DETERMINANTS OF FOREIGN DIRECT INVESTMENT IN KENYA

PRESENTED BY: MAJUNE KRAIDO SOCRATES

X75/27622/2009

SUPERVISOR

Mr. KABANDO RAPHAEL MUTHEE

A PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE AWARD OF THE DEGREE OF BACHELOR OF ECONOMICS AND

STATISTICS

UNIVERSITY OF NAIROBI

MAY, 2012

ii

DECLARATION

This research is my original work, and has not been presented for the award of a degree in any

other University.

Signature…………………………………… Date …………………………….

MAJUNEKRAIDO SOCRATES

This research paper has been submitted with my approval as University Supervisor.

Signature ………………………………… Date …………………………......

Mr. KABANDO RAPHAEL MUTHEE

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ACKNOWLEDGEMENT

I wish to acknowledge the great support and guidance from my supervisor Mr. Kabando Raphael

Muthee. He has committed his time and devotion to read and scrutinize this research paper. He has

offered his guidance and constructive criticism that has greatly influenced my research paper. In

addition, he has equipped me with competitive research skills which I shall carry on to even more

advanced levels of my academic pursuits.

I am eternally grateful to my family and friends for their support both material and otherwise

during the pursuit of my studies. Mostly my loving Sister Brenda, Ms. Muriuki Diana and

Ms.Apunda Rose and family especially when I lost my laptop on the verge of completing my

project and perhaps felt very discouraged.

Finally I wish to thank God Almighty for the ability to pursue my studies. Any errors and

omissions are however mine.

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ABSTRACT

This research paper investigates the main drivers of foreign direct investment (FDI) in Kenya. It

is now widely acknowledged that FDI has potential benefits that can accrue to developing

countries. Kenya’s FDI record over the years has not been impressive. Even though Kenya was

among one of the most favoured destinations for FDI in the 1970s in East Africa, it is now among

the countries with very low levels of FDI. Few studies have investigated the reasons for low levels

of FDI in Kenya and most of these studies are based on macroeconomic data in conjunction with

other countries. This paper provides fresh evidence of the determinants of FDI based on several

factors deemed to determine FDI with political instability being the new factor on board. The study

findings reveal that most of the foreign firms in Kenya using a study period of 1980 to 2010 deem

the following as factors that encourage FDI inflow; political instability, exchange rate and inflation

rate while GDP growth rate deters FDI inflow.

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TABLE OF CONTENTS

DECLARATION ............................................................................................................................ ii

ACKNOWLEDGEMENT ............................................................................................................. iii

ABSTRACT ................................................................................................................................... iv

TABLE OF CONTENTS ................................................................................................................ v

LIST OF FIGURES AND TABLES............................................................................................. vii

CHAPTER ONE ........................................................................................................................... 1

1.0 INTRODUCTION .................................................................................................................... 1

1.1 Background ............................................................................................................................... 1

1.1.1 General Overview of FDI ................................................................................................................ 1

1.1.2 FDI in Kenya ..................................................................................................................................... 2

1.2 PROBLEM STATEMENT ....................................................................................................... 8

1.3 Objectives ................................................................................................................................. 9

1.4 Significance of the Study .......................................................................................................... 9

CHAPTER TWO ........................................................................................................................ 10

2.0 LITERATURE REVIEW ....................................................................................................... 10

2.1 THEORETICAL LITERATURE REVIEW ........................................................................... 10

2.2 EMPIRICAL LITERATURE REVIEW ................................................................................. 10

2.3 OVERVIEW OF LITERATURE ........................................................................................... 11

CHAPTER THREE .................................................................................................................... 17

3.0 METHODOLOGY ................................................................................................................. 17

3.1 Model Specification ................................................................................................................ 17

3.2 Testing for Autocorrelation..................................................................................................... 18

3.3 Data Sources and Data Analysis ............................................................................................. 19

vi

CHAPTER FOUR ....................................................................................................................... 20

4.0 DATA ANALYSIS AND RESULTS ..................................................................................... 20

Introduction ................................................................................................................................... 20

4.1 Regression Results .................................................................................................................. 20

4.2 Results of test for autocorrelation ........................................................................................... 21

4.3 Discussion of Results .............................................................................................................. 21

CHAPTER FIVE ........................................................................................................................ 23

5.0 SUMMARY, POLICY IMPLICATIONS AND CONCLUSION .......................................... 23

5.1 Summary ................................................................................................................................. 23

5.2 Policy implications.................................................................................................................. 24

5.3 Conclusions ............................................................................................................................. 24

5.4 Areas of further research ......................................................................................................... 24

REFERENCES ............................................................................................................................ 25

APPENDIX I: DATA ................................................................................................................... 27

APPENDIX 11: AUTOCORRELATION REMEDIAL MEASURE RESULTS ........................ 28

APPENDIX 111: CORRELATION MATRIX ............................................................................. 28

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LIST OF FIGURES AND TABLES

Figure 1FDI inward stock trend in East Africa (1980 to 2010) ...................................................... 5

Figure 2 Net FDI inflows trend in Kenya (1980 to 2010) .................................................................... 7

Table 1-FDI stock in Africa (millions of dollars) ........................................................................... 2

Table 2 -FDI inward stock in East Africa. (US Dollars at current exchange rate in millions of

shillings) .......................................................................................................................................... 4

Table 3-Net FDI flows and FDI stock in Kenya (1980-2010) in Millions of US Dollars. ............. 6

Table 4-Main impediments to Foreign Direct Investment in Kenya ............................................ 13

Table 5-GDP Growth Rates, FDI inflow, FDI Stock and FDI inflow as a percentage of GDP in

Kenya from 1980 to 2010 ............................................................................................................. 15

Table 6: Decision rules ................................................................................................................. 18

Table 7: Descriptive statistics ....................................................................................................... 20

1

CHAPTER ONE

1.0INTRODUCTION

1.1 Background

1.1.1 General Overview of FDI

Foreign Direct Investment (FDI) refers to an investment involving 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) (UNCTAD, 2008). FDI can be

in form of either “greenfield” investment (also known as “brick and “mortar” investment) or

merger and acquisition, which involves the acquisition of existing interest rather than new

investment.

