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The study set out to investigate the determinants of coffee exports of Uganda from 1991 to 2010. The hypotheses tested were that; Real exchange rates have an influence on coffee exports in Uganda, Coffee prices affect coffee exports in Uganda and Coffee quality affects its export in Uganda.

The study applied Cointegration technique and error correction modeling to Uganda quarterly data starting for 1991 to 2010. The results indicate the existence of long-run relationships. The econometric results show that the exchange rate is negatively correlated with coffee export volumes with elasticity of -2.164. The coffee price has a positive and statistically significant effect on coffee export volumes with price elasticity of 0.789. However, quality of coffee has statistically insignificant effects in the short-run.

From the results, it is concluded that an increase in coffee price, increase coffee exports while a depreciation of exchange rate and a reduction of coffee quality reduce the coffee export volumes. The study recommends the establishment of agreements with international coffee buyers to increase prices prevent exchange rate depreciation and also improve on quality of coffee thereby leading to a high coffee production and hence increase in coffee exports.

CHAPTER ONE

1.0 Introduction

This chapter presents the background of the study, the problem statement, and objectives of the study, research questions, scope of the study, and significance of the study and final definition of key terms.

Background of the study

Exporting is one of the most important channels through which developing countries can link with the world economy (World Bank, 2001). Exporting allows firms in developing countries to enlarge their markets and benefit from economies of scale. Additionally, several scholars have pointed out the importance of exporting as a channel of technology transfer (Pack, 1993). Thus, for better performance of a developing country, it is vital to identify the major determinants of its export supply. In order to formulate trade and industrial policies aimed at stimulating exports, it is important to understand which factors stimulate or deter firms from entering foreign markets.

Redding and Venable (2004), investigate the relative contribution towards export performance. They find that internal components related to supply capacity such as internal geography and institutional quality played a significant role in explaining the observed differential in export performance. According to Redding and Venables (2004), the relative export performance of the African and Middle Eastern countries tended to deteriorate over 1980s and 1990s. This was driven by relatively poor performance in supply capacity. However, since the late 90s, East Asian and Pacific countries in particular have been among the main beneficiaries of foreign market access which coincides with their successful diversification efforts. Real exchange rate which reflects the underlying relative movement of prices at home and abroad is proved to have a significant effect on the export performance of the lowest performers.

One of the world’s most widely traded commodities is coffee. Coffee beans when roasted produce a flavorful, aromatic and caffeine filled drink that is popular all over the world, with over 600 billion cups sold each year (ICO, 2014). Two botanically different trees can produce coffee. Arabica coffee trees produce coffee beans that are more labor intensive in its cultivation and are grown at higher altitudes. This coffee is milder, more aromatic and more complex than its Robusta counterpart (ICE, 2014). For many countries, over 50% of their total export earnings can be accounted for by coffee exports. In fact, the top 10 Arabica coffee exporting countries in the world are considered developing. The UCDA estimates that approximately 77 million 60 Kilogram bags were exported from the top 10 coffee exporting countries in 2011-2012.

Coffee is one of the most significant exported commodities by Brazil Colombia, Guatemala and Honduras, and as previously noted, these are among the top exporting countries of Arabica coffee. Naturally, coffee plays a significant role in the composition of the Gross Domestic Product and Agricultural Gross Domestic Product of these countries, Coffee makes up the highest percentage of both GDP and AGDP for Honduras with 7.37% and 48.17 %, respectively. Honduras is similarly followed by Guatemala with 2.49% and 21.08%; Colombia with 0.86% and 12.53%; and finally Brazil with 0.35% and 6.41%, (ICE, 2014).

For most low developed economies in Sub-Sahara Africa (SSA), agriculture has been the main source of livelihood both in terms contributing 34% to Gross Domestic Product (GDP) and 64% to employment, either directly or indirectly, Dependence on agricultural commodities like coffee for exports has been accompanied by a high degree of price risk in terms of both volatile and declining prices, a phenomenon which has not only affected the way households allocate their resources but also affected their welfare in terms of consumption and export volume to the world market.

Uganda’s export sector is dominated by primary products (about 74.1 %), (Roberta, 2004). These include agricultural products; mainly coffee, cotton, flowers, simsim, fish; unprocessed minerals such as gold; live animals, hides and skins among others. At independence time (1962), Uganda’s traditional exports constituted agricultural commodities and unprocessed minerals. By the end of the 1970s, coffee was the largest foreign exchange earner accounting for about 51 percent leaving cotton, copper, tea and tobacco sharing the other portion of the earnings (Musinguzi, 2002).

