2011-E-1
Impacts of Globalization on Quality of Life:
Evidence from Developing Countries

Jeet Bahadur Sapkota, Ph.D.
Visiting Assistant Professor, Global Institute for Asian Regional Integration,
Waseda University
Research Associate, Asian Development Bank Institute, Tokyo
Email: jbsnepal@gmail.com

Abstract:
This paper evaluates the impacts of globalization on quality of life, particularly on humandevelopment, gender development and human poverty in developing countries. Applying the
fixed effect model to the annual panel data of 124 developing countries covering nine years
from 1997, it shows that globalization (in terms of its comprehensive indexes and key
elements) not only promotes human and gender development, but also significantly reduces
human poverty. Not surprisingly, all the three aspects of globalization (economic, social andpolitical) contribute to the overall effect of globalization. In general, the results from the key
elements of globalization are consistent with the results from the comprehensive indexes.
However, it is also observed that political and social globalization, FDI, and internationalmigration were insignificant to gender-related development. Thus, further research is suggested
for appropriate policy recommendations to make these variables significant on promoting
gender aspects of development.

Key Words:
Globalization, human development, gender development, human poverty, developing countries

Acknowledgement: I am grateful to the Global Institute for Asian Regional Integration(GIARI), Global Center of Excellence Program, Waseda University for excellent researchfunding. I thank Prof. Shujiro Urata and Prof. Nobuhiko Fuwa for helpful comments.

2

1.

INTRODUCTION

Due to its ever-accelerating trend, globalization has been one of the most closely observed
processes among scholars, policymakers, politicians and even the general public in recent years(Collier and Gunning 2008, 1-2). Consequently, scholars have devoted their efforts toanalyzing the impacts of globalization on different aspects of human life and society. Theirarguments are, however, highly contested in terms of impact mechanisms and the gainers andlosers of globalization. Some are quite critical as they observe the negative effect of
globalization on QOL and society in the form of job elimination, especially in the
manufacturing sector (Scott, 2001). These “hyperglobalists” argue that globalization is creatinga new era of human history in which nation-states and governments are powerless to improve
the quality of life of their citizens (e.g., Guillen, 2001; Soros, 2000). In their view, globalization
is a great threat to society and they strongly oppose a globalized world.
In contrast, many others observe the positive impact of globalization on QOL (e.g.,
Thorbecke and Eigen-Zucchi, 2002). For these “pro-globalists,” trade liberalization andincreased marketing integration are opportunities to increase productivity and wages, whichlead to improved QOL of workers (Zoellick, 2001). They claim that the negative impact of
globalization, such as the elimination of manufacturing jobs, is highly overstated and thedecline of the manufacturing industry is the result of rapid changes in technology rather thanglobalization (Krugman, 1996).
In the debate on the possible consequences of globalization, non-economists generally
tend to oppose globalization as they expect the costs associated with globalization to exceed
its benefits, particularly in developing countries. On the contrary, supported by a number of
empirical studies, most economists strongly believe the net effect of globalization is positive(Dreher, 2006:1091). Although Sirgy et al. (2004) and Tsai (2007) have investigated thisquestion by assessing globalization’s effects on human and social aspects of development,
their efforts are still exploratory and further empirical examinations are necessary. This paper
attempts to contribute to the study of globalization with an empirical analysis that tests thetheories proposed by earlier research, with a focus on the human aspects of development.
The paper proceeds as follows. Section 2 defines the variables and explains its linkages
with recent trends. Section 3 discusses the methodology employed, the data used and thevariables analyzed. Section 4 presents the results and Section 5 concludes the paper.

3

2. GLOBALIZATION AND QOL: DEFINITION, TRENDS AND LINKAGES
Most empirical studies related to the so-called third wave of globalization 1 employ proxies,
such as trade, capital flows and openness as measures of globalization using cross-section data
(Dreher, 2006). For example, Heinemann (2000) shows that more globalized countries havelower increments in government outlays and taxes and lower government consumption. Rodrik(1998) also used cross-sectional data and found no effects of capital that account for opennessin economic growth. Recently, however, some scholars have used panel data to find the effects
of globalization and showed positive impacts of openness on growth and poverty but mixed
impacts on income inequality (Dollar and Kraay, 2004; Greenaway et al., 1999).
These detailed studies, however, failed to consider the overall effect of globalization, as
they focused on individual sub-dimensions. As all the dimensions of globalization are strongly
related and are important in explaining the consequences of globalization, omitting importantvariables from the regression equation can generate severely biased estimates (Dreher, 2006).
In addition, most of these studies, motivated by conventional wisdom, focus solely on
economic growth, income poverty and income inequality (ibid). To avoid these shortcomings,
this study intends to use QOL indicators as dependent variables and a comprehensive indicatorof globalization, along with its major elements, as explanatory variables.
2.1

Globalization

Globalization is a highly contested concept that means different things to different people(Bardhan, 2006), raising both positive and negative emotions in different groups and circles(Scholte, 2005). Consequently, development practitioners and scholars mostly refrain fromexplicitly defining globalization, and rather vaguely interpret it as real cross-border interactions
and exchanges (Goldin and Reinert, 2007:2). In the literature, globalization is mostly viewed ineconomic terms. However, we cannot ignore its social and political dimensions, which areusually not considered properly. In fact, cross-national connections are created in the economic,
political, cultural, social, and environmental domains.
Measuring these variables into a single indicator is a major challenge in empirical work.
In fact, if any concept can be expressed in numbers, it gives more meaning than explanation.
1

According to Martell (2007), “some recent contributions in globalization literature have identified three waves orperspectives in globalization theory—the globalists, skeptics, and transformationalists or post-skeptics (for details
see Held et al. 1999; Holton 2005).”4

