Final Report

FOREIGN DIRECT INVESTMENT AND ITS IMAPCT ON

ECONOMIC GROWTH AND INCOME INEQUALITY:

A CASE FOR BANGLADESH

MD AHAD UDDIN

ID – 072249020

IN FULFILLMENT OF ECO 495 [SUPERVISED RESEARCH]

RESEARCH PAPER SUBMITTED TO

DR. AFM ATAUR RAHMAN

ASSOCIATE PROFESSOR, DEPARTMENT OF ECONOMICS

NORTH SOUTH UNIVERSITY

19TH SEPTEMBER 2011

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ACKNOWLEDGMENT

I would like to take this opportunity to express my gratitude to All Mighty

Allah and to my parents whose dedication and sacrifice have allowed me to

undertake this research project. They took care of everything else while I

concentrated solely on this research. I am highly indebted to my research

supervisor, Dr. AFM Ataur Rahman who managed to allocate time despite his

busy schedule as core a member of the faculty in the department of

economics at NSU. His suggestions and expert advice regarding selection of

the research topic and its methodology have been invaluable. I am also

grateful to everyone who has helped with this research including my friends

at North South University.

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Foreign Direct Investment and its Impact on Economic

Growth and Income Inequality : A Case for Bangladesh

Md Ahad Uddin

ABSTRACT

Foreign Direct Investment is viewed as a major stimulus for economic growth

for developing countries like Bangladesh which lacks sufficient domestic

financing. This paper is intended to empirically analyze this theoretical

perspective and analyze the impact of FDI on Bangladesh’s economic growth

and income inequality. Using time series data of FDI, GDP and other relevant

variables, it was found that FDI inflow into Bangladesh did not show any

direct significant impact on economic growth; however, it had a negative

impact on income inequality, though this impact was found to be small.

Key words: FDI , economic growth, income inequality, Bangladesh.

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Table of Contents

Acknowledgements…………………………………………………………………………2

Abstract………………………………………………………………………………………3

Introduction….……………………………………………………………………………….5

Rationale and Objective of the study

…………………………………………………….7

Structure of the study……………………………………...……………………………….7

History of FDI and its current situation

…………………………………………………..7

Literature review …………………………………………………..………………………13

Theoretical framework…………………………………………………………………….20

Methodology and model

framework……………………………………………………..21

Empirical model

estimation……………………………………………………………….25

Results and Discussion …………………………………………………………………..28

Limitations ………………………………………………………………………………….30

Conclusion …………………………………………………………………………………31

References………………………………………………………………………………….32

Appendix……………………………………...............................................................

.39

A. Time series data for

Bangladesh……………………………………………….39

B. Sector wise FDI inflow……………………………………………………………40

C. Regression Output……………………………………………………………..…41

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i) Impact of FDI on economic growth………………………………..

….41

ii) Impact of FDI on income inequality……………………………..……

42

INTRODUCTION

Globalization offers an unprecedented opportunity for developing countries to achieve faster

economic growth through trade and foreign investment. During the 1970s, international trade

grew more rapidly than foreign investment and thus it was the most important economic activity.

However this situation changed dramatically during the 1980s when the supply of Foreign Direct

Investment increased sharply. Foreign Direct Investment or FDI in accordance with the United

Nations (UN) conference on Trade and Development (UNCTAD) and its World Investment

Report 2006 is” an investment involving a long-term relationship and reflecting a lasting interest

and control by a resident entity in one economy (foreign direct investor or parent enterprise) in

an enterprise resident in an economy other than that of the foreign direct investor (FDI enterprise

or affiliate enterprise or foreign affiliate)”. In this period, the world FDI has increased its

importance by transferring technologies and establishing efficient network for production and

sales. Through FDI, foreign investors benefit through efficient utilization of their assets and

resources while the recipients of FDI benefit from acquiring technology and getting involved in

international production and trade network.

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The determining factor for a particular firm to establish production facilities abroad is the

prospect of earning higher profit which induces firms to invest abroad, primarily because of

lower labor costs. Traditional theories on trade and investment assumed that factors of

production, such as labor and capital, were not internationally traded. However, in reality, factors

are internationally mobile and at least since the nineteenth century, international labor movement

and international investments have been very important in the global economy (Jayasuriya and

Weerakoon, 2000). Although differences in labor costs may sometimes help influence firms’

decisions to locate abroad, this is far from being the whole story. As the FDI data showed, the

majority of FDI still goes to the advanced countries, in particular the United States where wages

are high relative to those in developing countries. The interesting point here is that there will

normally be extra costs involved, at least initially, for a firm investing in a foreign country where

it is not familiar with the local market and the institutions. At a theoretical level, economic

analysis offers various explanatory approaches which attempting to show why, despite these

disadvantages, firms may still wish to invest abroad. According to John Dunning (1977) firms

undertake FDI when three factors are present and the resulting advantages are sufficient to offset

the natural disadvantages of having to operate in a foreign country. These three advantages are;

ownership advantages (Hymer, 1960), locational advantages (Vernon, 1966), and international

advantages (Buckley and Casson, 1976).

FDI provides much needed resources to developing countries such as capital, technology,

managerial skills, entrepreneurial ability, brands, and access to markets. These are essential for

developing countries to industrialize, develop, and create jobs attacking the poverty situation in

their countries. As a result, most developing countries recognize the potential value of FDI and

have liberalized their investment regimes and engaged in investment promotion activities to

attract various countries. While world FDI inflows increased almost 500% ,i.e. five times from

1990 to 2010 , FDI inflows to developing countries in particular increased by 1546% .ie. almost

15 times during the same period. FDI inflow into developing countries started to grow at a steady

pace from the 80s and after the year 2000 in declined until 2003. After this period FDI inflows to

developing countries picked up sharply and reached its peak in 2008 and again declined in the

year 2009.

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From the early stage of 1980s, many of the Least Developed Countries, including Bangladesh,

were skeptical of the intentions of FDI and perceived it as a tool for promoting foreign interests.

Consequently, a wide array of restrictions were imposed to control FDI inflows through

regulations on profit and dividend repatriations, limits on foreign equity and capital, and required

royalty payments. In an increasingly globalized world economy, many countries including

Bangladesh have now lifted such barriers to open their economies and take advantage of the

benefits of foreign investment.

RATIONALE AND OBJECTIVE OF THIS STUDY

This study is intended to examine the impact of Foreign Direct Investment on the economy of

Bangladesh particularly its effect on economic growth and income distribution. Though there are

a number of papers assessing the impact of FDI on economic growth, this paper will be the first

of its kind to simultaneously examine the impact of FDI on both economic growth and income

inequality for Bangladesh.

STRUCTURE OF THE STUDY

The structure of the study will constitute a discussion of the history of FDI inflow in Bangladesh

followed by theoretical and economic concepts on the impact FDI in an economy. Past relevant

literature will be explored before empirically analyzing the effect of FDI inflow for Bangladesh.

Separate regression models will be presented to examine the impact of FDI on economic growth

and income inequality. Finally a conclusion will be given based on the results of regression

analysis.

