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

3.

48E+

09

6.38

1568

6.

81E+

09

13.7

46

58.2

22

0.87

13

K

enya

2.

95E+

09

4.40

6217

9.

05E+

09

22.9

66

8.4

84.0

3 33

La

tvia

2.

30E+

09

-0.8

1116

86

4.90

E+09

0.

205

278.

5 14

4.15

17

9.6

Lith

uani

a 3.

41E+

09

3.29

1193

6.

44E+

09

0.54

8 25

3.5

108.

26

72.6

M

alay

sia

8.36

E+10

9.

8290

85

8.88

E+10

15

.627

16

5.7

322.

94

4,17

8 M

aurit

ius

5.25

E+08

4.

6057

34

1.07

E+09

62

.36

4 15

7.18

19

M

exic

o 8.

70E+

10

-6.1

6699

4 2.

86E+

11

10.1

58

93.8

14

6.82

9,

526

Nic

arag

ua

5.84

E+08

4.

3194

54

1.84

E+09

35

.327

22

.1

155.

26

75

Paki

stan

9.

76E+

09

5.11

9828

6.

12E+

10

60.6

69

16.6

52

.03

723

Phili

ppin

es

2.69

E+10

4.

6786

91

7.41

E+10

5.

99

20.5

16

3.86

1,

478

Pola

nd

3.22

E+10

7

1.27

E+11

0.

322

148.

4 18

6.21

3,

659

Rom

ania

9.

80E+

09

7.11

6245

3.

55E+

10

2.40

4 13

0.8

9.08

41

9 Th

aila

nd

7.02

E+10

9.

3061

44

1.68

E+11

5.

874

60.5

12

5.06

2,

068

Trin

idad

and

Tob

ago

2.87

E+09

3.

9548

29

5.33

E+09

7.

343

167.

7 33

4.37

29

9 U

krai

ne

2.31

E+10

-1

2.15

059

4.91

E+10

0.

476

160.

9 15

53.1

26

7 U

rugu

ay

3.67

E+09

-1

.447

599

1.93

E+10

2.

75

194.

9 27

80.8

6 15

7 Zi

mba

bwe

2.72

E+09

0.

1580

264

7.11

E+09

15

.245

14

37

.81

118

Puget Sound eJournal of Economics

Works Cited

Asiedu, Elizabeth., �On the Determinants of Foreign Direct Investment to Developing Countries: Is Africa Different?� World Development. (v30 2002) 107-120. Billington, N. (1999). The Location of Foreign Direct Investment: An Empirical Analysis, Applied Economics. 31:65-76. Chakrabarti, Avik., �The Determinants of Foreign Direct Investment: Sensitivity Analyses of Cross-Country Regressions.� Kyklos (v 54 2001): 87-114. Choi, C. J. (1999). Global Competitiveness and National Attractiveness, International Studies of Management and Organization. 29: 3-13l. Culem, C. G. (1988). The Locational Determinants of Direct Investment Among Industrialized Countries, European Economic Review. 32:885-904. �Global Development Finance: Financing the Poorest Countries.� The World Bank. 2002. Flamm, K. (1984). The Volatility of Offshore Investment, Journal of Development Economics. 16: 231-248. International Labor Organization. International Labor Organization. Retrieved October 28, 2002, From: http://www.ilo.org/public/english/ Jun, Kwang W. and Harinder Singh. �The Determinants of Foreign Direct Investment in Developing Countries.� Transnational Corporations (August 1996): 67-106. Kemsley, D. (1998). The Effect of Taxes on Production Location, Journal of Accounting Research. 36: 321-341. Kennedy, Peter. (1986). A Guide to Econometrics: 2nd Edition, Cambridge Massachusetts: MIT Press. Kravis, I.B and R.E. Lipsey (1982). The Location of Overseas Production and Production for Export by U.S. Multinational Firms, Journal of International Economics. 12: 201-223. Noorbakhsh, Farhad, Alberto Paloni and Ali Youssef, �Human Capital and FDI Inflows to Developing Countries: New Empirical Evidence.� World Development (v29 2001): 1593-1610. Ross, Robert J.S. and Anita Chan, �From North-South to South South.� Foreign Affairs (Sep/Oct 2002): 8-15.

22

Saunders, R.S. (1982). The Determinants of Foreign Direct Investment, Canadian Journal of Economics. 15: 77-84. Schneider, F. and B. Frey (1985). Economic and Political Determinants of Foreign Direct Investment, World Development. 13: 161-175. Shamsuddin, A.F. (1994) Economic Determinatns of Foreign Direct Investment in Less Developed Countires, The Pakistan Development Review. 33:41-51. Tsai, P. (1994). Determinants of Foreign Direct Investment and Its Impact on Econmoic Growth, Journal of Economic Development. 19: 137-163. Wheeler D. and A. Mody (1992). International Investment Location Decisions: The Case for U.S. Firms, Journal of International Economics. 33: 57-76. World Development Indicators. World Bank. Retrieved October 28, 2002, from: http://devdata.worldbank.org.