fdi as a factor of globalization and its effects on wages

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FDI as a Factor of Globalization and its effects on Wages in Least Developed Countries

Robert F. Wiley

May 2008

1 Introduction

Since World War II, the global economy has seen two periods of international

integration and while different instances of globalization can be characterized by specific

circumstances and effects, the current period (approximately 1980‐2003) is generally associated

with the expansion of the international economy through trade, capital flow and technology

sharing. 1 This expansion follows a trend of trade liberalization facilitated by several

international organizations whose goal is to discourage protectionism and promote trade

liberalization. The most notable of these organizations is the General Agreement on Tariffs and

Trade, GATT, (1948) and its successor, the World Trade Organization, WTO, (1995). Proponents

of globalization, sometimes called “globalists,” argue that global economic expansion leads to

increased prosperity, and more efficient allocation of resources.2 The shining example is China,

where the liberalization of economic policies in past years has corresponded with several years

of high, sustained growth. The reality, however, is that China is an anomaly and a large number

of countries and their people continue to struggle and live on dollars per day. As of March 2008,

fifty countries remain on the United Nation’s Least Developed Countries list.3 Anti‐globalization

advocates would argue that these poorer countries aren’t able to compete effectively (through

subsidization) in the market for selling goods and are therefore forced to sell their goods at

lower prices and this ultimately decreases their economic well‐being. The following research will

not attempt to make an assumption about whether the effects of globalization have been either

favorable or unfavorable, but rather to discern whether there exists a meaningful relationship

between factors of globalization and some aspects of developing economies for a given time

period. While globalization can be measured as the result of four factors: capital and labor flow,

trade openness, and technology sharing, the following research will only focus on capital flow in

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the form in the form of foreign direct investment. Additionally, the effects of globalization on

any specific country can be widely varied, from political and legal to cultural and ecological. In

order to obtain meaningful results then, we will focus on globalization’s effect on labor in LDCs

with an emphasis on wages.

FDI statistics will show a degree of endogeneity for wages in any given country because

of the innumerable factors affecting investment and wage at the aggregate level. In order to

create a usable model, an instrument will attempt to explain the variation in FDI while also

showing no correlation with the error terms in the estimates of wage levels. This instrument

represents a factor that may influence the level of FDI flow while not affecting the relative wage

levels. Every year the World Bank collects data on a large number of investment related factors

from around the world. Included in that database are investment risk ratings given for whole

countries as rated by institutional investors. It is possible that these risk ratings will make a

suitable instrument for the model because they are presumably highly correlated with the

amount of investment inflow, while being uncorrelated with the wages.

In the following pages we will first look at a short description of the World’s LDCs,

followed by a brief theoretical framework outlining the possible relationship of FDI and wages

with some empirical evidence on the topic. Additionally, the paper will outline and run the

proposed model and finally we will examine the results with an emphasis on the instrumental

variable regression.

LDCs

Since the late 1960’s the UN has paid special attention to the world’s LDCs. These

countries represent the poorest and most vulnerable people in the world and classification as an

LDC, now requires formal guidelines to be met. According the UN‐Office of the High

Representative for the Least Developed Countries, the criteria for an LDC are as follows: “a low‐

income criterion, based on a three‐year average estimate of the gross national income (GNI) per

capita (under $745 for inclusion, above $900 for graduation), a human capital status criterion,

involving a composite Human Assets Index (HAI) based on indicators of: (a) nutrition: percentage

of population undernourished; (b) health: mortality rate for children aged five years or under;

(c) education: the gross secondary school enrolment ratio; and (d) adult literacy rate, an

economic vulnerability criterion, involving a composite Economic Vulnerability Index (EVI) based

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on indicators of: (a) population size; (b) remoteness; (c) merchandise export concentration; (d)

share of agriculture, forestry and fisheries in gross domestic product; (e) homelessness owing to

natural disasters; (f) instability of agricultural production; and (g) instability of exports of goods

and services.”4 Of the fifty countries satisfying the criteria, 34 are located in Africa, 15 in Asia

and the Pacific, and 1 in Latin America.5 As of 2005 the population in the LDCs was

approximately 750 million and almost 50% of that population are living on less than 1$ per day.6

While LDCs contain more than 10% of the world’s population, the countries

accounted for only 1.6% of the world’s FDI inflow in 2001.7 While the factors of globalization

have been increasing for the global economy, some argue that LDCs have been excluded from

this expansion. Ajit Ghose examines this hypothesis in his book Jobs and Incomes in a

Globalizing World. Ghose proposes that adverse effects seen by many of the world’s developing

countries associated with globalization are often the result of exclusion from the global

economic expansion rather than exploitation of cheap labor and commodities. In Jobs, Ghose

focused his research on trade as a factor of globalization, rather than FDI, on the premise that

trade makes up a much larger percentage of the world GDP than FDI. It is useful, however, to

look at the trends that can be seen in both trade and FDI in the last 25 years. In 1980 the FDI

stock to world GDP ratio was around 5 and by 1998 it had jumped to 14. In the same period

trade‐GDP ratio first declined, then rose back to the 1980 level in 1992 and grew rapidly

thereafter.8 These trends are evaluated with a focus on how the changes affect the labor market

structure of the world. In the end, Ghose concludes that increased trade between industrialized

and manufactures‐exporting developing countries has affected the structure of both countries’

labor markets. In regards to the labor markets, much of the research was concerned with

income inequality and the types of jobs created or destroyed, but interestingly, Ghose found no

evidence that trade had any effect on real wages for either type of country.

