Acknowledgements
I would like to show my deepest gratitude to my supervisor, Dr. J. Bolt for her invaluable guidance, support and encouragement from the beginning to the end of my thesis. I also would like to thank Dr. H. de Jong for his contribution, my family and friends for their unfailing support and encouragements.
Abstract
1. Introduction
Natural resources play an important role in the production of Gross Domestic Product and thus influence an economy’s short term and long term economic growth path. Expanded natural resource exploitation and exportation increases economic output and foreign exchange earnings. Natural resource wealth ought to be what Jeffrey Sachs and Andrew Warner (1995) call a virtual sine qua non of national wealth implying that natural resources ought to add to national wealth. However, in literature, one often finds strong and definite arguments based on empirical studies that natural resource abundance is an obstacle for economic growth with very few exceptions such as Norway1. There is a tendency for natural resource rich countries to grow slower than natural resource poor countries the paradox Auty (1990) referred to as the ‘’Resource Curse’’. The natural resource curse describes how natural resource rich countries have been unable to grow based on the abundant natural resources and paradoxically recorded even lower economic growth than the natural resource poor countries.
The observed copper price boom may have the Dutch disease effects on the Zambian economy. Therefore, following Gylfason (2001), Calì & Velde (2007) and Ruehle and Kulkarni (2011) who studied the similar issues with the traditional Dutch disease theory in other countries, we will apply this theory to the Zambian case. It will provide us with a logical framework to analyze the structural changes in the economy in response to the booming mining sector2. The Zambian economy has for a long time been dependent on copper exports for its revenue, and copper production has long been the engine of industrial, social development and economic growth. Copper production currently accounts for 10 % of the Gross Domestic Product and accounts for 80 % of foreign exchange3. Therefore, copper revenues have to a large extent dictated how and at what position of development the Zambian economy has been at any point in time. For example, Zambia’s GDP per capita grew slowly since the early 1970’s when copper prices hit the low levels and economic growth remained stagnant for the period copper prices were low and moreover, efforts to diversify the economy also failed lamentably. Simutanyi (2008) also noted that the collapse of the Zambian economy in the 1980s was closely related to poor performance of the copper mining industry. However, the past decade has witnessed an unprecedented increase in copper prices.
To this end, the boom in copper prices can be expected to raise the question of the Dutch disease in Zambia as it is a small open economy with a booming natural resource sector. The aim of this thesis therefore, is to investigate if Zambia has contracted the Dutch disease as a result of the boom in copper prices since 2003. We will do this by analyzing the impact of real exchange rate on the lagging sector output, the output correlations between the mining sector with output of other key sectors, and economic structural changes following the recent boom in the mining sector. This thesis will be organized as follows; next section, Literature Review, will provide an account of studies relevant to ours undertaken before to help us learn as well as help us be in a position 2 See Ruehle and Kulkarni (2011) who discusses the Dutch disease in the Chilean economy 3
to justify our study. Section three, Theoretical Framework and methodology will present the theoretical model of the Dutch disease and relate it to the Zambian economy. We will also describe the method of data analysis. Section four, Background, will provide an overview of the consequences of high copper price on the Zambian economy and we will compare it with the Chilean economy since Chile is another small open economy with a large dependence on copper earnings. In section five, Empirical Findings and Analysis, the logic of the Dutch Disease theory will be followed to establish sector output correlations and draw conclusions based on what economic theory predicts. Section six will summarize and conclude the results.
2. Literature Review
2.1. The Natural Resource Curse
Before Sachs and Warner studied and found out that natural resource exploitation retarded economic growth rather than promote it, economists held for a long time as conventional wisdom that economic growth was a function of comparative advantage, raw materials (natural resources) and exports of finished goods4. It was understood that the economy would consequently experience high per capita incomes and high standards of living. Researchers and academicians observed and published on the irony that countries whose exports were particularly dominated by relatively unprocessed goods grew slower compared to countries that exported manufactured or processed goods. Such researchers include Neary and Van Wijnbergen (1986), Gelb (1988) and Auty (1990). The reasons attributed to the relative slower growth are that primary goods exporters often suffered from macroeconomic instabilities. And in a recent study, Jakob (2010) also argued that many natural resources rich countries rank among the most spectacular growth disasters of the last decades citing examples of Nigeria, Venezuela, Zambia and Republic of Congo. He suggested several channels through which the resource curse could arrive; we list them here accordingly without giving detailed accounts of how each one affects economic growth.
