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. Phiri 1 Bank of ZambiaApril 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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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].
8.3 Annex iii: Stationarity and Cointegration Test Results ...................................................................62
8.4 Annex iv: Summary Table of Data Description, Sources and Limitations .....................................63
iv
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.
1
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).
2
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.
3
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
4
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
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,
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.
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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***
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.
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.
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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.
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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