Because of its inevitability in empirical research, a number of composite indicators have beendeveloped recently. Constructing such composite indicators has made a significant contribution
to globalization research, which is making it possible to consider a wide range of issues ofglobalization in scholarly works. Although using such composite indexes has its own
limitations, the development of a composite indicator is essential to globalization research dueto the complexity and comprehensibility of issues involved. Of course, one should be carefulwhen using and interpreting results from such composite indexes. Particularly, conclusions
cannot be generalized from such composite indexes for particular sectors or issues, as theresults always come from combined effects. Keeping these points in mind, this study uses the
KOF index of globalization developed by Axel Dreher (2006), which is considered one of themost comprehensive indicators of globalization.
To define globalization, Dreher followed the explanations of Clark (2000), Norris (2000), andKeohane and Nye (2000:4). He summarized that “globalization is meant to describe the processof creating networks of connections among actors at multi-continental distances, mediatedthrough a variety of flows including people, information and ideas, capital and goods.
Globalization is conceptualized as a process that erodes national boundaries, integrates national
economies, cultures, technologies and governance and produces complex relations of mutualinterdependence” (Dreher, 2006:1092).
More specifically, Dreher summarized the definition of the KOF index in the following
three dimensions. Firstly, “economic globalization,” which is measured by the long-distanceflow of goods, capital and services as well as information and perceptions that accompany
market exchanges. Secondly, “social globalization” is defined as the spread of ideas,
information, images and people, which is measured by personal contacts, information flows
and cultural proximity. Finally, “political globalization” is defined as the level of diffusion of
government policies, which is proxy by the number of embassies and high commissions in acountry and, the number of international organizations to which the country is a member andthe number of UN peace missions a country participated in. To construct the indices ofglobalization, Dreher transformed each of 24 variables under the above categories to an index
on a scale of one to 100, where 100 is the maximum value for a specific variable over theperiod 1970 to 2007, and one is the minimum value. Higher values denote greater globalization.
For detail see Dreher (2006).
Following the definition and measurement of globalization in terms of KOF indexes,
Figure 1 presents the comparative trends of the three types of globalization from 1970 to 2007.

5

As expected, economic globalization is the strongest of the three factors. In fact, most literature
considers economic indicators as a proxy of globalization. This is not only because of data
availability and the clarity of economic indicators, but also because of its influence in global
integration. Figure 1 also shows that economic globalization has the steadiest trend, without
much fluctuation. Even during the Asian Financial Crisis (1997-1998), the trend did not drop
sharply. To observe the effect of the current global financial crisis, however, we do not yet
have data.

60
50
40
30

KOF Index of Globalization

70

Figure 1 Global Trend of Overall Globalization and Its Sub-Indexes (1970-2007)

1970

1975

1980

1985

1990

Overall Globalization
Social Globalization

1995

2000

2005

2010

Economic Globalization
Political Globalization

Source: Calculations done by the author based on data from Dreher (2006).
Updated data can be found at: http://globalization.kof.ethz.ch/ (accessed May 5, 2010).

Representing the high turmoil in international security as well as the increasing role ofglobal governance, political globalization fluctuates more. Interestingly, political globalizationshows much more of a contribution to the overall globalization trend than social globalization.
Social globalization, on the other hand, is continuously the lowest of all trend lines. However,its progress goes along with the pace of other forms of globalization.
Interestingly, globalization has moved faster since the beginning of the 1990s. This can

6be explained by the end of the Cold War in the 1990s, which lead to the opening of many
countries in Eastern Europe, Russia and the states that emerged after the collapse of Soviet
Union, and many other developing countries. The dramatic increase of political globalization
since 1990 has contributed much to the accelerating globalization process.
To complement the analysis from the KOF index, this study considers some of the realvariables of globalization in a regression analysis. This not only provides an understanding
about the relationships between such key elements of globalization and QOL, but also checks
the consistency of the results that ultimately enhance the reliability of the study. The selectionof the key globalization variables was based on the existing literature in the sense that the mostproxy variables for globalization were considered. Therefore, this study uses international trade,
FDI, ICT and international migration for its disaggregated analysis. The comparative trends ofthese key elements of globalization between DCs and the world are shown in Figure 2 on its
four parts (2a-2d).
Figure 2(a) shows the comparative trend of international trade between DCs and theworld from 1970 to 2007. Interestingly, the trends not only reveal that the trade to GDP ratio isincreasing sharply in DCs and the world, but also that international trade has become more andmore important for developing countries in recent decades. Figure 2(b) shows the comparative
trends of FDI inflows in percentage of GDP for both the world and DCs from 1970 to 2007,
which reveals that FDI inflows in DCs are less volatile than that of the world. The FDI inflow
to GDP ratio has continued to increase since 1990 for both DCs and the world. Again, thefluctuation in the world’s trend is mostly consistent with the trend of DCs in terms of time anddirection. However, in terms of magnitude, the world trend has fluctuated sharply in recent
years. See the World Investment Reports (WIRs) of 2001 and 2002 of the United Nations
Conference on Trade and Development (UNCTAD) for the explanation of such sharp
fluctuation, which mainly comes from big mergers and acquisition in developed world (seeUNCTAD 2003 and 2008 for detail).
Figure 2(c) shows the dramatic increasing trend of ICT development in recent decades in
DCs. In 1990, there were less than 5 persons per 100 people in DCs who had telephone ormobile phone services. However, this number increased rapidly to more than 55 persons per
100 people in 2007. Finally, Figure 2(d) shows the importance of remittances to DCs vis-à-vis
the world. The DCs’ remittance trend is not only much higher than the world, but has been
increasing dramatically since 1995. If the unofficial flow of remittance were also included, the7

picture would be more interesting. However, information on unofficial flow is difficult to
explore. These trends also suggest that the significance of globalization is much higher for DCs.

Figure 2 Comparative Trends of Key Elements of Globalization between DCs and the World

3
2
0

1995

2000

2010

1970

1975

Telephone and/or mobile
subscribers (per 100 people)

1980

1985

1990

1995

2000

Developing Countries
2

World

phone

2005

2010

2005

2010

World

a) Remittance flows (% of GDP)

0

20

40

60

a)

2005

1.5

1990

1

1985

.5

1980

Workers' remittances (% of GDP)

1975

Developing Countries
80

1

50
40
30
1970

Mobile/telephone Subscriber (per 100 people)

4a) FDI inflow (% of GDP)
FDI Inflows (% of GDP)a) Trade flows (% of GDP)

20

Trade (% of GDP)60

5(1970-2007)

1975

1980

1985

1990

1995

Developing Countries

2000

2005

2010

1975

1980

1985

1990

1995

Developing Countries

World

2000

World

Source: Calculations done by the author using data from WDI online database of the World Bank
(accessed May 5, 2010).

