GLOBALIZATION’S CONVERGENCE EFFECT ON
HUMAN QUALITY OF LIFE (QOL) IN ASIA
EVIDENCE FROM THE KOF INDEX OF GLOBALIZATION1JEET BAHADUR SAPKOTA
Research Fellow, Global COE Program, Global Institute for Asian Regional Integration (GIARI)
PhD Candidate, Graduate School of Asia Pacific Studies (GSAPS), Waseda University
Address: 1-21-1 Nishi-waseda, Shinjuku-ku, Tokyo169-0051 JAPAN
Phone: 81-90-8569-5177; Fax: 81-3-5286-2169; E-mail: jbsnepal@gmail.com

Abstract: The impact of globalization on human quality of life (QOL) is an issue highly debated byacademics, policy makers, private sectors, social organizations and even by the general public in Asiaand elsewhere in the world. There is no doubt that globalization has had a significant impact on the
lives of millions of Asian people; however, it is unclear that globalization has reduced or increased
the gap in human QOL among the rich and poor countries. This paper examines whether human QOL
in the Asian countries is converging (or diverging), and assesses the impact of globalization on QOL
trends in the region. Using panel data of selected countries from 1975 to 2005 over five-year intervals,
and applying the dynamic panel data model, the study finds that human QOL of most countries in theregion is moving closer to that in Japan (the benchmark country) and that globalization has
significant impacts on this convergence. More specifically, overall indicators of human QOL,
measured by the human development index (HDI), is converging in Asia and the overall as well as
economic, social and political indicators of globalization are highly significant in this convergenceprocess. Similarly, a disaggregated analysis of the sub-constructs of HDI shows that health andeducation indicators are also converging and globalization indicators have a significant impact on theconvergence process. Although the income aspect of QOL is found diverging sharply, theglobalization indicators are not significant on the divergence process and the shrinking gap in healthand education indicators overweight the income gap, which leads the overall human QOL
convergence in the region. To complement these findings, the paper calls for a study of some country
cases, as well as a study of the impact of globalization on human QOL inequality within countries.
Such studies, the paper concludes, will facilitate the development of specific policy recommendations
tailored towards countries in their cultural context, with a positive (or at least neutral) impact of
globalization on the reduction of human QOL gaps within them.
[Key words: globalization, convergence, human QOL, KOF Index, dynamic panel data model, Asia]

I.

Introduction

The modern world economy and society are globalizing at a more rapid pace than ever before(Urata, 2002; Dreher, 2008) and Asia is one of the most affected regions from the current wave ofglobalization politically, economically and socially (Rondinelli and Heffron, 2007). Consequently, theimpacts of globalization and its various dimensions have been widely debated and examined byacademics, politicians, policymakers, the private sector and even by the general public in Asia.
This paper was presented in the ―2nd Summer Institute on Regional Integration‖ at Waseda University inAugust 2008 organized by the GIARI. The author is highly indebted to the professors and participants whose
comments and suggestions during and after the presentation have been instrumental for refining the paper.
Especially, sincere thanks go to Prof. Shujiro URATA, Prof. Shunji MATSUOKA, Prof. Tsuneo AKAHA,
1

Prof. Binod K. AGRAWAL, Dr. Giovanni CAPANNELLI, Prof. Kazuhiko YOKOTA, Prof. Kyotsuke
KURITA, and Dr. Stephen R. NAGY. The author also sincerely thanks the Waseda University Global COE
Program, Global Institute for Asian Regional Integration (GIARI), and its Program Leader Prof. Satoshi
AMAKO for the generous funding and encouragement for conducting this research project in GIARI.

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However, there is no consensus regarding how the benefits of globalization are distributed among
Asian countries and their populations. Although most of the theoretical and empirical literature on theconvergence hypothesis claims globalization should help reduce the gap between the rich and poor
(Milanovic, 2006), the convergence effect of globalization on the human quality of life (QOL) within
Asia is still a matter of debate and rarely explored.
Nonetheless, there are a significant number of studies that assess the impact of globalization
within Asia. For instance, a comprehensive study by the World Bank (1993) confirmed that thesustained and rapid economic growth in East Asia is more equally distributed than in any other region.
Furthermore, Yusuf (2001) claimed that the so-called ―East Asian Miracle‖2 helped global income
convergence, as a number of economies from the region grew significantly faster than the average for
the Organization for Economic Cooperation and Development (OECD). However, these studies
mainly focused on income or materialistic achievements following the conventional wisdom ofglobalization research (Garrett, 2000; Nyahoho, 2001; Dreher, 2006). Of course, income is an
important part of QOL, but health and education are as well (Stieglitz, 2006); hence, these aspects
should also receive similar research efforts. In this regard, recent works by Kenny (2007) assessed
empirically the global convergence on QOL variables. Kenny (2008) also evaluated the case of East
Asia. However, in his works, he did not introduce any aspect of globalization as an explanatory
variable. Nor did he assess the overall convergence of QOL, but instead measured the convergence ofdifferent elements separately and found some were converging and some were not. The present study
attempts to narrow the gap in this area of research by offering an analysis of the current situation of
convergence of overall human QOL in terms of human development index and its components within
the region and an exploration of the effect of globalization in this convergence (or divergence)
process.
The paper is organized as follows. Section II introduces the KOF indexes as the working
definition and measurement of globalization and the human development index as the measure ofhuman QOL. The section also highlights the current trends of these key indicators in the Asian
countries. Section III examines the convergence of human development in the region and finds clear
evidence of convergence. It also briefly discusses the reasons behind this finding. Section IV
empirically tests the impacts of globalization on this convergence process. The section presents themethodology and the results simultaneously and shows, as expected, a significant contribution of
globalization to the convergence of human development. Finally, Section V offers a brief discussion
on the findings and suggests further research.
II.

Globalization, human QOL and its trend in Asia

Since the end of the Cold War and the resurgence of a neo-liberal economic policy, contemporary
globalization has not only become a central concern to the donor community and policymakers but it
also draws due attention from academics, non-governmental organizations and even from the generalpublic (Beynon and Dunkerley, 2000; Bircham and Charlton, 2001; DFID, 2000; Oxfam, 2002).
Globalization is one of the most controversial issues whose many aspects are questioned and debated(Wiarda, 2007), which include scope, structure, reality and meaning of globalization itself (Croucher,2009). Thus, it is very hard to define globalization in a way that satisfies all stakeholders. This study,
however, tries to take the most comprehensive definition and indicator of globalization based on theacademic literature on the impact of globalization.
2

After the publication of ―East Asian Miracle‖ by the World Bank in 1993, the phrase became one of the best
known in development studies. It is because of the region‘s fast growth in the past three decades. The economies
of Japan and the Newly Industrializing Economies (NIEs) of Hong Kong, Singapore, Korea, and Taiwan are still
considered as models of development for other countries.

