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Puget Sound eJournal of Economics
The Effect of Wage Rate on Foreign Direct Investment Flows to
Individual Developing Countries.
Koben Calhoun Sarah Yearwood Andrew Willis
November 2002
Econometrics 374 Professor Matt Warning
2
1. Introduction and Literature Review
Foreign Direct Investment (FDI) has become increasingly important for many
low-income countries. Over the ten year period from 1990 to 2000, global FDI flows
have increased more than 83% from 198.3 billion US$ to 1.169 trillion US$.
(http://devdata.worldbank.org/dataonline/). The FDI allocated to developing countries
has also seen substantial increase. The FDI flows arriving in developing nations
increased from 24.1 billion US$ in 1990 to 168.2 billion US$ by the year 2000 (Global
Development Finance). With the level of domestic saving in the third world often low,
and loans approved for allocation to the third world declining over the last ten years, FDI
has become an increasingly important source of investment capital for many low-income
nations (Asiedu, 2002). As a source of investment capital, FDI stimulates employment,
but further, it is often seen as a means to provide developing countries with a source for
acquiring the superior technology, business methods, and market access of the developed
world (Noorbakhsh, Paloni, Youssef 2001).
The importance of FDI to the developing world has led many low-income
countries into what Wheeler and Mody (1992) refer to as �location tournaments,�
situations in which third world nations compete against each other to attract foreign
investment dollars. Ross and Chan (2002) describe the outcome of these tournaments as
a �race to the bottom� in which low-income countries try to offer more incentives, lower
wages, and less stringent labor conditions than other developing nations, in hopes of
attracting FDI dollars and the benefits which they provide. Without a set of international
labor standards in place, wage rate represents a factor which developing nations are
3
unfettered to manipulate downwards in their efforts to attract FDI. In their recent study
Ross and Chan (2002) found that wages are dropping in developing countries in direct
relation to these nations attempts to attract FDI dollars (11). Although much controversy
exists over the implications of these �location tournaments,� this study proceeds with the
purpose of investigating whether the nature of the characteristics which attract FDI
promote such potentially destructive competition among nations (Wheeler and Mody 57).
In particular this study investigates whether wage rate is a significant factor attracting
FDI inflows to developing countries.
A comprehensive view of Foreign Direct Investment must recognize two distinct
subgroups of FDI based on the motivation of the investment: host market motivated, and
export market motivated (Choi, 5). Host market motivated FDI is investment motivated
by the economic potential of the customer market within the destination country (Asiedu,
109). In this case the good or service is produced within the destination country for
consumption by the local market, as noted by Asiedu (2002), often to avoid high import
tariffs (111). Export market motivated FDI is investment with the purpose of establishing
production facilities within the destination country for export back to the source country
or to the greater global market (Asiedu, 109). Export market motivated FDI is much
more cost conscious, as its sole intent is to search the world market for a low cost
production environment. The fact that each type of FDI is distinctly motivated means
that each type seeks out different characteristics within the host country. Since the
available FDI data is not segregated by motive, in order to accurately investigate one
individual factor such as wage rate, factors which potentially attract each type of FDI
must be included in our analysis.