Furthermore FDI has three components namely; equity capital, reinvested earnings and intra-

company loans (UNCTAD, 2008) with ownership of at least 10% of ordinary shares or voting

stock in corporate governance as the criterion for existence of a direct investment relationship

while a less than 10% ownership is recognized as portfolio investment. It is important to note that

on average close to three-quarters of annual FDI flows to Africa majorly goes to 24 countries

which are classified by the World Bank as oil-and mineral-dependent. Studies have shown that the

following countries are the biggest recipients of FDI in Africa (UNCTAD 2009 report): Nigeria,

South Africa, Morocco, Libya, the Sudan, Equatorial Guinea, Egypt, Algeria, Tunisia and

Madagascar. Africa’s share of world FDI inflows has for a large part remained below 3 percent in

spite of being at 5.88 percent in 1980. For instance Africa’s FDI share marginally declined from a

level of (2.07 percent in 2000 to a share level of 2.02 percent in 2001 with successive shares as

follows; 2002(2.23 percent), 2008(2.59 percent), 2009(2.72 percent) and 2010 (2.89 percent).

The following table shows FDI trends in Africa and Sub-Saharan Africa with Africa’s world share.

2

Table 1-FDI stock in Africa (millions of dollars)

YEAR AFRICA Sub-Saharan Africa Africa’s world

share (%)

1980 41,097.00 29,832.00 5.88

1985 42,898.00 27,762.00 4.34

1990 60,675.00 36,768.00 2.92

1995 89,308.00 56,089.00 2.63

2000 154,268.00 109,938.00 2.07

2001 151,186.00 102,481.00 2.02

2002 167,658.00 114,274.00 2.23

2003 203,015.00 141,263.00 2.16

2004 240,793.00 171,263.00 2.17

2005 261,753.00 184,864.00 2.27

2006 317,038.00 212,118.00 2.29

2007 395,240.00 259,355.00 2.21

2008 395,726.00 240,361.00 2.59

2009 488,824.00 317,222.00 2.72

2010 553,972.00 368,648.00 2.89

Source: UNCTAD data

Thus, several African countries have put much initiative (in form of signing of treaties, incentives

and investment promotion activities) to attract FDI due to the following perceived benefits ranging

from employment creation, transfer of management skills to local managers, increased export

competitiveness, employment creation and capital formation all of which contribute to economic

development and help to cushion Africa’s low savings rate.

1.1.2FDI in Kenya

Kenya is a country found in East Africa with land area of 580,000KM square and a population of

approximately 40 million. The country’s geography is diverse with its multi-ethnic population.

Agriculture is the major contributor to the economy with other sectors like manufacturing also

making significant contribution.

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FDI in Kenya is characterized by investment in foreign assets such as foreign currency, credits,

rights, benefits or property, undertaken by foreign national (like a person who is not a Kenyan

citizen) for the purpose of production of goods and services which are to be sold either in the

domestic market or exported overseas (Investment Promotion Center Act, Chapter 518).

Furthermore the Central Bank of Kenya acts as the custodian of records related to FDI transactions

in Kenya.

Kenya had about 114 foreign-affiliated firms by 2001 located in the economy according to UN

Conference on Trade and Development (UNCTAD) majorly in form of multinational firms

operating in the tertiary sector (trade, transport and telecommunications) with parent countries

being majorly Britain, United States, Germany, South Africa, Netherlands, Switzerland and of late

China and India(UNCTAD,2005) . FDI is majorly distributed in the manufacturing, service,

tourism and telecommunication sectors with 55 percent of the foreign firms concentrated in

Nairobi while Mombasa accounts for about 23 percent. Furthermore the main form of FDI

establishment has been through the form of greenfields establishment.

Basing on this, it is important to note that Kenya was one of the favorite destinations for FDI in

East Africa in 1960s and 1970s (87 percent of foreign ownership of companies) with most

investments in floriculture, horticulture and manufacturing sectors. This can be attributed to the

high development levels, good infrastructure, market size growth and openness to FDI that Kenya

had. However, Kenya lost its appeal to foreign investment over the years with a general decline in

FDI in 1980s and 1990s making Tanzania and Uganda to by-pass it. For instance during the period

2005 to 2010, Kenya’s FDI inflows averaged $204.67 million a year while Tanzania and Uganda

had $531.02 million and $691.85 million respectively. Furthermore, according to UNCTAD’s FDI

performance index findings, Kenya has never ranked better than position 111 at any time since

1990. Perhaps this can be linked to factors like political instability, declining economic

performance, slow rate of reforms, declining economic growth, corruption, rising cost of service,

poor governance and policy environment that made investment unattractive to both foreign and

domestic investors.

In particular Kenya has been left behind in the global surge in FDI flows that started in the mid

and late 1990s that benefited neighbors according to studies.

The following table shows trends of FDI inflows in Kenya, Uganda and Tanzania.

4

Table 2 -FDI inward stock in East Africa. (US Dollars at current exchange rate in millions of

shillings)

YEAR/COUNTRY KENYA UGANDA TANZANIA

1980 386.00 10.00 47.00

1985 476.00 8.00 91.00

1990 668.00 5.00 93.00

1995 732.00 276.00 325.00

2000 931.00 807.00 2,778.00

2001 937.00 962.00 2,960.00

2002 964.00 1,147.00 3,243.00

2003 1,064.00 1,349.00 4,139.00

2004 1,092.00 1,645.00 4,759.00

2005 1,113.00 2,074.00 4,390.00

2006 1,164.00 2,669.00 5,342.00

2007 1,893.00 3,461.00 5,942.00

2008 1,989.00 4,190.00 6,621.00

2009 2,129.00 5,006.00 7,266.00

2010 2,262.00 5,853.00 7,966.00

Source: UNCTAD data

5

Figure 1FDI inward stock trend in East Africa (1980 to 2010)

From the table 2 and graph above, it is clear that Kenya ranks below Uganda and Tanzania as a

major recipient of FDI.