Sapkota 3

Most of the empirical studies in relation to the so-called third wave of globalization3 employ
proxies such as trade, capital flows and openness as measures of globalization using cross-section data
(Dreher, 2006). For example, Heinemann (2000) showed that more open countries had lower
increments in government outlays and taxes and Vaubel (1999) found them to have lower government
consumption. Rodrik (1998) also used cross-section data and found no effects of capital accountopenness on economic growth. Recently, however, some scholars have used panel data to find theeffects of globalization and have shown positive impacts of openness to growth and poverty but
revealed mixed impacts on income inequality (Dollar and Kraay, 2004; Greenaway et al., 1999;
Edition et al., 2002).
These detailed studies, however, fail to consider the overall effect of globalization, as they focus
on individual dimensions of globalization. As all the dimensions of globalization are strongly related
to each other and are important in explaining the consequence of globalization, omitting suchimportant variables from the regression equation can generate severely biased coefficients (Dreher,2006). In addition, as mentioned earlier, most of these studies, motivated by conventional wisdom,
focus solely on economic growth, income poverty and income inequality (Garrett, 2000; Nyahoho,
2001; Dreher, 2006).
To correct these shortcomings, this study uses the KOF Index of globalization developed Dreher(2006), which is considered the most comprehensive indicator of globalization available. Although
there is another measure of overall globalization developed by A.T. Kearney/Foreign Policy
Magazine (2002), it only ranks the countries in terms of globalization and the rank is only available
for recent years. Thus, the A.T. Kearney Index of Globalization cannot be used for the purpose of thisstudy.
To define globalization by formulating the KOF Index of Globalization, Dreher (2006) referred tothe definition given by Keohane and Nye (2000: 4) among others, and he summarized the definition of
globalization in the following three dimensions: ―economic globalization, characterized as long-distance flow of goods, capital and services aswell as information and perceptions that accompany market exchanges;
 political globalization, characterized by a diffusion of government policies; and, social globalization, expressed as the spread of ideas, information, images and people‖(Dreher, 2006, p 1092).
Dreher then considered all possible elements for each dimension of globalization and developed
the indexes of economic, social and political globalization by employing appropriate weights
systematically for each component following the methodology of Gwartney and Lawson (2001). 4 The
components of each aspect of globalization were transformed on a zero to ten scale before theprinciple components technique was used to construct a weighted summary index for individual
dimensions of globalization. Then the indexes of economic, social and political globalization werecombined into a single index of overall globalization, giving the respective weights for eachdimension and named it the KOF Index of Globalization, which is the working definition and measureof globalization in this study. Appendix I presents the elements considered and weight placed to
calculate the KOF Index of Globalization, which provides its broad concept and comprehensive
methodology used to obtain the index.
Figure 1, based on the KOF Indexes, presents the trends of the three types of globalization.
Representing the high turmoil in international security as well as an increasing role of global
3

According to Luke Martell (2007), some recent contributions in the globalization literature have identified
three waves or perspectives in globalization theory—the globalists, skeptics, and transformationalists or
postskeptics (for example, Held et al. 1999; Holton 2005). For detail, see Martel (2007).
4
See Dreher, Axel (2006) for details.

Sapkota 4

governance, political globalization fluctuates more at higher levels. However, economic globalization
has a rather steady upward trend. Although social globalization has the lowest index value, theprogress goes along with the pace of other forms of globalization. Overall, globalization has moved
faster since the beginning of the 90s, when the five-decade long Cold War ended.
Figure 1. Global trends of globalization as per KOF Indexes (1970-2006)

Data Source: Dreher (2006); KOF Economic InstituteSapkota 5

Figure 2. Asian trends of globalization as per KOF Indexes (1970-2006)

Data Source: Dreher (2006); KOF Economic Institute

The Asian trends of globalization are somehow different from the global trends. As shown in
Figure 2, Asian globalization is highly driven by economic factors. Political globalization was very
weak before 1990; however, it has gained significant momentum since. The trend of social
globalization is much more similar to the trend at the global level.
Figure 3 shows the country-level trends of globalization in Asia. As a huge continent lacking aclear-cut common definition of ―Asia―, this study considers two world regions as constituting Asia,
namely East Asia and the Pacific and South Asia. 5 Thus, it includes some Pacific countries but
excludes Central Asia and Arab regions as these regions are much more integrated with Europe andNorth Africa than with East and South Asia. The study considers all the countries from these tworegions, for which all required data are available, but excludes Australia and New Zealand because
these countries have had nearly the same human development levels with Japan since 1975. As can beseen, globalization trends are not very different among Asian countries. However, poorer countries
such as Nepal and Bangladesh appear to be on low levels of globalization. As expected, newly
industrialized countries, such as Singapore and the Republic of Korea (South Korea) have higher
levels of globalization. Surprisingly, these countries are marked by even higher levels ofglobalization than Japan, the 2nd largest economy in the world in terms of real GDP value.

5

The regions are based on the World Bank classification of countries and regions.

Sapkota 6

Figure 3. Globalization trends of selected Asian countries (1975-2005)

Data Source: Dreher (2006); KOF Economic Institute

The main dependent variable of this study is human QOL. Conventionally, the impact of
globalization on economic efficiency and growth outcomes has drawn the most research attention(Garrett, 2000; Nyahoho, 2001; Dreher, 2006). However, income, health and education are also very
important elements of human QOL (Stieglitz, 2006); hence, this study focuses on the effect of
globalization on human QOL. Considering the popularity as well as availability of data, the present
study uses the Human Development Index (HDI) developed by the United Nations Development
Program (UNDP). Although HDI does not include all the aspects of human QOL, it broadly
summarizes the level of human QOL in a single indicator (Sen, 2000). According to the Human
Development Report 2007/08 (UNDP, 2008), the HDI involves three key sub-constructs withcorresponding measures: health or longevity (measured by life expectancy at birth indicators),
knowledge or education (measured by adult literacy rates and combined enrollment ratios), and an
adequate standard of living or income (measured by adjusted per capita income in dollar purchasepower parity). See a detailed explanation of the index in Appendix II.

Sapkota 7

Figure 4. Human development trends of world regions (1975-2005)

Data source: Human Development Report 2007/08- updated database, UNDP (2009)

Figure 4 presents the trends of HDI across world regions from 1975 to 2005. The generalobservation is that Sub-Saharan Africa has the lowest HDI, followed by South Asia (SAS); this fact isconsistent with globalization trends. Notably, the East Asia and the Pacific (EAP) region had been
taking a more rapid pace of human development from the beginning of the 90s and surpassed theglobal average around the beginning of the 21st century. This is because of its high and sharedeconomic growth as per the World Bank (2002).