Coffee continues to play a leading role in the economy of Uganda, contributing 18% of the export earnings between 2000 and 2010, despite the vigorous efforts by Government to diversify the economy. Though large scale coffee producers are gradually emerging, the coffee sub-sector is almost entirely dependent on about 500 000 smallholder farmers, 90 percent of whose average farm size ranges from less than 0.5 to 2.5 hectares (UCDA, 2012). The coffee industry employs over 3.5 million families through coffee related activities. Though large scale coffee producers are gradually emerging, the coffee sub-sector is almost entirely dependent on about 500000 smallholder farmers, Domestic consumption of the commodity in Uganda is relatively small ranging from 4-10% of production. As such, coffee is primarily an export crop, between 2005 and 2010, (Sayer, 2002).

Coffee has continued to play a leading role in the economy of Uganda (UBOS 2011). It contributes between 20-30 percent of the foreign exchange earnings (Uganda Coffee Development Authority, 2009). In 1995, the National Union of Coffee Agribusinesses and Farm Enterprises (NUCAFE), this has led to the coming up of some large scale coffee farmers. Though large scale coffee producers are gradually emerging, the coffee sub-sector is almost entirely dependent on about 500,000 smallholder farmers, 90 percent of whose average farm size ranges from 0.5 to 2.5 hectares. The coffee industry employs over 3.5 million family members through coffee related activities. From the 1920s, coffee was grown for export and in the 1950s an extensive coffee production programme was launched. In 1972, coffee production reached 4.2 million bags of 60Kgs each. Thereafter, coffee production declined tremendously because of civil strife, poor marketing systems, and low producer prices arising from government monopoly and controls (Rudaherenwa, et al 2003).

Uganda ranks fourth after Burundi, Ethiopia and Honduras in terms of contribution of coffee exports in total export earnings in the period 2000-2010 with an average share of 18% during this period (ICO, 2012). The post-1997 coffee price decline has had a negative effect on production and exports (Baffes, 2006). However, production kept declining even when prices recovered until 2006 and has recently been declining.

Although coffee contributed as much as $400 million annually to total merchandise exports during the mid-1990s, it currently (2010) contributes about $280 million (MAAIF, 2011). Understandably, the sector’s poor performance raised concerns among policy makers. However, despite the declining foreign earnings compared to the mid-1990s, coffee remained the main foreign exchange earner for the country. Its share in total export earnings declined marginally from 17.9 percent in 2009 to 17.5 percent in 2010.

Despite a significant decline in quantity exported coffee export earnings in 2010 increased by 13.1 percent as a result of higher global prices although there was an overall 14.3 percent decline in the quantity of coffee produced in 2010. Coffee exports in 2010/11 were 156,000 MT valued at US$ 338 million. European Union is the main market for Uganda coffee export accounting for over 70% of total exports followed by Sudan importing over 10% of Ugandan coffee and USA with 3% of coffee exports of Uganda (Figure 3) (UCDA, 2011). However, the export market of Uganda is quite diverse with a total of 16 importing countries. The export market is controlled by 29 national and multi-national companies with ten companies controlling about 85% of the export market. The leading company Ugacof (U), Ltd) controlled 15% of the coffee export in 2011 (UCDA, 2011). The top ten importing companies held a market share of 73.4% in 2011.

In Uganda, Robusta Coffee is mainly grown in the low altitude areas of Central, Eastern, Western and South Eastern Uganda up to 1,200 meters above sea level. Arabica coffee requires cool, moist and higher altitude. It is mainly grown on Uganda’s mountain fringes, on Mount Elgon in the east (notably in Bugisu, on the western slopes of Mount Elgon in Mbale district) and on the Ruwenzori’s and West Nile (Nebbi and Okoro districts) on the border with Congo. Some Arabica is also grown in Mbarara district in Western Uganda (Sayer, 2002).

