Protectionist measures and trade restrictions have far-reaching consequences on the world economy. The increasing popularity of economic nationalism is creating challenges to attain sustainable development goals (SDGs) and their indicators. Therefore, the study aims to investigate the consequences of economic nationalism on the SDGs in the 2030 Agenda for Sustainable Development framework.
The research administered a mixed-methods approach to integrate quantitative analysis and qualitative insights. The research deepens the complex interaction between economic nationalism and sustainable development.
The study’s findings identified the direct effects of economic nationalism on various SDGs, including poverty eradication, gender equality, industry and innovation, environmental sustainability, and global partnerships. The research found that low-income countries are the most suffering due to the protectionism policies of the world. The study also noticed the role of far-right political outfits in promoting economic nationalism.
This study contributes to the ongoing dialogue on how economic nationalism can be aligned with the 2030 Agenda, facilitating the realization of the SDGs and a more sustainable future for all.
The outcomes of this research provide valuable insights for policymakers, enabling them to navigate the complexities of economic nationalism and its effects on sustainable development.
The insights of the study offer interconnectedness between economic nationalism and sustainable development, taking into consideration the complexity and potential trade-offs.
1. Introduction
The international economy has been exposed to a variety of non-economic shocks, including the COVID-19 pandemic, geopolitical unrest and heightened tensions among major global powers (Howse and Langille, 2023). The world economy has witnessed different situations, either due to uncontrollable environmental factors or domestic political skepticism towards economic integration, which are highly significant. Economic nationalism is one such concept that has evolved to advocate protectionist economic policies to protect domestic industries (Girvin, 1992). These developments have raised legitimate concerns that the open global economic system, which has been widespread since the 1950s, may be at risk of closing the globalized free trade concept (Frieden and Torres, 2022). The 21st-century world economy is marked by high levels of interconnectedness and interdependence among domestic economies. It has become more inclusive as a result of technological advances, advanced supply chains, and effective communication, which makes it reliable for countries to trade goods and services, and for industries to cross-border activity (Grant and Yaffe, 2020). In recent decades, globalization has catalyzed the deepening integration of national economies and the exponential growth of multinational corporations, ushering in a dual reality of transformative opportunities and systemic challenges (Buckley et al., 2023). The data from World Development Indicators shows that globalization has driven significant economic growth and poverty reduction, particularly through trade liberalization and foreign direct investment, as seen in nations like Vietnam and India (Barbier and Burgess, 2019). It has accelerated technological diffusion, enabling small businesses to access global markets via digital platforms like Zoom and Amazon Web Service (AWS) while fostering cross-border innovation in fields such as renewable energy and biotechnology. However, a study by Pandian (2024) globalization has exacerbated economic inequality, with wealth concentrated among the top 1% and billions still lacking Internet access, deepening the digital divide. Geopolitical tensions, such as U.S.-China trade wars and tech decoupling, disrupt supply chains and inflate costs, while environmental degradation from carbon-intensive industries threatens climate goals (Saint Akadiri and Ozkan, 2025). Nations also grapple with eroded sovereignty, as seen in Sri Lanka’s debt crises and corporate influence over policies, and cultural homogenization has sparked backlash, exemplified by movements like France’s “Buy Local” campaigns. Finally, pandemic-induced supply chain disruptions and reshoring efforts highlight systemic fragility in overly globalized networks. This mutual link poses a threat to the rise and popularity of economic nationalism, which is a political approach that gives priority to local industries and limits foreign entries (Lincicome and Obregon, 2022). Economic nationalism leads to protectionist policy, trade wars and the emphasis on short-term economic development at the expense of long-term sustainability (Rammal et al., 2022). This approach leads to adverse impacts on the global economy, including an increase in imbalance, diminished international cooperation and a slower pace of progress toward achieving the United Nations’ sustainable development goals (SDGs) (Qadeer et al., 2022).
The current study examines the effects of economic nationalism on the SDGs of the United Nations by applying district tools and data sources. From a nationalistic viewpoint, economic nationalism is a policy approach that favors domestic industries and controls foreign competition. Economic nationalism is a philosophy that advocates state interventionism over international market mechanisms, with policies such as national control of the economy, labor and capital formation, including if this requires the imposition of tariffs and other cross-border limitations (Hieronymi, 1980). It appeared as the response to globalization and the rising interconnectedness of national economies, with proponents claiming that it is important to protect local manufacturing plants industries, and jobs from external competition (Pryke, 2012). The very roots of economic nationalism can be traced back to the early 20th century, when several countries began to embrace protectionist policies to protect their economies from the impact of world economic turmoil. During the 1920s and 1930s, economic nationalism became increasingly widespread as the Great Depression prompted a collapse of international trade and an increase in protectionist policies (Helleiner, 2021). Numerous countries imposed tariffs and other barriers to trade to safeguard their domestic industries, which only intensified economic decline and contributed to the outbreak of the Second World War. Postwar, there has been a shift towards greater openness and interconnected national economies, with the establishment of institutions such as the World Bank, the International Monetary Fund and the General Agreement on Tariffs and Trade (Fajgelbaum and Khandelwal, 2022). Nevertheless, economic nationalism remains a significant factor in national economic policies, especially in developing countries that seek to establish their industries and protect their economies from foreign dominance (Bin, 2022).
The United Nations and the World Trade Organization (WTO) both oppose economic nationalism after identifying it as harmful to the global economy. In 2019, the United Nations issued a resolution against economic nationalism by calling on the world to recommit to multilateralism and free trade (Andersen, 2019). This institution opined that economic nationalism leads to trade wars, protectionism, and economic instability. A study by Buckley (2022) observed that due to the US-China-centered bloc, the global economy splintered into regional and national groups. This has reignited the rise of supply chain nationalism and hindered the idea of technological sharing. The impact on productivity growth in advanced economies would be significant due to the loss of economies of scale, and a more turbulent disruption of supply chains could lead to increased fluctuations in both output and inflation. These two blocks may escalate into confrontation and lead to a broader breakdown of economic and financial ties. This situation would abruptly destabilize the major global economy and create a shortage of essential requirements for the supply chain and rampant price rises. Previous research has highlighted distinct aspects of economic nationalism, including its role in shaping trade policies, its political motivations (e.g. populism or protectionism) and its sector-specific impacts (e.g. manufacturing or technology). However, there remains no comprehensive analysis identifying how economic nationalism systematically hinders the attainment of SDGs. Therefore, current research is intended to make a methodological, conceptual and quantitative observation about how economic nationalism could be harmful to attaining the SGDs of the United Nations.
2. Literature review
Economic nationalism is a political ideology that highlights the importance of national economic interests over global economic integration (Helleiner, 2019). Economic nationalists believe that the state must play a powerful role in the economy and that economic policies should aim to safeguard domestic industries and jobs from overseas competition. Research has identified a growing amount of literature on economic nationalism. Some scholars argue that economic nationalism is a necessary response to the challenges of globalization, while others argue that it is a harmful and counterproductive ideology (MacIsaac and Duclos, 2020). Trade restriction and protectionism are political measures identified to have a significant impact on the attainment of SDGs (Ferrannini et al., 2021; Stephenson et al., 2021). This literature review is intended to explore the link between trade restriction, protectionism and difficulties in achieving SDGs. By examining previous research work, the review highlights key arguments, empirical evidence and theoretical structures that surround this subject.
