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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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International Conference on Business and Policy Studies

CONF-BPS 2022: Proceedings of the 2022 International Conference on Business and Policy Studies pp 556–562 Cite as

Research on Globalization Impact Factors and Sustainable Development Strategies

• Mengtong Liu 7  
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• First Online: 27 September 2022

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Part of the book series: Applied Economics and Policy Studies ((AEPS))

Economic globalization itself is known for facilitating the distribution of the means of production, but it often has a negative impact because the distribution process is not efficient enough. COVID-19 has largely curbed globalization since its massive outbreak in late 2019 and shifted the process of globalization, which had been experiencing protectionist shocks, into reverse globalization. Globalization seems to be in a tense situation and at risk of extinction: Differences in policy responses among nation-states reveal different attitudes toward economic globalization; Individual psychological satisfaction with the sense of recognition and the rapid development of the market economy are opposing and circular features. This paper addresses the question of the pros and cons of globalization, used literature research to study the imbalance rules of globalization, the mechanism of populism’s influence on globalization, found the cyclical development trend of globalization, and used China’s response as a practical case to support and analyze, with a view to providing reference for other countries to deal with globalization in the current internationally situation.

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Liu, M. (2022). Research on Globalization Impact Factors and Sustainable Development Strategies. In: Li, X., Yuan, C., Ganchev, I. (eds) Proceedings of the 2022 International Conference on Business and Policy Studies. CONF-BPS 2022. Applied Economics and Policy Studies. Springer, Singapore. https://doi.org/10.1007/978-981-19-5727-7_56

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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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Globalisation, economic uncertainty and labour market regulations: Implications for the COVID‐19 crisis

Jianchun fang.

1 Zhejiang University, Hangzhou China

Giray Gozgor

2 Istanbul Medeniyet University, Istanbul Turkey

3 CESifo, Munich Germany

James H. Nolt

4 New York University (NYU), New York New York, USA

Associated Data

All data used in this paper are publicly available, and the details are provided in the "Online Data Appendix Table II". The data that support the findings of this study are available from the corresponding author upon reasonable request.

This paper empirically analyses the effects of globalisation on labour market regulations. We also interact globalisation measures with economic uncertainty, and they serve as potential determinants of de jure labour market conditions. For this purpose, we consider new innovative globalisation and economic uncertainty indices (the Revisited KOF Globalisation and the World Uncertainty) in a panel dataset of 136 countries from 2000 to 2017. The findings indicate that globalisation promotes labour market flexibility, while economic uncertainty decreases it. We also find that the interaction of globalisation with economic uncertainty positively affects labour market flexibility. The findings are robust to various sensitivity analyses, that is, different estimation procedures and globalisation indicators, including various controls and excluding outliers.

1. INTRODUCTION

One of the crucial economic problems of the 21 st century in many countries is the deterioration of labour market conditions and protections, especially for unskilled workers (Berliner et al., 2015 ). Various studies indicate that this is happening due to globalisation and offshoring (Davies & Vadlamannati, 2013 ; Olney, 2013 ; Ranjan, 2013 ; Rudra, 2008 ). There is also a significant impact of globalisation on collective bargaining, and the potential weakening of unions due to globalisation is one important mechanism through which the labour market conditions of unskilled workers are deteriorating (Hessami & Baskaran, 2015 ). Jobs of unskilled workers are even disappearing altogether due to globalisation (Autor et al., 2013 ). Globalisation also reduces public employment, an alternative to private‐sector jobs (Gozgor et al., 2019 ).

These arguments about the impact of globalisation on labour markets stem from two issues. First, through global value chains (GVCs), the production process is internationalised. Production is divided into various stages, and each stage is completed in a different country. GVCs create a large‐scale extension of labour division and stimulate competitive pressure (Wood, 1998 ). GVCs put pressure on real wages, standardised production regulations and demanded high levels of efficiency of workers. Thus, GVCs provide cheaper production, and most countries can choose to import products (e.g. from China) rather than produce them domestically. Overall, GVCs affect labour market institutions and outcomes (Autor et al., 2013 ).

The second issue is the race to the bottom. Governments deregulate labour markets to provide more profit to investors by increasing their revenues or decreasing their costs. By providing benefits, countries expect to receive higher investments in a competitive global system. At this stage, governments can decrease hiring/firing costs, severance payments and minimum wages. They can also change union laws to decrease workers’ collective bargaining rights and military conscription duration to control the labour supply. Overall, GVCs, trade (imports) shocks and increased competition cause a race to the bottom for workers’ wages. These issues are mainly responsible for the deterioration of labour market conditions and protections (Autor et al., 2013 ; Potrafke, 2013 ).

As discussed in Section 2 below, the empirical findings on globalisation's effects on labour market regulations are mixed. There are several reasons for the lack of coherence across studies. First, there are various indicators of labour market regulations (e.g. employment protection, minimum wage, unemployment benefits, union density, etc.). These measures vary significantly, even across countries with similar development levels (Gwartney et al., 2020 ). For example, labour markets are less regulated in Anglo‐Saxon countries than Scandinavian countries, and these countries are at similar per capita income levels. Labour market regulations also vary significantly across developing countries (Gwartney et al., 2020 ). Therefore, we use the overall index of labour market regulations to handle this issue, capturing different labour market dynamics (Potrafke, 2013 ). Second, various globalisation measures (e.g. financial openness, foreign direct investments, offshoring, outsourcing, trade openness, etc.) are available. However, these are not the sole variables representing globalisation. Globalisation is a concept that includes these economic variables as well as social and political dimensions. Therefore, it is necessary to consider the impact of globalisation's economic, social and political aspects on labour market regulations as a whole. The KOF globalisation indices provided by Gygli et al. ( 2019 ) are designed for this purpose. Our paper uses the revisited KOF globalisation indices to address this issue in empirical studies (see, e.g. Potrafke, 2019 ).