Figure 1: Graph showing the changes in the quantity of coffee exported by Uganda since 1991-2010

The figure 1 above shows that Uganda’s exports of coffee in 1994 increased 1000,000 Kgs and Uganda exported the largest amount of coffee in 1996 which was above 4,000,000 Kgs , this figure also indicates that the export of Uganda coffee has been fluctuating over the years and by 2010 there was a great general decline of the coffee exports as compared to other years like 1996, 1998, 2000, 2002 this volatility in exports therefore indicates that Uganda’s coffee faces various challenges in its export of coffee to the world markets. From 1991 to 1998, coffee exports increased mainly due to fair prices on the international market. Thereafter, coffee exports declined

almost every subsequent year. This is mainly due to adverse prices on the international market, and there exists a huge value gap between the global revenues generated from coffee and what producing countries earn, due to a long supply chain with very many participants. For instance, in the year 2006/2007, the global coffee revenues were US$90 billion but farmers in producing countries all combined including Brazil earned only US$9 billion which is 10 percent of the global value share (UCDA, 2009). Basing on this background, this study therefore intends to investigate into the determinants of coffee exports in Uganda from 1991-2010.

1.2 Problem statement

Uganda ranks fourth after Burundi, Ethiopia and Honduras in terms of contribution of coffee exports in total export earnings in the period 2000-2010 with an average share of 18% during this period (ICO, 2012). According to figure 1 above there has been unstable export volume of coffee in Uganda from 1991 up to 2010, this is also shown by the fact that there was a rise in coffee export to the world market from 159,983 tons in 2004 up to 200,640 tons in 2008 however the coffee exports generally declined from 200,640 tons in 2008 to 159, 433 tons in 2010. Coffee exports have been declining since 1998 (refer to fig 1) despite the measures undertaken by the government to boost the sector.

Objectives of the study

General objective

The general objective of the study was to find out the determinants of coffee export volumes in Uganda between 1991 and 2010.

1.3.2 Specific objectives

To investigate the effect of real exchange rate on coffee export in Uganda
To establish the effects of coffee prices on coffee exports in Uganda.

To assess the effects of coffee quality on its exports in Uganda.

Hypothesis

Real exchange rates have an influence on coffee exports in Uganda.
Coffee prices affect coffee exports in Uganda.

Coffee quality affects its export in Uganda.

1.5 Scope of the study

1.5.1 Content scope.

The study specifically provided in-depth examination on the effects of real exchange rates on coffee exports in Uganda, the effects of coffee prices on coffee exports in Uganda and the effects of coffee quality on its exports in Uganda.

Time scope.

The study focused on the determinants of export volumes of Uganda’s coffee covering the period 1991-2010. The period was chosen because reforms in the coffee export sub-sector were introduced in 1991. These were expected to have contributed to a sustained increase in coffee exports by 2010.

1.6 Significance of the study

The findings of the study will be beneficial in the following ways;

The study provides academicians with information regarding the effects of real exchange rates on coffee exports.
The study helps researchers have information regarding the effects of coffee prices on coffee exports.

The study adds on existing literature regarding the effects of coffee quality on its exports.

CHAPTER TWO

LITERATURE REVIEW

2.0 Introduction

In this section, the researcher reviewed literature related to the topic under study. It basically looked at what different scholars wrote about the determinants of coffee exports.

2.1 Determinants of coffee exports

These include real exchange rate, coffee prices and coffee quality

2.1.1 Real exchange rate and export performance

For a study of the Fridman (1953) proposition that floating exchange rate regimes allow the nominal exchange rate to act as a shock absorber in small, open economies and thus help insulate them against real external shocks, see among others Broda (2004), who found that the short-run real GDP impact of negative terms of trade shocks was smaller in floats than in pegs, and Hoffman (2007), who also found that external shocks were less contractionary under floating than under pegged exchange rates.

Gonenc and Yilmaz (2008) point out the high effect of exchange rate on the competitiveness. They did find out that imported input costs act as a natural hedge against exchange rate movements and have a substantial effect on competitiveness. Aysan and Hachasanoglu (2007), on the other hand, found that real exchange depreciation does not induce a huge increase in exports.

Exchange rate volatility is defined as the risk associated with unexpected movements in the exchange rate. Economic fundamentals such as the inflation rate, interest rate and the balance of payments, which have been more volatile in the 1980s and early 1990s, by themselves, are sources of exchange rate volatility (Ozturk, 2006). The NEWS impacts suggested by Tibesigwa and Kaberuka (2014) could also be added onto the list of the factors that bring about volatilities or abrupt shifts in the exchange rates of any economy.

The last decades have brought on board various models, modelling techniques and modelling software in modelling exchange rate volatility, since the early 1980s to date researchers like Engle (1982), Bollerslev (1986) have advanced various time series models and edited the ARCH model to come up with a more generalized ARCH model, Tibesigwa and Kaberuka (2014) however, state that though these models have been used in developed countries, their applicability is still minimal in the analysis of developing countries like Uganda.