4
Volumes of scholarly research have investigated the impact of wage rate on
foreign direct investment, with varied results. In theory, as investing entities search for
potential investment locations, decision makers will prefer locations with lower wage
rates to those with higher labor costs. However, the cost of labor, as a major production
expense, would factor heavily into investment decisions motivated by export markets,
and far less so into those decisions seeking to gain access to customers within the host
market. This may help explain the lack of unanimity in the results of studies
investigating wage rate as a determinant of FDI. A number of studies including
Suanders (1982), Flamm (1984), Schneider and Frey (1985), Culem (1988), Tsai (1994)
and Shamsuddi (1994) have returned results supporting the wage rate theory, such that
higher wages were found to discourage FDI (Chakrabarti 90). For example, in his 1994
cross-country analysis of 51 countries from 1975 to 1978 and 62 countries from 1983 to
1986 Tsai found, in general support of the wage rate theory, that increases in the nominal
wage rate in the manufacturing sector tend to discourage FDI (152). Further, Wheeler
and Mody in their 1992 investigation of general manufacturing and electronics industry
investment across 42 countries found that while wage rate was relatively unimportant in
determining FDI flows to industrialized countries, the wage rate theory was one of the
most important determinants of FDI inflows to developing nations (69). Although the
majority of research has found a significant negative relationship between wages and
foreign direct investment, the wage rate as a determinant of FDI has been far from
unanimous. Several studies have found that wage rate was insignificant as a determinant
of foreign direct investment flows, and a number of studies have even uncovered a
5
positive relationship, such that increases in wage rate encourage FDI inflows
(Chakrabarti 90).
Despite the varied results found by studies investigating wage rate as a
determinant of FDI, when looking at the determinants of FDI into developing countries
we expect to find wage rate significant. With typically smaller markets, less per capita
spending power, and lower wages than the north, it could be expected that a greater share
of export market motivated FDI alights to the developing world (Noorbakhsh, Paloni and
Youssef, 1597). Thus, despite the variety in results returned by past studies examining
wage rates, we expect wage rate to prove a significant factor attracting FDI to developing
countries.
Beyond wage rate, a number of other important factors must be considered when
investigating the determinants of foreign direct investment. Much scholarly work has
been devoted to investigating the determinants of FDI in developing countries. Our study
has employed this rich history of published research in determining the significant factors
to include in our analysis. Among the factors found consistently significant among
scholarly research, market size is the one most unanimously agreed upon as significant in
attracting FDI. The theory, especially important for host market motivated FDI, follows
that a country with a larger market will have a greater ability to consume the production
capacity established by the inflows of FDI, and will thus appear more attractive to
potential investment. The size of the host market has been repeatedly found to hold a
positive relationship with FDI, such that as GDP increases FDI is expected to increase as
well. Kravis and Lipsey (1982), Schneider and Frey (1985), Culem (1988), Wheeler and
Mody (1992), Tsai (1994), Shamsuddin (1994) and Billington (1999) have each found
6
the size of the host country market a significant determinant of FDI inflows. Similarly,
the growth rate of a nation�s GDP has been found consistently significant. The thinking
follows that a nation whose economy is experiencing rapid growth will provide a better
investment climate, especially for host market motivated FDI, than one growing at a
slower rate. The findings of studies such as Schneider and Frey (1985), Culem (1988)
and Billington (1999) have each supported the GDP growth rate theory.
The quality of the labor force available to investing entities is another factor
accounted for in this study. Conceptually, greater productivity could be expected from a
better educated and trained workforce. Thus, higher quality of labor, often measured in
education levels, would be expected to attract FDI inflows. Noorbakhsh, Paloni and
Youssef (2001) in their econometric assessment of the determinants of FDI in 36
developing countries found the quality of human capital, measured in terms of literacy
and schooling, to be one of the most decisive factors in determining destinations for FDI.
The export orientation of the host country is another factor embodied in our study.
This proxy, often measured in terms of the dollar value of exports, would be especially
important for export market motivated FDI. Scholarly studies have shown that higher
levels of exports lead to higher FDI inflows. For example, in their 1996 study Jun and
Singh found export orientation to be the strongest determinant of FDI inflows. The
corporate and income tax rates of the host country represent another factor which
investing entities would consider in their investment decisions, such that higher corporate
and income tax levels of the host country would be expected to deter potential FDI.
Although research such as Wheeler and Mody�s 1992 study have found the tax rate of the
host country insignificant, the majority of work such as Kemsley (1998) and Billington
7
(1999) have found the host country tax rate to be a significant factor in determining FDI
inflows.
