Foreign Direct Investment in Zambia’s Mining and Other Sectors:...

Macroeconomic and Financial Management Institute of Eastern and Southern Africa

Foreign Direct Investment in Zambia’s Mining and

Other Sectors: Distinguishing Drivers and Implications for Diversification

A Technical Paper Submitted in Fulfilment of the MEFMI Fellowship Programme in

Foreign Private Capital Monitoring and Analysis

By Wilson C.K. Phiri1

Bank of Zambia

April 2011

1 Wilson C.K. Phiri- Economist, Balance of Payments, Economics Department, Bank of Zambia, P.O. Box 30080, Lusaka Zambia. Tel: +260 211 228888, Fax: +260 211 221722, E-mails: [email protected], [email protected].

9 Earls Road,

Alexandria Park, Harare

P.O. Box 66016 Kopje,

Harare, Zimbabwe

Tel: +263 4 745988-94,

Fax: +263 4 745547-8

Email: [email protected],

Web: www.mefmi.org

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FDI in Zambia’s Mining and Other Sectors: Distinguishing Drivers and Implications for Diversification

By Wilson C.K. Phiri

Bank of Zambia

April 2011

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TABLE OF CONTENTS

i. List of Figures ............................................................................................................................. iv

ii. List of Tables ............................................................................................................................... iv

iii. List of Acronyms and Abbreviations ............................................................................................ v

iv. Acknowledgements ...................................................................................................................... vi

v. Abstract ...................................................................................................................................... vii

1. INTRODUCTION ...................................................................................................................... 1

2. RECENT TRENDS AND PROSPECTS OF FDI IN ZAMBIA ............................................ 2

3. LITERATURE REVIEW .......................................................................................................... 9

3.1 Determinants of Foreign Direct Investment: Econometric Studies ............................................. 9

3.2 Determinants of Foreign Direct Investment: Qualitative and Other Studies ..............................15

3.3. Key Findings from the Literature Survey ...................................................................................26

4. METHODOLOGY AND DATA ..............................................................................................28

4.1 Conceptual Framework ...............................................................................................................28

4.2 Empirical Model .........................................................................................................................29

4.3 Stationarity, Cointegration and Diagnostic tests .........................................................................35

5. RESULTS AND ANALYSIS ....................................................................................................37

5.1 Stationarity, Cointegration and Diagnostic Test Results ............................................................37

5.2 Estimation Results and Analysis .................................................................................................38

5.3 Explanations for Inconsistent Findings .......................................................................................46

5.4 Comparison of Mining and Non-Mining ....................................................................................48

5.5 Correlation Tests and Analysis ...................................................................................................49

6. CONCLUSION AND POLICY RECOMMENDATIONS ....................................................52

7. REFERENCES/BIBLIOGRAPHY .........................................................................................57

8. ANNEX ......................................................................................................................................61

8.1 Annex i: Cointegration and Error Correction Methodology ..........................................................61

8.2 Annex ii: Diagnostic Tests ..............................................................................................................62

8.3 Annex iii: Stationarity and Cointegration Test Results ...................................................................62

8.4 Annex iv: Summary Table of Data Description, Sources and Limitations .....................................63

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i. List of Figures

Figure 1: Zambia's FDI Inflows by Sector, (2001, 2007 and 2009), US $ million ...................................... 4 Figure2: Stock of Zambia's FDI by Sector, (2000, 2001, 2006- 2009), US $ million ................................. 4 Figure 3: Share of FDI Stock by Sector, (2000, 2001, 2006- 2009), percent .............................................. 5 Figure4: Trend of Mining, Non-Mining and Overall FDI inflows (1970 - 2009), US $ million ................. 6 Figure5: Movements in the Monthly Average LME and Realised Copper Price 1999-2010 ...................... 7 Figure 6: Trends of Zambia’s GDP, GDP Per Capita and Degree of Urbanisation ....................................11 Figure 7: Major Investor Pull Factors .........................................................................................................16 Figure 8: Zambia’s Top Investment Catalysts and Constraints in Mining 2010 ........................................16 Figure 9: Zambia’s Top Investment Catalysts and Constraints in Non-Mining 2010 ................................17 Figure11: Zambia’s Top Investment Catalysts and Constraints in Mining ................................................19 Figure12: Zambia’s Top Investment Catalysts and Constraints in Non-Mining ........................................20 Figure13: Recent Trends of FDI in Mining and Selected Variables (1999-2008) ......................................30 Figure14: Recent Trends of FDI in Other Sectors and Selected Variables (1999-2008) ............................30 Figure 15: Movements in the K/US$ Exchange Rate and Copper Prices ...................................................47

ii. List of Tables

Table 1: Major Pull Factors-Other Country FPC CBP Studies ...................................................................21 Table 2: Top Catalysts-Other Country FPC CBP Studies ..........................................................................22 Table 3: Top Constraints-Other Country FPC CBP Studies .......................................................................22 Table 4: Zambia: Ease of Doing Business Overall Ranking, 2010-2011 ...................................................25 Table 5: Zambia Ease of Doing Business Ranking by Factor, 2011 ..........................................................25 Table 6: Summary Table of Literature Findings from Econometric Studies ..............................................26 Table 7: Summary Table of Major Literature Findings from Qualitative and Other Studies .....................27 Table 8: Mining & Non-Mining Regression Models ..................................................................................31 Table 9: Correlation Tests of FDI with Malaria, HIV Aids, Corruption, ...................................................35 Table 10: Diagnostic Test Results for Mining Equation (A) ......................................................................37 Table 11: Diagnostic Test Results For Non-Mining FDI Equation (A) ......................................................37 Table 12: Results of the Mining FDI Equations-Dependent Variable: DLFDIM .......................................38 Table 13: Long Run Elasticity of FDI in Mining Equations .......................................................................41 Table 14: Results of the Non-Mining FDI Equations .................................................................................42 Table 15: Long Run Elasticity Non-Mining ...............................................................................................46 Table 16: Correlation of FDI in Mining with Selected Variables ..............................................................49 Table 17: Correlation of FDI in Non-Mining with Selected Variables .......................................................51

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iii. List of Acronyms and Abbreviations

ADI African Development Indicators ARCH Auto Regressive Conditional Hetroscedasticity BoZ Bank of Zambia DFI Development Finance International ECM Error Correction Model CSO Central Statistical Office ERB Energy Regulation Board EU European Union FDI Foreign Direct Investment FDIM Foreign Direct Investment in Mining FDINM Foreign Direct Investment in Non-Mining FPC Foreign Private Capital FPC CBP Foreign Private Capital Capacity Building Programme GDP Gross Domestic Product HIPC Highly Indebted Poor Country Initiative HIV/AIDS Human Immune Virus Acquired Immune Deficiency Syndrome IFS International Financial Statistics IMF International Monetary Fund KCM Konkola Copper Mines LME London Metal Exchange MEFMI Macroeconomic and Financial Management Institute of Eastern and Southern Africa MFEZ Multi Facility Economic Zones MMD Movement for Multiparty Democracy MMMD Ministry of Mines and Minerals Development MNEs Multinational Enterprises NTEs Non-Traditional Exports OLS Ordinary Least Squares QSBOE Quarterly Survey of Business Opinion and Expectations SNDP Sixth National Development Plan TI CPI Transparency International Corruption Perception Index UNCTAD United Nations Conference on Trade and Development VAT Value Added Tax WDI World Development Indicators WEF World Economic Forum WEO World Economic Outlook WHO World Health Organisation ZDA Zambia Development Agency ZIC Zambia Investment Centre ZESCO Zambia Electricity Supply Corporation

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iv. Acknowledgements

I wish to express my sincere gratitude to my Mentor Dr. Matthew Martin from Development

Finance International (DFI) for his guidance in the fellowship programme including the

preparation of the technical paper. Special thanks go Mr Nils J. Bhinda –Programme Officer DFI

for his technical insight and tremendous support from the onset of the fellowship programme to

the finalisation of this technical paper. Appreciation is extended to other DFI staff such as David

and Jeannette who supported and facilitated my two (2) weeks attachment at DFI in the United

Kingdom.

Great thanks go to management and staff at the Macroeconomic and Financial Management

Institute of Eastern and Southern Africa (MEFMI) including Dr Elias Ngalande (Executive

Director), Dr. Ephraim Kaunga, Ms Nomusa Tibane, Mr Charles Assey, Mr Simon Namagoa,

Mr Evarist Mugangaluma, Mr Amos Cheptoo, Mr Jean Havugimana, Ms Fungisai, Ms Farirai

Katongera, Ms Esther Murahwa and Ms Sharon Wallet. Sincere appreciation is extended to the

entire 2009 MEFMI candidate fellow intake for their valuable comments during the formulation

of the research topic at the fellows workshop held in Gaborone in Botswana.

My earnest appreciation go to the Bank of Zambia management for the opportunity and their

continued support during the entire fellowship programme. I also wish to extend my earnest

appreciation to the Staff at the Ministry of Mines and Minerals Development geological survey

office in Lusaka for provision of data and guidance on copper mining in Zambia. Thanks go to

all who in one way or another supported me in the fellowship programme. I most sincerely

express my heartfelt gratitude to my wife Dines and my daughter Mphaso for their continuous

encouragement and support throughout the fellowship programme.

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v. Abstract

Zambia is highly dependent on Foreign Direct Investment (FDI) inflows, which are highly concentrated in the mining sector. This sector, however, is highly vulnerable to commodity price shocks, posing a challenge of sustainability of FDI inflows and overall economic growth. This study explored and assessed the factors that drive FDI in Zambia’s mining and into other sectors. This was done via two error-correction time-series econometric models, one for mining and another for non-mining. The findings, suggest that copper prices and external copper demand were major drivers of FDI in mining, while electricity supply was the major constraint. The non-mining sector was largely driven by the degree of urbanisation, GDP growth, exchange rate depreciation, supply of telecommunication services and the boom in the mining sector, while lending rates were the main constraints. To minimise Zambia’s vulnerability to commodity price shocks and ensure sustainability of FDI inflows, it is critical for Government to pursue a diversification strategy targeted at accelerating FDI inflows to other sectors such as agriculture, tourism, and manufacturing (non-mining-related), which are not only less vulnerable to commodity price shocks, but also contribute highly to employment creation technology and skills transfer. There is need for accelerated infrastructure development in electricity supply, roads, rail and telecommunication, among others, especially in rural areas. In addition, efforts should be directed towards sustaining robust GDP growth, maintaining a competitive exchange rate, exploring new source markets of FDI and enhancing business linkages of investors with domestic SMEs.

Key Words: Foreign Direct Investment, Drivers, Mining, Other sectors, Diversification.

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1. INTRODUCTION

A number of low-income countries heavily rely on Foreign Direct Investment (FDI) to finance

current account deficits. Given limited local resources, FDI is seen to be one of the most

effective ways of enhancing productivity and developing an internationally competitive private

sector. In this regard, FDI is viewed as an important driver of growth and development in many

developing countries including Zambia. It is generally assumed that FDI yields various benefits

including; employment creation, technology and skills transfer, increased government tax and

non-tax revenue, multiplier effects via forward and backward linkages, market access for utility

service providers, contribution to overall Gross Domestic Product (GDP) and exchange rate

stability.

Some empirical studies, however, such as Sun (2002) have shown limitations of the benefits of

FDI. Investors, among other things, have the primary objective of maximising their global

profits, with or without benefits to the host country. Benefits of FDI inflows may be limited

particularly in instances where there are limited jobs created for residents in host economies due

to heavy reliance on foreign personnel. In addition, if there is no technology transfer, investors

enjoy protracted tax holidays, and inputs are largely imported rather than obtained from the

domestic market, FDI may not have a notable positive impact on the host economies.

Foreign direct investment2 has three components, these being equity capital, reinvested earnings

and borrowing from affiliated non-resident entities. Despite differences in the types of FDI

inflows, they tend to have a relatively long lasting impact on the host economies compared with

other inflows. In crisis periods like the recent global economic crisis, however, FDI could

equally be short term and volatile. As witnessed during the recent global economic crisis, new

investments in form of equity were postponed or cancelled while, retained earnings sharply

declined as companies accelerated remittance of profits and dividends. Debt from affiliates

became short-term despite the investment relationship.

Zambia has in recent years recorded notable amounts of FDI inflows. The mining sector

continues to dominate, accounting for about 60 percent of Zambia’s FDI inflows. This sector,

however, is highly vulnerable to commodity price shocks as evidenced by the impact of the

recent global financial and economic crisis. In light of this, there is need to diversify FDI

inflows to other sectors. To do so, factors that drive investment into mining and into other

sectors must be clearly identified and distinguished to guide policy and promotion efforts. A 2Foreign Direct investment arises when an investor resident in one economy makes an investment that gives control or a significant degree of influence on the management of an enterprise that is resident in another economy (IMF 2008).

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clear understanding of the drivers of FDI in mining and in other sectors is critical in guiding

policy and investment promotion efforts. This would help ensure sustainability of FDI inflows

through diversification, thereby minimise the vulnerability of the Zambian economy to

commodity price shocks.

The main objective of the study is to identify the factors that drive foreign direct investment

inflows into Zambia’s mining and other sectors with a view to highlighting policy implications

in order to promote diversification and enhance sustainability of FDI inflows. The study is

intended to contribute to discussion on policy among Government policy makers, business

leaders, donors, international and regional organisations and other stake holders on critical issues

to address in order to speed-up the diversification of FDI inflows and the overall economy. This

will in turn contribute to the reduction in vulnerability of the Zambian economy to commodity

price shocks. Zambia’s Sixth National Development Plan (SNDP) for the period 2011-2015

clearly outlines growth and diversification among its major objectives. The report stresses the

need to aggressively diversify the economy to other sectors, in order to cushion the negative

effects of external shocks. The agriculture, tourism, manufacturing, mining and energy sectors

were itemised as growth areas in the SNDP.

In the context of the need to diversify FDI inflows and the economy as a whole, to our

knowledge, no econometric study has been done to specifically distinguish drivers of FDI into

mining and into other sectors in Zambia. Against this background, this study, adopts two time-

series error-correction models to assess what factors drive FDI3 in mining and other sectors in

Zambia. The rest of the paper is structured as follows: in Section 2, the trend of FDI in Zambia

is presented, followed by the review of literature in Section 3.Section 4 discusses the

methodology and data, while in Section 5 the results are presented and analysed and section 6

concludes and highlights policy recommendations.

2. RECENT TRENDS AND PROSPECTS OF FDI IN ZAMBIA

This section presents the sectoral breakdown of FDI in Zambia, recent trends and prospects. In

addition, an analysis of various FDI episodes since independence and the associated factors that

explain these developments are given.

3The study is limited to the analysis of the impact of various factors on overall FDI in mining and non-mining and not its specific components (shareholder funds, retained earnings and borrowing from affiliates). This is due largely to non-availability of a reliable and disaggregated time series.

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2.1 Recent Trends of FDI Stocks and Flows and Sectoral Breakdown

At a global level, foreign direct investment inflows in 2009, at US $1,114.0 billion, were 37.0

percent lower than US $1,770.9 billion recorded in 2008. In the preceding year 2008, FDI

inflows declined by 16.0 percent from record levels of US $2, 100.0 billion recorded in 2007.

The decline in recent years was mainly attributed to the effects of the global financial and

economic crisis (UNCTAD, 2010).

Foreign Direct Investment inflows to developing and transition economies, declined by 27.0

percent to US $548.0 billion in 2009, after recording uninterrupted growth for six years. Though

FDI flows to these economies declined, these economies were more resilient to the crisis than

developed countries. With regard to Africa, FDI inflows declined by 19.0 percent to US $59.0

billion following the contraction in global demand and falling commodity prices. This decline,

however, was relatively lower than recorded in other regions. Global FDI inflows are showing

signs of recovery and expected to rise to over US $1.2 trillion in 2010 and further to US $1.3-1.5

trillion in 2011 and US $2.0 trillion by 2012.