Sapkota 8

Figure 5. Human development trends of selected Asian countries (1975-2005)

Data source: Human Development Report2007/08- updated database, UNDP (2009)

To give a detailed picture of HDI trends in the Asia-Pacific region, the study plots the trends foreach selected country in Figure 5. The graph shows that all the countries have an increasing trend of
human development over time, though a few countries show some fluctuations and others exhibit
steady trends. Interestingly, poor countries have a more rapid growth of HDI. This finding gives ageneral impression of convergence of human QOL in the region. The next section demonstrates thetrends of convergence more concretely.
III.

Is human QOL converging in Asia?

The mainstream literature on convergence argues that globalization raises the living standard of allparticipants in the globalization process. Theoretically, increased international trade raises the real
incomes of all participating countries. Nevertheless, accessing the superior technology embodied ingoods or capital, or simply through intellectual exchange allows greater productivity gains in poorer
countries. Similarly, international capital flows bring new technology and allow countries to tap into alarger savings pool, which also helps the poor more. Finally, the free flow of labor force also
contributes to convergence, as people from poor countries migrate to richer nations. A number of
empirical papers on convergence support this view. For example, Baumol (1986) and Baumol and
Wolff (1988) tested the convergence among industrialized countries over the period of a century
beginning in 1870. Other notable papers are on the convergence among OECD countries (Barro andSala-i-Martin, 1992), among European Community members (Ben-David, 1993), among individual
U.S. states (Barro and Sala-i-Martin, 1992), among European regions (Cannon & Duck, 2000, p. 418),

Sapkota 9

among Spanish provinces (Goerlich & Mas, 2001), and so forth. As economic theory suggests, allthese papers have shown income convergence.
However, many other scholars show diverging trends of income and human QOL. For example,
Maddison (1995, 2001) and Pritchett (1997) demonstrated the historical process of income divergence.
However, they did not fully contradict the mainstream belief of a strong causal link between
globalization and income convergence because a major diversion was created by the discrete
technological breakthroughs of the Industrial Revolution. And, the recent income divergence among
countries over the last 20 years (see Milanovic, 2005, chapter 4; Kanbur and Lustig, 1999) was
explained by the fact that globalized countries were attempting to reach the same industrial standards
and those who did not globalize were left behind (World Bank, 2002). Nevertheless, there are alsostrong counter-arguments that question the convergence hypothesis as the world is becoming moreunequal in terms of per capita income. Indeed, looking back over the last 100 years or so, initiallypoorer countries have tended to experience lower subsequent growth rates (Pritchett, 1997). Poor
countries are not catching up with rich ones; rather it seems the opposite has been happening. For
example, Ravalian (2004) showed that the average income of the richest countries in the world wasabout 10 times that of the poorest around the end of the nineteenth century but is closer to 60 times
higher today. Furthermore, Milanovic (2004) argued that inequality between countries had increased
sharply since the beginning of the 80s.
The convergence literature is not very focused on the Asian region. Instead, the focus is generally
on groups of countries that have similar characteristics, such as OECD countries, European countries,
or American states. In addition, the convergence literature concentrates heavily on the income aspect.
Although there are several papers that deal with the convergence of human QOL (for example Kenny,
2005, 2008; Bakhtiari, 2006; and Ram 1992, Ingram 1992, and so on), most of them tend to use
disaggregated factors of QOL, which cannot give an overall picture of human QOL. For instance,
Kenney, (2008) analyzed the convergence of income, health and education separately for the East
Asian countries. Ram (1992) studied the cross-country inequality of calorie supply, life expectancy
and adult literacy, and found that the inequality of these indicators across the world was minimalcompared to income inequality. Ingram (1992) found that there was strong evidence of convergencein life expectancy, caloric intake, primary enrollment ratios and urbanization – fairly strong evidenceof convergence. All of the studies, however, fail to consider the overall human QOL as a dependent
variable, even though the single index of human development has been available since 1990.
Conventional methodology for testing the ―convergence hypothesis‖ is also critically questioned.
Ravallian (2004) pointed out the methodological differences and the data type and quality variation
are the main causes of different findings and arguments on convergence. More importantly, Quah
(1993) theoretically rejected using the so-called sigma, coefficient of variation and beta convergence.
‗‗Sigma‘‘ convergence is defined as decline over time of the cross-sectional dispersion of a variable,
which can be measured by looking at the size of standard deviation. For variables that trend upward(or downward) across the world over time, it is argued, the coefficient of variation (standard deviation
divided by the mean) might provide a better reflection of convergence or divergence. A thirdconventional approach is the beta convergence, which is used frequently in the literature on
cross-country economic growth. Beta convergence is defined if the variable displays mean reversion,
meaning that the value of a variable at the start is inversely correlated with its growth over that period.
Quah‘s point is valid as he showed how results were misleading because of the famous Galton‘sfallacy of regression towards mean.6 Thus, as Quah suggests, this study follows an alternative method,
6

In fact, many phenomena tend to be attributed to the wrong causes when regression to the mean is not taken
into account. Indeed, conclusion of Galton‘s (1886) famous work, which actually gave the popularity ofregression analysis itself, was wrong as he could not understand the nature of regression towards mean.

Sapkota 10

which is more transparent about whether convergence occurs or does not occur. However the study
does not take Quah‘s model as it is; rather it follows his wisdom.
First, this study directly calculates the human QOL indicator gap between each country and thebenchmark country over time. As the most developed country in Asia, the study takes Japan as thebenchmark country to calculate the human QOL gaps, which are also taken as the dependent variables
for regression analysis. Symbolically, the human QOL gaps between Japan and each country arecalculated using simple mathematics as follows:∆γct = γjt ― γct………………………….…………………………………………………………..……………… (1)Where, ∆γct is the difference in a particular human QOL indicator between Japan and country c at yeart; γjt is Japan‘s human QOL indicator at year t; and γct is country c‘s human QOL indicator at time t.
The calculated human QOL indicator gaps between Japan and each country for each five-year
interval are plotted as lines-graphs to observe convergence (or divergence). First, the HDI gaps arecalculated as the overall measure of human QOL and then the gaps on individual elements of HDI are
assessed. As can be seen in Figure 6, most of the countries are catching up with Japan in terms ofhuman development. The trend line for Japan is constant with 0 value because the line represents thedifference of HDI value with Japan herself. For almost all other countries, trend lines of this HDI gap
are downward sloping, meaning that the gap with Japan is closing. Clearly, human QOL in Asia isconverging.
Figure 6. Trends of HDI-gaps of Asian countries with Japan (1975-2005)

Data source: Human Development Report2007/08- updated database, UNDP (2009)