Colantone and Stanig (2019) identified numerous theoretical structures that have been employed to figure out the motives behind economic nationalism and its impact on trade. Economic nationalism analyzed through theoretical lenses like realism (power maximization), mercantilism (trade surpluses), dependency theory (resisting exploitation) and behavioral economics (public sentiment), drives motives such as industrial protection and cultural sovereignty, while fragmenting global trade, inciting retaliatory tariffs and reshaping supply chains in pursuit of domestic priorities. Barcus and Moseley (2022) noted that small and medium-scale industries face challenges due to protectionist measures until they become internationally competitive. The strategic trade theory suggests that governments can strategically protect and sustain key industries to gain a competitive advantage in the global marketplace. Empirical studies, for example, Ndukwe et al. (2021) and Lilly (2020) have studied the relationship between economic nationalism and trade flows, highlighting the implications of protectionist policies. Some research indicates that higher trade barriers cut down on imports and protect domestic industries but also lead to countermeasures by trading partners (Bolton, 2011). The impact of economic nationalism on the attainment of sustainable development goals can also be considered in business results Peña et al. (2023), emerging and disruptive technologies Dadkhah et al. (2024), and industry, innovation and infrastructure (Brodny and Tutak, 2023). Studies like Strauss (2019), Woods (2017), and MacIsaac and Duclos (2020) also highlight the potential negative effects of economic nationalism on foreign direct investment (FDI) flows, as it generates uncertainty and hinders confidence among investors. The literature examines the role of economic nationalism in the framework of global value chains (GVCs). GVCs engage in cross-border manufacturing networks where various phases of production are spread across several countries. Economic nationalism can disrupt GVCs by increasing trade costs, disrupting supply chains, and inducing firms to relocate production. For example, the U.S.-China trade war (2018–2023) saw tariffs on $550 billion worth of goods, raising costs for firms reliant on trans−Pacific supply chains and prompting companies like Apple to diversify production to Vietnam and India (International Monetary Fund, 2022). Similarly, Japan’s 2020 subsidy program incentivized 87 firms to shift operations out of China, illustrating state−driven reshoring (Zeng and Kim, 2025). Such policies fragment GVCs, as seen in the semiconductor sector, where U.S. CHIPS Act subsidies ($52.7 billion) aim to reduce dependency on Asian suppliers, reshaping global tech trade (Wong et al., 2024). These disruptions reorient trade patterns toward regional blocs (e.g. nearshoring in North America under USMCA) and weaken economic integration, with UNCTAD (2024) estimating a 0.4% annual GDP loss from trade tensions. Thus, economic nationalism not only destabilizes GVC efficiency but also accelerates deglobalization trends.
Economic nationalism is motivated by a complex mix of economic, political and cultural factors. Notable experts identified different forms of economic nationalism. For example, in political ideology, the recent rise of far-right political movements in Europe and Asian countries often advocates for economic nationalism (Ausserladscheider, 2019). Since these political movements hold a controlling capacity in governance, their policies usually support a nationalistic economic agenda. For example, the imposition of heavy tariffs on foreign products by the United States Forster (2022) and China Yu (2022), the introduction of “Swadeshi or nationalism” in business by India Patel (2023), discrimination against foreign companies by Brazil and Russia Lee and Osgood (2022), and the introduction of tariffs and subsidizing domestic industry by Europe (Bin, 2022; Varian, 2022). Notably, advanced and emerging economies are shifting towards more significant skepticism and protectionism. A recent study by Gupta (2023) identified that during the Modi-Trump period (2017–2020), both nationalist leaders implemented protectionist policies. However, the effectiveness and nature of their actions, as well as their selection of instrumentation and their ability to influence each other’s policies, were significantly impacted by the power abilities of their respective states and the level of reciprocal economic dependence. Furthermore, Colantone and Stanig (2019) and Matsunaga (2021) identified the role of political institutions, advocacy groups and economic crises in stimulating economic nationalism. Additionally, the literature has investigated the link between economic nationalism and income inequality, finding that protectionist actions can worsen inequalities within societies.
Protectionist policies, which are designed to protect domestic industries from international competition, have mixed implications for economic growth and sustainable development outcomes (Barros and Martínez-Zarzoso, 2022). Restrictions on trade, such as tariffs, quotas and non-tariff barriers, are found to be an obstacle to economic growth, increase market inefficiency and prevent access to goods and services that are essential to achieving SDGs (Singh and Kapuria, 2022). A study from Nand et al. (2023) and Carballo Perez and Corina (2024) has explored that exaggerated trade restrictions decrease foreign direct investment, restrict technology transfer and hinder global value chains, eventually having an impact on progress toward SDGs, such as poverty reduction, food security, healthcare access and ecological sustainability.
Although previous research identified the key area of impact due to trade restrictions and protectionism policies, the available literature is unable to identify the direct impact of economic nationalism on the SDGs of the United Nations. Therefore, by identifying these gaps, current research is intended to explore the consequences of economic nationalism on SDGs.
3. Methodology
The study was employed to identify the impacts of economic nationalism on SDGs. The research is intended to explore the direct and indirect consequences of economic nationalism on various SDGs, such as zero poverty, hunger, environmental sustainability, gender equality, industry and innovation, and employment. The methodology involves data collection, analysis and interpretation to offer valuable insight into the link between economic nationalism and SDGs.
3.1 Research design
The research design for the study was adapted by applying a mixed-methods approach to comprehensively examine the effects of economic nationalism on SDGs. The research has considered combining quantitative analysis of relevant data sets with qualitative analysis of case studies, expert interviews and policy documents. The mixed-methods approach allowed for the exploration of various trade policies of the countries, political decisions on protectionism policies and tension between commercial policy intervention and SDGs more comprehensively.
3.2 Data collection and analysis
The quantitative data were collected from reliable sources, including national statistical agencies, and international organizations such as the World Bank, the United Nations, the International Monetary Fund, SDG Global Database and UNCTADstat. The researcher also considered case studies of different countries to identify the deeper impact of economic nationalism on SDGs. The data has been analyzed using statistical software to identify relationships, trends and models. Data visualization techniques, regression analysis and descriptive statistics were employed to study the relationship between economic nationalism indicators and SDG results.
3.2.1 Calculation of tariff
The study administered three measuring tools to identify the tariff based on (World Tariff Profiles, 2022). The Tariff score was allocated based on the following Formula. As per the World Bank (2024), the tariffs are classified into three categories, i.e. most favored nation (MFN), bond tariff (BND) and effectively applied (AHS). The tariff scores and non-tariff scores calculation have been administered as per the calculation pattern adopted by the WTO’s tariff measurement scale. The non-tariff testimonies are categorized into eight, i.e. export subsidies (ES), quantitative restrictions (QR), safeguards (S), special safeguards (SS), sanitary and phytosanitary (SPS), tariff rate quotas (TRQ), anti-dumping (AD) and countervailing duties (CVD).
3.2.2 Measures and criteria for equations
Equation (1) measures the tariff-related scores that provide the cumulative effects of various tariff measures applied by the developed countries. The components considered for these equations are MFN, AHS tariff, MFN applied line tariff and duty-free line. The rationale behind this equation is to capture the tariff burden faced by the importer and exporter, thereby exploring the reflection of protectionism and economic nationalism policies on trade.