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HISTORY OF FDI AND THE CURRENT SITUATION OF FDI INFLOW

IN BANGLADESH

Bangladesh inherited a small reserve of FDI after its independence. It however became relatively

significant with the start of the privatization process in 1974-75. Net inflows of FDI into

Bangladesh have grown from a trickle during the 1980s to above $179 million towards the end of

1990s and reached the highest level of more than $1 billion in 2008. Figure 1 illustrates the rising

trend of FDI inflows into Bangladesh.

19801982

19841986

19881990

19921994

19961998

20002002

20042006

2008

-200,000,000

0

200,000,000

400,000,000

600,000,000

800,000,000

1,000,000,000

1,200,000,000

FDI inflow

FDI inflow

Figure 1 : Inward FDI trend in Bangladesh

Source : WDI data World Bank

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To better understand the factors that have led to this dramatic rise in FDI, it is necessary to

discuss the history of economic policy implemented the Government of Bangladesh since the

country’s independence from Pakistan in 1971. Immediately after the birth of the sovereign

nation, the new government attempted to establish a socialist state and adopted the

Nationalization Order of 1972 to foster economic growth. 86% of the industrial sector was

brought under government control, including key industries such as sugar, jute, and cotton

textiles. The First Five Year Plan was undertaken from 1973 through 1978 and focused on a state

directed economy. The nationalized industries, however, were inefficient and the economy

experienced slow growth. The losses incurred by the public sector and its State Owned

Enterprises created a build-up of political pressure and the government initiated more laissez-

faire measures to encourage a larger role of the private sector.

Consequently, Bangladesh underwent a series of policy reforms to induce a more capitalistic

economy by progressively increasing funding allocations to the private sector; these reforms

include the 1978-1980 Two Year Plan, the 1980-1985 Second Five Year Plan, the 1985-1990

Third Five Year Plan, and the 1990-1995 Fourth Five Year Plan.

However with the lack of financial ability, knowledge, and management within the nascent

economy of a new nation, the government could not solely rely on the domestic financial market

for economic growth. While other low income countries experienced a 3.8% growth of GNP per

capita, Bangladesh struggled at 0.4% per year till 1985. To accelerate the development of the

economy, foreign investment became a priority and in 1980, the Bangladesh Parliament

approved the Foreign Private Investment Act. FDI, however, rose very little owing to the upheld

trade restrictions and the Investment Act of 1989 soon followed to establish the Board of

Investment (UNCTAD 2000), the primary objective of which is aimed at attracting and

facilitating investment from abroad.

Figure 1 demonstrates the efforts of the Bangladesh Board of Investment which shows increases

in FDI inflows, particularly throughout the 1990s. It is important to emphasize the years between

1995 and 1998 which saw a sharp and sudden rise in FDI flows. This period can be attributed to

a variety of factors. During the mid1990s, numerous foreign enterprises led exploratory research

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campaigns into the nation’s natural gas reserves, which have an estimated capacity greater than

10 trillion cubic feet according to the U.S. Geographical Survey. Given the world’s scarce

resources, external pressure finally urged the Bangladeshi government into liberalizing the

energy sector, a move which almost immediately attracted increasing levels of FDI.

Concurrently, the government also eased capital controls and reduced its bureaucratic red tape to

allow private firms to borrow foreign loans without governmental permission, thus encouraging

more joint ventures with international companies. In 1995, the Bangladesh government opened

up the mobile telecommunication industry for private investment, an area which has fostered

technology transfers as well as hundreds of millions of dollars in FDI. All these reforms and

policies combined to shape Bangladesh into the nation that it is today.

Considering policy brief, the Bangladesh Board of Investment has taken measures to transform

the country into the most liberalized investment regime in the South Asian region. This is largely

reflective of the increasingly capitalistic model of the economy where growth is fueled primarily

by the private sector. Thus, foreign enterprises are allowed to reduce associated business risks by

undertaking joint ventures with domestic private firms. A number of other advantages make

Bangladesh a prime destination for FDI. With a 150 million population, the most abundant factor

of production is low-cost labor. This attribute makes the country ideal for labor-intensive

industries. The densely populated city centers also provide for an untapped, sizeable market.

However the only limitation to such a market is that the products offered will only appeal to the

upper socioeconomic strata or the products will have to low cost items to cater the needs of the

general population. The country is also abundant in natural resources such as natural gas and

coal.

Furthermore, the infrastructure of Bangladesh remains underdeveloped and this provides a wide

array of markets for incoming foreign investment with little or no domestic competition. It is also

important to realize that the government has neither the capital nor the resources to expand many

areas of its infrastructure and consequently has attempted to open its economy towards foreign

capital, particularly in areas such as power plants, construction, transportation, financial services

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etc. Hence, the country has adopted a sequence of liberalized industrial policy reform for inward

FDI.

Figure 2 below represents a cross sectional FDI inflow distribution into different sectors of

Bangladesh. The sectors included are Textiles, Telecommunication, Banking, Petroleum, Power

and Others sectors. Here ‘Others’ include Manufacturing (excluding textiles), Agriculture and

Services (excluding financial service Banking). The data for analysis have been collected from

Bangladesh Bank website on FDI survey.

Figure 2 : Cross sectional distribution of FDI inflows to Bangladesh

Source: Bangladesh Bank, Publication of FDI survey 2008.

33%

12%21%

9%

4%

22%

Sector wise FDI in 2005TelecommunicationBankingPetroleumTextilesPowerOthers

36%

17%

24%

10%

4%9%

Sector wise FDI in 2006TelecommunicationBankingPetroleumTextilesPowerOthers

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38%

12%

26%

13%

3%8%

Sector wise FDI in 2007TelecommunicationBankingPetroleumTextilesPowerOthers

39%

20%

17%

12%

3%8%

Sector wise FDI in 2008TelecommunicationBankingPetroleumTextilesPowerOthers

From the above figure, we find that over the years 2005 to 2008, Telecommunication has

received the largest share of FDI and its share has increased over the years from 33% in 2005 to

39 % in 2008. However the second largest recipient in 2005 ‘Others’ which included

manufacturing, agriculture and services has decreased from 22% in 2005 to a mere 8 % in 2008.

The Textiles sector however saw in increase in its share from 9% to 12% in 2008. The petroleum

sector’s increased from 21% in 2005 to 26% in 2007, however it declined to 17% in 2008. The

Banking sector also experienced an increase in share of FDI from 12% in 2005 to 20% in 2008.

The smallest recipient of FDI is the Power sector with on 4% in 2005 and only 3% in 2008.

It is important to note that FDI inflows have increased each of these years and the above only

represents the share of FDI each sector has received relative to the other sectors. The pie charts

therefore only show us how the dimensions of FDI inflow have changed over the years.

The success of the Telecommunication sector is largely due to increased privatization efforts by

the government, telecom has emerged as one of the fastest growing sectors in the Bangladesh

economy. Much of this can be explained by the increased competition between large private

corporations that have magnified efforts to attract FDI and attain better technology to optimize

profits. At the same time, Grameen Phone’s efforts to loan out mobile phones to female

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operators in remote villages have also increased the demand for foreign investment in telecom

and satellite communication technologies. The success of the Textiles sector in attracting FDI is

mainly due to the success of the Ready Made Garments (RMG) industry which exploited the

availability of cheap labor.