In addition, the COVID‐19 pandemic can increase the pressures of globalisation on labour since it shows the potential of decoupling work from physical presence in the workplace. When the COVID‐19 pandemic ends, most work will be done using online tools, that is, there will be less sharing of physical offices and factory‐based work. The COVID‐19 pandemic shows the potential of labour flexibility since workers can work from home with flexible hours instead of working offices or factories with regular hours. Therefore, we should expect more flexibility in labour market regulations in the post‐COVID‐19 era. In line with the race to the bottom hypothesis, labour competition will increase once the pandemic ends.

In this paper, unlike previous studies, we include economic uncertainty in investigating globalisation's effects on labour market regulations. When examining the impact of globalisation on labour market regulations, it is important to consider the role of economic uncertainty for several reasons. First, firms may stall hiring in times of economic uncertainty since economic uncertainty increases the risk of making the costly mistake of new hiring (Pries, 2016 ). This is referred to as the ‘ real option’ channel (Bernanke, 1983 ). 1 Second, when economic uncertainty increases, the risk premium rises and the cost of investing with external financing (credit) increases, reducing opportunities for new job creation. This is referred to as the ‘risk premium’ channel (Arellano et al., 2019 ). Third, risk aversion may cause firms to avoid investing in new projects on the supply side. On the demand side, risk aversion may also increase households’ precautionary saving motivation, reducing aggregate demand. This is referred to as the ‘risk aversion’ channel (Basu & Bundick, 2017 ). Therefore, policymakers can increase the labour market's rigidities to protect jobs and earnings to prevent further household demand decline. Policymakers can even try to increase demand with fiscal policies, for example increase public employment and workers’ compensation (Fernández‐Villaverde et al., 2015 ).

Governments can create a more flexible labour market to reduce the cost of hiring ‘inefficient workers’ in times of economic uncertainty. In other words, governments can relax hiring and firing regulations. In addition, in times of increased economic uncertainty, union bargaining power will decrease since workers have fewer opportunities to find better jobs. Simultaneously, economic uncertainty can lead to more flexible working hour regulations, such as working at night, on weekends, or having fewer holidays. In short, economic uncertainty should cause more flexible labour market regulations due to the real option and the risk premium channels; however, it should cause stricter labour market conditions due to the risk aversion channel.

This paper analyses the effects of economic globalisation and economic uncertainty measures on labour market flexibility using under‐researched 21 st century data (from 2000 to 2017) in 136 countries. Following the empirical strategy in Potrafke ( 2013 ), where he uses traditional globalisation indices, our paper uses the new datasets of Gygli et al. ( 2019 ) to measure globalisation. More importantly, we include the role of economic uncertainty in this debate. We find empirical support for the race to the bottom hypothesis, since globalisation promotes labour market flexibility. However, we observe that globalisation has a less positive impact on labour market flexibility during times of greater economic uncertainty. Therefore, we should expect stricter labour market conditions when globalisation decreases and economic uncertainty increases, such as in the COVID‐19 era. Following our results, we suggest that a more flexible labour market will emerge for the post‐COVID‐19 period if globalisation increases and economic uncertainty decreases.

The structure of the remaining parts of the paper is as follows. In Section 2 , we explain our theoretical models and review the previous empirical literature. In Section 3 , we outline our empirical model and estimation procedures and explain the data. In Section 4 , we present our empirical findings. In Section 5 , we check the robustness of the results. Section 6 concludes the paper.

2. LITERATURE REVIEW

2.1. theoretical models.

Globalisation causes systems competition based on the mobility of production factors (Sinn, 2004 ). Competition among free‐market economies can produce a ‘race to the bottom’ in labour market regulations (Rudra, 2008 ; Sinn, 1997 ). Following the race to the bottom hypothesis, various theoretical models show how globalisation can affect labour market regulations. For instance, Boulhol ( 2009 ) indicates that globalisation affects labour market regulations via capital mobility and trade liberalisation, which leads to an inter‐sectoral reallocation of the factors of production (from the highly unionised sectors to deregulated sectors). 2 In a globalised world economy with capital mobility and free trade, investors may invest in countries where workers have less bargaining power. Baskaran and Hessami ( 2012 ) and Schulze and Ursprung ( 1999 ) also point out how nation‐states adjust fiscal (or generally social) policies due to the pressures created by globalisation. Therefore, governments may choose to deregulate labour markets because they fear global firms will invest in other economies.

In terms of the theoretical background, a standard Heckscher‐Ohlin (H‐O) framework may also be useful. Here, foreign capital flows to countries with abundant production factors; if idle labour in particular countries is abundant and foreign direct investments (FDIs) are labour‐intensive, they will settle there. In this framework, factor abundance matters and the regulatory framework in which such abundance ‘occurs’. For instance, a country may be diving in large unemployment, but procedures to fire a worker may be that stringent, that abundance (low wages) itself is not a trigger for FDIs to settle there. Therefore, expanding the H‐O framework, for example, the theory early developed by Olson ( 1971 ) on workers’ bargaining power, can explain globalisation's effects on labour market regulations. In line with the H‐O framework, Hessami ( 2011 ) shows that globalisation affected various social groups differently.

Global value chains and trade liberalisation enhance firms’ bargaining power (leading to labour market flexibility) since they can import production factors instead of producing them (Boulhol, 2009 ). This can change the unemployment rate and labour market regulations. However, the model in Felbermayr et al. ( 2013 ) predicts that more generous unemployment benefits in one open economy affect unemployment in the domestic economy and other countries. Countries’ size, trade costs and real wages’ rigidity determine unemployment spillover at this stage. Felbermayr et al. ( 2013 ) also consider the panel dataset of 20 OECD countries and confirm the model's predictions when controlling for institutions and business cycles. Therefore, following Felbermayr et al. ( 2013 ), we also control for business cycles. Differently, we use the novel measure of the World Uncertainty Index (WUI), and we also include various institutional quality indicators in the robustness checks.