The communications infrastructure of the host country, often measured in the
literature with a proxy such as telephones per 1,000 people, is another factor which our
study will explore. The quality of the groundwork present in the host country has the
potential to increase the efficacy of investments into the region and thus presents a
characteristic attractive to entities looking to invest abroad. As noted by Asiedu, (2002)
the proxy of the number of telephones per 1,000 people is imperfect as it provides a
measure of the availability of infrastructure (and even here with limited scope), but says
nothing of the reliability of the resources present (111). However, with limited data
availability and a scholarly precedent in place, this study will employ telephones per
1,000 people as a measure of infrastructure.
This study explores factors that determine the flows of FDI into individual
developing nations. In particular, this study seeks to explore the nature of the
relationship between the prevailing wage rate in developing countries and inflows of FDI
to those nations. We hypothesize that wage rate and FDI have a negative relationship
such that nations with a lower wage rate will receive higher levels of FDI inflows,
compared to developing nations with higher wage rates. With some FDI flows host
market motivated and some export market motivated, but no division in the available data
the host country characteristics attractive to each type of FDI must be embodied in our
study. Drawing on the thorough scholarly attention to the determinants of international
FDI flows this study arrives at a number of variables, beyond wage rate consistently
8
found to influence flows of FDI. In particular, the study will embody, market size, GDP
growth rate, labor quality, tax rate, export orientation and infrastructure.
Data Description
Our study draws its data from three primary sources. The first source is the
�World Development Indicators�(WDI), released annually by the World Bank. This,
development focused data source compiles data on 800 development indices over 152
national economies and 14 country groups. The majority of the data contained in the
WDI is gathered from national statistical agencies, central banks and customs services
within the individual countries themselves. The second source is the annual �Global
Development Finance� (GDF) report also compiled by the World Bank. The GDF is
composed of statistical tables summarizing the external debt structure of 136 countries.
Like the WDI, the GDF relies primarily on host country agencies for the collection of
data.
Our third source of data is the International Labor Organization (ILO) and their
yearly compilation of labor statistics. ILO collects data on over 200 countries in the areas
of employment, unemployment, labor costs, wages, hours of work, consumer price
indices, and several other factors. Through ILO we were able to gather specific data on
the wage rates within many developing countries. The practice of employing in-country
data sources for the compilation of the WDI, GDF and ILO leads to a number of
problems. First, the data collection techniques may vary from country to country, thus
reducing the validity of inter country comparison. Further, many developing nations
have limited resources available for application to data collection processes leaving
significant holes in some portions of the data. However, the WDI, GDF and ILO
9
represent some of the most comprehensive development data available, and are thus
employed to the extent which the data availability allows.
Our study explores the relationship between the wage rate and the levels of
foreign direct investment inflows into individual low and middle-income countries. We
believe that this relationship is most pronounced within developing countries; therefore
we have concentrated our study on countries defined as low or middle-income. The
World Bank defines low and middle-income countries as those whose year 2000 Gross
National Income was $9,265 or less. Based on this World Bank Definition, 155 nations
qualify as low or middle income. As mentioned above, however, holes exist in the
available data for many of these developing countries, thus, a study analyzing low and
middle-income nations must adjust its analysis to the data available. Our study looks at
29 representative low and middle-income countries in the year 1995. The 29 countries
were selected based on the data available for their analysis. The year 1995 is selected as
it represents the most recent year for which the necessary data is available on the widest
pool of low and middle-income nations. Beyond wage rate, there are several other
factors that play an important role in attracting foreign direct investment. The factors
anticipated in this study to attract foreign direct investment are: market size, GDP
growth, labor quality, export orientation, and infrastructure. The regression equation
used for this econometric analysis is:
FDIt = β1t + β2tMarketSize + β3tGDPGrowth + β4tLaborQuality + β5tExportOrientation + β6tInfrastructure + β7tWageRate
10
One of the main determinants of foreign direct investment is the size of the host
countries market. The gross domestic product (GDP) is used as a proxy for market size.