A strong inducement of FDI is that the macro economy of Bangladesh, even with some slumps,

has been stable on a whole over a long period of time. Besides, the ability of the workforce to

adapt to the requirements and training of foreign funded enterprises is noted to be relatively

good. However, the country’s infrastructure needs to be upgraded. Certainly, the conditions for

FDI in Bangladesh can be further improved or needs to be improved particularly in terms of

lowering the costs of starting and doing business. Bangladesh should have been a notable

investment destination for foreign investors by now from whatever opportunities it currently

extends to them.

Bangladesh however still has to improve the overall investment climate to remove some of the

problems. Some of the problems which are hindering FDI include:

1. Lack of proper advertisements to attract foreign investors in the vital agricultural sector.

2. Poor law and order condition which results in violence and labor unrests.

3. Bureaucracy and corruption which increases the cost of doing business.

4. Lack of reliable and sufficient power supply.

Despite the pros, because of these problems, Bangladesh lags behind its neighboring counterparts

such as India and Sri Lanka. In many aspects, it is still viewed as an FDI underperformer and the

country is far from achieving its full potential. It will take time before Bangladesh achieves

better results in attracting FDI but as long as the inflows continue to increase, the possibilities for

the country’s future remain hopeful.

LITERATURE REVIEW

The relationship between Foreign Direct Investment (FDI) and economic growth has been a hot

topic of debate in the field of international development and attracted the interest of economists

over the past decades. The Neo-classical models of growth identifies FDI as an important source

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of capital which compliments private domestic investment and creates new job opportunities,

enhances productivity through technology transfer and fosters economic growth in developing

countries, particularly the Least Developed Countries (LDCs) .

Several studies have been conducted among both developed and developing countries to

investigate the relationship between FDI and economic growth. The earliest works include the

classical modernization perspective of Lewis (1948) who argued that the export of capital to

undeveloped countries promoted economic growth by creating industries, transferring

technology, and fostering a “modern” perspective in the local population. However, the

Dependency School in the 1970’s challenged this pervasive belief.

Dependency theorists Amin (1974); Frank (1979), argued that an economy controlled by foreign

interests would not develop organically but grow in a disarticulated manner. The natural linkages

that would evolve from locally controlled capital would not occur. Profits would be exported and

the interests of the ruling elite would be allied with those of owners of the foreign capital.

Income inequality would grow and economy would stagnate. Chase-Dunn (1975) and Bornschier

and Chase-Dunn (1985) supported the above theoretical argument. With their pooled data of both

developed and developing countries, they found that their measure of foreign capital penetration

(PEN: a ratio of foreign investments to total capital stocks) in 1967 had a negative effect on GNP

per capita growth, 1965–75.

Studies from the 1990’s produced an ambiguous picture of the relationship between FDI and

economic growth as some studies found positive relationship while others found a negative

relationship and some of them shows no relationship between the two.

Firebaugh (1992) in his paper “Growth Effects of Foreign and Domestic Investment” found a

positive relationship between FDI and economic growth. In this paper, he challenged the validity

of Bornschier and Chase-Dunn’s (1985) findings and argued that their findings of the negative

effects were spurious. Firebaugh concluded from his reanalysis of Bornschier and Chase-Dunn’s

data that penetration of foreign capital has a positive effect on economic growth but one that is

smaller than the positive effect that domestic capital investment has.

Borensztein et al (1998) studied the effect of FDI on economic growth in a cross-country

regression framework and found a positive relationship; they utilized data on FDI flows from

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developed countries (DCs) to 69 less developed countries (LDCs) for the two decades, the 1970s

and 1980s. Their findings suggest that FDI is an important vehicle for the transfer of technology,

contributing relatively more to growth than domestic investment. They also observed that FDI

has the effect of increasing domestic investment suggesting a complementary relationship.

Agrawal ( 2000) presented empirical evidence on the impact of foreign direct investment (FDI)

inflows on domestic investment by national investors and on GDP growth for South Asian

countries . He found that increases in the FDI inflows in South Asia were associated with a

many-fold increase in the investment by national investors, suggesting that there exist

complementarity and linkage effects between foreign and national investment. The impact of

FDI on economic growth is found to be positive for South Asian countries with more open

economy. He also found that since 1980, FDI inflows contributed more to GDP growth in South

Asia than did an equal amount of foreign borrowing. This suggests that FDI is more preferable to

foreign borrowing in order to achieve a higher growth. Lensink and Marrissey (2001) also found

a positive relationship by estimating the standard model using cross-section, panel data and

instrumental variable techniques whereas volatility of FDI has a negative effect. They also found

that the evidence for a positive effect of FDI was not sensitive to other explanatory variables

which were included.

Ramirez (2006) analyzed the theoretical and empirical links between FDI and private investment

spending in Latin America for the period 1981- 2000 and found a positive relationship. Their

pooled model using data from Latin American countries tested the complementarity hypothesis,

which suggests that increases in FDI raise the marginal productivity of private capital via the

transfer of more advanced technology and managerial knowhow, thereby inducing higher rates

of private investment spending; and found that FDI does compliment private investment in Latin

America and thus helps to achieve a faster rate of economic growth.

Samsu et al (2008) tested the long run relationship between FDI and Malaysian exports. They

found these two time series variables to be cointegrated implying a long term relationship

between foreign investment and exports. Though they did not find any short term relationship,

they found a positive relation in long run, i.e. foreign investment into Malaysia leads to higher

exports in the long run.

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Amin and Eskander (2006) examined the long run relationship between FDI and economic

growth in Bangladesh. Using three different cointegration techniques, they found that there exists

a long run positive relationship between FDI and economic growth even though short run error

correction models showed negative relationship between the two.

Though many researches showed a positive relationship between FDI and economic growth,

there are also papers which show a negative relationship.

Dixon and Boswell (1996) followed up on Firebaugh’s (1992) work and found a negative

relationship where Firebaugh found a positive one. They constructed two new measures of

foreign capital penetration: the ratios of (1) foreign stocks to total capital stocks and (2) foreign

stocks to gross domestic product (GDP) and their reanalysis of the data with these new measures

of foreign investment dependence supported Bornschier and Chase-Dunn’s (1985) earlier

findings of the negative effect of foreign investment dependence. A similar work was done by

Kentor (1998) in his paper “The Long‐Term Effects of Foreign Investment Dependence on

Economic Growth, 1940–1990” examined the long-term effects of foreign capital penetration

and found that peripheral countries with relatively high dependence on foreign capital had a

slower economic growth than those less dependent peripheral countries. His works further

showed that a structure of dependency is created that perpetuates these effects. The consequences

of these effects are unemployment, over-urbanization, income inequality, and social unrest.

Robertson and Watson (2004) examined the impact of changes in the level of FDI on corruption.

Using Corruption Perceptions Index (CPI) scores computed by Transparency International for a

panel of 99 countries, they showed that the more rapid the rate of change in FDI, the higher the

level of corruption. This implies that a developing country experiencing an influx of foreign

investment will experience higher level of corruption.

