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Research Article

Globalization and Economic Growth: Empirical Evidence on the Role of Complementarities

* E-mail: [email protected]

Affiliations Faculty of Management, Universiti Teknologi Malaysia (UTM), Johor, Malaysia, Department of Management, Mobarakeh Branch, Islamic Azad University, Isfahan, Iran

Affiliation Applied Statistics Department, Economics and Administration Faculty, University of Malaya, Kuala Lumpur, Malaysia

• Parisa Samimi, 
• Hashem Salarzadeh Jenatabadi

• Published: April 10, 2014
• https://doi.org/10.1371/journal.pone.0087824
• Reader Comments

This study was carried out to investigate the effect of economic globalization on economic growth in OIC countries. Furthermore, the study examined the effect of complementary policies on the growth effect of globalization. It also investigated whether the growth effect of globalization depends on the income level of countries. Utilizing the generalized method of moments (GMM) estimator within the framework of a dynamic panel data approach, we provide evidence which suggests that economic globalization has statistically significant impact on economic growth in OIC countries. The results indicate that this positive effect is increased in the countries with better-educated workers and well-developed financial systems. Our finding shows that the effect of economic globalization also depends on the country’s level of income. High and middle-income countries benefit from globalization whereas low-income countries do not gain from it. In fact, the countries should receive the appropriate income level to be benefited from globalization. Economic globalization not only directly promotes growth but also indirectly does so via complementary reforms.

Citation: Samimi P, Jenatabadi HS (2014) Globalization and Economic Growth: Empirical Evidence on the Role of Complementarities. PLoS ONE 9(4): e87824. https://doi.org/10.1371/journal.pone.0087824

Editor: Rodrigo Huerta-Quintanilla, Cinvestav-Merida, Mexico

Received: November 5, 2013; Accepted: January 2, 2014; Published: April 10, 2014

Copyright: © 2014 Samimi, Jenatabadi. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The study is supported by the Ministry of Higher Education of Malaysia, Malaysian International Scholarship (MIS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Globalization, as a complicated process, is not a new phenomenon and our world has experienced its effects on different aspects of lives such as economical, social, environmental and political from many years ago [1] – [4] . Economic globalization includes flows of goods and services across borders, international capital flows, reduction in tariffs and trade barriers, immigration, and the spread of technology, and knowledge beyond borders. It is source of much debate and conflict like any source of great power.

The broad effects of globalization on different aspects of life grab a great deal of attention over the past three decades. As countries, especially developing countries are speeding up their openness in recent years the concern about globalization and its different effects on economic growth, poverty, inequality, environment and cultural dominance are increased. As a significant subset of the developing world, Organization of Islamic Cooperation (OIC) countries are also faced by opportunities and costs of globalization. Figure 1 shows the upward trend of economic globalization among different income group of OIC countries.

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https://doi.org/10.1371/journal.pone.0087824.g001

Although OICs are rich in natural resources, these resources were not being used efficiently. It seems that finding new ways to use the OICs economic capacity more efficiently are important and necessary for them to improve their economic situation in the world. Among the areas where globalization is thought, the link between economic growth and globalization has been become focus of attention by many researchers. Improving economic growth is the aim of policy makers as it shows the success of nations. Due to the increasing trend of globalization, finding the effect of globalization on economic growth is prominent.

The net effect of globalization on economic growth remains puzzling since previous empirical analysis did not support the existent of a systematic positive or negative impact of globalization on growth. Most of these studies suffer from econometrics shortcoming, narrow definition of globalization and small number of countries. The effect of economic globalization on the economic growth in OICs is also ambiguous. Existing empirical studies have not indicated the positive or negative impact of globalization in OICs. The relationship between economic globalization and economic growth is important especially for economic policies.

Recently, researchers have claimed that the growth effects of globalization depend on the economic structure of the countries during the process of globalization. The impact of globalization on economic growth of countries also could be changed by the set of complementary policies such as improvement in human capital and financial system. In fact, globalization by itself does not increase or decrease economic growth. The effect of complementary policies is very important as it helps countries to be successful in globalization process.

In this paper, we examine the relationship between economic globalization and growth in panel of selected OIC countries over the period 1980–2008. Furthermore, we would explore whether the growth effects of economic globalization depend on the set of complementary policies and income level of OIC countries.

The paper is organized as follows. The next section consists of a review of relevant studies on the impact of globalization on growth. Afterward the model specification is described. It is followed by the methodology of this study as well as the data sets that are utilized in the estimation of the model and the empirical strategy. Then, the econometric results are reported and discussed. The last section summarizes and concludes the paper with important issues on policy implications.

Literature Review

The relationship between globalization and growth is a heated and highly debated topic on the growth and development literature. Yet, this issue is far from being resolved. Theoretical growth studies report at best a contradictory and inconclusive discussion on the relationship between globalization and growth. Some of the studies found positive the effect of globalization on growth through effective allocation of domestic resources, diffusion of technology, improvement in factor productivity and augmentation of capital [5] , [6] . In contrast, others argued that globalization has harmful effect on growth in countries with weak institutions and political instability and in countries, which specialized in ineffective activities in the process of globalization [5] , [7] , [8] .

Given the conflicting theoretical views, many studies have been empirically examined the impact of the globalization on economic growth in developed and developing countries. Generally, the literature on the globalization-economic growth nexus provides at least three schools of thought. First, many studies support the idea that globalization accentuates economic growth [9] – [19] . Pioneering early studies include Dollar [9] , Sachs et al. [15] and Edwards [11] , who examined the impact of trade openness by using different index on economic growth. The findings of these studies implied that openness is associated with more rapid growth.

In 2006, Dreher introduced a new comprehensive index of globalization, KOF, to examine the impact of globalization on growth in an unbalanced dynamic panel of 123 countries between 1970 and 2000. The overall result showed that globalization promotes economic growth. The economic and social dimensions have positive impact on growth whereas political dimension has no effect on growth. The robustness of the results of Dreher [19] is approved by Rao and Vadlamannati [20] which use KOF and examine its impact on growth rate of 21 African countries during 1970–2005. The positive effect of globalization on economic growth is also confirmed by the extreme bounds analysis. The result indicated that the positive effect of globalization on growth is larger than the effect of investment on growth.

The second school of thought, which supported by some scholars such as Alesina et al. [21] , Rodrik [22] and Rodriguez and Rodrik [23] , has been more reserve in supporting the globalization-led growth nexus. Rodriguez and Rodrik [23] challenged the robustness of Dollar (1992), Sachs, Warner et al. (1995) and Edwards [11] studies. They believed that weak evidence support the idea of positive relationship between openness and growth. They mentioned the lack of control for some prominent growth indicators as well as using incomprehensive trade openness index as shortcomings of these works. Warner [24] refuted the results of Rodriguez and Rodrik (2000). He mentioned that Rodriguez and Rodrik (2000) used an uncommon index to measure trade restriction (tariffs revenues divided by imports). Warner (2003) explained that they ignored all other barriers on trade and suggested using only the tariffs and quotas of textbook trade policy to measure trade restriction in countries.

Krugman [25] strongly disagreed with the argument that international financial integration is a major engine of economic development. This is because capital is not an important factor to increase economic development and the large flows of capital from rich to poor countries have never occurred. Therefore, developing countries are unlikely to increase economic growth through financial openness. Levine [26] was more optimistic about the impact of financial liberalization than Krugman. He concluded, based on theory and empirical evidences, that the domestic financial system has a prominent effect on economic growth through boosting total factor productivity. The factors that improve the functioning of domestic financial markets and banks like financial integration can stimulate improvements in resource allocation and boost economic growth.

The third school of thoughts covers the studies that found nonlinear relationship between globalization and growth with emphasis on the effect of complementary policies. Borensztein, De Gregorio et al. (1998) investigated the impact of FDI on economic growth in a cross-country framework by developing a model of endogenous growth to examine the role of FDI in the economic growth in developing countries. They found that FDI, which is measured by the fraction of products produced by foreign firms in the total number of products, reduces the costs of introducing new varieties of capital goods, thus increasing the rate at which new capital goods are introduced. The results showed a strong complementary effect between stock of human capital and FDI to enhance economic growth. They interpreted this finding with the observation that the advanced technology, brought by FDI, increases the growth rate of host economy when the country has sufficient level of human capital. In this situation, the FDI is more productive than domestic investment.

Calderón and Poggio [27] examined the structural factors that may have impact on growth effect of trade openness. The growth benefits of rising trade openness are conditional on the level of progress in structural areas including education, innovation, infrastructure, institutions, the regulatory framework, and financial development. Indeed, they found that the lack of progress in these areas could restrict the potential benefits of trade openness. Chang et al. [28] found that the growth effects of openness may be significantly improved when the investment in human capital is stronger, financial markets are deeper, price inflation is lower, and public infrastructure is more readily available. Gu and Dong [29] emphasized that the harmful or useful growth effect of financial globalization heavily depends on the level of financial development of economies. In fact, if financial openness happens without any improvement in the financial system of countries, growth will replace by volatility.

However, the review of the empirical literature indicates that the impact of the economic globalization on economic growth is influenced by sample, econometric techniques, period specifications, observed and unobserved country-specific effects. Most of the literature in the field of globalization, concentrates on the effect of trade or foreign capital volume (de facto indices) on economic growth. The problem is that de facto indices do not proportionally capture trade and financial globalization policies. The rate of protections and tariff need to be accounted since they are policy based variables, capturing the severity of trade restrictions in a country. Therefore, globalization index should contain trade and capital restrictions as well as trade and capital volume. Thus, this paper avoids this problem by using a comprehensive index which called KOF [30] . The economic dimension of this index captures the volume and restriction of trade and capital flow of countries.

Despite the numerous studies, the effect of economic globalization on economic growth in OIC is still scarce. The results of recent studies on the effect of globalization in OICs are not significant, as they have not examined the impact of globalization by empirical model such as Zeinelabdin [31] and Dabour [32] . Those that used empirical model, investigated the effect of globalization for one country such as Ates [33] and Oyvat [34] , or did it for some OIC members in different groups such as East Asia by Guillaumin [35] or as group of developing countries by Haddad et al. [36] and Warner [24] . Therefore, the aim of this study is filling the gap in research devoted solely to investigate the effects of economic globalization on growth in selected OICs. In addition, the study will consider the impact of complimentary polices on the growth effects of globalization in selected OIC countries.

Model Specification

Methodology and Data

This paper applies the generalized method of moments (GMM) panel estimator first suggested by Anderson and Hsiao [38] and later developed further by Arellano and Bond [39] . This flexible method requires only weak assumption that makes it one of the most widely used econometric techniques especially in growth studies. The dynamic GMM procedure is as follow: first, to eliminate the individual effect form dynamic growth model, the method takes differences. Then, it instruments the right hand side variables by using their lagged values. The last step is to eliminate the inconsistency arising from the endogeneity of the explanatory variables.

The consistency of the GMM estimator depends on two specification tests. The first is a Sargan test of over-identifying restrictions, which tests the overall validity of the instruments. Failure to reject the null hypothesis gives support to the model. The second test examines the null hypothesis that the error term is not serially correlated.

The GMM can be applied in one- or two-step variants. The one-step estimators use weighting matrices that are independent of estimated parameters, whereas the two-step GMM estimator uses the so-called optimal weighting matrices in which the moment conditions are weighted by a consistent estimate of their covariance matrix. However, the use of the two-step estimator in small samples, as in our study, has problem derived from proliferation of instruments. Furthermore, the estimated standard errors of the two-step GMM estimator tend to be small. Consequently, this paper employs the one-step GMM estimator.

In the specification, year dummies are used as instrument variable because other regressors are not strictly exogenous. The maximum lags length of independent variable which used as instrument is 2 to select the optimal lag, the AR(1) and AR(2) statistics are employed. There is convincing evidence that too many moment conditions introduce bias while increasing efficiency. It is, therefore, suggested that a subset of these moment conditions can be used to take advantage of the trade-off between the reduction in bias and the loss in efficiency. We restrict the moment conditions to a maximum of two lags on the dependent variable.

Data and Empirical Strategy

We estimated Eq. (1) using the GMM estimator based on a panel of 33 OIC countries. Table S1 in File S1 lists the countries and their income groups in the sample. The choice of countries selected for this study is primarily dictated by availability of reliable data over the sample period among all OIC countries. The panel covers the period 1980–2008 and is unbalanced. Following [40] , we use annual data in order to maximize sample size and to identify the parameters of interest more precisely. In fact, averaging out data removes useful variation from the data, which could help to identify the parameters of interest with more precision.

The dependent variable in our sample is logged per capita real GDP, using the purchasing power parity (PPP) exchange rates and is obtained from the Penn World Table (PWT 7.0). The economic dimension of KOF index is derived from Dreher et al. [41] . We use some other variables, along with economic globalization to control other factors influenced economic growth. Table S2 in File S2 shows the variables, their proxies and source that they obtain.

We relied on the three main approaches to capture the effects of economic globalization on economic growth in OIC countries. The first one is the baseline specification (Eq. (1)) which estimates the effect of economic globalization on economic growth.

The second approach is to examine whether the effect of globalization on growth depends on the complementary policies in the form of level of human capital and financial development. To test, the interactions of economic globalization and financial development (KOF*FD) and economic globalization and human capital (KOF*HCS) are included as additional explanatory variables, apart from the standard variables used in the growth equation. The KOF, HCS and FD are included in the model individually as well for two reasons. First, the significance of the interaction term may be the result of the omission of these variables by themselves. Thus, in that way, it can be tested jointly whether these variables affect growth by themselves or through the interaction term. Second, to ensure that the interaction term did not proxy for KOF, HCS or FD, these variables were included in the regression independently.

In the third approach, in order to study the role of income level of countries on the growth effect of globalization, the countries are split based on income level. Accordingly, countries were classified into three groups: high-income countries (3), middle-income (21) and low-income (9) countries. Next, dummy variables were created for high-income (Dum 3), middle-income (Dum 2) and low-income (Dum 1) groups. Then interaction terms were created for dummy variables and KOF. These interactions will be added to the baseline specification.

Findings and Discussion

This section presents the empirical results of three approaches, based on the GMM -dynamic panel data; in Tables 1 – 3 . Table 1 presents a preliminary analysis on the effects of economic globalization on growth. Table 2 displays coefficient estimates obtained from the baseline specification, which used added two interaction terms of economic globalization and financial development and economic globalization and human capital. Table 3 reports the coefficients estimate from a specification that uses dummies to capture the impact of income level of OIC countries on the growth effect of globalization.

https://doi.org/10.1371/journal.pone.0087824.t001

https://doi.org/10.1371/journal.pone.0087824.t002

https://doi.org/10.1371/journal.pone.0087824.t003

The results in Table 1 indicate that economic globalization has positive impact on growth and the coefficient is significant at 1 percent level. The positive effect is consistent with the bulk of the existing empirical literature that support beneficial effect of globalization on economic growth [9] , [11] , [13] , [19] , [42] , [43] .

According to the theoretical literature, globalization enhances economic growth by allocating resources more efficiently as OIC countries that can be specialized in activities with comparative advantages. By increasing the size of markets through globalization, these countries can be benefited from economic of scale, lower cost of research and knowledge spillovers. It also augments capital in OICs as they provide a higher return to capital. It has raised productivity and innovation, supported the spread of knowledge and new technologies as the important factors in the process of development. The results also indicate that growth is enhanced by lower level of government expenditure, lower level of inflation, higher level of human capital, deeper financial development, more domestic investment and better institutions.

Table 2 represents that the coefficients on the interaction between the KOF, HCS and FD are statistically significant at 1% level and with the positive sign. The findings indicate that economic globalization not only directly promotes growth but also indirectly does via complementary reforms. On the other hand, the positive effect of economic globalization can be significantly enhanced if some complementary reforms in terms of human capital and financial development are undertaken.

In fact, the implementation of new technologies transferred from advanced economies requires skilled workers. The results of this study confirm the importance of increasing educated workers as a complementary policy in progressing globalization. However, countries with higher level of human capital can be better and faster to imitate and implement the transferred technologies. Besides, the financial openness brings along the knowledge and managerial for implementing the new technology. It can be helpful in improving the level of human capital in host countries. Moreover, the strong and well-functioned financial systems can lead the flow of foreign capital to the productive and compatible sectors in developing countries. Overall, with higher level of human capital and stronger financial systems, the globalized countries benefit from the growth effect of globalization. The obtained results supported by previous studies in relative to financial and trade globalization such as [5] , [27] , [44] , [45] .

Table (3 ) shows that the estimated coefficients on KOF*dum3 and KOF*dum2 are statistically significant at the 5% level with positive sign. The KOF*dum1 is statistically significant with negative sign. It means that increase in economic globalization in high and middle-income countries boost economic growth but this effect is diverse for low-income countries. The reason might be related to economic structure of these countries that are not received to the initial condition necessary to be benefited from globalization. In fact, countries should be received to the appropriate income level to be benefited by globalization.

The diagnostic tests in tables 1 – 3 show that the estimated equation is free from simultaneity bias and second-order correlation. The results of Sargan test accept the null hypothesis that supports the validity of the instrument use in dynamic GMM.

Conclusions and Implications

Numerous researchers have investigated the impact of economic globalization on economic growth. Unfortunately, theoretical and the empirical literature have produced conflicting conclusions that need more investigation. The current study shed light on the growth effect of globalization by using a comprehensive index for globalization and applying a robust econometrics technique. Specifically, this paper assesses whether the growth effects of globalization depend on the complementary polices as well as income level of OIC countries.

Using a panel data of OIC countries over the 1980–2008 period, we draw three important conclusions from the empirical analysis. First, the coefficient measuring the effect of the economic globalization on growth was positive and significant, indicating that economic globalization affects economic growth of OIC countries in a positive way. Second, the positive effect of globalization on growth is increased in countries with higher level of human capital and deeper financial development. Finally, economic globalization does affect growth, whether the effect is beneficial depends on the level of income of each group. It means that economies should have some initial condition to be benefited from the positive effects of globalization. The results explain why some countries have been successful in globalizing world and others not.

The findings of our study suggest that public policies designed to integrate to the world might are not optimal for economic growth by itself. Economic globalization not only directly promotes growth but also indirectly does so via complementary reforms.

The policy implications of this study are relatively straightforward. Integrating to the global economy is only one part of the story. The other is how to benefits more from globalization. In this respect, the responsibility of policymakers is to improve the level of educated workers and strength of financial systems to get more opportunities from globalization. These economic policies are important not only in their own right, but also in helping developing countries to derive the benefits of globalization.

However, implementation of new technologies transferred from advanced economies requires skilled workers. The results of this study confirm the importance of increasing educated workers as a complementary policy in progressing globalization. In fact, countries with higher level of human capital can better and faster imitate and implement the transferred technologies. The higher level of human capital and certain skill of human capital determine whether technology is successfully absorbed across countries. This shows the importance of human capital in the success of countries in the globalizing world.

Financial openness in the form of FDI brings along the knowledge and managerial for implementing the new technology. It can be helpful in upgrading the level of human capital in host countries. Moreover, strong and well-functioned financial systems can lead the flow of foreign capital to the productive and compatible sectors in OICs.

In addition, the results show that economic globalization does affect growth, whether the effect is beneficial depends on the level of income of countries. High and middle income countries benefit from globalization whereas low-income countries do not gain from it. As Birdsall [46] mentioned globalization is fundamentally asymmetric for poor countries, because their economic structure and markets are asymmetric. So, the risks of globalization hurt the poor more. The structure of the export of low-income countries heavily depends on primary commodity and natural resource which make them vulnerable to the global shocks.

The major research limitation of this study was the failure to collect data for all OIC countries. Therefore future research for all OIC countries would shed light on the relationship between economic globalization and economic growth.

Supporting Information

Sample of Countries.

https://doi.org/10.1371/journal.pone.0087824.s001

The Name and Definition of Indicators.

https://doi.org/10.1371/journal.pone.0087824.s002

Author Contributions

Conceived and designed the experiments: PS. Performed the experiments: PS. Analyzed the data: PS. Contributed reagents/materials/analysis tools: PS HSJ. Wrote the paper: PS HSJ.

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The impact of economic, social, and political globalization and democracy on life expectancy in low-income countries: are sustainable development goals contradictory?

• Published: 18 January 2021
• Volume 23 , pages 13508–13525, ( 2021 )

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• Arif Eser Guzel   ORCID: orcid.org/0000-0001-5072-9527 1 ,
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The 17 Sustainable Development Goals announced by the United Nations are important guides for the development processes of developing countries. However, achieving all of these goals is only possible if the goals are consistent with each other. It has been observed in the literature that possible contradictions between these goals are ignored. Therefore, the main purpose of this study is to investigate whether two sustainable development goals (SDGs) of the UN are contradictory or supporting each other in low-income countries. These SDGs are “Good Health and Well-Being” (SDG3) and “Partnerships for the Goals” (SDG17). For this purpose, the role of globalization and democracy in life expectancy is empirically investigated in 16 low-income countries over the period 1970–2017. While globalization has been used as an indicator of the partnership between countries, democracy has been used as an indicator of accountability and cooperation between governments and societies. According to estimations of the continuous-updated fully modified (CUP-FM) and bias-adjusted ordinary least squares (BA-OLS), globalization and its subcomponents such as economic, social, and political globalization affect life expectancy positively. Democracy also increases life expectancy in those countries. The GDP per capita is also used as a control variable. Our results show that a higher level of per capita income is positively associated with higher levels of life expectancy. In conclusion, no contradiction was found between SDG3 and SDG17 in those countries. Achieving a healthier society requires economic, social, and political integration between governments and societies.

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1 Introduction

The main problem of economics is to increase economic development and social welfare. Increasing the social welfare level is a complex process that depends on economic and non-economic factors. Achieving economic development or increasing the level of welfare depends on achieving and sustaining the main objectives in political, economic, and social areas. Today, development is no longer a process that can be realized through policies implemented by governments alone. It requires cooperation between governments and societies. While cooperation between different countries requires globalization in the economic, social, and political fields, democracy is the way to ensure cooperation between governments and societies.