Consistent with the global trends, Zambia’s FDI inflows fell to US $694.8 million in 2009 (US

$938.6 million in 2008) after rising to record levels of US $1,323.9 million in 2007, from US

$145.8 million recorded in 2001. The decline above was largely attributed to the effects of the

recent global financial and economic crisis. The share of FDI in overall foreign private

investment inflows rose to 74.3 per cent in 2009from 68.5 per cent in 2007. Other investments

(borrowing from non-affiliates) inflows share of total foreign investment inflows, however,

declined to 14.5 percent in 2009 from 29.2 percent recorded in 2007. Portfolio investment

inflows had a relatively lower share of 1.3 percent in 2009 compared with 2.3 percent recorded

in 2007. In addition, financial derivates liabilities were recorded for the first time in 2009,

accounting for 9.9 percent of total foreign investment inflows. Though portfolio investments

(particularly debt securities) were relatively smaller, they tend to be more volatile and

devastating on host economies as they generate exchange rate shocks particularly in flexible

exchange rate regimes in crisis periods.

In terms of a sectoral distribution of FDI inflows in Zambia, the commodity sector (mining)

continued to dominate, though its share in total FDI inflows declined to 52.8 percent in 2009

from about 60.0 percent in 2007. In addition, the mining sector has continued to dominate the

export sector accounting for over 80 percent of Zambia’s export earnings. In 2009, the

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manufacturing sector’s share in total FDI inflows surged to 41.1 percent from 8.2 percent

recorded in 2007(see Figure 1).

Figure 1: Zambia's FDI Inflows by Sector, (2001, 2007 and 2009), US $ million

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200

400

600

800

1000

1200

1400

2001 2007 2009

Other

Construction

Real Estate

Agriculture

Tourism

Transport & Communication

Wholesale & Retail Trade

Manufacturing

Bank & Non‐Bank Financial

Institution

Mining

Source: Foreign Private Investment & Investor Perceptions Survey 2002, 2008& 2010

With regard to the stock of FDI, the partern is similar. Based on survey findings, Figure2 below

shows the extent of concentration of FDI in mining with respect to the stock at end of each year.

Figure2: Stock of Zambia's FDI by Sector, (2000, 2001, 2006- 2009), US $ million


0

1000

2000

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2000 2001 2006 2007 2008 2009

US $ M

illion

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Construction

Agriculture

Tourism



Manufacturing

Bank & Non‐Bank FinancialInstitution

Mining

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The concentration of FDI stock in the mining sector rose to 60.7 percent in 2009 after falling to

58.5 percent in 2008 from 72.3 percent in 2006.Despite the decline in recent years, FDI

concentration in mining is substantially higher than 22.2 percent recorded in 2001 (see Figure 3).

Figure 3: Share of FDI Stock by Sector, (2000, 2001, 2006- 2009), percent


Foreign direct investment in the construction,manufacturing and transport sectors is largely

driven by the performance of the mining sector. In the construction sector, a number of foreign

owned construction firms are set up to undertake major construction works at mining companies.

Similarly, in the manufacturing sector, new foreign owned companies are setup and existing

ones increase their investments to service the mining sector,particularly in periods of mining

boom. This underscores how strongly linked FDI inflows in manufacturing are to the

performance of the mining sector. The transport sector (particularly road transportation) equally

responds to the boom in mining. In periods of mining boom, new and existing foreign owned

transport companies expand their fleet to transport copper to major ports for export. This shows

how strongly linked these three sectors are to the mining sector.

Though the mining sector accounted for the largest share in overall FDI, its contribution to

employment was albeit lower at about 33,503 compared with 36,490 for other sectors in 2007

(Balance of payments Statistics Committee, 2008). This entails that the challenge of employment

creation could only be effectively addressed through increased FDI in other sectors.

In terms of the source of the stock of FDI in Zambia, Canada, India, Australia, and Switzerland

dominated at US $1,433.0 million, US $1,277.9 million, US $810.4 million and US $805.4

million, respectively. These countries collectively accounted for about 58.0 percent of the Stock

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2000 2001 2006 2007 2008 2009

Percent

Other

Construction

Agriculture

Tourism



Manufacturing

Bank & Non‐Bank FinancialInstitution

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in 2009. Other major source countries include China, which accounted for 8.0 percent,

Netherlands (7.0 percent), South Africa (6.8 percent) and the United Kingdom (6.2 percent).

2.2 Trend in Mining, Non-Mining and Overall FDI inflows

In this section, we analyse various episodes of notable changes in FDI inflows in Zambia. Figure

4 below presents the trend of FDI inflows and shows the booms and bursts that occurred.

Figure4: Trend of Mining, Non-Mining and Overall FDI inflows (1970 - 2009), US $ million

Source: Bank of Zambia, IMF and WDI

As presented in Figure 4 above, Zambia experienced several episodes with regard to FDI

inflows. One major episode was the nationalisation of the mining companies in 1970, which saw

government gaining a dominant stake in the mining sector, leading to a decline in FDI inflows.

After the oil crisis of 1979, Zambia’s economy collapsed following the unprecedented decline in

copper prices relative to the price of imports. Consistent will the slide in copper prices, FDI

inflows declined and mining sector output fell, while associated losses increased. This was

followed by the liberalisation of the economy after the Movement for Multiparty Democracy

(MMD) assumed power in 1991, the removal of capital controls, the opening up of the Lusaka

Stock exchange in 1994, Privatisation of the mining companies from about 1997 to 2000.

In 2002, Anglo American Corporation gave a twelve months’ notice of its pulling out of

Konkola Copper Mines (KCM). The withdrawal of this major investment destabilised the

mining sector in Zambia, leading to high job losses, and a slide in export earnings.

Following the macroeconomic gains made by Zambia, after reaching the Highly Indebted Poor

Country (HIPC) completion point in 2005, coupled with the steady increase in copper prices on

the international market, the mining sector recorded a notable recovery. Zambia attracted

‐36

64

164

264

364

464

564

664

764

864

964

1,064

1,164

1,264

1,364

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

FDI FDIm FDInm

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substantial inflows of FDI in existing mines. Besides these, Green field investments at

Kansanshi Mine, Lumwana Copper Mines, Albidon Nickel Mine, and Chambishi Copper

smelter, contributed to the surge in FDI inflows. In addition, the on-going Konkola Deep Mining

expansionary project has continued to contribute to the surge in FDI in the sector.

The non-mining sector has equally been buoyant in recent years, driven by macroeconomic gains

Zambia has recorded such as concurrent robust GDP growth, exchange rate stability and the

decline in inflation. In addition, improvement in Zambia’s global rankings, with regard to the

ease of doing business, has contributed to the notable improvement in the investment climate.

The upward trend in FDI inflows recorded in recent years, however, slowed down following the

onset of the recent global financial and economic crisis. The slide in global demand associated

with the financial and economic crisis resulted in the collapse of commodity prices on the

international market. The London Metal Exchange (LME) copper prices fell by 67.7percent to

US $2,902.0 per ton in December 2008 from US $8,985.9 per ton recorded in July 2008.

Consistent with the movements in the LME Price, the monthly average realised price of copper

in Zambia fell by 61.5percent to US $2,953.2 per ton in December 2008 from US $7,665.0 per

ton recorded in July (see Figure5:below).

Figure5: Movements in the Monthly Average LME and Realised Copper Price 1999-2010

Source: Bank of Zambia & WDI

Following the slide in copper prices, FDI inflows fell to an estimated US $938.6 million and US

$694.8 million in 2008 and 2009, respectively. Some planned investment projects such as

Albidon Nickel Mine were postponed, while other projects were cancelled following the sharp

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

Jan‐99

Jun‐99

Nov‐99

Apr‐00

Sep‐00

Feb‐01

Jul‐01

Dec‐01

May‐02

Oct‐02

Mar‐03

Aug‐03

Jan‐04

Jun‐04

Nov‐04

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Feb‐06

Jul‐06

Dec‐06

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Oct‐07

Mar‐08

Aug‐08

Jan‐09

Jun‐09

Nov‐09

Apr‐10

US $ Per To

n

LME Price Realised Price

8

decline in commodity prices. Some companies such as Luanshya Copper Mines suspended

operations and a number of workers lost their jobs. The global crisis inevitably slowed down

Zambia’s growth during this period to about 5.7 percent in 2008 from 6.3 percent in 2007.

Zambia, however, proved resilient to the effects of the global crisis by registering real GDP

growth of 6.4in 2009, following buoyant performance in mining, agriculture and construction

sectors. Consistent with the above developments, a recent cross sectional study on selected Sub

Saharan African countries (including Zambia) by Macias and Massa (2010) showed that the

recent global financial crisis caused a notable slowdown in foreign private capital inflows to the

region.

2.3 Prospects of FDI in Zambia

Foreign direct investment inflow prospects are bright in Zambia in the short to medium term. In

2010 for example, FDI inflows were projected to rise to over US $1,100.0 million premised on

the rebound in copper prices due to the recovery of the world economy from the impact of the

global economic crisis. In addition, further improvement in the investment climate is expected to

spur investments into the country. The favourable investment climate is clearly demonstrated by

the recently announced Zambia FITCH Sovereign ratings where Zambia was rated B+ (Fitch

2011).

In line with these developments, Zambia’s pledged investments over the period January to

December 2010 surged to US $4.8 billion, with the manufacturing sector accounting for 40.0

percent, followed by mining (20.6 percent), energy (11.9 percent), and real estate (8.6 percent).

Other sectors, with pledged investment in excess of US $130.0 million were agriculture,

education, and tourism. Foreign Direct Investment pledges in 2010 were largely concentrated in

agro-processing, mineral processing, production of bio fuels, property development,

underground mining and energy sub-sectors. In the 2011, FDI inflows in Zambia are projected to

rise to US $1, 525.2 million, surpassing the 2007 record level of US $1,323.9 million.

9

3. LITERATURE REVIEW

The literature is replete with theories of foreign direct investment. Despite the diverse theories,

the literature distinguishes the major motivations of Foreign Direct Investors and Multinational

Enterprises (MNEs) into one or more of the following categories: market seeking, resource

seeking, efficiency seeking, and strategic positioning (Dunning 1997). This section surveys the

current literature, looking not only at econometric studies (Section 3.1), but also at other studies,

including work done by the Bank of Zambia and other countries in the Sub-Saharan African

region, and related international initiatives (Section 3.2).

3.1 Determinants of Foreign Direct Investment: Econometric Studies

Various factors account for the flow of direct investment in many countries. Researchers in both

developed and less-developed countries stress the role of political, economic, social, and policy

variables as determinants of FDI flows (Sun 2002). Others emphasise the role of institutional,

historical and geographical factors. In this section, we present the factors that affect FDI as

highlighted by econometric studies in the following categories: Profitability/Rate of Return

factors, Market factors, Political factors, Macroeconomic factors, Infrastructure factors,

Regulation, Investment Promotion, Natural Resource, and Environment and Health factors

Profitability/Rate of Return Factors

Among other things, the main objective of foreign investors in investing anywhere is to

maximise their global profits, with or without benefits to the host country. Firms invest abroad

when the expected return exceeds the costs (Caves 1982). Among other factors, the rate of return

on investments positively affected FDI inflows in Sub Saharan Africa (Opolet et al 2008). Using

the inverse of GDP Per Capita as a proxy for the rate of return, the study suggests that though

Africa is perceived to be inherently risky, the risk-adjusted rate of return is high enough to

compensate for possible political and investment risks. This therefore entails that the factors in

the source and host countries that affect net profitability are critical to investment. Commodity

prices, on the revenue side, are a good indicator of profitability particularly for FDI in

commodity sectors.

Most studies on FDI have tended to focus on a number of other determinants of FDI with no

specific reference to the role of commodity prices. There are, however, a number of authors who

have debated the role of commodity prices on economic growth. Some studies have linked

commodity price booms to increased growth, while others such as Collier and Goderis (2008)

10

suggest the existence of a resource curse that undermines sustainable growth. Other studies such

as Dehn (2000a) and Dehn (2000b) analysed the impact of commodity prices on aggregate

domestic investment but not FDI.

The IMF (2008b) research on the role of commodity prices in developing countries found that

the recent (2007) commodity price boom had been more favourable in bringing rapid economic

growth, and attracting investments to developing countries. In previous booms, the report notes

that foreign investment accelerated, reflecting primarily portfolio inflows, while domestic

investment responded weakly. In the recent boom (2007), however, both foreign direct

investment and domestic investment grew substantially.

Most studies on the impact of commodity prices have largely been cross sectional and have a

potential bias of omitting key variables. Some of the cross sectional studies that have

incorporated Zambian data largely covered periods under which the mining companies were

under Government ownership. The periods under which mining companies in Zambia were

predominantly foreign owned and when they were largely Government owned need to be clearly

distinguished. As argued by Elva Bova (2009), mine ownership has significant implication on

the impact of commodity price shocks on an economy.

Market Factors

A number of studies such as (Pigato 2001) argue that FDI is positively influenced by the size of

a country’s market demand as measured by the size of GDP or GDP per capita. Where the

domestic market is small, efforts should be directed towards attracting export-oriented FDI.

Most studies show that FDI flows mostly to countries experiencing rapid economic growth. It is

worth noting, however, that FDI also contributes significantly to economic growth and that

faster economic growth attracts more FDI because it increases foreign investors’ confidence in

the economy, which in turn results in higher growth rates.

Studies done for least developed countries have shown that FDI follows some initial growth or at

least the promise of growth (Sun 2002). This suggests that investors focus on the growth

prospects as they make their investment decisions. Other studies such as Aseidu (2004) and

Opolet et al (2008) equally support the fact that market size and GDP growth promote FDI in

Africa. With regard to Zambia, the GDP, GDP per Capita and the degree of urbanisation are the

key market factors. Figure 6 suggests that Overall FDI inflows respond positively to the

movements in all these market factors.

11

Figure 6: Trends of Zambia’s GDP, GDP Per Capita and Degree of Urbanisation

Source: BoZ and WDI

The above literature on market factors appears to be consistent with the category of “market-

seeking” FDI where especially the actual size and potential of the national market is

fundamental. This, however, does not necessarily explain FDI that targets export markets or

takes place with different kinds of motivations. In the context of Zambia, external demand for

copper is expected to be the major driver of FDI in the mining sector.

Political Factors

With regard to political stability, studies have shown that incidences of political coups,

assassinations, riots, or armed conflicts exert a dominant negative influence on foreign

companies’ investment decisions (Sun 2002). It has been noted that frequent changes of

governments and the associated policy changes can significantly affect investments. Asiedu

(2004) also shows that political stability and a reliable legal system are critical factors in

attracting FDI in Africa. The study argues that regional economic cooperation could help to

enhance FDI in Africa through promoting political stability, and regional coordination of

policies especially with regard to curbing corruption.

It is exciting to note that Zambia has continued to enjoy some measure of political stability and

policy consistency in recent years. There were, however, periods of political uncertainty

particularly the time of military coups in the 1990s, industrial riots prior to multiparty

democracy, elections and uncertainty associated with the death of the president in 2008. These

political uncertainties have the potential to negatively impact on FDI inflows.

0

5

10

15

20

25

30

35

40

45

‐40

160

360

560

760

960

1,160

1,360

1,560

1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009

US $ bn & Percent

US $

Degree of Urbanisation GDP Per Capita FDI GDP

GDP Per Capita, FDI GDP & Urbanisation Axis

12

Among other factors, corruption is one of the factors that affect investments in most economies.

A study on foreign direct investment in Africa shows that corruption was a major constraint in

attracting FDI in Africa (Asiedu, 2004). It shows that a decline in corruption from the level of

that of Nigeria to that of South Africa had the same positive effect on foreign direct investment

as increasing the share of fuels and minerals by about 35per cent.

Macroeconomic Factors

Among other factors, studies such as Opolet et al (2008), Asiedu (2002),and Onyiewu (2005)

show that a high rate of macroeconomic instability, as measured by the level of inflation,

discouraged investment in Sub Sahara African countries. Similarly, Reinhart et al (2002) found

that high inflation rates in Africa associated with civil unrest and high levels of distortions such

as capital controls have adverse effects on FDI on the continent. It is clear that macroeconomic

instability makes it difficult for investors to evaluate the true costs and returns of their

investment. A study by Nonnemburg and Mendonca (2004) shows that FDI inflows in selected

developing countries (including Zambia), were largely driven by macroeconomic factors such as

inflation, risk, and the average rate of economic growth. Other factors included availability of

trained staff and the performance of the stock market.