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Declining terms of trade and high volatility explanations claim that a secular decline in the terms of trade coupled with high volatility form a kind of ‘staple‐trap’ (a relatively unprocessed commodity which dominates an economy’s exports) for commodity exporters The Dutch Disease literature attributes the natural resource curse to the crowding out of the manufacturing sector or other growth‐generating economic activities by resource booms The entrenched inequality hypothesis claims that ownership of natural resources usually is concentrated and that the resulting inequality is detrimental to growth
Institutional failure explanations argue that abundant natural resource endowments counteract the formation of high‐quality institutions (and consequently hamper economic development)
Rent seeking models lay down how natural resource rents can create perverse incentives for individual actors’ behavior
between natural resource intensity and growth between 1970 and 1990 as can be seen from the graph 1 below. They noted that countries that grew more rapidly during this period started as resource poor countries and not resource rich countries. However, in spite of the confirmation of their claim, they did not show what channels made natural resource endowments retard economic growth of a country.
Graph 1. Growth and Natural Resource Intensity
Source: Sachs and Warner 2001
2.2. The Dutch disease Literature
relative to price of tradables. The result was a real appreciation of the domestic currency5 which reduced the trading of other exports (other tradables). Other export industries lost competitiveness following the real appreciation of the domestic currency which consequently led to a decline in total output6 through de‐industrialization and de‐agriculturalization. A similar effect on the manufacture sector and agriculture sector takes place when resources (labor) move to the booming sector. De‐industrialization and de‐agriculturalization therefore, refers to the negative output effect of the loss of a country’s international competitiveness (through appreciated RER) and resource (labor) movement towards the booming sector from the manufacturing and agriculture sectors.
This Dutch disease phenomenon has been of interest to researchers in countries which discover new natural resources deposits and/or experience increased commodity prices.
Following the arguments of the Dutch disease theory, Stijns (2005) showed that oil and gas sectors tended to move resources (labor) from the manufacturing sector. He found evidence of the Dutch disease in Venezuela, Iran, Congo and Trinidad and Tobago. His findings also showed that the Dutch disease symptoms were not strong in mineral exporting countries and further, that the Dutch disease effects were absent in economies with good institutions citing Norway and Australia. And for Chile, Bolivia, Spain and Peru, he found evidence of a slight positive correlation between mineral reserves and growth in non resource sector (and their share of exports in total merchandise exports).
disease they observed both an appreciating real exchange rate and increasing inflation rate, an attribute they considered having been brought about by the extra incomes as a result of the copper boom (spending effect)7.
To determine the evidence of the labor movement to the booming copper sector (resource movement effect)8 in Chile, they compared the performance of the copper industry (booming sector), the agricultural and manufactured goods export sectors (lagging sector) and the construction and public administration sectors (non‐tradable). The effect of the copper boom on the economic sector structure through the resource movement was studied by correlations of output during the relevant periods. They studied the correlations of output of the three sectors for periods before the copper boom (1997‐2001) and periods after the copper boom (2001‐2006). The period before the copper boom was characterized by very low correlations between sectors with an exception of the correlation between public administration and copper mining sectors. They argued that the observed a strong positive correlation of public administration and copper mining was due to 10 % of revenue the copper industry contributes to total government revenue. During the boom period however, they observed strong positive correlations between the copper mining sector and construction, manufacturing and public administration sectors. The copper mining sector and agricultural sector remained weakly correlated during this period.
influence of the resource (labor) movement from the manufacturing and agriculture sectors towards the booming sector.
Below we present summaries and lessons from literature reviewed above on the relationship between natural resources and economic growth, and the Dutch disease.
Natural resources abundance presents an opportunity for economic growth and at the same time, challenges to economic growth. From Sachs and Warner (1995), we learn that natural resource abundance retards economic growth, that there exists a negative correlation between natural resources abundance and economic growth. The reasons leading to this negative outcome are macroeconomic instabilities (for example, volatile world commodity prices) which are largely due to external factors and also internal factors such as institutional qualities.