Health is one of the most important indicators of social welfare. Besides being one of the indicators of development, it is one of the determinants of human capital formation which is necessary for economic development. Individuals living in developed countries live a healthier life compared to those living in less developed countries. While the differences between the levels of development of countries determine the health conditions, at the same time, improvement of public health paves the way for economic development. Healthy people have higher opportunities to earn a higher income than unhealthy people. Individuals with higher incomes can benefit from better nutrition and access to health services. Therefore, economic development and improvement of health conditions represent a two-way process. In this context, the determination of the variables that will enable the achievement of the goal of a healthier society is especially important in explaining the economic differences between developing countries and developed countries. Because of its importance, health-related goals have an important place both among the Millennium Development Goals (MDGs) and the Sustainable Development Goals (SDGs) announced by the United Nations.

The world leaders with the support of international funding organizations announced the Millennium Declaration in September 2000 at the United Nations Headquarters in New York. They committed their nations to a new international partnership to achieve some development targets having with the final deadline of 2015. The Millennium Development Goals (MDGs) consist of 8 goals, 21 targets, and 60 related indicators covering a wide spectrum of development areas such as “End Poverty and Hunger (MDG 1),” “Universal Education (MDG 2),” “Gender Equality (MDG 3),” “Child Health (MDG 4),” “Maternal Health (MDG 5),” “Combat HIV/AIDS (MDG 6),” “Environmental Sustainability (MDG 7),” and “Global Partnership (MDG 8).” As we see, three of the goals are directly associated with the health status of the people. In the deadline of 2015, according to “Health in 2015: From MDGs to SDGs” report of the World Health Organization (WHO), there are improvements in health-related targets such as child health, maternal health, and combat with HIV/AIDS. Globally, HIV, tuberculosis, and malaria targets have been met. Also, the child mortality rate was reduced by 53% and maternal mortality by 43% (WHO 2016 ). On a global view, although health-related problems are largely resolved, the situation is not as good for low-income countries. As shown in Fig.  1 , significant differences exist between developing countries and developed countries in achieving health-related goals.

Source Halisçelik and Soytas (2015)

World Bank Income Groups’ MDGs Index Values in 2015.

According to MDGs, indexes in the context of health status show that the goals desired in terms of health are not attained in low-income countries compared to other income groups. After the deadline of MDGs, the United Nations has announced 17 SDGs, and “Good Health and Well-Being” takes its place as the third goal. Since achieving these goals requires the cooperation of countries and societies, “Partnership for the Goals” is determined as the seventeenth SDG. According to the United Nations ( 2019 ), the main indicators of global partnerships are trade, foreign direct investments, remittances, financial integration technology transfers, data monitoring and accountability, internet usage, and political integration among countries. In our study, while globalization is used as a proxy indicator of global cooperation, democracy is an indicator of cooperation between societies and governments. Democracy also refers to accountability levels of governments.

Globalization can simply be defined as the process of international integration which has economic, social, and political dimensions (Dreher 2006 ). Many countries have adapted to this process and have enjoyed the welfare effects of globalization by implementing necessary economic and institutional transformation. However, some countries still suffer from poor adaption to global markets. According to the KOF Globalization Index published by the Swiss Economic Institute ( 2020 ), low-income countries have the lowest globalization level compared to other income groups. They also suffer from bad health conditions such as low life expectancy, communicable diseases, and high mortality rates according to MDG indexes given above. At this point, the literature is divided into two parts. The first one blames globalization and argues that poverty and as a result of this, low life expectancy derives from the inequality created by globalization itself (Buss 2002 ). The second group mostly focuses on the benefits of free trade, capital mobility, and technology transfers (Rao and Vadlamannati 2011 ). The low-income countries also suffer from low institutional quality in the context of democracy and political rights. According to Freedom House’s list of electoral democracies, the countries without electoral democracy are mostly the low-income countries in the Middle East, North Africa, Sub-Saharan Africa, and Southeast Asia (Freedom House 2019 ).

The main question of our study is to determine whether the problem of low life expectancy in low-income countries is due to the low levels of globalization and weak political institutions in these countries. To answer this question, the role of economic, social, and political globalization and democracy in life expectancy in those countries is empirically investigated. This study provides several contributions to previous literature. First, we provide a new perspective in the context of sustainable development goals. Previous studies mostly focused on how to achieve SDGs, while possible conflicts between the goals were mostly ignored especially in the context of health. Such conflicts between sustainable development goals in the literature have mostly focused on the impact of economic growth and globalization on the sustainable environment (Ulucak and Bilgili 2018 ; Zafar et al. 2019a ). Those studies are mostly addressed the relationship between SDG7, SDG8, SDG13, and SDG17 (Zafar et al. 2019b ). To the best of our knowledge, it is the first study that investigates the relationship between SDG3 and SDG17. It is also important to examine this relationship in low-income countries since they still suffer from low levels of life expectancy, less adaptation to globalization, and poor democratic institutions compared to other income groups. Previous works mostly provide global evidence, while only a few studies focus on less developed countries. Achieving these 17 goals put forward by the United Nations at the same time is possible only if these goals do not conflict with each other. Second, empirical works in previous literature consist of traditional estimation methods called first-generation tests. In the analysis of panel data, the estimators considering cross-sectional dependence are called the second-generation estimators. Cross-sectional dependency simply refers to the situation when the shock that occurs in one country affects other countries as well. The source of this problem encountered in panel data analysis is the economic, financial, and political integration among countries (Menyah et al. 2014 ). The ignorance of cross-sectional dependence results in biased and inconsistent estimates and wrong inferences (De Hoyos and Sarafidis 2006 ; Chudik and Pesaran 2013 ). Low-income countries are mostly African countries where there is a rising trend in terms of integration to global markets and institutions (Beck et al. 2011 ). Using estimation techniques that consider cross-sectional dependence in those countries prevents misleading results. As the literature is divided into two parts about the effects of globalization on human well-being, fresh evidence via robust estimation methods is required in order to provide proper policy implications. To fill this gap, our work provides second-generation estimations.

2 Literature review

To improve the health conditions of a country, the welfare of the poor should be improved as well. Poverty is detrimental to access to health services. Therefore, the positive impact of globalization on health first emerged with its positive effects on economic growth (Labonté et al. 2009 : 10). The effects of globalization on growth were mostly driven by free trade, international specialization, technology transfers, knowledge spillovers, and competitive markets. It also offers broader opportunities for entrepreneurs and paves the way for innovation (Grossman and Helpman 2015 : 101). As expected, poverty rates significantly reduced in the last two decades because of the integration of developing economies to global markets (Harrison 2006 ). When trade liberalization and income increases are considered together, people's access to treatments and medications can be easier and life expectancy may be prolonged. However, we should consider other possibilities in the context of spreading communicable diseases. As Deaton ( 2004 ) mentioned before, access to cheap and easy travel can increase the rate of spread of communicable diseases. Migration is also another fact to take into account. Particularly rising sexual tourism and migrant sex workers increase the spread of sexually transmitted diseases such as HIV/AIDS. But today there are improved treatment methods to solve these problems. Even HIV-infected people can survive with antiretroviral therapy, and it also reduces sexual transmission of the infection (Dollar 2001 ; Cohen et al. 2011 ). Due to the high cost of advanced drugs as in the case of antiretroviral therapy, it should be accepted that people in low-income countries will have trouble accessing the drugs (Buss 2002 ). There are approaches known as the unequal exchange that globalization increases inequality among countries and that developed countries are more profitable from the globalization process (Love, 1980 ). It may also increase domestic income inequality. There are a few studies that came with the conclusion that globalization rises inequality (Dreher and Gaston 2008 ; Ha 2012 ), but Bergh and Nilsson ( 2010 ) suggested a different perspective. Due to extensive R&D investments and scientific activities, developed countries can find new treatment methods and supply advanced drugs. The only way to access that knowledge and these drugs are trade and integration between developed and underdeveloped countries. Globalization can play an important role in improving the health conditions of low-income countries to the extent that it can provide these linkages. One should also notice that wider markets and higher returns are important factors that motivate entrepreneurs. Buss ( 2002 ) claimed that the intellectual property rights of advanced drugs belong to private firms in developed countries, and because of the strong protection of property rights, less developed countries have trouble accessing them. However, rising global human rights became an important step to advance public health issues against economic concerns in the trade of pharmaceutical products.

The human rights approach focuses on how globalization affected disadvantaged people worldwide (Chapman 2009 ). It is an important instrument in the suppression of the inequality created by economic globalization. Because of the pressure on the government about human rights, disadvantaged people are becoming able to meet their basic human needs. The role of political globalization on this point is forcing governments to adopt global institutions. It increases the number of international organizations in which a country is a member. This makes governments more accountable in the global area and forcing them to pay attention to protect human rights. Gelleny and McCoy ( 2001 ) also claimed that integration among countries leads to political stability. Therefore, governments' tendency to violate human rights in order to maintain their power becomes lesser. Moreover, as social dimensions of globalization expand and communication opportunities among people in different countries increase, the possibility of human rights violations being discovered by other people increases (Dreher et al. 2012 ). Governments that know the international sanctions required by these violations have to be more cautious against human rights violations. Social globalization also provides cultural integration among the world’s people, and it changes lifestyles and consumption patterns worldwide. The consequences of this change can have positive and negative effects. First, increased urban population and sedentary lifestyles may enhance prepared food consumption and reduce daily movements which result in rising obesity and diabetes (Hu 2011 ). Second, although rapidly increasing consumption options and diversity are known as welfare indicators, they also can cause stress which is known as an important determinant of many diseases both psychological and physical (Cutler et al. 2006 ). Third, due to knowledge spillovers and communication technology, people can learn about healthy nutrition and protection from communicable diseases. Thus, unhealthy but traditional consumption patterns and lifestyles may change. These days we experience the coronavirus epidemic and we see once again the importance of globalization. Countries are aware of infectious diseases in different parts of the world in a very short time and can take measures to stop the spread of the virus. The changes created by social and political globalization play a major role in this emergence. Social globalization enables people in very remote areas of the world to communicate with each other, while political globalization forces governments to be transparent about infectious diseases.

With economic globalization, increased economic activity may lead to urbanization. One may think about unhealthy conditions of an urban area such as environmental degradation, air and water pollution, higher crime rates, and stress which reduce life expectancy. However, according to Kabir ( 2008 ), people living in an urban area can benefit from improved medical care, easy access to pharmacy, and to the hospitals that use higher technology. They can also get a better education and can enjoy better socioeconomic conditions.

Democracy can be considered as another determinant of life expectancy. In order to solve the health problems of the poor, people should draw the attention of the government. Sen ( 1999 ) claimed that the instrumental role of democracy in solving problems is enabling people to express and support their claims. Thus, the attention of politicians can be attracted to the problems of the poor. Politicians who have never tasted poverty do not have the urge to take action against the problems of the poor at the right time. Another linkage can be established through accountability (Besley and Kudamatsu 2006 ). In democracies, governments have an obligation to account to citizens for what purposes the resources were used. Thus, resources can be allocated to solve important public issues such as quality of life, communicable diseases, and mortality.

Compared to theoretical discussions, previous literature provides a lack of empirical evidence. Barlow and Vissandjee ( 1999 ) examined the determinants of life expectancy with cross-sectional data available in 1990 for 77 developed and developing countries. According to regression results, per capita income, literacy rate, and lower fertility are important determinants of life expectancy while living in a tropical area decreasing it. Another finding in this study shows that health expenditures in those countries failed to increase life expectancy. Following this study, Or ( 2000 ) analyzed the determinants of health outcomes in 21 industrialized OECD countries covering the period 1970–1992. This study presents gender-specific estimates separately for men and women. Fixed effects estimation results reveal a significant negative relationship between public health expenditure and women's premature death. The relationship also occurs for men, while GDP per capita dropped from the regression model due to high collinearity. Furthermore, GDP per capita and the proportion of white-collar workers reduce premature death for both men and women, while alcohol consumption increases it.

Franco et al. ( 2004 ) analyzed the impact of democracy on health utilizing political rights data of 170 countries. Empirical results show that people living in democracies enjoy better health conditions such as longer life expectancy, better maternal health, and lower child mortality. Following this, Besley and Kudamatsu ( 2006 ) investigated the nexus between democracy and health outcomes utilizing panel data from the 1960s to the 2000s. In their study, they used life expectancy at birth and child mortality variables for 146 countries as indicators of health outcomes. According to results, democracy has a positive and significant effect on life expectancy at birth and it also reduces child mortality. Safaei ( 2006 ) also investigated the impact of democracy on life expectancy and adult and child mortality rates with the data of 32 autocratic, 13 incoherent, and 72 democratic countries. According to the OLS estimation results, improving democratic institutions increases life expectancy and reduces child and adult mortality rates. Another finding of the study is that socioeconomic factors such as income, education, and access to health care services are important determinants of health status.

Owen and Wu ( 2007 ) found a positive relationship between trade openness and health outcomes using a panel of 219 countries. Health outcome measures of this study are infant mortality and life expectancy. Trade openness is one of the most important dimensions of globalization.

Kabir ( 2008 ) analyzed the determinants of life expectancy in 91 developing countries. Empirical results obtained are the opposite of the expected. According to results, per capita income, literacy rate, per capita health expenditure, and urbanization have no significant impact on life expectancy. On the other hand, the number of physicians has a positive and significant impact on life expectancy, while malnutrition reduces it. As a dummy variable, living in Sub-Saharan Africa is another factor that reduces life expectancy due to communicable diseases like HIV, malaria, etc.

Bergh and Nilsson ( 2010 ) used a panel of 92 countries in the period 1970–2005 to investigate the relationship between globalization and life expectancy. They used social, political, and economic globalization data separately, and the results show a significant positive effect of economic globalization on life expectancy at birth. But no significant relationship was found between social globalization, political globalization, and life expectancy. They also used average years of education, urban population, the number of physicians, and nutrition as control variables and the effect of economic globalization was still positive and significant.

Welander et al. ( 2015 ) examined the effects of globalization and democracy on child health in their panel data analysis for 70 developing countries covering the period 1970–2009. According to the results, globalization significantly reduces child mortality. In addition, democracy improves child health and it also increases the beneficial effects of globalization on child health. Following this study, Tausch ( 2015 ) analyzed the role of globalization in life expectancy in 99 countries. The results of OLS estimates show that globalization leads to inequality, and therefore, it reduces health performance in terms of life expectancy and infant mortality. These results are contradictory to positive views on the role of globalization in public health. However, in 19 of 99 countries, globalization increases public health performance. Ali and Audi ( 2016 ) also analyzed the role of globalization in life expectancy in Pakistan. According to ARDL estimation results, life expectancy is positively associated with higher levels of globalization. Another study on the Pakistan case proposed by Alam et al. ( 2016 ) concluded that foreign direct investment and trade openness which are important indicators of economic globalization affects life expectancy positively.

Patterson and Veenstra ( 2016 ) concluded that electoral democracies provide better health conditions compared to other countries. Their analysis includes annual data from 168 countries covering the period 1960–2010. Empirical results show democracy has a significant positive impact on life expectancy and it reduces infant mortality.

In their recent study, Shahbaz et al. ( 2019 ) investigated the impact of globalization, financial development, and economic growth on life expectancy. The authors used nonlinear time series analysis methods utilizing the data of 16 Sub-Saharan African countries over the period 1970–2012. Their results show that globalization, financial development, and economic growth affect life expectancy positively in 14 of 16 Sub-Saharan African countries.

The previous literature provides a lack of evidence in the context of globalization, democracy, and life expectancy relationship. There are also methodological weaknesses in previous empirical studies. First, it can be observed that previous studies are mostly based on traditional estimation methods. Second, the panel data analyses are based on the first-generation estimators that assume cross-sectional independence. This assumption is hard to satisfy due to integration among countries. In addition, ignoring the cross-sectional dependence results in inconsistent estimations. Particularly in empirical work in the context of globalization which refers to economic, political, and cultural integration among countries, considering the cross-sectional dependence becomes more important. Therefore, in order to make a methodological contribution to previous literature, we used second-generation panel time series methods considering cross-sectional dependence.

3 Methodology and data

According to the United Nations, achieving sustainable development goals requires global cooperation and partnership. Therefore, “partnerships for goals” has taken its place as the 17th sustainable development target. However, it was emphasized that some sub-goals should be realized in order to reach this goal. These include improving international resource mobility, helping developing countries to attain debt sustainability, promoting the transfer of information and technology between developed and developing countries, an open and rule-based free trade system, encouraging public–private and civil society partnerships, increasing transparency and accountability, and high quality and reliable data (United Nations 2019 ). In our empirical work, economic, social, and political globalization and democracy variables were used as proxies of the subcomponents of SDG17. In addition, the life expectancy at birth variable that mostly used in related literature as a proxy of health status and well-being, it is used in our study as a proxy of SDG3. In this study, we investigated the role of globalization and democracy in life expectancy in 16 low-income countries. Footnote 1 Following Barlow and Vissandjee ( 1999 ) and ( 2000 ), GDP per capita is used as a control variable in order to mitigate omitted variable bias. Our dataset is covering the period 1970–2017. Following the related literature, we present our model as follows:

where lex is life expectancy at birth which refers to the average number of years a newborn is expected to live. Life expectancy at birth data is provided by World Bank ( 2019 ) World Development Indicators. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. The dataset is consisting of a weighted average of collected data from several co-founders. In Eq.  1 , X refers to the KOF Globalization Index developed by Dreher ( 2006 ). This index has been used in previous literature as a proxy of SDG17 (Saint Akadiri et al. 2020 ). The current version of the data published by the Swiss Economic Institute is revised by Gygli et al. ( 2019 ). The globalization variables are between 0–100, and 100 refers to the highest globalization level. In our analysis, we used subcomponents of globalization index such as economic (EC), social (SOS), and political (POL) globalization in addition to overall globalization (GLB). Due to high collinearity, the effects of different types of globalization are analyzed separately. Models 1, 2, 3, and 4 represent the estimations with overall, economic, social, and political globalization indexes, respectively. The democracy variable ( dem ) is provided from the Polity IV project dataset (Marshall and Jaggers 2002 ). While the increases in this indicator represent a more democratic regime, the decreases represent a more autocratic regime. Finally, gdp is real GDP per capita (constant 2010 $) and it is provided from World Bank World Development Indicators. All variables transformed to the logarithmic form except democracy due to negative values. In the estimation of the model, the panel data analysis methods are used.

3.1 Cross-sectional dependence

Traditional panel data methods are based on the assumption that no cross-sectional dependence exists among cross section units. However, this assumption is hard to satisfy due to rising economic, social, and political integration between countries. The estimations do not take this process into account may cause inconsistent results. Such results may also lead to incorrect inferences (Chudik and Pesaran, 2013 ). The existence of cross-sectional dependence in variables and the error term is obtained from the model analyzed with Pesaran ( 2004 ) \({\text{CD}}_{{{\text{LM}}}}\) and Pesaran et al. ( 2008 ) bias-adjusted LM test. These techniques are robust whether N > T and T > N. Therefore, \({CD}_{LM}\) and bias-adjusted LM ( \({LM}_{adj})\) tests are found to be appropriate and their test statistics can be calculated as follows:

Equation  2 shows the calculation of Pesaran ( 2004 ) \({CD}_{LM},\) and Eq.  3 is Pesaran et al. ( 2008 ) bias-adjusted LM test statistic. \({V}_{Tij}\) , \({\mu }_{Tij}\) , and \({\widehat{\rho }}_{ij},\) respectively, represent variance, mean, and the correlation between cross section units. The null and alternative hypothesis for both test statistics; \({H}_{0}\) : No cross-sectional dependence exist; \({H}_{1}\) : Cross-sectional dependence exist.

In the selection of stationarity tests and long-run estimators, the existence of cross-sectional dependence will be decisive. If the null of no cross-sectional dependence is rejected, second-generation methods that assume cross-sectional dependence should be used in order to provide unbiased and consistent estimation results.

3.2 Slope homogeneity

Pesaran and Yamagata ( 2008 ) proposed a method to examine slope heterogeneity in panel data analysis based on the Swamy ( 1970 )’s random coefficient model.

The calculation of the test statistic of Swamy’s model is given in Eq.  4 .

In Eq.  4 , \({\stackrel{\sim }{\beta }}_{i}\) and \({\overbrace{\beta }}_{WFE},\) respectively, indicate the parameters obtained from pooled OLS and weighted fixed effects estimation, while \({M}_{T}\) is the identity matrix. The test statistic obtained from Swamy’s model is improved by Pesaran et al. ( 2008 ) as follows:

where \(\stackrel{\sim }{S}\) is the Swamy test statistic and k is a number of explanatory variables. \({\stackrel{\sim }{\Delta }}_{adj}\) is a bias-adjusted version of \(\stackrel{\sim }{\Delta }\) . \({\stackrel{\sim }{Z}}_{it}\) =k and \(Var\left({\stackrel{\sim }{Z}}_{it}\right)=2k(T-k-1)/T+1\) . The null and alternative hypothesis for both test statistics is given below.

The rejection of the null hypothesis shows that slope coefficients of Eq. 1 are heterogeneous. In the selection of panel data estimation methods, the results of those preliminary analysis are taken into account.

3.3 Unit root test

Pesaran ( 2006 ) suggested a factor modeling approach to solve the cross-sectional dependency problem. This approach is simply based on adding cross-sectional averages to the models as proxies of unobserved common factors. The Cross-sectionally Augmented Dickey–Fuller (CADF) unit root test developed by Pesaran ( 2007 ) is based on that factor modelling approach. This method is an augmented form of Augmented Dickey–Fuller (ADF) regression with lagged cross-sectional average and its first difference to deal with cross-sectional dependence (Baltagi, 2008 : 249). This method considers the cross-sectional dependence and can be used, while N > T and T > N. The CADF regression is:

\({\stackrel{-}{y}}_{t}\) is the average of all N observations. To prevent serial correlation, the regression must be augmented with lagged first differences of both \({y}_{it}\) and \({\stackrel{-}{y}}_{t}\) as follows:

After the calculation of CADF statistics for each cross section ( \({CADF}_{i}\) ), Pesaran ( 2007 ) calculates the CIPS statistic as average of CADF statistics.