2.2

Quality of Life (QOL)

QOL, the dependent variable of this study, is also a vague term that has different meanings fordifferent people. QOL is a multifaceted and elusive concept (e.g. Nussbaum and Sen 1993,
Doyal and Gough 1991). Generally, QOL definitions emphasize degree of choice, meaning thegreater the degree of choice, the higher the QOL. Mainly, choice is explained in economic
terms. Thus, for many decades, national QOL measures have been defined by the level of the
GNP per capita. Although GNP per capita is a significant part of a development strategy, itcannot capture all aspects of development. Of course, there is a systematic positive relationship8between GNP per capita and social and human welfare (McGillivray 1991), but the social andphysiological aspect of QOL cannot be measured correctly by the income variable. Thus, arange of socio-economic indicators should be considered to measure QOL. For instance, it isestimated that economic development adds approximately 30 years to life expectancy andreduces infant mortality by 140 deaths per thousands live births (Sagan and Afifi, 1978).
Additionally, the World Economic Forum’s World Competitiveness Report (WCR, 1993) also
recognizes the importance of measuring social and economic indicators of competitivenessamong nations, including human resources as the fifth of the 10 most important economic
components of a nation’s environment. However, there has been no individual attempt to createsuch a composite index, as it requires massive efforts and cost.
To cover this shortcoming, the United Nations Development Program (UNDP 1990)
introduced the Human Development Index (HDI), a comprehensive measure of QOL, in 1990
aiming to provide a yardstick of human development of all member countries of the United
Nations. Since then, the UNDP has published the Human Development Report (HDR) annuallyfor the world, and occasionally for regions and member states, in detail. The HDR’s basic
principle is that the essential components of QOL are the combination of a long and healthy life,
education, and a decent standard of living. As a result, the HDI has measured humandevelopment through the use of three factors; longevity, knowledge and GDP per capita
measured in purchasing power parity (PPP).
Because of its popularity and data availability for a wide range of countries since before
1990, this study uses the HDI as an overall measure of QOL. Apart from the HDI, there are
four other key indexes that have been reported by the HDR: the Gender Development Index(GDI), the Gender Empowerment Index (GEM), and the two Human Poverty Indexes fordeveloping (HPI-1) and developed (HPI-2) countries. This study, however, takes the GDI and
HPI-1 along with HDI as dependent variables. Detailed explanations and calculations
techniques of the indexes can be found in Technical Note 1 of Human Development Report,
2007/08 (UNDP, 2008: 356-357).
Figure 3 presents the trends of HDI across regions from 1975 to 2005. In general, theHDI for highly globalized regions is much higher. However, HDI trends for less globalized
regions also seem to be catching up. This is more valid for the East Asia & Pacific (EAP)
region, which saw an even more rapid pace of human development from the same period andexceeded the global average in 2000. This was due to the region’s high and shared economic
growth (World Bank, 1993).

9

.9
.8
.7
.6
.5
.4

Human Development Index (HDI)

Figure 3 Regional Comparisons of Human Development Trends (1975-2005)

1975

1980

1990

1985

Sub Saharan Africa
Latin America & Caribbean

1995
South Asia
OECD

2000

2005

East Asia & PacificWorld

Source: Calculations done by the author based on UNDP (2009). Updated data also available at:
http://hdr.undp.org/en/statistics/data/ (accessed in May 3, 2009).

C. Linkages between Globalization and QOL
As was observed by the general trends of globalization and human development, it is easy to
predict their positive relationship. Although most literature agrees that globalization is a current
reality (Guillen 2001), the human consequences of globalization remain controversial. Twocontradicting theories prevail in how globalization affects human welfare. First, neo-liberalists
believe that globalization enhances productive efficiency and generates extraordinary
prosperity. Although wages for unskilled workers fall, especially in developed nations,
globalization helps workers manage these potential threats by acquiring additional skills, which
benefits the whole of society (Grennes 2003). Additionally, globalization has spreadindustrialization to developing countries, and has thus reduced global income inequality(Firebaugh and Goesling 2004). Economic globalization, in terms of trade liberalization, wasfound to be effective in increasing productivity and institution-building in societies, which

10leads to faster economic growth (Urata and Yokota 1994; Rodrik et al. 2004). However, manyargue that some policy measures are essential to make globalization work for all (Mayer 2007)
pointed out that globalization alone is not a sufficient condition for development. For example,
trade liberalization without roads and ports would not lead to more trade or help the poor
(Stiglitz 2004). Even though scholars point out the defects of the current form of globalizationand suggest better options, they ultimately tend to favor globalization.
On the contrary, many others see globalization as a new hegemonic project thattransnational capitals operate in a way that promises little betterment for most countries. This
perspective claims that the current form of globalization is the creation of a new world orderthat facilitates capitalist accumulation in the so-called free market environment (Petras andVeltmeyer 2001). Promoting private interests in a limitless free market ignores personal andsocial interests (Smart 2003). As a result, benefits from globalization primarily go mainly to thealready advantaged (Scholte 2005).
Within the context of these controversies, this paper attempts to answer how and whichtypes of globalization generate the most favorable and unfavorable human consequences. It
founded on the theoretical base provided by the Sirgy et al. (2004) and improves the empiricalmodel offered by Tsai (2007) to evaluate the human consequences of globalization.

3.

DATA AND METHODOLOGY

Most empirical analyses use cross-country data at a certain point in time (Rodrik 1988).
Although this is useful to find differences between countries, such studies fail to observechanges in structural features and their correlates over time. These studies are also limited by
fewer numbers of observations, which lead to weak results. Thus, this study builds a panel of
124 DCs, covering nine years of annual data of globalization and human development from1997 to 2005. The selected countries are listed in Appendix 1. Data from 1997 onward is usedbecause there is no HPI data available for years prior to 1997.
In another analysis, Tsai (2007) used a similar methodology on panel data of 112
developing countries. However, he had several methodological weaknesses. Firstly, the results
were weak in capturing time effects as the data were in intervals of 10 years from 1980 to 2000.
Secondly, Tsai uses economic, social and political globalization separately in the regression
instead of regressing simultaneously, which can create the problem of biased, as Dreher (2006)
argues, “The effects reported for one dimension of globalization might therefore appear only
because other important aspects of globalization are omitted from the regressions” (p. 1092).