Sapkota 11

If we see the trends of each individual country, the pace of catching up is rapid for many countries.
For instance, Singapore and South Korea are catching up quickly and nearly reaching the fullconvergence stage with Japan. This can be explained by their very high level of globalization (seeFigure 3 for respective globalization trends). Similarly, countries with a higher initial gap also tend tohave a rapid pace of catching up. Nepal and Bangladesh fall in this category. However, in spite of
having a higher initial HDI gap, Cambodia and Papua New Guinea are slow to catch up. On the other
hand, the Philippines and Samoa also have very slow trends, despite having relatively low initial HDI
gaps with Japan. Thus, based on this analysis the paper concludes that the Asian-Pacific countries areconverging in terms of HDI.
The remaining part of this section presents the convergence (or divergence) of individualcomponents of HDI. Although, there is a clear convergence of HDI, some of the individual variables
are converging and some are not. For examples, the health indicator, measured by life expectancy atbirth, is converging as shown in Figure 7. Similarly, one of the major education indicators used toconstruct HDI, adult literacy, are also found converging (Figure 8). As in the case of HDI, the speedof convergence is higher for some countries, such as Nepal, Bangladesh and Vietnam. On the contrary,another indicator of education, gross school enrollment from primary to tertiary level, is neither
converging nor diverging (Figure 9). Some countries, such as Singapore, the Philippines, Bangladesh
and Nepal, were able to reduce their gap with Japan, whereas Papua New Guinea, Cambodia andMongolia, among others, increased their gap. Interestingly, South Korea fully converged with thebenchmark country – Japan - around 1992 and crossed over the benchmark in terms of gross school
enrollments.
Figure 7. Trends of the “life expectancy at birth”-gaps with Japan (1975-2005)

Data source: Human Development Report2007/08- updated database, UNDP (2009)

Sapkota 12

Figure 8. Trends of the “adult literacy”-gaps with Japan (1975-2005)

Data source: Human Development Report2007/08- updated database, UNDP (2009)

Figure 9. Trends of the “gross school enrollment”-gaps with Japan (1975-2005)

Data source: Human Development Report 2007/08- updated database, UNDP (2009)

Sapkota 13

Figure 10. Trends of the “GDP per capita”-gaps with Japan (1975-2005)

Data source: World Development Indicators online database (Accessed Aug. 27, 2009)

Supporting the existing literature, Figure 10 shows that the income aspect of human QOL isdiverging. The gap between the benchmark country Japan and the rest of the countries is increasing
sharply. Only Singapore was able to converge (fully) with Japan around 1992, and the rapid growthcontinued so that the gap increased sharply, leaving Japan far behind. Overall, the gap was raised
sharply during the period of 1980-1990. Since then, the gap increased continuously for all thecountries except South Korea.
Based on this simple and transparent assessment, the overall measure of human QOL in terms of
HDI in Asia was found to be converging. The health aspect of QOL was also converging, howevereducation aspects of QOL showed mixed results, with adult literacy converging and the gross school
enrollment neither converging nor diverging. On the contrary, the income aspect of human QOL
measured by GDP per capita was sharply diverging.
The following section assesses the impact of globalization on these convergence (and divergence)trends, which is the main question of interest in the present study.
IV. Has globalization affected human QOL convergence?
This section addresses the main question of this study. It estimates combined cross-section time-series
regressions using the human QOL-gaps of each country with Japan for each period (as reported in theprevious section) as the dependent variables. These variables are calculated using the HDI values andthe individual components of HDI as reported in the Human Development Reports, which is publicly
available on the UNDP home page. HDI is based on the achievement of three basic aspects of human
QOL: health, education and income. A detailed explanation of HDI and its components is presented inSapkota 14

Appendix II. All data are taken over five-year intervals from 1975 to 2005. Similarly, the KOF Index
of Globalization is the main explanatory variable, which is taken from Dreher (2006). A detailed
explanation of the KOF Index is given in Appendix I.
As some of the data are not available for all countries in the region, only 19 countries have been
selected, and the panel is strongly balanced, meaning that most of the data are available for theselected countries for all the periods. However, as the value of all dependent variables (human
QOL-gap) for Japan is 0 over the period, Japan is excluded from the regression analysis. Column [1]reports the results when economic, social and political globalization are regressing separately, where
as Column [2] reports the results using a single indicator of overall globalization. All the control
variables and their precise definitions and data sources are listed in Appendix III. The descriptive
statistics and the correlation matrix are reported in Appendix IV and Appendix V, respectively.
The regression estimation model is as follows;yi yi 1t 'G'C
i t i ….…………
(2)
t 
i 
t
i 
t 
t
Where y represents the difference between the human QOL-gaps between Japan and the specificcounty (i) in specific time (t), yit-1 is the lagged dependent variable, G represents the measures ofglobalization -- negative and positive coefficient of G explains the convergence and divergence effectrespectively--, C represents the vector of control variables, ηi is the country fixed effect, ηt is theperiod fixed effect and ε is an error term.
The lagged dependent variable is included because HDI-inequality tends to change slowly overtime. However, it creates several serious methodological problems. Given the inclusion of the lagged
dependent variable and fixed country effects, the OLS estimator is biased and inconsistent in shortpanels (Nickell, 1981). To deal with this problem, the analysis uses the system GMM (generalized
method of moments) estimator, as suggested by Arellano and Bover (1995) and Blundell and Bond(1998). Results are based on the one-step estimator implemented by Roodman (2005), which is
explained in detail by Roodman (2009) in Stata, including Windmeijer‘s (2005) finite sample
correction.7
In choosing the set of control variables, the study follows standard practice as much as possible.
First, it includes GDP per capita at the initial point of each 5 year-period to capture the effect of theinitial level of development on convergence. It also includes the share of the under-15 year-olds andthe over-64 year-olds relative to the total population (referred to as the ―age-dependency ratio‖). Thisratio controls for demographic factors and is expected to vary positively with the HDI gap with richer
countries. Similarly, overall and urban population growth rates are taken as they affect human QOL
significantly. It is expected that the overall population growth rate correlates positively and urban
population growth rates are expected to correlates negatively to the QOL-gaps.
Similarly, irrigated land in percent of total cropland, electricity consumption per capita, andannual growth rate of manufacturing sector value added are also included as control variables.
Irrigated land captures the effect of agricultural infrastructure, which is assumed to be an importantfactor to uplift millions of poor who mostly depend on subsistent agriculture. Electricity consumption
per capita is expected to effect human QOL positively as it is one of the key elements to make humanlife easier and more efficient. Manufacturing value added is one of the main indicators ofindustrialization, which is supposed to be crucial to improve people‘s life by creating employment andproducing cheaper goods. The data on these variables are taken from the WDI online database of theWorld Bank.
7

This also accounts for the potential endogeneity of globalization, e.g., HDI and other human QOL-gap may
induce poorer countries to pursue more inward-oriented policies. However, the Sargan test indicates that
endogeneity is not an issue.