If the calculated CIPS statistic exceeds the critical value, it means that the unit root hypothesis is rejected. After the preliminary analysis of unit root, the existence of a long-run relationship between the variables in our model will be investigated via Westerlund and Edgerton ( 2007 ) cointegration test. After this, the long-run coefficients will be estimated using the continuous-updated fully modified (CUP-FM) estimator developed by Bai and Kao ( 2006 ) and Bias-adjusted OLS estimator developed by Westerlund ( 2007 ).

3.4 Cointegration test and long-run relationship

In this study, the cointegration relationship was investigated by Westerlund and Edgerton ( 2007 ) LM bootstrap test. This method considers cross-sectional dependence and provides robust results in small samples (Westerlund and Edgerton, 2007 ). This method is based on the following equation

where \({n}_{ij}\) is an independent and identically distributed process with zero mean and var( \({n}_{ij})\) = \({{\sigma }_{i}}^{2}\) . Westerlund and Edgerton ( 2007 ) suggested following LM test in order to test the null of cointegration

where \({S}_{it}\) is partial sum process of the fully modified estimate of \({z}_{it}\) and \({\widehat{w}}_{i}^{-2}\) is the estimated long-run variance of \({u}_{it}\) conditional on \(\Delta {x}_{it}^{^{\prime}}\) . If the calculated LM statistic is below the critical value, the null of cointegration will be accepted. The critical values will be provided using the bootstrap method in order to prevent cross-sectional dependence.

In the estimation of long-run coefficients, the CUP-FM estimator was used and this method is based on the following regression

where \({\widehat{\lambda }}_{i}^{^{\prime}}\) refers to the estimated factor loadings and \(\hat{y}_{{i,t}}^{ + } = y_{{i,t}} - \left( {\lambda _{i} ^{\prime } \hat{\Omega }_{{F \in i}} + \hat{\Omega }_{{\mu \in i}} } \right)\hat{\Omega }_{{ \in i}}^{{ - 1}} {{\Delta }}x_{{i,t}}\) indicates the transformation of the dependent variable for endogeneity correction. According to Bai and Kao ( 2006 ), CUP-FM estimator is robust under cross-sectional dependence. However, the assumption that the number of common factors (k) is known cannot be satisfied in practice (Westerlund, 2007 ). Therefore, Westerlund ( 2007 ) suggested a bias-adjusted estimator (BA-OLS) following the methodology of Bai and Kao ( 2006 ) except in the context of determining the number of common factors. The author suggested the estimation of k using an information criterion as

where \(IC\left(k\right)\) is the information criterion. In this study, we determined the number of common factors via the Bayesian information criterion (BIC) as follows.

In the equation above, V(k) is the estimated variance of \({\widehat{u}}_{it}\) based on k factors. By minimizing the BIC, we obtain \(\widehat{k}\) . Westerlund ( 2007 ) showed that the estimation of k provides better results compared to CUP-FM estimator assuming k is known. Both of the estimators require cointegrated variables in the long run.

3.5 Empirical results and discussion

The results of Pesaran ( 2004 ) \({CD}_{LM}\) and Pesaran et al. ( 2008 ) bias-adjusted LM tests are given in Table 1 .

The results given in Table 1 show that the null of no cross-sectional dependence is rejected at 1% according to both \({CD}_{LM}\) and \({LM}_{adj}\) test statistics in all variables. In addition, in the error terms obtained from models 1, 2, 3, and 4 the null of no cross-sectional dependence is rejected at 1%. These results show that the methods to be used in the analysis of the stationarity of the variables and the determination of the long-run relationship should consider the cross-sectional dependence.

The results of homogeneity tests developed by Pesaran and Yamagata ( 2008 ) are given in Table 2 . According to the results, the null of homogeneity is accepted at %1 in all models. Therefore, estimators assume parameter homogeneity are used in our analysis.

After the preliminary analysis of cross-sectional dependence, the CADF unit root test developed by Pesaran ( 2007 ) is found to be appropriate for our model because of its robustness under cross-sectional dependence. The results of the CADF unit root test are given in Table 3 .

In the analysis of unit root, constant and trend terms are both considered at level, while only constant term is added at first difference. Maximum lag level is determined as 3, while optimum lag level is determined by F joint test from general to particular. According to results, the null of unit root is accepted for all variables, while calculated CIPS statistics of first-differenced variables exceed 1% critical value. All variables have a unit root, and their first differences are stationary ( \({I}_{1})\) . Therefore, in order to determine the existence of a long-run relationship, we applied Westerlund and Edgerton ( 2007 ) panel cointegration test. This method considers cross-sectional dependence and can be used, while the series are integrated in the same order. The results are shown in Table 4 .

Constant and trend are both considered in the analysis of cointegration, and critical values are obtained from 5000 bootstrap replications. The results show that the null of cointegration is accepted for all models. There is a long-run relationship between life expectancy, globalization, democracy, and GDP per capita. After determining the cointegration relationship, we estimated long-run coefficients utilizing CUP-FM and BA-OLS estimators proposed by Bai and Kao ( 2006 ) and Westerlund ( 2007 ), respectively.

The long-run estimation results given in Table 5 show that overall, economic, social, and political globalization are positively associated with life expectancy at 1% significance level according to both CUP-FM and BA-OLS estimators. The results show that a 1% increase in globalization index increases life expectancy %0.014 and %0.015 according to CUP-FM and BA-OLS estimators, respectively. The impact of economic, social and political globalization indexes is 0.013%, 0.011%, and 0.015% according to CUP-FM estimation results while 0.014%, 0.012%, and 0.017% according to both estimators, respectively.

Our results confirms the findings of Owen and Wu ( 2007 ), Ali and Audi ( 2016 ), and Shahbaz et al. ( 2019 ) who found a positive relationship between globalization and life expectancy. Our empirical work also supports the evidence of Bergh and Nilsson ( 2010 ) in terms of positive effect of economic globalization on life expectancy. While the authors found no significant impact of social and political globalization on life expectancy, our results show that life expectancy is positively associated with both social and political globalization. The results we found contradict Tausch ( 2015 )’s evidences in 80 of 99 countries. However, according to his results, in 19 of 99 countries, globalization affects health positively. When these countries are examined, it is seen that 14 of them are countries in the low and lower-middle income groups. In this sense, it can be said that the evidence we found for low-income countries is in line with the author's evidence. As Dreher ( 2006 ) mentioned, despite its possible inequality effects, the net effect of globalization on development is mostly positive and our empirical work supports that idea. The effect of democracy on life expectancy is also positive and significant at 1% which confirms the findings of Franco et al. ( 2004 ) and Besley and Kudamatsu ( 2006 ). In electoral democracies, people living in poverty and suffering from health problems can easily attract the attention of policymakers compared to autocracies. This leads to the reallocation of resources to solve the primary problems of the society. In the context of sustainable development goals, our results show that there is no conflict between SDG3 (good health and well-being) and SDG17 (partnerships for the goals). The improvement of the health conditions of the poor countries depends on global partnership and economic, social, and political integration among countries. In addition, democracy is an important tool in achieving the goal of a healthy society, as it fosters accountability, transparency, and partnership between governments and the societies they rule. As stated in the introduction section, low-income countries show low performance in terms of health-related sustainable development goals, and their connections with global markets are weak compared to other countries. At the same time, democratic institutions are not developed. Our work supports the idea that in order to achieve SDG3, global partnership and democracy are required.

The GDP per capita that used as a control variable has a positive impact on life expectancy at a 1% level. These results support the evidence of Barlow and Vissandjee ( 1999 ), Or ( 2000 ), and Shahbaz et al. ( 2019 ). Individuals living in countries with high per capita income are expected to have higher welfare and have a longer life expectancy (Judge, 1995 ). In low-income countries where people still suffer from having difficulty in meeting basic human needs, increasing per capita income may lead to better nutritional status, easier access to advanced treatment methods and technology.

4 Conclusion

In this study, the effects of globalization and democracy on life expectancy are empirically investigated in low-income countries. While globalization and democracy indexes are used as proxy indicators of “Partnerships for the Goals (SDG 17),” life expectancy used a proxy of “Good Health and Well-Being (SDG 3).” With this, it is aimed to examine the existence of contradiction between those SDGs. In the estimation of the long-run relationship between the variables, second-generation panel data analysis methods that consider cross-sectional dependency are used. According to the results, the globalization index and its subcomponents such as economic, social, and political globalization are important instruments to achieve a healthier society. In addition, higher levels of democracy lead to higher levels of life expectancy. Finally, GDP per capita growth improves health status of countries.

The findings obtained from our study show that economic, social, and political integration of countries and democracy accelerate the process of achieving a healthier society. Therefore, it is seen that SDG3 and SDG17 targets are compatible with each other. In order to achieve SDG3, economic, social, and political integration between countries should be encouraged and democratic institutions should be improved. Policy makers should remove the barriers on globalization, and they should promote participation on international organizations and public–private and civil society partnerships.

Those countries are Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, The Gambia, Haiti, Madagascar, Malawi, Mali, Nepal, Niger, Rwanda, Sierra Leone, and Togo.

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Guzel, A.E., Arslan, U. & Acaravci, A. The impact of economic, social, and political globalization and democracy on life expectancy in low-income countries: are sustainable development goals contradictory?. Environ Dev Sustain 23 , 13508–13525 (2021). https://doi.org/10.1007/s10668-021-01225-2

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The State of Globalization in 2021

• Steven A. Altman
• Caroline R. Bastian

Trade, capital, and information flows have stabilized, recovered, and even grown in the past year.

As the coronavirus swept the world, closing borders and halting international trade and capital flows, there were questions about the pandemic’s lasting impact on globalization. But a close look at the recent data paints a much more optimistic picture. While international travel remains significantly down and is not expected to rebound until 2023, cross-border trade, capital, and information flows have largely stabilized, recovered, or even grown over the last year. The bottom line for business is that Covid-19 has not knocked globalization down to anywhere close to what would be required for strategists to narrow their focus to their home countries or regions.

Cross-border flows plummeted in 2020 as the Covid-19 pandemic swept the world, reinforcing doubts about the future of globalization. As we move into 2021, the latest data paint a clearer — and more hopeful — picture. Global business is not going away, but the landscape is shifting, with important implications for strategy and management.

• Steven A. Altman is a senior research scholar, adjunct assistant professor, and director of the DHL Initiative on Globalization at the NYU Stern Center for the Future of Management .
• CB Caroline R. Bastian is a research scholar at the DHL Initiative on Globalization.

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The world economy will need even more globalization in the post-pandemic 2021 decade

Farok j. contractor.

Management and Global Business Department, Rutgers Business School, Rutgers University, 1 Washington Park, Newark, NJ 07102 USA

Instead of the dire predictions of a post-pandemic world characterized by increased global risks, decoupling of economies, shake-up of global value chains, and the retreat of globalization, this article proposes that the changes induced by heightened nationalism and protectionism will be marginal rather than fundamental in nature. These marginally higher risks can easily be handled and ameliorated by multinational enterprises through alternate cross-border business strategies and emerging technologies. Moreover, the paper gives reasons why the future world economy will need even more globalization.

Résumé

Au lieu des sombres prédictions d’un monde post-COVID 19 caractérisé par des risques mondiaux accrus, le découplage des économies, le bouleversement des chaînes de valeur mondiales et le recul de la mondialisation, cet article propose que les changements induits par le renforcement du nationalisme et du protectionnisme soient de nature marginale plutôt que fondamentale. Ces risques marginalement plus élevés peuvent facilement être gérés et atténués par les entreprises multinationales (EMN) grâce à des stratégies transfrontalières alternatives et aux technologies émergentes. En outre, l’article donne les raisons pour lesquelles la future économie mondiale aura besoin d’une mondialisation encore plus poussée.

En lugar de las predicciones nefastas de un mundo post-COVID-19 caracterizado por un aumento de los riesgos globales, la desarticulación de las economías, la sacudida de las cadenas de valor global, y la marcha atrás de la globalización, este artículo propone que los cambios inducidos por el nacionalismo y proteccionismo intensificado serán marginales y no fundamentales en su naturaleza. Estos riesgos marginalmente más altos pueden ser manejados fácilmente y mitigados por las empresas multinacionales (MNEs por sus iniciales en inglés) mediante estrategias de negocios transfronterizos y tecnologías emergentes. Más aún, este artículo da razones por las cuales la economía mundial futura necesitará aún más la globalización.

Em vez das terríveis previsões de um mundo pós-COVID19 caracterizado pelo aumento de riscos globais, desacoplamento das economias, desarranjo de cadeias de valor globais e o recuo da globalização, este artigo propõe que as mudanças induzidas pelo nacionalismo e protecionismo exaltados serão de natureza marginal, em vez de fundamental. Esses riscos marginalmente mais altos podem ser facilmente tratados e atenuados por empresas multinacionais (MNEs) por meio de estratégias de negócios internacionais alternativas e tecnologias emergentes. Além disso, o artigo apresenta razões pelas quais a futura economia mundial precisará ainda mais de globalização.

抽象

本文不是对后COVID19世界是以全球风险增加、经济脱钩、全球价值链改组、以及全球化退缩为特征的可怕预测, 而是提出由加剧的民族主义和保护主义所引起的变化在本质上将是边际的而不是根本的。这些稍高的风险跨国企业(MNE)可以通过替代的跨境业务战略和新兴技术轻松地处理和改善。此外, 本文给出了为什么未来的世界经济将需要进一步全球化的原因。

INTRODUCTION

Much has been written about how the global economy will change as a result of the Covid-19 pandemic, including the operations of multinational enterprises (MNEs), and patterns of trade (e.g., Baldwin & Tomiura, 2020 ). Particular attention has been focused on the reconfiguration of international supply chains (e.g., Ivanov & Dolgui, 2020 ; Verbeke, 2020 ) since it was reported, early in the pandemic, that 94% of the Fortune 1000 companies were encountering coronavirus supply chain disruptions (Sherman, 2020 ). The importance and complexity of cross-border supply or value chains may be gauged from an UNCTAD report that estimated that 60% of global trade consisted of intermediate goods and services (i.e., components and semi-finished items), with around a quarter re-crossing borders at least twice before final assembly or release as a finished product, software or service package (UNCTAD, 2013 ).

In a post-pandemic world, it is proposed that a fundamental shift in MNE strategies and managerial thinking will occur and will be skewed towards greater risk aversion, nationalism, and protectionism (Fontaine, 2020 ), pre-existing trends that they say the pandemic has now precipitated. Some go even further, presaging a “legitimacy crisis” for the post-war neoliberal economic order (Abdelal, 2020 ). Others even proclaim the coming “end of globalization” (Young, 2020 ).

This article instead proposes that the fundamental rationales for globalization have not eroded, and that, in a post-pandemic world, there will be an even greater need and utility for globalization. Certainly, the shifts proposed, such as rethinking of global value chain overdependence, have already begun to occur. However, I argue here that these shifts will be marginal rather than fundamental, and that the basic efficiency, comparative advantage, and rationalization arguments for global investment and trade will remain irresistible, even in a post-Covid-19 world.

HOW THIS PAPER IS ORGANIZED

It would first be useful to outline how globalization has multiple dimensions measured by scholars and consultancies, in order to frame the argument. Next, the paper will review the fundamental rationales or justification for international business, while recognizing its occasional negative externalities. 1 The following sections will argue that changes in the organization and configuration of multinational operations, in response to external factors such as rising nationalism and risk-aversion, will be marginal rather than fundamental. The concluding section will highlight why the world will need even more globalization and cross-border collaboration in the future.

MEASUREMENTS FOR GLOBALIZATION

International Business scholarship is about tracking cross-border movements. The most common measures include traded goods and services, and foreign direct investment (FDI) flows in and out of nations. Data on these are easily found in sources such as the World Investment Report ( 2020 ), the World Bank, and OECD. Critics decry the stagnation of the value of FDI and trade measured in current dollars in the post-2008 period, but this belies the fact that the 2008–2019 numbers averaged as much as ten times their 1990 levels. 2 Some scholars, from a short-term perspective focusing only on the post-2008 period, have taken an excessively pessimistic view, ignoring the fact that most FDI and trade indicators, taken over a long-term trend line, i.e., the 1990s–2020 period, show a five- to ten-fold growth (Witt, 2019 ).

To these as globalization indicators, Verbeke, Coeurderoy and Matt ( 2018 ) add cross-border movements of ideas, people, technology, portfolio capital, and “effective institutional practices” by which they imply that multinational companies are the catalysts and conduits of higher standards and practices in the nations in which they invest. Using different data points, the DHL Global Connectedness Index (Altman & Bastian, 2019 ) paints a rosier picture over the 2001–2018 period, with information flows (bandwidth, telephone, and printed publications) shown as growing by 76%, people flows (migrants, tourists and foreign students) growing at 20%, and the FDI Stock/GDP ratio showing an increase of 16 percentage points (or 71% in terms of percentage growth) from 2001 to 2018). The DHL index shows no growth after 2001 in the geographical breadth coverage of multinational enterprises, echoing Rugman and Verbeke’s ( 2004 ) assertion that most MNEs limit themselves to a regional coverage. However, Rosa, Gugler and Verbeke ( 2004 ) calculated an increased global coverage from the Fortune Global 500 list for 2017 3 , stating that “…many large firms are still home-region oriented, but to a lesser extent than before.”

Another significant globalization indicator, almost totally ignored by international business scholars, is the cross-border payments (mainly royalties) for the licensing of intellectual property, which increased from US$26.74 billion (current dollars) in 1990 to $397.23 billion in 2019. 4 Apart from their dramatic growth, international licensing is far less affected by recessions and pandemics, 5 and is likely to continue its fast growth in the knowledge economy of the future. The royalty numbers seem small in absolute terms, until one probes their strategic significance to global commerce and economics. Foreign sales by licensees at least partially substitute for exports or FDI affiliate sales. How do the sales of these three international business strategies compare? Royalty rates range from 2% or less for some music and publications to over 8% for valuable technologies and medicines, so that dividing $397.23 by the 0.08 or 0.02 royalty rates yields estimates of licensee sales (achieved by international licensing of intellectual assets) of $4965 billion to $19,862 billion, respectively. 6 (Notice that the latter number is comparable to the 2019 world merchandise export sales total).

That MNEs are instruments or channels for the upgrading of institutional standards by foreign host governments is documented in studies such as Jude and Levieuge ( 2015 ). The Contractor, Dangol, Nuruzzaman, and Raghunath ( 2020 ) study covering 189 nations shows that better institutional quality attracts larger FDI inflows. The “demonstration effect” of MNEs, in upgrading productivity, sustainability, and environmental and labor standards has been well documented for 45 years (e.g., Caves, 1974 or Moran, Graham & Blomstron, 2005 ). 7 Also, in recent years, the knowledge spillover effects (intended as well as inadvertent) diffusing into the host nation in which the MNE operates have drawn increased attention (Contractor, 2019 ; Prud’homme, 2019 ).

Starting in February 2020, unsurprisingly, most measures of globalization declined. However, the fundamental rationales for international business remain unassailable and even more valid in the post-pandemic period.

THE CONTINUING RATIONALES OF INTERNATIONAL BUSINESS

The inescapable fact is that, into the long future, the world will remain fragmented (into nation states) and unequal (in terms of income, culture, laws, institutions, and business practices). Therein lies the fundamental rationale for international business, which will persist in the post-pandemic period, since inequalities and fragmentation will continue to create aggregation and arbitrage opportunities for MNEs, and for traders and alliances (Ghemawat, 2007 ). This cross-border “bridging” function performed by international firms will continue to benefit not only them, but also the citizens and companies in host nations to which better managerial, productivity, technological and institutional practices are “demonstrated” (Caves, 1974 ; Swenson & Chen, 2014 ). Consumers in both home and host nations benefit from improved methods and organization, and from heightened competition that results in better quality and design, at lower prices. 8 Such fundamental justifications for international business will not diminish but may even increase in the future (as discussed in a later section).

Global Production Scale Economies : Combining demand from several markets to achieve economies of production scale is a core argument of International Business theory (Chandler & Hikino, 2009 ; Dunning, 2015 ; Cantwell, 2015 ). This is especially pertinent when one considers that the vast majority of national markets are small. Among the 20 biggest markets, going downward we quickly have, at ranks 18 and 20, respectively, Saudi Arabia and Turkey, whose GDP is a mere 0.8% each of global total GDP. Below rank 50, we have not only tiny but also politically risky nations. The bottom 174 countries put together comprise only 19.3% of the world economy – a highly skewed distribution indeed. Of course, aggregating standardized demand across nations to achieve scale economies is not always easy. For one thing, there is the contrary pull of local adaptation as a marketing strategy. Katsikeas, Samiee and Theodosiou ( 2006 ) identify inhibiting factors, such as varying customs and traditions, customer characteristics, stage of product life cycle, regulations, technology, and intensity of competition. Their study shows, however, that, when these barriers are overcome and standardization enables scale economies, this does result in superior performance. The skewed distribution of national economic size and inequality will continue to justify the existence of MNEs.

Global Amortization Scale : Most of the costs of a multinational company are not at the factory but in central organizational and R&D overheads. Generally, MNEs exhibit a greater technology intensity and spend more on R&D than comparable domestic firms. Innovations initially have a local root and most R&D is still carried out in the home nation of the firm. However, the technological and other overheads incurred in the MNE’s home country, if spread over many foreign affiliates and markets, reduce the overhead burden (and hence cost) per unit of final production – a luxury that domestic competitors cannot replicate. 9 As economies become more technology-intensive in the future, this attribute of multinational companies will become even more strategically relevant. 10

Specialization and Global Value Chain (GVC) Orchestration : Since February 2020, when the pandemic began, much attention has been focused on the coming need to deepen cross-border integration of global value chains, i.e., to make them more resilient (e.g., Verbeke, 2020 ).The reasoning is that unexpected shocks, such as pandemics, rising nationalism, geo-political frictions, and protectionism, can adversely affect GVCs, which can delay vital supplies, and in the worst case create “stock-outs” and shortages (Ivanov & Dolgui, 2020 ; Sherman, 2020 ). In brief, the hypothesis is that the design or orchestration of GVCs in the future will exhibit greater risk-aversion (Aylor et al., 2020 ), although, undoubtedly, this will vary depending on the sector in question.