The situation in Kenya has even been worsened by a worrying trend of foreign investors moving

out of Kenya, with few new investors planning significant expansion.

However in spite of a previous decline the performance of FDI has improved recently and averaged

US$123.6 million in 2000-2007 as shown on table 3.NetFDI increased to an average of 3.2% of

gross investment in 2000-2007 majorly due to investment by mobile phone companies.

6

Table 3-Net FDI flows and FDI stock in Kenya (1980-2010) in Millions of US Dollars.

Year FDI inflows FDI Stock

1980 79 386.65

1981 14.2 399.8

1982 13.0 412.8

1983 23.7 436.54

1984 10.8 447.29

1985 28.9 476.14

1986 32.7 508.87

1987 39.4 548.25

1988 0.4 548.64

1989 62 610.83

1990 57 667.93

1991 19 686.73

1992 6 692.73

1993 145 694.73

1994 7 699.03

1995 42 732.03

1996 108 742.5771

1997 62 795.5771

1998 26 806.5771

1999 42 820.399

2000 127 931.00

2001 50 937.00

2002 52 964.00

2003 81 1,064.00

2004 46 1,092.00

2005 21 1,113.00

2006 51 1,164.00

2007 729 1,893.00

2008 95 1,989.00

2009 116 2,129.00

2010 185 2,262.00

Sources: UNCTAD, World Investment Report (various issues) and KNBS Statistical Abstracts

(1980-2010)

7

Figure 2 Net FDI inflows trend in Kenya (1980 to 2010)

From the graph, it can be noted that FDI inflow in Kenya has been fluctuating.

Hence with the disappointing FDI inflow, Kenya has put forward several astutes to attract FDI

which is hoped to assist in the development process. Notably, Kenya launched lunched its long

term development blue print, Vision 2030(covers period from 2008 to 2030 with successive five

year Medium Term Plans) after the elapse of the term for the Economic Recovery Paper in 2007.

The country has five export processing zones with the government owning two (Mombasa and

Athi) and the private owns the rest (Nairobi, Della Rue and Nakuru) which strengthen the operating

environment for zone based industries. Special incentives are given to transnational corporations

that invest in lesser developed areas with abolition of exports and import licensing besides

rationalization and reducing import tariffs. Furthermore there are no restrictions to foreign

8

investment or on foreign ownership with unrestricted repatriation of profits and also unrestricted

borrowing by foreign as well as domestic companies.

The aim is to make Kenya globally competitive which among other aims is to attract FDI to assist

in Industrialization. However despite of these strategies, Kenya still ranks as an underperformer

with low FDI potential and performance according to UNCTAD in 2007.

The issue of FDI determinants in Kenya has mostly been conducted in cross-country studies

usually employing comparative analysis using some African or developing countries with Kenya

included. Therefore, it is prudent to carry out an empirical study to find out key drivers of FDI in

Kenya. The most important determinants of FDI in Kenya as identified by most studies are; size

of the market (Gross Domestic Product (GDP) growth.), political and economic instability both in

Kenya and her neighbors (insecurity).

1.2 PROBLEM STATEMENT

The deteriorating trend of FDI inflow as shown in table 3 has raised eyebrows of Kenya’s capacity

to attract foreign investment. Kenya has proved to have a potential of attracting FDI given the

significant success in the horticulture, the airline industry and mobile telephony industry which

have had a blessing of foreign investors. Furthermore Kenya risks losing a lot of benefits that come

along with FDI:

i. Capital and Investment; FDI could play an important role in providing extra sources of

capital and investment.

ii. Technology and skills; Technological transfers can benefit industry growth as witnessed

in the horticulture and floriculture industries.

iii. Employment creation; FDI has been crucial in employment creation witnessed by the

employment of over 35,000 people due to rise in lobour-intensive garment production

iv. Diversification of output and exports; FDI has played a major role in the more dynamic

sectors of horticulture and garment sectors, tourism and tourism related services.

With all this benefits measures need to be put in place to create an enabling environment for FDI

in Kenya. The purpose of this study is to look at the possible determinants of FDI attraction in

Kenya with a major view on GDP growth (Market size), political instability, exchange rate and

inflation rate.

9

1.3 Objectives

GENERAL OBJECTIVE

The general objective of this study is to identify the determinants of Foreign Direct Investment in

Kenya and establish their relative importance in determining the flows.

SPECIFIC OBJECTIVES

a) To identify the determinants of Foreign Direct Investment in Kenya.

b) To establish the relationship between the determinants identified above and the FDI Flows in

Kenya.

c) To determine the relative importance of the determinants identified above on FDI inflow in

Kenya.

d) To use (a) and (b) above to suggest policy recommendations for the country.

1.4 Significance of the Study

There is need to critically examine the extent of the current fluctuating levels of FDI and whether

the current policy environment is conducive to FDI in the country. In this regard it is necessary to

empirically analyze the relationship between FDI and its determinants in Kenya.

10

CHAPTER TWO

2.0 LITERATURE REVIEW

The purpose of this chapter is to review both theoretical and empirical literature on FDI. The first

section reviews theoretical literature under the Global Horizon Theory, International Product

Cycle, Neoclassical Trade Theory, Keynesian Theory and Portfolio Theory.The second section

presents a summary of empirical studies which is followed by an overview of literature review.

2.1 THEORETICAL LITERATURE REVIEW

It is important to note that various theories have been advanced to explain the determinants of FDI

but there is no single unanimously accepted single factor determining the flow of investment.