Sapkota 15

Finally, democracy indicator is used to gauge the effect of political as well as social liberty on
human QOL and is expected to have a converging impact of human QOL. Indeed, there is a positive
relationship between democracy and human QOL (Frey and Al-Roumi, 1999). The measures of
democracy are taken from Freedom House (2009) and are available from 1972 to 2008. The
democracy index consists of two key rights. Firstly, the political rights measure is a subjective
indicator that annually ranks each country on a scale from one (the highest level of political rights) toseven (the lowest level of political rights). Secondly, the civil liberties measure is used to capture
personal rights such as those to free expression and to organize or demonstrate and is placed on thesame scale from one to seven. These two Freedom House measures of democracy are averaged andnormalized to range from zero to 100, with 100 representing full democracy. The time dummies areincluded in the equation and it is revealed that time and country fixed effects are jointly significant but
the results are omitted from the result tables. The variables are logged if they have absolute values.
The percentage form and index numbers are not logged.
Tables 1 to 5 report the results, each table for each dependent variable. In Column [1], results
come from regressing all the three indexes of globalization (economic, social and political) including
the control and lag dependent variables. While in Column [2], a single index of overall globalizationis regressed instead of three separate indexes. The magnitude and the sign of the coefficients explain
the strength and the direction of the effect of regressors. Thus, the negative sign of a coefficient means
convergence effect and vice versa. The three, two and one asterisks (***, **, and *) denote thesignificance of the coefficients at 1%, 5% and 10%, respectively. The standard errors are given inparenthesis.
In Table 1, the dependent variable is HDI, which measures the overall ―human QOL.‖ The
results show that the HDI gap closes with globalization, with the coefficient being significant at 1
percent level for economic, social as well as overall globalization and just at 10 percent for politicalglobalization. This confirms that globalization helps to increase the human QOL more in poorer
countries than in richer ones, resulting in HDI convergence. This result is consistent with thetheoretical arguments by Sirgy et al. (2004) and the empirical findings by Tsai (2007) on the positive
impacts of globalization on human QOL.
The impacts of the control variables are mixed and are consistent for both columns. The lag
dependent variable is drooped due to a high degree of collinearity with the dependent variable.
Unexpectedly, the HDI gap with Japan rises with higher GDP per capita and the coefficient issignificant at one percent. This might be explained by the increasing income inequality as shown in
the Figure 10. Other results are as expected. Population growth has a diverging effect on human QOL
with the coefficients significant at 1 and 5 percent level for Column [1] and Column [2], respectively.
As expected, the population growth and age dependency ratio found to have diverging effects onhuman QOL. The effects are statistically significant at the 1 percent level. This shows the importanceof controlling the demography of the country to improve human QOL.
On the contrary, although the significance level is weak (at 10 percent), irrigated land anddemocracy index have converging effects on QOL. This indicates that the agricultural infrastructure isimportant in reducing the QOL-gap as most of the poorer countries, and their poor households, arehighly dependent on agriculture. If the governments of poor countries cannot provide alternative
employment through industrialization, the improvement of the agricultural sector is required to uplift
the life quality of their people. Similarly, democracy provides an opportunity to people to decide theirown destiny in life and can improve their quality of life. Indeed, no one can make a better decision
than people themselves about their life. Democracy‘s significant impact on the converging process ofhuman QOL further reconfirms both the theories and empirics that suggest democracy as one of thevital factors to improve the life of poor people in any country.

Sapkota 16

Table 1: Globalization and human development gaps (1975-2005)
Dependent variable: HDI -gap with Japan (△HDIct ﹦HDIjt - HDIct)[1]
Lagged dependent variable (△HDIt-1)[2]
DroppedDropped0. 2 *** (0.056)

0. 278 *** (0.097)

Globalization: Overall globalization index---0.031*** (0.009)

Economic globalization index-0.005*** (0.002)--

Social globalization index-0.014*** (0.003)--

Political globalization index-0.002* (0.001)--

0.119*** (0.049)

0.174** (0.076)

Urban-population growth rate

-0.004 (0.009)-0.004 (0.076)

Age dependency ratio-0.002 (0.002)-0.002 (0.003)

Irrigated land (% of cropland)-0.002* (0.001)-0.004* (0.002)

Manufacturing value-added (annual growth rate)-0.001 (0.002)-0.001 (0.002)

Log of electricity consumption (kWh per capita)-0.016 (0.02)-0.004 (0.03)

Democracy index-0.001 (0.001)-0.002* (0.001)

droppeddropped53

53

Log of GDP per capita

Population growth rate

Constant
Number of Observation

Notes: Dynamic panel one step system GMM estimations are reported. Standard errors are in parentheses. ***, **,and * denote the significance of the coefficients at 1%, 5% and 10%, respectively.
Sources: Globalization data are taken from Dreher (2006) (http://globalization.kof.ethz.ch/); GDP per capita(current US$), population growth rate, urban-population growth rate, age dependency ratio, irrigated land,
manufacturing sector value-added, and electricity consumption per capita are taken from the World Development
Indicators (WDI) online database of the World Bank; Democracy index is calculated from the data of Freedom
House web-site (http://www.freedomhouse.org/); and human development index (HDI) is taken from the Human
Development Report (updated online version of HDR 2007/08) of United Nations Development Program (UNDP).
The data cover the period from 1975 to 2005 in 5-year intervals.

Disaggregated analyses of the human QOL are presented in Table 2 to Table 5. Each variable -health, education and income -- is a dependent variable in each Table. These analyses not only testify
to the impact of globalization on each aspect of human QOL, but also check the validity of themethodology that is applied in this study. In Table 2, log of life expectancy at birth is taken as adependent variable. As in Table 1, a lag dependent variable is dropped for both columns. The impactof overall and social globalization on the convergence of life expectancy at birth has been foundsignificant at 1 percent, whereas economic globalization is significant at 5 percent. Politicalglobalization has no significant effect on this convergence process.
The control variables have mixed impacts. As in Table 1, GDP per capita has a diverging effect at 1
percent level for both columns. Irrigated land has been found to have a significant converging effect at 5
percent in Column [1], and the urban-population growth and electricity consumption are also significant
in reducing the gap at 10 percent and 1 percent in Column [2]. The effects of the remaining control
variables are not significant. However, the overall results are consistent with the case of HDI in Table 1.

