Does globalization contribute to widening the Global Gap

Running Head: Global North and Global South

Global North and Global South 12

Global North and Global South

How does globalization influence the widening gap between the Global North and the Global South?

Abstract

This study seeks to evaluate the hypothetical belief that globalization has dramatically enhanced the gap between the global North and the global South. Notably, different scholars have taken different views as far as globalization relates to inequality between the global North and the global South. This Paper’s findings will therefore inform on the accurate contributions of globalization towards the global north-south inequality.

According to some scholars, there is overwhelming evidence to relate globalization to the widening gap between the global North and South compared to Africa and western Europe’s per capita parity. However, some studies have also claimed that merging national economies to create a global economy has significantly contributed to reducing poverty and inequality across the globe.

In the literature, Zhou et al. (2011) argue that, after studying the effects of globalization on 60 countries’ inequality distribution, globalization can minimize or increase the global North and global South’s income inequality. Also, Bergh and Nilsson (2011) concluded that reforms geared towards economic freedom appear to heighten inequality between the Global North and global South. However, in their findings, they also argue that legal, political, and monetary globalization does not influence the global north-south gap.

The methodology of this study uses globalization indices such as the Kearney index and the principal component index. Also, to look at the cross-country inequality trends, this study will use inequality data from the most current World Bank Povcal database, supplemented with the Luxembourg Income Study (LIS) data that gives high-quality coverage of developed economies (Global North). Consequently, the study will adopt the Gini coefficient, which will help the researcher observe the inequality gap among the sampled countries within the last two decades. Subsequently, each country’s Gini coefficient will be regressed on principal component (PC) indices and Kearney indices for all the 50 sampled countries.

Introduction.

Global North is loosely translated as more economically developed countries, while Global South refers to less economically developed countries (Reuveny & Thompson 2007). The North primarily refers to the western (First) world countries, while the South is mainly the third world countries (Sofie,2013). However, irrespective of significant development gains worldwide, there is tangible evidence indicating a widening gap between the world’s richest and poorest countries. For instance, in 1820, West Europe’s per capita income was three times higher than Africa’s; otherwise, by 2000, it was thirteen times bigger (Kolb,2018). 

Moreover, globalization has political, social, and cultural origins; however, much concentration is directed to economic globalization and the consequences. Some scholars have defined economic globalization as the “integration of national economies to international economy through trade, technological transfer, capital flows within short terms, foreign investments and international transfer of workers. (Bhagwati, 2004) To the economists, their primary concern is globalization’s impacts on developing countries’ societies and economies. (Silbey, 2017) According to Bhagwati 2004, the merging of national economies into one global economy alleviates poverty, enhances economic growth, reduces the global North and global south inequality gap, and helps solve pollution and democracy issues worldwide in China and India. (Ogunyomi et al. 2013)

Nevertheless, the view has faced opposition from some quarters claiming that globalization has resulted in economic insecurity instead and has hugely contributed to the growing inequality between the global North and Global south. Marjit et al. 2004; Bergh & Nilsson 2011) both have argued that globalization has not only contributed to the enhancement of a wider gap between the global North and global South but has also resulted in increased inequality between the rich and the poor in over 95% specific countries across the globe. Hence, the rich are continuously getting richer, while the poor are getting poorer. (Borjas & Ramey 1994)

The widening gap of inequality between the North and South globe can damage social cohesion and trust, contributing to conflicts and hindering investment (Kolb,2018). Consequently, inequality can result in a poor choice of public policies, therefore hampering poverty reduction (Fletcher & Weinstein 2018). On the other hand, while the anti-globalists have criticized globalization as the producer of inequality, others have considered it an equalizer that expands the poor’s horizons. Therefore, the study seeks to evaluate if globalization contributes to the widening gap between the Global North and the Global South. The knowledge will inform various policies that can bridge the inequality gap between the North and South globes. (Borjas & Ramey 1994) Furthermore, there is no agreement on the contributions of globalization (Reuveny & Thompson 2007). Thus, the perception that globalization is problematic considering various divergent and contradictory claims around it (Kolb,2018).