Equation (2) measures the non-tariff scores that calculate the various non-tariff measures affecting international trade. Equation (2) comprises two categories namely, non-tariff barriers related to the administrative procedure (NTB-AP) and non-tariff barriers related to barriers (NTB-B). The components considered for this equation are ES, QR, S, SS, SPS, TRQ, AD, and CVD. The rationale for considering equation (2) is to recognize the non-tariff barriers, namely quotas and technical regulations, that are significant aspects of trade patterns and policies.
Tariff-related scores:
Non-Tariff scores:
4. Results
Trade protectionism policy has been prevalent throughout global history, particularly in developed countries like the United States, China, Germany and India through various restrictive policies, and import and export tariffs, protecting domestic business and jobs. Statistical data from various institutions like IMF, UN Trade and Development and World Bank reveal that countries’ policies acknowledging economic nationalism created a negative impact on economic growth, reasoned for de-globalization and increased the cost of traded services in the global market. It suggests that protectionist actions and restrictions on trade impede overall economic progress, which in turn affects multiple SDGs of the UN. Additionally, the analysis of the research indicates a direct relationship between upper levels of economic nationalism and the rise in poverty, hunger rates, environmental uncertainty, gender inequalities, poor global cooperation and partnerships which create more challenges in achieving SDGs 2030.
The International Trade Barrier explores the global trade barriers status and countries with the highest number of protectionist policies between 2009 and 2023 in Figure 1. During this period, the United States implemented the highest number of harmful trade policy measures with nearly 9,489 policies that resulted in a negative impact on trade liberalization. Similarly, China, which is another country with a higher global trade partnership in the world, ranked second with 6,130 harmful policies registered against trade liberalization during this period. The results also explored Germany in third position, followed by Italy and India in the top five list.
The horizontal bar chart is titled “Number of Protectionist Policy”. The horizontal axis ranges from negative 2000 to 10 000 in increments of 2000 units. The vertical axis lists 25 countries from top to bottom as follows: “Romania”, “Hungary”, “Czechia”, “Greece”, “Belgium”, “Austria”, “Denmark”, “Sweden”, “Finland”, “Argentina”, “Netherlands”, “Portugal”, “Japan”, “Russia”, “Poland”, “Brazil”, “United Kingdom”, “Spain”, “France”, “Canada”, “India”, “Italy”, “Germany”, “China”, and “United States of America”. Each country has a horizontal bar. A dotted diagonal line extends across the bars. The bars increase in length moving downward. The data for the bars is as follows: Romania: 992. Hungary: 1000. Czechia: 1004. Greece: 1039. Belgium: 1041. Austria: 1055. Denmark: 1056. Sweden: 1063. Finland: 1074. Argentina: 1084. Netherlands: 1084. Portugal: 1087. Japan: 1194. Russia: 1322. Poland: 1325. Brazil: 1563. United Kingdom: 1677. Spain: 1806. France: 1808. Canada: 1903. India: 1935. Italy: 2336. Germany: 3274. China: 6130. United States of America: 9489.Countries with the highest number of protectionist policies between 2009 and 2023. Source: Created by authors; Global Trade Alert Database, 2025
The horizontal bar chart is titled “Number of Protectionist Policy”. The horizontal axis ranges from negative 2000 to 10 000 in increments of 2000 units. The vertical axis lists 25 countries from top to bottom as follows: “Romania”, “Hungary”, “Czechia”, “Greece”, “Belgium”, “Austria”, “Denmark”, “Sweden”, “Finland”, “Argentina”, “Netherlands”, “Portugal”, “Japan”, “Russia”, “Poland”, “Brazil”, “United Kingdom”, “Spain”, “France”, “Canada”, “India”, “Italy”, “Germany”, “China”, and “United States of America”. Each country has a horizontal bar. A dotted diagonal line extends across the bars. The bars increase in length moving downward. The data for the bars is as follows: Romania: 992. Hungary: 1000. Czechia: 1004. Greece: 1039. Belgium: 1041. Austria: 1055. Denmark: 1056. Sweden: 1063. Finland: 1074. Argentina: 1084. Netherlands: 1084. Portugal: 1087. Japan: 1194. Russia: 1322. Poland: 1325. Brazil: 1563. United Kingdom: 1677. Spain: 1806. France: 1808. Canada: 1903. India: 1935. Italy: 2336. Germany: 3274. China: 6130. United States of America: 9489.Countries with the highest number of protectionist policies between 2009 and 2023. Source: Created by authors; Global Trade Alert Database, 2025
Over the years, trade restrictions and policies have had varying impacts on trade, especially for foreign direct investment (FDI) in all states of the economy. Figures 2 and 3 explore the trade fluctuation from 1990 to 2022, supported by statistical figures and data from UNCTAD (United Nations Conference on Trade and Development). Data shows trade protectionism policies reduced FDI inflows in developing economies. By imposing tariffs, quotas, or other trade barriers, these policies restrict market access and increase the cost of doing business for foreign investors. In Asia, for example, Japan, India and South Korea, heavily rely on global trade and investment. The study explores how trade barriers disrupt supply chains, increase costs and deter foreign investors. UNCTAD’s data shows that FDI inflows in developed economies in Asia experienced fluctuations over the years. For example, in 1990, FDI inflows to these economies amounted to $34 billion, which increased to $256 billion during the 2019 pre-pandemic peak, in 2022 it rose to $378 billion and based on UNCTAD forecasts and regional recovery trends $420 billion at the end of 2024. However, in each declining year, other than a routine economic recession, policies and trade restrictions played crucial roles. Trade protectionism policies also created uncertainties in least-developed countries as well as the European Union with nationalistic policies. The impact has reduced market access and increased costs for foreign investors, impacting FDI inflows in these countries.