The GDP data was collected in current US$ from the WDI. As defined by the World
Bank the dollar values for GDP were converted from domestic currencies using single
year official exchange rates (World Development Indicators). From the data we collected
we were able to calculate a mean value for the market size of $82.965 billion US$.
Another important factor in attracting foreign direct investment inflow is the
amount of GDP growth a country experiences. Data for this variable was collected from
the WDI in the form of 1995 GDP percentage growth. The World Bank defines the data
as an annual percentage growth rate of GDP at market prices (World Development
Indicators).
Labor quality is an additional independent variable included in our study. Labor
quality is interpreted as the education and skills of the workers within a country. This
study uses a nation�s adult illiteracy rate as a measure of the labor quality within a nation.
Illiteracy rate data was collected for each of our 29 countries from the WDI. The WDI
definition of the adult illiteracy rate is the percentage of people above the age of 15 who
cannot, read and write a basic statement about their everyday life (World Development
Indicators). The adult illiteracy rate for our 29 focus nations had a mean of 15.37
percent. This means that on average 15.37 percent of the adult population within our
focus nations are, by definition, illiterate.
The exports of goods and services for the year 1995, measured in current US$, is
used as a proxy for the export orientation of the developing nation�s in our study. The
US$ value for exports of goods and services was acquired from the WDI. The mean
11
level of annual exports for the 29 low and middle-income nations in our study is 20.246
billion US$. We employ the number of telephone mainlines per 1,000 people as a
measure of the quality of infrastructure within the host countries. A larger number of
telephone mainlines available indicates a higher level of infrastructure, which potentially
attracts more investment. Data was collected from the WDI and is based on figures from
1995. The mean value is 126.8 telephone mainlines per 1,000 people, with a maximum
of 304.7 in Bulgaria and a minimum of 4 in Mauritius.
The wage rate is the focus independent variable in our analysis. Wage rate in the
manufacturing sector has been used as a proxy for the cost of labor in such studies as
Wheeler and Mody (1992) and Tsai (1994). Following this lead, the 1995 average
manufacturing wage rate for International Standard Industrial Classification numbers 30-
39 is used as a proxy for wage rate in this study. This encompasses country specific
wage rate information on the manufacturing of food, beverage and tobacco items,
textiles, wood products, paper products, chemicals and petroleum, non-metallic minerals,
metals, metal fabrication and other manufacturing. The wage rate data is collected from
the ILO�s annual compilation of labor statistics. The data is presented in the host country
currency and has been converted into US$ with oanda.com using the exchange rates
posted on December 25, 2001. Cross country comparison between wage rates is
facilitated by placing all wage rate data in terms of an employee�s monthly earnings.
Monthly earnings vary widely across our 29 low and middle-income countries. A
manufacturing employee�s monthly earnings in Bulgaria stand, on average, at 4,118 US$
while an employee in Romania will make the equivalent of just over 9 US$ per month.
The mean wage for manufacturing is 452.7 US$ per month, with a standard error of 166
12
US$. The large standard error illustrates the wide spread of the data. Rather than tightly
clumped around the mean, the wage rate has a lot of variance across different countries.
For example, 6 of the 29 countries in our study report manufacturing wage rates of less
than 100 US$ per month, while manufacturing employees in 3 of the countries earn over
1000 US$ per month.
Our dependent variable is foreign direct investment inflows to our 29 individual low and
middle income nations in 1995, measured in millions of current US$. Foreign direct
investment is defined as a measure of investment made with the purpose of acquiring at
least a 10% voting share of a business entity in a country distinct from the capital source
country (Global Development Finance, xvi). The measure includes inflows of the sum of
investment capital and reinvestment of earnings with the aforementioned purpose. As
found with our independent variables, the data for our dependent variable is widely
spread. The mean of FDI inflows to our 29 focus nations is 1,504.83 million US$ with a
standard error of 399.7 million US$. The range of our data is 9,513 million US$, from
Jordan with 1995 FDI inflows of 13 million US$ to Mexico with 1995 inflows of 9,526
million US$.