Hence, this study will recognize the following theories as trying to outline the determinants of

FDI: (1) Global Horizon Theory (GTH) which identifies internal and external forces that make a

firm go to for international markets; (2) International Product Cycle (IPC) which suggests that

firms undertake FDI at particular stages in life cycle of products they have innovated; (3)

Determinants according to Neoclassical Trade Theory in which capital moves across countries

owning to differences in returns while seeking the highest return. Therefore, early neoclassical

theories explained that international capital flows with differentiated rates of return across

countries that lead to capital arbitrage, with capital seeking the highest return; (4) Determinants of

FDI according to Keynesian Theory of Economics in which developed countries invest in

developing ones to raise their welfare and bring about transition; (5) Portfolio Theory which takes

into account the element of uncertainty that is missing in other theories. Thus, investors are

postulated to consider not only returns but also the risk in selecting their portfolio.

2.2 EMPIRICAL LITERATURE REVIEW

Several studies have tried to identify determinants of FDI notably: Ajayi (2007) identified market

size and growth, costs and the skills of workers, availability of good infrastructure, country risk,

openness, institutional environment, natural resources, agglomeration effects, returns on

investment, macroeconomic policies among others. However, very few studies have been

conducted on FDI determinants in Kenya. Just to outline a few, Kinaro (2006) using time series

analysis found that FDI in Kenya is determined by economic openness, human capital, real

exchange rate, inflation and FDI in previous periods. Opolot et al (2008) while using panel data

for sub-Saharan Africa including Kenya noted that market potential, openness to trade,

11

infrastructure, urbanization and the rate of return on investment positively affect FDI inflows to

Sub-Saharan Africa while macroeconomic instability is a disincentive to FDI. In addition Mwega

and Rose (2007) while using panel data of 43 countries found that Kenya was not different from

other countries and that FDI is determined by growth rates, terms of trade shocks, external debt

ratio and quality of institutions. UNCTAD (2005) argue that Kenya’s inability to attract FDI is due

to growing problems of corruption and governance, inconsistencies in economic policies and

structural reforms, deteriorating public service and poor infrastructure.

Wanjala (2001) using panel data analysis for 20 Sub-Saharan Africa countries to study

determinants of Foreign Direct Investment with reference on Kenya from 1990 to 1999,identified

taxation policy, rate of return (captured by the real GDP growth rate) and openness of an economy

as significant factors of FDI inflows.

Also according to Nyamwange (2009) while using annual time series data identified market size

(proxied by GDP), stable macroeconomic policies and a level of human capital that is tolerable by

investors as the main determinants of FDI in Kenya.

Kinuthia and University of Nairobi School of Economics (2010) in their paper submitted for the

annual African International Business and Management (AIBUMA) Conference in Nairobi in

August 2010 identified political instability, crime and insecurity, and institutional factors mostly

corruption as the major determinant of FDI in Kenya. Therefore In general, most studies identified

human capital, market size and openness as major determinants of FDI. Perhaps the issue of

political instability was not looked at intensively.

Hence this paper seeks to expound on the issues of political instability, exchange rate, market size

(GDP growth rate) and inflation as major determinants of FDI in Kenya.

2.3 OVERVIEW OF LITERATURE

a. Political Instability

Kenya risks losing a lot in terms FDI in the presence of political instability. Perhaps this can best

be understood by basing on the findings of a study by Edwards (1990). The study suggests that

inclusion of political instability as a determinant of FDI emanates from the theory of transaction

cost in which FDI is negatively affected by risk of expropriation of investment by host country’s

government .This effect can be higher on the foreign investor than on the political instability of

the host country by causing sunk costs of investing to be high (Altomonte, 1998). Thus, with a

porous political stability in Kenya, investors will most likely shy away investing in Kenya,

12

terminate operations, cancel agreements with government or force negotiation. Further actions can

also be taken in form of enactment of new laws that make doing business more expensive, currency

restrictions and changes on Trade-Related Measures (TRIMs) or equity participation rules..

Furthermore, the political system is the one that sets policies to allow foreign investors to operate

in Kenya. Of significance to note is that Kenya has witnessed cases of political instability with the

most notable case being the 2007-2008 Post-election violence which had a major negative impact

on the Gross Domestic Product (GDP). In the same line, it can be noted that FDI Inflow declined

in 2008 after a high in 2007 as shown in table 5.

From table 5, it can be noted that there was a sharp decline in GDP growth from 6.993% in 2007

to 1.528% in 2008 which is majorly attributed to the post-election violence. Notably, cases of

political instability have been majorly witnessed at the nearing of elections in Kenya. Furthermore,

a study by Kinuthia and University of Nairobi School of Economics in a paper submitted for the

Annual African International Business and Management (AIBUMA) conference in Nairobi in

August 2010 confirms that political instability is a vital factor determining FDI in Kenya. From

table 4, political instability in Kenya and its neighbors was rated highest at 27 percent among the

impediments of FDI in Kenya by respondents.

13

Table 4-Main impediments to Foreign Direct Investment in Kenya

FACTOR PERCENTAGE

Political instability in Kenya and its neighbors 27

Crime and Insecurity 19

Climate change 4

Competition 5

Unreliable infrastructure 7

Delays in licenses and work permits 6

Lack of law enforcement/weak legal infrastructure 4

Lack of proper knowledge of regional blocks 2

Lack of clear policies and regulatory impediments 5

Exchange rate volatility/Currency risk 3

Economic growth 3

Lack of skilled labour/High cost of production 3

Source: Kinuthia and University of Nairobi School of Economics paper (2010)