The horizontal bar chart is divided into two sections. The left section is labeled “Import Restriction” and the right section is labeled “Export Restriction”. Both sections display data for the years “2010”, “2020”, and “2022”. The regions listed along the left side from top to bottom are “Developed countries”, “Africa”, “East Asia”, “Latin America”, “Rest of Asia”, and “South Asia”. The data for the bars is as follows: Import Restriction: Developed countries: 2010: 1.2 percent; 2020: 1.9 percent; 2022: 1.8 percent. Africa: 2010: 8.3 percent; 2020: 7.3 percent; 2022: 7.4 percent. East Asia: 2010: 4.0 percent; 2020: 3.1 percent; 2022: 3.0 percent. Latin America: 2010: 4.2 percent; 2020: 4.1 percent; 2022: 4.0 percent. Rest of Asia: 2010: 3.8 percent; 2020: 3.9 percent; 2022: 3.9 percent. South Asia: 2010: 7.8 percent; 2020: 7.9 percent; 2022: 7.8 percent. Export Restriction: Developed countries: 2010: 2.5 percent; 2020: 2.2 percent; 2022: 2.1 percent. Africa: 2010: 1.8 percent; 2020: 2.0 percent; 2022: 2.0 percent. East Asia: 2010: 3.1 percent; 2020: 3.6 percent; 2022: 3.5 percent. Latin America: 2010: 2.0 percent; 2020: 2.5 percent; 2022: 2.4 percent. Rest of Asia: 2010: 2.2 percent; 2020: 2.3 percent; 2022: 2.2 percent. South Asia: 2010: 4.6 percent; 2020: 3.9 percent; 2022: 4.0 percent.Region-wise trade restriction. Sources: Created by authors; Data source from WTO Report, 2025
The horizontal bar chart is divided into two sections. The left section is labeled “Import Restriction” and the right section is labeled “Export Restriction”. Both sections display data for the years “2010”, “2020”, and “2022”. The regions listed along the left side from top to bottom are “Developed countries”, “Africa”, “East Asia”, “Latin America”, “Rest of Asia”, and “South Asia”. The data for the bars is as follows: Import Restriction: Developed countries: 2010: 1.2 percent; 2020: 1.9 percent; 2022: 1.8 percent. Africa: 2010: 8.3 percent; 2020: 7.3 percent; 2022: 7.4 percent. East Asia: 2010: 4.0 percent; 2020: 3.1 percent; 2022: 3.0 percent. Latin America: 2010: 4.2 percent; 2020: 4.1 percent; 2022: 4.0 percent. Rest of Asia: 2010: 3.8 percent; 2020: 3.9 percent; 2022: 3.9 percent. South Asia: 2010: 7.8 percent; 2020: 7.9 percent; 2022: 7.8 percent. Export Restriction: Developed countries: 2010: 2.5 percent; 2020: 2.2 percent; 2022: 2.1 percent. Africa: 2010: 1.8 percent; 2020: 2.0 percent; 2022: 2.0 percent. East Asia: 2010: 3.1 percent; 2020: 3.6 percent; 2022: 3.5 percent. Latin America: 2010: 2.0 percent; 2020: 2.5 percent; 2022: 2.4 percent. Rest of Asia: 2010: 2.2 percent; 2020: 2.3 percent; 2022: 2.2 percent. South Asia: 2010: 4.6 percent; 2020: 3.9 percent; 2022: 4.0 percent.Region-wise trade restriction. Sources: Created by authors; Data source from WTO Report, 2025
The horizontal axis is marked with years and ranges from 1990 to 2022 in increments of 2 years. The vertical axis is marked with monetary units and ranges from 0 to 2 million in increments of 200 units. The graph shows six lines, each representing a different group labeled “World”, “Developing economies”, “Developed economies”, “Asia”, “European Union”, and “Least developed countries (L D C s)”. The “World” line begins above 200K in 1990, rises sharply to a peak below 1.4M around 2000, then declines, rises again to a second peak near 1.9M around 2007, drops, and rises again to peak above 2M around 2014, then declines rapidly and rises again to end below 1.6M around 2022. The “Developing economies” line begins above 0 in 1990, rises steadily, increases sharply around 2006, reaches a peak near 700K around 2016, then rises and ends near 900K in 2022. The “Developed economies” line begins above 100K in 1990, rises to a peak above 1.1M around 2000, declines, increases again to a secondary peak near 1.4M around 2015, then decreases and ends near 600K. The “Asia” line begins above 0 in 1990, rises gradually with noticeable fluctuations, reaches a peak near 500K around 2015, decreases, and ends above 600K in 2022. The “European Union” line begins just above 40K in 1990, rises gradually with multiple fluctuations, reaches a peak above 600K around 2015, then declines and ends below 200K. The “Least developed countries (L D C s)” line begins near 0 in 1990, remains low with minor fluctuations throughout the timeline, and ends slightly above 0 in 2022. Note: All numerical data values are approximated.Foreign direct investment flows. Sources: Created by authors; Data source: UNCTAD World Investment Report 2024
The horizontal axis is marked with years and ranges from 1990 to 2022 in increments of 2 years. The vertical axis is marked with monetary units and ranges from 0 to 2 million in increments of 200 units. The graph shows six lines, each representing a different group labeled “World”, “Developing economies”, “Developed economies”, “Asia”, “European Union”, and “Least developed countries (L D C s)”. The “World” line begins above 200K in 1990, rises sharply to a peak below 1.4M around 2000, then declines, rises again to a second peak near 1.9M around 2007, drops, and rises again to peak above 2M around 2014, then declines rapidly and rises again to end below 1.6M around 2022. The “Developing economies” line begins above 0 in 1990, rises steadily, increases sharply around 2006, reaches a peak near 700K around 2016, then rises and ends near 900K in 2022. The “Developed economies” line begins above 100K in 1990, rises to a peak above 1.1M around 2000, declines, increases again to a secondary peak near 1.4M around 2015, then decreases and ends near 600K. The “Asia” line begins above 0 in 1990, rises gradually with noticeable fluctuations, reaches a peak near 500K around 2015, decreases, and ends above 600K in 2022. The “European Union” line begins just above 40K in 1990, rises gradually with multiple fluctuations, reaches a peak above 600K around 2015, then declines and ends below 200K. The “Least developed countries (L D C s)” line begins near 0 in 1990, remains low with minor fluctuations throughout the timeline, and ends slightly above 0 in 2022. Note: All numerical data values are approximated.Foreign direct investment flows. Sources: Created by authors; Data source: UNCTAD World Investment Report 2024
Over the past several years, there has been a fundamental global inclination towards trade liberalization, with a consistent drop in tariffs on commodities. From 1994 to 2017, the average global tariff rate has seen a significant decrease, dropping from 8.5% to 2.5% (Figure 4). However, a reversal of this trend has been identified since mid-2018, particularly a noticeable significant increase in the bilateral tariffs on trade between the United States and China. This change in the trade policy trend has had a notable impact on global business confidence. The surge in trade and economic policy uncertainty has been a direct consequence of these changes. Throughout 2019, there was a dramatic rise in the measures of uncertainty over trade, as well as economic policies. The research identified that investors recognize a trade war as the most important risk to the global economic outlook. These events unfolded during a period when the global economy was already suffering a slowdown. This slowdown could be attributed to the tightening of financial conditions in 2018, especially in emerging markets, as well as the deceleration of China’s growth. Overall, the ongoing trajectory of trade policies and the accompanying increase in uncertainty have ignited new challenges to the global economic landscape. These dynamics have the potential to impact business decisions and investment activities, further influencing the underlying pace of global economic growth.