Empirical Analysis
The Halbert White regression analysis run on our 26 low and middle-income
countries indicates that a large portion of FDI inflows can be explained by the
independent variables (exports, GDP growth, GDP, illiteracy rate, communications
infrastructure, and wage rate) encompassed in our study. The independent variables
accounted for in our model account for 15.21 percent of the variance in per country FDI
13
inflows. Although this represents a relatively small percentage, the findings are strong
for a cross-sectional analysis.
Correlation coefficients were calculated and examined for each independent
variable, and multicollinearity was not found to be a problem. The highest correlation
coefficient, at 0.62, was found to exist between annual exports (measured in current US$)
and GDP (measured in current US$), however, this result falls well below the 0.8 to 0.9
commonly accepted to indicate the presence of multicollinearity (see table 2) (Kennedy,
1986). Heteroskedasticity, on the other hand is a problem. The residual plots prompted
suspicions of heteroskedasticity which were confirmed by running the Goldfeld-Quandt
test (see table 3). Upon discovery of heteroskedasticity, the Halbert White estimation
technique was used in place of the least squares estimator. The results of the regression
are presented in table 5.
Barring GDP growth rate, all of the independent variable coefficients returned in
our multiple regression have the sign anticipated by the respective theories which
prompted their initial inclusion in our model. Further, each of the independent variables
included in our study were found to be significant determinants of FDI inflows to
developing nations. The export orientation of low and middle-income nations, measured
in current US$, was found significant at the 1% level with a P-value of 0.003. The
coefficient of 0.0000000351 indicates that for every billion US$ increase in a nations
exports, FDI inflows to that nation will increase by 35.1 million US$.
GDP growth rate represents the only independent variable included in our model,
whose coefficient returned a sign counter to the expectations of the study. The
relationship between the GDP growth rate of a particular developing nation and FDI
14
inflows to that nation were found to be �156.23 (see table 4). This means that for each
percentage increase in GDP growth, FDI inflows to the nation in question are expected to
decrease by 156.23 million US$. This result runs counter to the expectations of this
study and the majority of previous scholarly research such as Schneider and Frey (1985),
Culem (1988) and Billington (1999).
The independent variable, market size, was also found to be a significant
determinant of FDI inflows at the 5% level with a P-value of 0.032 (see table 4). The
coefficient of the GDP variable was determined to be 0.00000000239 (see table 4). This
means that for each billion US$ increase in a nation�s GDP, FDI is anticipated to increase
by 2.86 million US$.
The illiteracy rate of a nation, used by this study as a proxy for labor quality, was
found to be a significant determinant of country specific FDI flows at the 5% level. The
coefficient for illiteracy rate was �6.8994, which is interpreted to mean that for each
percentage increase in the illiteracy rate FDI inflows to the country in question are
anticipated to decrease by 6.89 million US$. The sign of the coefficient match the
expectations of the study and the results of previous scholarly research, such as
Noorbakhsh, Paloni and Youssef (2001). The number of telephone mainlines per 1,000
people, used as a proxy for communications infrastructure, with a P-value of 0.000, was
shown to be a significant determinant of country-specific FDI inflows at the 1% level
(see table 4). From the regression we calculated a coefficient of 7.7953, which can be
interpreted such that an increase of one telephone mainline per thousand people will
result in a 7.8 million US$ increase in FDI. From this coefficient we can see that
infrastructure plays a large role in attracting foreign direct investment.
