), but the enduring divergence indicates a rather sluggish percolation of knowledge frontiers-an important transmission channel according to technology-based growth models. Taken together, these figures indicate that while some of the more basic comparative statics of development hold over time, when adding China and India to an extended panel, only models incorporating these country-specific policy and growth regimes can be accurate.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9133472/v1/0bf83a60431e0f00f154e40a.png"},{"id":105033789,"identity":"800ca914-cc48-4557-bb87-efe53e639243","added_by":"auto","created_at":"2026-03-20 07:21:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":676572,"visible":true,"origin":"","legend":"","description":"","filename":"2026.03.06ROLAPAPAERtopuplish4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9133472/v1_covered_bc2432d1-734c-46a8-a1f3-a58237241ea1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eThe Asymmetric Economic Effects of COVID-19 and the Russia–Ukraine War: Panel Evidence from G7 \u0026amp; BRICS+ (1970–2024)\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"EMU UNIVERSITY","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GDP Growth, COVID-19, Russia-Ukraine War, Error Correction Model, Cross-Sectional Dependence, Panel Cointegration, BRICS, G7, Conditional Convergence, Investment, Fiscal Policy","lastPublishedDoi":"10.21203/rs.3.rs-9133472/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9133472/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research looks at how COVID-19 and the Russia-Ukraine war differently impact the GDP growth rates of BRICS\u0026thinsp;+\u0026thinsp;and G7 countries, using a strong panel ECM model that accounts for connections between countries, from 1970 to 2024. Diagnostic tests show that the countries are linked and have significant errors in their data, which is why Diagnostic tests confirm cointegration and significant cross-country error correlation, hence the necessity for PCSEs. The results indicate a rapid error correction speed of 81.5% annually, underlining rapid adjustment to long-run equilibrium. We find strong evidence of conditional convergence, where poorer economies tend to grow faster in the longer run. The shock caused by the COVID-19 pandemic contributed a\u0026thinsp;\u0026minus;\u0026thinsp;5.8 percentage point decline to annual growth, implying a sharp and simultaneous contraction of the global economy. In comparison to the above, the shock from the Russia-Ukraine conflict had a marginally positive and differential impact (insignificant), suggesting that it favoured export-oriented countries while harming import-dependent countries. The growth of investment becomes evident as the most significant short-term engine behind growth, while government spending growth shows crowding-out effects, indicating that increased investment is driving economic recovery more effectively than government spending in the current context. It is critical to note that trade openness becomes insignificant when accounting for cross-sectional dependency, refuting widely recognized paradigms on growth benefits from globalization. According to the study, sustainable growth, as well as the ability to overcome a crisis, is only achievable through strategic investment in education and green energy, the establishment of contingent fiscal buffers, support for a good governance environment (stable government), and improvement of global coordination regarding asymmetric shocks. Methodologically, we demonstrate that accounting for cross-country dependence has a significant impact on inference regarding growth determinants and provides a clearer foundation for making economic policies in a world economy in a future study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"The Asymmetric Economic Effects of COVID-19 and the Russia–Ukraine War: Panel Evidence from G7 \u0026amp; BRICS+ (1970–2024)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 14:16:13","doi":"10.21203/rs.3.rs-9133472/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af0c024a-24cf-4ccf-8b49-4d2a1c1f223f","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64604682,"name":"Macroeconomics"}],"tags":[],"updatedAt":"2026-03-17T14:16:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 14:16:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9133472","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9133472","identity":"rs-9133472","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}