The horizontal axis is marked with years and ranges from 1988 to 2020 in increments of 2 years. The vertical axis is marked with percentage values 1, 1.5, 3, 5, 10, 15, 30, and 50. The graph shows four lines labeled “Trade (percent of G D P)”, “percent of G D P”, “World Tariff Rate”, and “F D I”. The “Trade (percent of G D P)” line begins above 30 percent in 1988, shows a gradual upward trend with fluctuations, peaks above 50 percent around 2008, and ends slightly below that level in 2020. The “percent of G D P” dotted line begins near 18 percent, increases gradually with minor variations, peaks near 30 percent around 2008, and ends slightly below 30 percent by 2020. The “World Tariff Rate” line begins slightly below 5 percent in 1988, rises with fluctuations to a peak near 10 percent around 1994, declines steadily, and ends near 3 percent in 2020. The “F D I” line begins below 1 percent in 1988, rises steadily with fluctuations to reach a peak near 5 percent around 2008, declines sharply afterward, rises again to a secondary peak around 2007, then continues downward with fluctuations and ends below 3 percent in 2020. Note: All numerical data values are approximated.Reflection of global tariff rate on trade. Sources: Created by authors; Data source: OECD trade and tariff statistics, 2024
The horizontal axis is marked with years and ranges from 1988 to 2020 in increments of 2 years. The vertical axis is marked with percentage values 1, 1.5, 3, 5, 10, 15, 30, and 50. The graph shows four lines labeled “Trade (percent of G D P)”, “percent of G D P”, “World Tariff Rate”, and “F D I”. The “Trade (percent of G D P)” line begins above 30 percent in 1988, shows a gradual upward trend with fluctuations, peaks above 50 percent around 2008, and ends slightly below that level in 2020. The “percent of G D P” dotted line begins near 18 percent, increases gradually with minor variations, peaks near 30 percent around 2008, and ends slightly below 30 percent by 2020. The “World Tariff Rate” line begins slightly below 5 percent in 1988, rises with fluctuations to a peak near 10 percent around 1994, declines steadily, and ends near 3 percent in 2020. The “F D I” line begins below 1 percent in 1988, rises steadily with fluctuations to reach a peak near 5 percent around 2008, declines sharply afterward, rises again to a secondary peak around 2007, then continues downward with fluctuations and ends below 3 percent in 2020. Note: All numerical data values are approximated.Reflection of global tariff rate on trade. Sources: Created by authors; Data source: OECD trade and tariff statistics, 2024
The recent President Trump’s 25% tariffs in 2025 and retaliation from Mexico and Canada witnessed a wipeout of cross-border trade in major sectors like mining, computer, electronic and lumber. This policy is projected to reduce trade across USMCA countries (US from −6 to −9%; Canada from −9.3 to −19% and Mexico from −133.9 to −25.7%). The tariffs are reshaping the trade flows; however, at a cost, the growth of global GDP is downgraded to 3.1% according to results amid rising protectionism.
Table 1 provides a comprehensive overview of several policies representing advanced and emerging economies. The results expose the existing policies and depict regression coefficients that assess the influence of right-wing/left-wing political ideologies and populist/non-populist orientations on economic nationalism, particularly regarding trade policies, FDI, immigration and protectionist measures. The study utilizes the World Bank Political Database 2021, Beck et al. (2001) as a primary source of data and the Comparative Political Dataset (1960–2020) by Armingeon et al. (2022) as a secondary source of data.
Number of observations and correlation with post-global financial crisis
| Policy | No. of policy advanced economy | No. of policy emerging economy | Regression and p values | |||
|---|---|---|---|---|---|---|
| Left | Right | Populist | Non-populist | |||
| Trade protectionism | 88 | 75 | 0.13 | −0.28 | 0.94 | 0.13 |
| (0.731) | (0.469) | (0.004) | (0.359) | |||
| FDI protectionism | 27 | 71 | −0.47 | 0.19 | 0.43 | −0.29 |
| (0.468) | (0.737) | (0.400) | (0.576) | |||
| Immigration restrictions | 82 | 17 | −0.33 | 1.07 | 1.20 | 0.25 |
| (0.353) | (0.010) | (0.028) | (0.101) | |||
| Anti multilateralism | 97 | 79 | −0.90 | −0.19 | 1.53 | 0.09 |
| (0.041) | (0.697) | (0.000) | (0.418) | |||
| Trade restrictions | 88 | 75 | 0.27 | −0.29 | 0.94 | 0.20 |
| (0.458) | (0.461) | (0.002) | (0.188) | |||
| Industrial policies toward specific sectors | 94 | 96 | 0.15 | −0.62 | 0.47 | 0.14 |
| (0.710) | (0.092) | (0.210) | (0.457) | |||
| Tolerance of concentration | 67 | 58 | −0.20 | −0.35 | 0.39 | 0.04 |
| (0.697) | (0.363) | (0.476) | (0.671) | |||
| Macroeconomic populism | 94 | 96 | 0.23 | −0.01 | 1.44 | 0.24 |
| (0.644) | (0.988) | (0.001) | (0.177) | |||
| Policy | No. of policy advanced economy | No. of policy emerging economy | Regression and p values | |||
|---|---|---|---|---|---|---|
| Left | Right | Populist | Non-populist | |||
| Trade protectionism | 88 | 75 | 0.13 | −0.28 | 0.94 | 0.13 |
| (0.731) | (0.469) | (0.004) | (0.359) | |||
| FDI protectionism | 27 | 71 | −0.47 | 0.19 | 0.43 | −0.29 |
| (0.468) | (0.737) | (0.400) | (0.576) | |||
| Immigration restrictions | 82 | 17 | −0.33 | 1.07 | 1.20 | 0.25 |
| (0.353) | (0.010) | (0.028) | (0.101) | |||
| Anti multilateralism | 97 | 79 | −0.90 | −0.19 | 1.53 | 0.09 |
| (0.041) | (0.697) | (0.000) | (0.418) | |||
| Trade restrictions | 88 | 75 | 0.27 | −0.29 | 0.94 | 0.20 |
| (0.458) | (0.461) | (0.002) | (0.188) | |||
| Industrial policies toward specific sectors | 94 | 96 | 0.15 | −0.62 | 0.47 | 0.14 |
| (0.710) | (0.092) | (0.210) | (0.457) | |||
| Tolerance of concentration | 67 | 58 | −0.20 | −0.35 | 0.39 | 0.04 |
| (0.697) | (0.363) | (0.476) | (0.671) | |||
| Macroeconomic populism | 94 | 96 | 0.23 | −0.01 | 1.44 | 0.24 |
| (0.644) | (0.988) | (0.001) | (0.177) | |||
The findings of the study, based on a regression analysis, reveal significant associations between political ideologies and policy preferences. The Right-wing political parties tend to exhibit higher ratings, indicating a more nationalist or illiberal stance. Conversely, left-wing parties show a negative correlation with higher ratings. Notably, the analysis highlights that right-wing orientation is linked to policies related to FDI protectionism, restrictions on immigration and anti-multilateralism (with the first two being statistically significant). On the other hand, a notable left-wing orientation is associated with a preference for sector-specific industrial or international trade-related policies and a more lenient stance on macroeconomic populism and acceptance of concentration. Furthermore, the study explores the correlations between policy preferences and populism, using a similar regression analysis. It reveals that nationalist or illiberal policy preferences have a significant relationship with populism across all policy categories, with statistically significant coefficients seen in most categories. These findings shed light on the complex relationship between political ideologies, populism, and policy preferences in the context of economic nationalism. The analysis presented in Table 2 provides valuable insights into how different ideological orientations shape policy agendas and can inform discussions on the impact of political ideology on economic decision-making.