This coming “resilience” of supply chains will be manifested in four ways, (1) an increase in the number of suppliers for the same component or item (or lower likelihood of reliance on one sole-source foreign supplier), (2) geographical diversification of supply sources to more than one country, (3) propinquity of supply sources, in terms of both geographical and political “distance”, and (4) Increase in inventory levels at the point of use – all of which represent an increase in cost per unit.

However, I argue that this future shift will only be marginal and not fundamental. For one thing, as Miroudot ( 2020 ) argues, past experiences show how quickly supply chains recover from disruptions, in some cases more than making up for the business lost during the supply interruption. However, in a longer-term sense, the overarching fact remains that much of international business relies on price-based competitiveness. The strategic imperatives of efficiency or cost-reduction, through the “fine-slicing” of a company’s value chain, the dis-internalization (outsourcing) of many of the “slices”, and their dispersion internationally (offshoring), will remain a powerful, inescapable competitive mandate (Contractor et al., 2010 ). This will limit the coming reconfiguration of GVCs to only a marginal or slight shift. As illustrated in Figure  1 , the vertical axis tracks “Cost Per Unit of Procured Item” as well as the company’s “Risk”, while the horizontal axis tracks increasing “Resilience” of a GVC (a composite index constructed from four sub-indicators.

Trade-offs between GVC resilience and cost per unit.

Figure  1 is a representation of the trade-offs calculated by an MNC before and after the pandemic period. That is to say, the Figure  1 framework enables a company to assess whether it should reconfigure its GVC to make it more risk-resistant and resilient (or not). “Resilience” of the GVC increases along the horizontal axis based on four strategic components to be decided by the firm:

• (i) Number of suppliers worldwide for the item,
• (ii) Geographical diversification (number of distinct source countries),
• (iii) Propinquity: weighted average of the political and geographic distance from supply sources to the point of assembly or demand, and
• (iv) Overall inventory levels.

The cost per unit procured is a J-curve, I hypothesize. 11 Often, a sole-supplier (being a quasi-monopoly) charges a higher cost per unit than a situation where competitive pressures between two or more suppliers reduces the cost. However, beyond two or three suppliers, the cost per unit is likely to increase simply because the multiplicity of supply sources increases (1) global logistics cost and (2) transaction costs (Berghuis, & den Butter, 2017 ).

Risk reduces from left to right in the graph. Resilience of the GVC increases towards the right of the x -axis, which reduces supply chain risk – as sources are diversified to more countries, as the weighted average distance from sources to demand points reduces, and as inventory/sales ratios carried for the item in question increase. 12 The optimum position is neither close to the extreme left of the x -axis nor far to the right, but somewhere in the middle.

Optimizing the supply chain is a balance between it being too lean on the above four indicators (in the interest of efficiency and low cost) and being too resilient or risk-averse. For instance, being too risk-averse and increasing inventory by more than a slight extent can put a firm at a competitive disadvantage. There is an echo of one of the variables in the J-curve hypothesis, found in Chen, Frank and Wu ( 2005 ) who showed that the stock market performance was best for US firms that held an intermediate level of inventory, as compared to rival firms that held too low, or too high, an inventory-to-sales ratio. This suggests that firms that are overly cautious in the future and carry too much inventory compared to rivals, or increase the number of worldwide suppliers by more than a marginal extent, will suffer a worse performance. (The exact shape of the J-curve will vary from one subsector to another.)

“Risk” can be a strategic perception, but can also be estimated by the MNE’s Supply Chain Department using probabilistic models that include the likelihood of “stock-outs” and their consequences for each GVC configuration, in terms of lost sales or profits, as well as reputation. 13 This will of course vary firm by firm. Figure  1 is a schematic representation.

Prior to February 2020, when perceived risks were lower, a MNE could be content to have two or three suppliers worldwide. With higher nationalism and protectionism, the post-pandemic risk curve lifts upwards (the higher dashed line in Figure  1 ), which calls for, or allows, increased resilience (e.g., more suppliers worldwide) – but only to a limited extent – because costs per unit also rise to the right of the x -axis. Sensitivity to risk has increased. However, that will be offset by risk-reducing organizational and technical developments in GVCs, described below.

Hence the argument that the reconfiguration of GVCs will be small or marginal rather than radical. 14 There are two reasons. First, much of globalization is driven by competition between companies based on price and cost. Efficiency and competitiveness require cost-cutting. Only a marginal increase in GVC costs can be tolerated. Second, supply chain risks can (and will) be mitigated by three digital technologies still in their infancy, (1) Blockchain, (2) Integration of Vendor–Buyer Computer Systems and (3) Artificial Intelligence (AI) which produces predictive analytics (Lund et al., 2020 ; Kano & Oh, 2020 ). In general, over the past 30 years, information technology and closer communication between buyers and suppliers has led to the growth of GVCs (Gunasekaran et al., 2017 ). However, even in 2020, to a surprising extent, the computer systems of MNEs are only loosely integrated with those of their foreign suppliers, so that a MNE procurement manager often does not exactly know the status of an order in the foreign factory or service provider.

Blockchain-based contracts lead to greater assurance, lower information asymmetry, and real-time information which reduces uncertainties, risks, and transaction costs (Schmidt & Wagner, 2019 ; Kamilaris, Fonts & Prenafeta-Boldu, 2019 ). Integration of computer systems, or the ability of the MNE to monitor, at any time, the status of the vendor’s production by accessing their servers, reduces uncertainty and helps schedule the MNE’s own sales, inventories, and other processes (Frazzon et al., 2018 ). Systems integrated via 5G will further reduce GVC risks by providing real-time information in transportation pathways (Rundle, 2020 ). Finally, the use of AI that incorporates data from weather, volcanoes, politics, economic cycles, competitor moves, commodity and other price levels, etc., should lead to more accurate forecasting of demand and hence lower risk in the management of GVCs (Lund et al., 2020 ).

In summary, while sensitivity to risk will increase in the post-pandemic era, at the same time there will be countervailing risk-reducing effects from new technologies which will reduce risks by improving the management and coordination of foreign supplier systems. (A fuller discussion of new technologies that reduce risks is taken up in a later section of this paper.) Most pertinently, as noted above, in competition with other global firms, price and cost cutting are of paramount importance. Hence, I argue that the numerical and geographical diversification of suppliers will occur only to a limited or marginal extent.

The imperatives of globalization will continue.

THE OTHER IMPERATIVES OF GLOBALIZATION: MNEs AS INTERNATIONAL BRIDGING AGENTS, TRANSFERORS, AND ARBITRAGEURS

Why do multinational firms exist? In an atomistic, autarkic world, companies would remain domestic or national, and would deal across borders with other firms through contracts. International trade (sales of approximately $23 trillion in goods and services in 2019), as well as an unnoticed but huge set of substitute transactions in the form of international licensing of intellectual assets (also resulting in foreign sales by licensees of $5 trillion upwards 15 in 2019), is legally covered by contracts although some significant portion is between related parties. 16 Sales by MNE affiliates (not counting sales in the MNE’s home nation) trump both at approximately $30 trillion. Whatever the foreign market entry strategy, the multinational firm plays a dominant bridging role.

The MNE as the carrier or transmitter of internalized proprietary capabilities to affiliates in foreign locations, or Internalization Theory (Buckley & Casson, 1976 ), has long lain at the heart of international business scholarship, and this core argument will not disappear in the future, even in a multi-polar, protectionist, or politics-driven world. The proprietary, internalized advantages or capabilities of successful international firms are alternatively described by Verbeke et al. ( 2018 ) as “firm-specific assets” which similarly result in the transfer of technologies (Monteiro, Arvidsson & Birkinshaw, 2008 ), including the occasional reverse flow of ideas and knowledge from affiliates back to headquarters (e.g., Kumar, 2013 ).

MNEs also result in the spread of best practices in management (e.g., Kostova & Roth, 2002 ), human resource management (HRM) (e.g., Ahlvik, Smale & Sumelius, 2016 ), gender equality (e.g., Abe, Javorcik & Kodama, 2016 ), sustainability (e.g., Marcon, de Medeiros & Ribeiro, 2017 ), and ethics (e.g., Johnson, 2017 ). 17 These contributions of MNEs will not disappear but will remain valid, even in a future world that may possibly be more nationalistic or fragmented (Petricevic & Teece, 2019 ).

MNEs are diffusers of knowledge, both unconsciously (like birds or insects that propagate flora), as well as consciously. Even while attempting to keep their core proprietary technologies internalized, there is inevitably a leakage or “spillover” of some knowledge and best practices to local firms through employee mobility or simple imitation. This may be a negative for the international firm, reducing its competitive advantage vis-à-vis local competitors, but for the latter there is a beneficial learning process. For example, Swenson & Chen ( 2014 ) found that the presence of international companies in locations in China resulted in improvement in the productivity, quality, frequency, and revenue capture of exports by local Chinese competitors in those regions.

Similarly, local firms learn as licensees of foreign companies. The World Bank ( 2020 ) reported that cross-border royalty payments for intellectual property crossed $400 billion in 2018. These transactions are covered under a contractual alliance agreement where the licensor, or intellectual asset provider, has a self-interested incentive to teach their foreign partner the auxiliary production techniques beyond just the patent, design, brand, or licensed intellectual asset – for the simple reason that royalties are typically linked to licensee sales, and therefore the licensor has an incentive to help the licensee succeed. Even imports of physical products and services have a learning value to the importer (Grosse & Fonseca, 2012 ).

International investment, trade, and licensing occur because of an arbitragable gap, or “distance”, between nations in terms of knowledge, capital, know-how, and corporate capabilities – a gap that is unlikely to disappear after 2021.

WHY, POST-PANDEMIC, THE WORLD ECONOMY WILL SEE EVEN MORE GLOBALIZATION

The indispensable role played by the multinational enterprise (MNE) as a bridging agent that aggregates demand and arbitrages differences across nations, as well as orchestrates and conduits the cross-border flows of capital (FDI), goods and services (trade), and intellectual assets (in affiliates and in contractual sharing of knowledge and capabilities with licensing and alliance partners 18 ), will not diminish, but remain even more needed in a post-pandemic world. In a world remaining fragmented and unequal, the MNE also plays a salutary role as a catalyst of higher institutional, governance, sustainability, HRM, environmental and ethics standards, both through its own affiliate network (Foss & Pedersen, 2019 ) and by its external influence in countries that still have to catch up with “best practices.”

The pandemic is more an accelerator of changes that were already under way rather than an event that enforces radically new patterns globally. Moreover, the impact of Covid-19 will affect a few nations and sectors more strongly than others. We have indeed seen, in the past three years and only in some nations, marginally more protectionism, nationalism, 19 and calls for greater self-sufficiency. Mimicking trends espoused by the Trump Administration, India’s Modi declared his hope to “… transform India into a more self-reliant country, making the goods and providing the services consumed in the country largely at home” (Roy, 2020 ). However, these trends are not entirely orthogonal to globalization. Waldman and Javidan ( 2020 ) describe this as a “false dichotomy.”

Protectionism and nationalism can even increase the geographical “footprint” of the MNE if trade barriers lead to increased tariff-jumping FDI (Buckley, 2020 ). For example, China’s long tradition of protecting its automobile sector has resulted in substantial FDI investments by western companies from Volkswagen to General Motors to Tesla. Not only do the foreign companies dominate but, for some of them, China is their largest and most profitable market; moreover, Chinese industry has benefited greatly from the transfer of technology, designs, productivity, and best practices to China (Buckley, Clegg, Zheng, Siler, & Giorgioni, 2010 ).

Nationalist policies can sometimes increase globalization, a seemingly paradoxical effect. Glennon’s ( 2020 ) study concludes that the more stringent enforcement of H1-B visas by the Trump Administration has already seen an increase in the offshoring of technological jobs. As a global orchestrator or network organizer, the international firm has more than one conduit of opportunity to enable cross-border transfers. If migration of talent is constrained, it can be replaced by remote virtual work. Observers suggest that, post-pandemic, more service functions will be carried out remotely (Tilley, 2020 ). However, by the same logic (i.e., the “Zoom Effect”), that job can be done even more remotely from Sofia or New Delhi. True, geographical and cultural distances impose higher organizational and transaction costs on the firm (Larson, Vroman, & Makarius, 2020 ), but these can be more than offset by the labor cost saving. Since there is no proposal to restrict the hiring of remote foreign employees, the “Zoom effect” and the growing worldwide familiarity with the “gig economy” can lead to even more offshored work. For example, while cross-border telemedicine faces significant regulatory barriers in advanced nations (Ferreira & Rosales, 2020 ), this is not the case everywhere. Instead of the patient crossing borders to visit the hospital abroad, some diagnoses and treatments will increasingly occur remotely.

Petricevic and Teece ( 2019 ) correctly identify the rekindling of the idea of government intervention in the foreign direct investment process. While most of the rest of the world has been lifting restrictions – liberalizing incoming FDI and eliminating lists of sectors requiring prior governmental approval (UNCTAD, 2019 ) and under the general rubric of “Ease of Doing Business” (World Bank, 2019 ) – the two biggest investors, China and the US, have been tightening scrutiny and vetoing a few proposed investments. The CFIUS (Committee on Foreign Investment in the United States) scrutinizes large FDI proposals for national security 20 concerns and is comprised of nine cabinet members, with the Treasury Secretary as Chair, and aided by senior intelligence officials. Ostensibly, China proclaims itself as a “champion of globalization” (Wang & Quan, 2019 ). China’s new “Foreign Investment Law” promulgated in January 2020 has slightly relaxed inward FDI regulations, reduced its “negative list”, and promises “national treatment” (Dresden & Xia, 2020 ). However, the interventionist hand of the state remains just below the surface.

The few vetoes of FDI proposals in the US, and even rarer such occurrences in Europe, constitute an insignificant fraction-of-one-percent of overall global flows. Anxieties elevated by the pandemic having abated, most countries may become more vigilant, but will resume their welcome towards FDI simply because it adds net value to the host nation. Petricevic and Teece ( 2019 ) go too far in characterizing the future of globalization as a “structural reshaping.” They are correct in highlighting the rising techno-political rivalry between the US and China. Almost their entire paper (except for the first two pages) refers to – and is colored by – this bilateral relationship. 21 While China and the US remain the two biggest economies and direct investors, and they may partially decouple from each other, it is too much of a stretch to extrapolate this possible rivalry to the rest of the 191 nations on the planet. Only a handful of other nations will add some sectors to their list of “strategic industries.” The fact remains that the vast bulk of FDI is in “…non-strategic sectors, such as agriculture, fashion, consumer goods, and even insurance.” (Petricevic & Teece, 2019 , p. 1502). Even in the US, an examination of Chinese FDI investments between January 2007 and June 2020 shows only a small percentage in technology-related sectors (American Enterprise Institute, 2020 ). 22 For all countries’ MNEs seeking to invest in the US, CFIUS conducted 561 reviews for the entire 9-year period, 2009–2017, of which 145 FDI proposals were withdrawn during the investigation, and only 3 or 5 were vetoed by presidential order (Jackson, 2020 ). 23

Buckley ( 2020 ) takes a balanced view, stating that “the fracture (between the US and China) may not be complete, nor be the only global policy change of significance in the post-virus world” (parentheses added). As noted in this piece above, I propose that, after the post-pandemic hiatus, globalization will resume and that changes will be marginal or incremental rather than structural.

REDUCING RISKS IN A POST-PANDEMIC WORLD

We are likely to see a world where perception of risks will be marginally increased. However, these risks will be ameliorated or counteracted by changes that were already underway, which augur an even more coordinated global economy:

• More sophisticated information systems amongst MNEs and Traders : Volatility, Uncertainty, Complexity and Ambiguity (VUCA) are reduced “…by the increased collection of information …with greater transmission and coordination of informational resources…” (Buckley, 2020 ). Liesch and Welch ( 2019 ) make a similar argument. In practical terms, this means linking and integrating the computer systems of GVC buyers and vendors in real-time, so that the exact status of an order under production, as well as a vendor’s schedules, are instantly available and transparent to the MNE or importer. Second, in transit across borders, 5G and satellite technology will further pinpoint the tracking of shipments (Rundle, 2020 ). Third, Bughin et al. ( 2017 ) show the huge – as yet unutilized – potential for the increased use of AI in global scanning and strategic planning, including demand forecasting in various national markets, forecasting and managing political or weather-related risks, input costs and selling prices, and optimization of transport and logistics, as well as culturally-adaptive marketing. These coming information technology advances are poised to reduce risk.
• Closer relationships between suppliers and buyers : Verbeke ( 2020 ) and Kano ( 2018 ) suggest that even stronger joint “relational governance”, accompanied by a willingness to be flexible when disruptions threaten GVCs, can further handle risk by substituting for, or augmenting, the digitized information flows discussed above. This echoes somewhat with the rather venerable concept of “keiretsu” in Japanese supply chains, where interfirm cooperation, homophily, and a familial relationship were aided by symbolically small cross-shareholdings between the focal firm and its constellation of suppliers (e.g., Lincoln, Gerlach, & Takahashi, 1992 ).

Risk can also decrease with the use of alliance partners in R&D. The increased complexity of development and finished product design means that even large MNEs do not possess internally sufficient knowledge or efficiency for all aspects of research. The R&D portion of the value chain is increasingly “dis-internalized” and slices of the development process shared with partners. This results in speedier and lower-cost results; moreover, developmental risks are shared and reduced for the focal MNE (Contractor et al., 2010 ). Occasionally, valuable novel or idiosyncratic ideas can be accessed by including innovation partners in emerging nations (Ramamurti, 2016 ).

• A weighted - average decrease in “distance” : This can be measured multidimensionally as per Berry, Guillén and Zhou ( 2010 ) in terms of cultural, political, and geographical distances between the MNE and its network partners). In addition, the GVC network will see (only a partial) locational shift or decoupling between US MNEs and Chinese sources. For example, Ha & Phuc ( 2019 ) show the benefit derived by Vietnam from the relocation of sourcing from China as a result of the Trump tariffs. However, that geographical shift had begun years earlier in reaction to rising Chinese labor rates. 24

Internationally adopted standards lower risk by reducing information asymmetries, providing transparency, comparability, interoperatibility, scale advantages, and accountability, and supplying a common technical language that facilitates global commerce. Technology standards “…directly affect at least 80% of international trade,” according to Purcell, Kushnier and Law ( 2016 ).

I do not aver that common standards cause or trigger globalization. Rather, common technical standards are a necessary precondition and concomitant of globalization – its hidden plumbing. A technological civilization cannot exist without the world, or at least large enough coalitions of firms, adopting common standards. In a quiet, unheralded way under the aegis of organizations such as the ISO (International Organization for Standardization) and the World Bank, cross-border industry conferences and multinational committees have quietly hammered out jointly acceptable protocols on almost the entire range of products and services, from clinical trials 25 (Idänpään-Heikkilä, 1994 ), to air traffic (International Civil Aviation Guidelines), to financial transactions and remittances, to satellite and GPS receivers, to mobile telephony, to phyto-sanitary standards in food, horticulture, and medicine (Ramakrishnan, 2016 ), to insurance, to cybersecurity (Wilkins, 2020 ), to piping and instrumentation, to smart buildings, to corporate social responsibility (CSR), and to ethics (Nadvi, 2008 ), etc. A complete list of products and services under international standards would require thousands of pages.

A huge boost to the global expansion of trade occurred in 1965 when, after a three-year negotiation, ISO delegates from a dozen nations finalized the design of standardized shipping containers, resulting in an at least three-quarters reduction in freight and insurance costs, compared with the old system of “breakbulk freight”, or the loading of individual cargo of miscellaneous shapes and sizes into the belly of a ship (Levinson, 2020 ).

The standardization process is not only incomplete, but, with new developments and accelerating technical growth, international standardization will be even more needed in the future.

CONCLUSIONS

After the pandemic, the “new normal” may be marginally different, but globalization in its various manifestations will continue, and global coordination will be even more important for collective intergovernmental action to meet future pandemics, climate change, emerging technologies, and international tax-avoidance, to set common product and technical standards, and to address the growing sensitivity of customers worldwide to sustainability, ethics, and CSR issues.

A greater degree of nationalism and protectionism need not impede FDI and in some cases may even increase it by inducing more tariff-jumping investments. Alliances such as international joint ventures and contractual alliances such as licensing of intellectual property, circumvent protectionism, and substitute for exports or FDI as a means of reaching foreign customers. In fact, over 1990–2019, licensing royalties (and the foreign sales resulting from the transfer of intellectual assets) have been the fastest-growing method of international business (10.13% compound annual growth rate or CAGR) versus world exports (6.62% CAGR) versus FDI flows (6.37% CAGR). 26 In comparing the global strategic importance of FDI, trade, and licensing (loose contractual alliances), scholars have to be careful with the raw numbers. All three foreign market entry alternatives are biased by international tax avoidance, double-counting, and under-reporting biases. Nevertheless, it is clear that all three international business indicators have grown at a faster rate than the average growth rate of GDP (4.9% CAGR). This illustrates the value and continued rationale for cross-border commerce, and the unique role played by the MNE as a agent that aggregates demand and arbitrages differences across nations, as well as orchestrates and conduits the cross-border flows of capital (FDI), goods and services (trade), and intellectual assets (in affiliates and in contractual sharing of knowledge and capabilities with licensing and alliance partners),

The art of global management has always been to seek the optimum middle ground between integration and fragmentation, between standardization and adaptation, and between resilience or assurance on the one hand and efficiency/cost reduction on the other. The global manager knows how to manage risk. In MNEs and governments, there will be a greater awareness of political and GVC risks. However, at the same time, this article has highlighted several risk-reducing methods and emerging technologies whereby global risks can be ameliorated. A small or marginal increase in surge or spare capacity (for “strategic” items), a small increase in inventories at point-of-demand, and the number of suppliers can slightly increase procurement costs per unit.

On the other hand, these incremental costs can be reduced or avoided altogether by implementing better information-gathering systems, 5G surveillance and monitoring, blockchain and other integration of vendor–buyer computer systems, AI-based demand and inventory prediction, relationship-based alliances (Kano & Oh, 2020 ), and the continuing evolution and consensus on common technical and governance standards.