Damooei and Tavakoli (2006) estimated the output elasticity of FDI and imports in Thailand and

in the Philippines during the period 1970-1998. They found that FDI contribution to every one

percentage growth point in GDP is about only 0.05 percentage point in each country whereas

imports contribute about 0.47 of a percentage point in Thailand and 0.31 of a percentage point in

the Philippines. This shows that imports contributed more to economic growth than foreign

investment.

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Recent studies on the relationship of FDI and economic growth reveals a new dimension in the

literature. These studies show that FDI has very little or no significant impact on economic

growth rendering it unable to change the fate of developing countries.

Maria Carkovic and Ross Levine (2002) using pooled data, concluded in their econometric study

on FDI and GDP growth that the exogenous component of FDI does not exert a robust,

independent influence on growth. They found the impact of FDI on GDP growth rate to be

insignificant.

A similar conclusion was reached by Athukorala (2003) who examined the relationship between

FDI and GDP using time series data from Srilankan economy. His econometric results showed

that FDI inflows do not exert an independent influence on economic growth and also the

direction of causation is not towards from FDI to GDP growth but GDP growth to FDI.

Bekeke and Mekonnen (2004) examined the impact of FDI on economic growth of Sub-Saharan

African countries through its impact on savings and found that the impact of FDI on economic

growth was unsatisfactory holding the assumption of efficient market and perfect mobility of

factors of production. They concluded that it was difficult to generalize that FDI has positive

contribution to economic growth of other developing countries like Sub-Saharan African.

Sarkar (2007) casts doubt on examined the growth-promoting effect of foreign direct investment,

which is widely supported by the proponents of financial globalization, for a panel of 51 less

developed countries. His analysis shows a rising relationship between growth and FDI (relative

to gross capital formation) only for the group of 11 relatively rich and open-economy countries,

however, the time-series analysis observes meaningful positive relationships between FDI and

growth only for 3 countries belonging to this group and some other countries. But by and large

no long-term positive relationship exists between the two irrespective of income levels, openness

and FDI-dependence.

In the case of Bangladesh, Ahamad and Tanin (2010) reviewed the long-run trend of FDI and

economic growth in Bangladesh over the period 1975- 2006. Their econometric analysis of FDI

and economic growth shows a positive relationship, however, this result is misleading as further

analysis revealed that it is actually the other way around. Their findings conclude that economic

growth of Bangladesh is actually a determinant of FDI.

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From the above literatures, it can be concluded that analysis using pooled panel data of both

developed and developing countries yields an ambiguous result where some shows a positive

relation between FDI and economic growth and some shows negative relation. Analysis using

data from only LDCs shows no relation between FDI and growth. Region specific analysis

shows that in Latin America and South Asia, FDI has a positive impact nn economic growth but

for Sub-Saharan countries, there is no impact. The study for only the Srilankan economy shows

no relationship between the two. For Bangladesh, it is seen that one study shows a long run

positive relationship but another more recent study reveals no relation between FDI and

economic growth.

The effect of globalization on income inequality has been one of the hottest research interests as

globalization has deepened in the 1990s. There has been plenty of research on the relationship

between trade and income inequality within countries. As foreign direct investment has increased

recently, concern about the effect of FDI on income inequality has heightened.

Foreign direct investment can have direct and indirect impacts on poverty reduction in the host

country. The direct impact of FDI on poverty can be seen through the increase in employment

and the reduction of people living below the poverty line resulting from the increase in the

demand for labor. The indirect impact of FDI on the reduction of poverty is through economic

growth which results in the improvement of living standards due to the increase in GDP,

improvement of technology and productivity, as well as the economic environment. As poverty

goes down in an economy, the level of income inequality also decreases.

Deardorff and Stern (1994) in their paper “The Stolper–Samuelson Theorem: A Golden Jubilee”

found that FDI helps to reduce income inequality when implemented to utilize abundant low-

income unskilled labor. Tsai (1995) also concluded a similar result that FDI helps to reduce

income inequality when capital, domestic or foreign, stimulates economic growth and its benefits

eventually spread throughout the whole economy.

Jensen and Rosas (2007) explored the relationship between FDI and income inequality in

Mexico and they also found that increased FDI inflows are associated with decrease in income in

inequality in Mexico’s states.

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However, other researchers have found that higher FDI leads to increase in income inequality.

(Girling, 1973;Rubinson, 1976; Bornschier and Chase-Dunn, 1985;Tsai, 1995) have reached the

same conclusion that inward FDI deteriorates income distribution by raising wages in the

corresponding sectors in comparison with traditional sectors.

Feenstra and Hanson (1997) using Mexican data from 1975 to 1988, found that the rising wage

inequality in Mexico is caused by higher foreign capital inflows.

Mah (2002) investigated the impact of changes in trade values and FDI inflows on the Gini

coefficients in Korea and concluded that globalization tends to deteriorate the income

distribution there. Taylor and Driffield (2004) also found that inward flows of FDI contributed to

increasing wage inequality based on an empirical analysis with the three-digit industry level for

UK manufacturing sectors over the period 1983 to 1992.

Choi (2006) using pooled Gini coefficient from 1993 to 2002 data for 119 countries from World

Development Indicators 2004, World Bank, found that income inequality increases as FDI stocks

as a percentage of GDP increase.

Though income inequality has a negative impact on economic growth as concluded by Persson

and Tabellini ( 1994); Voitchovsky(2005) ; and others, there are also researches which shows

that though FDI has an impact on economic development but it doesn’t affect the profile of

income inequality.

Lindert and Williamson (2001) and Milanovic (2002) did not find any significant relationship

between FDI and income inequality. Tsai (1995) after comparing models with and without

geographical dummies –such as Asia and Latin America – over the period from 1967 to 1981,

also concluded that the statistically significant correlation between FDI and income inequality

might capture more of the geographical difference in inequality than the negative influence of

FDI.

In the case of Bangladesh, there is no study which specifically tests the impact of FDI on income

inequality, however, the next alternative of foreign funds, i.e. Foreign Aid, Quazi (2005) finds

that aid in the form of loans significantly increases GDP growth rate.

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From the literature, it can be concluded that the overall impact of FDI on income inequality is

ambiguous and mainly depends on the type of FDI, i.e. the investment is capital intensive or

labor intensive. However it is clear that FDI does gives rise to wage inequality whether it be

sectoral wage or regional wage.

THEORETICAL FRAMEWORK

Neo classical theory of growth maintains that economic growth is caused by improvements in the

quality if labor (through training and education), increase in capital (through higher savings and

investment), improvement in technology ( through research and development) , and increase in

labor quantity ( population growth) . Neo-classical economists advocate the following strategies

should be encouraged:

a. Perfectly competitive market.

b. Privatization of state owned enterprises.

c. A move from closed to an open economy.

d. Opening up the domestic economy through encouraging free trade and FDI.

These policies will stimulate investment, higher output and a faster rate of economic growth.