Furthermore, we learn from Ruehle and Kulkarni (2011)’s study that the Dutch disease was contracted by the Chilean economy following the increase in copper prices as could be seen in increased inflation, real exchange rate appreciating and the sectoral output variations. Although the Dutch disease was contracted by the Chilean economy, its adverse effects on economic output were minimized by good economic policies and abundant resources (labor). We learn from Ruehle and Kulkarni that the Dutch disease is a specific channel through which natural resource abundance could affect economic growth negatively during commodity booms in Chile.
coping with the high copper prices. Our study is also interesting as it presents an opportunity to test how diversified the Zambian economy is. During this period of high copper prices, no similar study has been conducted in Zambia to investigate structural changes in the economy’s sector outputs following the copper price boom.
3. Theoretical framework and Methodology
3.1. Dutch Disease defined by the ‘’Core Model’’
To determine and examine the possible symptoms of the Dutch Disease in Zambia as a result of the increased copper prices, we adopt the ‘’core model’’ due to Corden and Neary (1982). It allows us to systematically analyze structural changes in an economy following a natural resource price boom and ultimately total output. As in the core model, we will also ignore the monetary considerations in our study.
The model assumes that the economy consists of three sectors among which two are tradable sectors and one is non‐tradable sector. The three sectors are the Booming Tradable (Xe) sector which produces for exports only (in our study is the mining sector), the Lagging Tradable sector (Xm) which is the lagging manufacturing (and agriculture) sector. And the final sector of the model is non‐ tradable (XS) sector consisting of the service sectors (construction sector and wholesale and retail sector).
In the ‘’ core model’’, the real exchange rate is defined on the basis of the tradable and non‐ tradable goods rather than on the basis of the Purchasing Power Parity (PPP). The definition takes the relative price of the tradable goods and non‐tradable goods in the country as an indicator of the country’s competitiveness level in foreign trade. Assuming therefore, that the price of tradable goods will be equal all around the world, the real exchange rate defined on the basis of tradable and non tradable goods distinction is t r n P r P
Where Pt denotes domestic price of tradable goods and Pn denotes price of non‐tradable goods. A decline of rr indicates a real appreciation in the domestic currency. We now consider the two effects that dictate the outcome of a natural resource boom on the structure of the economy.
3.2. The Resource Movement Effect
The resource movement effect implies that the booming sector attracts resources (capital and labor) from the lagging tradable sector and the non‐tradable sector. Since production increases in the booming sector, the marginal productivity of the factors of production increases. The high productivity in the booming sector dictates that factors earn higher wages and interest in the booming sector compared to the other two sectors. As a consequence of high wages on labor and high returns on capital in the booming sector, labor and capital moves from the other two sectors to the booming sector. The capital and labor movement affects the output of services negatively which makes demand for services to be in excess of services supplied leading to higher prices. Since prices of non tradables are locally determined, a rise in the price of services (non‐tradable goods) leads to an appreciation of the real exchange rate.
output declines and the resulting restructuring of the economy is referred to as ‘’direct de‐ industrialization’’. And in a later article published by Corden (1984), he noted that when few people are employed in the manufacturing sector, de‐industrialization effect is often negligible.
3.3. The Spending Effect
Further, we note that the resource movement effect could be redundant if technologically advanced machinery is used in the copper mining industry which would reduce labor use in the copper mining industry.
To determine evidence of resource movement effect, like Ruehle and Kulkarni, we shall compare the performance of the booming sector, lagging sector and non tradable sector. We do this for periods before the high copper prices and the period during the high copper prices. We will study output correlations between the relevant sectors between 1996 and 2002 and the period during the copper price boom between 2003 and 2009.
When studying the spending effect, we shall observe the real exchange rate behavior and inflation (increasing or decreasing) during this period. Since real exchange rate is given
by t r n P r P , if demand for non‐tradables increases, real exchange rate will appreciate. Therefore,
if we observe an appreciation and increasing inflation during the copper price boom, we will suspect the presence of the Dutch disease as a result of the spending effect.
In the next section, we turn to economic and demographic background information about the Zambian economy.