Literature Review

With international trade and free movements of people and goods globally, poverty and privileges are no longer geographically isolated (Kacowicz,2007). Although globalization is primarily presumed to be very important, there is a scant idea of what precisely it entails (Prasad et al. 2005). Scholte notes that despite many publications on globalization, it remains shallow and politically naïve (Ortiz-Ospina et al., 2018). However, according to Dreher and Gaston (2008) and Roser (2013), globalization has indeed been part of history. Notably, most critics of globalization come from the North and not the South. (Dabla-Norris et al. 2015) According to some critiques, the third world economy’s marginalization is due to the Cold War’s end and not globalization. From a radical perspective, one can claim that globalization deepens inequality among nations (Dreher & Gaston,2008). However, according to the “Washington consensus,” globalization is considered the cure of poverty. (Fletcher & Weinstein 2018)

Consequently, the widening gap between the global North and global South due to globalization is of significant academic interest. Most of the scholars studying this issue have got a different conclusion. According to Zhou et al. (2011), after studying the effects of globalization on 60 countries’ inequality distribution, globalization can minimize or increase the income inequality between the global North and global South. (Bhagwati, 2004) Also, Bergh and Nilsson (2011) concluded that reforms geared towards economic freedom appear to heighten inequality between the Global North and global South. (Dreher et al. 2008) However, in their findings, they also argue that legal, political, and monetary globalization does not influence the global north-south gap. (Bergh & Nilsson 2010) When Edwards (1997) evaluated the link between income and trade policy distribution by regressing the Gini coefficient over various trade openness indicators, he concluded a lack of evidence to relate globalization to any significant effect on the global north-south gap enhancement. (Bergh & Nilsson 2010)

On the other hand, irrespective of global significant developmental gains that have helped many countries transition from adverse poverty, there is adequate reason and evidence to believe that globalization in the same measure contributes to the widening gap between the global North and global South. (Bergh & Nilsson 2010) For instance, the per capita difference between Africa and Western Europe has widened over the last century. (Bhagwati, 2004) Not to say that globalization has not benefited developing countries; however, as these emerging countries experience relative growth due to globalization, the trend seems to favor more developed countries much more, resulting in the widening gap. (Dreher et al. 2008)

Nevertheless, some factors have also been blamed for having contributed to global north-south inequalities. (Borjas & Ramey 1994) That includes; specific country’s industrial and economic sectors, education level and health status of the citizens, access to market and international trade relationships with other countries, and conflicts between and within countries. (Bergh & Nilsson 2010)

Theoretical Approach

Nonetheless, the realism theory of international relations will be employed as the guiding framework for the proposed study. According to realism, Nations work only to increase their power relative to other states (Galston,2010). Assumptions of realism include; That state is the principal actor in international relations. Second, that state is a unitary actor. (Bergh & Nilsson 2010) Third, decision-makers are rational actors because rational decision-making leads to the pursuit of national interest. Finally, states desire power to ensure self-preservation. The theory will inform the study to evaluate whether the state’s aggression to retain power could significantly thwart globalization’s expected gains if that could widen the gap between the North-South divide (Galston,2010).

Methods and Data

This study aims to table tangible empirical evidence on this fundamental issue of the relationship between globalization and the Global North-South gap. Therefore, the following globalization indices will be used. (Borjas & Ramey 1994)

a.) Kearney index and b.) the principal component index. The study data will also entail the derivation of four aspects of globalization: political activities, technological connection, economic integration, and personal contact. (Bhagwati, 2004)

This study will use inequality data from the most current World Bank Povcal database, supplemented with the Luxembourg Income Study (LIS) data that gives high-quality coverage of developed economies (Global North) cross-country inequality trends. LIS is a trans-national data center based in Luxembourg providing data to the global community of policymakers, researchers, and educators. (Bergh & Nilsson 2010) LIS dataset includes; employment, demographic, employment, and income data from the middle- and high-income countries. (Borjas & Ramey 1994) The data are organized to enable comparison between countries.

Moreover, LIS is an honored international venue for social sciences research across various countries. On the other hand, PovcalNet produces official world bank country, regional, and global poverty estimates. (Ogunyomi et al. 2013) PovcalNet provides information on a specific country’s efforts towards development goals and enables national governments, international organizations, civil society, journalists, and researchers to evaluate inequality and poverty across all countries’ development stages. (Bergh & Nilsson 2010)