Furthermore, the compensation hypothesis indicates that globalisation (trade and financial openness) redounds the government's role in markets (Rodrik, 1998 ). Because workers (the losers of globalisation) will demand compensation (e.g. unemployment benefit payments, unemployment insurance and job‐protection regulations) against risks, economies will be more globalised. Governments should respond to these demands by providing broader public facilities (Rodrik, 2011 ). In short, there should be more labour protection in countries with hyper‐globalisation. Following the compensation hypothesis provided by Rodrik ( 1998 , 2011 ), we control for the size of government and absolute redistribution in our empirical estimations.

2.2. Previous empirical papers

Globalisation (trade openness or financial openness) affects labour market indicators (e.g. unemployment rates and wages) and labour market regulations (see, e.g. Autor et al., 2013 ; Dutt et al., 2009 ; Gaston & Nelson, 2004 ). Researchers have also investigated the effects of globalisation on domestic economic policies, including labour market regulations (see, e.g. Mosley, 2008 ; Neumayer & De Soysa, 2006 ; Rudra, 2008 ). 3

2.2.1. Studies on OECD countries

Several empirical studies examine the effects of globalisation indicators on labour market regulations in OECD countries. For instance, Algan and Cahuc ( 2006 ) observe that trade openness was positively associated with the index of employment protection in OECD countries from 1970 to 1999. Using data for 17 OECD countries from 1980 to 1999, Dreher and Gaston ( 2007 ) indicate that social globalisation reduced union membership. However, the economic and political aspects of globalisation did not significantly affect union membership. Fischer and Somogyi ( 2009 ) also find that trade openness increased unemployment benefits in OECD countries from 1961 to 2007. In another empirical analysis from 1985 to 2003, the authors observe that globalisation decreased the protection of full‐time jobs but increased the protection of part‐time jobs in OECD countries.

Similarly, Felbermayr et al. ( 2012 ) indicate that trade openness was positively related to unemployment benefits from 1961 to 2007. However, using a panel dataset of 20 OECD countries from 1982 to 2003, Potrafke ( 2010 ) shows that globalisation (measured by KOF indices of globalisation) does not significantly affect various indicators of labour market regulations. Olney ( 2013 ) also demonstrates that FDI led to fewer employment protection rules in a panel dataset of 30 OECD countries from 1985 to 2007. This evidence aligns with the race to the bottom hypothesis and means that OECD countries competitively provide more flexible labour standards to attract FDI.

2.2.2. Studies focused on other countries

Several studies focus on both developing and developed economies. For instance, Busse ( 2004 ) indicates that trade openness positively affected labour standards in a panel dataset of 71 developing economies from 1970 to 2000. However, Mosley and Uno ( 2007 ) state that trade openness negatively affected labour rights, but FDI promoted labour rights in a panel dataset of 90 countries from 1985 to 2002. Davies and Vadlamannati ( 2013 ) consider the panel dataset of 135 countries from 1985 to 2002, separating them into OECD and non‐OECD countries. They find that increased trade openness leads to more flexible labour standards due to competition for investment among countries. Potrafke ( 2013 ) uses different estimation procedures and concludes that globalisation (measured by KOF indices of globalisation) does not significantly affect labour market regulations (measured by the economic freedom of the world indicators). This evidence is robust to the panel dataset of 49 countries from 1970 to 2009 with the 5‐year averaged data and for 137 countries from 2000 to 2009 with annual data. Our paper uses similar indicators for globalisation and labour market regulations and the annual dataset estimation procedure in Potrafke ( 2013 ). We also include the role of business cycles (economic uncertainty) measured by the WUI.

Furthermore, Vadlamannati ( 2015 ) observes that social globalisation promotes labour rights in developing economies—a finding in line with the compensation hypothesis. Hessami and Baskaran ( 2015 ) use a panel dataset of 44 countries from 1980 to 2009 and show that globalisation decreases unionisation. However, there are no significant effects of globalisation on decentralisation and government intervention in collective bargaining. Berdiev and Saunoris ( 2018 ) study globalisation's effects on the shadow economy using an annual panel dataset of 119 countries from 2000 to 2007. They find that globalisation reduces the development of the shadow economy, meaning that globalisation suppresses the benefits of the shadow economy, such as avoiding higher labour costs through hiring and firing regulations and minimum wage laws.

Moreover, Reinsberg et al. ( 2019 ) investigate globalisation's effects (measured by the International Monetary Fund's policy interventions) on labour rights in a panel dataset of 70 developing countries from 1980 to 2014. The authors obtain mixed results for the effects of globalisation indicators on collective labour rights. Petreski ( 2020 ) also examines globalisation's effects (measured by manufacturing exports and FDI) on labour market indicators in 25 transition economies from 1996 to 2016. The author obtains heterogeneous results: While globalisation promotes the labour market conditions in the Central European transition economies, it decreases collective bargaining and wages and worsens working conditions in the Commonwealth of Independent States. Furthermore, it creates more jobs in the Southeast European transition economies, distorting wages and working conditions. In a further study, Petreski ( 2021 ) analyses globalisation's effects on labour share (a measure of workers’ bargaining power) in an industry‐level panel dataset for 23 transition economies from 2000 to 2015. The author observes that globalisation results in stagnant labour shares in low‐skilled industries. However, labour shares in highly‐skilled industries are not significantly affected by globalisation shocks.