Theoretically, there are several potential ways in which FDI can cause economic growth. For

example, Solow-type standard neoclassical growth models suggest that FDI increases the capital

stock and thus growth in the host economy by financing capital formation (Brems, 1970). In

neoclassical growth models with diminishing returns to capital, FDI has only a short-run growth

effect as countries move towards a new steady state. Accordingly, the impact of FDI on growth

is identical to that of domestic investment. In endogenous growth models, in contrast, FDI is

often assumed to be more productive than domestic investment. The logic behind this is that FDI

encourages the incorporation of new technologies in the production function of the host economy

(Borensztein et al., 1998). In this view, FDI-related technological spillovers offset the effects of

diminishing returns to capital and keep the economy on a long-term growth path. Moreover,

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endogenous growth models imply that FDI can promote long-run growth by augmenting the

existing stock of knowledge in the host economy through labor training and skill acquisition, on

the one hand, and through the introduction of alternative management practices and

organizational arrangements, on the other (de Mello, 1997). Hence, through capital accumulation

and knowledge spillovers, FDI may play an important role for economic growth.

Furthermore, FDI can possibly stimulate economic growth through the international trade

channel by augmenting domestic capital for exports, helping transfer of technology and new

products for exports, facilitating access to new and large foreign markets, and providing training

for the local workforce and upgrading technical and management skills.

All of these benefits are expected to contribute to higher economic and employment growth

which is an effective tool for achieving improvement in the redistribution of income and

reduction of poverty. FDI can reduce income inequality when implemented to utilize abundant

low-income unskilled labor (Deardorff and Stern, 1994) or when the benefits of economic

growth caused by capital both domestic and foreign spread throughout the economy (Tsai, 1995).

However, FDI may not stimulate host economy because it may lower or replace domestic

savings and investment. Moreover, FDI may target primarily the host country’s domestic market

and thus not increase exports, or it will not help developing the host country’s dynamic

comparative advantages by focusing solely on local cheap labor and raw materials.

It might even adversely deteriorate income distribution by raising wages in the corresponding

sector compared to the traditional sector which does not receive FDI. If the size of the traditional

sector is relatively large then income inequality may increase.

Therefore an empirical analysis of this issue is needed in order to better understand the role of

FDI in a country.

METHODOLOGY AND MODEL FRAMEWORK

The purpose of this study is to empirically analyze the impact foreign direct investment has on

Bangladesh’s economic growth and income inequality. In this study, annual GDP has been

selected as the measure of economic growth. GDP is the sum of gross value added by all resident

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producers in the economy plus any product taxes and minus any subsidies not included in the

value of the products. It is calculated without making deductions for depreciation of fabricated

assets or for depletion and degradation of natural resources.

Theoretically, Neo- classical models of growth identifies FDI as an important source of capital

which compliments private domestic investment and create new job opportunities, enhances

productivity through technology transfer and fosters economic growth in developing countries.

This view has been supported by Firebaugh (1992), Borensztein et al (1998), Agrawal ( 2000),

and many others. However, there are also literature against this view where it is said that FDI

actually has a negative effect on economic growth, Amin (1974); Frank (1979), Chase-Dunn

(1975) and Bornschier and Chase-Dunn (1985).

To test the impact of FDI on growth, a Cobb-Douglas production function is used.

Y = β0 . Lβ1 . K β

2 ……………………………………………. (i)

Where, Y= GDP ; L = labor inputs ; K = capital and β1 β

2 are the output elasticities of labor and

capital respectively.

If the production function is denoted by P = P(L,K), then the partial derivative dP/dL is the rate

at which production changes with respect to the amount of labor. Economists call it the marginal

production with respect to labor or the marginal productivity of labor. Likewise, the partial

derivative dP/dK is the rate of change of production with respect to capital and is called the

marginal productivity of capital.

In these terms, the assumptions made by Cobb and Douglas can be stated as follows :

1. If either labor or capital vanishes, then so will production.

2. The marginal productivity of labor is proportional to the amount of production per unit

of labor.

3. The marginal productivity of capital is proportional to the amount of production per unit

of capital.

For the purpose of this study, certain modifications have been made. Total labor force has been

used as a proxy for labor inputs. The variable K is broken down into three components. Foreign

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capital is used as an additional variable. In addition, official development aid also known as

foreign aid has also been included. From a theoretical perspective, the main role of foreign aid in

stimulating economic growth is to supplement domestic source of finance such as savings, thus

increasing the amount of investment and capital stock. Morrissey(2001) points a number of

mechanisms through which aid can contribute to economic growth, a) aid increases investment in

both physical and human capital, b) aid increases the capacity to import capital goods or

technology , c) aid does not have indirect effect that reduce investment or savings rate. Previous

empirical studies on foreign aid and economic growth generate mixed results. For example,

Papanek (1973), Dowling and Hiemenz (1982), Gupta and Islam (1983), Hansen and Tarp

(2000), Burnside and Dollar (2000), Gomanee, et al. (2003), Dalgaard et al. (2004), and Karras

(2006), find evidence for positive impact of foreign aid on growth; Burnside and Dollar (2000)

and Brautigam and Knack (2004) find evidence for negative impact of foreign aid and growth,

while Mosley (1980), Mosley, et al. (1987), Boone (1996), and Jensen and Paldam (2003) find

evidence to suggest that aid has no impact on growth. It should be noted that, although Burnside

and Dollar (2000) concluded that foreign aid has positive effects, this conclusion applies only to

economies in which it is combined with good fiscal, monetary, and trade policies. Domestic

capital is represented by ‘Gross capital formation’. Gross capital formation, formerly gross

domestic investment, consists of outlays on additions to the fixed assets of the economy plus net

changes in the level of inventories. Economic theory suggests that higher the investment, higher

the productive capacity of the economy increases hence higher the economic growth. This notion

was empirically tested. Khan and Reinhart (1990) using data from developing countries found

that private investment has a large direct effect on economic growth. This result was also

obtained by Anderson (1990) in his paper “Investment and Economic growth” where he found

that capital accumulation greatly benefits growth in both developing and industrial countries.

Tang; Selvanathan and Selvanathan (2008) investigated the causal link domestic investment and

economic growth in China and found that there exists a bi-directional causality between

domestic investment and economic growth.

Thus the new model is

Y = β0 . L β1 . FDInf β2 . ODA β3 . GCF β4 ………………. (ii)

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Where , L = labor ; FDInf = FDI inflow ; ODA = official development aid ; GCF = Gross capital

formation net of FDI inflow and ODA received.

Since this is a non-linear model, a “ln” transformation is made to make the model linear. Hence

the model becomes,

Ln Y = β0 + β1 ln L + β2 ln FDInf + β3 ln ODA + β4 GCF ……. (iii)

The coefficients of the independent variables now give the partial elasticity of output of the

respective variable.

To examine the impact of FDI on income inequality, the following function will be used,

GINI = F ( FDInf , ODA , GCF )

GINI coefficient has been selected as the measure of income inequality. It is an aggregate

numerical measure ranging from 0 (perfect equality) to 1 (perfect inequality), therefore higher

the value of the coefficient, higher the inequality in income distribution. Though there are other

measures of income inequality, GINI coefficient has been selected because it satisfies four highly

desirable properties: a. the anonymity, b. scale independence, c. population independence, and d.

transfer principles.

As mentioned in the literature review, theoretically, FDI reduces income inequality through

increase in employment and demand for labor. Deardorff and Stern (1994) , Tsai (1995), Jensen

and Rosas (2007) and other researchers found inward FDI reduces income inequality whereas

other found that inward FDI deteriorates income inequality (Girling, 1973;Rubinson, 1976;

Bornschier and Chase-Dunn, 1985;Tsai, 1995).