4. Zambia economic background and a brief comparison with the Chilean economy
Zambia is Africa’s largest copper producer9 just as Chile is South America’s (and world) largest copper producer. And therefore, in this section we will attempt to give an economic background on the Zambian economy and compare it to the Chilean economy on the basis of some selected macroeconomic indicators and government policy in response to high copper prices. This brief comparison is important as it also reflects the relevance of our study particularly how small open economies respond to increase in price of the main export commodity.
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Although these two countries differ in geographic locations, history, culture and politics, the Chilean economy offers an interesting country comparison. This is also because like Zambia, it has a significant amount of its foreign exchange earnings coming from commodity exports particularly copper exports and it is also a small open economy. However, despite the similarity in the amount of contribution the mining sector has in foreign exchange earnings, the ownership of mines differ significantly in these two countries. Copper mines in Zambia are privately owned whereas the Chilean government owns and controls a significant share of the mining industry. We will also be quick to point out that Chile has done well to manage and benefit from its commodity exports compared to Zambia. This is in accordance with researchers such as Ruehle and Kulkarni (2011) and Cali and Velde (2007) and also as can be seen from the macroeconomic indicators in the table below. Furthermore, the Chilean economy’s copper production is greater than that of Zambia. For example 5,320,000 tonnes of copper were produced by Chile in 2009 which was by far larger than Zambia’s 655,000 tonnes of copper produced10 during the same year and with a more diversified economy.
4.1 Macroeconomic indicators
Zambia’s main export commodities are copper accounting for 64% of total exports and the rest include cobalt, electricity, tobacco, flowers and cotton and Chile’s export commodities are copper, fruits, fish products, paper and pulp, chemicals and wine. Other facts11 are presented in table 1 below.
Clearly, the economic indicators below look by far better for Chile compared to those for Zambia with a striking difference in GDP per capita. Further, the agriculture sector in Zambia employs 85% of the labor force compared to 13.2% in Chile and consequently Zambia employs lower percentage of its labor force in both industry and services sector comparatively. This could indicate the difference in the levels of diversification between the two economies.
10
See http://en.wikipedia.org/wiki/List_of_countries_by_copper_production. Accessed on 6th August, 2011
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Furthermore, Chile exports more value added commodities such as chemicals, wine and paper whereas Zambia’s exports are mainly characterised by unprocessed commodities which also imply lower value and are more subject to international price volatility. It should be noted also that most of agriculture production in Zambia is at a subsistence level and therefore, not much cash crops are grown on a large scale except tobacco and cotton which are grown in Zambia for exports.
We also observe that Chile is characterised by both lower levels of inflation rate and unemployment rate a probable indication of well managed fiscal and monetary policy within the economy.
Table 1: Macroeconomic indicators comparison between Zambia and Chile
Zambia Chile
Population 13,881,336 (2011 est.) 16,888,760 (2011 est.)
Median age 16.5 years 32.1 years
Main industries (% of GDP) Agriculture (19.7%) Services (46.6%) Industry (33.7%) Agriculture (5.6%) Services (40.56%) Industry (53.9%)
GDP per capita $ 1,500 (2009 est.) $ 15,400 (2010 est.)
Total labor force 5.524 million (2009) 7.58 million (2010 est.)
Unemployment rate 14.6% (2006 est.) 9.6% (2009 est.)
Inflation rate(consumer price) 8.5% (2010 est.) 1.7% (2010 est.)
Labor force by occupation Agriculture 85% Industry 6% Services 9% Agriculture 13.2% Industry 23% Services 63.9%
Chile on the other hand conducts a rule‐based countercyclical fiscal policy, accumulating surpluses in sovereign wealth funds during periods of high copper prices and economic growth, and allowing deficit spending only during periods of low copper prices and economic growth12.
4.3 The response of copper production and employment to high copper price
The increasing demand for copper and high copper prices has made copper production profitable and attractive to home and foreign investors. The high return on capital gave incentives to domestic producers to increase production and thus raise the profit margins. In Zambia, copper production has increased over the years since 2003 due to increased world demand for the commodities as well as increased commodity prices. Figure 3 below shows the evolution of copper prices since 1971 to present. Graph 2: Yearly World copper prices (US$/tonne) 2003 20 00 40 00 60 00 80 00 10 0 0 0 U S $ pe r to n n e 1985 1990 1995 2000 2005 2010 year
Copper Price over the years
Source: London Metal Exchange
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Copper prices stayed low until 2003 and increased sharply afterwards but with a deep in 2009 following the Global Financial Crisis. However, the copper prices have since 2010 remained high.