One should not overstate the current rift between the US and China as a portent of the business environment to come. In certain technologies, such as 5G, there may indeed be an unfortunate bifurcation of technical standards. However, overall, the “Brussels Effect” 27 is likely to play a more powerful, albeit quiet role in shaping global commerce (Bradford, 2020 ). EU rules and standards, adopted around the world, on issues ranging from green technology, data protection (GDPR), antitrust and competition rules, ethics, international law, arbitration, and technical standards ranging from AI to zucchini 28 (and to a lesser extent California standards) exert a disproportionate extraterritorial influence leading to “harmonization” and a lower-risk strategic planning environment.

We are building a global technological civilization, undergirded by common understandings, consensus, and cooperation. History has had examples of U-turns. The glories of Rome, Chang An, and Pataliputra were followed by some darker periods. However, today, the cross-border flow of information, spread of education, literacy, knowledge, and technology have progressed to a global scope and developed a nascent global consciousness, which makes it more difficult (although not impossible) for regression. The post-pandemic world is likely to need, and witness, even more globalization.

• Individual examples can easily be found where the direct and indirect costs of an international investment project, or a particular kind of trade, are higher than the benefits it produces. However, that does not obviate the unequivocal overall net benefits produced by globalization. Admittedly also, the net benefits produced by globalization are not shared equally across nations, some of which may have had their industrialization stage in economic development prematurely aborted by the shift of manufacturing to more dynamic producers like China (Rodrik, 2016 ; Larson et al., 2016 ), as well as the general shift to a services-based global economy (Levinson, 2020 ). However, these are not issues pursued in this Point article.
• From World Bank data: https://data.worldbank.org/indicator/BX.KLT.DINV.CD.WD . And, of course, the numbers for 2020, and a year or two following, are likely to represent a significant drop.
• The number of MNEs from the Fortune Global 500 list, deemed by the authors to be “global,” increased from a count of 9 in 2004 to 36 firms in 2017, using their perhaps overly-stringent criterion that a global firm must have “at least 20% of their sales in all three regions of the triad, but less than 50% in any one region.” This does not measure sales of a company’s products worldwide through trade, contractual alliances, and minority equity joint ventures, all of which are not separately counted in UNCTAD or World Bank data. Nevertheless, Rugman & Verbeke’s ( 2004 ) overall conclusion is correct, that most MNEs principally serve their home and contiguous regions.
• https://data.worldbank.org/indicator/BX.GSR.ROYL.CD .
• Most royalties are linked by a formula as a percentage of sales achieved by the licensee/alliance partner. Compared to the profit of a FDI affiliate, licensee sales are axiomatically far less volatile, for two reasons/ First, sales of any firm are far less volatile than profits. Secondly, royalties are steady because the agreement remains in force for the number of years of the alliance agreement. Returns from licensing out intellectual assets are hence intrinsically more steady and assured compared with foreign affiliate dividends. FDI flows are also more volatile and sensitive to business cycles, because a FDI involves a conscious initial investment decision, made in return for the expected discounted cash flow of future affiliate profits. Hence FDI falls off in recessionary periods.
• The latter figure is an overestimate. However, unfortunately we have no comprehensive information on international royalty rates, this being a gaping data hole in international business and economics studies. We only have some sketchy figures from consultants (e.g, Podlogar, 2018 ). Using the typical “reasonable” average royalty rate of 5%, touted by licensing negotiators, by dividing the royalty remittances by a factor of 0.05, we get an estimate for foreign licensee sales stemming from licensed intellectual property at $7945 billion. This is smaller than “World Exports  (i.e. Sales)” or “Sales by MNEs Outside Their Home Country”. Nevertheless, licensing of intellectual assets constitutes an inescapably important, albeit neglected, component of global strategy.
• Undoubtedly, a tiny minority of FDI cases produce negative effects on the host country. However, that does not obviate the overall conclusion of the beneficial impact of FDI.
• Of course, there are some net costs of international business and globalization. However, these are, on average, more than offset by the benefits.
• This argument sounds similar to the advantage of larger firm size, except that the MNE, by expanding abroad, transcends or escapes the operational size limitation that constrains domestic competitors. Also, this paragraph addresses the benefits of size or global scale but with a specific focus on the amortization of R&D and central overheads in the MNE as opposed to scale economies in production, where factory-level fixed costs, spread over more units of output, reduce average cost per production unit.
• Easy scalability, accompanied by network effects, can also occasionally lead to oligopolies and monopolies, as we see in digital services such as Google or Facebook (e.g., Smyrnaios, 2018 ). However, this is not a widespread phenomenon and is not the focus of this article.
• The author, despite many searches, has been unable to find a Supply Chain Management paper where the cost per unit of procurement has been theorized or mapped as a function of the number of supply sources. This is likely a research opportunity.
• For US-based firms, the inventory-to-sales ratio had been declining since 1981 but then increased from a low of 1.25 in 2010 to 1.39 in June 2020 according to the US Census Bureau. https://www.census.gov/mtis/www/data/pdf/mtis_current.pdf .
• Again, the author has been unable to find papers that go in this direction, in which case this is a research opportunity for Supply Chain Management or IB scholars.
• With rising geo-political tensions, perhaps the most noticeable changes in global GVCs will be for supply sources from China, where the plateauing labor force has also seen labor costs escalate at well above China’s inflation rate between 2010 and 2020.
• Estimates can range up to an unlikely $19 trillion, depending on our assumption of the global average royalty rate, which is unknown.
• The data have to be interpreted with great circumspection, however, because of double-counting and interrelatedness. UNCTAD ( 2013 ) estimated that a multinational firm functioning as either exporter or importer was involved in three-quarters of world trade. Some reports suggest that intrafirm trade is 40% of the world total. In the licensing or contractual alliance category, an unknown fraction of deals, for tax-avoidance reasons, are between a MNE and its own foreign affiliate as licensee. All said, the MNE plays a dominant role in all three modes of foreign entry.
• The upgrading of standards may be weaker, but only in some cases, when FDI is between emerging nations. For example, the literature on Chinese FDI in Africa admits that there is an overall economic benefit, but takes a more circumspect view of managerial and HRM practices used by Chinese managers within their affiliates and projects in Africa (e.g., Jackson, 2014 ).
• Many IB scholars still seem to be not fully aware that IJVs are today covered by as detailed and long an agreement as in contractual or “non-equity” alliances, both of which are based on the letter of the agreement, as well as the relationship, although the relationship is, on average, stronger and deeper in IJVs than in contractual alliances (Velez-Calle, 2018 ; Contractor & Reuer, 2019 ). Both lie along a spectrum that can be described as “quasi-internalization.”
• The various aspects and nuances of nationalism are a complex subject which deserves a more richly textured analysis than can possibly be covered in this article.
• What comprises “national security” is of course open to question and to political considerations.
• The word “China” is not seen in the first 771 words of the introduction to Petricevic and Teece’s ( 2019 ) paper. However, “China” then occurs as many as 224 times throughout the rest of their article.
• In the largest 20 Chinese investments between 2009 and 2017 which exceeded $2 billion, aircraft leasing and food (pork) companies were the two biggest American targets, others including innocuous sectors such as entertainment, textiles, tourism, real estate, and consumer white goods. In the top-20 list, there were four technology companies, but these included peripherals such as printers (Lexmark) and personal computers (IBM personal computer division purchased by Lenovo).
• Of course, the numbers do not include prospective Chinese investments that may not have been initiated in the first place, because of fear of refusal.
• For the foreseeable post-pandemic future, the shift away from Chinese sources is likely to be small, partial and manageable because (1) other nations like Vietnam do not have as large a labor pool, (2) to some extent, rising Chinese labor costs have already been offset by the greater use of automation in Chinese factories, (3) it is not easy to replicate the sub-contractor and knowledge clusters in Chinese cities that have specialized in certain product types, and (4) the anti-China animus in the US and some other nations may not escalate further.
• Good clinical practice guidelines developed by the International Conference on Harmonization and first published in May 1996.
• World Bank Data: data.worldbank.org.
• While the US federal government has, at least temporarily, abdicated its role as an exemplar and standard-setter, the European Union (EU) has quietly had a big impact in establishing standards of corporate conduct and trade, as well as technology.
• The EU name for zucchini is “courgettes”.
• https://data.worldbank.org/indicator/BX.GSR.ROYL.CD.

is Distinguished Professor of Management and Global Business at Rutgers Business School, a Fellow of the AIB, author of ten books and over 150 scholarly articles. He holds a PhD and MBA from Wharton, two engineering degrees, MS (Michigan), and BSE (Bombay), and is currently on the Board of the Academy of International Business as President-Elect (2020–2021) and President (2021–2022). Farok’s research on corporate alliances, emerging markets, outsourcing and offshoring, valuation of intangible assets, the technology transfer process, licensing, and FDI has been cited more than 12,000 times. He has taught at leading schools on four continents. Previously, he was an executive with the Tata Group, a Fulbright Fellow, Unilever Fellow, and consultant for UNCTAD. Farok’s blog covering International Business issues, https://globalbusiness.blog, is read by viewers worldwide.

APPENDIX: IDEAS FOR FURTHER RESEARCH STEMMING FROM THIS POINT ARTICLE

As Figure  1 indicates, GVC resilience can be operationalized using four variables: (1) the number of vendors for the same component or item, (2) geographical diversification of supply sources to several countries, (3) propinquity of supply sources, in terms of both geographical and political “distance”, and (4) increase in inventory levels at the points of use. This lowers risk (from left to right in the dashed lines in Figure  1 exhibiting a negative slope) and increases resilience. However (1) through (4) also could represent an increase in procurement cost per unit.

• • How do companies arrive at a balance between these contrary considerations?
• • How are the four measures to be operationalized? What weightage should be given to each of the variables?

This represents a research opportunity to international business and supply chain scholars, as well as being a fundamental post-Covid-19 question for companies.

It has been hypothesized that the shock of the pandemic will, to some extent, make GVCs more “regional”, and that MNEs will trim excessively long-distance sources of supply. While it is unlikely that there will be large-scale reshoring (Miroudot, 2020 ) or substantial decoupling, as suggested by Petricevich & Teece ( 2019 ), nevertheless some reduction in geographic coverage could occur. If so, in which sectors, regions or products?

Instead of restructuring value chains, as indicated above, Kano ( 2018 ) and Verbeke ( 2020 ) suggest a behavioral or micro-foundational approach. They propose a more “relational” interaction between buyer and supplier. That is to say, a stronger and more intimate linkage between the two, accompanied by a willingness to be flexible – with mutual accommodation – in the face of exogenous shocks, would be congruent with the “structural” adjustments suggested in (1) above, and would make the GVC even more resilient.

Another aspect of strengthening this relationship is technological (Schmidt & Wagner, 2019 ; Kamilaris et al., 2019 ). Integration of buyer–seller computer systems, which would give the MNE access to the vendor’s servers and help it more closely monitor, in real-time, the status of their orders, reduces uncertainty and helps the MNE’s own scheduling of sales and inventories (Frazzon et al., 2018 ). However, here again, giving such access requires trust, which is a microfoundational or behavioral issue.

Interfirm relationships (and GVCs are one example) cover a spectrum from purely contractual ordering, or basic patent licensing unaccompanied by any significant interaction between licensor and licensee, all the way to forming an international joint venture where the managers, engineers, and personnel of the partners “rub shoulders” on a daily basis (Contractor & Reuer, 2019 ). Much of past alliance literature, unfortunately, has used bifurcated dependent variables such as “Equity” JV (EJV) versus “Non-equity” alliances. This a distortion of reality. Actually, the majority of alliances involve some degree of interaction or relationship. For example, even in a contractual alliance, the licensor, after transferring the intellectual property rights and accompanying “know-how” (or unregistered knowledge), will continue to support and help their licensee, out of self-interest. Even more pertinently, in recent years, EJVs are covered by as detailed, or even more detailed, an agreement as are contractual alliances (Velez-Calle, 2018 ; Contractor & Reuer, 2019 ). Research has only partially provided answers to questions such as how to construct or structure an agreement (i.e., with what clauses, depth, and length) depending on the strategic objectives (e.g., resilience, flexibility, irreversibility (Verbeke, 2020 ), and duration) – questions that occur in a world of increasing interorganizational relationships and supply chains.

IB Scholars have long known that, instead of FDI or exporting as a means of achieving sales in foreign markets, the licensing of intellectual assets (registered property such as patents, brands, and copyrights, as well as unregistered trade secrets and tacit “know-how”) results in sales by the licensee in the assigned country/territories. These licensee sales can act as a substitute strategy to FDI or trade—in terms of reaching the foreign customer. The licensee pays the licensor royalties, which are typically a percentage of the sales achieved for the licensed item. Even GVC and IJV agreements often have a licensing component, because the supplier or partner first needs to receive the legal permission to produce the MNE’s designs. Moreover, payment of royalties is most often a deductible expense, reducing the licensee’s corporate tax liability.

We know from World Bank and other data that cross-border royalty payments in 2019 amounted to $397.23 billion, and that these have grown faster, over recent decades, than the growth rate of FDI or trade. 29 The $397.23 billion number is not a sales number, it is only royalties, which are a small percentage of the licensees’ achieved sales.

Astonishingly, nobody knows the strategic significance of international licensing (in terms of foreign market sales, compared with exporting or FDI affiliate sales) because we have no basis, yet, for estimating the foreign sales that result from the international licensing of intellectual assets. If we assume that the global average royalty rate is 8% of sales, by dividing 397.23 by 0.08, we obtain an estimate of foreign licensee sales (achieved by international licensing of intellectual assets) of $4965 billion. If, on the other hand we assume a global average royalty rate of 2%, by dividing 397.23 by 0.02, we arrive at a foreign sales estimate of $19,862 billion (resulting from international licensing). The actual figure is likely somewhere in between. However, we just do not know, because there is no available datum about the global average royalty rate. Either estimate tells us that international licensing is a substantial substitute strategy to FDI or trade as a means of serving foreign markets.

However, there is another conundrum. Is licensing really a substitute strategy to FDI and trade? Only partially. In many cases, licensing is a complement to FDI and trade (as in GVC agreements) and not the main strategic driver. For parties related by ownership such as Parent–Subsidiary or IJV partner–JV company, the licensing portion of the agreement may only be an “add-on” clause for tax-avoidance and legal purposes. On the other hand, when the licensor and licensee are unrelated parties, then the royalty payment is based on an arms-length negotiation. Here again, astonishingly, we have no firm idea of the proportions of related party versus arms-length transactions in international business.

This investigation should be of great interest to MNEs, IB scholars, and tax authorities around the world.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Accepted by Alain Verbeke, Editor-in-Chief, 27 October 2020.

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Effects of Economic Globalization

Globalization has led to increases in standards of living around the world, but not all of its effects are positive for everyone.

Social Studies, Economics, World History

Bangladesh Garment Workers

The garment industry in Bangladesh makes clothes that are then shipped out across the world. It employs as many as four million people, but the average worker earns less in a month than a U.S. worker earns in a day.

Photograph by Mushfiqul Alam

Put simply, globalization is the connection of different parts of the world. In economics, globalization can be defined as the process in which businesses, organizations, and countries begin operating on an international scale. Globalization is most often used in an economic context, but it also affects and is affected by politics and culture. In general, globalization has been shown to increase the standard of living in developing countries, but some analysts warn that globalization can have a negative effect on local or emerging economies and individual workers. A Historical View Globalization is not new. Since the start of civilization, people have traded goods with their neighbors. As cultures advanced, they were able to travel farther afield to trade their own goods for desirable products found elsewhere. The Silk Road, an ancient network of trade routes used between Europe, North Africa, East Africa, Central Asia, South Asia, and the Far East, is an example of early globalization. For more than 1,500 years, Europeans traded glass and manufactured goods for Chinese silk and spices, contributing to a global economy in which both Europe and Asia became accustomed to goods from far away. Following the European exploration of the New World, globalization occurred on a grand scale; the widespread transfer of plants, animals, foods, cultures, and ideas became known as the Columbian Exchange. The Triangular Trade network in which ships carried manufactured goods from Europe to Africa, enslaved Africans to the Americas, and raw materials back to Europe is another example of globalization. The resulting spread of slavery demonstrates that globalization can hurt people just as easily as it can connect people. The rate of globalization has increased in recent years, a result of rapid advancements in communication and transportation. Advances in communication enable businesses to identify opportunities for investment. At the same time, innovations in information technology enable immediate communication and the rapid transfer of financial assets across national borders. Improved fiscal policies within countries and international trade agreements between them also facilitate globalization. Political and economic stability facilitate globalization as well. The relative instability of many African nations is cited by experts as one of the reasons why Africa has not benefited from globalization as much as countries in Asia and Latin America. Benefits of Globalization Globalization provides businesses with a competitive advantage by allowing them to source raw materials where they are inexpensive. Globalization also gives organizations the opportunity to take advantage of lower labor costs in developing countries, while leveraging the technical expertise and experience of more developed economies. With globalization, different parts of a product may be made in different regions of the world. Globalization has long been used by the automotive industry , for instance, where different parts of a car may be manufactured in different countries. Businesses in several different countries may be involved in producing even seemingly simple products such as cotton T-shirts. Globalization affects services, too. Many businesses located in the United States have outsourced their call centers or information technology services to companies in India. As part of the North American Free Trade Agreement (NAFTA), U.S. automobile companies relocated their operations to Mexico, where labor costs are lower. The result is more jobs in countries where jobs are needed, which can have a positive effect on the national economy and result in a higher standard of living. China is a prime example of a country that has benefited immensely from globalization. Another example is Vietnam, where globalization has contributed to an increase in the prices for rice, lifting many poor rice farmers out of poverty. As the standard of living increased, more children of poor families left work and attended school. Consumers benefit also. In general, globalization decreases the cost of manufacturing . This means that companies can offer goods at a lower price to consumers. The average cost of goods is a key aspect that contributes to increases in the standard of living. Consumers also have access to a wider variety of goods. In some cases, this may contribute to improved health by enabling a more varied and healthier diet; in others, it is blamed for increases in unhealthy food consumption and diabetes. Downsides Not everything about globalization is beneficial. Any change has winners and losers, and the people living in communities that had been dependent on jobs outsourced elsewhere often suffer. Effectively, this means that workers in the developed world must compete with lower-cost markets for jobs; unions and workers may be unable to defend against the threat of corporations that offer the alternative between lower pay or losing jobs to a supplier in a less expensive labor market. The situation is more complex in the developing world, where economies are undergoing rapid change. Indeed, the working conditions of people at some points in the supply chain are deplorable. The garment industry in Bangladesh, for instance, employs an estimated four million people, but the average worker earns less in a month than a U.S. worker earns in a day. In 2013, a textile factory building collapsed, killing more than 1,100 workers. Critics also suggest that employment opportunities for children in poor countries may increase negative impacts of child labor and lure children of poor families away from school. In general, critics blame the pressures of globalization for encouraging an environment that exploits workers in countries that do not offer sufficient protections. Studies also suggest that globalization may contribute to income disparity and inequality between the more educated and less educated members of a society. This means that unskilled workers may be affected by declining wages, which are under constant pressure from globalization. Into the Future Regardless of the downsides, globalization is here to stay. The result is a smaller, more connected world. Socially, globalization has facilitated the exchange of ideas and cultures, contributing to a world view in which people are more open and tolerant of one another.

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• Published: 13 March 2024

Global supply chains amplify economic costs of future extreme heat risk

• Yida Sun   ORCID: orcid.org/0000-0001-8160-1358 1   na1 ,
• Shupeng Zhu   ORCID: orcid.org/0000-0002-8446-9329 2 , 3   na1 ,
• Daoping Wang   ORCID: orcid.org/0000-0001-5221-4965 4 , 5   na1 ,
• Jianping Duan 6 ,
• Hui Lu   ORCID: orcid.org/0000-0003-1640-239X 1 , 7 ,
• Hao Yin 8 ,
• Chang Tan 1 ,
• Lingrui Zhang 9 ,
• Mengzhen Zhao 10 ,
• Wenjia Cai   ORCID: orcid.org/0000-0002-4436-512X 1 ,
• Yong Wang   ORCID: orcid.org/0000-0002-5831-9466 1 ,
• Yixin Hu   ORCID: orcid.org/0000-0002-3986-9059 11 ,
• Shu Tao   ORCID: orcid.org/0000-0002-7374-7063 12 &
• Dabo Guan   ORCID: orcid.org/0000-0003-3773-3403 1 , 13  

Nature volume  627 ,  pages 797–804 ( 2024 ) Cite this article

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Evidence shows a continuing increase in the frequency and severity of global heatwaves 1 , 2 , raising concerns about the future impacts of climate change and the associated socioeconomic costs 3 , 4 . Here we develop a disaster footprint analytical framework by integrating climate, epidemiological and hybrid input–output and computable general equilibrium global trade models to estimate the midcentury socioeconomic impacts of heat stress. We consider health costs related to heat exposure, the value of heat-induced labour productivity loss and indirect losses due to economic disruptions cascading through supply chains. Here we show that the global annual incremental gross domestic product loss increases exponentially from 0.03 ± 0.01 (SSP 245)–0.05 ± 0.03 (SSP 585) percentage points during 2030–2040 to 0.05 ± 0.01–0.15 ± 0.04 percentage points during 2050–2060. By 2060, the expected global economic losses reach a total of 0.6–4.6% with losses attributed to health loss (37–45%), labour productivity loss (18–37%) and indirect loss (12–43%) under different shared socioeconomic pathways. Small- and medium-sized developing countries suffer disproportionately from higher health loss in South-Central Africa (2.1 to 4.0 times above global average) and labour productivity loss in West Africa and Southeast Asia (2.0–3.3 times above global average). The supply-chain disruption effects are much more widespread with strong hit to those manufacturing-heavy countries such as China and the USA, leading to soaring economic losses of 2.7 ± 0.7% and 1.8 ± 0.5%, respectively.