FACTORS OF GLOBALIZATION

It could be argued that international migration is the most contentious of the factors of

globalization. It is often the view that migrants undermine employment prospects and wages for

domestic workers and the result is that in an era of unprecedented levels of trade, technology

and capital flow, migration restrictions are as stiff as they have ever been. While international

migration was the central feature of the nineteenth‐century period of globalization, the current

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expansion has not seen the large increases in labor flow of migration. From 1980 to 1998 the

stock of the world migrant population as a percentage of the world population went from 2.2%

to 2.4%.9 The debate over international migration becomes even more contentious when the

topic of the so‐called “brain‐drain” is addressed. However, discussions of the South‐North

migration and issues of skilled and un‐skilled labor will not be addressed here. In fact, migration

as a factor of globalization will be viewed as a minor feature of globalization’s effect on LDCs

and as such will not enter into the formal research.

The advancement and sharing of technology is without a doubt a major contributor to

all episodes of globalization. In the 16th century improvements in sailing and navigating

technology led to the connection of the Old World with the New World. More recently,

however, two factors have led to the current global economic expansion. The first has already

been touched on briefly as the increase in trade liberalization policies. The second and equally

important factor is the improvement in transportation and communication technologies. These

improvements have decreased costs to the point that it is profitable to split production into

smaller parts at locations throughout the world‐ a process called “supply‐chaining.” It can be

argued that FDI and trade statistics can be indicators of the amount of technology sharing

between countries because practices like “supply‐chaining” require elements of foreign

investment and trade. It has also been documented that technology transfer is an effect of

foreign direct investment. This and the fact that technology transfer presents measurement

problems leads us to exclude technology transfer as its own factor from this evaluation.

The idea of investment into a foreign country isn’t necessarily a novel concept, however,

the rapid growth in the amount of FDI flow and stock and the increasing number of trans‐

national corporations (TNCs) can be viewed as a fairly recent development. Between 1982 and

1999 it is estimated that foreign divisions of TNCs doubled their share of gross world GDP. 10 The

year 2007 saw the highest levels ever recorded for FDI, with significant increases in all major

country groupings, presenting evidence that the trend is continuing.11 Traditionally, in

economics, the impact of trade has been the focus of international relations, but it can now be

useful to analyze the impact of investment as well as FDI plays an increasingly important role in

the international economy. International trade involves the reciprocal transfer of goods and

services across boundaries and this differs from FDI in several ways. The first is that FDI occurs

most often between a parent company located domestically and a subsidiary or affiliate located

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in a foreign territory and the transfer takes the form of capital. FDI is measured as the amount

of investment made to acquire a lasting interest in a foreign enterprise with the intention of

gaining a voice in the management. In this framework the “direct investor” makes the

investment and the “direct investment enterprise” receives it. The important stipulation

pertaining to the definition of FDI that will help in our analysis is that the investment must be

made with the intention of gaining a voice in the management of the investment enterprise.

This is important because we are attempting to look at the labor‐related effects of a foreign

enterprise’s ability to make decisions in another country. Traditional economic theory generally

aims to examine the effects of trade on labor, while it is a more recent development to examine

the effects of FDI separately from trade. The prescriptive framework of international trade tells

us that when two countries enter into trade with their only difference being the composition of

the labor force, the result is an increase in the real wage for skilled workers (and a subsequent

decrease in real wage for unskilled labor) in the country where skilled manufactures increases

and the opposite in the country where unskilled manufactures increase. Empirical evidence has

shown that these assumptions do not always hold true in the real world where access to

technology is not homogenous and capital and labor are not fully mobile, but it is generally

assumed that trade does have some impact on wage inequalities, either between or within

countries. As outlined above, trade and FDI have distinct differences, so it is not unreasonable to

assume that their relative effects on wages may also be different. Before we can examine the

results of FDI on relative wage levels it would be prudent to examine whether FDI has a

statistically significant effect on wages at all. The hypothesis of how FDI affects wages, starts

with how we assume the investment will affect production. The presumption is that when a

subsidiary receives investment their aim is to increase production. Increased production in

developing countries can be associated first with increased employment and indeed, Ghose

found that in some countries this held true. 12 Additionally, we can make the assumption that

increased investment into developing countries will increase productivity. Ghose found that

changes in wage levels were the result of productivity changes, not trade, and empirical

evidence exists that suggests that FDI may have impact on wages through productivity changes.

Yasin Mesghena of Morehead State University published research in 2006 examining the long

run relationship between wages in manufacturing and globalization using panel data from three

developing countries with globalization represented by FDI inflows and trade openness.13 His

results show that there is evidence of a long run relationship between wages in manufacturing

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and both measures of globalization. In this basic model, falling transportation and

communication costs lead firms to desire to supply chaining. In order for firms to stretch their

supply‐chains to developing countries they will need to increase their capital investment there.

The increased capital investment will result in increased output and higher productivities that

will in‐turn lead to higher wage levels.

2 Variables

To assess the impact of FDI on wages in LDCs requires a compilation of relevant

statistics. This compilation must include variables that effectively account for the factors

affecting the amount of investment a county receives as well as the determinants of wage levels

in that country. The UNCTAD gathers data on FDI yearly for its World Investments Report as this

information is seen as valuable to evaluating the changing tides of the global economy. FDI

inflows per year will hopefully capture the degree by which globalization has increased or

decreased across time. Wage level information can sometimes be difficult to come by for the

World’s LDCs because of their inherent lack of infrastructure. Fortunately, a large amount of

data is available through the Occupational Wages Around the World database (OWW) as kept by

Richard Freeman and Remco Oostendorp. The wage data are derived from the International

Labor Office’s October Inquiry by calibrating the data into a normalized wage rate for each

occupation. This wage rate represents the average monthly wage for men in each industry.

While the entire database covers 161 occupations in over 150 countries from 1983 to 2003, only

12 countries have been chosen from the LDCs list because of data availability. The sample will

hopefully be representative of wage levels within the country as well as across all LDCs.