Thus political instability is a vital factor that potential foreign investors would be keen to look at

knowing that expropriation (politically motivated and forceful acquisition and redistribution of

private property by the host country outside the common law) might occur leading to loss of

assets.This study will analyze major historical political violence cases in Kenya: 1982 (coup

attempt to overthrow the government), 1991/1992 (tribal clashes in 1991 followed by election

violence in 1992 due to calls for multi-party democracy and elections), 1997 (politically motivated

violence), 2002 (mild politically motivated violence) and 2007/2008 (wide spread country-wide

politically motivated violence)

b) Gross Domestic Product Growth (Market Size)

The size of the market is a very vital catalyst of FDI in a country. It is important to note that the

market size of a country is represented by the GDP which in turn represents the host country’s

economic muscles and the potential demand for their output. According to Altomonte (1998),

market size has a positive sign and positive correlation with FDI in that the bigger the GDP, the

larger the inflows of FDI, since larger economies tend to attract more capital in accordance with

the gravity approach. The rate of GDP growth has proved a vital factor for FDI perhaps confirming

14

Altomonte’s notion. Table 5 will show how the trend of FDI has been in relation to GDP trend in

Kenya.

15

Table 5-GDP Growth Rates, FDI inflow, FDI Stock and FDI inflow as a percentage of GDP

in Kenya from 1980 to 2010

Year GDP Growth Rate FDI inflows(million) FDI Stock(million) FDI inflows as % of GDP

1980 5.572 79 386.65 1.086997173

1981 4.1 14.2 399.8 0.206398387

1982 5.052 13.0 412.8 0.202141099

1983 1.593 23.7 436.54 0.397023332

1984 1.6 10.8 447.29 0.17368417

1985 4.73 28.9 476.14 0.4701835

1986 6.983 32.7 508.87 0.452066859

1987 5.811 39.4 548.25 0.494069136

1988 6.091 0.4 548.64 0.004720678

1989 4.554 62 610.83 0.751836887

1990 4.134 57 667.93 0.664461938

1991 1.339 19 686.73 0.230995267

1992 -1.08 6 692.73 0.077403617

1993 -0.095 145 694.73 2.532352589

1994 2.531 7 699.03 0.103976829

1995 4.287 42 732.03 0.467473986

1996 4.011 108 742.5771 0.902161732

1997 0.22 62 795.5771 0.473452965

1998 3.33 26 806.5771 0.188375901

1999 2.407 42 820.399 0.402863318

2000 0.599 127 931.00 0.873864259

2001 4.726 50 937.00 0.040831747

2002 0.299 52 964.00 0.210037979

2003 2.785 81 1,064.00 0.548445031

2004 4.616 46 1,092.00 0.286180527

2005 5.981 21 1,113.00 0.113201906

2006 6.326 51 1,164.00 0.225198582

2007 6.993 729 1,893.00 2.68290644

2008 1.528 95 1,989.00 0.318285505

2009 2.645 116 2,129.00 0.39576014

2010 5.552 185 2,262.00 0.591535424

Sources: UNCTAD, World Economic Outlook Database September 2011 and KNBS Statistical

Abstracts (1980-2010).

16

From the table, it can be noted that FDI inflow seems to have a positive relationship with GDP

growth in that an increase in GDP leads to an increase in FDI inflow. For instance consider years

like 1980 which had a GDP growth of 5.572% with FDI inflow of 79 million US dollars followed

by a decline in 1981 which had a GDP growth of 4.1% with FDI inflow of 14.2 million US dollars.

However, not all years that witnessed GDP growth had an increase in FDI inflow. Never the less,

GDP growth rate is a vital indicator of FDI as it shows the prospects of investing in Kenya.

c) Inflation

It is important to note that inflation leads to high prices besides increasing production cost thereby

having a negative impact on the FDI. In addition, a high inflation rate reflects macroeconomic

instability, which increases uncertainty and makes it less attractive for FDI in terms of decision

making by foreign investors. In this case a high inflation rate makes it difficult for investors to

evaluate the true costs and returns of their investments.

Hence the worrying trend of inflation fluctuations in Kenya may have a large impact on the

decision making aspects of foreign investors. This study will seek to find the impact of inflation

on FDI it having been scantly researched on by studies.

d) Exchange rate

Devaluation changes in the Exchange Rate captures the effect of the host country relative to the

sourcing country. Exchange rates are expected to affect FDI in so far as they affect a company’s

cash flow, attractiveness of domestic assets to foreign investors and expected profitability. Hence,

real depreciation of the local currency affects investment through its effect on the real cost of

capital and real output. On the other hand, unanticipated devaluation affects the profitability of

local businesses by raising the price level and increasing the cost of imported intermediate inputs.

Exchange rate volatility leads results in uncertainty, macroeconomic instability and high exchange

risk while devaluation will increase FDI if investment is export oriented according to Wanjala

(2001).

17

CHAPTER THREE

3.0 METHODOLOGY

3.1 Model Specification

The study uses time series data to analyze the determinants of Foreign Direct Investment in Kenya

for a period ranging from 1980 to 2010. The choice of the period is because Kenya faced major

political instability cases within it and also FDI inflow remained unpredictable within this period

.This study will use regression analysis to analyze the econometric model where by FDI to GDP

ratio will be the dependent variable while explanatory variables will include; political instability,

inflation, exchange rate and market size (GDP growth rate).

Hence, FDI to GDP ratio will be measured as a linear relation to factors that impact it directly i.e.

political instability, inflation rate, exchange rate and market size/GDP growth.

Hence the model will be of the form;

…………................. (Model 1)

Where:

t=1,…, T Is time

Is the Net Foreign Direct Investment

Is political instability

Is the market size

Is the inflation rate

Is the real exchange rate

Is the stochastic error term

, , and are the coefficients to be estimated

Description of variables

1. FDI to GDP ratio is the rate of Foreign Direct Investment stock divided by GDP (all in US

dollars multiplied by one hundred.)

2. Political instability is also a vital determinant of FDI in Kenya. It is widely acknowledged that

when a country is politically unstable its FDI is hindered. Political instability is usually measured

by the probability of a change of government, as well as political violence as measured by the sum

18

of frequency of political assassinations, violent riots and politically motivated strikes. Asiedu

(2001) used average number of assassinations and revolutions to measure political instability.