Overall, previous papers indicate that the ‘race to the bottom’ hypothesis can be valid (e.g. Davies & Vadlamannati, 2013 ) as countries compete to lower production costs by increasing labour market flexibility. Alternatively, globalisation leads to stricter (protective) labour market regulations, as the compensation hypothesis suggests (e.g. Vadlamannati, 2015 ). In addition, globalisation does not impact labour market regulations (e.g. Potrafke, 2013 ). We contribute to this literature by including the novel index of the WUI to capture the effects of changing economic policies and business cycles as a potential driver of labour market flexibility. In so doing, we observe the validity of the ‘race to the bottom’ hypothesis in the globalisation‐labour market regulation nexus in a panel dataset of 136 countries from 2000 to 2017.

3. DATA, MODELS AND ESTIMATION PROCEDURES

3.1.1. panel data sample.

Our sample covers the period between 2000 and 2017. Following Berdiev and Saunoris ( 2018 ) and Potrafke ( 2013 ), the data frequency is annual. The beginning date of the sample is related to the availability of annual data on labour market flexibility. We also use annual data to model short‐term fluctuations (business cycles), which is why we consider the WUI. The sample includes 136 developed and developing countries, as listed in the Table S1 . Following World Bank ( 2021a ), we classify countries as low‐income, middle‐income and high‐income economies. The high‐income economies are defined as those with a per capita gross national income (GNI) higher than $12,536 in the fiscal year 2021. The countries with a GNI lower than $12,536 are defined as low‐income and middle‐income economies (World Bank, 2021a ). Finally, we consider the OECD and non‐OECD countries.

3.1.2. Labour market flexibility

We use the labour market flexibility ( LMF ) index as the dependent variable. We also consider six sub‐indices of the LMF : (i) hiring regulations and minimum wage ( HRMW ), (ii) hiring and firing regulations ( HFR ), (iii) centralised collective bargaining ( CCB ), (iv) hours regulations ( HORE ), (v) mandated cost of worker dismissal ( MCWD ) and (vi) conscription ( CON ). The related data are obtained from Gwartney et al. ( 2020 ). All indicators are defined as the index from 0 to 10, and higher levels indicate greater flexibility (freedom). 4

3.1.3. Uncertainty measure

We consider the WUI to capture business cycles’ effects on labour markets (Felbermayr et al., 2013 ). The WUI is a novel indicator that tracks uncertainty across countries by text mining the country reports of the Economist Intelligence Unit (EIU). The data are obtained from Ahir et al. ( 2019 ). The WUI is calculated by counting the word ‘uncertain’ (or variant) in the EIU’s country reports. A greater value of the WUI indicates a higher level of uncertainty. 5

3.1.4. Globalisation measures

One of the main variables of interest is the KOF indices of globalisation, and the data are obtained from Gygli et al. ( 2019 ). The revisited version of the KOF indices of globalisation provides de facto and de jure measures of the overall index and economic, political and social globalisation indices. Following Gozgor ( 2018 ), we use the indices in logarithmic form. In addition, following Potrafke ( 2013 , 2015 ), we use the indices’ lagged values to address potential endogeneity and reverse causality concerns.

We first provide the results for the overall globalisation index ( lnKOF_OGI t ‐1 ), the de facto overall globalisation index ( lnKOF_OGdfI t ‐1 ), and the de jure overall globalisation index ( lnKOF_OGIdj t ‐1 ) as well as the economic globalisation index ( lnKOF_EGI t ‐1 ), the social globalisation index ( lnKOF_SGI t ‐1 ), and the political globalisation index ( lnKOF_PGI t ‐1 ). Secondly, we further investigate the components of the economic globalisation index and provide the results for indices of de facto economic globalisation ( lnKOF_EGIdf t ‐1 ) and de jure economic globalisation ( lnKOF_EGIdj t ‐1 ), as well as trade globalisation ( lnKOF_TRGI t ‐1 ), de facto trade globalisation ( lnKOF_TRGIdf t ‐1 ), de jure trade globalisation ( lnKOF_TRGIdj t ‐1 ) , financial globalisation ( lnKOF_FINGI t ‐1 ), de facto financial globalisation ( lnKOF_FINGIdf t ‐1 ) and de jure financial globalisation ( lnKOF_FINGIdj t ‐ 1 ). In short, we use 14 indices of overall globalisation and economic globalisation. Note that de jure indices are based on globalisation policies, such as agreements, regulations and taxes. De facto indices are based on globalisation outcomes, such as foreign investments, international trade and technology. 6

3.1.5. Main controls

Following Potrafke ( 2010 , 2013 ) and Felbermayr et al. ( 2013 ), we use the total population in the logarithmic form to capture country size. Following Potrafke ( 2010 , 2013 ) and Rodrik ( 1998 ), we also consider government consumption (percentage of current purchasing power parity (PPP) and gross domestic product (GDP)) to capture the size of government in the economy. The related data are obtained from Feenstra et al. ( 2015 ). Both indicators are defined as the main controls. Note that these controls are in line with the empirical models in Potrafke ( 2013 ).

3.1.6. Additional controls

We use various additional controls in the robustness checks. We include the age‐dependency ratio to capture the ageing population's effect on labour market flexibility (Gozgor & Ranjan, 2017 ). Urban population and population density are included in the model and are considered alongside city size and migration within the country. (Bernal‐Verdugo et al., 2012 ). Note that population is treated as exogenous in the system generalised method of moments (GMM) estimations. The macroeconomic stance is measured by the inflation rate and the current account balance (Potrafke, 2019 ). These data are obtained from the World Bank ( 2021b ). Furthermore, we include income inequality and redistribution indices to control their effect on labour market flexibility (Dabla‐Norris et al., 2015 ; Rodrik, 2011 ). The related data are obtained from Solt ( 2020 ).

We also include various measures of institutional quality, which can also affect labour market flexibility (Felbermayr et al., 2013 ; Potrafke, 2010 , 2013 , 2015 ). Therefore, we consider the indices of democracy, executive constraints and polity2 , and the related data are obtained from Marshall and Gurr ( 2020 ). We also consider the index of the legal system and property rights, and the related data come from Gwartney et al. ( 2020 ). Furthermore, we use indices of political regimes and a typology of political institutions, with related data provided by Bjørnskov and Rode ( 2020 ). A higher level of indices indicates a higher level of institutional quality. Another measure is the index of the freedom of the press, with related data obtained from Freedom House ( 2020 ). A higher level of this index means a lower level of institutional quality.