Appendix i contains a list of the included industries and illustrates that the wage figures are

representative of both skilled and un‐skilled types of employment. This database contains

individual variables for the raw data as well as country‐specific calibrated data with

lexicographic weighting and imputated data in order to increase the amount the number of

observations. This obviously increases the degree of uncertainty, but the raw data statistics have

not shown to have enough observations for this application. The inclusion of different industries

presents a bit of problem when the other data represent yearly aggregates for the country. The

data for each industry for each year and for each country have been given as separate

observations. A similar problem exists in that the industries vary across countries. More

specifically, we are presented with an equal contributions problems. The hope is the large

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number of observations created will keep the estimator consistent. Unfortunately, this is means

that our evaluation will be based on pooled data rather than the intended panel‐style. A list of

the included industries can be found in Appendix i.

The independent variables here are attempting to explain variation in FDI and wages in

order to find the ceteris paribus relationship of FDI and wages. The first variable is simply the

GDP per capita for each country in each year. This variable is colloquially assumed to be a

measure of a countries relative wealth. The variables inclusion addresses the idea that relatively

richer countries may have different wages with everything else constant. Also included will be

the total GDP level. The aim of this variable is to control for differences country size in regards

to a number of variables including population, natural resources and location. The least

developed of the World’s countries are also the most susceptible to economic crisis and this

presents a problem with the price levels and other factors that might be affecting wages. To

account for this, data were included that represent the yearly percentage changes in inflation.

Although the trend over the last 60 years has been the liberalization of trade policies, the

degree of trade openness varies with the country. Trade openness will be included then because

it may indicate how conducive the country is to foreign investment as well as accounting for the

possibility of global exclusion. The trade openness variable is given as the sum of exports and

imports as a percentage of GDP. Similar to trade openness, but representative of the domestic

side of investment is the inclusion of data on the percentage of GDP from investment. As A.K.

Ghose surmised, global exclusion may result from a country’s inability to shift from agricultural

commodities production to manufactures. Manufactures export may be correlated to the

amount of FDI inflow. As a way to account for this factor of FDI, agricultural raw material

exports as a percentage of merchandise exports will be included. While at the aggregate level it

may be difficult discern, education can certainly be a factor of wage levels and as such, data on

the level of educational attainment across a country has been included. For any TNC the object

of FDI is to create output from a number of inputs, the most obvious of which is capital. It is

possible that the amount of capital formation within a country can impact both the amount FDI

inflow and the wage. It is useful then to include capital formation statistics when modeling FDI

and wages. Included are the estimated ratio of physical capital to output as well as the FDI

inflows as a percentage of gross fixed capital formation. It is possible that any given year in the

set was more conducive to FDI than year preceding or following it. This could be the result of

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any of the economic, political, or social transgressions occurring during that year and to account

for this possible impact on any given country, included is the total world inflow for each year.

More detailed descriptions of the above variables can be found in Appendix i.

3 Descriptors

We first look at some descriptive statistics in order to gauge the characteristics of the

data. Appendix ii shows the twelve selected countries. While the selection was based on data

availability, these countries show characteristics of being representative of the larger group of

LDCs. None of the selected countries are categorized as “oil‐exporting,” by the UN‐OHRLLS. The

six “oil‐exporting” LDCs received 70% of all the FDI inflow that went to all LDCs in 2004.14 The

inclusion of any of the oil‐exporting LDCs without the ability to control for the data could result

in an outlier problem and skew the estimations.

Table 1 has some interesting information with economic significance. For instance the

mean monthly wage for these twelve LDCs was only $138, while the minimum was less than $7.

For comparison, the average monthly wage for all the 150 countries in the set was $732 and the

maximum was $22650. The statistics are similarly bleak for GDP per capita. The twelve LDCs

have a mean GDP/cap. of $289 per year with a maximum value of $722. For the same period,

the United States averaged a GDP/cap. around $25500.15

Table 1

Wage FDI GDP/cap

Mean 138.54 33.93 289.18

Std. Dev. 174.16 91.62 97.96

Min. 6.8 ‐140.311 120.94

Max 3695.32 578.7 721.77

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From a simple correlation matrix (Table 2) we can see that the log of wage (lwage) and

level wage (x2wlus) have mild positive correlation with FDI (fdi), (.162 and .0489 respectively).

The wage variables are only slightly less correlated with the instrument (invrat), but the

direction or the correlations support the model and intuition.

Table 2

Correlations lwage X2wlus Fdi GDP/cap. invrat

LogWage 1

Wage X 1

FDI .1623 .0489 1

GDP/cap. .3280 .2946 .2217 1

invrat .1361 .0295 .8301 .0711 1

With separate observations for each industry within 12 countries each year for 21 years

the total number of possible observations is 7095, but there is a downside to creating the large

number of observations. Because there are multiple sets of observations for every year we

effectively lose the ability to analyze the data in the panel format. Each year becomes a different

set of observations for each country and its industries and as mentioned earlier this presents a

problem with consistency. Another important characteristic is that the number of observations

will change dramatically with the variables in the regression because of the availability of the

data. Agricultural raw materials exports as a percentage of merchandise exports contains the

least amount of data with 2954 observations.

4 The Model

The model will start with a simple OLS estimation of the significance of a number of

variables on the level wage (x2wlus), but as mentioned earlier we can assume that this

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estimation produces an inconsistent estimator. Further analysis will consist of a two‐stage least

squares estimation with log wage as the dependent variable of the instrumented fdi and the

other independent variables. The two‐stage least squares procedure is an attempt to account

for a violation of the Zero Conditional Mean assumption in the data. In order to effectively

create a two‐stage least squares model we need a legitimate instrument and a good

instrument is characterized by having non‐zero correlation with the endogenous variable

and zero correlation with the dependent variable. It is also necessary that the instrument be

statistically significant as a factor of the endogenous variable. If we look at Table 2, the

correlation matrix shows that the investment rating (invrat) is correlated with fdi with a

coefficient of .83 and x2lus with a coefficient of .0295. These coefficients indicate that invrat

may make a suitable instrument. When we regress fdi on the remaining variables (Table 3),

invrat does indeed show statistical significance with a p‐value of 0.000.