A more recent study by Amal et al (2010) equally found a strong positive relationship between

FDI and economic stability, growth and openness in Latin America, over the sample period 1996

to 2008. Ramirez (2010) study on economic and institutional determinants of FDI flows in Latin

America also shows that market size (Using the real GDP as a proxy), credit provided by the

private banking sector, government expenditure on education, the real exchange rate and the

level of economic freedom had a positive and significant impact of FDI flows. In addition,

public investment spending, debt-service ratio and the volatility of the real exchange rate had a

negative and significant effect.

Infrastructure Factors

Cardoso (2002), Ribakova and Wu (2005) found that infrastructure development (such as roads,

power) was positively and significantly related to FDI. These studies have shown that a well-

developed infrastructure network and a well-trained labour force are major elements that attract

foreign investments. Asiedu (2004) established that among other key factors, good infrastructure

was a major driver of FDI in Africa. The study shows that good infrastructure, low inflation, an

educated population, openness, political stability and a reliable legal system were critical in

attracting FDI to Africa.

13

Regulation

In terms of the regulatory environment, most studies have showed that they have a considerable

impact on FDI flows. While large investors may be in a position to endure cumbersome and

costly procedures, this may prove fatal to the entry and growth of small and medium enterprises

(Sun 2002). Arbitrary, discriminatory and non-transparent regulations often lead to corruption,

which has been a critical deterrent to FDI in most countries. A study on Determinants of Foreign

Direct Investment in Latin America by Amal M. et al (2010) shows that there is a strong

positive relationship between FDI inflows and a countries institutional and political

environment.

Nonnemburg and Mendonca (2004) also found that the degree of openness of the economy

measured by the ratio of exports plus imports over GDP was a key factor in driving FDI in

developing countries.

Investment Promotion

Though a stable and appealing investment climate is critical in attracting FDI, these inflows also

depend on the marketing or investment promotion efforts of a country. When other factors are

similar among competing countries, investment promotion efforts do make a difference.

With regard to the role of investment incentives in attracting FDI, most economies often feel

compelled to offer tax incentives in order to draw FDI inflows. A number of economic studies

including (Sun 2002), however, have repeatedly shown that incentives are not as influential as

usually believed. Other factors such as those discussed above, are seen to be far more important

than special fiscal incentives. Most multinationals attach more importance to the simplicity and

stability of a country’s tax system than to tax incentives.

In the context of Zambia, Government through the Zambia Development Agency has come up

with investment incentives aimed at increasing levels of investment, international trade and

domestic economic growth. These incentives are in form of allowances, exemptions and

concessions. In an effort to reduce dependence on the mining sector, Government has identified

agriculture, manufacturing, energy, tourism, information and communication technology, health

and education as priority sectors. The major focus has been on value addition to various raw

materials produced in Zambia.

Investors investing not less than US $10.0 million are entitled to negotiate with the government

for additional incentives other than what they might have already qualified for under the ZDA

14

act of 2006. Investors with not less than US $0.5 million in the Multi Facility Economic Zones

(MFEZ) and /or in a priority sector or product (in addition to the general incentives), are entitled

to Zero per cent tax rate on dividends for 5 years from year of first declaration of dividends. In

addition, they enjoy zero percent tax on profits for 5 years from the first year profits are made (in

year 6 to 8, only 50.0 percent of profits are taxable and years 9 and 10, only 75.0 percent of

profits are taxable). They are also entitled to zero per cent import duty on raw materials, capital

goods, machinery including trucks and specialized motor vehicles for five years, deferment of

Value Added Tax (VAT) on machinery and equipment including trucks and specialized motor

vehicles (ZDA, 2010).

Natural Resource Factors

Dindia (2009)’s empirical investigation on factors attracting FDI to Nigeria using a vector error-

correction model for the period 1970 to 2006 showed a number of pull factors. The endowment

of natural resources was a major factor followed by degree of openness and macroeconomic risk.

The study showed that the bulk of foreign direct investment in Nigeria was resource seeking.

Nigeria’s natural resource export ratio in world resource exports was used as a proxy for natural

resource endowments.

Similarly, a study by Asiedu (2004) on Foreign Direct Investment in Africa: The role of natural

resources, market size, Government policy, institutions and political stability for the period 1984

to 2000 shows interesting findings. Natural resource endowment and market size were found to

be the major drivers of FDI in Africa. The study goes further to suggest that countries that are

small or lack natural resources could attract FDI by improving their institutions and policy

environment.

Zambia is highly endowed with natural resources such as minerals (particularly copper),

wildlife, land, water, forests and other tourist attractions such as the Victoria Falls-one of the

Seven Wonders of the World. Due largely to the high returns on minerals, the presence of

mineral resources is a major pull factor of foreign investments. Other natural resources such as

wildlife and other tourist attractions, land and forests also have tremendous investment potential.

Health and Environmental Factors

Health and environmental factors have a potential to impact on FDI inflows in a country. To our

knowledge, no econometric study had been done incorporating health factors such as HIV Aids,

and Malaria. Similarly, other environmental factors such as drought and floods have not been

15

incorporated in econometric studies. Our study seeks to include such factors in our econometric

model, particularly those with a long time series. Those with a short time series will be

incorporated in the correlation tests.

3.2 Determinants of Foreign Direct Investment: Qualitative and Other Studies

3.2.1 Zambia Foreign Private Capital Surveys (2010, 2008 and 2002)

Zambia has thus far conducted three (3) surveys under the Foreign Private Capital Capacity

Building Programme (FPC CBP). The first (2002) covered data for 2000-1, and the second

(2008) for 2006-7. A third survey conducted in 2010 was recently completed capturing data for

2008-9.These surveys are implemented by a team led by the Bank of Zambia and comprise other

stakeholders including Zambia Development Agency (ZDA) and the Central Statistics Office.

The surveys capture data on foreign assets and liabilities in line with international codes and

standards, including FDI, portfolio, other investment, and recently financial derivatives, broken

down by sector of economic activity, source country and recipient region.

With respect to determinants, they also cover investor perceptions: not just on the initial decision

to invest in Zambia, but also the factors that affect the current investment decision for businesses

already in operation, and the medium term outlook. Unfortunately, the data is not sufficient yet

for an econometric study. However, the surveys have been very useful in highlighting variables

and other information that will be included in this paper, in particular looking into the

motivations to invest in much more detail than other studies, and including sector breakdown.

3.2.1.1 Zambia Foreign Private Capital 2010 Survey

The 2010 survey findings on investor perceptions indicated that domestic economic and political

scenarios were the major investor pull-factors. Tax incentives, however, ranked highest in the

mining and tourism sectors (see Figure 7).

16

Figure 7: Major Investor Pull Factors

0 20 40 60 80

Domestic political scenario

Domestic economic situation

Regional economic situation

Availability of qualified management and …

Climate conditions

Road network

Telecommunication

Availability of Natural Resources

Energy

Regulatory Institutions

77.6

75.3

57.7

56.2

56.2

55.8

55.5

54.6

53

51.2

In terms of the catalysts, most respondents indicated that in the macro category, financial system

stability was the main positive factor, while depreciation of the Kwacha and interest rates were

the major negative factors. In the political and governance category, corruption and bureaucracy

were the dominant constraints. The cost of electricity supply and inland transport costs were the

major inhibiting factors in the cost and efficiency category, while HIV/AIDS and malaria were

cited as major constraints in the environmental and health category.

Figures 8 and 9 below show the major catalysts and constraints of investment in mining and

other sectors.

Figure 8: Zambia’s Top Investment Catalysts and Constraints in Mining 2010

‐90 ‐70 ‐50 ‐30 ‐10 10 30 50 70 90

HIV/AIDS

Malaria

Global Economic Scenario

Bureaucracy/Regulatory Framework

Corruption

Fiscal Policy

Corporate and other taxes

Interest rates

Depreciation of domestic currency

Availability of International Business Finance

Regional Economic Scenario

Trade Policy

Financial System Stability

Source: Foreign Private Investment & Investor Perceptions Survey 2010


17

As depicted in figure 8, financial system stability, trade policy, regional economic scenario and

availability of international business finance were the major catalysts of investments. In terms of

constraints of investment, HIV/AIDS, malaria, the global economic scenario, bureaucracy,

corruption and fiscal policy ranked highly in the mining sector.

With regard to the non-mining sector, the 2010 survey findings show that market share

expansion, the domestic economic scenario, financial system stability, trade policy, fiscal policy

and the domestic market size were major catalysts (see Figure 9). Major constraints included

malaria, depreciation of the Kwacha, the global economic scenario, HIV/AIDS, corruption,

bureaucracy and interest rates.

Figure 9: Zambia’s Top Investment Catalysts and Constraints in Non-Mining 2010

‐80.0 ‐60.0 ‐40.0 ‐20.0 0.0 20.0 40.0 60.0 80.0

Malaria

Depreciation of the Kwacha

Global Economic Scenario

HIV/AIDS

Corruption

Bureaucracy/Regulatory Framework

Interest rates

Foreign Exchange management

Domestic Market Size

Fiscal Policy

Trade Policy


Domestic Economic Situation

Market Share expansion


Similarities

The above 2010 survey findings show that there are some similarities as well as differences

between the factors that affect investment in mining and non-mining. In terms of similarities, it

is clear that financial system stability and trade policy were important catalysts in both sectors.

With regard to constraints, Malaria, HIV/AIDS, the global economic scenario, bureaucracy,

corruption, depreciation of the kwacha and interest rates were major constraints in both sectors.

18

Differences

There were some marked differences with regard to certain factors. The domestic market size

and the domestic economic scenario were only important catalysts in the non-mining sector and

not the mining sector. Fiscal policy was rated one of the major catalysts of investments in the

non-mining sector, but ranked among the major constraints in the mining sector.

Outlook of Investment

The 2010 survey captured investor perceptions on whether they would expand, maintain or

contract their operations in the short to medium term. The survey findings indicate investor

optimism with most enterprises forecasting increased turnovers (73.1 percent), and profits (62.8

percent). The above survey findings are consistent with the 2001 and 2008 surveys results,

suggesting the recovery from the impact of the recent global crisis as well as continued investor

confidence in the Zambian economy.


The 2008 survey ranked environmental and natural resource endowments, political and

governance, and investment policy as the major investor pull factors (see Figure10). The

environment and natural resource endowment factor ranked highest at 92.0 percent. This was

understandably particularly high in the mining sector (84.0 percent), construction (80.0 per cent)

and tourism (71.1percent). This was followed by the domestic political scenario, at 80.0 percent,

with the wholesale and retail, construction, manufacturing and agriculture ranking highly on this

factor. Similarly, investment policy was rated favourable in attracting foreign investments in the

country.

Figure10: Zambia’s Top Investor Pull Factors (Mining & Non-Mining)


‐

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

Environmental and NaturalResource

Political and Governance factors Investment Policy

Mining Non‐Mining

19

With regard to subsequent investments made by businesses already established in Zambia,

domestic economic situation, political scenario, market size, fiscal policy and the regional

economic situation were the major catalysts. Factors such as electricity supply efficiency, HIV

/AIDS, inland transport costs, corruption and malaria were the major constraints

Figures 11 and 12 below, present some interesting similarities and contrasts between mining and

other enterprises.

Figure11: Zambia’s Top Investment Catalysts and Constraints in Mining

Source: Foreign Private Investment & Investor Perceptions Survey 2007/2008

Similarities

In terms of catalysts of foreign direct investment, the domestic political scenario was the major

catalyst for both the mining and non-mining sectors. In addition, the domestic political scenario

and fiscal policy ranked highly in both sectors. This underscores the importance of these factors

in driving FDI in general. Similarly, on the constraints side, corruption, bureaucracy, malaria,

availability of technically trained staff, HIV/AIDS and inland transport costs ranked highly in

both sectors.

‐75.0 ‐55.0 ‐35.0 ‐15.0 5.0 25.0 45.0 65.0

Banking Services

Appreciation of the Kwacha

Corruption

Malaria

Bureacracy & Regulatory Framework

Availability of Technically Trained Staff

HIV/AIDS

Inland trasnport Costs

Electricity Suply efficiency

Telecom Service Cost

Interest rates

Licence Fees


Capital Account Liberalisation

Tax Incentives


Regional Market Size

Telecom Service Efficiency

Monetary Policy

Trade Policy

Fiscal Policy

Domestic Political Scenario


20

Figure12: Zambia’s Top Investment Catalysts and Constraints in Non-Mining


Differences

There were however notable differences on selected factors. Among the major catalysts, the

domestic market size was more important in the non-mining sector than the mining sector, while

financial system stability ranked higher in the mining than in the non-mining sector. Similarly,

on the constraints side, there were notable differences. Banking services cost ranked higher in

mining than other sectors, while electricity supply inefficiency, HIV/AIDS, and interest rates

were much stronger in the non-mining than the mining sector. In addition, the appreciation of the

kwacha was a major constraint in the mining sector but ranked among the major catalysts in the

non-mining sector.

Outlook of Investments

With regard to the enterprises’ performance prospects, the 2008 survey findings showed a

favourable outlook, with most investors (78.0 percent) forecasting increased profitability and

turnovers. These findings should, however, be interpreted with caution as these findings relate to

investor perceptions prior to the onset of the recent global financial and economic crisis.

‐75.0 ‐55.0 ‐35.0 ‐15.0 5.0 25.0 45.0 65.0

Electricity Suply efficiency

HIV/AIDS

Corruption

Bureacracy & Regulatory Framework

Malaria

Interest rates

Banking Services

Licence Fees

Availability of Technically Trained Staff

Inland trasnport Costs

Telecom Service Cost

Telecom Service Efficiency

Capital Account Liberalisation

Trade Policy

Tax Incentives

Regional Market Size

Appreciation of the Kwacha

Monetary Policy

Fiscal Policy



Domestic Political Scenario


21


The Foreign Assets, Liabilities, and Investor Perceptions survey conducted in 2002 highlighted

the domestic economic situation, political stability and access to domestic markets as major pull

factors of investment in Zambia. Infrastructure and services were perceived to be deterrents to

investment. Electricity was perceived to be costly despite the fact that the cost of energy was

very competitive compared to neighbouring countries at that time. Similarly, HIV AIDS was

considered a great risk to human capital in Zambia, while concerns about corruption,

bureaucracy and security were raised as major constraining factors to investment in Zambia.

3.2.1.3 Similarities and Differences of Zambia Investor Perception Survey Findings

A careful comparison of the 2002, 2008 and 2010 investor-perception survey findings shows that

the domestic economic scenario, trade policy, political and financial system stability continue to

be major catalysts of investment in Zambia. In addition, HIV/AIDS, Malaria and bureaucracy

continue to be rated among the major constraints. Infrastructure and services such as roads and

electricity supply were major impediments to investment inflows in 2002 and 2008. Factors such

as security, however, were not rated among the major constraints in 2008 and 2010 compared

with 2002. This suggests that there has been a general improvement in the security situation in

Zambia during this period and that investors are not generally worried about security/crime

compared to previous years. The global economic scenario only became a major constraint in

2010, while fiscal policy was rated negatively in the mining sector in 2010 though it was

considered favourable in 2008 by the same sector.

3.2.2 Other Country FPC CBP Survey Findings

A number of countries have been conducting similar surveys under the FPC Capacity Building

programme. Results of these surveys show similarities and differences. In terms of similarities,

the key initial pull factors in most countries were domestic political stability, access to markets,

productivity, cost and availability of labour. Other important factors included domestic

economic stability and pro-investment legal framework (see Table 1).

Table 1: Major Pull Factors-Other Country FPC CBP Studies

Major Pull Factor Countries where factor was important Domestic Political Stability Bukina Faso, The Gambia, Nicarágua, Zambia

Domestic Economic Stability Senegal Labour Productivity, cost and availability Gambia, Nicaragua, Senegal Pro-investment legal framework Bolivia Access to markets Bolivia, Burkina Faso, The Gambia, Nicaragua, Senegal Source: Bhinda and Martin (2009)

22

With regard to catalysts and constraints of investments, a cross-country analysis of countries

participating in the FPC CBP shows that there are a number of similarities. Major catalysts of

FDI in most countries included the domestic political scenario, domestic market size, and the

domestic economic scenario (see Table 2). These factors were important catalysts in most

countries including Zambia. Other important factors in some countries included domestic

institutions and banking services.