11

The current study corrects these shortcomings by using the components of globalization
simultaneously with annual data.
3.1

The Data and Its Sources

The HDI, the GDI and the HPI-1 are the dependent variables of this study. Data are from the
1998-2007/08 annual HDRs. The KOF indexes (sourced from the updated database of Dreher2006) and some key elements of globalization (sourced from the WDI online database), as
defined in the previous section are explanatory variables. Contrary to the conventional
approach of using proxies of globalization through specific factors such as trade and investment,
this paper adopts a more multidimensional and pluralistic approach. A multidimensional
approach is far more effective in preventing the over-simplification of complexities involved inunderstanding globalization. This over-simplification is usually due to the omitting ofimportant variable(s) in addition to problems of measurement and interpretation (Dreher et al.
2008: 121).
In choosing the set of control variables, this paper follows standard practice as much as
possible. Firstly, a country’s level of development is considered a critical element in improving
human development (Tsai 2007). Thus, it includes per capita GDP to differentiate developmentlevels across countries. Secondly, as this study focuses on developing countries, population
growth is also included because the literature has long documented the harmful impacts ofunchecked fertility against limited resources, such as attenuated health and educational
expenditures, insufficient housing, sanitary and water, etc. (Goldthorpe 1996). Data of both thecontrol variables are taken from the WDI online database. Finally, this study also introduces adummy variable to compare the effect of globalization across different income group ofcountries. The dummy for lower middle-income countries (LMCs) and upper middle-income
countries (UMCs) are created for comparison. The classifications are based on the WorldBank’s classification of countries and regions (World Bank 2008). Then an interaction term isgenerated with multiplying each dummy variable and major elements of globalization andindexes that are used in each regression equation. Natural logarithms of all variables (except
dummies) are taken to reduce the skewness of the data. The summary statistics of each variable
are presented in Appendix 2.

12

3.2

The Model

Throughout this paper, the fixed effect (FE) estimation model is used to analyze the data. The
FE model explores the relationship between predictor and outcome variables within an entity(country, person, company, etc.). When using the FE model, it is assumed that something
within the individual may impact the predictor or outcome variables, so this needs to becontrolled for. Another important assumption of the FE model is that time-invariant
characteristics are unique to the individual and should not be correlated with other individual
characteristics. Each entity is different, therefore the entity’s error term and the constant (which
captures individual characteristics) should not be correlated with the others. If the error terms
are correlated, then FE is not suitable since inferences may not be correct and that relationshipneeds to be modeled (probably by using random-effects). The Hausman test indicates that thedataset good fit the FE model. The model is described as follows.
log Yit= α + β1 logCit + β2 logGit + ui +∈it
Here, Y is the vector of dependent variables (i.e. HDI, GDI and HPI-1), C is the vector ofcontrol variables (i.e. GDP per capita and population growth) and G is the vector of
explanatory variables (i.e. the indexes of globalization). α is the constant term. β is thecoefficient of explanatory variable that explains the strength and direction of impact on QOL
indicators. In addition, i represents the group identifier (i.e. 124 countries), and t represents thetime identifier (i.e. 9 years from 1997 to 2005). Similarly, ui +∈it is the composite error term,
where ui is the unobservable country effect fixed over time. Time dummies were included in theequation and found that the time effect is jointly significant; however, results for time dummies
are not reported in result tables.
A number of diagnostic tests were performed before and after running the regression in
Stata. A unit root test for panel data developed by Levin, Lin and Chu (2002) was performedthat confirmed all the variables are free from the unit root problem. Similarly, tests for
autocorrelation and heteroskedasticiy also showed no significant such problems on the model.
The correlation matrix of each variable is presented in Appendix 3.

4.

RESULTSFirst, the study uses the key elements of globalization as major explanatory variables. Then, it

13

uses the KOF indexes to capture the overall effects of globalization. Table 1 shows the impactsof the major elements of globalization on the HDI. Column 1 reports the results without theinteraction terms, whereas Column 2 reports the results of regression including all theinteraction terms. The effects of trade and access to ICT were found to significantly increasehuman development at the 1 % level in both the columns. These results are consistent withmuch of the existing literature, theoretical as well as empirical, which argues that trade helps
overall socio-economic development by increasing employment, productivity, government
revenue, educational access and standards, among other things, all which lead to the overall
improvement of human development (Seker, 2009; Bernard et al., 2007). Similarly, UNDP
(2001) claims that using ICT in a development strategy allows DCs to achieve a wider diffusionof benefits, which ultimately leads to a broad-based economic growth.
Table 1 Human Development and Four Major Elements of Globalization (1997-2005)

Dependent Variable: Human Development Index (HDI)
Explanatory Variables
GDP per capita
Population Growth
Trade (% of GDP)
FDI Inflow (% of GDP)Log of ICT
Log of Remittances
LMC Dummy × Trade
UMC Dummy × TradeLMC Dummy × FDI
UMC Dummy × FDI
LMC Dummy × ICT
UMC Dummy × ICT
LMC Dummy × Remittances
UMC Dummy × Remittances
Constant
R -Square (Number of Observations)[1]
0.02** ( 0.01)-0.01 (0.01)
0.06*** (0.01)
0.00 (0.00)
0.02*** (0.01)
0.00 (0.00)---------0.93*** (0.08)
0.28 (913)[2]0.04*** ( 0.01)-0.01 (0.01)
0.05*** (0.02)
0.02*** (0.01)
0.02*** (0.00)
0.003 (0.003)
0.03 (0.03)
0.06* (0.03)-0.03*** (0.01)-0.03*** (0.01)
0.004 (0.01)-0.001 (0.01)
0.003 (0.01)-0.01** (0.01)-1.13*** (0.09)
0.27 (913)

Notes: Fixed effect (FE) estimations are reported. Standard errors are in parentheses. * means the coefficient issignificant at 10%, ** is significant at 5%, and *** is significant at 1%. LMC=lower-middle income countries and
UMC=Upper-middle income countries. All the variables, dependent as well as explanatory, are logged toneutralize the skewness of data. The data covers 1997 to 2005 annually.
Sources: The data of GDP per capita, population growth rate, trade, FDI inflow, mobile/fixed line subscribers (asICT) and remittances are taken from the WDI online database of the World Bank. HDI are taken from the HDRs
from 1997 to 2007/08 of the UNDP.