Figure ​ Figure1 1 illustrates the main indices (labour market flexibility, the WUI, log overall globalisation and log economic globalisation) in different country groups (full sample, high‐income, low‐ and middle‐income, OECD and non‐OECD countries) from 2000 to 2017.

Main indicators, 2000–2017

Details of all variables and a summary of descriptive statistics are reported in Table S2 . The correlation matrix for the LMF , the WUI , globalisation indices and main control variables are also provided in Table S3 .

3.2. Empirical models and estimation procedures

We estimate the following empirical models:

As the dependent variable, we consider LMF i , t , the index of labour market flexibility. We also use various sub‐indices of LMF i , t in country i at time t . In the system GMM estimations, we include the lagged labour market index ( LMF i , t −1 ) in country i at time t –1. ln KOF_GI i , t −1 and ln KOF_EGI i , t −1 are various KOF overall globalisation and economic globalisation indices in country i at time t –1. WUI i , t −1 is the World Uncertainty Index (WUI) in country i at time t –1. We also include the interaction term of the WUI with globalisation ( WUI i , t −1 *ln KOF_GI i , t −1 ) and the interaction term of the WUI with economic globalisation ( WUI i , t ‐1 * lnKOF_EGI i , t ‐1 ). X i , t ‐1 indicates a vector of control variables in the country i at time t–1 . ϑ t , ϑ t , and ε i , t captures ‘period fixed‐effects (FE)’, ‘country FE’ and ‘error terms’, respectively. Robust standard errors are clustered by country level, which pools annual observations to account for the possible autocorrelation in the error term.

Equations ( 1 ) and ( 2 ) are estimated using FE estimations, a traditional estimation procedure in the empirical literature (see, e.g. Potrafke, 2015 ). However, due to their global size and importance, many multinational companies negotiate with governments and agree on labour regulation changes to invest in the country. Hence, reverse causation occurs between the current globalisation level (e.g. FDI inflows) and future variation in labour market regulations. From that viewpoint, fixed‐effect estimations could be easily contested for validity. Therefore, we also use system GMM estimations to estimate Equation ( 3 ) following previous papers (e.g. Potrafke, 2013 ). Our estimates with the annual data in level form do not suffer from spurious regression since we confirm that the dependent variables (indicators of labour market flexibility) are stationary according to Pesaran's ( 2007 ) panel unit root test.

The system GMM estimations, introduced by Arellano and Bover ( 1995 ) and Blundell and Bond ( 1998 ), can solve potential problems of endogeneity and reverse causality, that is, labour market flexibility can affect globalisation (Bellak & Leibrecht, 2011 ; Berliner et al., 2015 ). In the system GMM estimations, we provide the Sargan test results to determine whether there are over‐identifying restrictions. If we cannot reject the null hypothesis, there are no over‐identifying restrictions, and the required assumption is satisfied. Furthermore, we need to find the significant first‐order autocorrelation following the AR (1) autocorrelation test. However, we must not reject the null hypothesis of second‐order autocorrelation validity following the AR (2) autocorrelation test (Roodman, 2009 ). Instruments are also collapsed following Roodman ( 2009 ), and two‐stage GMM estimators are used.

4. EMPIRICAL FINDINGS

4.1. overall globalisation and labour market flexibility.

Table ​ Table1 1 reports the benchmark estimation findings for Equation ( 1 ), and the index of LMF is the dependent variable. The findings of lnKOF_OGI t ‐ 1 , lnKOF_OGIdf t ‐ 1 and lnKOF_OGIdj t ‐ 1 are provided in columns (1), (2) and (3), and the findings of lnKOF_EGI t ‐ 1 , lnKOF_SGI t ‐ 1 and lnKOF_PGI t‐1 are reported in columns (4), (5) and (6).

Globalisation, WUI and LMF (2000–2017)

The dependent variable is the index of labour market flexibility (LMF). The constant term, country FE and period FE are included. The robust standard errors are in parentheses. *** p  < 0.01 and ** p  < 0.05.

The findings show that lnKOF_OGI t ‐ 1 increases labour market flexibility. One standard deviation (0.276 per cent) increases in lnKOF_OGI t ‐ 1 leads to a 2.38 standard deviation (3.43 point) increase in the index of LMF . We also consider lnKOF_OGIdf t ‐ 1 and lnKOF_OGIdj t ‐ 1 . Finally, we use lnKOF_EGI t ‐ 1 , lnKOF_SGI t ‐ 1 and lnKOF_PGI t ‐1 . These globalisation indicators are positively associated with labour market flexibility. One standard deviation rises in the lnKOF_EGI t ‐ 1  lead to a 0.87 standard deviation increase in the index of LMF . In addition, the effects of one standard deviation increase in lnKOF_SGI t ‐ 1 and lnKOF_PGI t ‐1 are 2.01 standard deviation and 1.49 standard deviation increases in the index of LMF . Therefore, the impact is highest in social globalisation, and it is the lowest in economic globalisation. All related coefficients are statistically significant at the 1% level.

Furthermore, the lagged WUI ( WUI t ‐ 1 ) reduces the flexibility of the labour market. In addition, globalisation's interaction with the lagged WUI ( WUI t ‐ 1 * Globalisation Index t ‐1 ) also positively affects labour market flexibility. The related coefficients are statistically significant at the 1% level, except for column (4). The corresponding coefficients in column (4) are statistically insignificant.

When we analyse the controls, both lnPOP t ‐ 1 and log lagged lnGOV_SIZE t ‐ 1 are negatively associated with each regression's labour market flexibility. The related coefficients are statistically significant at the 5% level at least.