Table 2: Top Catalysts-Other Country FPC CBP Studies

Catalysts Countries Domestic Political Scenario Burkina Faso, Bolivia (Constraint), Senegal, Zambia Domestic Market Size Cameroon, Ghana, Malawi, Senegal, Tanzania, Uganda, Zambia. Domestic Economy Nicaragua, Zambia. Tanzania. Source: Bhinda and Martin (2009)

Key constraints to investment in most participating countries were electricity supply, corruption,

interest rates, inflation, tax related issues, diseases and smuggling (see Table 3). Electricity,

corruption, and malaria were dominant in a number of countries including Zambia. Other

dominant constraints included inflation, interest rates, tax, smuggling, and the exchange rate.

Table 3: Top Constraints-Other Country FPC CBP Studies

Constraints Countries

Tax Bolivia, Cameroon, Malawi, Gambia, Smuggling Cameroon, Malawi, Uganda Corruption Cameroon, Ghana, Tanzania, Senegal, Zambia. Malaria Burkina Faso, Ghana, Zambia. Inflation Burkina Faso, Ghana, Malawi, Senegal, Uganda. Exchange Rate Ghana, Malawi, Senegal, Interest Rates Gambia, Ghana, Malawi, Senegal, Uganda. Electricity Cameroon, Gambia, Nicaragua, Tanzania, Uganda, Zambia. Transport Cost Uganda, Zambia. Source: Bhinda and Martin (2009)

3.2.3 Fondad FPC CBP Study

An earlier FPC CBP study done on a number of African countries including Zambia showed

that the mining and oil sectors continued to account for the largest share of FDI stock and flows

to Africa [Bhinda et al(1999)]. Though there are general factors that pull investments to low

income countries like Zambia, there are special characteristics of mining investments that make

them thrive in areas that may have a number of other constraints. Bhinda et al(1999)shows that

due to the size of their investments, mining companies easily overcome problems which smaller

companies would not such as:

23

They can invest in their own infrastructure, such as roads in mining areas, water and

sanitation, and employee accommodation.

They face lower risks. Mining industries are less susceptible to exchange rate risks as

they are export oriented and lodge export proceeds in offshore escrow accounts. They are

also experienced in dealing with volatile commodity prices through hedging and forward

transactions.

Risk spreading is relatively easier. Most mining companies reduce their risk by

employing small prospecting companies to conduct mining exploration or feasibility

studies. These small companies take all initial risks, and in turn enjoy major tax breaks

from their home governments.

Financing is easily accessed. Due to the size and high credit ratings of these

multinational mining companies, they easily borrow on the international market against

their external assets or use their parents’ borrowing power. Mining ventures also find it

easier to attract syndicated international financing, largely because they have guaranteed

streams of hard currency revenues and privileged political status. This explains why

Lumwana Mine in Zambia secured a syndicated loan of close to US $600.0 million to

finance part of its Greenfield investment in Zambia.

Political instability is less worrying for existing investment as most mining projects are

strategically vital, generally influence political developments and are rarely obliged to

pull out in time of political uncertainty.

3.2.4 Bank of Zambia Quarterly Survey of Business Opinion and Expectations (Q1 2006 – Q2 2010)

The Bank of Zambia conducts surveys of business opinion and expectations on a quarterly basis.

This survey targets both foreign and domestically owned enterprises in manufacturing, trade,

services, tourism, agriculture and construction, but not the mining sector. A sample averaging

500 enterprises is drawn from along the line of rail and other major provinces. A questionnaire

that captures information on economic performance, sources of finance, operational constraints,

expectations for the following quarter, as well as the subsequent twelve months is administered

to the sampled companies. The survey summarises responses by using Thel’s Net balance

statistic (QSBOE 4th Quarter 2008). The net percentage is used to indicate the overall direction

of responses in the survey.

The survey results for the Second Quarter of 2010 show that respondents were of the view that

monetary policy remained generally tight, while commercial banks’ lending rates were still high

24

despite showing a downward trend. This was a major constraint in accessing credit. During the

first quarter of 2010, the following factors were of primary concern to doing business in Zambia

on a net basis: lack of demand, high cost of inputs, high cost of finance, shortage of raw

materials, lack of skilled labour, access to loans, and the appreciation of the Kwacha.

Over the period 2006 to 2009, respondents continued to express concern at lack of demand, high

cost of finance, non-availability of finance, appreciation of the local currency and high cost of

inputs. Other factors, however, such as the depreciation of the Kwacha and high rates of inflation

were only prominent in the fourth quarter of 2008 and in the first half of 2009, due largely to the

impact of the global financial and economic crisis.

3.2.5 Other Studies

This section covers the World Economic Forum (WEF) Africa Competitive findings and the

World Bank Investment Climate Profile. These studies, however, have their own limitations, as

they tend to focus on constraints rather than catalysts.

3.2.5.1 WEF Africa Global Competitiveness Findings

The WEF Africa Global Competitiveness 2009 Report shows that Zambia’s major inhibiting

factors to investment include access to financing, corruption, inadequate supply of infrastructure,

tax rates and tax regulations. Other than tax rates and tax regulations, the above constraints were

equally important in the Zambia CBP Surveys as constraints of investment in Zambia (World

Economic Forum 2009).

3.2.5.2 Doing Business in Zambia

The World Bank Doing Business studyfocuses on specific factors with the aim of measuring the

regulation and red tape relevant to the life cycle of a domestic small to medium-size firmin

respective countries.It does not necessarily identify the factors that drive FDI but issues relating

to the ease of doing business in different countries. Several factors are assessed and each country

is ranked based on its performance. The best performers are ranked lower while the worst

performers are given a higher rank.

The World Bank Doing Business 2011 report shows that Zambia registered notable

improvement with regard to ease of Doing Business in 2010. Zambia gained Ten (10) steps in

world rankings to 76 and was classified among the top eight in Africa, from the reweighted 84

the previous year (see Table 4).

25

Table 4: Zambia: Ease of Doing Business Overall Ranking, 2010-2011

Ease of Doing Business Ranking Country 2010 2011 Mauritius 20 20 South Africa 34 32 Botswana 50 52 Tunisia 58 55 Rwanda 70 58 Ghana 77 67 Namibia 68 69 Zambia 84 76 Egypt 99 94 Seychelles 92 95 Kenya 94 98

Source: World Bank Doing Business2011

Zambia ranked among the 10 economies improving the most in the ease of doing business in

2009/2010. Notable achievements were recorded with regard to starting a business, trading

across borders and enforcing contracts.Zambia recently eased business start-up by eliminating

the minimum capital requirement. It eased trade by implementing a one-stop border post with

Zimbabwe, launching web-based submission of customs declarations and introducing scanning

machines at border posts. In addition, Zambia improved contract enforcement by introducing an

electronic case management system in the courts that provides electronic referencing of cases, a

database of laws, real-time court reporting and public access to court records (World Bank

2011). These were major areas of weakness the previous year.

Table 5: Zambia Ease of Doing Business Ranking by Factor, 2011

Economy

Ease of Doing

Business Rank

Starting a Business

Dealing with Construction

Permits

Registering Property

Getting Credit

Protecting Investors

Paying Taxes

Trading Across

Borders

Enforcing Contracts

Closing a

Business

Mauritius 20 12 39 69 89 12 12 22 61 71 South Africa 34 75 52 91 2 10 24 149 85 74 Botswana 52 90 127 44 46 44 21 151 70 27 Tunisia 55 48 106 64 89 74 58 30 78 37 Rwanda 58 9 82 41 32 28 43 159 39 183 Ghana 67 99 151 36 46 44 78 89 45 109 Namibia 69 124 36 136 15 74 99 153 41 53 Zambia 76 57 158 83 6 74 37 150 86 97 Egypt 94 18 154 93 72 74 136 21 143 131 Seychelles 95 109 61 62 152 59 38 36 69 183 Kenya 98 125 35 129 6 93 162 144 125 85

Source: Doing Business 2011

Despite the notable improvements in the overall indicators of doing business, Zambia still faces

major challenges with regard to dealing with construction permits and trading across borders

(see Table 5). The World Bank Doing business survey underscores the challenge of bureaucracy

26

particularly in dealing with construction permits, which also ranked among the major constraints

to investment in Zambia in the FPC-CBP survey findings.

3.2.5.3 Other Selected Studies on Zambia

Other than the survey findings, some other non-econometric studies have been done on FDI in

Zambia. Using Dunning’s Eclectic Paradigm as an analytical framework, Mooya (2000) argued

that foreign direct investments in Zambia are constrained by location specific variables. These

include low GDP, lack of reliable and adequate infrastructure such as roads, telecommunication,

and electricity. Other studies of FDI such as Carmody et al (2009) analysed how inclusive or

exclusive Chinese investments in Zambia were, with no specific reference to the determinants of

these investments.

3.3. Key Findings from the Literature Survey

The literature survey shows that diverse factors affect FDI in low-income countries like Zambia.

Some factors are quantitative while others are qualitative. Most econometric studies analysed the

determinants of aggregate FDI, and largely incorporated quantitative factors. Table 6 below

summarises the major findings from the literature survey from econometric studies while Table

7 itemises the literature findings from qualitative and other studies.

Table 6: Summary Table of Literature Findings from Econometric Studies

Study/Group of Factors Factors with Positive Effect Factors with Negative Effect Profitability/Rate of Return Factors Caves (1982), WEO(2008), Opolet et al (2008) Expected Return, Commodity

Prices, Rate of Return,

Market Factors Sun(2002), Aseidu (2004) Asiedu(2002), Opolet et al (2008), Pigato (2001) Ramerez (2010)

Real GDP Growth, GDP, Urbanisation, GDP per Capita

Political Factors Sun (2002),Aseidu (2004) Political Instability,

Corruption. Macroeconomic Factors Opolet et al (2008), Aseidu(2002), Asiedu(2002), Onyiewu(2005), Nonnemburg and Mendola (2004), Amal et al (2010), Ramirez (2010)

GDP Growth, Availability of Trained Staff, Performance of the stock market, Credit to private sector, Expenditure of education

Inflation, Public investment spending, Debt service ratio, Volatility of exchange rate

Regulation Reinmart et al (2001), Sun(2002), Amal M. et al (2010), Nonnemburg and Mendola (2004),

Degree of Openness to trade (Export + imports) / GDP

Capital controls, Institutional environment.

Investment Promotion Sun (2002), Aseidu(2004) Incentives, Simplicity and

stability of tax system,

Infrastructure Factors Cardoso (2002), Ribakova and Wu (2005) Roads, Power,

telecommunication services,

Natural Resource Factors Dindia (2009), Asiedu (2004) Natural Resource

endowment,

27

Table 7: Summary Table of Major Literature Findings from Qualitative and Other Studies

Factor by Group Study / Effect (+/-) Zambi

a FPC 2002

Survey

Zambia FPC 2008

Survey

Zambia FPC 2010

Survey

Other Countrie

s FPC CBP

Findings

BoZ QSBOE

WEF Africa Global

Competiveness Findings

2009

World Bank Doing

Business 2011

M NM M NM Profitability/Rate of Return Factors Market Factors Access to Domestic Markets + + Domestic Market Size + + + Regional Market Size + + Low Demand - Political Factors Political and Governance Domestic Political Scenario + + + + + + Corruption - - - - - - - Security - Smuggling - Macroeconomic Factors

Domestic Economic Situation + + + + + Inflation - Monetary Policy + + - Financial System Stability + + + + Banking Services Cost - - Interest Rates - - - - - Appreciation of the Kwacha - + - Exchange Rate - Access to credit - - Availability of Tech. Trained Staff - - - High Cost of inputs - Regulation Investment Policy + + + Trade Policy + + + + Bureaucracy - - - - - Capital Account Liberalisation + + Licence Fees - Dealing with Construction Permits

-

Investment Promotion Fiscal Policy + + - + - - Tax Incentives + + + + Infrastructure Factors - - Telecom Service Efficiency + Telecom Service Cost - - Electricity cost - Electricity Supply Efficiency - - - Inland Transport Cost - - - Trading Across Borders - Natural Resource Factors Environmental & Natural Resource + Environmental and Health HIV/AIDS - - - - - Malaria - - - - -

28

Foreign Direct Investment data in the reviewed econometric studies was not disaggregated to

take away the effects of the dominant sector such as mining for Zambia. The findings therefore

may largely be biased towards the dominant sector. The factors that actually drive other sectors

may not be appropriately captured when analysis is aggregated. This therefore shows the need

to disaggregate FDI between mining and non-mining in order to arrive at meaningful

conclusions on the drivers of investments.

Investor perception findings show a number of pull factors, catalysts and constraints to

investments. As noted by Bhinda and Martin (2009), investor perceptions are not always an

accurate reflection of economic conditions or a good guide to policy priorities. They need to be

analysed carefully and compared with economic data. Econometric analysis may equally mislead

if findings are statistically significant even when the variables may not be major drivers of

investment decisions. This shows the importance of validating some of these findings using

econometric analysis tools. This would help make investor perceptions survey findings more

relevant and economically useful. In addition, findings from econometric studying could help in

identifying some key and economically important factors that should be included in investor

perception studies.

4. METHODOLOGY AND DATA

4.1 Conceptual Framework

There are several theoretical models on the determinants of foreign direct investment. These

include Hymer (1960), Caves (1982), Buckley and Casson (1976). As Faeth (2009), as well as

Bevan and Estrin (2000) argue, there is no single agreed theory of FDI to provide a basis for

empirical work. There are a variety of theoretical models that attempt to explain what drives

FDI. These include ownership advantages, location and internalization advantage, risk diversion

models, and policy variables.

Our estimation framework takes on board the fact that our study focuses on bilateral flows of

FDI from the source to the host country. Models on firm behaviour such as Jorghenson (1963)

and Tobin (1969) q approach put emphasis on the expected return, interest rates and cost

variables as the main determinants of investment. This approach is more focussed on variables

related to macroeconomic stability such as inflation rate, exchange rates, growth and

international trade. Caves (1982), also argued that firms invest abroad when the expected return

exceeds the costs. We therefore incorporate in our model factors in the host country; investing

29

country and the global economy that are expected to affect net profitability of investment. These

are, but not limited to market size, availability and costs of inputs (raw materials [natural

resource], energy and labour), risk both economic and political (Bevanet and Estrin, 2000). We

also incorporate location advantage and institutional factors. The framework is augmented by

findings from the investor perceptions survey on Zambia and other studies.

4.2 Empirical Model

The study employed time series econometric techniques for the period 1964 to 2009 to assess

and distinguish drivers of FDI in mining and non-mining sectors (see Annex i and ii). Our

empirical analysis is based on two error correction models, one for Mining FDI and another for

Non-Mining FDI. The models have their foundations in Opolet et al (2008) and Sawkut et al,

(2008) and are modified based on the Zambia FPC CBP survey findings on pull factors,

catalysts and constraints of investment in mining and non-mining as well as other qualitative and

quantitative studies.

The FDI model is as specified below:

1................0

2111

equationuLogxLogxLogFDIzLogFDI ttj

k

jjtttt

Where FDI is Foreign Direct Investment inflows in mining or non-mining sectors,

α are fixed effects, zt is a vector of dummies and xt is an m × 1 vector of m variables that are

expected to affect FDI both in the short run and long run. The models are estimated using

ordinary least squares (OLS) linear regression technique using E-Views 6.0 econometric

package.

In selecting variables for regression analysis, we assessed the linear association of FDI in mining

and non-mining with selected variables through graphical analysis as depicted in Figure 13 and

14. Figure 13 suggests the realised copper price, the external demand and the supply of

telecommunication services have a strong positive relationship with foreign direct investment in

the mining sector. This suggests that an increase in the above variables contribute to the growth

in investment in the mining sector. Electricity supply, however, seems to be have remained static

for some time, mainly explained by lack of investment in electricity generation for a long time.

Similarly, the volumes of identified copper reserves have been relatively unchanged until 2005,

indicating that there have been marginal discoveries of additional copper reserves since

independence.