Increased copper production is also evident by the ever increasing Chinese involvement in the copper mining industry. For example, the Chinese government through China Non‐Ferrous Metals Mining Company (CNMC) continued to increase its investments from US$ 20 million in Chambishi mine in 1998 to committing additional investments into the copper sector in 2006 worth US$ 200 million13. Apart from the Chinese investments in Zambia, capital into the mining industry continues to flow from different parts of the world to Zambia to expand copper production. Among other notable international minerals and mining companies are Vendata Resources, Glencore International AG and Canadian First Quantum Minerals.
During the period 2005 and 2009, employment in the mining sector also increased. The mining sector created 44,000 local jobs following expansions in copper production14. Expanded mineral production led to an increase in labor and capital employment. Therefore, high copper prices could be seen as having contributed to increased copper production and employment in the Zambian economy.
5. Empirical Findings and Analysis
5.1 Data Description, Sources and Limitations
For our study we rely on data from the United Nations National Accounts Main Aggregates Database and the World Bank’s World Development Indicators unless otherwise stated.
The main aim of the thesis is to study the way the output of the economy’s main sectors in Zambia have responded and have been restructured following the booming mining sector in the period between 2003 and 2009. We realize that it would have been interesting to show and predict the effect the real exchange rate has had on the manufacturing and agriculture sectors but the analysis has been constrained by the lack of data specific to these sectors such as
13
See www.consultancyafrica.com. Accessed 31st May 2011
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estimates of capital and labor employment in these sectors. Data is either unavailable or fragmented which renders such analysis difficult.
Another limitation is the size of data series we have, for meaningful and accurate predictions to determine the impact of real exchange rate on the lagging sectors, we needed at least quarterly data for several years which is not available.
We therefore, rely on sector output correlations to study the variations between the main sectors other than regressions since we expect that our coefficients will be biased due to either shortness of data series or due to omitted variables. But with the output sectoral correlations, one should be careful not make decisive conclusions or claim causality.
5.2 Examination and Discussion of the Dutch disease in Zambia
Graph 3 Real Effective Exchange Rate from 1986 to 2009 2003 40 60 80 100 120 140 index ( 2005= 100) 1985 1990 1995 2000 2005 2010 Year
Real Effective Exchange Rate
Source: World Bank’s World Development Indicators
In the graph above, we do observe that the real effective exchange rate entered a period of appreciation since 2003 as predicted by the Dutch disease theory.
On the other hand, inflation does not seem to have increased but rather is seen to follow a long moderate declining trend since the 1990s as seen the graph below. There is not an unusual trend during the boom period that is striking.
Graph 4 below plots annual inflation percentages against time from 1986 to 2008.
Graph 4 Annual Inflation (%) from 1986 to 2008 2003 0 50 10 0 15 0 20 0 A n nu al I n fl a ti o n ( % ) 1985 1990 1995 2000 2005 2010 year
Inflation
Source: World Bank’s World Development Indicators The observed trend in inflation is not in accordance with the expectations of the Dutch disease effects resulting from increased money supply following a surge in copper exports. However, this observed trend could be explained by Bova’s (2008) findings that in the short run, the Consumer Price Index (CPI) was not very responsive to changes in either money supply or exchange rate since food prices accounted for almost 70 per cent of Zambia’s CPI15. Bova also found that although non‐food inflation was sensitive to changes in money supply in the short run, it resulted in modest inflationary pressures because it did not account for a large share of the CPI.An important question following our observations above is what then is the link between copper prices and the real exchange rate? A correlation test at 5 percent level of significance
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between the two variables was performed and gave us a positive and significant correlation coefficient equal to 0.7915 for the period during the copper boom. The degree of linear association between the two variables is strong and simply put, Zambia’s international competitiveness is highly linked to copper prices. The result is however not surprising as it is consistent with the Dutch disease theory that the domestic real exchange rate appreciates due to a boom in the natural resource. Graph 5 below shows the link between the real exchange rate and copper prices. Graph 5 Relationship between Copper Price and Real Exchange Rate Source: London Metal Exchange and World Bank’s World Development Indicators
How then have the main sectors contributed to GDP during the copper boom, the period of declined competitiveness following an appreciated real exchange rate? We start by making some observations and describing how the manufacturing sector, the agriculture sector and the industry sector have changed with regards to their respective contributions to GDP in Zambia from 1986 to 2008. The leading question is how these sectors contributed to GDP over the years in Zambia with particular interest in the period between 2003 and 2009.