Impact of FDI was found to be significant at 1% only in Column 2. The finding is
consistent with the evidence drawn by Arnal and Hijzen (2008), which suggests that the14

OECD based MNEs tend to provide better pay than their domestic counterparts, especiallywhen they operate in developing and emerging economies. The positive impact on wages also
appears to spread to the employees of domestic firms that ultimately uplift the QOL of
workers in DCs.
As for the control variables, only GDP per capita has a positive impact on humandevelopment. The effect was significant at the 5 % level in Column 1 and the 1 % level inColumn 2. This result is in the line with the mainstream literature and the general intuition that
income improves overall QOL. However, the effect of population growth was not significant.
The results in Column 2 are firmly consistent with Column 1. The main purpose of thiscolumn is to observe whether the above observed effects are different in different income
groups of countries. As the model does not include the interaction term with the dummy of lowincome countries (LICs), the positive sign of the coefficient of the interaction term of therespective income group reveals a larger effect than LICs and vice versa. Thus, Column 2
reveals that the effect of FDI is significantly (at 1%) lower in lower-middle income countries(LMCs) and upper-middle income countries (UMCs) than LICs, as both have -0.3 coefficients.
This is a welcoming result that the HDI promoting effect of FDI is higher in LICs, as manyscholars worry that FDI works well only on those countries that have already reached a certain
level of development. Regarding the trade issue, the effect on UMCs was significantly (at 10%)more than other groups of countries.
In Table 2, the dependent variable is the GDI. The table reports that trade and ICT aresignificant at 1% in promoting gender development in both columns except for trade effects on
Column 2, which is only significant at 5%. These results support the arguments thatinternational trade reduces the gender gap in DCs. For example, evidence shows that job
creation for women in export sectors are growing rapidly (UNDP 1999), and girls, who
historically have low labor market participation rates and so have not benefited from thetraditional network, take greater advantage of available opportunities when their society
undergoes economic changes (Munshi and Rosenzweig 2006). Similarly, the impact of ICT on
gender development is consistent with the arguments that ICT brought some benefits to thedeveloping world as women are empowered with ICT access and they negotiate gender
relations in the household and community (Kelkar and Nathan 2002).
The remaining two elements of globalization and control variables are found to be
insignificant in affecting the GDI. The results after using the interaction term in Column 2 arequite consistent with Column 1. However, the effects are not found to be different across the15

income groups of countries except for the trade effect on LMCs, which is significantly higher(at 1%) than the other region.
Table 2 Gender-Related Development and Four Major Elements of Globalization (1997-2005)
Dependent Variable: Gender-Related Development Index (GDI)

Explanatory Variables[1]
0.00 ( 0.00)
0.01 (0.03)
0.19*** (0.07)
0.002 (0.03)
0.04*** (0.01)-0.01 (0.01)---------1.45*** (0.28)
0.41 (913)

GDP per capita
Population Growth
Trade (% of GDP)
FDI Inflow (% of GDP)Log of ICT
Log of Remittances
LMC Dummy × Trade
UMC Dummy × TradeLMC Dummy × FDI
UMC Dummy × FDI
LMC Dummy × ICT
UMC Dummy × ICT
LMC Dummy × Remittances
UMC Dummy × Remittances
Constant
R -Square (Number of Observations)[2]0.00 ( 0.00)-0.00 (0.03)
0.01** (0.001)
0.01 (0.04)
0.05*** (0.02)
0.05 (0.02)
0.67*** (0.14)
0.22 (0.16)-0.02 (0.06)-0.02 (0.06)-0.01 (0.03)-0.04 (0.05)-0.05 (0.04)-0.003 (0.04)-1.75*** (0.27)
0.26 (913)

Notes: Fixed effect (FE) estimations are reported. Standard errors are in parentheses. * means the coefficient issignificant at 10%, ** is significant at 5%, and *** is significant at 1%. LMC=lower-middle income countries and
UMC=Upper-middle income countries. All the variables, dependent as well as explanatory, are logged toneutralize the skewness of data. The data covers 1997 to 2005 annually.
Sources: The data of GDP per capita, population growth rate, trade, FDI inflow, mobile/fixed line subscribers (asICT) and remittances are taken from the WDI online database of the World Bank. GDI are taken from the HDRs
from 1997 to 2007/08 of the UNDP.

Table 3 presents the impacts of the major elements of globalization on human poverty indeveloping countries (HPI-1). The poverty effect of globalization elements is broader than thehuman and gender development effects because all the four elements of globalization (trade,
FDI, access to ICT and remittances (except in Column 1)) are found to be significant indecreasing human poverty at 1 %. Trade and FDI inflows are significant at 1% in both caseswith or without the interaction terms, whereas ICT was significant at 1% in Column 2 but notsignificant in Column 1. Migration was significant at 1 % in the case with and 10% without
interaction terms.
These results are consistent with the positive arguments of Winters (2000) and Urata(2002) for trade effect, Moran (2001) for FDI effect, the World Economic Forum (2010) for
ICT effect and Ozden and Schiff (2006) for remittance effect on poverty reduction and16

economic growth.
Table 3 Human Poverty and Four Major Elements of Globalization (1997-2005)
Dependent variable: Human Poverty Index for Developing Countries (HPI-1)

Explanatory Variables[1]-0.13*** ( 0.3)
0.06*** (0.02)-0.11*** (0.4)-0.06*** (0.02)-0.00 (0.001)-0.02* (0.01)--------4.64*** (0.17)
0.12 (798)

GDP per capita
Population Growth
Trade (% of GDP)
FDI Inflow (% of GDP)Log of ICT
Log of Remittances
LMC Dummy × Trade
UMC Dummy × TradeLMC Dummy × FDI
UMC Dummy × FDI
LMC Dummy × ICT
UMC Dummy × ICT
LMC Dummy × Remittances
UMC Dummy × Remittances
Constant
R -Square (Number of Observations)[2]-0.14*** ( 0.03)
0.08*** (0.02)-0.17*** (0.05)-0.09*** (0.02)-0.003**** (0.001)-0.03*** (0.01)
0.11 (0.09)
0.08 (0.10)
0.04 (0.03)
0.01 (0.05)
0.07*** (0.02)
0.16*** (0.04)
0.02 (0.02)
0.02 (0.02)
4.58*** (0.28)
0.17 (798)Notes: Fixed effect (FE) estimations are reported. Standard errors are in parentheses. * means the coefficient issignificant at 10%, ** is significant at 5%, and *** is significant at 1%. LMC=lower-middle income countries and
UMC=Upper-middle income countries. All the variables, dependent as well as explanatory, are logged toneutralize the skewness of data. The data covers 1997 to 2005 annually.
Sources:The data of GDP per capita, population growth rate, trade, FDI inflow, mobile/fixed line subscribers (asICT) and remittances are taken from the WDI online database of the World Bank. HPI-1 are taken from the HDRs
from 1997 to 2007/08 of the UNDP.