Table 3

Fdi Coefficient P‐value

Invrat ‐12.89 0.000

Inf 1.711 0.000

Agex 3.64 0.000

Wfdi .0003059 0.000

Trd ‐.88 .278

Fxc 10.28 0.000

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5 The Results

In a OLS regression with robust standard errors of the wage on fdi, cgdp, inf, trd, att, ky,

agex, ci, wfdi, fdi2, fxc, invrat and holding the years constant (Table 4) we can see that fdi holds

no significance with a t‐stat and p‐value of 1.15 and .249 respectively. Together the variables

explain 47% of the variation in the wage (x2wlus) and have an F‐statistic of 159.24. The

usefulness of this estimation is limited in that it probably represents a violation of the ZCM

assumption and we don’t get a consistent estimation of fdi’s impact on wages.

Table 4

X2wlus Coefficient P‐value

Fdi .036 .772

cgdp .5586 0.000

trd ‐5.502 .005

Att ‐44.304 .164

Ky 622.29 .009

Gdp ‐8.727 .001

agex ‐.6132 .558

When we estimate the relationship of fdi and some of the factors that may influence the

inflow of investment (Table 3) several variables show significance, but most importantly invrat is

highly significant with a t‐stat and p‐value of ‐11.81 and 0.00 respectively. The coefficient on

invrat shows that for every 1 point decrease in the investment rating, the FDI inflow increases

by $12.89 million and this makes sense because the investment rating variable is defined on a

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scale of 1‐100 where a rating of 100 represents the most risky investment. Several other

variables show significance at the 1% level. The direction of the coefficients and the t‐stats for

trade openness and capital to output ratio support intuitive outcomes. You would suspect that

the higher the trade openness and the amount of capital to output, the more likely a country

would be to receive FDI inflow. This regression has 2184 observations, holds the years constant

and is run with robust standard errors. Educational attainment was left out of this regression.

Using this information we then run a 2SLS estimation. Table 5 shows selected variables

from the second stage of the regression with x2wlus as the dependent variable, fdi as an

instrument of invrat, and the independent variables of cgdp, inf, att, ky, agex, ci, wfdi, fdi2, fxc,

gdp, trd and y0 to control for the year. Here you can see that fdi is the only variable that shows

significance at about the 5% level with a p‐value of .026. With a coefficient of 1.083 the

interpretation is that a $1 million increase in FDI flow in a year increases the wage level across

industries by $1 us dollar. However, the results show that both ky and fdi2 hold zero significance

when accounting for the other variables and instrumenting fdi. The regression was run with

robust standard errors and resulted in 1949 observations.

Table 5


Fdi^ 1.083 .026

Gdp ‐10.234 .038

NO OTHER VARIABLES SHOWING SIGNIFICANCE AT ANY LEVEL LOWER THAN 24%

Running the same regression with ky and fdi2 dropped (Table 6) results in the same

number of observations at 1949 and so it would seem that these two variables don’t add any

degree of explanation for the model. In this new regression fdi has a p‐value of .062 showing

significance at the 15% level with a similar coefficient. The coefficient implies that a $1 million

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increase in FDI inflow in a year estimates an increase of $1.1 in the monthly wage. $1.1 is

approximately .8% of the mean wage rate for all countries and industries. The R‐squared value

tells us that this model explains 5.9% of the variation in the wage data.

Table 6 shows some selected variables from this regression that show significance at

various levels.

Table 6


Fdi^ 1.11 .062

Gdp ‐10.44 .031

Inf ‐3.925 .081

Ci ‐38.33 .068

Wfdi ‐.00033 .094

6 Conclusions

The results of the 2SLS model show evidence that there is a statistically significant

relationship between wages and FDI inflows in some LDCs, however, the extent of that

relationship is unclear based on the available data. We were able to control for a number of

variables affecting the amount of FDI inflow as well as the relative wages between countries and

their industries, but the directions of the coefficients on several variables do not support

common intuition. In comparing the OLS regression with our 2SLS argument we can hope that

the instrument has given us a consistent estimator despite the data issues. Below is a quick

comparison reference.

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OLS 2SLS

Based on this information we can assume that the 2SLS model has helped our estimator

to become more consistent, however, there are a number of issues implying that the efficiency

of the estimator is out of control.

If we look at the R‐squared values for the OLS regressions of fdi on the independent

variables, it shows that the model is explaining 97% of the variation in FDI inflows. It is

unreasonable to assume that the chosen variables could account for nearly all of the factors that

influence the decision to transfer capital. When we think about the potential journey that

capital goes through from leaving the initial country to the point that it presents some influence

on the foreign labor market and all the factors that have influence in between, it becomes

increasingly clear that estimating the ceteris paribus relationship of FDI and wages is incredibly

complex and difficult.

Table 3 shows an interesting anomaly that contradicts the research of A.K. Ghose.

Ghose proposed that developing countries who continue to export primarily agricultural

commodities fail to engage in the global economic expansion and thus see adverse effects. In

this regression, agricultural exports as a percentage total merchandise exports shows statistical

significance at the 1% level, denoting more agricultural raw materials exports, coinsides with an

increase in FDI inflow. Put another way, for LDCs, production of primary commodities indicates

higher participation in globalization. This seems completely counter‐intuitive and explanation of

these results is very difficult. One potentially beneficial idea is the possibility of a reciprocal

relationship of between FDI inflows and wages. Here we make the argument that wage levels

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are a function of FDI inflows, but it could also be possible that firms looking to invest where the

wages are the lowest and this implies that FDI inflow may be a function of wage levels.

While the 2SLS results may hold some value, the characteristics of the data and the high

degree of endogeneity for a relationship of this magnitude can be preventing us from closely

estimating the true impact that FDI inflow has on the receiving labor market wages.