30

Figure13: Recent Trends of FDI in Mining and Selected Variables (1999-2008)

Source: BoZ, WDI and MMMD

With regard to other sectors, Figure 14 below suggests that FDI in the non-mining sector is

strongly and positively related with the supply of telecommunication services and the exchange

rate. This suggests that the increase in the supply of telecommunication services as well as the

depreciation of the Kwacha have been supportive of investment in other sectors.

Figure14: Recent Trends of FDI in Other Sectors and Selected Variables (1999-2008)

Source: BoZ, WDI and MMMD

The rate of inflation and the weighted average lending base rates have been trending downwards.

This suggests that the decline in these variables has a positive effect on investment in other

sectors. The supply of electricity has remained fixed for some time and it is not likely to

positively impact on FDI in the non-mining sector. The diesel pump-price maintained an upward

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

0

5001,000

1,5002,000

2,500

3,0003,500

4,0004,500

5,000

5,5006,000

6,5007,000

7,500

8,0008,500

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Copper Reserves

FDI in Mining

Realised CopperPrice

External Demand

Electricity Supply

Telecommunication

0

10

20

30

40

50

60

70

80

90

100

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

5,500

6,000

6,500

7,000

7,500

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Electricity Supply

FDI in Other Sectors

Disel Pump Price

K/US $ Exchange Rate

Telecommunication

Inflation

W/Ave Lending Rates

Secondary Axis

Secondary Axis

31

trend and does not seem to be tracking the growth in FDI in the non-mining sector. To verify the

above relationships, however, econometric analysis is required.

Table 8: Mining & Non-Mining Regression Models

Mining Model Non-Mining Model Profitability /Rate of Return Factors Variable Description Expect

ed Sign Variable Description Expecte

d Sign COPREA Copper Price (Realised) + COPREA Copper Price (Realised) + COPVOL Copper Price Volatility - Market Factors GDPCXMKT

Gross Domestic Product of Zambia’s Major Copper Export Markets

+ GDP Zambia’s GDP +

URB Degree of Urbanisation + Political Factors DUMPS Political Stability(Dummy) + DUMPS Political

Stability(Dummy) +

Macroeconomic Factors INF Level of inflation - INF Level of inflation - INFVOL Inflation Volatility - INFVOL Inflation Volatility -

GOR Gross Official Reserves + GOR Gross Official Reserves + EXR Exchange rate + EXR Exchange rate +

LLR London Interbank Lending Rate - WALBR Weighted Average Lending Base Rate

-

IRS Interest Rate Spread - IRS Interest Rate Spread - Regulation DUMCAL Capital Account

Liberalisation(Dummy) + DUMCAL Capital Account

Liberalisation(Dummy) +

DUMLI Trade Liberalisation(Dummy) + DUMLI Trade Liberalisation(Dummy)

+

DUMPRIV Privatisation of mining companies (Dummy)

+

+ OPEN Openness(Ratio of trade to GDP)

+

Investment Promotion DUMTAXI Tax Incentives (Dummy) + DUMTAXI Tax Incentives (Dummy) + Infrastructure Factors TELECOM Number of Telephone & mobile lines + TELECOM Number of Telephone

and mobile phone lines +

ELECG Electricity Generation + ELECG Electricity Generation + FUELP Diesel Pump Price - FUELP Diesel Pump Price - Natural Resource Intensity COPRES Volumes of identified Copper Reserves +

Health & Environmental Factors MALINC Malaria Incidence - MALINC Malaria Incidence -

Based on the above model, the literature findings summarised in table 7, figure 13 & 14 and the

availability of data, variables were selected for both the mining and non-mining models. These

variables, with their respective expected signs, are presented in Table 8 according to their

respective categories. The factors in the above table are elaborated below according to their

respective categories. The explanations of factors below relate to both models except where

explicitly indicated otherwise.

32

Profitability /Rate of Return Factors

With regard to profitability factors, the price of copper is used as the proxy in both the mining

and non-mining models. In the mining sector, copper accounts for about 90.0 percent of mining

output. The price of cobalt (a by-product of copper) has been found to move in line with copper

prices. For purposes of this study, the realised copper price is used, as it is reflective of the

applicable price to mining companies in Zambia compared with the LME prices. The price of

gemstones is not included due to their relative insignificance. The price of copper is also used in

the non-mining model as a proxy for the performance of the mining sector. An increase in

productivity of industries in the mining sector is expected to spur FDI inflows into other sectors,

largely through forward and backward linkages.

As a proxy for commodity price volatility, a standard deviation of the LME copper price will be

used as a measure of copper price volatility. An increase in commodity price volatility is

expected to discourage investments. This variable is only used in the mining equation.

Market Factors

Among other factors, the size of the market affects investment decisions. Given that copper is

primarily for export, investors are expected to be driven by the international, rather than the

domestic market size. Zambia’s major markets for copper and cobalt include Switzerland,

United Kingdom, the EU, China and South Africa. The total GDP of these countries will be used

as proxy of demand for copper in the mining model.

In the non-mining sector, however, the size of the Zambian market is important. Gross Domestic

Product is used as a proxy as it shows the country’s economic conditions and the potential

demand for the products of the investor. Both the value of GDP and real GDP growth are used to

assess the impact of both the size and the growth of the market. Another market factor is the

degree of urbanisation. In this study, the percentage of the population living in urban areas is

used as a proxy of the degree of urbanisation. This variable will only be included in the non-

mining model.

Political Factors

Political stability takes into account military coups (1990, 1997), riots (1989, 1990) risk

associated with change of Government through elections (1964, 1991, 1996, 2001, 2006), and

the death of the President (2008). This factor is critical in both mining and the non-mining

33

sectors. A dummy variable incorporating the above political risks is used in the regression

analysis.

Macroeconomic Factors

The level of inflation is included as a proxy for the domestic economic scenario. A fall in the

inflation rate is expected to make the domestic economy attractive for investment while a rise in

inflation is expected to discourage investments. Similarly, a stable macroeconomic environment

is considered a major pull factor of FDI. A rise in inflation volatility (a proxy for domestic

economic stability), therefore, raises uncertainty of the stability of the domestic economy and is

expected to negatively impact on foreign investment inflows.

Lending interest rates are incorporated as a proxy for the cost of borrowing for business

expansion. The weighted average lending base rate is used in the non-mining model while the

London interbank lending rate is used in the mining model. In addition, the study uses the

interest rate spread (difference between lending and savings rates) as a proxy for the cost of

banking services. The cost of banking services was a major constraint reported by the mining

companies in the 2008 Zambia investor perceptions survey.

Other macroeconomic factors include the level of reserves (proxy for external sector

vulnerability) the exchange rate (a proxy of international competitiveness), which are expected

to positively affect investment in both the mining and non-mining sectors. A weaker kwacha

implies that foreign financed investments (denominated largely in foreign currency) will have a

higher value in kwacha terms. In addition, because most of the investments are also meant to

satisfy foreign demand, a weaker kwacha entails higher export earnings (in kwacha terms) and

thereby encouraging foreign investments.

Regulation

With regard to regulation, three dummy variables are used – these being trade liberalisation,

capital account liberalisation and privatisation. Trade liberalisation refers to liberalisation of

exports and imports effective 1992, in the case of Zambia, while capital account liberalisation

relates to the removal of controls on repatriation of profits in form of foreign exchange and other

capital controls implemented in 1994. Both factors are expected to have a positive effect on

investment. Privatisation of mining companies which was done over the period 1997 to 2000,

however, is mainly expected to positively affect FDI inflows in the mining sector. The degree of

openness of an economy is a key factor that affects investments. The ratio of trade (exports plus

imports) to GDP will be used as a measure of openness. This variable will only be included in

34

the non-mining equation, as the mining sector is not likely to be mainly influenced by the level

of trade as it is the major driver of trade; accounting for about 80.0 percent of export earnings

and about 40 per cent of Zambia’s imports.

Investment Promotion

Tax incentives are used as a proxy for investment promotion. This is based on the survey

findings on investor perceptions, which showed that fiscal policy and tax incentives were critical

to investment. Fiscal policy in this context is closely tied to tax incentives. A dummy variable of

tax incentives is used in this study.

Infrastructure Factors Telecommunication, electricity supply and inland transport costs (using the diesel pump price as

a proxy) are used in this study. The number of telephone and mobile phone lines and electricity

generation are used as proxies for availability of infrastructure. Given that transportation in

Zambia is largely by road, the pump price of diesel per litre will be used as a proxy for inland

transport cost.

Natural Resource Intensity

As we have established in the earlier sections, most FDI inflows in Zambia has been resource

seeking. As argued by the eclectic theory, all things equal, countries endowed with natural

resources attract more FDI. In the context of Zambia, copper is the most important natural

resource. In this study, the volumes of identified copper ore reserves will be used as a proxy for

availability of natural resource endowments. This is only be used in the mining model.

Health and Environmental Factors

Malaria was rated a critical factor in business decision making according to FPC CBP surveys,

as it contributed to absenteeism, labour force instability, thus raising the costs of doing business.

The incidence of malaria per 1000 population is used as a proxy.

Correlation Tests

The factors that were among the major constraints to FDI in perception survey findings and

other studies had insufficient time series for regression analysis. We will use simple correlations

to assess the linear association of FDI inflows with HIV/AIDS, Corruption and Malaria

Mortality. Mining companies indicated that HIV/AIDS prevalence had a major impact on their

operations and for economic reasons have endeavoured in some cases to facilitate the provision

35

of health care and awareness creation to their workforce. HIV/AIDS deaths are equally expected

to negatively impact on investment particularly when they relate to the death of qualified

personnel, their relations and the associated costs. Similarly, corruption continued to be a

constraint to investments according to the investor perception surveys. Due to limited time

series, we test for the strength of the linear association of Corruption Perception Index, Control

of Corruption, and the Corruption Percentile Rank. The corruption perception index (CPI)

measures the degree to which corruption is perceived to exist among public officials and

politicians in a particular country. It focuses on perceptions and not hard data (Speech on Launch

of TI CPI 2009 Zambia).

Control of corruption is a World Bank governance indicator, which measures the effectiveness

of Governments in controlling corruption. An increase in the index implies improvement in

control of corruption. The corruption percentile rank is another World Bank governance

indicator, which shows a country’s ranking with regard to control of corruption. An increasein

the Index indicates improvement in control of corruption.The above-mentioned corruption

indicators were included to capture the impact of both perceptions and Government control of

corruption on investment. We also include malaria incidence in correlation tests due to data

limitations. Table 9 below presents the correlation test-factors and their expected signs in both

the mining and non-mining sectors.

Table 9: Correlation Tests of FDI with Malaria, HIV Aids, Corruption,

Mining FDI Non-Mining FDI Variable Description Expect

ed Sign Variable Description Expecte

d Sign Corruption

Corruption Perception Index (A rise in index implies a decline in corruption)

+ Corruption

Corruption Perception Index (A rise in index implies a decline in corruption)

+

Control of Corruption (Rise in index implies improvement in corruption control)

+ Control of Corruption (Rise in index implies improvement in corruption control)

+

Corruption Percentile Rank (Rise in index implies improvement in corruption control)

+ Corruption Percentile Rank(Rise implies improvement in control)

+

HIV/AIDS HIV Prevalence - HIV/AIDS HIV Prevalence -

HIV Deaths - HIV Deaths -

Malaria Malaria Incidence - Malaria Malaria Incidence -

4.3 Stationarity, Cointegration and Diagnostic tests

Most time-series data tend to exhibit a stochastic or deterministic trend over time, thereby

rendering the series non-stationary. We will first test for stationarity for each individual series

before estimating the equations using augmented Dickey-Fuller (ADF) test. We will also test for

Cointegration and carry out some diagnostic tests (see Annex iii)

36

4.4 Data Description, Sources and Limitations

Secondary time series data is used in the course of the analysis. This is augmented with both

primary and secondary data from Zambia’s Surveys of Foreign Private Investment and Investor

perceptions conducted in 2002, 2008 and 2010. A detailed table describing the data, sources and

limitations is presented in Annex IV.

The key variable FDI only had comprehensive and disaggregated survey data for a limited

period (2001, 2007 and 2009). Data for other years such as the period 1992-2000, 2002-2006,

2008-2009 were obtained from IMF/BOZ estimates. These non-metal FDI estimates were based

on implementation estimates of investment pledges made by new investors. This data has not

only the limitations of not reflecting the actual inflows, but does not break down FDI into the

three major components of equity, retained earnings and other capital (FDI debt).

During the period prior to the establishment of the Zambia Investment Centre (now Zambia

Development Agency), FDI data was estimated from exchange records from the banking sector.

This data has a major limitation of not accurately capturing the residency of the transactors. In

addition, financing of investments that do not necessary result in cash transfer are excluded.

Equipment could be imported and transported to the country without any clear record of how it

was financed. In some cases, the mother company to a resident entity may give its subsidiary

equipment or goods. These may not be captured as inward FDI if data is only obtained through

exchange records.

Other variables had a good time series except for selected years where the gaps were closed by

extrapolating data. Variables such as HIV/AIDS could not be extrapolated, as the incidence was

not well known in Zambia prior to the early 1990s.

37

5. RESULTS AND ANALYSIS

5.1 Stationarity, Cointegration and Diagnostic Test Results

5.1.1 Stationarity of Variables Results As indicated in the methodology section, variables were tested for stationarity using the

Augmented Dick Fuller test, the stationarity results are presented in Annex iii. The stationarity

results indicate that all the variables were I(1) [Stationary after first difference]. After

establishing the stationarity of the variables, we then proceed to test if the variables have a long

run relationship by testing for Cointegration.

5.1.2 Testing For Cointegration To test for Cointegration, we do so by running each equation and testing for stationarity of

residuals using augmented Dick Fuller test (without trend and intercept). If residuals are

stationary, then there is a Cointegration relationship. After generating and testing the residuals

for Stationarity, the results were as presented in Annex ii. The residuals for the both the mining

and non-mining FDI equations were I(0) stationary, implying that there is a cointegrating

relationship among the variables in each of the equations specified. This entails that there is a

long run relationship.

5.1.3 Diagnostic Test Results After verifying the stationarity of the residuals from the above equations, we then proceed to

undertake diagnostic tests on the error correction models for both mining and non-mining. The

results are as presented in the Tables 10 and 11 below:

Table 10: Diagnostic Test Results for Mining Equation (A) Test For Test Applied Observed Statistic P-value Conclusion

Normality Jarque Bera 0.3806 0.8267 Normally distributed Serial Correlation Breusch-Godfrey LM Test 1.4192 0.2615 No-significant serial correlation Hetroscedasticity Breusch-Pagan-Godfrey 0.7663 0.6349 No-Hetroscedasticity Hetroscedasticity ARCH White 0.5918 0.4475 No-Hetroscedasticity Stability Tests Ramsey Reset 0.5918 0.4475 Equation is correctly specified Recursive Chow Forecast Test 1.1471 0.4102 Stable and correctly specified

Table 11: Diagnostic Test Results For Non-Mining FDI Equation (A)

Test For Test Applied Observed Statistic

P-value

Conclusion

Normality Jarque Bera 1.5885 0.4519 Normally distributed Serial Correlation Breusch-Godfrey LM Test 0.4450 0.6470 No-significant serial correlation Hetroscedasticity

Breusch-Pagan-Godfrey 0.6797 0.7528 No-Hetroscedasticity ARCH White 0.4779 0.4946 No-Hetroscedasticity

Stability Tests Ramsey Reset 0.3198 0.5777 Equation is correctly specified Recursive Chow Forecast Test 1.1225 0.4517 Equation is stable and correctly specified

The above findings for both the mining and non-mining equations indicate that equations are

robust. The errors of both equations are normally distributed and there is no serial correlation.

38

Similarly, the Breusch-Pagan-Godfrey and the ARCH Test show that there is no

Hetroscedasticity. In addition, the Ramsey Reset test and the recursive tests (Chow Forecast

Test) show that the model is stable and correctly specified. Based on these findings, the results

can be analysed and used to draw inference.

5.2 Estimation Results and Analysis

This section presents and discusses the results of the estimation equations of both the mining and

non-mining. The results are summarised is a separate tables (Tables 12 and 13) for each sector.