Sapkota 17

Table 2: Globalization and “life expectancy at birth” gaps (1975-2005)
Dependent variable: Log of “life expectancy at birth”- gap with Japan (△lifexpct ﹦lifexpjt - lifexpct)[1]
Lagged dependent variable (△lifexpt-1)[2]

DroppedDropped0. 896*** (0.201)

0. 643 *** (0.19)---0.029*** (0.011)

Economic globalization index-0.014** (0.007)--

Social globalization index-0.058*** (0.012)---0.005 (0.005)--

Population growth rate

0.209 (0.174)-0.11 (0.157)

Urban-population growth rate

0.019 (0.033)

0.071* (0.04)

Age dependency ratio-0.002 (0.008)

0.011 (0.008)-0.011** (0.005)-0.001 (0.004)

Manufacturing value-added (annual growth rate)-0.005 (0.006)-0.001 (0.008)

Log of electricity consumption (kWh per capita)-0.087 (0.072)-0.284*** (0.101)

Democracy index-0.002 (0.003)-0.000 (0.003)

droppeddropped53

53

Log of GDP per capita
Globalization: Overall globalization index

Political globalization index

Irrigated land (% of cropland)

Constant
Number of Observation
Notes: See Table 1.
Sources: See Table 1.

Table 3 reports the impact of globalization on adult literacy gap. The main results are consistent
with the previous two tables. Globalization has converging effect with high degree of significance.
The main difference is that the political globalization is significant to reduce the literacy gap at 1
percent, and social globalization is not significant. This can be attributed by the fact that the elementsof political globalization, such as international non-governmental organizations (INGOs), multilateralorganizations as well as bilateral donor agencies, are playing significant role on education,
particularly they have greater role on non formal education compare to national government.
As for the control variables, GDP per capita and population growth have highly significant
diverging effect as expected. Electricity consumption is found to be significant to reduce the literacy
gaps at 5 percent for both the column. In fact, access to electricity, particularly in rural areas, greatly
improves the study environment for students.
In case of democracy, the result reconfirms the findings by Frey and Al-Roumi (1999) as
democracy index is significant to reduce adult literacy gap at 1 percent in Column [1] and 5 percent inColumn [2]. The effects of the remaining control variables are not significant. These results are also
highly consistent with the case of HDI in Table 1.

Sapkota 18

Table 3: Globalization and Adult literacy” gap (1975-2005)
Dependent variable: Log of “Adult literacy” - gap with Japan (△adultlrct ﹦adultlrjt - adultlrct)[1]
Lagged dependent variable (△adultlrt-1)[2]
DroppedDropped0. 916*** (0.381)

1. 488** (0.644)

Globalization: Overall globalization index---0.113** (0.05)

Economic globalization index-0.04*** (0.015)---0.035 (0.023)---0.023*** (0.008)--

0.977*** (0.349)

1.93*** (0.667)

Urban-population growth rate

-0.018 (0.089)-0.017 (0.129)

Age dependency ratio-0.011 (0.02)-0.011 (0.029)

Irrigated land (% of cropland)-0.008 (0.009)

0.013 (0.014)

Manufacturing value-added (annual growth rate)

0.014 (0.017)

0.022 (0.023)

Log of electricity consumption (kWh per capita)-0.436** (0.209)-0.547** (0.283)

Democracy index-0.019*** (0.007)-0.018** (0.01)

droppeddropped53

53

Log of GDP per capita

Social globalization index
Political globalization index
Population growth rate

Constant
Number of Observation
Notes: See Table 1.
Sources: See Table 1.

The dependent variable for Table 4 is gross school enrollment―another education-related
indicator―used to construct HDI. Gross school enrollment is the combined enrollment from primary totertiary level education. In this case, lag dependent variable is significant in increasing the gap at 1
percent level for both columns. However, GDP per capita is not significant.
Surprisingly, only economic globalization is found to have a converging effect at 10 percent level.
The other kind of globalization and overall globalization are insignificant. Control variables are also
found not to be significant except irrigated land, which has a significant converging effect at 10 percent
level, but only in Column 1. These results indicate that school enrollment is much more affected by thedomestic policies rather than globalization. Further analysis is essential to explore this issue.
The result for this particular dependent variable is different from that of previous dependent
variables. In fact, gross school enrollment has been found neither converging nor diverging (Figure 9),
which could be a reason for this different results.
However, it is interesting to find the significant converging effect of democracy on school
enrollment. This result is consistent with the findings by Brown (1999), who empirically showed thepositive impact of democracy on primary enrollment.

Sapkota 19

Table 4: Globalization and “gross school enrollment” gaps (1975-2005)
Dependent variable: Log of “gross school enrollment” gap with Japan (△enrollct ﹦enrolljt - genrollct)[1]
Lagged dependent variable (△genrollt-1)[2]

01.116*** (0.297)

1.343*** (0.354)

0.000 (0.000)

0.000 (0.000)

Globalization: Overall globalization index---0.043 (0.029)

Economic globalization index-0.037* (0.019)--

Social globalization index

0.033 (0.025)--

Political globalization index-0.001 (0.006)--

droppeddroppedUrban-population growth rate

-0.055 (0.06)-0.093 (0.074)

Age dependency ratio

0.014 (0.009)

0.01 (0.01)-0.013* (0.007)-0.004 (0.005)

Manufacturing value-added (annual growth rate)

0.002 (0.009)-0.003 (0.01)

Log of electricity consumption (kWh per capita)

droppeddropped-0.01 (0.006)-0.006 (0.005)

droppeddropped49

49

Log of GDP per capita

Population growth rate

Irrigated land (% of cropland)

Democracy index
Constant
Number of Observation
Notes: See Table 1.
Sources: See Table 1.

Finally, Table 5 presents the results for GDP per capita, the income measure of human QOL, as adependent variable. As in Table 4, the lag dependent variable is significant at the 1 percent level inincreasing the income gap. This means that the initial level of GDP per capita is for a very goodpredictor of future income levels. This phenomenon leads to income divergence. In fact, a clear
divergence is found by this study as well (see Figure 10 in the previous section).
However, globalization has no significant impact on the rising income gap in Asia. The resultshows that only political globalization has a diverging effect at the 10 percent level. This is consistent
with many existing studies, which claim that although the world income inequality is rising sharply, it is
not due to the effect of globalization but due to the lack of globalization in poorer countries (World
Bank, 2002).
The impact of control variables, however, is quite interesting. As expected, urban population
growth has a significant convergence impact on GDP per capita at the 5 percent level for both columns.
Indeed, urbanization leads to better living conditions, offering better facilities and opportunities
compared to rural areas. Irrigated land and manufacturing sector value-added are also highly
significant in reducing income gap in both cases. Expanding irrigated land generates higher
agricultural productivity and increasing the share of manufacturing value added to GDP enhances
technological advancement, industrial productivity and also generates more employment in poorer
countries, which ultimately leads to reduced income gaps. Finally, as the existing literature suggests(see Barrow, 1999, for detail), democracy is highly significant in reducing income gaps. It is significant
at the 5 and 1 percent levels for Column [1] and Column [2], respectively.