Protectionism policy and impact on the economy
| Impact on welfare | Impact on household consumption (import) | Impact on regional trade (import) | Impact on regional trade (export) | |
|---|---|---|---|---|
| East Asia and the Pacific | −2 | −22 | −17.6 | −12 |
| Latin America and the Caribbean | −1.1 | −35 | −35.2 | −26.4 |
| Middle East and North Africa | −1 | −16 | −13.9 | −13.7 |
| Sub-Saharan Africa | −0.7 | −27 | −19.1 | −21.9 |
| Europe and Central Asia | −0.1 | −11 | −9.3 | −12.8 |
| European Union | −0.4 | −2 | −2.4 | −11.9 |
| Other Advanced Economies | −0.5 | −10 | −5.6 | −8.3 |
| United States | −0.4 | −5 | −4.4 | −7.9 |
| South Asia | −4.2 | −47 | −32.6 | −15.9 |
| China | −0.8 | −6 | −3.5 | −3.9 |
| Impact on welfare | Impact on household consumption (import) | Impact on regional trade (import) | Impact on regional trade (export) | |
|---|---|---|---|---|
| East Asia and the Pacific | −2 | −22 | −17.6 | −12 |
| Latin America and the Caribbean | −1.1 | −35 | −35.2 | −26.4 |
| Middle East and North Africa | −1 | −16 | −13.9 | −13.7 |
| Sub-Saharan Africa | −0.7 | −27 | −19.1 | −21.9 |
| Europe and Central Asia | −0.1 | −11 | −9.3 | −12.8 |
| European Union | −0.4 | −2 | −2.4 | −11.9 |
| Other Advanced Economies | −0.5 | −10 | −5.6 | −8.3 |
| United States | −0.4 | −5 | −4.4 | −7.9 |
| South Asia | −4.2 | −47 | −32.6 | −15.9 |
| China | −0.8 | −6 | −3.5 | −3.9 |
Since 1 January 2016 (post announcement of the SDG of UN), the data reveal that a total of 19,672 trade-policy interventions were applied, impacting one or more of the 61 SDG indicators discussed in this study. These interventions were performed by 192 customs territories. Of these interventions, 45% are identified to have positively contributed to achieving an SDG indicator, while the other 27% likely worsened an SDG indicator. The remaining 28% were found not to affect any SDG indicator. Among the commercial policy interventions implemented since 2016, which influenced the studied SDG indicators, 24% involved some form of liberalization of cross-border commerce. Figure 4 provides an overview of the wide-ranging categories of commercial policy interventions implemented since 2016. The left panel of the figure illustrates the portion of interventions that enhanced an SDG indicator, while the right panel represents those that deteriorated an indicator. Each panel presents data on seven SDGs examined in this study (Figure 5).
The figure shows two line graphs arranged in a horizontal series. The horizontal axis in both panels is labeled with S D G categories from left to right as follows: “S D G 1”, “S D G 2”, “S D G 3”, “S D G 6”, “S D G 7”, “S D G 9”, and “S D G 14”. The vertical axis represents share values and ranges from 0.00 to 0.80 in increments of 0.20 units. Each graph contains four lines representing different intervention types labeled “Subsidies to local firms”, “Export measure”, “Import measure”, and “Behind-the-border measure”. The left graph is labeled “Improving S D G”. The “Import measure” line begins above 0.75 at “S D G 1”, fluctuates across the categories, and ends around 0.40 at “S D G 14”. The “Export measure” line begins near 0.20 at “S D G 1”, varies across the S D G values, and ends around 0.50 at “S D G 14”. The “Subsidies to local firms” line begins at 0.00 at “S D G 1”, remains low with minor fluctuations, and ends around 0.10 at “S D G 14”. The “Behind-the-border measure” line begins near 0.00 at “S D G 1”, shows changes across the categories, and ends around 0.02 at “S D G 14”. The right graph is labeled “Worsening S D G”. The “Import measure” line begins above 0.60 at “S D G 1”, varies across the categories, and ends above 0.10 at “S D G 14”. The “Export measure” line begins above 0.30 at “S D G 1”, rises and fluctuates across categories, and ends near 0.20 at “S D G 14”. The “Subsidies to local firms” line begins near 0.00 at “S D G 1”, fluctuates across the values, and ends at 0.70 at “S D G 14”. The “Behind-the-border measure” line begins around 0.05 at “S D G 1”, remains low throughout, and ends slightly above 0.00 at “S D G 14”. Note: All numerical data values are approximated.Shares of intervention and SDG response to measures. Sources: Created by authors, Data Source: UN Sustainable Development Goals Reports &WTO and the SDGs
The figure shows two line graphs arranged in a horizontal series. The horizontal axis in both panels is labeled with S D G categories from left to right as follows: “S D G 1”, “S D G 2”, “S D G 3”, “S D G 6”, “S D G 7”, “S D G 9”, and “S D G 14”. The vertical axis represents share values and ranges from 0.00 to 0.80 in increments of 0.20 units. Each graph contains four lines representing different intervention types labeled “Subsidies to local firms”, “Export measure”, “Import measure”, and “Behind-the-border measure”. The left graph is labeled “Improving S D G”. The “Import measure” line begins above 0.75 at “S D G 1”, fluctuates across the categories, and ends around 0.40 at “S D G 14”. The “Export measure” line begins near 0.20 at “S D G 1”, varies across the S D G values, and ends around 0.50 at “S D G 14”. The “Subsidies to local firms” line begins at 0.00 at “S D G 1”, remains low with minor fluctuations, and ends around 0.10 at “S D G 14”. The “Behind-the-border measure” line begins near 0.00 at “S D G 1”, shows changes across the categories, and ends around 0.02 at “S D G 14”. The right graph is labeled “Worsening S D G”. The “Import measure” line begins above 0.60 at “S D G 1”, varies across the categories, and ends above 0.10 at “S D G 14”. The “Export measure” line begins above 0.30 at “S D G 1”, rises and fluctuates across categories, and ends near 0.20 at “S D G 14”. The “Subsidies to local firms” line begins near 0.00 at “S D G 1”, fluctuates across the values, and ends at 0.70 at “S D G 14”. The “Behind-the-border measure” line begins around 0.05 at “S D G 1”, remains low throughout, and ends slightly above 0.00 at “S D G 14”. Note: All numerical data values are approximated.Shares of intervention and SDG response to measures. Sources: Created by authors, Data Source: UN Sustainable Development Goals Reports &WTO and the SDGs
The findings of the study were predictable, for instance, the largest category of interventions with a negative impact on the indicators in SDG 14 (Life underwater) was subsidies. Conversely, business subsidies played an important role in enhancing the indicators in SDG 9. The data outcome reveals that import measures comprised more than half of the implemented interventions, leading to improvements in three SDGs, but they exacerbated the situation for five SDGs (as illustrated in both panels of Figure 5). Similarly, the export measures played a more significant role in the panel that is linked to the deterioration of SDGs discussed in this study. In the case of behind-the-border (non-subsidized) measures, which accounted for a small portion of the implemented interventions in both figures, except for measures that enhanced indicators in SDG 7. It is important to note that while interpreting these outcomes, it must be noted that the frequency of the policy intervention has a crucial role rather than the impact of each policy intervention.
Table 2 summarizes how protectionism policy impacted various economic activities. One of the major impacts of protectionism is the effects on regional welfare due to the rise in tariffs. Table 3 illustrates the cumulative number of commercial policy interventions currently in effect that impact the seven SDGs examined in this study. It also compares these figures to the total volume of interventions implemented since the adoption of Agenda 2030. Notably, SDG 3 (Good Health and Well-Being) stands out, with more than half of the policy interventions introduced since 2016 having expired or become obsolete.