As expected, population growth has a poverty increasing effect at a 1% level ofsignificance. However, GDP per capita was insignificant in reducing poverty.
Column 2 reveals that there is no difference on the poverty reducing effects ofglobalization elements across income groups of countries except for ICT. It has a larger effecton higher income group countries and the effect is significant at the 1% level.
In the next step, it uses the KOF indexes of globalization as major explanatory variables.
Table 4 provides the relationship between human development and globalization in terms of the
KOF indexes. Column [1.a] includes overall globalization with the control variables GDP per
capita and population growth without the interaction terms with the income group dummy,
whereas Column [1.b] includes the interaction term. In these columns in this and the next two

17result-tables, the suffix ‘a’ is given for the result-columns without interaction terms, and suffix‘b’ is given for the result-columns with interaction terms.
The results from both the columns [1.a] and [1.b] show that the overall globalization ishighly significant (at 1%) to increase human development. However, Column [1.b] reveals thatthese effects are not different across income groups of countries. This result supports thetheoretical proposition made by Sirgy et al. (2004) that globalization enhances human qualityof life in many ways.
Table 4 Human Development and Globalization (1997-2005)
Dependent Variable: Human Development Index (HDI)

Explanatory Variables
GDP per capita
Population Growth
Overall Globalization (G.)
Economic G.
Social G.
Political G.
LMC × Overall G.
UMC × Overall G.
LMC × Economic G.
UMC × Economic G.
LMC × Social G.
UMC × Social G.
LMC × Political G.
UMC × Political G.
Constant
R-Square (No. of obs.)[1.a]
0.04*** ( 0.01)-0.01** (0.01)
0.18*** (0.02)
..
..

[1.b]
0.04*** ( 0.01)-0.01** (0.01)
0.16*** (0.03)
..
..

[2.a]
0.04*** ( 0.01)-0.01** (0.01)..
0.07***(0.02)
0.06*** (0.02)[2.b]
0.04*** ( 0.01)-0.01* (0.01)..
0.10***(0.03)
0.03* (0.02)

..
..
..
..
..
..
..
..
..
-1.51*** (0.08)
0.16 (987)..
0.06 (0.04)
0.01 (0.07)
..
..
..
..
..
..
-1.52*** (0.09)
0.16 (987)

0.08*** (0.02)
..
..
..
..
..
..
..
..
-1.45*** (0.11)
0.20 (791)

0.04* (0.02)..
..
-0.08* (0.04)
0.03 (0.06)
0.1** (0.04)
0.07 (0.08)
0.04 (0.03)-0.03 (0.05)-1.61*** (0.10)
0.21 (791)

Notes: Fixed effect (FE) estimations are reported. Standard errors are in parentheses. * means the coefficient issignificant at 10%, ** is significant at 5%, and *** is significant at 1%. LMC=lower-middle income countries and
UMC=Upper-middle income countries. All the variables, dependent as well as explanatory, are logged toneutralize the skewness of data. The data covers 1997 to 2005 annually. Column (2) has fewer observations
because some particular types of globalization data are not reported for some countries.
Sources: Globalization indexes are taken from Dreher (2006), GDP per capita (current US$) and population
growth rates are from the WDI online database of the World Bank, and HDI are from the HDR from 1997 to
2007/08 of the UNDP.

As expected, GDP per capita has a significant (at 1%) positive impact on HDI since
GDP per capita itself is one of the three components of HDI, which generates a direct effect on
it. Additionally, as richer countries generally have better health and educational status, GDP per
capita also indirectly generates a positive effect on HDI. Similarly, it is quite natural to observe
the negative impact of population growth on HDI. These impacts are significant at the 5% level.
This result is consistent with the previous findings that higher population growth rates have18

negative correlations with economic growth, health and educational indicators, particularly indeveloping countries (e.g. Kelly and Schmitd, 1995).
In Columns [2.a] and [2.b], instead of overall globalization, the three sub-indexes
economic, social and political globalization, are included in the equation. Such disaggregatedanalyses not only demonstrate the independent effect of each dimension of globalization, butalso cross-checks the validity of the model based on the consistency of the results. Here, theresult is highly consistent with Column [1.a] and Column [1.b]. All the three dimensions ofglobalization; economic, social and political, were found to be highly significant at the 1%
level to increase human development in both the equations with or without the interaction term,
except for the social and political globalization in Column 2.b in which the effect were foundsignificant at 10%. Interestingly, the magnitude and direction of the effect of GDP per capita
and population growth were also found to be exactly the same as in the previous columns [1.a]and [1.b]. These findings are similar with the outcomes of an empirical analysis by Tsai (2007),
but the relationship that he found was rather poor; Tsai showed the significance level of theoverall impact of globalization on human development at 10 % and also reported no significant
impact of economic and social globalization.
Unlike the case of overall globalization, the results from the disaggregated analysis showsthat the human development effect of economic globalization was significantly (at 10%) lower
in LMCs than LICs. However, there is no difference in effects between LICs and UMCs.
However, social globalization has a significantly (at 5%) larger effect on LMCs. This indicatesthat a certain level of development helps to optimize the benefits from social globalization.
Similarly, Table 5 presents the effects of globalization on GDI. Column [1.a] shows thatthe effect of overall globalization was positively significant (at 1%), which reveals that
globalization also helps to reduce gender disparity in human development. The result with theinteraction term in Column [1.b] also shows a significant (at 5%) effect. These results are
consistent with recent findings by Munshi and Rosenzweig (2006), who argue that lower-caste
girls in the Indian city of Bombay are taking full advantage of opportunities provided by the
globalized economy switching rapidly to English schools, whereas working class boys in thelower caste, who already have a dominating role within their ethnic group tend to continue togo local language schools that lead to traditional occupations with lower incomes. Furthermore,these results also support the cross-country evidence of Oostendorp (2004) that shows thesignificant impact of trade and FDI net inflows on narrowing the occupational gender wage gap
for low-skill occupations worldwide.