4.2. Economic globalisation and labour market flexibility

Table ​ Table2 2 provides the results of the benchmark estimation for Equation ( 2 ). The findings of lnKOF_EGIdf t ‐ 1 and lnKOF_EGIdj t ‐ 1 are reported in columns (1) and (2). The results of lnKOF_TRGI t ‐ 1 , lnKOF_TRGIdf t ‐ 1 and lnKOF_TRGIdj t ‐ 1 are provided in columns (3), (4) and (5). Finally, the findings of lnKOF_FINGI t ‐ 1 , lnKOF_FINGIdf t ‐ 1 and lnKOF_FINGIdj t ‐ 1 are reported in columns (6), (7) and (8).

Economic globalisation, WUI and LMF (2000–2017)

The dependent variable is the index of labour market flexibility (LMF). The constant term, country FE and period FE are included. The robust standard errors are in parentheses. *** p  < 0.01, ** p  < 0.05 and * p  < 0.10.

The results indicate that all indicators of economic globalisation promote labour market flexibility. The related coefficients are statistically significant at the 10% level at least, except for lnKOF_FINGIdj t ‐ 1 . In addition, WUI t ‐ 1 decreases labour market flexibility. On the other hand, ( WUI t ‐ 1 *Globalisation Index t ‐1 ) positively impacts labour market flexibility. However, most of the related coefficients are insignificant for these measures. When we look at the controls, lnPOP t ‐ 1 increases labour market flexibility. In contrast, lnGOV_SIZE t ‐ 1 is negatively related to labour market flexibility. The related coefficients are statistically significant at the 10% level at least.

4.3. Overall globalisation and sub‐indices of labour market flexibility

Table ​ Table3 3 reports the results of the benchmark estimation for the sub‐indices of the LMF . The findings of HRMW , HFR , CCB , HORE , MCWD and CON are provided in columns (1), (2), (3), (4), (5) and (6), respectively. The results state that lnKOF_OGI t ‐ 1 increases HRMW , HORE and CON . The coefficients of the related variables are statistically significant at the 5% level at least. However, KOF_OGI t ‐ 1 decreases the HFR , CCB and MCWD , and only the coefficient of MCWD is statistically significant.

Globalisation, WUI and sub‐indices of LMF (2000–2017)

The dependent variable is the sub‐indices of labour market flexibility (LMF). The constant term, country FE and period FE are included. The robust standard errors are in parentheses. *** p  < 0.01 and ** p  < 0.05.

Furthermore, WUI t ‐ 1 decreases the level of sub‐indices of labour market flexibility. In addition, ( WUI t ‐ 1 * Globalisation Index t ‐1 ) improves the sub‐indices of labour market flexibility. The coefficients of HFR and MCWD are statistically significant at the 5% level at least. Regarding the controls, both indicators of lnPOP t ‐ 1 and lnGOV_SIZE t ‐ 1  have mixed effects on the sub‐indices of labour market flexibility.

5. ROBUSTNESS CHECKS

5.1. different country groups.

We also consider the estimations for different country groups, and the related results are provided in Table ​ Table4. 4 . The first column in Table ​ Table4 4 provides the findings for low‐income and middle‐income economies, and the second column reports the findings for high‐income economies. We report the results for non‐OECD and OECD countries in columns (3) and (4), respectively.

Robustness test I: globalisation, WUI and LMF (Different Country Groups, 2000–2017)

The findings show that lnKOF_OGI t ‐ 1 increases labour market flexibility. The related coefficients are statistically significant at the 1% level. Furthermore, WUI t ‐ 1 decreases labour market flexibility. Note that ( WUI t ‐ 1 * Globalisation Index t ‐ 1 ) also positively affects labour market flexibility in all countries. However, the related coefficients are insignificant, except for the results for low‐income and middle‐income economies. When we analyse the controls, lnPOP t ‐ 1 decreases labour market flexibility. However, lnGOV_SIZE t ‐ 1 is positively related to labour market flexibility. The related coefficients are statistically significant at the 10% level at least.

5.2. System GMM estimations

Table ​ Table5 shows 5  shows the system GMM estimation results for the model in Equation ( 3 ). The results of lnKOF_OGI t ‐ 1 , lnKOF_OGIdf t ‐ 1 and lnKOF_OGIdj t ‐ 1 are reported in columns (1), (2) and (3), and the findings of lnKOF_EGI t ‐ 1 , lnKOF_SGI t ‐ 1 and lnKOF_PGI t ‐ 1 are provided in columns (4), (5) and (6). The results indicate that all lagged log globalisation indices spur labour market flexibility, and the related coefficients are statistically significant at the 1% level.

Robustness test II: system GMM estimations (Globalisation, WUI and LMF, 2000–2017)

The dependent variable is the index of labour market flexibility (LMF). The constant term and the lagged dependent variable are included. The robust standard errors are in parentheses, and the p –values are in brackets. *** p  < 0.01, ** p  < 0.05 and * p  < 0.10.

Furthermore, WUI t ‐ 1 decreases the flexibility of the labour market. In addition, ( WUI t ‐ 1 * Globalisation Index t ‐ 1 ) positively affects labour market flexibility. The related coefficients are statistically significant at the 10% level at least. When we investigate the controls, both the indicators of lnPOP t ‐ 1 and lnGOV_SIZE t ‐ 1 are negatively related to labour market flexibility in each regression. The coefficients for lnPOP t ‐ 1 are statistically significant at the 1% level, and however, the coefficients for lnGOV_SIZE t ‐ 1 are statistically insignificant. Potrafke ( 2013 ) finds the positive effects of population and government size on labour market flexibility in the system GMM estimations. However, the author finds a negative impact of population on labour market flexibility in cross‐sectional data estimations.