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The evolution and future of research on Nature-based Solutions to address societal challenges

Thomas Dunlop, Danial Khojasteh, … Stefan Felder

Research has been showing a trend in rising temperature and increasing occurrence of extreme heatwaves since the 1950s 1 , 2 . This continuous pattern raises concerns about the potential impacts of climate change and its associated socioeconomic costs. Notable effects of heat stress are on human health and labour productivity. On the one hand, global heat stress makes it difficult for the body to maintain its core temperature, thereby increasing morbidity and mortality from heat stroke 5 , 6 , 7 . Countries across all latitudes, including Russia 8 , the USA 9 , China 5 , Australia 10 and North Africa 11 have suffered from increased heat stress since the deadly European heatwave in 2003 12 , which caused considerable mortality and morbidity. On the other hand, biometeorological studies suggest that heat stress can seriously decrease labour productivity 13 , 14 , 15 , 16 , measured in terms of lost worktime from recommended work/rest ratios during heat stress, reduced work efficiency as estimated from exposure–response functions and self-reported reduced work efficiency 13 , 17 , 18 .

In the context of increasingly integrated global supply chains, the impacts of heat stress are not just confined to specific populations and industrial sectors in low latitudes but extend to wider regions and sectors 19 , 20 , 21 , 22 . For example, a Western European country such as the UK is rarely directly and severely affected by heat stress. However, consumption of beer or coffee in the UK can drop as a result of the severe impact of heat stress on wheat and coffee bean suppliers in Africa and South America 23 . This kind of spillover effect can have important consequences in terms of global food security 24 , 25 , 26 , energy supply 27 and the supply of various mineral products 28 .

The direct mortality and productivity loss resulting from heat stress have been extensively studied. However, the indirect losses due to supply-chain disruptions have not been fully analysed 29 , 30 , as previous literature has either devoted insufficient discussion to the indirect effects by only reporting the total/aggregated effects 31 , 32 , 33 or ignored the amplifying effect of the global trade system on direct losses. As climate change will make the impacts of heat stress worse over time, developing methodologies that allow comprehensive quantifications of both the direct and indirect impacts of heat stress on human systems can help policy-makers to develop more effective climate change mitigation and adaptation policies. In this study, a disaster footprint analytical framework, by integrating climate, epidemiological and hybrid input–output and computable general equilibrium global trade modules, was constructed to provide a comprehensive assessment of the impact of heat stress on socioeconomic systems to 2060, including health loss (excess mortality due to extreme heatwaves), labour productivity loss (decreased daily labour productivity due to higher temperature and humidity) and indirect loss (production stagnation due to lack of supply or demand) across 141 regions and 65 sectors worldwide. Details of our analytical approach are provided in the Methods . In summary, we use the sixth phase of the coupled model intercomparison project phase 6 (CMIP6) 34 , 35 , where 14 widely applied global climate models (GCMs) are averaged to assess future daily temperature and humidity parameters. Grid-scale daily excess mortality (health loss) and labour loss rates (labour productivity loss) are calculated on the basis of empirical functions and statistics from previous studies 36 . On the basis of the above labour constraints in different regions and industries, a hybrid input–output and computable general equilibrium global trade module was developed in the disaster footprint analytical framework to assess the pattern of heat-related economic losses transmitted through the global supply chain. By quantifying indirect effects that were hardly analysed before, this model provides insight into the far-reaching impacts of heat stress across global supply chains and how such impacts evolve spatially and over long time scales. The estimated results are based on static production and trade relationships which may not accurately address the dynamic nexus among industries and countries in the long-term.

This study examines three scenarios combining various representative concentration pathways (RCPs) and shared socioeconomic pathways (SSPs). RCPs represent greenhouse gas concentration trajectories as adopted by the Intergovernmental Panel on Climate Change (IPCC). Each RCP scenario implies different magnitudes of future heat stress. SSPs represent socioeconomic development pathways. Different SSP scenarios imply different amounts of risks of heat stress exposure and societal adaptive capacity. Three SSP–RCP scenarios were considered: SSP 585, SSP 245 and SSP 119. Scenario SSP 585 represents a world of rapid and unconstrained growth in economic output and energy use. Scenario SSP 245 represents the middle of the range of plausible future pathways 37 , reflecting the continuation of historical mitigation efforts 38 . In scenario SSP 119, the world shifts pervasively toward a more sustainable path, emphasizing more inclusive development that respects perceived environmental boundaries. These three scenarios, from high carbon to sustainable trajectories, allow the quantification of the potential economic benefits of ambitious emissions reduction policies that have previously received little attention.

Nonlinear growth trend of global heat-related losses

Figure 1a–d depicts the total global economic loss and the specific components. Under the SSP 119 scenario, the total global gross domestic product (GDP) loss is 0.9% (0.6–1.1%) in 2040 and each component is estimated as follows: health loss (0.5%), labour productivity loss (0.3%) and indirect loss (0.1%). In 2060, global GDP loss slightly decreases to 0.8% (0.4% health loss, 0.3% labour productivity loss and 0.1% indirect loss), amounting to about US $3.75 trillion (values are constant 2020 price). The number of global average heatwave days (definition and calculation detailed in the Methods ) would increase by 24% compared to 2022 and the average annual number of heatwave deaths would be around 0.59 million (0.44–0.74 million). In the case of a high-emissions, high-growth development path, SSP 585, economic losses in 2060 increase by 500% compared to the SSP 119 scenario, up to 3.9% (2.9–4.5%) (1.6% health loss, 0.8% labour loss and 1.5% indirect loss), with a value of about US $24.70 (18.36–28.80) trillion. The global annual heatwave days would be 104% higher compared to 2022 and the global average annual number of heat-induced deaths would increase to around 1.12 million (0.85–1.39 million). The labour and health loss on regional and global scales are close to the results of previous studies 39 , 40 , 41 .

a – d , Evolutionary trends of the four types of losses from 2040 to 2060 under different scenarios (health loss ( a ); labour productivity loss ( b ); indirect loss (supply-chain disruptions) ( c ); and the total losses ( d )). The colours from light to dark, represent the economic losses from the three scenarios SSP 119, SSP 245, SSP 585, respectively. e – p , The spatial distribution of global losses as a percentage of each country’s GDP at midcentury under the SSP 119 ( e – h ), SSP 245 ( i – j , l ) and SSP 585 ( m – p ) scenarios. The values shown are 10-year averages (for example, loss reported in 2060 represents the average loss calculated over the period between 2055 and 2065).

Global economic losses show a nonlinear growth trend with respect to time and degree of heat stress, driven by increased indirect losses. Over time, total losses grow from 1.5% of GDP in 2040 to 2.5% of GDP in 2050 and to 3.9% of GDP in 2060 (Fig. 1d ) under the SSP 585 scenario. However, the proportion of global GDP loss due to supply-chain disruptions is 0.1%, 0.3%, 0.7% and 1.5% per decade from 2030 onwards (Fig. 1c ), showing an exponential-like growth pattern (Extended Data Fig. 5 and Supplementary Figs. 7 and 8 ). Growing indirect losses gradually become the dominant contributor to total losses. Looking at the scenario scale, total GDP losses in 2060 are 0.8% under SSP 119, 2.0% under SSP 245 and 3.9% under SSP 585, of which the losses due to indirect effects are 0.1% of global GDP (13% of total) under SSP 119, 0.5% (25% of total) under SSP 245 and 1.5% (38% of total) under SSP 585. As the degree of heat stress increases progressively, the indirect effects gain more weight in the total losses.

Figure 1e–p explains the mechanism behind the growing weight of the indirect effect in the total losses as the degree of heat stress increases: in terms of spatial patterns, when direct losses are of low to medium magnitude, their impact on the supply network is limited to the regional area; however, when direct losses are severe, they have wider ramifications impacting the supply chain globally and giving rise to further, indirect, losses. Under the SSP 119 scenario, health losses are most significant in South-Central Africa and Eastern Europe (Fig. 1e ); labour productivity losses are concentrated in lower latitudes, including West Africa and South Asia ( Fig. 1f ); indirect losses are concentrated in Central America and East Asia ( Fig. 1g ); in general, Central and Southern Africa, Southeast Asia and Latin America have the most severe total losses ( Fig. 1h ). The spatial patterns of direct economic loss of labour and health under the three scenarios are similar. However, it is noteworthy that persistent and severe heat stress expected under the rapid growth SSP 585 scenario leads to substantial disruptions beyond the regional scale through to global value chains (GVCs). Countries such as Brazil, China and Norway all suffer substantial economic ripple losses. China’s indirect economic losses due to supply-chain disruptions soar from 0.4% under SSP 119 to 2.7% of GDP under SSP 585, Brazil from 0.2% to 2.5% and Norway from less than 0.1% to 2.1%. Although developed countries at high latitudes can mitigate most potential losses through adaptation strategies such as air conditioning under SSP 119 scenario, they remain exposed to risk of declining supply or demand in the GVCs under SSP 585 scenario (Fig. 1o and Supplementary Fig. 10 ). European Union (EU) countries will face considerable indirect losses due to their trading partners’ reduced production capacity of minerals and food products, especially developing countries. Although severely affected countries in South Asia or Africa are not core trading partners of the EU and trade volumes between such countries and the EU are relatively small, indirect economic losses in the EU will be amplified when many of those developing countries are affected by heat stress.

Different sensitivities to heat stress across countries

Different economies face different risk of losses from heat stress, depending on their geographical locations and the position they occupy in the global supply chain. First, countries whose densely inhabited districts are expected to suffer from severe future warming and temperature anomalies, are the most vulnerable to health losses in terms of excess mortality. Under the SSP 119 scenario, South-Central Africa’s GDP loss due to heatwave deaths is 1.8% (1.2–2.5%) in 2060, the highest in the world. It is followed by Trinidad and Tobago (1.7%), Sri Lanka (1.5%) and Indonesia (1.5%; Fig. 2a ). Vulnerability to health impacts depends on the frequency of extreme weather events and the amount of adaptive capacity. For example, Hungary and Croatia suffer considerable health losses, even though in these countries the climate is cooler than in the Middle East and North Africa. Unlike labour losses, which occur in regions with very high average temperature and humidity, health losses depend largely on the variance and abrupt changes in summer temperatures. As climate change will lead to more frequent and intense heatwaves, populations in cooler climatic zones will experience considerable loss of life if the adaptive capacity does not keep pace with the abrupt and sudden changes.

a – c , Top ten climate change-sensitive regions with the most severe health losses ( a ), labour productivity losses ( b ) and indirect losses ( c ) in 2060, under SSP 119 scenario. d – f , Top ten climate change-sensitive countries with the most severe health losses ( d ), labour productivity losses ( e ) and indirect losses ( f ) in 2060, under SSP 585 scenario. The countries marked with black triangles are newly ranked among the most vulnerable countries in 2060 under the SSP 585 scenario compared with SSP 119. The values shown are 10-year averages. Error bars represent 1 s.d. from the mean of decadal data. Upper and lower limits indicate mean + s.d. and mean − s.d., respectively. TTO, Trinidad and Tobago.

Second, low-income emerging economies in the warmest climatic zones are more likely to suffer labour productivity losses. Under the SSP 119 scenario for 2060, countries such as Botswana, Nepal and Nigeria suffer substantial labour productivity losses, up to 1.3%, 1.2% and 1.2% of GDP, respectively (Fig. 2b ). These emerging economies are predominantly located in southern and western Africa (except Nepal), where scorching climates combined with substantial warming over time result in labour-intensive activities during summer months being conducted under increasingly high temperatures. To add insult to injury, most of these countries depend on primary industries such as agriculture, forestry, mining and construction, where workers are mostly outdoors and will be severely affected by extreme heat. For example, agriculture accounts for 21.3% of Nepal’s GDP and 23.4% of Nigeria’s, whereas mining contributes to nearly 28% of Botswana’s GDP 42 . The widespread suspension and reduction of production in the agroforestry and extractive industries due to heat stress will have serious repercussions on national economies and international trade balances. Consequently, these countries are among the most affected by the loss of labour productivity.

Third, small to medium-sized economies with strong and diverse connections to the most affected regions in the GVC, are highly vulnerable to indirect effects. In the context of the SSP 119 scenario, value chains in Latin America and Southeast Asia are the most severely affected (Fig. 2c ). Puerto Rico suffered the highest losses, estimated at 0.8% (0.5–1.1%) of GDP, whereas Venezuela, Malaysia and other Latin American countries, including El Salvador, Panama and Dominican Republic, lost approximately 0.4–0.8% of GDP. Under the SSP 585 scenario, Southeast Asian economies such as Brunei, Malaysia, Singapore and Indonesia suffer the most. These losses stem from strong trade connections with highly vulnerable countries. For example, Brunei and Singapore are exposed to indirect effects as they import nearly 60% of their annual mineral and metal products from China, Malaysia and Indonesia. Caribbean countries like Puerto Rico and Panama generally have less economic diversity and depend heavily on the service sector and international trade. The complex mechanism of transmitting losses along the value chain necessitates thorough consideration by countries for managing future risk of instability across critical industries.

A comparison between the SSP 119 (Fig. 2a–c ) and SSP 585 (Fig. 2d–f ) scenarios shows that losses do not increase uniformly across developing and developed countries when faced with severe climate change impacts, indicating uneven exposures to climate risk. Under the high-emission SSP 585 pathway, a substantial portion of the rapidly escalating economic losses is shouldered by developing countries. Despite Africa contributing less than 5% of global greenhouse gas emissions, 12 countries in the continent, including Rwanda, Botswana, Uganda and Malawi, are projected to suffer some of the most substantial economic losses globally by the midcentury. Several East African countries such as Malawi, Madagascar and Tanzania are highly expected to suffer labour productivity losses of approximately 2.5–4.0% of GDP. Regarding health losses, South-Central Africa and Rwanda experience GDP losses of 8.6% and 7.2%, respectively, almost five times more compared to the SSP 119 scenario. In the SSP 585 scenario, indirect losses become more widespread, affecting both developed and developing economies. Brunei incurs the highest indirect losses at 4.7% (4.0–5.3%) of its GDP, whereas other emerging economies like Paraguay and Indonesia lose around 3.3% of their GDP. These findings demonstrated that the rapid growth of income and air-conditioning penetration in emerging economies under the SSP 585 scenario falls short of counteracting the immense impact of climate change on their economies.

Asymmetric effects of heat stress on global supply chains

Figure 3 highlights the three types of losses for sectors experiencing the highest losses across representative countries. The crop farming, construction and mining sectors are the most affected in most countries, especially in several African and Asian countries that rely on primary industries. For instance, an average summer wet bulb globe temperature (WBGT) above 30 in Tanzania challenges the ability of most outdoor workers to adapt in the midcentury. Sectors requiring workers to be directly exposed to sunlight, such as construction and farming, will suffer a loss of value-added (VA) of 1.9% in 2040 under the SSP 119 scenario. In 2060, rising incomes and a stable climate will result in a slightly reduced VA loss of 0.3% explained by lower labour productivity and health losses. However, under the high warming SSP 585 scenario, the same VA loss increases to 3.9% in 2040 and soars to 8.1% in 2060. In addition, most indoor manufacturing industries suffer a VA loss of 6.0–7.4% in 2060 under the SSP 585 scenario. As demonstrated in Extended Data Fig. 9 , countries like Tanzania, Zimbabwe and other African countries exhibit similar patterns of loss. Countries with comparable loss patterns tend to be situated at low latitudes, particularly in the Middle East, South Asia and Africa—regions most threatened by climate change. Most indoor manufacturing and service industries in developing countries have limited access to air conditioning and, as a result, labour capacity and economic development will be severely undermined by climate change.

a – l , Showing the top five most vulnerable sectors in Tanzania ( a – c ), India ( d – f ), Germany ( g – i ) and Sweden ( j – l ) in 2040 ( a , d , g , j ), 2050 ( b , e , h , k ) and 2060 ( c , f , i , l ). Sectors with absolute VA losses exceeding the median were ranked by percentage of VA losses, from highest to lowest. The length of the bar represents the 10-year average percentage VA loss of a sector. Sectors with the same percentage loss (for example, wheat, rice and cereals) were combined. Colours indicate the three categories of losses under SSP 119: health losses (yellow bars), labour productivity losses (purple bars) and indirect losses (azure bars). The orange and red bars represent the increment of the total loss under SSP 245 and SSP 585 (without differentiating by type of loss), respectively. The four types of countries were derived by machine-learning clustering based on sectoral patterns of economic loss (Supplementary Figs. 11 and 12 and Supplementary Table 3 ).

Non-metallic products and ferrous metals are vulnerable to climate change because of simultaneous supply-chain shocks from both upstream (supply) and downstream (demand). For example, a country such as India is affected directly by high temperatures and indirectly by the close links with countries severely impacted by heat stress. In 2040, losses in non-metallic manufacturing are second only to construction and agriculture sectors at 2.2% of sectoral VA, whereas ferrous metals industry loses 1.4% of VA. These losses can be attributed to both insufficient demand in the domestic construction sector and shortage of minerals and coal supplies from countries in Southeast Asia and Africa (for example, Indonesia and South Africa). In 2060, under SSP 585 scenario, with the increasingly frequent shutdowns in mining and construction industries under extreme summer heat stress, the ferrous metals industry in India suffers the most substantial VA loss at 5.0%, of which more than 70% is due to indirect losses, followed by the loss from non-metallic manufacturing industry at 3.9%. The sectoral patterns of loss in India are characterized by a combination of health, labour and indirect losses. The decline in labour productivity in the domestic construction and plantation industries leads directly to high economic losses in the country’s related value chains. As shown in Extended Data Fig. 10 , countries located at low and middle latitudes, such as China and Vietnam, exhibit similar patterns of loss.

Light manufacturing, including metal products, rubber and plastic products, food processing and beverages and tobacco, are vulnerable to indirect effects because of a lack of raw materials supply, such as minerals, metals, crops, oil seeds and vegetables. For example, under the SSP 119 scenario, metal products and tobacco and beverage manufacturing in Germany lose around 0.3% of VA in 2040. Under the SSP 585 scenario, the economic loss of beverages and tobacco would increase by more than six times in 2060, reaching 2.0% of VA as imports of plantation products (palm oil, soybeans, coffee, spices and so on) from South America, Southeast Asia and Africa decline by around 5% to 8% (Extended Data Fig. 8 ). Losses of metal products rise even faster, reaching 2.4% of VA in 2060. This is because the main producers of raw materials, such as coal and metals, which are essential for the metal products industry, are primarily located in regions that are vulnerable to climate change. This leads to higher losses in the metal product-related chain in most countries with developed manufacturing industries, including Germany, France and Australia (Extended Data Fig. 11 ). These countries have a relatively low share of agricultural GDP (less than 3%), with slight losses. Labour productivity losses are high only in the construction or mining sector, whereas indirect losses are higher in the metal-related manufacturing sector because of insufficient supply from foreign trading partners.

Similarly, high-end machinery, equipment and chemical products industries suffer indirect losses as a result of multilevel cascading effects, even in very cool climates. Losses in these industries, especially in developed countries such as European countries, emerge slowly and are not substantial under the SSP 119 scenario but increase sharply under SSP 245 and SSP 585 scenarios. For example, Sweden’s industry-wide production suffers mainly from indirect losses through supply-chain disruptions and excess mortality due to heatwaves. From 2040 to 2060 under the SSP 119 scenario, impacts on production activities are moderate given the cool climate and dependence on the stable EU supply chain. Sectors like electrical equipment and chemical products experience less than 1% of VA loss, mostly health loss due to sudden extreme heatwaves. However, sector VA losses soar under the SSP 585 scenario. Losses in the mechanical equipment sector increase rapidly, growing by approximately five times compared to the SSP 119 scenario. Ferrous metals (2.2%), electrical equipment (1.9%) and machinery and equipment (1.6%) experience the highest VA losses. Indirect losses become a main constraint in many sectors because national adaptive strategies or close regional trade flows (as in the EU) can no longer support production when heat stress becomes increasingly more severe globally. As shown in Extended Data Fig. 12 , developed economies located at high latitudes, such as Norway and the UK, are characterized by similar loss patterns.

We also analyse the mechanism through which indirect losses from disruptions in international trade flows propagate through national supply chains of specific sectors. Figure 4 illustrates how climate risk propagates through two supply chains, the Indian food production and the Dominican Republic tourism sectors, respectively (see Extended Data Fig. 8 for other typical supply chains). Each of these sectors is important to the respective economies of India (13% of GDP) and the Dominican Republic (18% of GDP) and each is largely dependent on international supply chains. In the case of India’s food sector, we see a pattern of ‘upstream constraint’ through which insufficient upstream supply of intermediates (such as palm oil from Indonesia) impacts the downstream sector and the entire value chain, whereas in the case of tourism in the Dominican Republic, we see a pattern of ‘downstream constraint’—the impact of insufficient downstream demand affects the upstream sector and the entire value chain.

a – d , Trade flows between India food production sector and upstream ( a , c ) and downstream ( b , d ) sectors in 2040 ( a , b ) and 2060 ( c , d ). e – h , Trade flows between Dominican Republic tourism sector and upstream ( e , g ) and downstream ( f , h ) sectors in 2040 ( e , f ) and 2060 ( g , h ). Each bar represents a key trading partner (sector with trade volume above the 50% quartile of trade volumes of the selected sector with all partner sectors) and the length represents the percentage decrease in product flow compared to the base period of 2014. The colours of the bars represent the cohesion level of the particular sector to the Indian food production sector from blue (weak) to red (strong), which is measured by the trade volume between the particular sector and the Indian food production sector. nec, not elsewhere classified.

The supply chain of the Indian food production industry relies heavily on its upstream suppliers, the oil and fat sectors of Indonesia and Malaysia, and as a result it is vulnerable to higher temperatures. The unmitigated warming under the SSP 585 scenario exacerbates the shortage of raw materials. By 2060, palm oil supplies from Malaysia and Indonesia fall by 5.3% and 4.9%. Additionally, Brazilian sugar, Southeast Asian and African vegetables, fruits and nuts are also less available, with a supply decreased by around 4–6%. Consequently, downstream countries, including India, Vietnam, Pakistan and other important trading partners, experience a contraction of imports between 3.7% and 5.1% (Extended Data Fig. 6 ). These impacts can negatively affect food prices and security in both developing and developed countries.