19Regarding the control variables, GDP per capita has a significantly (at 1%) positive
impact on GDI for both the columns. As expected, population growth was found significant (at5%) to reduce gender development, although there was no significant effect observed inColumn [1.a]. All the effects are found no different across the income group of countries.
Table 5 Gender-Related Development and Globalization (1997-2005)
Dependent Variable: Gender-Related Development Index (GDI)

Explanatory Variables
GDP per capita
Population Growth
Overall Globalization (G.)
Economic G.
Social G.
Political G.
LMC × Overall G.
UMC × Overall G.
LMC × Economic G.
UMC × Economic G.
LMC × Social G.
UMC × Social G.
LMC × Political G.
UMC × Political G.
Constant
R-Square (No. of obs.)[1.a]
0.10***( 0.04)-0.01 (0.03)
0.33*** (0.11)
..
..
..
..
..
..
..
..
..
..
..
-2.5*** (0.43)
0.60 (987)[1.b]0.10*** ( 0.04)-0.01** (0.03)
0.28** (0.14)..
..
..
0.23 (0.23)-0.31 (0.38)
..
..
..
..
..
..
-2.33*** (0.49)
0.03 (987)[2.a]0.13***( 0.05)-0.001 (0.04)..
0.39*** (0.11)-0.05 (0.11)-0.01 (0.09)
..
..
..
..
..
..
..
..
-2.76*** (0.50)
0.57 (791)[2.b]0.13*** ( 0.05)-0.001 (0.04)..
0.11*(0.16)
0.09 (0.14)
0.05 (0.14)..
..
0.82*** (0.25)-0.09 (0.34)-0.35 (0.24)-0.11 (0.48)-0.12 (0.18)-0.10 (0.28)-2.73*** (0.60)
0.05 (791)

Notes: Fixed effect (FE) estimations are reported. Standard errors are in parentheses. * means the coefficient issignificant at 10%, ** is significant at 5%, and *** is significant at 1%. LMC=lower-middle income countries and
UMC=Upper-middle income countries. All the variables, dependent as well as explanatory, are logged toneutralize the skewness of data. The data covers 1997 to 2005 annually. Column (2) has fewer observations
because some particular types of globalization data are not reported for some countries.
Sources: Globalization indexes are taken from Dreher (2006), GDP per capita (current US$) and population
growth rates are from the WDI online database of the World Bank, and GDIare from the HDR from 1997 to
2007/08 of the UNDP.

The gender impact of globalization mainly comes from the economic globalization.
Because, in Column [2.a] and [2.b], when the three major components of globalization
regressed replacing the overall globalization, only economic globalization found significant at
1% in Column [1.a] and 10% in Column [1.b]. Thus, it argues that globalization helps toincrease gender-related development as well. This finding complements earlier empiricalevidence on the gender development effects of globalization by Munshi and Rosenzweig(2006) and Ostendorp (2004), as explained above. The effect is significantly (at 1%) higher in
LMCs indicating that the certain level of economic development is helpful to gain gender
impact of globalization.
20

Table 6 shows the relationship between globalization and human poverty. As expected, all
the indexes of globalization are found to be significant (mostly at 1%) in reducing poverty, as
the coefficient of each indicator has a negative sign. Only in the case of Column [2.a] was theeffect of political globalization observed at a 5% level of significance. These results are
consistent with the findings by Dollar (2005) and many others that they found globalization has
a positive impact on improving the lives of poor people in developing countries and areas.
Table 6 Human Poverty and Globalization (1997-2005)
Dependent Variable: Human Poverty Index for Developing Countries (HPI-1)[1.a]
Explanatory Variables-0.03 (0.03)
GDP per capita
0.04** (0.02)
Population Growth-0.54***(0.07)Overall Globalization (G.)..
Economic G.
..
Social G.
Political G.
..
LMC × Overall G.
..
UMC × Overall G.
..
LMC × Economic G.
..
UMC × Economic G.
..
LMC × Social G.
..
UMC × Social G.
..
LMC × Political G.
..
UMC × Political G.
..
5.42*** (0.27)
Constant
0.10 (863)
R-Square (No. of obs.)[1.b]
0.04 (0.03)
0.05** (0.02)-0.66*** (0.08)..
..
..
0.45*** (0.14)-0.28 (0.26)
..
..
..
..
..
..
5.48*** (0.31)
0.12 (863)

[2.a]
[2.b]
-0.03 (0.03)-0.03 ( 0.03)
0.05** (0.02)
0.04** (0.02)..
..
-0.19*** (0.06)-0.22***(0.09)-0.23*** (0.06) -0.25*** (0.08)-0.11** (0.05) -0.166*** (0.08)
..
..
..
..
..
0.23* (0.13)..
-0.57*** (0.21)..
0.04 (0.13)..
-0.05 (0.26)..
0.12 (0.10)..
-0.08 (0.17)
5.35*** (0.35)
5.71*** (0.35)
0.46 (734)0.14 (734)Notes: Fixed effect (FE) estimations are reported. Standard errors are in parentheses. * means the coefficient issignificant at 10%, ** is significant at 5%, and *** is significant at 1%. LMC=lower-middle income countries and
UMC=Upper-middle income countries. All the variables, dependent as well as explanatory, are logged toneutralize the skewness of data. The data covers 1997 to 2005 annually. Column (2) has fewer observations
because some particular types of globalization data are not reported for some countries.
Sources: Globalization indexes are taken from Dreher (2006), GDP per capita (current US$) and population
growth rates are from the WDI online database of the World Bank, and HDI are from the HDR from 1997 to
2007/08 of the UNDP.

Regarding the control variables, contrary to case of the GDI, GDP per capita was foundto be insignificant, whereas population growth was found to increase poverty significantly at
the 5% level. The results are consistent with all four columns. These findings demonstrate thefact that income growth is highly concentrated on higher-income groups among the DCs, thus
doing little to help poor people. Population growth rates, being mostly higher among lowerincome groups and in poorer countries, certainly add a burden to the poor and ultimately
exacerbate poverty.
21

To compare the poverty effect of globalization across different groups of countries,
overall globalization was found to have a significantly high impact on LMCs. This clearlyshows that countries should reach a certain level of development to capture the optimum
poverty effect of globalization. The result is thus consistent with the argument of Sabi (2007)
that globalization is important for human development only after certain level of income
growth. This result actually comes from economic globalization as Column [2.b] shows that
economic globalization has a significantly high impact on LMCs (at 10%) but a low impact on
UMCs (at 1%), and the effect of social and political globalization are not different acrossincome groups of countries.
Overall, it is argued that globalization, in terms of the KOF index, is statisticallysignificant not only in increasing human development and gender development, but also in
reducing human poverty. Even for the segregated index of globalization (i.e. economic, socialand political globalization), the results are generally robust. The results are consistent with theanalysis using some of the key elements of globalization instead of globalization indexes at the
beginning of this section, which further strengthen the reliability of the mode used in this study.
Thus, the dataset used and model applied in this study is valid and appropriate.