Theoretically, Official development aid (ODA) helps to alleviate the problem of low savings and

availability of fund and acts as an alternate source of investment creating both employment

opportunities and increasing economic growth. Solow's growth model provides grounds for the

need of ODA to supplement domestic savings in augmenting growth (Dornbusch et al.,2004).

Later, the “big push” theory by Murphy et al., (1989) becomes impetus for providing aid to

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developing countries to assist them in taking-off to a steady state. However, a majority of

literature shows that aid tends to deteriorate income inequality. Chase-Dunn and Rubinson

(1978) find that stocks of foreign aid and investment had negative effects on income inequality

in both rich and poorer countries. Chong et al (2009) find aid to be conducive to improvement of

income distribution only when the quality of institution is taken into account otherwise the result

is not robust. Their finding is consistent with recent empirical research on aid ineffectiveness in

achieving economic growth.

The impact of Gross Capital formation on income inequality is theoretically more prominent

than other variables. Since this represents domestic investment, higher the level of GCF , higher

will be the demand for labor and more job opportunities will be created. This will improve the

income distribution in the economy.

For simplicity of the model and interpretation, the variables are taken in ‘ln’ form.

Therefore the model stands as ,

Ln GINI = β1. Ln FDInf + β2 Ln ODA + β3 Ln GCF …. … (iv)

EMPIRICAL MODEL ESTIMATION

Based on the model framework in previous section, equation (iii) will be used to examine the

effect of FDI inflow and other independent variables such as Labor force, official development

aid and Gross capital formation, on Bangladesh’s GDP.

Ln Y = β0 + β1 ln L + β2 ln FDInf + β3 ln ODA + β4 GCF ……. (iii)

In the above equation, FDInf which represents FDI inflow into Bangladesh is the main variable

of interest .Taking a partial derivative of any of the independent variable with respect to the

dependent variable gives us the coefficient β1, β2, β3 and β4 which are the partial elasticities of

output of the respected variable. A positive sign of the coefficient means that the variable has a

positive impact on GDP and a negative sign will indicate a negative impact. β2 will be equal to

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β4 if FDI inflow is equally productive as domestic gross capital formation. Similarly, β2 will be

equal to β3 if FDI inflow is just as efficient as Official development aid.

In order to estimate equation (iii) , data for all the variables in the equation have been collected

from World Development Indicators from World Bank website. Statistical software package

EVIEWS 7.0 has been used to estimate the equation. The data point ranges from 1980 to 2009.

Ln Y = 0.723 + 0.460 ln L + 0.011 ln FDInf + 0.270 ln ODA + 0.424 GCF + ut

S.E (2.477) (0.274) (0.009) (0.060) (0.091)

P value 0.772 0.10 0.22 0.00 0.00

R2 = 99% Adjusted R2 = 98% d= 1.09

From the estimation output of equation (iii), we see that all the coefficients have positive sign

which means that they all have a positive impact on GDP. The coefficient β1 has a value of

0.460. This means that if the quantity if the Labor force increases by 1% , then GDP is expected

to increase by 0.46 % . This coefficient has a low standard error and has a p value of 0.10. This

means that if we set up a hypothesis

Ho : β1= 0

H1 : β1≠ 0

We can reject the null hypothesis, Ho : β1= 0 , at 10% level of significance.

This can be restated as the variable is statistically significant at 10 % level of significance.

Our main variable of interest, FDI inflow (FDInf) has a coefficient of 0.011 which means that if

FDI inflow increases by 1% then GDP is expected to increase by 0.011%. However, this variable

is significant only above 22% level of significance. Therefore we can consider its effect to be

insignificant. ODA has a coefficient of 0.270 , this means that if ODA received by Bangladesh

increases by 1% then GDP is expected to increase by 0.27% . This variable is significant at 0%

level. The coefficient of GCF is 0.424 which indicates that if GCF increases by 1%, then GDP is

expected to increase by 0.424 %. Like ODA , GCF is also significant at 0% level of significance.

The intercept has a value of 0.723 however this value is insignificant due to its high standard

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error and high p value. The equation has a R2 = 99% . This means that 99 % of the variation in

GDP is explained by the regressors. The Adjusted R2 = 98% which means that 98% of the

variation in GDP is explained by the regressors after adjusting for degrees of freedom.

To examine the impact of FDI and other variables on inequality, equation (iv) from modeling

framework in the previous section is used.

Ln GINI = β1. Ln FDInf + β2 Ln ODA + β3 Ln GCF …. … (iv)

In the above equation, the coefficients β1, β2 and β3 give us the effect each of the variables has

on GINI coefficient. If the coefficients have a positive value, then an increase in the value of the

variables increases GINI coefficient which indicates that income inequality increases. If the

coefficients have a negative value, then a rise in the value of the variables decreases the value of

GINI coefficient which indicates that income inequality decreases.

To estimate the equation, data for GINI coefficient has been collected from “The Standardized

World Income Inequality Database”, (SWIID v.3), by Fredrick Solt. Data for FDI inflow,

Official development aid and Gross Capital Formation have been collected from World

Development Indicators, World Bank website. The data points used ranges from 1973 to 2005.

Ln GINI = 0.067 Ln FDInf + 0.359 Ln ODA – 0.231 Ln GCF + ut

S.E (0.0164) ( 0.0313) ( 0.0389)

P value 0.0004 0.0000 0.0000

R 2 = 25.29 % Adjusted R2 = 19.76% d= 1.405

From the estimation output, we see that if FDInf has a coefficient of 0.067. This means that if

FDI inflow increases by 1 % , then GINI coefficient is expected to increase by 0.067 % . This

means income inequality is expected to increase by 0.067 % with every 1% increase in FDI

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inflow. This variable is significant at 0% level of significance. The coefficient of ODA is 0.359

indicating that with every 1% increase in ODA received, income inequality is expected to

increase by 0.359 % . This is also a significant variable at even 0% level of significance.

Domestic Gross capital formation (GCF) on the other hand has a coefficient of -0.231 . This

means that if GCF increases by 1 % , then income inequality is expected to decrease by 0.231% .

This variable has a low standard error and is significant even at 0% level of significance. The

equation has a R 2 = 25.29 % , which means that 25.29% of the variability of GINI is explained

by the regressors.

RESULTS AND DISCUSSION

From the above regression estimation, we see that the impact of FDI inflow on economic growth

is insignificant. This is the only variable which has an insignificant impact. This finding

corresponds to the findings of Karkovic and Levin (2003) that came to the same conclusion

using pooled data, Bekeke and Mekonnen (2004) for Sub- Saharan African countries, Athukorala

(2003) for the Srilankan economy and Ahmad and Tanin (2010) for Bangladesh. However, it

would be incorrect to completely discard the importance of FDI inflow to economic growth.

Since only the direct contribution of FDI inflow to GDP shows an insignificant result, there is a

very high possibility that FDI inflow has a lagged beneficial effect on GDP and it compliments

domestic capital formation. Both of these effects have not been tested and there is a strong

possibility of their occurrence. This view is supported by Agrawal (2000) who found evidence

that FDI inflow in South Asia led to many fold increase in investment by domestic investors

suggesting that there exists complementarity and linkage effects between foreign investment and

national investment.