Sapkota 20

Table 5: Globalization and “GDP per capita” gap (1975-2005)
Dependent variable: Log of “GDP per capita” gap with Japan ( △gdppcpct ﹦gdppcpjt - gdppcpct)

Lagged dependent variable (△gdppcpt-1)

[1]

[2]

1.022*** (0.009)

1.028*** (0.009)---0.001 (0.001)

Economic globalization index

0.001 (0.001)--

Social globalization index-0.001 (0.001)--

Political globalization index

0.001* (0.001)---0.004 (0.019)

0.007 (0.019)-0.008** (0.004)-0.007** (0.004)

0.001 (0.001)

0.001 (0.001)

Irrigated land (% of cropland)-0.001** (0.001)-0.001*** (0.001)

Manufacturing value-added (annual growth rate)-0.002*** (0.001)-0.002*** (0.001)

Log of electricity consumption (kWh per capita)

0.009 (0.008)

0.005 (0.009)

Democracy index-0.001** (0)-0.001*** (0)droppeddropped

53

53

Globalization: Overall globalization index

Population growth rate
Urban-population growth rate
Age dependency ration

Constant
Number of Observation
Notes: See Table 1.
Sources: See Table 1.

Overall, although the converging effect of globalization is found insignificant to gross school

enrollment and GDP per capita, the effects are highly significant for the other variables. More
importantly, the effect is highly significant in reducing the overall human QOL convergence in termsof HDI. Although the income inequality is rising, thanks to the growing level of globalization, whichhelps to reduce the gap on overall quality of life of the people in Asia.
V. Discussion
In the context of contradicting arguments on the convergence hypothesis, the results of this study
reveal that globalization significantly reduces the gap between the rich country Japan and the rest of
the selected Asian and some Pacific countries in terms of human QOL. The results reconfirm theconvergence hypothesis as far as the human development index and the health and education
indicators are concerned. The results are also supported by Sab and Smith (2001)‘s findings about
education and health convergence over 1970-96. Even though GDP per capita is found diverging,
globalization does not have a significant effect on this diverging trend. Rather most of the control
variables are found to have converging effects. Thus, what are the factors that are playing a vital rolein income divergence? Further analysis is needed to explore this issue.
Similarly, in spite of the sharply diverging trend of GDP per capita, there is an obvious
convergence of HDI. It is more dichotomous when considering the literature on the relationship ofincome with health and education. However, the economic theory of diminishing marginal return of
the health and education expenditure explains this dichotomy. Precisely, even if poorer countries gainSapkota 21

less income than richer ones, they can improve their people‘s health and education levels at a fasterpace.
It is more important to find the significant impact, as we did in this study, of globalization on thisconvergence process because many politicians, policymakers, social organizations, activists and evensome academics criticize globalization for increasing income inequality. Of course, income is one of
the leading measures of human QOL, but health and education are also equally, and sometimes more,
important. Income is just a means of life, but arguably, health and education are ends or goals.
Therefore, it is argued here that convergence in human QOL matters more than convergence inincome.
Furthermore, in the context of growing discussion as well as initiatives for Asian regional
integration, and debate on regionalism versus globalization, the findings of this study support the viewthat globalization accelerates regional integration by reducing the human QOL gap between the richand poor countries in the region. Intuitively, the high level of developmental gap is one of the main
obstacles to regional integration in Asia. Thus, anything that is converging has a favorable impact on
the regional integration process.
What are the policy implications of these findings? Clearly, both rich and poor countries should
promote globalization. In view of the questions being raised about the effectiveness of development
aid,8 and the clear findings of converging effect of globalization, richer countries should be more
open towards poorer countries. Indeed, opening of country towards poorer ones helps more thanproviding development aid.
This study is not enough to offer specific policy recommendations, and to do so the author calls
for a study of some country cases, for an assessment of the impact of globalization on human QOL
convergence within countries. Such studies will facilitate the development of policy recommendations
tailored towards countries in their cultural context, with a positive (or at least neutral) impact on the
reduction of human QOL gaps within them.
※※※※※

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Appendices follow.

Sapkota 25(APPENDICES)

Appendix I. Components of the KOF Index of globalization(Main explanatory variables)
A. Economic Globalizationi) Actual Flows– Trade (percent of GDP)
– Foreign Direct Investment, flows (percent of GDP)
– Foreign Direct Investment, stocks (percent of GDP)
– Portfolio Investment (percent of GDP)
– Income Payments to Foreign Nationals (% of GDP)ii) Restrictions– Hidden Import Barriers– Mean Tariff Rate
– Taxes on Int‘l Trade (percent of current revenue)– Capital Account Restrictions
B. Social Globalizationi) Data on Personal Contact– Telephone Traffic– Transfers (percent of GDP)
– International Tourism– Foreign Population (percent of total population)– International letters (per capita)ii) Data on Information Flows– Internet Users (per 1000 people)– Television (per 1000 people)– Trade in Newspapers (percent of GDP)iii) Data on Cultural Proximity– Number of McDonald's Restaurants (per capita)– Number of Ikea (per capita)– Trade in books (percent of GDP)
C. Political Globalization– Embassies in Country– Membership in International Organizations– Participation in U.N. Security Council Missions– International Treaties

[38%]
(50%)
(19%)
(20%)
(23%)
(17%)
(21%)
(50%)
(21%)
(29%)
(25%)
(25%)
[39%]
(34%)
(26%)
(3%)
(26%)
(20%)
(26%)
(34%)
(36%)
(36%)
(28%)
(32%)
(37%)
(39%)
(24%)
[23%]
(25%)
(28%)
(22%)
(25%)

Notes: The number in parentheses indicates the weight used to derive the indexes.
Weights may not sum to 100 because of rounding. All indexes range between 0 (not globalized) and 10(globalized).
Source: Dreher, Axel (2006) Updated in Dreher, Axel; Noel Gaston and Pim Martens (2008).
Also available at:http://globalization.kof.ethz.ch/static/pdf/variables_2009.pdf

Sapkota 26

Appendix II. List of dependent variables and their definition
1. “Human development index (HDI) is a summary measure of human development. It measures theaverage achievements in a country in three basic dimensions of human development:• A long and healthy life, as measured by life expectancy at birth.
• Knowledge, as measured by the adult literacy rate (with two-thirds weight) and the
combined primary, secondary and tertiary gross enrolment ratio (with one-third weight).
• A decent standard of living, as measured by GDP per capita in purchasing power parity(PPP) terms in US dollars.‖
Source: Human Development Report 2007/08
2. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing
patterns of mortality at the time of its birth were to stay the same throughout its life. Source:
World Bank staff estimates from various sources including census reports, the United Nations
Population Division's World Population Prospects, national statistical offices, household surveys
conducted by national agencies, and Macro International.
3. Adult literacy rate is the percentage of people ages 15 and above who can, with understanding,read and write a short, simple statement on their everyday life. Source: United Nations Educational,
Scientific, and Cultural Organization (UNESCO) Institute for Statistics. Note: Break in series
between 1997 and 1998 due to due to change from International Standard Classification of
Education (ISCED76) to ISCED97. Recent data are provisional.
4. Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of theage group that officially corresponds to the level of education shown. Primary education provides
children with basic reading, writing, and mathematics skills along with an elementary
understanding of such subjects as history, geography, natural science, social science, art, andmusic. Source: United Nations Educational, Scientific, and Cultural Organization (UNESCO)
Institute for Statistics. Note: Break in series between 1997 and 1998 due to due to change fromInternational Standard Classification of Education (ISCED76) to ISCED97. Recent data areprovisional.
5. GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic productconverted to international dollars using purchasing power parity rates. An international dollar has
the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser's
prices is the sum of gross value added by all resident producers in the economy plus any producttaxes and minus any subsidies not included in the value of the products. It is calculated without
making deductions for depreciation of fabricated assets or for depletion and degradation of natural
resources. Data are in current international dollars.
Source: World Bank, WDI online database
Note: GDP per capita is also used as control variable for the rest of the dependent variables other thanGDP per capita.