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Appendix i

Variable Descriptions

x2wlus

Wage data has been sourced from the Occupation Wages from Around the World database which is derived from the ILO’s October Inquiry (http://laborsta.ilo.org, Table 01) by calibrating the data into a normalized wage rate for each occupation. The normalized wages refer to average monthly wage rates for male workers in US dollars. (http://www.nber.org/oww/). The variable x2wlus represents a wage with country‐specific and uniform calibration and lexicographic weighting. The database can be attributed to Remco H. Oostendorp of Tinbergen Institute, Amsterdam Institute for International Development and Free University Amsterdam and Richard B. Freeman of Harvard University, NBER and London School of Economics.

lwage

Log wage is the log of x2wlus.

fdi

Foreign direct investment observations are given as the yearly inward flow in US dollars at

current prices in the millions for 12 countries from the years 1983‐2003. The data has been

sourced from World Investment Reports 2007 compiled by the UNCTAD and accessed

through the online database. (http://stats.unctad.org/FDI/).

gdpcap

GDP per capita observations are given as the Gross Domestic Product in US dollars divided

by the population. Yearly values for 12 countries from 1983‐2003. The data has been

sourced from Handbook of Statistics 2007 compiled by the UNCTAD and accessed through

the online database. Observations for Ethiopia have been condensed between the former

Ethiopia (1983‐1992) and Ethiopia (1993‐2003). (http://stats.unctad.org/handbook/).

inf

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Inflation data is the annual % change inflation at average consumer prices, with an index for

the year 2000= 100. Data has been sourced from the IMF World Outlook Database for

October 2007. (http://www.imf.org/external/pubs/ft/weo/2007/02/weodata/index.aspx)

invrat

Institutional Investor’s Rating data is given on a scale of 0 to 100, with 100 representing the

least amount of risk. Institutional Investors magazine is published monthly by Institutional

Investors, Inc. which releases country credit ratings twice per year: March and September.

The data presents September values and has been accessed throught the World Bank’s

“New Database on Foreign Direct Investment.”

(http://www1.worldbank.org/economicpolicy/globalization/data.html)

trd

Trade data representing the openness of each country is given as the sum of exports and

imports of goods and services in current prices as a percentage of the gross domestic

product. Data has been sourced from the World Development Indicators Online database

available from the World Bank Data & Research. (http://www.worldbank.org/).

att

Attainment data represents the average educational attainment of individuals 25 years and

older in the given year. The data were available from 1983‐2000. The data has been sourced

from Barro, Robert J. and Jong‐Wha, Lee (2000), “International Data on Educational

Attainment .” NBER working paper #7911. Data was accessed via Klenow, Peter S. and

Rodriguez‐Clare, Andres. “Externalities and Growth.” Handbook of Economic Growth. Data

contains linear interpolations for the years in between those ending in 0 and 5 as calculated

by Klenow and Rodriguez‐Clare. (http://www.klenow.com).

ky

Physical capital data is given as the estimated ratio of physical capital to output for the

given countries, but was only available through the year 2000. Data has been sourced from

Klenow, Peter S. and Rodriguez‐Clare, Andres. “Externalities and Growth.” Handbook of

Economic Growth. volume 1A, P. Aghion and S. Durlauf, eds., 2005, 817‐861 (chapter 11).

(http://www.klenow.com).

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agex

Data for agricultural raw materials exports as a percentage of merchandise exports was

accessed through the World Development Indicators database available through the World Bank

Data & Research. Data were available for 12 countries from 1983‐2003.

(http://www.worldbank.org/).

ci

ci represents the percentage of total GDP attributed to investment in each year for 12 countries

from 1983‐2003. The data were accessed through the Penn World Table.

(http://pwt.econ.upenn.edu/)

wfdi

Data for the total World inflow of FDI were accessed through the UNCTAD’s “Key Data from WIR

Annex Tables.” Document # 21. Data were available for 12 countries from 1983‐2003 and given

in millions of US dollars. (http://www.unctad.org/Templates/Page.asp?intItemID=3277&lang=1).

Fdi2

Data for individual country’s inflow of FDI as a percentage of World FDI flow were calculated by

the author by taking fdi over wfdi.

gdp

GDP data are given in current prices in US dollars and accessed through the IMF’s World

Economic Outlook Database for October 2007.

(http://www.imf.org/external/pubs/ft/weo/2007/02/weodata/index.aspx)

fxc

fxc represents the FDI inflows as a percentage of fixed capital formation. Data were accessed

through the UNCTAD’s “Key Data from WIR Annex Tables.” Document # 20. Data were available

for 12 countries from 1983‐2003.

(http://www.unctad.org/Templates/Page.asp?intItemID=3277&lang=1).

19 | P a g e

Industries from OWW

y3: industry code AA Agricultural production (field crops) AB Plantations AC Forestry AD Logging AE Deep-sea and coastal fishing BA Coalmining BB Crude petroleum and natural gas production BC Other mining and quarrying CA Slaughtering, preparing and preserving meat CB Manufacture of dairy products CG Grain mill products CH Manufacture of bakery products DA Spinning, weaving and finishing textiles DB Manufacture of wearing apparel (except footwear)

DC Manufacture of leather and leather products (except footwear)

DD Manufacture of footwear EA Sawmills, planing and other wood mills EB Manufacture of wooden furniture and fixtures

FA Manufacture of pulp, paper and paperboard

FB Printing, publishing and allied industries

GA Manufacture of industrial chemicals

GB Manufacture of other chemical products

GC Petroleum refineries

IA Iron and steel basic industries JA Manufacture of metal products (except machinery and equipment)

JB Manufacture of machinery (except electrical)

JC Manufacture of electronic equipment, machinery and supplies

JD Shipbuilding and repairing KA Electric light and power LA Construction MA Wholesale trade (grocery) MB Retail trade (grocery) MC Restaurants and hotels NA Railway transport NB Passenger transport by road NC Freight transport by road ND Maritime transport NE Supporting services to maritime transport