15
The wage rate coefficient, of �0.4240 was found to have a negative sign in line
with the expectations of this study and the wage rate theory in general. Based on the
nature of the wage rate data, the coefficient can be interpreted to mean that for each US$
equivalent per month that a nation�s wage rate increases, country-specific FDI inflows
can be expected to fall by 432,100 US$. The P-value, of 0.015 reinforces the conclusion
that a relationship exists between wage rate and per country FDI inflows. This study
takes the monthly wage in US$ for the manufacturing sector in each of our focus
countries. However, studies such as Noorbakhsh, Paloni and Youssef criticize this type
of proxy for its failure to take into account work productivity. They suggest a proxy such
as wages divided by productivity, which would facilitate cross-country comparison of
payment per unit output rather than payment per unit time. Thus, use of a different
proxy, such as one which encompasses worker productivity, may yield different results.
Using a multiple regression to analyze the effect of export orientation, GDP
growth, GDP, illiteracy rate, communications infrastructure and wage rate on the FDI
inflows to 26 low and middle-income countries, each of the aforementioned independent
variables included in the study were found to be a significant determinant of country-
specific FDI inflows. However, the GDP growth rate was found to have a sign contrary
to the expectations of the study. This aforementioned unexpected result, along with the
relatively small coefficient between our observed and predicted values could be
explained by the relatively small sample size due to the unavailability of country specific
data. A study of this scope, focusing on low and middle-income nations must invariably
adjust itself around the existing data. The World Bank definition of low and middle-
income nations yields 156 countries for potential study. However, as independent
16
variables are added to the model, the data available for the chosen variables in the focus
countries restricts down the pool of nations. Thus, a cross country analysis of the
determinants of country specific FDI inflows in low and middle-income nations must
establish a balance between appropriate sample size and the inclusion of pertinent
independent variables. After selecting 6 independent variables repeatedly found
significant in scholarly research, the available data limited our focus country pool to 26
nations.
Further, the data available for low and middle income nations, such as the WDI
and the ILO often rely on in-country sources for their statistics. The differences in data
measurement and collection method used from country to country may compromise the
validity of cross country comparisons.
The Halbert White multiple regression analysis indicated that each of the
variables were found independently significant. This means that each of our independent
variables is a significant determinant of FDI inflows if scrutinized alone with all of the
other independent variables held constant.
Conclusion
Through the econometric analysis of the determinants of country specific FDI,
this study found support for its hypothesis that wage rate has a significant affect on
attracting foreign direct investment to developing countries. The empirical results
obtained from our regression supports this hypothesis and further, provides insight
toward discerning which economic factors are the most important in enticing foreign
direct investment to individual developing nations. From the data collected we were able
to determine that all of our independent variables were significant determinants of FDI,
17
however empirical analysis revealed that some factors are more important than others.
From our study we determined that the factors that have the greatest affect on attracting
FDI are: market size, labor quality, export orientation, infrastructure and wage rate.
The results obtained from this study have several policy implications for the
future. This study sought to determine whether wage rate was a significant determinant
of FDI within the broader context of its role in cross country location tournaments. Due
to the fact that wage rate has a significant affect on attracting FDI; it is possible that a
country would lower its wages as a way of enticing new foreign direct investment away
from other developing nations. However from our results one can see that there are
several other factors that are significantly larger determinants of FDI. Therefore if a
developing country is attempting to attract FDI it would be more efficient to focus on
developing their infrastructure, domestic market and industries, as well as labor quality
rather than just reducing the wages. FDI has become increasingly important to the
economic development of third world nations. Although the results of our study show
that FDI location tournaments can encourage countries to depress wage rates, our
findings also show a number of other significant determinants of foreign direct
investment which developing nations would be better served to follow in their pursuit of
FDI.