Finally, the required diagnostics are satisfied in each regression. The results of the Sargan test indicate that there is no issue with over‐identifying restrictions. The findings of the AR(1) and AR(2) autocorrelation tests show that there is a significant first‐order autocorrelation, but there is no second‐order autocorrelation.

5.3. Including macroeconomic indicators

According to previous literature (see, e.g. Potrafke, 2013 , 2015 ), macroeconomic indicators can change globalisation's effects on labour market flexibility. Therefore, we also include additional controls (defined in the lagged form) in the benchmark estimations. The related results are reported in Table ​ Table6 6 .

Robustness test III: including macroeconomic indicators (Globalisation, WUI and LMF, 2000–2017)

The dependent variable is the index of labour market flexibility (LMF). The robust standard errors are in parentheses.

Following Gozgor and Ranjan ( 2017 ), we add the age‐dependency ratio, which can change globalisation's effects on labour market flexibility due to the social security system's role. Urban population is also included to capture rural‐urban transformation and migration, affecting labour market flexibility (Bernal‐Verdugo et al., 2012 ). At this stage, country size is measured by population density. Furthermore, the macroeconomic stance is measured by the inflation rate and the current account balance (Potrafke, 2019 ). Finally, we include the roles of redistributive policies and income inequality in the globalisation‐labour market flexibility nexus (Dabla‐Norris et al., 2015 ; Rodrik, 2011 ). All results indicate that the coefficients of WUI t ‐ 1 ( WUIt ‐ 1 * lnKOF_OGIt ‐1) and lnKOF_OGIt ‐ 1 are in line with the benchmark estimations are statistically significant at the 1% level.

5.4. Including institutional quality measures

The previous papers also indicate that institutional quality can change globalisation's effects on labour market flexibility (Potrafke, 2010 , 2013 , 2015 ). At this stage, we add various institutional quality measures (again defined in the lagged form) in the benchmark estimations. The related findings are provided in Table ​ Table7 7 .

Robustness test IV: including institutional quality measures (Globalisation, WUI and LMF, 2000–2017)

We control for the indices of democracy, executive constraints and polity2 provided by Marshall and Gurr ( 2020 ). The index of the freedom of the press of Freedom House ( 2020 ) is also included. The index of the legal system and property rights introduced by Gwartney et al. ( 2020 ) is added to the benchmark estimations. Finally, the indices of political regimes and typology of political institutions of Bjørnskov and Rode ( 2020 ) are included. In each regression, the effects of globalisation on labour market flexibility align with the benchmark estimations. The related coefficients are statistically significant at the 1% level.

5.5. Additional sensitivity analyses

We provide additional sensitivity analyses to check the robustness of the main findings. The related findings are reported in Table ​ Table8. 8 . Firstly, we exclude the extreme observations for overall globalisation, world uncertainty and labour market flexibility indices. Following Fang et al. ( 2021 ) and Jha and Gozgor ( 2019 ), extreme observations are defined as observations more than two standard deviations away from the mean values. Secondly, we investigate whether our findings are sensitive to excluding specific regions. Following Jha and Gozgor ( 2019 ), we separately exclude the observations for the Middle East and North Africa (MENA), Latin America and the Caribbean (LAC), sub‐Saharan African (SSA) and developing East Asia countries. These additional results are also in line with the baseline regressions. The related coefficients are statistically significant at the 5% level at least.

Robustness test V: sensitivity analyses (Globalisation, WUI and LMF, 2000–2017)

Overall, we observe that globalisation increases labour market flexibility while economic uncertainty decreases it. Furthermore, the interaction of globalisation with economic uncertainty also positively affects labour market flexibility.

6. CONCLUSION

This paper investigates globalisation's impact on labour market flexibility via its interaction with economic uncertainty, serving as potential determinants of labour market flexibility. We consider new globalisation and economic uncertainty indices within a panel dataset of 136 countries from 2000 to 2017. We find that globalisation promotes labour market flexibility, while economic uncertainty decreases labour market flexibility. The interaction of globalisation with economic uncertainty also positively affects labour market flexibility. Our findings are robust to various sensitivity analyses, that is, different estimation procedures, different globalisation indicators (including various controls) and excluding outliers.

We conclude that globalisation has a less positive impact on labour market flexibility in times of greater uncertainty. At this stage, governments should monitor economic uncertainty at the domestic and global levels to analyse the potential impact of globalisation on labour market flexibility. Our findings indicate that policy decisions that will make labour markets more rigid (less flexible) should be expected during periods when globalisation decreases and the level of economic uncertainty increases, such as in the COVID‐19 era. The end of the COVID‐19 pandemic should increase the level of globalisation and decrease the level of economic uncertainty. Therefore, in line with the race to the bottom hypothesis, we should expect more flexibility in labour market regulations during the post‐COVID‐19 era.

Finally, the empirical evidence in this paper is limited over the period 2000–2017. Therefore, future papers can focus on globalisation's effects on other labour market indicators (e.g. public employment, unemployment, wages, etc.), including the data of the COVID‐19 era.

ACKNOWLEDGEMENTS

This research was supported by the National Social Science Fund of China (16BJY052), the Fundamental Research Funds for the Provincial Universities of Zhejiang (GB201902002 and GB202002003), and the Major Humanities and Social Sciences Research Projects in Zhejiang Province (2021QN050). We thank the editor and anonymous reviewers for their comments and suggestions to enhance the paper’s merit.

Supporting information

Table S1–S3

Fang, J. , Gozgor, G. , & Nolt, J. H. (2022). Globalisation, economic uncertainty and labour market regulations: Implications for the COVID‐19 crisis . The World Economy , 45 , 2165–2187. 10.1111/twec.13230 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

1 Therefore, economic uncertainty negatively affects labour market outcomes (e.g. higher unemployment) (Schaal, 2017 ).

2 Note that in newer models with heterogeneous firms, capital mobility and trade liberalisation can affect labour market regulations, which leads to an intra‐sectoral reallocation of the factors of production (see, e.g. Melitz and Redding, 2014 ).