NF Air transport

NG Supporting services to air transport

NH Communication

OA Banks OB Insurance OC Engineering and architectural services

PA Public administration

PB Sanitary services PC Education services PD Medical and dental services PF Repair of motor vehicles

Appendix ii

Bangledesh Benin Central African Republic Malawi Mali Niger Rwanda Senegal Sierra Leone Uganda Zambia Mozambique

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1 Bhagwati, Jagdish (2007). In Defense of Globalization. Oxford, New York: Oxford University Press. 2 Sachs, Jeffrey (2005). The End of Poverty. New York, New York: The Penguin Press. 1‐59420‐045‐9 3From The United Nations Office of the High Representative for the Least Developed Countries. http://www.unohrlls.org/ 4 From The United Nations Office of the High Representative for the Least Developed Countries. http://www.unohrlls.org 5 From The United Nations Office of the High Representative for the Least Developed Countries. http://www.unohrlls.org 6 Facts About Least Developed Countries, UN‐OHRLLS. http://www.unohrlls.org/en/ldc/related/63/ 7 Facts About Least Developed Countries, UN‐OHRLLS. http://www.unohrlls.org/en/ldc/related/63/ 8 Ghose, Ajit K., Jobs and Incomes in a Globalizing World. Geneva, International Labor Office, 2003. Ch.2, pp. 9. 9 Ghose, Ajit K., Jobs and Incomes in a Globalizing World. Geneva, International Labor Office, 2003. Ch.2, pp. 9. 10 Ghose, Ajit K., Jobs and Incomes in a Globalizing World. Geneva, International Labor Office, 2003. Ch.2, pp. 6 11 “FDI Surged to Record Levels in 2007.” UNCTAD Investment Brief, No. 1, 2008. (UNCTAD/PRESS/PR/2008/001). 12 Ghose, Ajit K., Jobs and Incomes in a Globalizing World. Geneva, International Labor Office, 2003. Ch.2, pp. 6 13 Yasin, Mesghena. “Globalization and Wages in Manufacturing in the Developing Countries: Evidence from Panel Cointegrations and Fully Modified OLS Regressions.” Journal of Economics (MVEA), 2006, v. 32, iss. 2, pp. 32. 14 Facts About Least Developed Countries, UN‐OHRLLS. http://www.unohrlls.org/en/ldc/related/63/ 15 Author’s calculation using data from the UNCTAD Handbook of Statistics 2007.

_cons -236.7168 60.80583 -3.89 0.000 -355.9687 -117.465 fxc -.0964128 .0359117 -2.68 0.007 -.1668426 -.025983 gdp -.0610209 .0163096 -3.74 0.000 -.0930073 -.0290346 fdi2 199.5056 417.6339 0.48 0.633 -619.5543 1018.565 wfdi 1.19e-06 5.68e-07 2.09 0.036 7.49e-08 2.30e-06 ci -.2309147 .054594 -4.23 0.000 -.337984 -.1238454 agex -.0034258 .0060708 -0.56 0.573 -.0153319 .0084803 ky 5.475018 1.524972 3.59 0.000 2.484256 8.465779 att -.6538386 .2024173 -3.23 0.001 -1.050818 -.2568597 trd -.0424874 .0123354 -3.44 0.001 -.0666794 -.0182954 invrat .1011271 .0199201 5.08 0.000 .0620599 .1401943 inf -.0056647 .003033 -1.87 0.062 -.011613 .0002837 cgdp .0046001 .0005326 8.64 0.000 .0035554 .0056447 fdi .0008733 .000757 1.15 0.249 -.0006113 .0023578 y0 .1201045 .0304347 3.95 0.000 .0604162 .1797929 lwage Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust

Root MSE = .58573 R-squared = 0.4706 Prob > F = 0.0000 F( 14, 1934) = 159.24Linear regression Number of obs = 1949

. reg lwage y0 fdi cgdp inf invrat trd att ky agex ci wfdi fdi2 gdp fxc, robust

Deleted: ¶

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Instruments: cgdp inf att agex ci wfdi fxc gdp trd y0 invratInstrumented: fdi _cons (dropped) y0 .5581458 .3288192 1.70 0.090 -.0863279 1.20262 trd .3590295 .4005255 0.90 0.370 -.425986 1.144045 gdp -10.44485 4.845979 -2.16 0.031 -19.9428 -.9469072 fxc -30.00766 19.27976 -1.56 0.120 -67.79528 7.779971 wfdi -.0003336 .0001992 -1.67 0.094 -.000724 .0000568 ci -38.33574 21.03915 -1.82 0.068 -79.57173 2.900246 agex -6.42971 4.270424 -1.51 0.132 -14.79959 1.940168 att -94.65063 70.75012 -1.34 0.181 -233.3183 44.01705 inf -3.924734 2.250875 -1.74 0.081 -8.336368 .4868995 cgdp -.1732138 .321278 -0.54 0.590 -.802907 .4564795 fdi 1.10821 .5949279 1.86 0.062 -.0578267 2.274248 x2wlus Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust

Root MSE = 101.46 R-squared = 0.0589 Prob > chi2 = 0.0000 Wald chi2( 11) = 1418.17Instrumental variables (2SLS) regression Number of obs = 1949

_cons 38235.52 9472.495 4.04 0.000 19658.16 56812.88 invrat .6108246 1.796032 0.34 0.734 -2.911535 4.133184 y0 -19.71207 4.755671 -4.14 0.000 -29.03884 -10.3853 trd 2.750148 .8839918 3.11 0.002 1.016472 4.483823 gdp 13.44705 1.773171 7.58 0.000 9.96953 16.92458 fxc 36.9347 3.424725 10.78 0.000 30.21817 43.65124 wfdi .0004102 .0000145 28.26 0.000 .0003817 .0004386 ci 11.73466 4.779864 2.46 0.014 2.360441 21.10888 agex 5.825221 .1922386 30.30 0.000 5.448205 6.202237 att 61.96269 7.311852 8.47 0.000 47.62277 76.30262 inf 2.977549 .1641995 18.13 0.000 2.655523 3.299576 cgdp .5386586 .0159686 33.73 0.000 .5073411 .5699761 fdi Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust

Root MSE = 41.9025 Adj R-squared = 0.9197 R-squared = 0.9201 Prob > F = 0.0000 F( 11, 1937) = 2519.64 Number of obs = 1949

First-stage regressions

. ivregress 2sls x2wlus cgdp inf att agex ci wfdi fxc gdp trd y0 (fdi = invrat), vce(robust) first

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Instruments: cgdp inf att agex ci wfdi fxc gdp trd y0 invratInstrumented: fdi _cons (dropped) y0 .0097128 .0039297 2.47 0.013 .0020106 .017415 trd .010719 .0039037 2.75 0.006 .0030678 .0183702 gdp -.1157024 .0579728 -2.00 0.046 -.229327 -.0020779 fxc -.4174774 .2324258 -1.80 0.072 -.8730236 .0380688 wfdi -4.79e-06 2.33e-06 -2.06 0.040 -9.35e-06 -2.22e-07 ci -.5925867 .2523571 -2.35 0.019 -1.087197 -.097976 agex -.0892427 .0504454 -1.77 0.077 -.1881138 .0096285 att -1.62639 .8251468 -1.97 0.049 -3.243648 -.0091321 inf -.0536738 .0260568 -2.06 0.039 -.1047442 -.0026034 cgdp -.0045549 .0037712 -1.21 0.227 -.0119462 .0028365 fdi .0153009 .00718 2.13 0.033 .0012284 .0293734 lwage Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust

Root MSE = .88035 R-squared = . Prob > chi2 = 0.0000 Wald chi2( 11) = 2125.36Instrumental variables (2SLS) regression Number of obs = 1949

_cons 38235.52 9472.495 4.04 0.000 19658.16 56812.88 invrat .6108246 1.796032 0.34 0.734 -2.911535 4.133184 y0 -19.71207 4.755671 -4.14 0.000 -29.03884 -10.3853 trd 2.750148 .8839918 3.11 0.002 1.016472 4.483823 gdp 13.44705 1.773171 7.58 0.000 9.96953 16.92458 fxc 36.9347 3.424725 10.78 0.000 30.21817 43.65124 wfdi .0004102 .0000145 28.26 0.000 .0003817 .0004386 ci 11.73466 4.779864 2.46 0.014 2.360441 21.10888 agex 5.825221 .1922386 30.30 0.000 5.448205 6.202237 att 61.96269 7.311852 8.47 0.000 47.62277 76.30262 inf 2.977549 .1641995 18.13 0.000 2.655523 3.299576 cgdp .5386586 .0159686 33.73 0.000 .5073411 .5699761 fdi Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust



. ivregress 2sls lwage cgdp inf att agex ci wfdi fxc gdp trd y0 (fdi = invrat), vce(robust) first

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Instruments: cgdp inf att agex ky fdi2 ci wfdi fxc gdp trd y0 invratInstrumented: fdi _cons (dropped) y0 .0094923 .0038351 2.48 0.013 .0019756 .0170091 trd .0105068 .3925593 0.03 0.979 -.7588953 .7799089 gdp -.1131534 .3637091 -0.31 0.756 -.8260103 .5997034 fxc -.4074027 1.630847 -0.25 0.803 -3.603804 2.788999 wfdi -4.69e-06 .0000101 -0.46 0.643 -.0000245 .0000151 ci -.5799987 3.266818 -0.18 0.859 -6.982844 5.822846 fdi2 0 .0816934 0.00 1.000 -.1601162 .1601162 ky 0 33.88556 0.00 1.000 -66.41447 66.41447 agex -.0862014 .0517503 -1.67 0.096 -.1876302 .0152274 att -1.560373 1.427929 -1.09 0.275 -4.359063 1.238317 inf -.0523903 .0341753 -1.53 0.125 -.1193727 .0145921 cgdp -.0043674 .0067591 -0.65 0.518 -.017615 .0088802 fdi .0149844 .0058557 2.56 0.010 .0035074 .0264614 lwage Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust

Root MSE = .87021 R-squared = . Prob > chi2 = 0.0033 Wald chi2( 13) = 31.05Instrumental variables (2SLS) regression Number of obs = 1949

_cons 7824.546 6343.948 1.23 0.218 -4617.146 20266.24 invrat -12.89403 1.015618 -12.70 0.000 -14.88585 -10.90221 y0 -4.078911 3.19784 -1.28 0.202 -10.35049 2.192663 trd -.8801053 .8114959 -1.08 0.278 -2.471604 .711393 gdp 3.042798 .7932303 3.84 0.000 1.487122 4.598474 fxc 10.28087 1.799564 5.71 0.000 6.751586 13.81016 wfdi .0003059 .0000817 3.74 0.000 .0001456 .0004662 ci 10.26798 12.97003 0.79 0.429 -15.16873 35.70469 fdi2 474126.4 13256.36 35.77 0.000 448128.2 500124.7 ky 40.69579 226.5445 0.18 0.857 -403.6012 484.9927 agex 3.647206 .599772 6.08 0.000 2.470939 4.823473 att 74.35934 32.79773 2.27 0.023 10.03674 138.6819 inf 1.710944 .3191257 5.36 0.000 1.085078 2.336811 cgdp .192037 .0862728 2.23 0.026 .0228396 .3612345 fdi Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust



. ivregress 2sls lwage cgdp inf att agex ky fdi2 ci wfdi fxc gdp trd y0 (fdi = invrat), vce(robust) first