In contrast to the Indian food production industry, the Dominican Republic’s tourism industry is more constrained by downstream demands. Under the SSP 585 scenario, the wealth generated by tourism in the Dominican Republic could drop substantially, as the largest source of foreign visitors, the USA, is likely to reduce annual demand for tourism in the Dominican Republic on average by around 5.5%. Demand from Malaysia and Indonesia is likely to fall by 6.1% and 8.1%. A drop in tourism output, the backbone of the Dominican Republic’s economy, is likely to reduce demand for upstream business services and manufacturing industries by approximately 4.5–4.7%, causing an extra impact on the Dominican national economy. The decline in the tourism sector is also likely to lead to a 4.7% and 4.3% drop in the Dominican Republic’s demand for insurance and financial services from the USA, as well as 4.2% and 4.5% drop in the demand for electronic equipment and chemical products from China (Extended Data Fig. 7 ). Furthermore, a smaller tourism sector leads to the slowdown in the construction of tourism infrastructure and the supply of tourism supporting products, posing considerable risks for tourism investment.

Implication for targeted risk governance and regional cooperation

By coupling climate, epidemiological and economic models, this study investigates the direct impact of heat stress on human activities and the indirect losses across the broader global supply chain. Focusing on the indirect effects of heat stress addresses a substantial gap in the literature. Comprehending the indirect effects of heat stress is crucial for devising effective and targeted adaptation strategies in the context of increasingly complex global supply-chain networks.

Our findings show that supply chains amplify the risk of future heat stress by causing nonlinear economic losses worldwide. In other words, the considerable adverse indirect effects of heat stress across interconnected markets cannot be overlooked. The indirect losses of heat stress highlight the need for countries to strengthen collaboration across global relevant supply-chain stakeholders to achieve successful heat stress adaptation. For instance, our results demonstrate that the impact of a heatwave on the agriculture and food manufacturing industry in India can further lead to a 0.9–2.3% loss of VA in the US food manufacturing industry. If the USA were to support India’s adaptation efforts through technology transfer, they would indirectly be reducing their own losses. These considerations could guide policy-makers working towards global cooperation for future climate change mitigation and heat stress adaptation efforts.

We also illustrate the sensitivity of different countries and sectors to the three types of losses caused by heat stress. For example, Caribbean and Central African countries are more likely to suffer health losses, whereas for low-income countries in Africa and Southeast Asia labour losses are more likely. By contrast, small to medium-sized economies dependent on international trade, such as Brunei, are more exposed to indirect losses. The way heat stress-related costs emerge demonstrates how extensive and diverse impacts from heat stress are propagated through global supply chains, resulting in economic losses to a country or sector that may not be immediately apparent. Our quantitative results provide valuable information for designing more targeted and effective heat stress adaptation strategies.

Our developed model and estimations are subject to uncertainties and limitation (detailed description in Supplementary Information sections  1.1 – 1.3 ). For example, although the disaster footprint module is widely used and performs well for single-country/single-region analyses, the substitutability of products in a multicountry scenario requires further discussion to ensure robustness. To quantify some of the uncertainties, we conducted a comprehensive sensitivity analysis, with details available in the Methods and Supplementary Information section  1 . Specifically, we used different years and versions of the input–output database for comparison to analyse the uncertainty in production and trade structures (Extended Data Fig. 4 ).

Globally, the estimate of the total amount of indirect losses is robust to changes in the data used (GTAP 2011 and GTAP 2014) for the base period. The results of the loss assessment at global scale differ by less than 5% in 2060. Most countries are distributed around the y  =  x line, which suggests a consistent assessment across different trade structures. Regionally, for a few countries, indirect loss assessments can show larger differences. By comparison, we find that when using GTAP 2014 data for the base period, indirect economic losses in East and Southeast Asian countries, such as Singapore, Korea and Japan, are amplified (Extended Data Fig. 4 and Supplementary Fig. 6 ). This can be explained by the fact that, in GTAP 2014, those countries have closer economic ties with climate-sensitive markets, including Malaysia, China, India and Vietnam. For instance, trade between Singapore and emerging economies such as China and Vietnam had increased substantially from 2010 to 2014. According to the Singapore Department of Statistics ( https://www.singstat.gov.sg/ ) and the United Nations Commodity Trade Statistics Database ( https://comtrade.un.org ), China became the largest trading partner of Singapore in 2014, up from fourth place in 2011, whereas Vietnam rose to the 13th largest partner in 2014, from the 20th place in 2011. Conversely, Singapore’s total trade share with the EU and the USA decreased slightly over the same period. Similarly, Japan, Korea and Myanmar developed closer trade relationships with emerging markets such as China, India and Vietnam.

The assessment of indirect losses under different trade relationships offers important insights into the likely supply-chain risks posed by climate change. As Africa, South America and Southeast Asia become increasingly involved in GVCs, the resilience of GVCs to the impacts of climate change must be properly assessed, rather than merely considering scale effects and comparative advantage in terms of economic efficiency.

For parameters such as the maximum stock ratio and excess production capacity, we conducted the experiment several times in the range of possible values from previous studies. For trade substitutability, upper and lower bounds of perfect substitution and non-substitution (traditional static input–output model) were used. We elaborate in more detail about the uncertainty intervals of the parameter for the three main modules and perform a Monte Carlo analysis, including simulation of economic loss dynamics for 10,000 periods (Supplementary Table 2 ). We have also conducted an historical validation using several authentic data sources (robustness tests and validation in the Supplementary Information ), encompassing government statistics, empirical studies and institution reports 3 , 43 , 44 , 45 , 46 , 47 (Supplementary Tables 1 , 6 and 7 , and Supplementary Fig. 1 ), in addition to a comparative analysis of previous studies 33 , 39 , 48 concerning future periods based on CMIP5 data and similar RCP scenarios (Extended Data Fig. 5 ).

Despite the uncertainties, our conclusion that projected climate change will continue to increase heat-related risks globally in the coming decades and that global supply chains will amplify economic losses by spreading indirect losses to wider regions, remain robust. Therefore, in the future, the organization of global supply chains should gradually shift from an exclusive focus on efficiency to one that places equal emphasis on efficiency and resilience. A concerted global strategy to reduce emissions will not only directly protect many people in developing economies from direct economic losses of heat stress but will also maintain resilient and efficient global supply chains and contribute to the long-term, sound development of the global economy.

Our methodology, in essence, combines three modules of climate, health and economy with full validation (Extended Data Fig. 1 ). The integrated model links climate module (estimating future climate parameters including surface air temperature and relative humidity and so on), demographic and health module (simulating future world population dynamics and exposure–response functions to warming) and economic module (dynamic footprint of heat-induced labour loss on global economy and supply chain).

Climate module

Fourteen GCMs involved in the framework of CMIP6 (Extended Data Table 1 ) with ten bias-corrected models from ISIMIP3b 49 , 50 are used to estimate the modelled heat stress projection for the end of the twenty-first century. Five models were randomly averaged several times from the climate model ensemble as a Monte Carlo uncertainty analysis. ERA5 re-analysis data 51 from 1985 to 2022 are used for bias-correction and validation. Climatic parameters such as maximum and average temperature and relative humidity on a daily scale are integrated, which are closely related to future working environment (Supplementary Fig. 9 ).

Many institutes, including International Standards Organization (ISO) and US National Institute for Occupational Safety and Health (NIOSH), use WBGT to quantify different amounts of heat stress and define the percentage of a typical working hour that a person can work while maintaining core body temperature. To facilitate the long-term calculation, we use 18 simplified WBGT, which approximates WBGT well using temperature ( T a ) and relative humidity (RH) 52 , 53 as parameters such as solar radiation and wind speed have higher uncertainty and weaker effects at the global scale. To take into account indoor heat exposures for industrial and service sector workers, we used the approximation that indoor WGBT indoor  = WBGT outdoor  − 4, based on a deduction of the radiation exposure factor from the formula below 18 :

We also calculated the spatial and temporal evolutionary trends in the occurrence of future heatwaves to calculate excess mortality. There is no consistent definition for heatwave worldwide because people may have acclimatized to their local climatic zones and different studies have applied various temperature metrics 54 , 55 . Heatwaves are usually defined by absolute or relative temperature threshold in consecutive days 56 . There are various ways to define a heatwave. For example, the IPCC defines heatwave as “a period of abnormally hot weather, often defined with reference to a relative temperature threshold, lasting from two days to months”, whereas the Chinese Meteorological Administration defined heatwave as “at least three consecutive days with maximum temperature exceeding 35 °C”. Others 31 identified heatwave using the TX90p criterion, that is, when the 90th percentile of the distribution of regional maximum temperatures spanned by data from the period 1981–2010 was exceeded for at least three consecutive days. In our study, two or more consecutive days above the 95% threshold of the 1985–2015 ERA5 daily mean temperature 51 , 57 were defined as a heatwave, which is considered to be a moderate estimation and is widely used in epidemiological studies 36 , 58 , 59 . Several definitions, such as four or more consecutive days above the 97.5% threshold, are used as sensitivity analysis. Considering certain amounts of climate adaptation of the local resident along the warming climate, dynamic heatwave thresholds 60 are defined as part of the uncertainty analysis in this study; that is two or more consecutive days above the 95% threshold of the daily mean temperature between 1985 and the year before the target year were defined as a heatwave (ERA5 data are used for 1985–2014; climate projection data are used after 2015). The use of a dynamic threshold based on both historical and climate projections data helps to incorporate the human adaptation of heat stress in a long-term warming scenario, as reported in recent studies 61 , 62 , 63 , 64 .

Health costs related to heat exposure

Some studies have shown that the health impact of heatwaves could vary substantially with location 65 , 66 . Few studies have investigated the heatwave-induced mortality risk at a global scale 41 , 67 . A primitive health risk function associating heatwave mortality risks with four different climate zones was established by ref. 36 on the basis of a comprehensive study using data from 400 communities in 18 countries/regions across several years (1972–2012). Here, we used the relative risk coefficients (Extended Data Table 2 ) from figure 4 of ref. 36 for four different climate zones (Extended Data Fig. 3 ) to estimate potential heatwave-related death due to climate change on a global scale. The simplified four-climate-zone-based estimation may neglect subregional characters and should be interpreted with caution, as further factors affecting heat-induced death (such as air condition accessibility 68 , age 69 , 70 , 71 , 72 and humidity 73 ) are not included in this study.

The number of excess deaths D hw during a heatwave period was calculated at each grid cell level (0.5°) with the following equation:

POP is the population at the given location consistent with the SSPs 74 . MR is the average daily mortality rate (2009–2019) at the country level obtained from the World Bank 75 . For 37 countries with large territory and more refined data (for example, European Union (including UK), Russia, Ukraine, China, the USA, Canada, Brazil, South Africa, India and Australia), we used state/provincial statistics based on data from national statistical offices (Source, World Bank; state/province level data for European Union, Eurostat 76 ; Russia, The Russian Fertility and Mortality database 77 ; China, China Statistical Yearbook 2019 78 ; the USA, National Institutes of Health 79 ; Brazil, Fundação Amazônia de Amparo a Estudos e Pesquisas 80 ; Canada, Statistics Canada 81 ; Australia, Australian Bureau of Statistics 82 ; India, Ministry of Finance Economic Survey 83 ). RR is the relative risk of mortality caused by heatwaves. HWN is the number of heatwave days for the given year and location (Extended Data Fig. 2 ).

The calculated excess deaths are translated to a social-economic loss on the basis of the value of statistical life (VSL). The concept of VSL is widely used throughout the world to monetize fatality risks in benefit–cost analyses. The VSL represents the individual’s local money–mortality risk tradeoff value, which is the value of small changes in risk, not the value attached to identified lives. The country-based VSL estimation used in this research is adopted from the global health risks pricing study by ref. 84 . The estimation is based on the estimated VSL in the USA (US$ 2019 11 million) and coupled with an income elasticity of 1.0 to adjust the VSL to other countries using the fixed-effects specification. A similar health valuation method has been adopted in past studies 85 , 86 and was recommended in the report of the World Bank 87 . Moreover, a sensitivity test is conducted under the assumption that all life would be valued equally across the world (Supplementary Figs. 2 and 3 ). For such a test, an averaged VSL is calculated by summing up each country’s income-based VSL times its population then dividing by the total population of the world.

Expose function of labour productivity

The increase in daily temperatures affects the efficiency of workers and reduces safe working time. A compromise in endurance capacity due to thermoregulatory stress was already evident at 21 °C. Different studies used similar methods to evaluate the labour loss function. The form of logistic function with ‘S’ shape has become the consensus of the academic community but the specific functional equation and parameters are various in different studies. The loss functions used in mainstream research include exponential function 88 as equation ( 4 ), cumulative normal distribution function 5 , 41 as equation ( 5 ) and so on. In this research, we adopt the cumulative normal distribution function (equation ( 5 )) as our benchmark function because it was extensively applied and case proven in 3-year reports of the Lancet Countdown on health and climate change 5 , 41 , 89 , 90 . Because the Hothaps function (equation ( 4 )) is subject to parameter uncertainty as a result of being based on a few empirical studies, we use it to test for the sensitivity of our estimates (Supplementary Figs. 4 and 5 ). Our methodology identifies three ISO standard work intensity amounts: 200 W (assumed to be office workers in the service industry, engaged in light work indoors), 300 W (assumed to be industrial workers, engaged in moderate work indoors) and 400 W (assumed to be construction or agricultural workers, engaged in heavy work outside). For example, to calculate workability loss fraction in India’s food production sector (300 W, indoor), we bring the corresponding parameters (Extended Data Table 3 ) and WBGT indoor into equation ( 5 ). Previous studies have tended to ignore indoor workforce loss, assuming that the indoor workforce was very low under current climate condition or protected by air conditioning 91 . However, a growing number of studies have proved that future indoor labour losses cannot be underestimated 31 . For example, only 7% of households in India possess an air conditioner, despite having extremely high cooling needs. Considering the severe adaptation cooling deficit in emerging economies 92 , indoor labour losses must be fully considered in global-scale studies. This study uses the climate–income–air conditioner usage function published by ref. 93 to assess the rate of air conditioning protection in conjunction with the per capita income of each country under each SSP scenario. Higher per capita income in each country leads to higher air-conditioning penetration, whereas the climate base determines the rate and trend of increase in air-conditioning penetration (elasticity of penetration to income). In our study, we improved the function by replacing cooling degree days (CDDs) with indoor WBGT, as CDDs only consider temperature neglecting humidity. Only the indoor workforce under air conditioning, will be protected from heat-induced loss.

Of which the parameters for a given activity level (Prod mean and Prod SD , defined as the amount of internal heat generated in performing the activity) are given in Extended Data Table 3 , and ERF is the error function defined as:

To calculate average daily impacts, we use an approximation for hourly data based on the 4 + 4 + 4 method implemented by ref. 14 . We assume that 4 h per day is close to WBGT max and 4 h per day is close to WBGT mean (early morning and early evening). The remaining 4 h of a 12 h daylight day is assumed to be halfway between WBGT mean and WBGT max (labelled WBGT half ). The analysis above gives the summer daily potential workability lost in each grid cell at each amount of work intensity and environment (200–400 W, indoor or outdoor). By combining this with the dynamic population grid under each SSP scenario (see Supplementary Fig. 13 for comparison with static population setting), we aggregate to obtain country-scale labour productivity losses. In the disaster footprint model, we adopt the approach presented by ref. 5 which defines the timeframe for computing labour productivity losses as the warm season (June to 30 September in the Northern Hemisphere and December to 30 March in the Southern Hemisphere) to adjust the overestimation of the risk of moderate hot temperature, as the model is more applicable to sudden and strong shocks rather than moderate changes throughout the year.

Global disaster footprint analysis module

The global economic loss will be calculated using the following hybrid input–output and computable general equilibrium (CGE) global trade module. Our global trade module is an extension of the adaptive regional input–output (ARIO) model 20 , 94 , 95 , which was widely used in the literature to simulate the propagation of negative shocks throughout the economy 96 , 97 , 98 , 99 . Our model improves the ARIO model in two ways. The first improvement is related to the substitutability of products from the same sector sourced from different regions. Second, in our model, clients will choose their suppliers across regions on the basis of their capacity. These two improvements contribute to a more realistic representation of bottlenecks along global supply chains 100 .

Our global trade module mainly includes four modules: production module, allocation module, demand module and simulation module. The production module is mainly designed for characterizing the firm’s production activities. The allocation module is mainly used to describe how firms allocate output to their clients, including downstream firms (intermediate demand) and households (final demand). The demand module is mainly used to describe how clients place orders to their suppliers. And the simulation module is mainly designed for executing the whole simulation procedure.

Production module

The production module is used to characterize production processes. Firms rent capital and use labour to process natural resources and intermediate inputs produced by other firms into a specific product. The production process for firm i can be expressed as follows,

where x i denotes the output of the firm i , in monetary value; p denotes type of intermediate products; \({z}_{i}^{{\rm{p}}}\) denotes intermediate products used in production processes; va i denotes the primary inputs to production, such as labour ( L ), capital ( K ) and natural resources (NR). The production function for firms is f ( · ). There is a wide range of functional forms, such as Leontief 101 , Cobb–Douglas and constant elasticity of substitution production function 102 . Different functional forms reflect the possibility for firms to substitute an input for another. Considering that heat stress tends to be concentrated in a specific short period of time, during which economic agents cannot easily replace inputs as suitable substitutes, might temporarily be unavailable, we use Leontief production function which does not allow substitution between inputs.

where \({a}_{i}^{{\rm{p}}}\) and \({b}_{i}\) are the input coefficients calculated as

where the horizontal bar indicates the value of that variable in the equilibrium state. In an equilibrium state, producers use intermediate products and primary inputs to produce goods and services to satisfy demand from their clients. After a disaster, output will decline. From a production perspective, there are mainly the following constraints.

Labour supply constraints

Labour constraints during heat stress or after a disaster may impose severe knock-on effects on the rest of the economy 21 , 103 . This makes labour constraints a key factor to consider in disaster impact analysis. For example, in the case of heat stress, these constraints can arise from employees’ inability to work as a result of illness or extreme environmental temperatures beyond health threshold. In this model, the proportion of surviving productive capacity from the constrained labour productive capacity ( \({x}_{i}^{{\rm{L}}}\) ) after a shock is defined as:

Where \({\gamma }_{i}^{{\rm{L}}}(t)\) is the proportion of labour that is unavailable at each time step t during heat stress; \((1-{\gamma }_{i}^{{\rm{L}}}(t))\) contains the available proportion of employment at time t .

The proportion of the available productive capacity of labour is thus a function of the losses from the sectoral labour forces and its predisaster employment level. Following the assumption of the fixed proportion of production functions, the productive capacity of labour in each region after a disaster ( \({x}_{i}^{{\rm{L}}}\) ) will represent a linear proportion of the available labour capacity at each time step. Take heatwaves as an example; during extreme heatwaves that last for days on end, governments and businesses often shut down work to reduce the risk of serious illnesses such as pyrexia. This imposes an exogenous negative shock on the economic network.

Constraints on productive capital

Similar to labour constraints, the productive capacity of industrial capital in each region during the aftermath of a disaster ( \({x}_{i}^{{\rm{K}}}\) ) will be constrained by the surviving capacity of the industrial capital 30 , 96 , 104 , 105 , 106 . The share of damage to each sector is directly considered as the proportion of the monetized damage to capital assets in relation to the total value of industrial capital for each sector, which is disclosed in the event account vector for each region \(({\gamma }_{i}^{{\rm{K}}})\) , following ref. 107 . This assumption is embodied in the essence of the input–output model, which is hard-coded through the Leontief-type production function and its restricted substitution. As capital and labour are considered perfectly complementary as well as the main production factors and the full employment of those factors in the economy is also assumed, we assume that damage in capital assets is directly related with production level and, therefore, VA level. Then, the remaining productive capacity of the industrial capital at each time step is defined as:

Where, \({\bar{K}}_{i}\) is the capital stock of firm \(i\) in the predisaster situation and K i ( t ) is the surviving capital stock of firm \(i\) at time \(t\) during the recovery process

Supply constraints

Firms will purchase intermediate products from their supplier in each period. Insufficient inventory of a firm’s intermediate products will create a bottleneck for production activities. The potential production level that the inventory of the p th intermediate product can support is

where \({S}_{i}^{{\rm{p}}}(t-1)\) refers to the amount of p th intermediate products held by firm i at the end of time step t  − 1.

Considering all the limitation mentioned above, the maximum supply capacity of firm i can be expressed as

The actual production of firm i , \({x}_{i}^{{\rm{a}}}(t)\) , depends on both its maximum supply capacity and the total orders the firm received from its clients, \({{\rm{TD}}}_{i}(t-1)\) (see section on the ‘Demand module’),

The inventory held by firm i will be consumed during the production process,

Allocation module

The allocation module mainly describes how suppliers allocate products to their clients. When some firms in the economic system suffer a negative shock, their production will be constrained by a shortage to primary inputs such as a shortage of labour supply during extreme heat stress. In this case, a firm’s output will not be able to fill all orders of its clients. A rationing scheme that reflects a mechanism on the basis of which a firm allocates an insufficient amount of products to its clients is needed 108 . For this case study, we applied a proportional rationing scheme according to which a firm allocates its output in proportion to its orders. Under the proportional rationing scheme, the amounts of products of firm i allocated to firm j , \({{\rm{F}}{\rm{R}}{\rm{C}}}_{j}^{i}\) and household h , \({{\rm{H}}{\rm{R}}{\rm{C}}}_{h}^{i}\) are as follows,

where \({{\rm{F}}{\rm{O}}{\rm{D}}}_{i}^{j}(t-1)\) refers to the order issued by firm j to its supplier i in time step t − 1, and \({{\rm{H}}{\rm{O}}{\rm{D}}}_{i}^{h}(t-1)\) refers to the order issued by household h to its supplier j . Firm j received intermediates to restore its inventories,

Therefore, the amount of intermediate p held by firm i at the end of period t is

Demand module

The demand module represents a characterization of how firms and households issues orders to their suppliers at the end of each period. A firm orders its supplier because of the need to restore its intermediate product inventory. We assume that each firm has a specific target inventory level based on its maximum supply capacity in each time step,

Then the order issued by firm i to its supplier j is

Households issue orders to their suppliers on the basis of their demand and the supply capacity of their suppliers. In this study, the demand of household h to final products q , \({{\rm{HD}}}_{h}^{q}\left(t\right)\) , is given exogenously at each time step. Then, the order issued by household (HOD) h to its supplier j is

The total order received (TOD) by firm j is

Simulation module

At each time step, the actions of firms and households are as follows in Monte Carlo simulations.