5.

CONCLUSION

In the context of disputing arguments among scholars, the empirical results of this study revealthat globalization enhances QOL by promoting human and gender-related development andsignificantly reducing human poverty. Not only the key elements of globalization, but the KOF
indexes are highly significant regarding all three dependent variables of QOL (i.e. HDI, GDI
and HPI-1) and have been shown to be quite robust in the FE regression model. Not
surprisingly, all of the aspects of globalization (economic, social and political) contribute to theoverall effect. Indeed, these three factors of globalization comprise a tripod of global
integration and move along together rather than separately. Furthermore, the selected key
elements of globalization were also found to significantly and positively affect the QOL
indicators. This rather conventional approach gives further evidence of the validity andcredibility of the data used and the methodology followed in this research. Therefore, it isargued that along with the results, the methodology applied in this study is also useful for
similar research of this kind.
The major message of the findings is that there should not be any fear of globalization, in
general. It argues that globalization has mostly favorable effects for all. However, globalization22

effects were found higher in LMCs in general. Thus, the international community should focus
their support to LICs to empower them to raise their income level up to a certain level.
The study also observed some unexpected outcomes. Political and social globalization,
FDI, and international migration (proxy by remittances) were all found to be insignificant
factors of gender-related development. Thus, how can these variables be made to significantly
promote gender aspects of development? This is a crucial question for policymakers and theright answers will enhance their capacity for successful globalization because there is no choice
but for globalization to improve QOL and other kinds of development. Indeed, better policies
will matter in making every element significant in this process. The paper suggests further
research for such policy recommendations.

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Appendices follow

26

APPENDICES
Appendix-1 List of developing countries included in the study (in alphabetical order)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.

Albania
Algeria
AngolaArgentina
Armenia
Azerbaijan
Bangladesh
Belarus
BelizeBenin
BhutanBolivia
Botswana
BrazilBulgaria
Burkina Faso
BurundiCambodia
Cameroon
Cape VerdeCentral African
Republic
Chad
Chile
China
Colombia
Comoros
Congo
Congo Rep.
Costa Rica
Cote d’Ivoire
Croatia
Djibouti
Dominica
Dominican Republic
EcuadorEgypt
El Salvador
Eritrea
Ethiopia
Fiji
Gabon
GambiaGeorgia
Ghana

45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.

GrenadaGuatemalaGuineaGuinea-Bissau
GuyanaHaiti
Honduras
India
Indonesia
Iran
Jamaica
JordanKazakhstan
Kenya
Kyrgyzstan
Lao PDR
LatviaLebanonLesotho
Libya
Lithuania
Macedonia
Madagascar
MalawiMalaysia
Maldives
Mali
Mauritania
Mauritius
MexicoMoldova
Mongolia
MoroccoMozambique
Namibia
Nepal
Nicaragua
Niger
Nigeria
Pakistan
PanamaPapua New Guinea
Paraguay
Peru
Philippines

27

90. Poland
91. Romania
92. Russia
93. Rwanda
94. Saint Kitts
95. Saint Lucia
96. Saint Vincent
97. Senegal
98. Seychelles
99. Sierra Leone
100. Solomon Islands
101. South Africa
102. Sri-Lanka
103. Sudan
104. Suriname
105. Swaziland
106. Syria
107. Tajikistan
108. Tanzania
109. Thailand
110. Togo
111. Tonga
112. Tunisia
113. Turkey
114. Uganda
115. Ukraine
116. Uruguay
117. Uzbekistan
118. Vanuatu
119. Venezuela
120. Vietnam
121. West Samoa
122. Yemen
123. Zambia
124. Zimbabwe

Appendix-2 Summary Statistics
Variables
Human Development Index (HDI)
Gender-related Development Index(GDI)
Human Poverty Index (HPI-1)
GDP per capita
Population Growth
Trade
FDI
ICT
Migration (remittances)
Economic Globalization
Social Globalization
Political Globalization
Overall Globalization

Obs.
1116

Mean
0.630986

Std. Dev.
0.156687

Min
0.25

Max
0.87

1116

1.166219

17.94408

0.24

600

869
1116
1116
1116
1116
1116
1053
909
1116
1116
1116

28.9441
1810.893
1.630654
81.650931.18571
19.93576
4.39116852.25267
45.5820457.10932
50.39887

15.40484
1846.233
1.241132
38.33903
32.31529
22.86311
6.358951
13.7725117.30288
19.85124
12.60756

3.3
81.58
-2.76
15.84
-16.59
0.04
0
17.64
8.56
19.1
20.82

65.5
8931.879.76
224.89202.3
150.97
44.3
84.6
89.69
93.6
80.02

Appendix-3 Correlation Matrix
Variables
HDI
GDI
HPI-1
GDP per capita(gdppc)Population Growth(pgrow)
Trade
FDI
ICT
remittances
Economic G. (ecog)
Social G. (socg)
Political G. (polg)
Overall G. (ovlg)

Economic G. (ecog)
Social G. (socg)
Political G. (polg)
Overall G. (ovlg)

GDI HPI-1 gdppc

HDI
1-0.0193-0.951810.0169-0.1824-0.0148 0.1932

0.1352-0.0297 -0.1413 -0.4296

0.0815-0.0811-0.1265-0.1299-0.1351-0.1128-0.125-0.1725-0.03-0.0318-0.0019-0.0089-0.0161-0.0237
0.06150.0006-0.1180.04020.13830.08680.1217
0.09130.13820.15640.09420.10230.7641-0.1370.562
0.6426
0.29710.7079

ecog

socg

polg

ovlg

1
0.104610.8543 0.4247

1

trade

fdi

ict

remit

1
1

1
0.6711
0.06630.8502

pgrow

28

1
-0.2033
1-0.1903 0.5246
1-0.4592 0.1978 0.1772
1-0.11850.267 0.1416 0.0366
1-0.4011 0.5077 0.5713 0.5689 0.1147-0.4293 0.4227 0.2998 0.6111 0.1649-0.2511 -0.3154 -0.2073 0.3546 -0.1369-0.5034 0.3607 0.3898 0.7136 0.1099