Moving on to the effects of the other variables, we find that of all the independent variables in

the model, Labor has the largest output elasticity of 0.46% of expected GDP increase with 1%

increase in labor force. Since Bangladesh is a developing country, its mainly based on agriculture

with labor intensive production process. Therefore this result is expected as higher number of

labor means more available inputs for the agricultural sector and labor intensive production

sector.

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The second largest impact can be seen from domestic gross capital formation. GCF has an

expected impact of 0.424 % on GDP for every one percent increase in GCF. This result is

compatible with economic theory as higher the investment, higher the productive capacity of the

economy increases hence higher the economic growth. Similar empirical conclusions have been

reached by Anderson (1990), Tang; Selvanathan and Selvanathan (2008). For an economy to

grow organically, domestic investment must have a significant contribution to economic growth.

In our estimation model, it has been found that Official development aid also has a significant

positive impact for Bangladesh’s economic growth. For every 1% increase in aid GDP is

expected to increase by 0.27%. This result is also predicted from a theoretical perspective as it

supplements domestic source of savings. A similar outcome is also predicted by Papanek (1973),

Dowling and Hiemenz (1982), Gupta and Islam (1983), Hansen and Tarp (2000), Burnside and

Dollar (2000), Gomanee, et al. (2003), Dalgaard et al. (2004), and Karras (2006), all these

researchers have found a positive impact of foreign aid on economic growth.

The value of the intercept is found to statistically insignificant. This means that in the absence of

Labor and other variables, GDP would be zero. Since the basis of the model is Cobb-Douglas

production function, this finding is compliant with the first assumption of the model.

When we look at the estimation of equation (iv), we see that even though the impact of FDI

inflow to economic growth was found to be insignificant, it has a negative impact on income

inequality. For every percent increase in FDI inflow, income inequality is expected to increase

by 0.06% . This view is also shared by Papanek (1973), Dowling and Hiemenz (1982), Gupta

and Islam (1983), Hansen and Tarp (2000), Burnside and Dollar (2000), Gomanee, et al. (2003),

Dalgaard et al. (2004), and Karras (2006). They also found FDI inflow to negatively affect

income inequality. One possible explanation maybe that most of the FDI inflow in Bangladesh

leads to the use of trained skilled labors such as in the telecom industry and other manufacturing

industries, instead of the abundant cheap unskilled labor. Even though the FDI in the textile

sector leads to employment generation, these sectors are mainly located in the urban areas and

demand skilled to semi-skilled labors. However the majority of the labor force is located in the

rural sector and thus cannot access any formal training program offered by these sectors. As a

result, a wage inequality is created between labors employed in the manufacturing and

agricultural sector.

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Unlike FDI, domestic gross capital formation shows to have a positive impact on income

inequality. Our estimation model shows that If GCF increases by 1 % , income inequality is

reduced by 0.23% . This finding is perfectly compliant with economic theory as higher level of

capital formation will lead to higher level of demand for labor and creation for more job

opportunities. Since this investment is made by domestic investors, there is more chance of using

labor intensive technology and hence employment of semi-skilled and low-skilled workers.

Official development aid, though a significant contributor to GDP, is a source of income

inequality. The estimation result shows that with every percent increase in Official development

aid received, income inequality is expected to increase by 0.35 % . Even though theoretically,

ODA is perceived as an alternative source of funds for investment leading to employment

opportunities and economic growth which ultimately leads poverty reduction , a lot of studies

have found foreign aid to cause income inequality. This result is consistent with the findings of

Chase-Dunn and Rubinson (1978), Chong et al (2009). One possible reason is the level of

corruption in Bangladesh where the ruling elites tend to realize maximum benefits from the aid.

Foreign aid can help income redistribution only when the quality of institution is taken into

account ( Chong et al , 2009 ). According to Auer (2007), without institutional reform, it is

useless for the donor country to transfer resources in the form of aid.

Overall, from the analysis, we find that foreign direct investment into Bangladesh does not have

a significant contribution in Bangladesh’s economic growth but instead it does lead to income

inequality; even though the impact is very small, it is still found to be statistically significant.

LIMITATIONS

Since Bangladesh gained its independence not so in the distant past, the number of post-

independence data is very limited. Hence low number of observation points is one the limitations

of this study especially since data for labor force has only been formally recorded since 1980.

Examining the lagged effect of FDI using advanced econometric model would also have given a

more detailed picture of the FDI scenario.

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CONCLUSIONS

This paper has examined the impact of FDI on economic growth and income inequality using

time series data from Bangladesh economy. In Bangladesh, major policy reforms regarding

favorable FDI conditions was made during the 80s and during the mid-90s, there was an influx

of FDI and it has grown ever since. Though theoretically, FDI is supposed to have a positive

impact on economic growth and income inequality, empirical studies have found mixed results.

In our econometric result, it was shown that FDI inflow into Bangladesh does not exert any

independent influence on its economic growth. Other variables such as Official development

aid, domestic gross capital formation and the number of people in the labor force all were found

to have a statistically significant positive impact on economic growth. Even though FDI inflow

did not show any significant impact on economic growth, we found it to a negative impact in

income distribution, however this impact was found to be small. Even though development aid

was found to be a significant contributor to growth we found it to have a significant negative

impact on income inequality.

The attitude of the government of Bangladesh towards FDI is positive. However the investment

climate has not improved in Bangladesh as a result of political instability and disturbance, poor

law and order condition, poorly developed infrastructure, lack of power and low level of human

capital. The importance of FDI cannot be ignored hence the investment climate in the country

Page | 31

must be improved through appropriate measures such as de-regulation in economic activity,

developing the port network, road network, railways and telecommunication facilities etc,

creating more transparency in the trade policy and more flexible labor markets and setting a

suitable regulatory framework and tariff structure. Currently Bangladesh provides an attractive

investment regime but the response from the investor has not been very encouraging. If the

ultimate objective of the government is to attract FDI for development, poverty reduction and

growth, then an appropriate policy mix is necessary to achieve these.