_cons -236.7168 60.80583 -3.89 0.000 -355.9687 -117.465 fxc -.0964128 .0359117 -2.68 0.007 -.1668426 -.025983 gdp -.0610209 .0163096 -3.74 0.000 -.0930073 -.0290346 fdi2 199.5056 417.6339 0.48 0.633 -619.5543 1018.565 wfdi 1.19e-06 5.68e-07 2.09 0.036 7.49e-08 2.30e-06 ci -.2309147 .054594 -4.23 0.000 -.337984 -.1238454 agex -.0034258 .0060708 -0.56 0.573 -.0153319 .0084803 ky 5.475018 1.524972 3.59 0.000 2.484256 8.465779 att -.6538386 .2024173 -3.23 0.001 -1.050818 -.2568597 trd -.0424874 .0123354 -3.44 0.001 -.0666794 -.0182954 invrat .1011271 .0199201 5.08 0.000 .0620599 .1401943 inf -.0056647 .003033 -1.87 0.062 -.011613 .0002837 cgdp .0046001 .0005326 8.64 0.000 .0035554 .0056447 fdi .0008733 .000757 1.15 0.249 -.0006113 .0023578 y0 .1201045 .0304347 3.95 0.000 .0604162 .1797929 lwage Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust

Root MSE = .58573 R-squared = 0.4706 Prob > F = 0.0000 F( 14, 1934) = 159.24Linear regression Number of obs = 1949

. reg lwage y0 fdi cgdp inf invrat trd att ky agex ci wfdi fdi2 gdp fxc, robust

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_cons -874.2832 3371.281 -0.26 0.795 -7485.558 5736.992 fxc -4.16757 .763182 -5.46 0.000 -5.664213 -2.670926 gdp -1.488478 .6144251 -2.42 0.015 -2.693401 -.2835557 fdi2 533214 6239.094 85.46 0.000 520978.7 545449.2 wfdi .0003193 .000019 16.84 0.000 .0002821 .0003565 ci 4.824656 2.104818 2.29 0.022 .6969883 8.952324 agex .5345695 .1302167 4.11 0.000 .279207 .7899319 ky 81.27426 21.93654 3.70 0.000 38.25545 124.2931 trd -2.015722 .3974353 -5.07 0.000 -2.795115 -1.236328 invrat -5.275755 .4466619 -11.81 0.000 -6.151685 -4.399826 inf .6281943 .1281006 4.90 0.000 .3769817 .8794068 cgdp .0458683 .0161404 2.84 0.005 .0142162 .0775205 y0 .4287422 1.691192 0.25 0.800 -2.887782 3.745267 fdi Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust

Root MSE = 25.073 R-squared = 0.9685 Prob > F = 0.0000 F( 12, 2171) = 2567.99Linear regression Number of obs = 2184

. reg fdi y0 cgdp inf invrat trd ky agex ci wfdi fdi2 gdp fxc, robust

fxc 0.3224 -0.3173 1.0000 gdp 0.5126 1.0000 fdi2 1.0000 fdi2 gdp fxc

fxc 0.1807 0.3315 0.2500 -0.2562 0.4167 0.4696 0.5826 0.5212 0.0447 -0.3158 -0.6771 0.7551 gdp -0.3035 0.4933 -0.3592 0.1927 -0.2554 0.4558 -0.8131 0.3038 -0.1509 -0.5142 0.6784 -0.0466 fdi2 0.0610 0.9711 0.1748 0.2738 -0.0877 0.8747 -0.0889 0.2150 -0.1924 -0.2337 0.2735 0.4944 wfdi 0.1086 0.5512 0.2067 -0.2200 0.3554 0.6608 0.4841 0.5241 -0.1479 -0.3308 -0.4996 1.0000 ci -0.1978 0.2753 -0.2375 0.3348 -0.5644 0.0430 -0.7628 -0.3778 0.0229 0.1247 1.0000 agex 0.1969 -0.2026 0.3251 0.0687 -0.4366 -0.3857 0.2568 -0.9396 -0.0352 1.0000 ky -0.1864 -0.1689 -0.2487 -0.5676 0.4948 -0.1581 0.2103 0.0779 1.0000 att -0.1276 0.1986 -0.2234 -0.2040 0.5341 0.4355 -0.0013 1.0000 trd 0.3198 -0.0768 0.4365 -0.1929 0.4886 0.0237 1.0000 invrat 0.0295 0.8301 0.1361 0.0711 0.0875 1.0000 inf -0.0663 -0.0741 -0.1055 -0.4780 1.0000 cgdp 0.2946 0.2217 0.3280 1.0000 lwage 0.8204 0.1623 1.0000 fdi 0.0489 1.0000 x2wlus 1.0000 x2wlus fdi lwage cgdp inf invrat trd att ky agex ci wfdi

(obs=1949). corr x2wlus fdi lwage cgdp inf invrat trd att ky agex ci wfdi fdi2 gdp fxc

fxc 7091 5.541499 21.17073 -121.7855 96.01546 gdp 7095 7.09759 11.58565 .636 54.476 fdi2 7091 .0001163 .0003265 -.001583 .001403 wfdi 7095 249066.9 221519.4 50681.7 1411366 ci 6931 6.526117 2.644092 1.591315 16.51379 agex 2954 14.51456 21.51094 .206838 92.06559 ky 6931 .7339658 .4121836 .103853 2.349445 att 6931 2.256922 1.092092 .582 5.457 trd 7094 44.04487 17.65893 14.32574 91.37796 invrat 4937 14.29082 5.542042 4.1 28.5 inf 7095 26.25046 39.44694 -14.9 215.4 cgdp 7095 289.1792 97.95237 120.9437 721.7669 lwage 5273 4.513989 .8814226 1.916578 8.214822 fdi 7091 33.93132 91.61605 -140.311 578.7 x2wlus 5273 138.5387 174.1627 6.797655 3695.318 Variable Obs Mean Std. Dev. Min Max

. summarize x2wlus fdi lwage cgdp inf invrat trd att ky agex ci wfdi fdi2 gdp fxc

fdi as a factor of globalization and its effects on wages

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