5.2.1 Mining FDI Equations

5.2.1.1 Results of the Mining FDI Equations

Table 12: Results of the Mining FDI Equations-Dependent Variable: DLFDIM

VARIABLE A B C D E LFDIM(-1) -0.999*** -0.888*** -0.988*** -0.966*** -0.971***

(-7.528) (-6.412) (-6.727) (-6.206) (-7.019) LCOPREA(-1) 1.132*** 0.705**

(6.134) (2.526) LGDPCXMKT(-1) 0.716** 0.817*** 1.12*** 1.145*** 0.973***

(2.674) (4.547) (5.634) (4.885) (4.168) LELECG(-1) -0.476** -0.472** -0.48** -0.508**

(-2.469) (-2.379) (-2.196) (-2.523)

LOG(TELECOM(-1)) 0.197* (1.453)

LOG(GOR(-1)) 0.537*** (3.455)

LOG(COPVOL(-1)) 0.254** (2.358)

LOG(INFVOL(-1)) -0.105* (-1.573)

C -14.897*** -11.519*** -14.228*** -9.589*** -10.096*** (-5.636) (-4.701) (-5.052) (-3.508) (-4.014)

D(LGDPCXMKT) 2.958*** 2.697*** 2.943*** 3.27*** 3.161*** (3.350) (9.915) (3.260) (3.389) (3.394)

D(LOG(GOR)) 0.43** 0.168 (2.594) (1.162)

D(INF) 0.011*** 0.009** 0.0106*** 0.008** 0.011*** (4.280) (2.709) (3.138) (2.485) (4.188)

D(LEXR) -0.606** -0.588** (-2.801) (-2.444)

D(IRS) -0.01 -0.004 -0.015 (-0.458) (-0.194) (-0.658)

D(LLLR) -0.417 (-0.940)

DUMPS 0.483** 0.389** 0.481** 0.534** 0.285 (2.777) (2.124) (2.708) (2.521) (1.592) DUMPRIV 0.085

(0.335) R-squared 0.791 0.784 0.791 0.789 0.795 Adjusted R-squared 0.726 0.706 0.715 0.703 0.722 F-Statistic 12.261 10.04 10.5 9.147 10.804 N0. Observations 35 35 35 35 35

Note: t –value in ( ),*, **, *** denote significance of t values at 10%, 5% and 1% levels, respectively

39

5.2.1.2 Significant Positive Effects in Mining

The FDI in mining equations show that the realised copper price and growth in external demand

for copper were the major drivers of FDI in mining. This is consistent with the graphical analysis

presented in Figure 13, which demonstrates a strong linear relationship of FDI in Mining with

the realised price of copper and external demand. From the above equations, a one percent (1%)

rise in growth of copper demand (GDP of copper export markets) results in about 1.2 percent

increase in growth of FDI inflows in the mining sector (Eqn. D).This finding is a unique

contribution of this study as no study from our literature survey had incorporated external

demand in econometric findings.

The above finding, however, is aligned to the Zambia FPC 2008 survey finding which showed

that the regional market size had a positive effect on FDI in mining. Consistent with the impact

of growth in external demand, a one percent (1%) rise in copper prices generates a 1.13 percent

increase in FDI inflows in mining. The results support the findings of IMF (2008 b) study on the

impact of globalisation which found that commodity prices had a positive effect on FDI inflows

in developing countries. Similarly, Caves (1982) and Opolet et al (2008) also found that the rate

of return and the expected rate of return on investment had a positive effect on FDI.

Other catalysts of FDI inflows in mining include; growth in Gross Official reserves (lower

external sector vulnerability), copper price volatility and political stability (see Table 13). The

findings on the role of political stability are consistent with studies such as Sun (2002), Aseidu

(2004) as well as all Zambia’s FPC Surveys and other countries FPC CBP findings which show

that the domestic political stability continued to be a major stimulant of foreign direct investment

inflows in both mining and other sectors.

5.2.1.3 Insignificant Positive Effects in Mining

Other factors such as availability of telecommunication services and privatisation of mining

companies had a positive but insignificant effect. These findings are not a surprise as mining

companies generally attract their own infrastructure. They do invest in places that may be

remote, without favourable infrastructure and in some cases they do build their own

infrastructure such as roads, houses etc. With regard to privatisation, the insignificant but

positive effect on FDI inflows could partly be explained by the fact that the mining companies

were sold at very low prices as they were seemingly not promising, given the dilapidated mining

infrastructure as well as the slide in realised copper prices which stood at about US $1,577.7 Per

tonne in 2002. The pulling out of Anglo American Corporation in 2002 demonstrated how

40

unfavourable the mining sector was to foreign investment during and soon after privatisation.

Foreign direct investment inflows, however, rose several years later driven by the steady

increase in realised copper prices to about US $7,103.0 in 2007.

The volume of identified copper reserves had a positive but insignificant effect on FDI in

mining. This finding is, however, not surprising. A quick look at the copper reserves data

depicted in Figure 13.1 and an analysis of the information obtained from the Ministry of Mines

and Minerals Development shows those minimal new discoveries of copper reserves we made

after the initial discoveries prior-to and soon after independence. Due, however, to low copper

content in ores, these reserves were not exploited until copper prices rose to levels that made

mining of such reserves viable. Some of the ores with low copper content include the Lumwana

Mine whose ores are estimated at about one (1) per cent copper content (MMMD, Mines Visit

report June 2010).

Similarly, other factors such as capital account liberalisation, trade liberalisation, openness and

fiscal incentives had insignificant positive effects on FDI inflows in the mining sector. Such

insignificant variables were dropped out of the final equations.

5.2.1.4 Significant Negative Effects in Mining

As presented in Table 12 above, the key constraint for FDI in the mining sector was electricity

supply. The marginal increase in electricity generation as depicted in Figure 13 does not seem to

catch up with the demand for energy in the country. The limitation in the electricity generation

capacity is a deterrent to investment in the sector. Zambia generates 1,400MW of electricity,

consumes about 800MW during the day but demand rises to 1,500MW at peak during the night,

according to ZESCO estimates. In 2009, Zambia's power consumption was expected to rise to

2,437MW owing to the projected 13.1percent growth in the mining sector.

5.2.1.5 Insignificant Negative Effects in Mining

Other factors such as inflation volatility, international lending interest rates, and cost of local

banking services (using interest rate spread as a proxy) had a negative but insignificant effect on

FDI inflows in the sector. These findings are not a surprise. Due to the size of most mining

companies, they easily import inputs and therefore, they are not significantly affected by

domestic inflation volatility. Similarly, the cost of local banking services does not significantly

impact on their investments as they keep most of their earnings in offshore accounts. This

finding, however, is contrary to the 2008 FPC CBP Zambia investor perception findings where

41

the cost of banking services ranked highest among the major constraints. Although mining

companies express concern over the cost of banking services, they are proportionately small,

given the size of these companies; hence the limited negative effect from the econometric study.

With regard to international lending rates, they do not have a significant impact on mining

companies as they largely borrow from or through their holding companies at no cost or

negotiable rates. Similarly, other factors such as transportation cost, and malaria incidence had

insignificant negative effects on FDI inflows in the mining sector. These findings do not confirm

the investor perception findings from the Zambia FPC CBP 2008, 2010, and other country FPC

CBP findings, which suggest that inland transportation costs and malaria had significant

negative effects.

5.2.1.6 Long Run Elasticity of FDI in Mining

Analysis of the responsiveness of FDI inflows to changes in various explanatory variables in the

model shows that FDI in mining is highly responsive to the realised price, external demand for

copper, the level of reserves and telecommunication services. A one per cent (1%) rise in

realised copper prices, external demand of copper, the level of reserves and telecommunication

services provision results in a 1.13percent, 1.2 percent, 0.6 percent and 0.5 percent increase in

FDI inflows, respectively(see Table 13).

Table 13: Long Run Elasticity of FDI in Mining Equations

Equation A B C D E Speed of Adjustment -0.999 -0.888 -0.988 -0.966 -0,977 LCOPREA(-1) 1.133 0.714 LGDPCXMKT(-1) 0.717 0.920 1.134 1.185 1.002 LELECG(-1) -0.476 -0.478 -0.523 LOG(TELECOM(-1)) 0.497 0.203 LOG(GOR(-1)) 0.605 LOG(COPVOL(-1)) 0.262 LOG(INFVOL(-1)) -0.118

Source: Own Computations

5.2.2 Non-Mining FDI Equations

In this section, the results of the estimation equations for non-mining are presented and discussed. The results of six equations A, B, C, D, E and F are summarised is Table 14.

42

5.2.2.1 Results of the Non-Mining FDI Equations

Table 14: Results of the Non-Mining FDI Equations

Dependent Variable: LDFDINM

VARIABLE A B C D E F

LFDIM(-1) -0.872*** -0.881*** -0.839*** -0.882*** -0.882*** -0.857*** (-8.886) (-8.78) (-7.026) (-8.569) (-8.601) (-6.655)

LCOPREA(-1) 0.752*** 0.725*** 0.728*** 0.724*** 0.788*** (4.188) (3.961) (3.855) (3.876 3.354

LOG(EXR(-1)) 0.534*** 0.42*** 0.494*** 0.419*** 0.432*** 0.323*** (5.096) (5.85) (3.916) (5.704) (4.957) (3.77)

RGDPG(-1) 0.145*** 0.148*** 0.123*** 0.147*** 0.146*** 0.173*** (4.675) (4.653) (3.192) (4.314) (4.336) (4.203)

LOG(ELECG(-1)) -0.147 -0.488 -0.206 -0.485 -0.469 0.041

(-0.398) (-1.677) (-0.466) (-1.621) (-1.523) (0.124) LOG(TELECOM(-1))

0.353** (2.394)

LOG(URB(-1)) 7.394*** 8.936*** 7.57** 8.819*** 8.66*** 5.474 (2.633) (3.352) (2.181) (3.089) (2.944) (1.693)

LOG(FUELP(-1)) -0.15 -0.179

(-1.463) (-1.43) C

-31.264*** -33.636*** -29.421*** -33.264***

-32.81*** -

26.138*** (-4.112) (-4.42) (-3.152) (-4.036) (-3.878) (-2.758)

D(RGDPG) 0.087*** 0.083*** 0.075*** 0.082*** 0.082*** 0.103*** (4.088) (3.84) (2.927 (3.709) (3.64) (3.836)

D(LOG(WALBR)) -0.956*** -0.996*** -0.871*** -1.002*** -1.022*** -0.454 (-3.289) (-3.357) (-2.466) (-3.27) (-3.185) (-1.366)

D(LOG(INF)) 0.77*** 0.79*** 0.721*** 0.789*** 0.788*** (4.011) (4.03) (3.035) (3.939) (3.931)

D(LOG(URB)) 42.59*** 40.332*** 36.332** 39.738*** 40.187*** 24.532 (3.628) (3.383) (2.419) (3.071) (3.298) (1.721)

D(LOG(INFVOL)) 0.088 (1.054)

DUMPS 0.813*** 0.749*** 0.822*** 0.749*** 0.751*** 0.838*** (5.101) (4.771) (4.277) (4.669) (4.679) (4.067)

DUMPFSI -0.029 (-0.136)

DUMTAXI -0.102 (-0.247 )

R-squared 0.893 0.883 0.848 0.883 0.883 0.809

Adjusted R-squared 0.835

0.827 0.765 0.819 0.82 0.718

F-Statistic 15.35 15.77

10.221 13.84 13.871 8.883 Number of Observations 35 35 35 35 35 35

Note: t –value in ( ),*, **, *** denote significance of t values at 10%, 5% and 1% levels, respectively.

5.2.2.2 Significant Positive Effects in Non-Mining

From Table 15, the results show that FDI in non-mining is largely driven by the degree of

urbanisation, political stability, real GDP growth, and exchange rate depreciation, improvement

in infrastructure such as telecommunication services and the boom in the mining sector.

43

A one per cent increase in the degree of urbanisation results in 8.5 percent rise in FDI inflows in

the non-mining sector. In order to stimulate FDI inflows into the non-mining sector, Zambian

policy makers must accelerate the speed of urbanisation. This should not be done by merely

attracting people from rural to urban areas, but by accelerated development of rural areas

through infrastructure development such as electricity, telecommunication and roads. As more

rural areas in Zambia are developed and transformed into urban areas, FDI inflows will

accelerate further. The role of urbanisation is consistent with findings of a study by Opolet et al

(2008) which suggests that the degree of urbanisation has a positive effect on FDI inflows in Sub

Saharan Africa. In the case of Zambia, however, the responsiveness of FDI in non-mining to the

degree of urbanisation was substantially higher than recorded in other studies.

Telecommunication services have been a major factor driving FDI into other sectors in Zambia.

This is clearly demonstrated by the tremendous increase in mobile cellular subscriptions to over

3.5 million by the year 2008, from as low as 1,500 in 1995 (see Figure 14). A further

improvement in telecommunication services in Zambia will spur growth in FDI inflows. These

findings are consistent with studies such as Cardoso (2002), Ribakova and Wu (2005) which

found that a well-developed infrastructure was a key driver of FDI. In terms of macroeconomic

factors, it is clear from the results that favourable macroeconomic performance evidenced by the

robust and sustained GDP growth is crucial in attracting foreign direct investment in Zambia.

Since a large proportion of investors in Non-mining are export oriented, the depreciation of the

Kwacha is a favourable development for such enterprises. A weakening of the local currency

raises their export earnings (in Kwacha terms) and makes it cheaper for them to meet their local

kwacha costs. The 2010 Zambia FPC CBP survey findings, however, suggest that the

depreciation of the Kwacha had significant negative effects. This could be partly explained by

the notable depreciation recorded in 2009 due to the effects of the global crisis (see Figure 14).

A boom in the mining sector (using the realised price of copperas a proxy) spurs investments in

the non-mining sector as well. This is mainly because a number of companies, particularly in the

construction and manufacturing sectors invest or step-up their investments, primarily to service

the mining sector. In times of copper price slides, the overall impact on the domestic economy

becomes much more severe due to the strong link of these sectors with mining. This clearly

shows how dominant the mining sector is in the overall economy and the challenge of economic

diversification.

44

Similarly, political stability has been a major catalyst of investments in Zambia. The favourable

political scenario reduces risks and encourages investors to invest in Zambia. These findings are

consistent with studies such as Sun (2002), Aseidu (2004) and all Zambia FPC CBP and other

country studies which stress the role of political stability in promoting FDI inflows. Zambia has

enjoyed a long spell of peace, which lowers political risk associated with investment in the

country.

5.2.2.3 Significant Negative Effects in Non-Mining

High local commercial banks’ lending interest rates were among the major constraints to FDI

inflows in the non-mining sector. High lending interest rates make credit costly and discourage

FDI inflows, while a decline in interest rates is expected to attract FDI inflows. Several studies

including Zambia and other country FPC CBP surveys and the Bank of Zambia quarterly

surveys of business opinion and expectations have repeatedly shown that high lending rates in

Zambia have been a major constraint to investment and business expansion.

Despite inflation sharply declining to single digits, commercial banks’ lending interest rates have

remained stubbornly high (see Figure 13.2). To this effect, the Bank of Zambia conducted a

survey in 2010 of all the 18 commercial banks to establish how they determine base lending

rates. The survey findings show that the key factors considered in the base lending rate

decision-making process were the cost of funds, economic conditions, market conditions and

political risks. With regard to cost of funds: cash reserve requirements – namely, the statutory

reserve ratio, core liquid asset ratio and the BoZ supervisory fee; operational costs; and yield

rates on Government securities were major considerations. This was followed by market

conditions: credit risk, industry trend, interbank rate, overnight facility, and demand and supply

of credit. Only half of the banks consider economic conditions as a major issue in determining

base lending rates (Bank of Zambia, Sept 2010).

Among other factors, inadequate competition in the banking sector due to a relatively lower

number of registered commercial banks could partly explain the high rates. To minimize this

problem, the Bank of Zambia attracted and registered about six (6) new banks in the last few

years in an effort to improve the cost and availability of banking services in order to facilitate

accelerated investment and growth.