Policy intervention and counties response to SDG
| Name of the SDGs | Intervention since 2016 | Intervention in force (2023) | Countries detracted most | Shares of negative contribution |
|---|---|---|---|---|
| SDG 1 | 10,946 | 6,855 | Lower middle income | 0.75 |
| SDG 2 | 7,070 | 3,765 | Lower middle income | 0.29 |
| SDG 3 | 1,825 | 886 | Low income | 0.67 |
| SDG 6 | 3,291 | 2,006 | Low income | 0.8 |
| SDG 7 | 7,198 | 4,921 | Low income | 0.47 |
| SDG 9 | 15,391 | 10,518 | Lower middle income | 0.24 |
| SDG 14 | 2,023 | 1,392 | Upper middle income | 0.75 |
| Name of the SDGs | Intervention since 2016 | Intervention in force (2023) | Countries detracted most | Shares of negative contribution |
|---|---|---|---|---|
| SDG 1 | 10,946 | 6,855 | Lower middle income | 0.75 |
| SDG 2 | 7,070 | 3,765 | Lower middle income | 0.29 |
| SDG 3 | 1,825 | 886 | Low income | 0.67 |
| SDG 6 | 3,291 | 2,006 | Low income | 0.8 |
| SDG 7 | 7,198 | 4,921 | Low income | 0.47 |
| SDG 9 | 15,391 | 10,518 | Lower middle income | 0.24 |
| SDG 14 | 2,023 | 1,392 | Upper middle income | 0.75 |
The results reveal that lower-middle-income economies bear the highest burden of trade policy interventions linked to SDG 1 (No Poverty) and SDG 2 (Zero Hunger), with negative impact shares of 0.75 and 0.29, respectively. Lower-income countries, meanwhile, are disproportionately affected by policies tied to SDG 3 (Good Health and Well-Being), SDG 6 (Clean Water and Sanitation), and SDG 7 (Affordable and Clean Energy), reporting negative shares of 0.67, 0.80 and 0.47, respectively. SDG 9 (Industry, Innovation and Infrastructure) most heavily impacts lower-middle-income countries, whereas SDG 14 (Life Below Water) has the strongest adverse effect on upper-middle-income countries, with a negative share of 0.75.
Collectively, these findings underscore that lower-income and lower-middle-income countries face disproportionate consequences from trade protectionism and economic nationalism across multiple SDGs.
5. Discussion
The current research identified the resurgence of economic nationalism in the form of various policies of the government and explored how the SDGs of the United Nations are under threat. The study measured the impact of economic nationalism by analyzing the impact of protectionist trade policies, restrictive FDI policies, restrictions on immigration, and anti-multilateralism. The study also extended how the role of political ideologies like left and right wings, populist and nonpopulist parties, private and electoral interests impacted SDG’s attainment through various economic and industrial policies (Figure 6).
The diagram shows a text box at the top center labeled “Economic Nationalism”. A line extends downward and branches into eight boxes from left to right labeled “Trade Protectionism”, “F D I Protectionism”, “Immigration Restrictions”, “Anti Multilateralism”, “Trade Restrictions”, “Industrial Policies”, “Tolerance of concentration”, and “Macroeconomic populism”. A line extends downward from “Trade Protectionism” to a box labeled “S D G 1 and 9”, and a line extends downward to a text box containing the phrases “Retaliation and reduced economic growth”, “Complacent and less productive”, “Reduced export opportunities”, and “Disruption of global supply chains”. A line extends downward from “F D I Protectionism” to a box labeled “S D G 8”, and a line extends downward to a text box containing “Limited capital and investment”, “Reduced job opportunities”, “Limited access to expertise and knowledge transfer”, and “Impeded competitiveness and market access”. A line extends downward from “Immigration Restrictions” to a box labeled “S D G 2, 3 and 10”, and a line extends downward to a text box containing “Lack of labor availability”, “Agricultural productivity”, “Healthcare workforce”, “Health equity and access”, “Social inclusion and diversity”, and “Discrimination and xenophobia”. A line extends downward from “Anti Multilateralism” to a box labeled “S D G 10”, and a line extends downward to a text box containing “Reduced support for development aid”, “Limited exchange of ideas and experiences”, “Weakened international norms and standards”, and “Impacted global governance mechanisms”. A line extends downward from “Trade Restrictions” to a box labeled “S D G 3 and 12”, and a line extends downward to a text box containing “Access to essential medicines and healthcare products”, “Disruption of global health supply chains”, “Sustainable consumption patterns”, “Circular economy and resource efficiency”, and “Access to sustainable technologies”. A line extends downward from “Industrial Policies” to a box labeled “S D G 7 and 3”, and a line extends downward to a text box containing “Hindered renewable energy development”, “Reduced investment in energy efficiency”, “Neglected occupational health and safety”, and “Limited pollution control measures”. A line extends downward from “Tolerance of concentration” to a box labeled “S D G 17”, and a line extends downward to a text box containing “Limited competition and innovation”, “Unequal participation in partnerships”, “Impacts on sustainable business practices”, and “Limited access to resources and markets”. A line extends downward from “Macroeconomic populism” to a box labeled “S D G 9”, and a line extends downward to a text box containing “Impacts on infrastructure investment”, “Disrupted industrial development”, “Reduced support for innovation”, and “Financial instability and uncertainty”.Economic nationalism framework and impact on SDGs. Source: Developed by authors; Data Source: UNCTAD, Trade and Development Report: Structural Transformation for SDGs
The diagram shows a text box at the top center labeled “Economic Nationalism”. A line extends downward and branches into eight boxes from left to right labeled “Trade Protectionism”, “F D I Protectionism”, “Immigration Restrictions”, “Anti Multilateralism”, “Trade Restrictions”, “Industrial Policies”, “Tolerance of concentration”, and “Macroeconomic populism”. A line extends downward from “Trade Protectionism” to a box labeled “S D G 1 and 9”, and a line extends downward to a text box containing the phrases “Retaliation and reduced economic growth”, “Complacent and less productive”, “Reduced export opportunities”, and “Disruption of global supply chains”. A line extends downward from “F D I Protectionism” to a box labeled “S D G 8”, and a line extends downward to a text box containing “Limited capital and investment”, “Reduced job opportunities”, “Limited access to expertise and knowledge transfer”, and “Impeded competitiveness and market access”. A line extends downward from “Immigration Restrictions” to a box labeled “S D G 2, 3 and 10”, and a line extends downward to a text box containing “Lack of labor availability”, “Agricultural productivity”, “Healthcare workforce”, “Health equity and access”, “Social inclusion and diversity”, and “Discrimination and xenophobia”. A line extends downward from “Anti Multilateralism” to a box labeled “S D G 10”, and a line extends downward to a text box containing “Reduced support for development aid”, “Limited exchange of ideas and experiences”, “Weakened international norms and standards”, and “Impacted global governance mechanisms”. A line extends downward from “Trade Restrictions” to a box labeled “S D G 3 and 12”, and a line extends downward to a text box containing “Access to essential medicines and healthcare products”, “Disruption of global health supply chains”, “Sustainable consumption patterns”, “Circular economy and resource efficiency”, and “Access to sustainable technologies”. A line extends downward from “Industrial Policies” to a box labeled “S D G 7 and 3”, and a line extends downward to a text box containing “Hindered renewable energy development”, “Reduced investment in energy efficiency”, “Neglected occupational health and safety”, and “Limited pollution control measures”. A line extends downward from “Tolerance of concentration” to a box labeled “S D G 17”, and a line extends downward to a text box containing “Limited competition and innovation”, “Unequal participation in partnerships”, “Impacts on sustainable business practices”, and “Limited access to resources and markets”. A line extends downward from “Macroeconomic populism” to a box labeled “S D G 9”, and a line extends downward to a text box containing “Impacts on infrastructure investment”, “Disrupted industrial development”, “Reduced support for innovation”, and “Financial instability and uncertainty”.Economic nationalism framework and impact on SDGs. Source: Developed by authors; Data Source: UNCTAD, Trade and Development Report: Structural Transformation for SDGs
The research highlighted the consequences of economic nationalism in attaining the SDGs of the United Nations. The analysis of the study explores how various forms of protectionism policies hinder the achievement of sustainable economic growth and create chaos in lower-income economies. There is increasing evidence that restrictive trade policy negatively impacts the attainment of sustainable poverty reduction. Trade restrictions are one of the key factors hindering higher living standards through lower productivity, lower competition, fewer choices for consumers, and higher prices in the marketplace. The results of Frieden and Torres (2022) justify this outcome by noting how free trade helps to reduce poverty. The study identified the commercial policy intervention in various policies and its impact on different SDGs. The research has identified that many developing and developed economies are tilting the commercial playing field in favor of domestic firms (or against international entry) in many ways. Therefore, the research has expanded its analysis to encompass policy interventions beyond traditional customs procedures that directly impact the import or export of goods. The study examined a broad spectrum of “behind the border” policy interventions, such as localization rules, financial incentives, subsidies allocated to import-competing and exporting firms, restrictions on cross-border payments for traded goods and measures affecting public procurement from foreign suppliers. It became apparent that certain SDGs and their corresponding indicators present challenges in establishing a direct connection to trade-related policies. For example, SDG 5 (“Achieve gender equality and empower all women and girls”) and SDG 16 (“Promote peaceful and inclusive societies for sustainable development, ensure access to justice for all, and build effective, accountable institutions at all levels”) illustrate this difficulty.