Graph 6 Share of agriculture, industry and manufacturing (value added) in GDP 2003 10 20 30 40 50 % of G D P 1985 1990 1995 2000 2005 2010 Year
Industry, value added Manufacturing, value added Agriculture, value added
GDP Breakdown
Source: World Bank’s World Development Indicators The share of industry contributing to GDP is seen to increase since the year 200316 from 26.5% in 2003 to 41.4% in 2008 before declining in 2009 to 34.1%. The decline in industry share (which includes the mining industry) in 2009 suggests a link between industry output and copper prices since copper prices declined during the same period following the Global Financial Crisis. The share of agriculture contributing to GDP entered a period of moderate decline in 2005 onwards from 23.3% in 2005 to 18.9% in 2008 and increased to 21.5% in 2009. The manufacturing sector’s relative contribution to GDP does not seem to have had a definite and remarkable change. However, the manufacturing sector’s share declined from 11.9% in 2003 to 9.6% in 2009. We should point out here that, although the relative contribution of the manufacturing sector declined, it increased in absolute terms over the years as seen in graph 7 below.
Furthermore, in graph 7 below, we attempted to visualize the evolution of real output of four sectors tracing them from 1970 to 2009 but with particular interest in the period between 2003 and 2009. Indeed we observe an increase in output of the mining sector, the construction and a moderate increase in the manufacturing sector in the period after 2003. One immediately sees that the construction sector output has increased considerably closing the gap with the mining sector. However, the agriculture output has exhibited up and down swings (as before) but with a slight upward trend and a sharp decline in 2009. Graph 7 Gross Domestic Product breakdown by sector 2003 0 5. 0e +0 8 1. 0e +0 9 1. 5e +0 9 o u tp u t @ 20 00 c o ns ta nt p ric e s 1970 1980 1990 2000 2010 year
Agriculture, hunting, forestry, fishing Mining, Utilities
during the 1990s which inspired substantial recapitalization following the change of ownership in the Zambian economy.
Graph 7 is however, important in giving us a visual picture of sector output levels in Zambia during the period of the copper price boom (2003 to 2009). One can argue that if the mining sector is the power source of the increase in the total economic output during the boom period, then clearly the construction sector, the manufacturing sector and the wholesale and retail sectors have benefited from the boom in the mining sector as seen from their upward trend. The trend in agriculture output is not definite and we can only rely on statistical tests to determine its correlation with the mining sector output which we shall do later in this paper.
In table 3 below we present a correlation matrix of the booming, lagging and non‐tradable sectors. We tested the significance of correlation of the various sector real outputs at 5 percent level of significance and the objective was to test for resource movement from the lagging sector towards the booming sector following Ruehle and Kulkarni (2011). In accordance with the core model, we recognize the mining sector as the booming natural resource sector, the agriculture and manufacturing sectors as the lagging sectors and lastly the construction and wholesale and retail as the non tradable sectors. For our statistical tests of correlation between sector outputs, the period 1996 to 2002 prior to copper price boom is chosen to represent the non boom period.
We observe that the mining sector was not significantly correlated with any sector. Worth noting however, is that although the correlation between the mining sector outputs was not significantly correlated with the lagging sector outputs, the mining sector output was negatively correlated with the agriculture sector output during this period. Similarly, the correlation between the mining sector output and the manufacturing sector output was negative.