Easterly and Levine (1997) and Anyanwu (1998) used the number of coups d’état a country suffers

to measure political instability. In this study a dummy variable was used with presence of political

instability in a year being the reference (=0) or (1) otherwise where a year has no political stability.

Using historical studies about Kenya, major political violence CASES occurred in1982,

1991/1992, 1997, 2002 and 2007/2008. We expect an indirect relationship between the measure

and FDI to GDP ratio.

3. GDP Growth rate represents the market size of an economy in terms of purchasing power and

general macroeconomic performance. Hence it is assumed to have a positive relationship with FDI

inflow.

4. Inflation rate is included as a measure of overall economic stability of the country. It is assumed

that an increase in inflation rate discourages FDI to GDP ratio therefore is expected to have a

negative relationship with inflation rate.

5. Exchange rate is in form of one US dollar to Kenyan shillings.

3.2 Testing for Autocorrelation

Autocorrelation is the “correlation between members of observations ordered in time (as in time

series data) or space (as in cross-sectional data)” (Essentials of Econometrics, 2006). The Durbin-

Watson d Test (ratio of the sum of squared differences in successive residuals to Residual Sum of

Squares) will be used to test for autocorrelation with the following decision

rules used to make conclusions:

Table 6: Decision rules

Null hypothesis Decision If

i.)No positive autocorrelation Reject 0< d <

ii.)No positive autocorrelation No decision

iii.)No negative autocorrelation Reject 4-

iv.)No negative autocorrelation No decision 4 - -

v.)No positive or negative autocorrelation Do not reject -

Source: Essentials of Econometrics, 2006 (page 439)

19

However, it is important to note that if the statistic lies in the indecisive zone, it might be

prudent to assume that autocorrelation exists and proceed to correct the condition.

In case autocorrelation is found, a remedial measure will be used where the regression model will

be lagged to one period to form;

……… (Model 2)

Then an Ordinary Least Squares will be applied which will form generalized least squares (GLS)

estimators.

3.3 Data Sources and Data Analysis

The data for FDI and the independent variables (GDP growth rate, exchange rate and Inflation

rate) will majorly be obtained from World Bank data on world development indicators and global

finance, Kenya economic survey reports, Kenya annual statistical abstracts, Central Bank of Kenya

publications, World Investment reports, IMF database and UNCTAD website. Political instability

will be an own computation. Both STATA and Gretl softwares have been used in data analysis for

this research paper.

20

CHAPTER FOUR

4.0 DATA ANALYSIS AND RESULTS

Introduction

This chapter is aimed at presenting empirically the results and finding of the study based on the

data on FDI to GDP ratio, GDP growth, exchange rate, inflation rate and political instability for

the period under review. By applying model 1 specified in the previous chapter and ordinary least

squares method, the results obtained are as shown in table 8 below.

By regressing FDI to GDP ratio as a function of Political instability, FDI to GDP ratio as a function

of GDP growth rate, FDI to GDP ratio as a function of exchange rate and FDI as a function of

inflation, we would show the explanatory power of these variables on the variations on FDI.

The following table has a summary of descriptive statistics;

Table 7: Descriptive statistics

Variable Observation Mean Std. Dev. Min Max

FDI 31 6.016226 1.151367 3.828 8.828

INFLATION 31 11.97968 8.956859 1.554 45.979

EXCHANGE RATE 31 47.3077 27.24759 7.420187 79.23315

GDP GROWTH RATE 31 3.577806 2.106793 0.095 6.993

4.1 Regression Results

The OLS regression results are as shown below:

Model: OLS, using observations from 1980 to 2010 with 31 observations

Dependent variable: (FDI/GDP)*100

(FDI/GDP)*100 Coefficient Std. Err. t P>t [95% Conf. Interval]

Inflation rate 0.0442931 0.0123849 3.58 0.001 0.0188356 0.0697506

GDP growth R -0.1217021 0.0552909 -2.20 0.037 -0.2353542 -0.0080499

Political inst 0.0460304 0.2331912 0.20 0.845 -0.4333009 0.5253618

Exchange rate 0.0333757 0.0036465 9.15 0.000 0.0258801 0.0408712

constant 4.306472 0.4085538 10.54 0.000 3.466677 5.146266

21

Mean dependent variable 6.016201 S.D. dependent variable 1.151433

Sum squared residual6.837685 S.E. of regression 0.512823

R-squared 0.828086 Adjusted R-squared 0.801638

F(4, 26) 31.30965 P-value(F) 1.35e-09

Log-likelihood -20.55826 Akaike criterion 51.11651

Schwarz criterion 58.28645 Hannan-Quinn 53.4537

Number of observations=31 t(29)right-tail probability = 0.025

Complementary probability = 0.975 two-tailed probability = 0.05

Critical value = 2.04523

4.2 Results of test for autocorrelation

0.233037 d=1.437786

5% critical values for Durbin-Watson statistic, n = 31, k = 3

dL (lower limit) = 1.2292

dU (upper limit) = 1.6500

4.3 Discussion of Results

The model explains 82.81% of the variations in the FDI to GDP ratio as shown by the R-Squared

value.

The regression results linked to model (1) formed the following regression model:

……………….model (3)

The t statistic values are 10.54, 0.20, -2.20, 3,58 and 9.15 respectively for 31 observations.

FDI to GDP ratio on average amounts to 4.306472 units when there is political Instability in a year

with no GDP growth, Inflation rate and exchange rate consideration.