The resulting complete sample data set will be more accurate within and country comparisons. The final dataset will include 50 countries (25 from the Global North and 25 from the Global South) chosen randomly. (Silbey, 2017) Gini coefficients for the 25 Global North economies within the sample will be constructed using survey data. Accordingly, Gini coefficients for the 25 Global South economies will use the consumer survey data. The World Bank’s Povcal database to construct our data set will help compare the income and consumption-based Gini indices attentive to concepts and survey methodology. Gini index refers to a gauge of income distribution among countries or populations usually used to measure economic inequality. (Borjas & Ramey 1994) The Gini coefficient will help the researcher observe the inequality gap among the sampled countries within the last two decades. (Ogunyomi et al. 2013) Subsequently, each country’s Gini coefficient will be regressed on principal component (PC) indices and Kearney indices for all the 50 sampled countries. (Bhagwati, 2004) The empirical model will be developed as below:

Ginii = ?0 + ?1 Globalization index + ?2 Education + ?3 Urbanization + ui

This study will also ensure that Kearney data’s flaws are alleviated by establishing two new global indices, i.e., PC index and Kearney index. (Silbey, 2017)

The consumer survey data will be made using primary data obtained from national representative surveys. (Ogunyomi et al. 2013) Finally, the researcher will conduct proper screening of the Povcal database to enhance comparability and consistency of consumption and income. The results will show Income Inequality between the global North and global south Groups and the Income Shares between global North and Global south Groups. (Silbey, 2017) Besides, a positive relationship between income inequality and globalization will be evaluated by identifying historical variations in economic indicators for the 50 countries over a given period. (Abakumova & Primierova 2018) That is, from 2010 to 2020. (Dreher et al. 2008) The analysis will use four different data modeling techniques to model possible variations in income inequality. At the same time, the analysis will adopt three explanatory variables, which are; urbanization level, globalization, and education level. (Silbey, 2017)

The model functional form will be as below; (Bhagwati, 2004)

Where ‘i’ is the country ranging from 1 to 50 while ‘t’ stands for the year. The other indicator’s sources and descriptions are as follows: (Bergh & Nilsson 2010)

Giniit stands for measuring income inequality. The data used in the Gini coefficient is drawn from the most current World Bank Povcal database, supplemented with the Luxembourg Income Study data that gives high-quality coverage of developed economies (Global North. (Borjas & Ramey 1994)

Git stands for the KOF index of globalization. The data for KOF will be obtained from the most updated Dreher.

Eit stands for education index, thus, helping in evaluating educational level. (Stiglitz, 2002)

Uit stands for urbanization index, calculated as the percentage of persons residing in urban places. Both the urbanization index and education index data will be gathered from Human Development Report data site. (Dreher et al. 2008)

Consequently, as opposed to Zhou et al. (2011), this research will use the KOF index as the globalization indicator and not the Kearney Index based on the superiority of KOF. For instance, according to the A.T. Kearney index, and globalization is grouped into four dimensions: technological activity, economic integration, political activities, and personal contact. (Borjas & Ramey 1994) Nonetheless, the A.T. Kearney index has some demerits associated with it; for example, it gives all the variables equal weights, thus, prompting bias against larger nations/states, translating that smaller states got the habit to occupy higher rank. (Bhagwati, 2004) However, the KOF index provides variables from political, social, and economic dimensions of globalization with relative weights and gives 208 nations/states data. (Bergh & Nilsson 2010)

Modeling

This study will adopt three distinct models to analyze the pooled data. The results obtained from various models will enable the researcher to develop a rigorous and concrete analysis due to comparing the same indicators within different dynamics. (Silbey, 2017)

Basic Linear Model (Pooled OLS)

The basic linear model is one of the models this study will employ to analyze the data. It can be devised as below; (Dreher et al. 2008)

???????????????????????? = ????0 + ????1. ???????????? + ????2. ???????????? + ????3. ???????????? + ???????? + ????????t?

In this case, ???????? depicts the impacts of time-invariant country-individual issues that may affect the Gini coefficient, like demographic dynamics and geographic dynamics. (Bhagwati, 2004)

???????????? refers to the idiosyncratic error term, which stands for the factors that fluctuate across the country and time. (Borjas & Ramey 1994)

Fixed Effects Model

Apart from the basic linear model, this study will as well use the fixed effects model. The model can be devised as:

In this model, the country-specific time-invariant effect will be eliminated, enabling the model to be more precise and consistent. (Bhagwati, 2004)

Random Effects Model

This model will need quasi-demeaning of the variables, rendering the impacts of unobserved variables reduced significantly, though not eliminated from the model, to estimate the longitudinal data. Below is the model: (Dreher et al. 2008)