Table 3: Correlation matrix of the booming, lagging and non‐tradable sectors (1996‐2002)
Agriculture Mining Manufacturing Construction Wholesale
Agriculture 1.0000 Mining ‐0.6590 1.0000 Manufacturing 0.5366 ‐0.1042 1.0000 Construction 0.2732 0.1855 0.9384* 1.0000 Wholesale 0.5450 ‐0.1149 0.9959* 0.9214* 1.0000 Source: United Nations National Accounts Main Aggregates Database In the period during the copper price boom (2003‐2009), we immediately see in table 4 below, that the mining sector output is positively and significantly correlated with the manufacturing, construction and wholesale and retail outputs. The observed strong association between the mining sector outputs and the manufacturing sector outputs is inconsistent with the predictions of the Dutch disease that resources (labor) will move towards the booming sector hence reducing production of the manufactures. Furthermore, the correlation between the mining sector outputs and the agriculture sector outputs remain insignificant and negatively correlated. It can also be observed that the relationship is weaker between the two sector outputs during the copper price boom period.
Table 4: Correlation matrix of the booming, lagging and non‐tradable sectors (2003‐2009)
Agriculture Mining Manufacturing Construction Wholesale
The interesting question arising from the above observed correlations particularly the one between the mining sector outputs and the manufacturing sector outputs during the boom is that of competitiveness position. Was Zambia’s competitiveness position not affected by the appreciated real exchange rate since manufacturing sector output also increased? This question can also be seen as being equivalent to asking the link between the real exchange rate and the manufacturing sector output. In table 5 below we tested for the association at 5 percent significance level between manufactures exports and agriculture exports with the real exchange rate. Manufactures exports and agriculture exports are both percentages of total merchandise exports during the period 2003 to 2009.
Table 5: Correlation matrix of the agriculture exports, manufacturing exports and real exchange rate
(2003‐2009)
Real exchange rate Agriculture exports Manufacturing exports
Real exchange rate 1.0000
Agriculture exports ‐0.8433* 1.0000
Manufacturing exports ‐0.8372* 0.4756 1.0000
Source: United Nations National Accounts Main Aggregates Database
The correlation coefficients for both the agriculture exports and manufactures exports with the real exchange rate are negative and significant during the period 2003 to 2009. This result suggests that the international competitiveness of the lagging sector was harmed following the real appreciation of the domestic currency. Although the results above cannot tell us by how much or to what extent the manufactures exports and agriculture exports declined following the appreciating domestic currency, they show us that Zambia’s international competitive position was affected negatively.
positive correlation between the mining sector output and the manufacturing sector output during the period of the boom. Indeed this observation is not consistent with our expectations. We speculate that the manufacturing sector’s contribution to total exports was probably not large enough in Zambia since the main exports are copper, cobalt, electricity, tobacco, flowers and cotton hence reducing the real exchange rate effect (breaking the link between manufactures and RER). And that if a larger proportion of manufactures are consumed domestically rather than exported, then the real exchange rate effect would be to a less extent. Graph 8 Manufactures exports in percent of total merchandise exports for Netherlands, Chile and Zambia 0 20 40 60 80 10 0 % of m e rc ha nd is e e x por ts 2000 2002 2004 2006 2008 2010 Year
Netherlands exports Chile exports Zambia exports
Manufacture as % of merchandise exports
Source: World Bank’s World Development Indicators
very large amounting to over 90 percent of total merchandise exports. The decline in manufactures was also significant from 94 % in 1962 to less than 60 % in 1963.
For Chile and Zambia, the picture is different. Manufactures exports have been below 20 per cent. This observation suggests that the link between the manufacturing sector output and the real exchange rate might not have been strong enough to affect the manufacturing sector output and total economy output negatively during periods of boom in the natural resources largely due to the limited size of the manufacture exports. This could also help explain why we observed an increase in real output of the manufacturing sector.
Other possible reasons why manufactures output did not decline could be as a result the government of Zambia’s tax incentives to increase production in manufactures such as17:
Suspension of import duty on machinery, equipment and goods for assembling of motor vehicles, trailers, motorcycles and bicycles.
Reduced import duty on inputs used in manufacturing such as crude coconut (copra) oil, plates sheets, film, foil and string of unsaturated polyesters to 5 per cent.
Income from chemical manufacturing of fertilizers is taxed at reduced rate of 15 per cent etc.
All such factors could help explain the observed results in the manufacturing sector output during the boom period.