A unit increase in inflation rate increases the FDI to GDP ratio by 0.0442931 units. Inflation rate

is a significant determinant of FDI inflow at 5 percent significance level. The positive relationship

of the inflation coefficient suggests that the development within the macro economy is such that it

encourages FDI inflows. The same result was found in Nigeria by Ayanwale (2007)

A unit increase in GDP growth rate reduces FDI to GDP ratio by 0.1217021 units. This negative

relationship suggests that GDP growth discourages FDI inflow in Kenya. However, this is contrary

to most study findings in Kenya where GDP growth was found to have a positive relationship with

22

FDI inflow for instance Wanjala (2001), Elbadawi and Mwega (1997). Never the less, GDP growth

rate is a significant determinant of FDI inflow at 5 percent significance level.

A unit increase in exchange rate increases FDI to GDP ratio by 0.0333757 units. Exchange rate is

a significant determinant of FDI inflow at 5 percent significance level.

Holding inflation, GDP growth rate and exchange rate constant, a year with political stability

realizes an increase in FDI inflow than a year with political instability with no GDP growth and

inflation rate consideration having an FDI to GDP ratio of 4.306472 by 0.0460304 units of FDI to

GDP ratio. Political instability is an insignificant determinant of FDI inflow at 5 percent

significance level because

The Durbin-Watson test has a value of 1.437786 with a rho ( ) value of 0.233037suggests that no

decision can be made with regards presence of positive autocorrelation in the model basing on

decision (ii) because 1.2292<1.437786<1.6500.Therefore, it is prudent to assume that

autocorrelation exists and carry out remedial measures by lagging to one period to obtain the

results in form of model 2 as shown below.

=

………..... Model (4)

23

CHAPTER FIVE

5.0 SUMMARY, POLICY IMPLICATIONS AND CONCLUSION

5.1 Summary

The main objective of this study was to determine the determinants of FDI in Kenya. The data was

collected from secondary sources and analyzed using ordinary least squares to get the stated

objective. It was expected that there would be four probable results: (a) exchange rates determine

FDI; (b) political instability determines FDI; (c) inflation rate determines FDI ; (d) GDP growth

determines FDI.

From the findings we can conclude that exchange rate, political stability (where =1) and inflation

rate have a positive effect on FDI inflow while GDP growth has a negative effect on FDI.

Of major interest is that economic growth negatively impacts on FDI which is contrary to what

was expected meaning that the government should focus more on improving exchange rate and

political stability. The negative effect of GDP growth rate can best be explained using data in table

5 where it can be shown that on most occasions FDI inflow declined regardless of GDP growth.

Notably, some years experienced increase in GDP growth rate but had less FDI inflow compared

to the previous year for instance 1981 had a GDP growth of 4.1% followed by 5.052% in 1982 but

FDI inflow declined from 14.2 million to 13.0 million respectively. The same happened for

2003/2004 where GDP growth was 2.785% to 4.616% while FDI was 81 million to 46 million

respectively.

On the other hand, some years experienced GDP growth decline but had more FDI inflow than

previous years. For instance 1983 had a GDP growth of 1.593 while 1982 had 5.052 but FDI inflow

in 1983 was 23.7 million while 1982 had 13.0 million. Also 2002 had a GDP growth of 0.2995

while 2001 had 4.726% but 2002 had FDI inflow of 52 million to 50 million of 2001.

Furthermore inflation has a positive impact on FDI meaning that an increase in inflation increases

FDI inflow. This might be due to more currency circulation in the domestic Kenyan economy

which increases foreign investor’s willingness to invest in the market mostly through capital

projects according to the Tobin effect. However, caution should be taken as high inflation impacts

negatively on the domestic market.

24

5.2 Policy implications

The results obtained in this study have a number of policy implications having found that exchange

rate, political stability and inflation rate have a positive effect on FDI inflow while GDP growth

has a negative effect on FDI. Hence the government more so the parties involved in the financial

sector should ensure that a favorable exchange rate is maintained as the country is bound to benefit.

More so the government should create a conducive political environment and adopt more liberal

policy to attract new FDI and maximize the net benefit to avoid political instability. Inflation rate

has been found to be favorable for FDI attraction although caution should be taken to ensure that

inflation does not rise to levels that hurt the domestic market. Perhaps appropriate monetary

policies should be set by the Central Bank coupled with appropriate fiscal policies by the

government to control inflation.

5.3 Conclusions

Based on the empirical studies, there is majorly a need by the government to ensure political

stability is maintained. Although it was found that GDP growth has a negative relationship with

FDI, the study recognizes that GDP growth is still vital for FDI inflow in Kenya.

5.4 Areas of further research

A policy issue that still needs to be addressed is whether the FDI inflows as a source of external

finance is both efficient for and supportive of poverty reduction and development that Kenya as a

country urgently yearns for. Hence more studies can be done to identify whether FDI addresses

the problems of poverty and development in Kenya.

25

REFERENCES

Ajayi, S.I (2007) “The Determinants of Foreign Direct Investment: A Survey of the Evidence” in

AjayiS.I (eds) Foreign Direct Investment in Sub Saharan Africa: Origins, Targets, Impact and

Potential. Africa Economic Research Consortium.

Anyanwu, J.C. 1998. “An econometric investigation of determinants of foreign direct investment

in Nigeria”. In Investment in the Growth Process: Proceedings of the Nigerian Economic

Society Conference 1998, pp. 219–40. Ibadan, Nigeria.

Asiedu, E. 2001. “On the determinants of foreign direct investment to developing countries: Is

Africa different?” World Development, 30(1): 107–19.

Ayanwale B. (2007).Foreign Direct Investment: Evidence from Nigeria. Africa Economic

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Edwards, S. (1990). Capital flows, foreign direct investment, and debt-equity swaps

in developing countries, NBER Working Paper No. 3497.

Gujarati, Domadar N.: Essentials of Econometrics, 3 rd ed., McGraw-Hill, New York, 2006

http://Data.worldbank.org .World Data Bank. Data Catalogue. World Development indicators and

Global Development Finance.