18
Table 1: Descriptive Statistics
Table 2: Correlation Between Independent Variables
Exports GDP growthGDP (current
US$) Illiteracy
rate Telephone mainlines
Wage Rates/month
Exports 1 GDP growth 0.049 1 GDP 0.623 0.040 1 Illiteracy rate -0.076 0.201 0.100 1 Telephone mainlines -0.121 -0.181 -0.218 -0.672 1 Wage Rates/month -0.151 -0.322 -0.132 -0.246 0.462 1
Variables Mean Standard
Error Range Minimum Maximum
Market Size (billions US$) 82.96 26.98 703.09 1.06 704.168
GDP Growth (annual %) 3.502 0.96 22.78 -12.15 10.63
Labor Quality (Illiteracy rate
%) 15.37 3.746 62.16 0.206 62.36
Export Orientation
(billions US$) 20.246 4.54 86.523 0.524 87.048
Infrastructure (telephone
mainlines per 1000 people)
126.8 17.15 300.7 4 304.7
Wage Rate (per month
US$) 452.7 166 4109 9.08 4118
FDI Net Inflows
(millions US$) 1,504.83 399.7 9,513 13 9,526
19
Table 3: Goldfeld-Quandt Test for Equal Variances
Table 4: Regression Results (Halbert White Estimator)
Data Input T1 13 T2 13 k 7 sigma hat squared 1 2345968 sigma hat squared 2 300998.1 alpha 0.05
Computed Values df-numerator 6 df-denominator 6 GQ 7.79 One-Tailed Test: Right Critical Value 4.28 Decision Reject H0 p-value 0.012 Two-Tailed Test: Right Critical Value 5.82 Decision Reject H0 p-value 0.0061
Variable Estimated Standard T-stat P-value Standardized Elasticity Coefficient Error Coefficient At Means
Export 3.51E-08 1.17E-08 2.987 0.003 0.576 0.4001 0.4753
GDP Growth -156.23 38.32 -4.077 0.000-0.693 -0.2067 -0.5419
GDP -2.39E-09 1.12E-09 -2.143 0.032-0.451 -0.1618 -0.1387
Illiteracy -6.8994 3.046 -2.265 0.023-0.471 -0.0586 -0.0691
Telephone Mainlines
7.7593 2.164 3.585 0.000 0.645 0.3225 0.6057
Wage Rate -0.42405 0.1736 -2.443 0.015-0.499 -0.1768 -0.1356
Intercept 1049 261.3 4.014 0.000 0.687 0 0.6841
Puge
t Sou
nd e
Jour
nal o
f Eco
nom
ics
Tab
le 5
: Var
iabl
e D
ata
Cou
ntri
es
Exp
orts
of
good
s and
se
rvic
es
(cur
rent
US$
)
GD
P gr
owth
(a
nnua
l %)
GD
P (c
urre
nt
US$
)
Illit
erac
y ra
te,
adul
t tot
al (%
of
peop
le a
ges 1
5+)
Tel
epho
ne
mai
nlin
es (p
er 1
,000
pe
ople
)
Wag
e R
ates
/mon
th
(US$
) FD
I Net
(in
mill
ions
) Br
azil
5.44
E+10
4.
22
7.04
E+11
16
.766
85
.1
323.
109
4,85
9 Bu
lgar
ia
5.85
E+09
2.
8601
89
1.31
E+10
2.
126
304.
7 41
18.0
3 90
C
hile
1.
99E+
10
10.6
2759
6.
52E+
10
4.99
2 12
7.3
265.
22
2,95
7 C
olom
bia
1.37
E+10
5.
2024
37
9.25
E+10
9.
778
100.
4 53
3.99
96
9 C
osta
Ric
a 4.
40E+
09
3.92
0883
1.
17E+
10
5.20
9 14
3.8
165.
6 33
7 C
roat
ia
7.26
E+09
6.
8338
61
1.88
E+10
2.
332
282.
8 20
6.19
2 11
5 Eg
ypt
1.35
E+10
4.
6652
33
6.02
E+10
48
.872
46
.6
383.
7 59
8 H
unga
ry
1.66
E+10
1.
4895
25
4.47
E+10
0.
778
210.
5 14
3.98
4,
519
Indi
a 3.
97E+
10
7.67
9543
3.
53E+
11
46.7
35
12.9
26
.57
2,14
4 Jo
rdan
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118
Puget Sound eJournal of Economics
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