The agriculture sector seems to be the only sector to show some negative developments during the copper boom period. Firstly, its relative contribution to GDP during the boom period entered moderate decline period as observed in graph 6 above. Secondly, its output levels have exhibited instability during this period as seen in graph 7. Lastly, the competitiveness of this sector suffered consistent with the Dutch disease proposition as seen from its link with the real
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exchange rate in table 5. The graph below shows the decline in agriculture exports as percentage of merchandise exports from about 5.2 percent in 2003 to 1.3 percent in 2009. Graph 9 Agriculture exports in percent of total merchandise exports for Zambia 0 2 4 6 8 10 % of A g ri c u lt ur e ex por ts 1995 2000 2005 2010 Year
Agriculture exports as % of merchandise exports
Source: World Bank’s World Development Indicators
6 Summary and conclusion
One symptom of the Dutch disease present and evident in the Zambian economy is the appreciated real exchange rate. We linked the real exchange rate to the manufacturing and agriculture sector exports, and consistent with the Dutch disease theory, we found out that the real exchange rate and the manufacturing and agriculture exports were negatively and significantly correlated. This link serves as evidence of the negative effects the copper boom had on the manufacturing and agriculture sectors (lagging sectors). We cannot however conclude that the resource curse and the Dutch disease have hurt the overall economy output since the manufacturing sector and the services sector output increased during the period.
Nevertheless, the case of agriculture is different as not only did the agriculture sector’s relative contribution to aggregate output exhibit slight decline during the copper boom period, but so did the agriculture exports as percentage of total merchandise exports. The absolute output was observed to continue with the up and down swings in 2003 but with a steep decline in 2009.
Applying the Dutch disease theoretical framework shows mixed results. The Dutch disease theory suggests that the real exchange rate will appreciate and that the resources (labor) will move from the lagging sector and lead to de‐industrialization and de‐agriculturalization. After an examination of the Zambian economy, we have evidence to believe that de‐industrialization did not occur in Zambia since the manufacturing sector’s relative, absolute contribution to total output increased during the copper boom period. And the strong positive correlation between the manufacturing sector output and the mining sector output suggests that resources (labor) were attracted to both the manufacturing sector and the booming mining sector (labor from the agriculture sector and the pool of unemployment).
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Data Used and Sources
Central Statistical Office of Zambia
London Metal Exchange, Accessed via The University of Groningen DataStream on 11th May, 2011.
United Nations National Accounts Main Aggregates Database,
http://unstats.un.org/unsd/snaama/dnlList.asp Accessed on 23rd May, 2011.
The World Factbook, https://www.cia.gov/library/publications/the‐world‐factbook/ Accessed on 8th July, 2011.
APPENDIX
And below are some selected economic industry indicators for Zambia from 2002 to 2009.
Table A1: Sector growth rates (constant 1994 prices) PROJECTIONS (In annual
percentage change) 2002 2003 2004 2005 2006 2007 2008 2009
CONSTANT 1994 PRICES
Primary sector 3.8 4.5 7.5 6.8 5.5 5.1 4.4 4.2 Agriculture, forestry, and
fishing ‐1.7 5.1 4.4 4.0 4.0 4.0 4.0 4.0 Mining and quarrying 16.4 3.4 13.7 12.0 8.0 7.0 5.0 4.5 Secondary sector 7.2 10.9 5.6 5.8 6.5 6.2 6.2 6.3 Manufacturing 5.7 7.6 5.1 5.0 5.0 6.0 6.0 6.0 Electricity, gas, and water ‐5.2 0.6 ‐2.6 0.0 9.0 5.0 5.0 5.0 Construction 17.4 21.6 9.8 9.0 8.0 7.0 7.0 7.0 Tertiary sector 1.9 3.4 3.1 3.9 4.2 4.5 4.8 4.9 Wholesale and retail trade 5.0 6.1 5.5 5.0 5.0 5.2 5.4 5.5 Restaurants and hotels 4.8 6.8 5.2 5.5 7.0 6.0 6.0 6.0
Transport, storage, and
communications 1.8 5.0 4.6 4.8 4.8 5.0 5.0 5.0
Financial intermediation and
insurance 3.5 3.4 3.5 3.5 3.5 4.0 5.0 5.0
Real estate and business
services 4.4 4.0 4.0 4.0 4.0 4.0 4.0 4.0
Community, social, and
personal services 1.6 1.5 0.6 1.6 3.1 4.1 5.0 5.3