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APPENDIX

Time series data for Bangladesh

Source: WDI World Bank website for GDP, FDI, ODA and GCF

SWIDD v.3 for Income Inequality

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YEAR GDP FDI net inflow ODA GCF GCF net of FDI and ODA Labor GINI NET1972 6,288,245,866$ 90,000$ 223,760,000$ 295,402,700$ 71,552,7001973 8,067,027,104$ 2,340,000$ 421,470,000$ 702,799,900$ 278,989,900 42.645611974 12,459,282,561$ 2,200,000$ 522,370,000$ 918,854,300$ 394,284,300 43.549831975 19,395,903,916$ 1,543,333$ 1,015,390,000$ 1,192,442,000$ 175,508,667 45.194091976 10,083,163,547$ 5,420,000$ 497,460,000$ 999,378,800$ 496,498,800 47.693351977 9,632,469,659$ 6,980,000$ 783,570,000$ 1,109,970,000$ 319,420,000 50.615211978 13,299,358,553$ 7,700,000$ 988,430,000$ 1,535,650,000$ 539,520,000 47.891441979 15,585,961,410$ (8,010,000)$ 1,162,660,000$ 1,746,231,000$ 591,581,000 47.068051980 18,114,645,161$ 8,510,000$ 1,286,720,000$ 2,615,644,000$ 1,320,414,000 36,252,006 46.244671981 19,762,945,710$ 5,360,000$ 1,099,040,000$ 3,482,515,000$ 2,378,115,000 37,288,659 45.421291982 18,087,000,000$ 6,960,000$ 1,337,400,000$ 3,223,050,000$ 1,878,690,000 38,421,813 42.955141983 17,155,798,869$ 400,000$ 1,042,430,000$ 2,911,639,000$ 1,868,809,000 39,550,784 40.488981984 19,670,160,944$ (550,000)$ 1,186,820,000$ 3,131,486,000$ 1,945,216,000 40,784,043 38.627271985 21,613,230,769$ (6,660,000)$ 1,126,530,000$ 3,526,885,000$ 2,407,015,000 42,071,631 36.765561986 21,160,234,384$ 2,440,000$ 1,424,560,000$ 3,534,047,000$ 2,107,047,000 43,414,541 37.906441987 23,781,404,932$ 3,210,000$ 1,787,610,000$ 3,808,889,000$ 2,018,069,000 44,814,312 36.547611988 25,638,749,373$ 1,840,000$ 1,613,870,000$ 4,182,468,000$ 2,566,758,000 46,274,227 35.188781989 26,825,240,347$ 250,000$ 1,799,240,000$ 4,486,145,000$ 2,686,655,000 47,861,956 33.041151990 30,128,776,344$ 3,238,780$ 2,092,760,000$ 5,138,199,000$ 3,042,200,220 49,519,879 30.893521991 30,957,444,767$ 1,390,440$ 1,878,790,000$ 5,230,560,000$ 3,350,379,560 51,315,651 28.74591992 31,708,863,730$ 3,721,850$ 1,818,030,000$ 5,487,230,000$ 3,665,478,150 52,484,161 29.223781993 33,166,530,060$ 14,049,900$ 1,368,850,000$ 5,952,339,000$ 4,569,439,100 53,680,951 31.190651994 33,768,661,427$ 11,147,800$ 1,742,550,000$ 6,214,297,000$ 4,460,599,200 54,902,478 33.157521995 37,939,752,960$ 92,300,000$ 1,281,540,000$ 7,254,002,000$ 5,880,162,000 56,220,929 35.12441996 40,666,015,641$ 231,600,000$ 1,228,060,000$ 8,130,447,000$ 6,670,787,000 57,483,162 36.594091997 42,318,798,538$ 575,310,000$ 1,010,630,000$ 8,769,668,000$ 7,183,728,000 58,757,641 35.753011998 44,091,754,148$ 576,460,000$ 1,162,140,000$ 9,538,113,000$ 7,799,513,000 60,115,459 34.911921999 45,694,072,379$ 309,100,000$ 1,219,300,000$ 10,140,910,000$ 8,612,510,000 61,462,072 34.070842000 47,124,925,462$ 578,700,000$ 1,171,730,000$ 10,850,030,000$ 9,099,600,000 62,785,830 33.229762001 46,987,842,847$ 354,500,000$ 1,043,720,000$ 10,848,090,000$ 9,449,870,000 64,629,422 33.464422002 47,571,130,071$ 328,300,000$ 914,580,000$ 11,011,530,000$ 9,768,650,000 66,473,861 33.699092003 51,913,661,485$ 350,200,000$ 1,392,150,000$ 12,150,550,000$ 10,408,200,000 68,320,175 33.933762004 56,560,744,012$ 460,400,000$ 1,413,780,000$ 13,587,650,000$ 11,713,470,000 70,073,044 34.168422005 60,277,560,976$ 845,300,000$ 1,317,650,000$ 14,784,410,000$ 12,621,460,000 71,828,639 34.403092006 61,901,116,736$ 792,500,000$ 1,219,830,000$ 15,259,010,000$ 13,246,680,000 73,481,2552007 68,415,421,373$ 666,400,000$ 1,514,590,000$ 16,737,270,000$ 14,556,280,000 75,126,7072008 79,554,350,678$ 1,086,300,000$ 2,061,400,000$ 19,258,450,000$ 16,110,750,000 76,765,0422009 89,359,767,442$ 716,000,000$ 1,226,940,000$ 21,778,970,000$ 19,836,030,000 78,619,079

SECTOR WISE FDI INFLOW

Source: Bangladesh Bank FDI survey Jan- Jun 2008

Unit: Million US dollar

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Sector 2005 2006 2007 2008Telecommunicatio

n 261.89 267.97 304.71 299.91Banking 94.88 129.96 91.83 156.8

Petroleum 168.74 181.86 204.99 132.82Textiles 74.98 73.53 105.45 93.42Power 29.64 27.45 24.96 25.1Others 173.64 63.85 60.83 60.63Total 803.77 744.62 792.77 768.68

Sector wise FDI inflow in percentage

Sector 2005 2006 2007 2008Telecommunication 33% 36% 38% 39%Banking 12% 17% 12% 20%Petroleum 21% 24% 26% 17%Textiles 9% 10% 13% 12%Power 4% 4% 3% 3%Others 22% 9% 8% 8%Total 100% 100% 100% 100%

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Regression Output

Impact of FDI on economic growth

Dependent Variable: LOG(GDP)

Method: Least Squares

Date: 09/19/11 Time: 17:33

Sample (adjusted): 1980 2009

Included observations: 28 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.723507 2.476696 0.292126 0.7728

LOG(LABOR) 0.460130 0.273669 1.681337 0.1062

LOG(FDINF) 0.011619 0.009334 1.244911 0.2257

LOG(ODA) 0.270806 0.060877 4.448434 0.0002

LOG(GCF) 0.424082 0.091300 4.644918 0.0001

R-squared 0.990176    Mean dependent var 24.33942

Adjusted R-squared 0.988468    S.D. dependent var 0.459069

S.E. of regression 0.049298    Akaike info criterion -3.021433

Sum squared resid 0.055897    Schwarz criterion -2.783540

Log likelihood 47.30007    Hannan-Quinn criter. -2.948707

F-statistic 579.5804    Durbin-Watson stat 1.097778

Prob(F-statistic) 0.000000

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Regression output

Impact of FDI on income inequality

Dependent Variable: LOG(GINI)

Method: Least Squares

Date: 09/19/11 Time: 17:37

Sample (adjusted): 1973 2005

Included observations: 30 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

LOG(FDINF) 0.066913 0.016419 4.075287 0.0004

LOG(ODA) 0.359290 0.031364 11.45565 0.0000

LOG(GCF) -0.230644 0.038923 -5.925656 0.0000

R-squared 0.252986    Mean dependent var 3.614882

Adjusted R-squared 0.197652    S.D. dependent var 0.156526

S.E. of regression 0.140206    Akaike info criterion -0.996765

Sum squared resid 0.530760    Schwarz criterion -0.856645

Log likelihood 17.95147    Hannan-Quinn criter. -0.951939

Durbin-Watson stat 1.405427

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