Firms plan and execute their production on the basis of three factors: (1) inventories of intermediate products they have, (2) supply of primary inputs and (3) orders from their clients. Firms will maximize their output under these constraints.

Product allocation

Firms allocate outputs to clients on the basis of their orders. In equilibrium, the output of firms just meets all orders. When production is constrained by exogenous negative shocks, outputs may not cover all orders. In this case, we use a proportional rationing scheme proposed in the literature 20 , 108 (see section on ‘Allocation module’) to allocate products of firms.

Firms and households issue orders to their suppliers for the next time step. Firms place orders with their suppliers on the basis of the gaps in their inventories (target inventory level minus existing inventory level). Households place orders with their suppliers on the basis of their demand. When a product comes from several suppliers, the allocation of orders is adjusted according to the production capacity of each supplier.

This discrete-time dynamic procedure can reproduce the equilibrium of the economic system and can simulate the propagation of exogenous shocks, both from firm and household side or transportation disruptions, in the economic network. From the firm side, if the supply of a firm’s primary inputs is constrained, it will have two effects. On the one hand, the decline in output in this firm means that its clients’ orders cannot be fulfilled. This will result in a decrease in inventory of these clients, which will constrain their production. This is the so-called forward or downstream effect. On the other hand, less output in this firm also means less use of intermediate products from its suppliers. This will reduce the number of orders it places on its suppliers, which will further reduce the production level of its suppliers. This is the so-called backward or upstream effect. From the household side, the fluctuation of household demand caused by exogenous shocks will also trigger the aforementioned backward effect. Take tourism as an example, when the temperature is well beyond the comfort range of the visitor, the demand for tourism from households all over the world will decline significantly. This influence will further propagate to the accommodation and catering industry through supplier–client links.

Economic footprint

We define the VA decrease of all firms in a network caused by an exogenous negative shock as the disaster footprint of the shock. For the firm directly affected by exogenous negative shocks, its loss includes two parts: (1) the VA decrease caused by exogenous constraints and (2) the VA decrease caused by propagation. The former is the direct loss, whereas the latter is the indirect loss. A negative shock’s total economic footprint (TEF i,r ), direct economic footprint (DEF i,r ) and propagated economic footprint (PEF i,r ) for firm i in region r are,

Global supply-chain network

We build a global supply-chain network based on v.10 of the Global Trade Analysis Project (GTAP) database 109 and use GTAP 9 (ref. 110 ), EMERGING database 111 for robustness analysis. GTAP 10 provides a multiregional input–output (MRIO) table for the year 2014. Also, the database for the year 2011 was used for robustness testing. This MRIO table divides the world into 141 economies, each of which contains 65 production sectors (Supplementary Tables 4 and 5 ). If we treat each sector as a firm (producer) and assume that each region has a representative household, we can obtain the following information in the MRIO table: (1) suppliers and clients of each firm; (2) suppliers for each household and (3) the flow of each supplier–client connection under the equilibrium condition. This provides a benchmark for our model. We also used a dynamic CGE model consistent with the SSP scenarios for a parallel assessment and as part of the robustness check of the ARIO results. Specifically, the CGE model we used is a G-RDEM 112 with aggregated ten regions and ten sectors 113 , 114 , 115 (Supplementary Information section 1.3 ).

When applying such a realistic and aggregated network to the disaster footprint model, we need to consider the substitutability of intermediate products supplied by suppliers from the same sector in different regions 115 , 116 , 117 . The substitution between some intermediate products is straightforward. For example, for a firm that extracts spices from bananas it does not make much of a difference if the bananas are sourced from the Philippines or Thailand. However, for a car manufacturing firm in Japan, which uses screws from Chinese auto parts suppliers and engines from German auto parts suppliers to assemble cars, the products of the suppliers in these two regions are non-substitutable. If we assume that all goods are non-substitutable as in the traditional input–output model, then we will overestimate the loss of producers such as the case of the fragrance extraction firm. If we assume that products from suppliers in the same sector can be completely substitutable, then we will substantially underestimate the losses of producers such as the Japanese car manufacturing firm. To alleviate these shortcomings in the evaluation of losses under the two assumptions, we allow for the possibility of substitution for each sector depending on the region and sector of the supplier (Supplementary Information section 1.3 ).

Nonetheless, our estimates of economic damages from heat stress are subject to some important uncertainties 118 and our methods may not capture all types of economic damages. We only include economic losses caused by heat stress on human activities without considering the impacts on infrastructure, crop growth and other factors. Considering the challenges of predicting changes to socioeconomic systems globally, we have followed the approach from the literature 23 , 31 , 91 , 119 to simulate supply-chain indirect losses by considering the impact of future climate risks on current socioeconomic settings. We have not considered the potential substitution of labour with capital resulting from technological advances, such as mechanization. Our analysis ignores the different levels of trade openness and globalization among SSP narratives, as well as the role of dynamic factors such as technology and price. Again, although we have conducted robustness tests for different degrees of trade substitutability, the relevant parameter is set randomly in the Monte Carlo simulation rather than derived through a general equilibrium model. The results should therefore be interpreted with caution as indicating potential future climate change risks to the existing economy rather than as quantitative predictions, given that the static representation of the economic structure in our model inevitably skews the assessment in the long run.

Data availability

Data for the numerical results of this research are provided at https://zenodo.org/records/10032431 . The global trade dataset used to simulate the presented results are licensed by the Global Trade Analysis Project at the Centre for Global Trade Analysis, Department of Agricultural Economics, Purdue University. The GTAP v.10 can be obtained for a fee from its official website: https://www.gtap.agecon.purdue.edu/databases/v10/index.aspx . Owing to the restriction in the licensing agreement with GTAP, the authors have no right to disclose the original dataset publicly. Multimodal meteorological data are derived from World Climate Research Programme (WCRP CMIP6): https://esgf-node.llnl.gov/search/cmip6/ . Socioeconomic data for the different SSP scenarios are derived from IIASA: https://secure.iiasa.ac.at/web-apps/ene/SspDb/ . Global population projection grids are from Socioeconomic Data and Applications Center (SEDAC) ( https://sedac.ciesin.columbia.edu/data/set/popdynamics-1-8th-pop-base-year-projection-ssp-2000-2100-rev01/data-download ).

Code availability

The climate and epidemiological module processes daily surface temperature, dynamic population grid and baseline mortality data to determine heatwave days and the associated excess deaths. The economic module simulates changes of values and flows in global multiregional input–output table under shocks. All of the codes can be accessed at https://zenodo.org/records/10334260 . The minimal input for the code is multiregional input–output table. The sample code and test data for the minimal inputs are also provided.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (grant nos. 72242105, 72091514 and 72250710169) and the startup funding from Zhejiang University to S.Z.

Author information

These authors contributed equally: Yida Sun, Shupeng Zhu, Daoping Wang

Authors and Affiliations

Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

Yida Sun, Hui Lu, Chang Tan, Wenjia Cai, Yong Wang & Dabo Guan

Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, China

Shupeng Zhu

Advanced Power and Energy Program, University of California Irvine, Irvine, CA, USA

Department of Geography, King’s College London, London, UK

Daoping Wang

Centre for Climate Engagement, Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

State Key Laboratory of Earth Surface and Ecological Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China

Jianping Duan

Tsinghua University (Department of Earth System Science)—Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping, Beijing, China

Department of Economics, University of Southern California, Los Angeles, CA, USA

Department of Economics, University of Waterloo, Waterloo, Ontario, Canada

Lingrui Zhang

School of Management and Economics, Beijing Institute of Technology, Beijing, China

Mengzhen Zhao

School of Economics and Management, Southeast University, Nanjing, China

College of Urban Environment, Peking University, Beijing, China

The Bartlett School of Sustainable Construction, University College London, London, UK

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Contributions

D.G. designed and supervised the study. Y.S., S.Z. and D.W. conducted the study, collected the data, analysed the results and drafted the paper. J.D. and Y.W. collected and processed the meteorological data. H.Y., M.Z. and W.C. provided guidance on the calculation of health and labour productivity losses. C.T., Y.H. and L.Z. participated in the writing of the manuscript. S.T. and H.L. guided the uncertainty analysis and validation.

Corresponding author

Correspondence to Dabo Guan .

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Extended data figures and tables

Extended data fig. 1 schematic diagram of the methodological framework..

Coupling mechanisms for climate, health and economic modules.

Extended Data Fig. 2 Heatwave days in the 2040 s and 2060 s under SSP585 Scenario.

The number of heatwave days in each cell was calculated from the ten-year average.

Extended Data Fig. 3 Climate zones classification of relative risk.

Cold area: mean temperature of hot season: ≤<= 20.7 °C; moderate cold areas: mean temperature of hot season: 20.7–24.1 °C; moderate hot areas: mean temperature of hot season: 24.1–27.6 °C; and hot areas: mean temperature of hot season: >27.6 °C, based on ERA5 1985–2010.

Extended Data Fig. 4 Estimates for the ten countries with the highest indirect losses under the SSP585 scenario using different base period trade data.

Estimates are displayed as 10-year averages for the year 2060, using the GTAP2011 (a), GTAP2014 (b) and EMERGING 2019 (c) databases separately. The colours of the bars represent GDP per capita from low to high. (d), indirect losses under different benchmark trade structures in each region. The horizontal axis measures the indirect losses as a percentage of GDP using the GTAP2014 trade structure and the vertical axis measures the indirect losses as a percentage of GDP using the GTAP2011 trade structure. Details of Extended Data Fig. 4d can be checked in Supplementary Fig. 6 .

Extended Data Fig. 5 Global economic losses for each scenario under Monte Carlo simulations.

The assessment results of existing studies are marked with symbols for comparison. None of the previous studies were based on CMIP6 SSP119 scenario, so we use RCP2.6 to compare with the SSP119 scenario in our study. The studies above did not simulate health loss and the mean values of the health loss simulations in this paper were added for consistency.

Extended Data Fig. 6 Impacts of heat stress on India food manufacturing supply chains.

( a ), ( c ) panels represent the upstream sectors of the India’s food production sector in 2040 and 2060, respectively. ( b ), ( d ) panels represent the downstream sectors. Each bar represents a key trading partner (i.e. sector with trade volume above the 50 percent quartile of trade volumes of the selected sector with all partner sectors) and the length represents the percentage decrease in product flow compared to the base period of 2014. The colours of the bars represent the cohesion level of the particular sector to the Indian food production sector from blue (weak) to red (strong), which is measured by the trade volume between the particular sector and the Indian food production sector.

Extended Data Fig. 7 Impacts of heat stress on Dominican Republic tourism supply chains.

( a ), ( c ) panels represent the upstream sectors of the Dominican Republic’s tourism sector in 2040 and 2060, respectively. ( b ), ( d ) panels represent the downstream sectors. Each bar represents a key trading partner (i.e. sector with trade volume above the 50 percent quartile of trade volumes of the selected sector with all partner sectors) and the length represents the percentage decrease in product flow compared to the base period of 2014. The colours of the bars represent the cohesion level of the particular sector to the Dominican Republic’s tourism sector from blue (weak) to red (strong), which is measured by the trade volume between the particular sector and the Dominican Republic’s tourism sector.

Extended Data Fig. 8 Impacts of heat stress on Germany beverages and tobacco products supply chains.

( a ), ( c ) panels represent the upstream sectors of the Germany’s beverages and tobacco products sector in 2040 and 2060, respectively. ( b ), ( d ), panels represent the downstream sectors.

Extended Data Fig. 9 Sectoral loss patterns of type 1 countries.

The top 5 most vulnerable sectors in Tanzania (a–c), Botswana(d–f) and Malawi (g–i). The column length represents each sector’s percentage loss of annual value-added. Sectors with the same loss percentage (e.g. wheat, rice, cereals, etc.) were combined. Colours indicate the three categories of losses in SSP119: health losses (Yellow bars), labour productivity losses (Blue bars) and supply-chain disruption losses (Green bars). The orange and red bars represent total loss increments for SSP245 and SSP585 (no distinction between types of loss in this part), respectively. The red dashed line indicates the mean value of losses for all sectors in the SSP585 scenario.

Extended Data Fig. 10 Sectoral loss patterns of type 2 countries.

The top 5 most vulnerable sectors in India (a–c), Vietnam (d–f) and China (g–i). The column length represents each sector’s percentage loss of annual value-added. Sectors with the same loss percentage (e.g. wheat, rice, cereals, etc.) were combined. Colours indicate the three categories of losses in SSP119: health losses (Yellow bars), labour productivity losses (Blue bars) and supply-chain disruption losses (Green bars). The orange and red bars represent total loss increments for SSP245 and SSP585 (no distinction between types of loss in this part), respectively. The red dashed line indicates the mean value of losses for all sectors in the SSP585 scenario.

Extended Data Fig. 11 Sectoral loss patterns of type 3 countries.

The top 5 most vulnerable sectors in Germany (a–c), France (d–f) and Australia (g–i). The column length represents each sector’s percentage loss of annual value-added. Sectors with the same loss percentage (e.g. wheat, rice, cereals, etc.) were combined. Colours indicate the three categories of losses in SSP119: health losses (Yellow bars), labour productivity losses (Blue bars) and supply-chain disruption losses (Green bars). The orange and red bars represent total loss increments for SSP245 and SSP585 (no distinction between types of loss in this part), respectively. The red dashed line indicates the mean value of losses for all sectors in the SSP585 scenario.

Extended Data Fig. 12 Sectoral loss patterns of type 4 countries.

The top 5 most vulnerable sectors in Sweden (a–c), Norway(d–f) and United Kingdom (g–i). The column length represents each sector’s percentage loss of annual value-added. Sectors with the same loss percentage (e.g. wheat, rice, cereals, etc.) were combined. Colours indicate the three categories of losses in SSP119: health losses (Yellow bars), labour productivity losses (Blue bars) and supply-chain disruption losses (Green bars). The orange and red bars represent total loss increments for SSP245 and SSP585 (no distinction between types of loss in this part), respectively. The red dashed line indicates the mean value of losses for all sectors in the SSP585 scenario.

Supplementary information

Supplementary information.

Supplementary sections 1–7, including Figs. 1–13, Tables 1–7 and references.

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Sun, Y., Zhu, S., Wang, D. et al. Global supply chains amplify economic costs of future extreme heat risk. Nature 627 , 797–804 (2024). https://doi.org/10.1038/s41586-024-07147-z

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Scientific Consensus

It’s important to remember that scientists always focus on the evidence, not on opinions. Scientific evidence continues to show that human activities ( primarily the human burning of fossil fuels ) have warmed Earth’s surface and its ocean basins, which in turn have continued to impact Earth’s climate . This is based on over a century of scientific evidence forming the structural backbone of today's civilization.

NASA Global Climate Change presents the state of scientific knowledge about climate change while highlighting the role NASA plays in better understanding our home planet. This effort includes citing multiple peer-reviewed studies from research groups across the world, 1 illustrating the accuracy and consensus of research results (in this case, the scientific consensus on climate change) consistent with NASA’s scientific research portfolio.

With that said, multiple studies published in peer-reviewed scientific journals 1 show that climate-warming trends over the past century are extremely likely due to human activities. In addition, most of the leading scientific organizations worldwide have issued public statements endorsing this position. The following is a partial list of these organizations, along with links to their published statements and a selection of related resources.

American Scientific Societies

Statement on climate change from 18 scientific associations.

"Observations throughout the world make it clear that climate change is occurring, and rigorous scientific research demonstrates that the greenhouse gases emitted by human activities are the primary driver." (2009) 2

American Association for the Advancement of Science

"Based on well-established evidence, about 97% of climate scientists have concluded that human-caused climate change is happening." (2014) 3

American Chemical Society

"The Earth’s climate is changing in response to increasing concentrations of greenhouse gases (GHGs) and particulate matter in the atmosphere, largely as the result of human activities." (2016-2019) 4

American Geophysical Union

"Based on extensive scientific evidence, it is extremely likely that human activities, especially emissions of greenhouse gases, are the dominant cause of the observed warming since the mid-20th century. There is no alterative explanation supported by convincing evidence." (2019) 5

American Medical Association

"Our AMA ... supports the findings of the Intergovernmental Panel on Climate Change’s fourth assessment report and concurs with the scientific consensus that the Earth is undergoing adverse global climate change and that anthropogenic contributions are significant." (2019) 6

American Meteorological Society

"Research has found a human influence on the climate of the past several decades ... The IPCC (2013), USGCRP (2017), and USGCRP (2018) indicate that it is extremely likely that human influence has been the dominant cause of the observed warming since the mid-twentieth century." (2019) 7

American Physical Society

"Earth's changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe. While natural sources of climate variability are significant, multiple lines of evidence indicate that human influences have had an increasingly dominant effect on global climate warming observed since the mid-twentieth century." (2015) 8

The Geological Society of America

"The Geological Society of America (GSA) concurs with assessments by the National Academies of Science (2005), the National Research Council (2011), the Intergovernmental Panel on Climate Change (IPCC, 2013) and the U.S. Global Change Research Program (Melillo et al., 2014) that global climate has warmed in response to increasing concentrations of carbon dioxide (CO2) and other greenhouse gases ... Human activities (mainly greenhouse-gas emissions) are the dominant cause of the rapid warming since the middle 1900s (IPCC, 2013)." (2015) 9

Science Academies

International academies: joint statement.

"Climate change is real. There will always be uncertainty in understanding a system as complex as the world’s climate. However there is now strong evidence that significant global warming is occurring. The evidence comes from direct measurements of rising surface air temperatures and subsurface ocean temperatures and from phenomena such as increases in average global sea levels, retreating glaciers, and changes to many physical and biological systems. It is likely that most of the warming in recent decades can be attributed to human activities (IPCC 2001)." (2005, 11 international science academies) 1 0

U.S. National Academy of Sciences

"Scientists have known for some time, from multiple lines of evidence, that humans are changing Earth’s climate, primarily through greenhouse gas emissions." 1 1

U.S. Government Agencies

U.s. global change research program.

"Earth’s climate is now changing faster than at any point in the history of modern civilization, primarily as a result of human activities." (2018, 13 U.S. government departments and agencies) 12

Intergovernmental Bodies

Intergovernmental panel on climate change.

“It is unequivocal that the increase of CO 2 , methane, and nitrous oxide in the atmosphere over the industrial era is the result of human activities and that human influence is the principal driver of many changes observed across the atmosphere, ocean, cryosphere, and biosphere. “Since systematic scientific assessments began in the 1970s, the influence of human activity on the warming of the climate system has evolved from theory to established fact.” 1 3-17

Other Resources

List of worldwide scientific organizations.

The following page lists the nearly 200 worldwide scientific organizations that hold the position that climate change has been caused by human action. http://www.opr.ca.gov/facts/list-of-scientific-organizations.html

U.S. Agencies

The following page contains information on what federal agencies are doing to adapt to climate change. https://www.c2es.org/site/assets/uploads/2012/02/climate-change-adaptation-what-federal-agencies-are-doing.pdf

Technically, a “consensus” is a general agreement of opinion, but the scientific method steers us away from this to an objective framework. In science, facts or observations are explained by a hypothesis (a statement of a possible explanation for some natural phenomenon), which can then be tested and retested until it is refuted (or disproved).

As scientists gather more observations, they will build off one explanation and add details to complete the picture. Eventually, a group of hypotheses might be integrated and generalized into a scientific theory, a scientifically acceptable general principle or body of principles offered to explain phenomena.

1. K. Myers, et al, "Consensus revisited: quantifying scientific agreement on climate change and climate expertise among Earth scientists 10 years later", Environmental Research Letters Vol.16 No. 10, 104030 (20 October 2021); DOI:10.1088/1748-9326/ac2774 M. Lynas, et al, "Greater than 99% consensus on human caused climate change in the peer-reviewed scientific literature", Environmental Research Letters Vol.16 No. 11, 114005 (19 October 2021); DOI:10.1088/1748-9326/ac2966 J. Cook et al., "Consensus on consensus: a synthesis of consensus estimates on human-caused global warming", Environmental Research Letters Vol. 11 No. 4, (13 April 2016); DOI:10.1088/1748-9326/11/4/048002 J. Cook et al., "Quantifying the consensus on anthropogenic global warming in the scientific literature", Environmental Research Letters Vol. 8 No. 2, (15 May 2013); DOI:10.1088/1748-9326/8/2/024024 W. R. L. Anderegg, “Expert Credibility in Climate Change”, Proceedings of the National Academy of Sciences Vol. 107 No. 27, 12107-12109 (21 June 2010); DOI: 10.1073/pnas.1003187107 P. T. Doran & M. K. Zimmerman, "Examining the Scientific Consensus on Climate Change", Eos Transactions American Geophysical Union Vol. 90 Issue 3 (2009), 22; DOI: 10.1029/2009EO030002 N. Oreskes, “Beyond the Ivory Tower: The Scientific Consensus on Climate Change”, Science Vol. 306 no. 5702, p. 1686 (3 December 2004); DOI: 10.1126/science.1103618

2. Statement on climate change from 18 scientific associations (2009)

3. AAAS Board Statement on Climate Change (2014)

4. ACS Public Policy Statement: Climate Change (2016-2019)

5. Society Must Address the Growing Climate Crisis Now (2019)

6. Global Climate Change and Human Health (2019)

7. Climate Change: An Information Statement of the American Meteorological Society (2019)

8. American Physical Society (2021)

9. GSA Position Statement on Climate Change (2015)

10. Joint science academies' statement: Global response to climate change (2005)

11. Climate at the National Academies

12. Fourth National Climate Assessment: Volume II (2018)

13. IPCC Fifth Assessment Report, Summary for Policymakers, SPM 1.1 (2014)

14. IPCC Fifth Assessment Report, Summary for Policymakers, SPM 1 (2014)

15. IPCC Sixth Assessment Report, Working Group 1 (2021)

16. IPCC Sixth Assessment Report, Working Group 2 (2022)

17. IPCC Sixth Assessment Report, Working Group 3 (2022)

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