However, for other SDGs where trade policies may have a more direct impact, the study identified 61 SDG indicators linked to seven specific SDGs (1, 2, 3, 6, 7, 9 and 14), demonstrating clear connections to various policy measures. These findings align with prior research, such as Gehlen et al. (2020) and Mikecz (2019), which highlighted how economic nationalism has fueled trade conflicts. The analysis also explored contrasting perspectives, including Donnelly (2018), who argued that trade liberalism fosters economic growth and national prosperity.
Political ideology is another important factor associated with economic nationalism. The growing far-right political ideology advocates the idea of economic nationalism in various countries, notably in Brazil in the West and India in Asia (Kumral, 2024). The analysis reveals that, in advanced countries, there has been a significant increase in the coverage of trade policy, immigration restrictions, industrial policy, anti-multilateralism and macroeconomic policy within party platforms over the studied period. These subjects are gaining significance and getting more attention. Parties in emerging-market economics, on the other hand, have witnessed significant increases in the importance attributed to FDI policies and macroeconomic policy. Conversely, trade policy, anti-multilateralism and competition policy (specifically, tolerance of concentration) have become less obvious in their political platform. Previous studies conducted by De Bolle and Zettelmeyer (2019) identified similar outcomes by applying a regression approach and scoring each policy.
The findings show that immigration has consistently been a fringe issue in the political parties’ agenda of emerging-market economies, while it is still a major concern in the agenda of advanced economies. Conversely, FDI policies have received greater attention in the platforms of emerging-market parties, particularly in the post-Global Financial Crisis (GFC) period, compared to those of advanced countries (Ogbuabor et al., 2024). Although the share of advanced country party platforms referencing FDI has increased, it remains relatively low, at just 27%. It is important to consider these trends when examining the priorities and policy orientations of political parties in different contexts. The data provides valuable insights into the changing landscape of electoral platforms and the issues that shape political debates in advanced and emerging-market economies. Restricting immigration can limit access to labor markets and opportunities for economic mobility, particularly for migrants seeking better employment prospects. This can hinder poverty reduction efforts, especially if immigrants are unable to escape poverty in their home countries due to limited economic opportunities (Ganga, 2024). Further, this restriction impedes access to healthcare services for migrants, including preventive care, treatment and health promotion programs. This can lead to increased health disparities and challenges in achieving universal health coverage and well-being for all, including migrants. These issues hinder the attainment of SDGs 1 and 2. This result is associated with the outcomes study conducted by Ain et al. (2025), which explored how policies are essential to overcome and eradicate SDGs 1 and 2.
In terms of promoting sustainable development, there are remarkable variations among income groups concerning their adoption of commercial policy interventions. The most significant improvements were seen in both low-income and high-income countries. Interestingly, middle-income countries, including both lower and upper-middle-income nations, have shown minimal changes in their commercial policies since the initiation of Agenda 2030. This finding is particularly relevant in examining the evidence about the seven SDGs examined in this study. It is worth noting that the World Bank classifies 108 nations as middle-income World Bank (2022), which holds significant weight in assessing the overall global progress recorded since the establishment of Agenda 2030.
6. Conclusion
The research has identified various protectionism policies of different countries instigated by the economic nationalist ideology and their impact on the attainment of a sustainable agenda. The findings of the research explored the direct and indirect impacts of economic nationalism on the SDGs with valid outcomes with the support of data. It is noticeable from the study that trade barriers and protectionist policies cut down on global trade, limit market access, and disrupt supply chains. This leads to impaired economic growth, diminished employment opportunities and higher consumer prices. Moreover, results also highlighted that trade wars and retaliatory actions induced by economic nationalism created uncertainties and undermined global cooperation, hindering efforts to confront common global issues.
The intertwined nature of the SDGs requires global collaboration, open markets, and free exchange of goods, services and capital expenditure. Economic nationalism, with its trade restrictions and protectionist practices, often weakens these critical factors. It hampers the progress made in achieving the goals associated with poverty reduction, climate action, gender equality and sustainable economic growth. By identifying the difficulty and possible trade-offs, policymakers can work towards a more sustainable approach that encourages both domestic economic interests and the attainment of the SDGs. International collaboration and inclusiveness are vital to address the challenges raised by economic nationalism and encourage a more affluent and sustainable future for all.
7. Practical implications
The research results of this study have numerous practical consequences for policymakers, stakeholders and international organizations participating in the pursuit of the SDGs in the framework of economic nationalism. Policymakers need to incorporate SDGs into their economic nationalist policies. This consolidation should consider the potential impacts of protectionist measures on different aspects of sustainable development. By aligning economic nationalist policies by using the SDGs, countries are recommended to ensure that their economic strategies make a positive contribution to the broader global sustainability agenda. It is essential that Policymakers actively participate in multilateral forums, trade negotiations and regional partnerships, promoting dialogue and collaboration. Through cooperation, countries can easily find common challenges, avoid trade conflicts, and advance the SDGs more efficiently.
The finding underlined the negative consequences of protectionist policies on low-income countries. Since all these countries struggle to meet sustainable resources, poverty and hunger rates are alarming, developed and developing countries must prioritize policies that encourage a more inclusive and mutually supportive approach. Further, expanding the supply chain based on economy of scale fosters resource allocation and reduces the burden of unemployment and inequalities. Although there is an existing policy that discourages protectionism and trade barriers, it is essential to introduce more effective policies and regulations that prevent countries from adopting stringent, politically motivated regulations that lead to obstacles for overseas trade and commerce.