Sapkota 27

Appendix III. List of control variables and their definition(Note: All the variables, except Democracy Index, are taken from WDI online database of the World
Bank, hence the definitions sourced from WDI online data base)
1. Annual population growth rate (annual %): Population is based on the de facto definition of
population, which counts all residents regardless of legal status or citizenship--except for
refugees not permanently settled in the country of asylum, who are generally considered partof the population of the country of origin.
Source: World Bank staff estimates from various sources including census reports, the United
Nations Population Division's World Population Prospects, national statistical offices,
household surveys conducted by national agencies, and Macro International.
2. Urban population growth (annual %): Urban population is the midyear population of areas
defined as urban in each country and reported to the United Nations.
Source: World Bank staff estimates using United Nations, World Urbanization Prospects.
3. Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--tothe working-age population--those ages 15-64. For example, 0.7 means there are 7 dependents
for every 10 working-age people. Source: World Bank staff estimates from various sources
including census reports, the United Nations Population Division's World Population Prospects,
national statistical offices, household surveys conducted by national agencies, and Macro
International.
4. Electric power consumption measures the production of power plants and combined heat andpower plants less transmission, distribution, and transformation losses and own use by heat andpower plants. Source: International Energy Agency, Energy Statistics and Balances of
Non-OECD Countries and Energy Statistics of OECD Countries.
5. Irrigated land refers to areas purposely provided with water, including land irrigated bycontrolled flooding. Cropland refers to arable land and permanent cropland. Source: Food andAgriculture Organization, Production Yearbook and data files.
6. Democracy indicator consists of two key rights; political and civil. Political rights measure
is based on subjective indicators that annually ranks each country on a scale from one (highest
level of political rights) to seven (lowest level of political rights). Similarly, the civil liberties
measure is used to capture personal rights such as those to free expression and to organize or
demonstrate and measured on the same scale from one to seven. These two Freedom Housemeasures of democracy are averaged and normalized to range from zero to hundred, withhundred representing full democracy. The measures of democracy are taken from Freedom
House (2009), and are available from 1972 to 2008.
(Source: Freedom house, 2009. http://www.freedomhouse.org/template.cfm?page=1 )Note: GDP per capita is also used as control variable for the rest of the dependent variables other thanGDP per capita.

Sapkota 28

Appendix IV. Summary statistics
Variable

Obs.

Mean

Std. Dev.

Min

Max

HDI gap
Life exp. gap
Adult literacy gap
Gross enrollment gap
GDP per capita gap

126
126
126
122
126

0.285
15.56427.46620.91914801.7

0.128067
6.953864
23.84905
14.42645
8408

0.031
1.935
0
-10.1
-13023.5

0.56
36
81
54
29349.9

Overall globalization Index
Economic globalization
Social globalization
Political globalization

126
119
126
126

42.46744.61333.41953.25115.75620.02920.87519.250

14.45
9.84
8.05
12.07

86.36
96.34
91.04
90.62

Population growth rate
Urban-population growth rate
Age dependency ratio
Irrigated land (% of cropland)
Manufacturing value-added(annual growth rate)
Electricity consumption (kWh per
capita)Democracy index

126
126
126
78
107

1.806
2.971
69.63122.276
5.767

0.814
4.549
15.09416.4358.516-0.557-42.905
37.0870-22.77

4.178
11.27796.42285.36530.29

99992.333

1707.165

6.569

8507.197

126

46.49527.026091.667

Sapkota 29

Appendix V. Correlation matrix

HDI gap (dhdi)
Life exp. Gap (dlifexp)
Adult literacy gap (dadultlr)
Gross enrollment gap (denrol)
GDP per capita gap (dgdppcp)
Index of overall globalization (gblz)
Index of economic globalization (egblz)
Index of social globalization (sgblz)
Index of political globalization (pgblz)
Population growth rate (Png)
Urban-population growth rate (urpgrwth)
Age dependency ration (agedepr)
Irrigated land % of cropland (irrland)
Manufacturing value-added (Manvadd)
Electricity consumption (Elecpcp)
Democracy Index (dindex)
Population growth rate (Png)
Urban-population growth rate (urpgrwth)
Age dependency ration (agedepr)
Irrigated land % of cropland (irrland)
Manufacturing value-added (Manvadd)
Electricity consumption (Elecpcp)
Democracy Index (dindex)

dhdi

dlifexp

dadultlr

denroldgdppcp

gblz

egblz

sgblz

pgblz

1
0.87710.92120.85110.1623-0.6588-0.6156-0.7597-0.0855
0.382
0.40340.56390.0497-0.081-0.6006-0.20231
0.79240.69660.0836-0.5358-0.4796-0.641-0.07210.46280.53670.6174-0.1594-0.0991-0.4951-0.08711
0.7984-0.0121-0.5819-0.6222-0.6450.02350.47680.45550.51730.0829-0.0831-0.419-0.098510.1249-0.5127-0.5004-0.5661-0.0667
0.33810.32480.39340.1015-0.139-0.5077-0.12141
0.33220.27130.18450.4099-0.2812-0.2101-0.24540.24880.2199-0.1058-0.15961
0.89640.92150.5444-0.1751-0.2221-0.4863-0.14410.206
0.61520.22461
0.77950.2116-0.0833-0.1105-0.2809-0.3290.2116
0.43650.008

10.3576-0.2358-0.3061-0.5358-0.0729
0.11340.69790.33861-0.1138-0.1249-0.4067
0.184
0.1792
0.34110.2659png

urpgrwth

agedeprirrlandmanvadd

elecpcp

dindex10.62790.8036-0.3819-0.1445-0.2782-0.00891
0.5282-0.2709-0.0741-0.2099-0.26491-0.3629-0.2062-0.593-0.15091
0.10420.1312-0.180110.2041-0.243810.30281