3 There is also extensive literature examining the impact of globalisation on employment using either industry‐level or firm‐level data. Refer to Hummels et al. ( 2018 ) for a survey of these papers.

4 For details, visit https://www.fraserinstitute.org/economic‐freedom/approach .

5 For details, visit https://worlduncertaintyindex.com/ .

6 For details, visit https://www.kof.ethz.ch/en/forecasts‐and‐indicators/indicators/kof‐globalisation‐index.html .

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Exploring the impact of globalization on research in the United States

Jonathan Adams

Chief Scientist, Institute for Scientific Information

Our latest Global Research Report from the Institute for Scientific Information (ISI)™, “U.S. research trends: The impact of globalization and collaboration,” raises important questions about how well past investment has prepared the U.S. scientific enterprise to achieve its goals. Our findings suggest that while the U.S. remains a leading science and technology power, it must acknowledge its shrinking domestic research capacity and work collaboratively with resourceful competitors to maintain its position.

The U.S. has a diverse and extensive research landscape, with government agencies, universities, and private companies playing a significant role in funding and conducting research. For many years the U.S. has led in areas such as AI, biotech, space exploration and healthcare. The research landscape is highly competitive on the global stage, with a focus on driving innovation and making breakthroughs.

Our latest ISI Global Research Report draws on Web of Science™ data to shed light on the trajectory of research in the U.S. over the last 15 years. Our findings suggest that while the U.S. remains a leading science and technology power, it faces increasing competition from new science-based economies in Asia and an expanded EU network. Our report also raises questions about how past investment has prepared the U.S. scientific enterprise to achieve its goals.

Keeping pace with global science and technology R&D

Our data-driven analysis demonstrates that globalization is shaping the R&D landscape in the U.S. Key findings include:

• U.S. research investment, domestic research student numbers, and the output of research articles and reviews have not grown at the same rate as other parts of the world – resulting in increasing competition from other countries and regions that have diversified and grown to challenge the U.S.’s historical strengths.
• The U.S.’s research portfolio remains extensive and diverse, but its research subject diversity has declined due to the science budget expanding much faster in biomedicine than in technology areas.
• The U.S. is strong but no longer dominates the research landscape as it did in the past. This is because other G7 nations are producing papers of the highest citation impact, while more U.S. papers are now of world average citation impact.

Silver linings: Spot the trends

One notable trend in the U.S. research landscape is the increasing importance of international research collaboration. Collaboration has become pervasive across the globe, with most of the growth in U.S. research output attributable to collaboration. Collaborative research has also helped to mitigate a decline in overall U.S. impact indicators.

• U.S. research collaboration accounts for over 50% of output in most science/engineering areas and includes a diverse network of partners.
• The physical sciences and technology areas are the subjects where the U.S. has the greatest degree of international collaboration and the smallest component of purely domestic research output.

Another trend is the shift towards greater equity in the distribution of research excellence, with a focus on addressing over-concentration of innovation and development resources in historically strong research areas. This has resulted in rising impact in U.S. states with historically low research output.

What comes next for U.S. research?

Our findings emphasize the need for the U.S. to acknowledge its shrinking domestic research capacity, particularly in engineering, and work pragmatically with resourceful competitors such as Mainland China.

Overall, while the U.S. research landscape is facing challenges from competitors, it remains a strong and influential player in the global research community. However, continued investment and collaboration will be necessary to maintain this position and address areas of weakness.

Complete the download form on the right to read the full report and find out more about the impact of globalization on the U.S. R&D landscape, or get in touch to talk to us about your research analytics, evaluation and management needs.

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Paranoid Publics: Conspiracy Theories and the Public Sphere

Oedipus & the Cabal: Conspiracy Theories and the Decline of Symbolic Efficiency Provisionally Accepted

• 1 American University, United States

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Conspiracy theories are a means by which people make sense of vastly complex webs of cause and effect, contingency and random chance. The impersonal operations of political economy thus become personified in the figure of sinister puppet-masters controlling the world. Overdetermined historical events become narrativized as plots executed with clockwork timing and perfect secrecy. This paper proposes a model for the motives and mechanisms by which complex and ambiguous systems are made fodder for paranoid interpretation in the form of conspiracy theory. Using an exegetical close-reading methodology and psychoanalytic interpretive lens, it takes three meta-conspiracies as symptoms of the process described above: Antisemitic theories of globalization, the “Deep State” as seen from the political Left, and vulgar UFOlogy. This paper will argue that the impenetrable complexity of our globalized, financialized, hypermediated world agitates our experience of the Symbolic order, that is, the realm of language, logic, law, which acts as the scaffolding to the individual’s psychic sense of place in society. Lacan associates entry into the Symbolic order with the reconciliation of the Oedipal conflict and the Symbolic itself with the figure of the father. But the complexity and ambiguity of the Symbolic in our contemporary world produce a crisis in this dynamic, which some have described as a “decline in Symbolic efficiency.” This is particularly acute around events which give rise to conspiracy theories. The will of the father becomes unknowable, and the order upon which he insists appears dark and chaotic. Conspiracy therefore represents a turning away from this chaotic order, and a “dark return” to the pre-symbolic maternal, via the recession of the Symbolic into the Real, as a chaotic totalized presence (the conspiracy) stretching to occupy every corner of reality.

Keywords: Conspiracy theories, Digital Media, media ecology, Psychoanalysis, Lacan, Symbolic Order. (Min.5-Max. 8 Zizek's is not only psychotic, but multiply so

Received: 29 Jan 2024; Accepted: 02 Apr 2024.

Copyright: © 2024 Hughes. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Brian Hughes, American University, Washington, DC, United States

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