45

Apart from lack of intense competition in the market, the poor credit culture prevailing in

Zambia contributes to the high non-performing loans. The poor credit environment significantly

raises the credit risk, leading to high lending rates. To address this challenge, a Credit Reference

Bureau (CRB) was recently created to mitigate credit risk. It should, however, be noted that the

CRB is still new and there is need to increase its scope and quality of coverage to effectively

contribute to lowering interest rates in the country.

Other factors such as electricity generation, transport costs (Diesel pump price), financial system

instability and incidence of malaria had negative but insignificant effects on FDI inflows in non-

mining. Electricity supply has largely been inadequate for some years. To overcome some of

these challenges, a number of companies bought standby generators to minimise productivity

loss arising from load shedding. This may partly explain the insignificant impact. Electricity

supply however continues to be a major challenge in the country as the electricity deficit

continues to widen. Transport costs had insignificant negative effects, though they ranked among

the major constraints in the Zambia’s FPC CBP studies. Most enterprises adjust their product

selling prices to take on board transportation costs. This could partly explain the insignificant

effect. In addition, most of the non-mining foreign investment enterprises generally set up their

operation near the targeted local markets (in urban areas). Other than the costs of imported

inputs (which are included in the price of their finished products), the transportation costs to

domestic markets are minimal.

Financial system instability particularly experienced during the period1990 to1998, which saw

the closure of a number of locally owned banks, had a negligible negative impact on FDI

inflows. In 1995 alone, Zambia experienced a turbulent period with three locally owned

commercial banks failing due to weak corporate governance and risk management structures

(Beyani et al 2008). During this period, there was a general discontent in the stability of the

locally owned banks. Most savers therefore, moved to the foreign owned banks. The instability

was short lived and quickly addressed by the Central Bank and brought again confidence in the

banking system. The negligible impact of this instability could be explained by the fact that most

foreign investors held their accounts with foreign owned banks. The closure of locally owned

banks, therefore, was not a threat to investments during that period.

46

5.2.2.4 Long Run Elasticity of FDI in Non-Mining

As shown in Table 15 below, FDI inflows are highly responsive to the degree of urbanisation. A

one per cent rise in the degree of urbanisations results in about 10.0 percent increase in FDI

inflows in the non-mining sector. This is followed by the performance of the mining sector, the

exchange rate and telecommunication services provision. A one per cent rise in the realised price

of copper, one per cent depreciation of the kwacha as well as a one per cent increase in the

supply of telecommunication services result in a 0.8 percent, 0.5 percent and 0.4 percent rise in

FDI inflows, respectively (see Table 15).

Table 15: Long Run Elasticity Non-Mining

VARIABLE A B C D E F Speed of Adjustment -0.87 -0.88 -0.84 -0.88 -0.88 -0.86 LCOPREA(-1) 0.86 0.82 0.83 0.82 0.92 LOG(EXR(-1)) 0.61 0.48 0.59 0.48 0.49 0.38 RGDPG(-1) 0.17 0.17 0.15 0.17 0.17 0.20 LOG(ELECG(-1)) -0.17 -0.55 -0.25 -0.55 -0.53 0.05 LOG(TELECOM(-1)) 0.42 LOG(URB(-1)) 8.48 10.14 9.02 10.00 9.82 6.39 LOG(FUELP(-1)) -0.17 -0.21

Source: Own Computations

5.3 Explanations for Inconsistent Findings

5.3.1 Mining Model

In the mining model, factors with results contrary to expectation were the inflation rate and the

exchange rate. A rise in inflation is expected to make the domestic economic scenario

unfavourable for investment. The findings however show that high domestic inflation does not

necessarily discourage investments in the mining sector. This finding however is not surprising

since most mining costs such as capital equipment and other mining inputs (apart from salaries

etc.), are incurred internationally. In light of this, they would not necessarily be vulnerable to

increases in domestic inflation.

With regard to the exchange rate, an appreciation of the local currency is expected to discourage

investments in the sector. The results above show that FDI inflows rise when the currency

appreciates. This is equally not surprising as investments in mining are predominantly driven by

the copper price. A surge in the copper price contributes to exchange rate appreciation due to

increased export earnings. The impact of the rise in copper prices on investment outweighs the

associated negative effects of exchange rate appreciation. Figure 15 shows the strong negative

relationship between copper prices and the exchange rate.

47

Figure 15: Movements in the K/US$ Exchange Rate and Copper Prices

0.00

1,000.00

2,000.00

3,000.00

4,000.00

5,000.00

6,000.00

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

400.00

450.00

Jan‐04

Jul‐04

Jan‐05

Jul‐05

Jan‐06

Jul‐06

Jan‐07

Jul‐07

Jan‐08

Jul‐08

Jan‐09

Jul‐09

Jan‐10

Jul‐10

K/US

$

US$/lb

Copper Prices Nominal Exchange Rate

Source: Bank of Zambia

It is also worth noting the peculiarity of the mining sector with regard to investments. There are

special characteristics of mining investments that make them thrive in areas that may have a

number of other constraints. As presented by Bhinda et al (1999), mining companies easily

overcome problems that smaller companies would not due to the size of their investments. They

can invest in their own infrastructure, they are less susceptible to exchange rate risks as they are

export oriented and lodge export proceeds in offshore escrow accounts. In addition, they have

ability to spread risk and enjoy major tax holidays.

5.3.2 Non-Mining Model

In the non-mining model, the level of inflation was found to be positively related with FDI

inflows. This surprising finding may be explained by the fact that Zambia has in the last couple

of years experienced relatively lower inflation rates. At lower levels of inflation, such as single

digit levels, a further reduction in inflation may not necessarily result in a proportionate rise in

FDI inflows. This suggests that there is an acceptable level of inflation, which investors can

accommodate, and therefore they would not respond proportionately to any increase if

considered small. Inflation volatility equally had an insignificant impact on investment. This

could well be attributed to the fact that at lower levels of inflation, volatility does not affect

investment decisions significantly.

Similarly, tax incentives had an insignificant effect on investment inflows in the non-mining

sector. These findings are contrary to Zambia’s investor perceptions survey findings of 2008

and 2010. This, however, is not surprising, as several studies including Sun (2002) have

repeatedly shown that tax incentives are not as influential as usually believed. In perception

surveys, investors highly raate tax incentives highly mainly to encourage Governments keep

taxes low, though they do not play a significant role in their investment decisions. Investors

48

often look at other key issues such as the profitability prospects and policy consistence as they

make investment decisions. Zambia should therefore moderate the lengths of tax holidays

extended to foreign investors, as there are huge tax revenue losses, which are not commensurate

to the benefits of investments inflows associated with these incentives. Zambia, through the

Zambia Development Agency has been offering ‘blanket’ incentives to investors irrespective of

the region they invest in. Given the high responsiveness of foreign investment to the degree of

urbanisation and infrastructure development elaborated above, it is appropriate to consider

offering specially targeted incentives to those wishing to invest in specific regions such as rural

areas.

Other reasons for inconsistent findings in both models could partly be as a result of aggregating

FDI data (equity capital, retained earnings and FDI debt). These various components of FDI

respond differently to various factors. New investments in form of equity are predominantly

driven by the investments climate, availability of natural resources and other pull factors.

Retained earnings might be pro-cyclical, rising in booms and falling in busts. Retained earnings

might also decline as profits remittances and dividend payments rise due to political and

economic uncertainty. They might also be depend on the individual company’s investment

strategy and the scope the company has to expand on existing or new projects. The debt

component of FDI inflows is largely driven by the availability of credit, level of interest rates

and expected return on investments.

Zambia’s experience during the recent global financial and economic crisis clearly demonstrates

how different these components of FDI are impacted. Despite the global crisis, foreign direct

investment inflows in form of equity capital in 2009 surged substantially to US $419.2 million

from US $131.6 million recorded in 2007. This clearly demonstrates how favourable the

investment climate in Zambia is. Retained earnings, however, shrunk to US $52.4 million from

US $776.4 million recorded in 2007, explained by the slide in profits following the collapse in

commodity prices. As a result of drying up of credit on the global market due to the crisis, FDI

inflows in form of borrowing declined by 46.3 percent to US $223.2 million from US $415.9

million recorded in 2007.

5.4 Comparison of Mining and Non-Mining

A careful comparison of regression results for mining and non-mining show a number of

similarities and differences. In terms of similarities, results show that FDI inflows in both sectors

are positively influenced by realised copper prices, political stability, and improvements in

49

telecommunication. In terms of differences, the non-mining equations is more robust that the

mining equations as evidenced by the higher adjusted . This could be explained by the fact

that some factors that are critical to investment in the mining sector such as availability of

technically trained staff, bureaucracy and corruption were not included due to inadequate time

series. In addition, FDI in the mining sector is highly responsive to growth in external demand,

while the non-mining sector is spurred by the increase in urbanisation and growth in real GDP in

Zambia. Electricity supply constraints are more critical in mining than non-mining, while

exchange rate depreciation is favourable for investment in non-mining but unfavourable in the

mining sector.

5.5 Correlation Tests and Analysis

It is important to note that some variables driving investments cannot easily be quantified and

where such data is available, the series is too short to carry out time series econometric tests and

analysis. Due to the above limitations, we assess the strength and significance of the linear

relationship of FDI with other explanatory variables. If the linear relationship is strong and

significant, it suggests that there is a strong and significant relationship between the variables.

We use a t test defined as:

Where r is the Pearson sample correlation coefficient (computed using excel), n is the sample

size, and critical degrees of freedom. If the absolute value of t calculated is high than the

critical degrees of freedom, then we reject the null hypothesis that r = 0 (that there is no

linear relationship).

The results of the correlation tests of FDI in mining with selected variable are presented in the

table 16 below:

5.5.1 Correlation of FDI in Mining with Selected Variables

Table 16: Correlation of FDI in Mining with Selected Variables

Variable

Malaria Incidence

Corruption Perception

Index

HIV/ AIDS Death

HIV/AIDS prevalence

Control of Corruption

Corruption Percentile

Rank Period 1970-2009 1998-2009 1990-2009 1990-2009 1999-2009 1999-2009 Sample size (n) 40 12 18 18 11 11 Degrees of Freedom (n-2) 38 10 16 16 9 9 Correlation coefficient (r ) 0.41 -0.38 0.31 0.01 0.83 0.82 6.8 -1.3 1.3 0.1 4.5 4.3 Critical df @ 5% C.I. 2.1 -2.23 2.12 2.12 2.26 2.26 Decision Reject ho Accept ho Accept ho Accept ho Reject ho Reject ho

50

Malaria Incidence

Table 17 suggests that despite huge investments recorded in mining in recent years, very little

has been done by the mining companies to curb the spread of malaria in the surrounding

communities as part of their corporate social responsibility. Sharp et al (2003) in a study entitled

‘Malaria Control by Residual Insecticide Spraying in Chingola and Chililabombwe, Copperbelt

Province, Zambia’ show that Malaria incidence rates in Zambia had nearly tripled to 321.4 per

1000 population by 1999 from as low as 121.5 cases per 1000 in 1976.

On the Copperbelt province, by the year 2000, the incidence rates of Malaria had increased to

158/1000 p.a., for Chililabombwe and 68/1000 p.a., for Chingola from as low as 20/1000 p.a. in

the 1970’s. Prior to the 1980s, effective malaria control was achieved in the northern mining

towns of Chingola and Chililabombwe by means of annual residual spraying programmes by

mining companies. These spraying programmes were not done for some time, leading to the rise

in malaria incidence. This house-spraying programme demonstrates the need for private/public

sector collaboration in malaria control, which ultimately benefits the community and the

investors as well.

HIV/AIDS

The above results indicate that there is no significant linear relationship between FDI inflows in

mining and HIV/AIDS prevalence or death. This suggests that other factors included in the

regression analysis are more important to investors than HIV/AIDS. On the Copper belt,

increased mining activities has triggered an influx of commercial sex workers raising fears of

further spread of sexually transmitted diseases and HIV/AIDS (Times of Zambia , 2005). By

2005, the Copper belt was the country’s second hardest hit province by HIV with a prevalence

rate of 20.0 percent while the national average was 16.0 percent. Three of the Copper belt

districts: Ndola, Kitwe and Chingola, were among the worst affected in terms of HIV prevalence

rates. These, are the towns where most of the major mines are located.

Though the effect on investment is seemingly insignificant, HIV/AIDS estimates indicate that as

of 2009, about 82,600 new HIV/AIDS related infections of people between the ages of 15 and 49

were recorded in Zambia, which translates into 226 new infections every day (Kapembwa,

2010).

51

Corruption

The Control of Corruption Index and Corruption Percentile Rank show that improvement in the

control of corruption in Zambia has a strong positive linear relationship with FDI inflows in the

mining sector. The Corruption Perceptions Index score, however, shows that there is an

insignificant linear relationship between FDI inflows in the mining sector and the worsening in

corruption perception. The corruption perception index (CPI) measures the degree to which

corruption is perceived to exist among public officials and politicians in a particular country. It

focuses on perceptions and not hard data (Speech on Launch of TI CPI 2009 Zambia). Given the

strenth of the linear relationship of corruption control and FDI in mining, it would have been

appropriate to include this variable in regression analysis, if we had an adequate time series.

5.5.2 Correlation of FDI in Non-Mining with Selected Variables

Table 17: Correlation of FDI in Non-Mining with Selected Variables

CorruptionPerceptionIndex

HIV/AIDSprevalence

ControlofCorruption

CorruptionPercentileRank

Malaria Incidence

Period 1998‐2009 1990‐2009 1999‐2009 1999‐2009 1970-2009 Samplesize n 12 18 11 11 40 DegreesofFreedom n‐2 10 16 9 9 38 Correlationcoefficient r ‐0.23 0.10 0.85 0.86 0.25 ‐0.7 0.4 4.8 5.0 6.4 Critical df@5% ‐2.23 2.12 2.26 2.26 2.1 Decision acceptho acceptho rejectho rejectho Reject ho

HIV/AIDS

Consistent with the findings for the mining sector, results show that HIV/AIDS prevalence has

no significant linear relationship with FDI inflows in non-mining (see Table 17). This suggests

that other factors are more important to investors than HIV/AIDS.

Malaria Incidence

Similarly, in the non-mining sector, the incidence of malaria had statistically significant positive

linear relationship with FDI. This simply illustrates the rise in malaria incidence over time and

the slowdown in Malaria prevention measures. Though Malaria was rated among the major

constraint to investment in the non-mining sector in the Zambia 2008 FPC CBP study, not much

has been done to address the high incidence.

52

Corruption

Consistent with the mining sector findings, the Control of Corruption Index and Corruption

Percentile Rank show that improvement in the control of corruption in Zambia has had a strong

positive linear relationship with FDI inflows in Non-Mining. The Corruption Perceptions Index

score, however, shows that there is no significant linear relationship between FDI inflows in the

mining sector and the decline in corruption perception. These findings demonstrate the

importance of the control of corruption to investors as opposed to mere perceptions about

corruption. Due to lack of adequate time series, the control of corruption, however, was not

included in reression analsysis.

5.6 Limitations of the Study

This study, like many others, has some limitations particularly related to data. Due to inadequate

time series, key variables that drive investment such as the avalaiability of technially trained

staff, bureaucracy, control of corruption, HIV/AIDS, length of laved roads were not included in

regression analysis. These variables could have enhanced the robustness of the regression

results. In addition, unavailability of high frequency data such as monthly or quarterly on a

number of variables was also a major limitation as some of the drivers of investments such as

copper prices change substantially within a short period. Their effects therefore on investment

may be moderated by using annual figures. Further research would be critical to deal with

findings that are contrary to general economic expectations particularly on the role of inflation,

Malaria and HIV/AIDS, which ranked highly in perception findings.

6. CONCLUSION AND POLICY RECOMMENDATIONS

Zambia is highly dependent on FDI inflows, which are highly concentrated in the mining sector.

This sector, however, is highly vulnerable to commodity price shocks, hence the need for

diversification to ensure sustainability of these flows and minimise vulnerability of the economy

to price shocks. To do so, this study clearly distinguished the factors that drive FDI in mining

and into other sectors with a view to guiding policy in order to enhance diversification of FDI in

particular, and the economy at large. The findings show that FDI in the mining sector is largely

driven by growth in external demand for copper and the realised copper price. Other factors

facilitating growth in investment in the mining sector include the level of gross official reserves,

53

political stability and the control of corruption. Electricity supply, however, is a major constraint

to investment in the mining sector.