Kenya National Bureau of statistics (1980-2010). Statistical Abstracts. Nairobi, Kenya

Kinaro, E.O (2006) “Determinants of Foreign Direct Investment in Kenya” Institut African de

DéveloppementEconomiqueet de Planification. Dakar

Kinuthia, B. (2010). Determinants of Foreign Direct Investment in Kenya: New Evidence.

Annual African International Business and Management (AIBUMA). Africa Studies Centre,

Leiden.

Mwega .F.M and R.W. Ngugi (2007) “Foreign Direct Investment in Kenya” in AjayiS.I (eds)

Foreign Direct Investment in Sub Saharan Africa: Origins, Targets, Impact and Potential. Africa

Economic Research Consortium.

Nyamwange, M (2009) “Foreign direct investment in Kenya” Munich Personal RePEc Paper

No.34155

Opolot, J., Mutenyo. J and A. Kalio (2008) “Determinants of Foreign Direct Investment: Evidence

From Sub-Saharan Africa Using A Generalized Method Of Moments Dynamic Panel Estimator”.

Research Bank of Uganda.

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United Nations (2005) United Nations Conference on Trade And Development. World

Investment Report. Transnational Corporations and internationalization of Research and

Development.

United Nations (2007) United Nations Conference on Trade And Development. World

Investment Report. Transnational Corporations, Extractive Industry and Development.

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Investment Report. Transnational Corporation and Infrastructure challenge.

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Wanjala, Bernadette Mukhwana (2001). Determinants of foreign direct investment in Sub-

Saharan Africa, with inferences on Kenya. Thesis (M.A.) - University of Nairobi

www.imf.org. World Economic Outlook data base, September 2011

27

APPENDIX I: DATA

Year FDI STOCK GDP

(FDI/GDP)

*100

Inflation,averag

e consumerprice

(Index).

GDP Growth

Rate Exchange rate

Political

instability

1980 386650000 10099652750 3.828 13.866 5.572 7.420187 1

1981 399800000 9512698076 4.203 7.895 4.1 9.047498 1

1982 412800000 9158446616 4.507 13.821 5.052 10.92232 0

1983 436540000 8470110264 5.154 11.603 1.593 13.31152 1

1984 447290000 8788245492 5.090 20.667 1.6 14.41387 1

1985 476140000 8745981720 5.444 11.398 4.073 16.43212 1

1986 508870000 10387431472 4.899 10.284 6.982 16.22574 1

1987 548250000 11387130726 4.815 13.007 5.811 16.45449 1

1988 548640000 11806444580 4.647 4.804 6.091 17.7471 1

1989 610830000 11704630130 5.219 7.617 4.554 20.57247 1

1990 667930000 12179395067 5.484 11.2 4.134 22.91477 1

1991 686730000 11501231789 5.971 19.104 1.339 27.50787 0

1992 692730000 11327072510 6.116 27.332 -1.08 32.21683 0

1993 694730000 7869288768 8.828 45.979 -0.095 58.00133 1

1994 699030000 9421865295 7.419 28.814 2.531 56.05058 1

1995 732030000 11943769568 6.129 1.554 4.287 51.42983 1

1996 742577000 12045797460 6.165 8.862 4.011 57.11487 1

1997 795577000 13281241188 5.990 11.924 0.22 58.73184 0

1998 806577000 13767069608 5.859 6.716 3.33 60.3667 1

1999 820399000 12882667700 6.368 5.753 2.407 70.32622 1

2000 931000000 12316172400 7.559 9.955 0.599 76.17554 1

2001 937000000 13058650640 7.175 5.73 4.726 78.5632 1

2002 964000000 13190924666 7.308 1.97 0.299 78.74914 0

2003 1064000000 15036116649 7.076 9.81 2.785 75.93557 1

2004 1092000000 16090725920 6.787 11.79 4.616 79.17388 1

2005 1113000000 19366499380 5.747 9.87 5.981 75.55411 1

2006 1164000000 23303933604 4.995 6.041 6.326 72.10084 1

2007 1893000000 28964414115 6.536 4.265 6.993 67.31764 0

2008 1989000000 27052658052 7.352 15.101 1.528 69.17532 0

2009 2129000000 31359141800 6.789 10.552 2.645 77.35201 1

2010 2262000000 32114596134 7.044 4.086 5.552 79.23315 1

28

APPENDIX 11: AUTOCORRELATION REMEDIAL MEASURE RESULTS

Dependent variable: uhat

Breusch-Godfrey test for first-order autocorrelation

OLS, using observations 1905/06/02-1905/07/02 (T = 31)

coefficient std. error t-ratio p-value

constant -0.0633307 0.407077 -0.1556 0.8776

Inflation Rate 0.000851319 0.0128712 0.06614 0.9478

GDP Growth Rate 0.0143713 0.0560468 0.2564 0.7997

Exchange Rate 0.000442848 0.00367806 0.1204 0.9051

Political instability -0.0183900 0.240147 -0.07658 0.9396

uhat_1 0.237134 0.204765 1.158 0.2578

Unadjusted R-squared = 0.050915

Test statistic: LMF = 1.341149,

with p-value = P(F(1,25) > 1.34115) = 0.258

Alternative statistic: TR^2 = 1.578353,

with p-value = P(Chi-square(1) > 1.57835) = 0.209

Ljung-Box Q' = 1.57199,

with p-value = P(Chi-square(1) > 1.57199) = 0.21

APPENDIX 111: CORRELATION MATRIX

FDI INFLATION

RATE

GDP GROWTH

RATE

EXCHANGE

RATE

POLITICAL

INSTABILITY

1.0000 0.3128 -0.4953 0.7692 -0.1135 FDI

1.0000 -0.5058 -0.1700 -0.0846 INFLATION

RATE

1.0000 -0.1467 0.3567 GDP

GROWTH

RATE

1.0000 -0.0388 EXCHANGE

RATE

1.0000 POLITICAL

INSTABILITY