The non-mining sector is mainly driven by the degree of urbanisation, GDP growth, exchange

rate depreciation, supply of telecommunication services, political stability, control of corruption

and the boom of the mining sector. High lending interest rates continue to be a key constraint to

investment in the non-mining sector.

In order to minimise the vulnerability of Zambia to commodity price shocks and enhance

diversification of FDI in particular and the economy at large, Government should address the

following:

6.1 Cross-Cutting Recommendations

Government should continue to improve the investment climate in Zambia by sustaining a robust

GDP growth, maintaining lower and stable inflation and a competitive exchange rate. In

addition, maintenance of political, financial system stability and the control of corruption are

crucial in sustaining FDI inflows. Other priority issues include accelerated infrastructure

development such as electricity supply, roads, rail and telecommunication. In this regard, there is

need for financial support and collaboration between Government, international donor agencies

as well as the private sector, particularly for large-scale infrastructure projects.

Apart from physical infrastructure, there is also need to build social infrastructure such as

tertiary/vocational training and health services. Vocational training is important as it reduces the

cost of training. Adequate health services help in making available a healthy labour force and

reduces loss of man-hours associated with illnesses. In this regard, there is need for the private

sector to support Government in reducing the incidence of diseases through their corporate

social responsibility programmes.

Tax incentives were found to have an insignificant effect on investment in both mining and other

sectors, suggesting that investments would continue to rise with or without huge tax incentives.

It is therefore critical for Government to moderate incentives and target them much more

effectively. This would help to minimise loss of tax revenues due to protracted tax holidays both

in the mining and other sectors. Special incentives, where necessary, could be targeted at

promoting investment in rural areas.

54

In terms of monitoring and analysing FDI inflows and investor perceptions under the FPC CBP

programme, Zambia should endeavour to include other key variables that were found to have a

substantial impact on investment inflows from this econometric study, but were not captured in

the surveys. The major variable being the ‘degree of urbanisation’ as it was the major and most

responsive driver of FDI in the non-mining sector. In addition, factors such as GDP growth of

export markets, level of gross international reserves, level of commodity prices on the

international market and fuel prices, could be included. These had significant effects on FDI

inflows in Zambia.

6.2 Recommendations for Mining

In order to minimise the negative effects of the slide in demand for copper from some export

markets, mining companies must explore new markets. To facilitate investments in the mining

sector, there is need to address infrastructure gaps such as electricity supply, roads and rails.

Accelerated investment in electric power generation is critical given the current power deficit

and the projected growth in mining activities. In addition, Government through the Bank of

Zambia should continue to maintain a relative high level of gross official reserves and a stable

exchange rate. These will help minimise fears of external sector vulnerability, given the size of

investments in the mining sector.

Mining companies should also go a step further by supporting workers and their families with

access to health services and education. For example, the fight against Malaria through spraying

of communities in mining towns should be supported by the mining companies, as it would

reduce the incidence of Malaria. Such programmes have mutual benefits for the employees and

the employer as well.

6.3 Recommendations for Non-Mining

The SNDP recommends diversification of the economy to other sectors with a strong focus on

agriculture, tourism, manufacturing, mining and energy. At the core of this, is the promotion of

rural investments and the acceleration of infrastructure development. The findings of this study

tie in closely as they bridge the gap of the general diversification objectives and the specific

strategies to achieve such objectives. Promotion of rural investment as outlined in the SNDP

should be based on the fact that an increase in urbanisation spurs FDI. Given the high

responsiveness of FDI to the degree of urbanisation, the issue of infrastructure development

particularly in rural areas should be a priority of Government. This should not be done by merely

55

attracting people from rural to urban areas but by developing rural areas through infrastructure

development such as electricity, telecommunication, roads and rail.

Given the favourable economic performance recorded in recent years, Zambia needs enough

energy to continue to support the growth of the economy and attract new investments. Increased

investment in rural areas will inevitably contribute to employment creation and poverty

reduction. There is also need to maintain a competitive exchange rate, given that a high

proportion of investors in other sectors are export oriented and are generally adversely affected

by exchange rate appreciation. To enhance investment in non-mining, there is need to lower the

cost of finance (lending interest rates). This could be achieved through promotion of intense

competition in the banking sector and reduction of credit risk by increasing the scope and quality

of coverage of the recently created Credit Reference Bureau (CRB).

To ensure sustainability of FDI and help minimise the vulnerability of Zambia to commodity

price shocks, there is need for investment diversification. Investment promotion should focus on

sectors that are not only less vulnerable to commodity price shocks but also contribute highly to

job creation, transfer of skills, technology and international standards such as the

manufacturing, agriculture and tourism sectors. In addition, investment promotion should be

targeted at Greenfield FDI in sectors or projects where the domestic private sector does not

presently have sufficient financial or technical capacity.

To enhance diversification of the economy away from mining, maximise benefits of FDI and

effectively contribute to poverty reduction, Government through ZDA should support and

encourage both joint ventures and support Small and Medium Enterprises (SMEs). There is need

to enhance the role of FDI enterprises in developing domestic business and in national

development strategies. Potential domestic business partners both in terms of financial capacity

and knowledge should be helped to join with foreign investors in setting up investments in

Zambia. Foreign investors should be encouraged to explore linkages with domestic SMEs by

giving them enhanced access to finance. In this regard, ZDA through its Business Linkages

Programme should enhance linkages of big and small businesses in an effort to boost exports,

create jobs, transfer technology and skills.

The Coca Cola Company and SAB Miller, for example, are seeking to build capacity at certain

points along the value chain through the provision of technical assistance and credit

programmes. The Coca Cola Company in Zambia has launched a programme to boost

entrepreneurial skills at retail outlets, in which sales representatives mentor retailers to improve

56

business skills (Oxfam 2011). There is scope to learn from and encourage the rollout of such

initiatives much more widely.

With regard to target source markets for FDI, Government through ZDA should explore new

sources such as intra-Africa rather than rely entirely on the Organisation for Economic

Cooperation and Development (OECD) and Asian countries. It is equally important for members

countries to accelerate regional integration efforts particularly macroeconomic convergence in

both SADC and COMESA in order to make these regions more conducive destinations for

investment. Given its strategic central position, Zambia has potential to benefit highly from

investments targeted at supplying goods and services to the regional market.

57

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Massachusetts Institute of Technology.

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8. ANNEX

8.1 Annex i: Cointegration and Error Correction Methodology

Working with non-stationary data series in the estimation process may yield a meaningless or

spurious result, that is, there is danger of obtaining apparently significant regression results from

unrelated data. When non-stationary time series are used in regression analysis, there is a need to

test further for Cointegration amongst the series. In testing for this, Engle and Granger (1987)

two-step procedure is widely used. In addition, Johansen (1988) proposed a general framework

for testing cointegration4. The Engle and Granger test for Cointegration is residual based test

which is based on the assumption that there is only one cointegrating vector in the equations.

A cointegrating relationship allows us to not only estimate the long-run relationship but also

analyse the short-run dynamics and how adjustment to equilibrium is attained. According to the

Granger representation theorem, the existence of a stable long-run relationship between the

variables enables us to estimate an error correction model (ECM). These error correction models

are premised on the behavioural assumption that two or more time series exhibit an equilibrium

relationship that determines both short- and long-run behaviour. ECMs are important in as far as

they reconcile the short and long run behaviour of the variables by shedding light on the speed or

rate of adjustment towards long-run equilibrium.

Generally two different econometric methodologies are used in the construction of the ECM

these being the generalized one-step procedure5, and Engle and Granger two-step procedure. As

argued by (Susana De Boef, 2000), the single-equation generalized error correction model

(GECM) has proved to be both theoretically appealing and statistically superior to the two-step

estimator by Engle and Granger (1987) in many cases. In this study, therefore, we will employ

the one-step method.

If there exists a long-run relationship between Z and X, such as ∝ ∝ Ɛ theGECM

is estimated in one-step as follows:

μ ..................................................................................2

Where Δ is the first difference and is the coefficient of adjustment to equilibrium. Theory

predicts that coefficient must be negative and significantly different from zero. A negative

entails that, in the event of a deviation between the short-run and the long-run equilibrium,

there would be an adjustment back to the long-run relationship in subsequent periods, which

eliminate this discrepancy.

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8.2 Annex ii: Diagnostic Tests

In order to establish the robustness of the estimation results, the following diagnostic tests were

done: Jarque-Bera to test for Normality, ARCH White to test for Hetroscedasticity, Breuch-

Godfrey LM Test for serial correlation, Chow Forecast test and Recursive Coefficients (visual

test) for stability of the model. Both models were tested for significance at 5% level of

significance using the F-test. Each explanatory variable were tested for significance by way of a

t-test at 5% level of significance. These tests were done on refined specifications of both the

overall FDI and the non-mining FDI models.

8.3 Annex iii: Stationarity and Cointegration Test Results

Stationarity Test Results for Variables

Note: **, ***

denote significance of t values at 5% and 1% levels, respectively.

Cointegration Test Results for Mining Equations

Equation Variable Critical t

Value@5% Observed t

value P-value Conclusion

Eqn A Resid_Ma -1.9513 -7.0369*** 0.0000 I(0) Eqn B Resid_Mb -1.9513 -5.7352*** 0.0000 I(0) Eqn C Resid_Mc -1.9513 -7.0241*** 0.0000 I(0) Eqn D Resid_Md -1.9544 -4.4880*** 0.0000 I(0) Eqn E Resid_Me -1.9521 -4.6558*** 0.0000 I(0)

Note: *** denote significance of t values at 1% levels.

Variable Critical t Value@5% Observed t value

P-value Conclusion

LFDIM -1.9507 -8.3552*** 0.0000 I(1) LFDINM -1.9507 -7.9249*** 0.0000 I(1) LCOPREA -1.9485 -5.4600*** 0.0000 I(1) LCOPVOL -1.9485 -9.1313*** 0.0000 I(1) LGDPCXMKT -1.9487 -2.8663** 0.0052 I(1) RGDPG -1.9489 -6.8746*** 0.0000 I(1) LINF -1.9499 -6.2859*** 0.0000 I(1) INF -1.9501 -6.3465*** 0.0000 I(1) LINFVOL -1.9499 -11.5178*** 0.0000 I(1) LTELECOM -1.9485 -1.9527** 0.0496 I(1) LELECG -1.9485 -4.3416*** 0.0001 I(1) LIRS -1.9507 -6.3744*** 0.0000 I(1) IRS -1.9504 -7.7516*** 0.0000 I(1) LWALBR -1.9499 -5.3393*** 0.0000 I(1) LURB -1.9487 -2.0275** 0.0420 I(1) LGOR -1.9487 -8.6464*** 0.0000 I(1) LEXR -1.9485 -2.6414*** 0.0094 I(1) LFUELP -1.9485 -3.9594*** 0.0002 I(1) LMALRINC -1.9487 -5.894*** 0.0000 I(1)

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Cointegration Test Results for Non- Mining Equations

Equation Variable Critical t

Value@5% Observed t

value P-value Conclusion

Eqn A Resid_NMa -1.9513 -5.2053*** 0.0000 I(0) Eqn B Resid_NMb -1.9513 -5.5838*** 0.0000 I(0) Eqn C Resid_NMc -1.9513 -4.2134*** 0.0000 I(0) Eqn D Resid_NMd -1.9513 -5.5718*** 0.0000 I(0) Eqn E Resid_Nme -1.9513 -5.5148*** 0.0000 I(0) Eqn F Resid_NMf -1.9602 -5.2560*** 0.0000 I(0)

Note: *** denote significance of t values at 1% levels.

8.4 Annex iv: Summary Table of Data Description, Sources and Limitations

Variable Description Sources Years Limitations Estimations

Dependent Variables Foreign Direct Investment in Mining

Aggregate FDI inflows in Mining

BoZ, ADI, IMF(IFS),UNCTAD,

1972-2009

Comprehensive data only available for 2001 and 2007. Other years obtained from Ministry of mines and report forms from mines from 1992-2009.

FDI Data for the period 1970-1991 is disaggregated by applying the share of metal exports in Total exports

Foreign Direct Investment in non-mining

Aggregate FDI inflows in other sectors

BoZ, ADI, IMF(IFS),UNCTAD,

1972-2009

Comprehensive data only available for 2001 and 2007. 1992-2009 from BoZ & IMF Estimates

1970-1991 FDI is disaggregated by applying the share of Non-traditional exports (NTEs) in Total Exports

Natural Resource Intensity Identified Copper Reserves

Volumes of identified copper reserves in mt

MMMD, BOZ, Bureau of Mines Minerals Yearbooks (1964-2008).http://minerals.usgs.gov/minerals/pubs/usbmmyb.html

1964-2009

Gaps for some years Estimated by subtracting production from previous years stock plus new stock in subsequent year

Profitability Factors Realised Copper Price

Realised average Copper price in US Dollar per ton

Constructed from BoZ, IMF (IFS), WDI,

1964-2009

missing realised prices for selected years

Estimated by applying the LME price growth rate on the previous year’s data

Copper Price Volatility

Standard deviation of daily LME Copper prices

Constructed from BoZ, IMF(IFS), WDI,

1964-2009

Short time series of realised price

Estimated using LME prices.

Macroeconomic Factors Inflation Year on year

change in CPI CSO, BoZ, IMF(IFS), WDI

1970-2009

None

Inflation Volatility

Standard deviation of monthly Inflation

Constructed from CSO, BoZ, IMF (IFS),WDI

1970-2009

None

Interest rates Weighted average lending base rates

BOZ 1970-2009

None

Interest Rate Spread

Difference between lending and savings rate

Constructed for BOZ data

1970-2009

None

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Annex iv: Summary Table of Data Description, Sources and Limitations (Continued)

Variable Description Sources Years Covered

Limitations Estimations

Macroeconomic Factors Gross Official Reserves

Gross Official Reserves BoZ, IMF IFS,

1965-2009 None

Exchange Rates Kwacha US $ nominal exchange rates

BoZ, IMF IFS,

1965-2009 None

Market Factors Gross Domestic Product

Gross Domestic Product (in US $m)

CSO, BOZ, IMF, WDI,

1964-2009

None

GDP Growth Annual % change in Real GDP

CSO, BOZ, IMF, WDI,

1965-2009

None

Gross Domestic Product of for Zambia Copper Markets

Aggregate GDP of Switzerland, UK, EU, China and South Africa

WDI, IMF, 1964-2009

Exports markets are dynamic over time

Dominant countries over time were used

Degree of Urbanisation

Percentage of population in urban areas

WDI/CSO 1964-2008

None None

Environmental & Health HIV/AIDS Prevalence

HIV prevalence rate, adult 15-49 years (%)

ADI, CSO, WHO,

1991-2008 Short time series None

HIV Deaths AIDS deaths in adults and children (low estimate)

WDI 1990-2007 Short time series None

Malaria Incidence Malaria Incidence/100,000 pop

WDI 1990-2008 Short time series Extrapolated series by estimating from available graph from 1970 to 2000

Malaria Mortality Malaria Mortality/ 100,000 pop

WDI 1990-2008 Short time series None

Regulation Openness (X+M/GDP) Constructed from

CSO and BOZ data 1964-2009 None

Infrastructure Transport Cost (Diesel Price)

Diesel Price Per litre

BoZ, ERB, WEO 1964-2009 Short Diesel Pump price

Filled gaps by extrapolating series using the growth in oil prices

Electricity Generation

hydroelectric production (millions kWh)

WDI 1964-2009 None

Telephone & Mobile lines

Number of telephone + mobile lines

WDI 1964-2009 None

Other Social Factors Corruption PI Corruption

Perception index score.

Transparency International,

1998-2009 Short series None

Control Corruption

Corruption Control(index)

World Bank 1999-2009 Short series None

Corruption Percentile Rank

Corruption Percentile Rank

World Bank 1999-2009 Short series None

Investment Promotion Tax Incentives (Dummy)

Years of tax Incentives

ZDA 1964-2009 Short time series of tax rates

Used a dummy for years when tax incentives strongly came on board after setting up ZIC in 1993.

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