Their paper tried to check the applicability of the GARCH model to exchange rate of Rwanda and found that volatility periods existed in the exchange rate data of Rwanda implying the GARCH models were applicable, further analysis and forecasts done using the models obtained showed that joining the East African community reduced the volatility of rates of Rwanda (Tibesigwa and Kaberuka, 2014).

The period of late 90’s, Uganda was characterized by general high prices of conventional traditional exports and market determined exchange rate as a result of abolition of the auction in 1993, which in turn led to inflows of foreign currency and appreciation of a shilling thus negatively impacting on coffee exports (Bigstern and Kayizzi 1999).

Harberger (2003) studied the impact of economic growth on real exchange rate. He found that there is no systematic connection between economic growth and real exchange rate. Husain et al. (2004) found in their study that little access to international capital is available for the weaker and less developed countries, so low rate of inflation and higher level of durability is associated with fixed exchange rate regime in those countries. However, they found no robust relationship between economic performance and exchange rate regime in the developing economies. They also found that advanced economies may experience durable and slightly higher level of growth rate without higher level of inflation in flexible exchange rate regime.

According to Edwards and Alves (2006), the exchange rate has a strong impact on manufacturing export performance in any country in the world. A one percent increase in the relative price of exports is estimated to raise average manufacturing export volumes by 0.99 percent to 2.33 percent in the long-run. The very elastic response of export volumes to changes in relative prices found in these estimates, suggests that much of the improvement in export performance during the 1990s can be attributed to the real depreciation of the currency during this period. Yusuf and Edom (2007) analyzed the factors influencing the exports of timber in Nigeria with the aid of Error Correction Model (ECM) representation procedures. The analysis was carried out with the data collected on round wood and sawn wood over 33 years (1970 -2003).

Using the long run restricted ECM. The study proved the lagged values of the official exchange rate to be one of the most important factors determining the quantities of export of sawn wood from Nigeria. Amin (1996) estimated the effects of exchange rate policies on Cameroon’s agricultural export competitiveness.

Depreciation of real exchange rate stimulates about one percent increase in coffee. Rudaheranwa, et al. (2003), in their study of supply response of selected export commodities in Uganda, got the following elasticity 0.09 for maize and beans, 0.67 for cotton and tobacco and 0.03 for coffee and tea. The results confirmed that the inelastic nature of Uganda’s agricultural commodities renders the exchange rate depreciation ineffective in terms of improving the competitiveness of agricultural products in external markets, (Kaberuka,W (2014)

According to Faruk and Yavuz (2007), the real effective exchange rate is statistically significant and negative (-0.333). This means that a one percent increase in real exchange rate reduces export growth by 0.3 percent. This supports the hypothesis that exchange rate policies may not be successful in promoting export growth.

Cushman (1986) shows that an increase in exchange rate volatility has adverse effects on the volume of international trade, by increasing the riskiness of trading activity. Viaene and de Vries (1992) and Franke (1991) have demonstrated that increased exchange rate volatility can have ambiguous effects (negative or positive) on the volume of trade. They go on to say that an increase in risk has both a substitution and an income effect. The substitution effect decreases export activities as an increase in exchange rate risk induces agents to shift from risky export activities to less risky ones. The income effect on the other hand induces a shift of resources into the export sector when the expected utility of export revenues declines as a result of the increase in exchange rate risk.

Kasekende and Ssemogerere (1994) investigated the role of exchange rate policy in export performance in Uganda. The study took center stage on the impact of devaluation as a form of exchange rate reform. Results showed that the elasticity of total export supply re-switching from a unit change in the real exchange rate was inelastic (0.28). This implies that a one percent increase (devaluation) in the exchange rate increases exports by only 0.28 percent.

Cline (2004) in his study used pooled data for over 100 developing countries for the period 1981-2001. He ran an Ordinary Least Squares regression and his results showed that real exchange rate has a significant effect (7.76) on export growth. This means that a depreciation of the real exchange rate greatly increases export growth. According to Njuguna, et al. (2002) in their analysis of Kenya’s export performance, the supply response to price incentive (real exchange rate depreciation) for exports of goods and services is significant. This means that the depreciation of exchange rate increases the export of goods and services.

Yusuf and Edom (2007) analyzed the factors influencing the exports of timber in Nigeria with the aid of Error Correction Model (ECM) representation procedures. The analysis was carried out with the data collected on round wood and sawn wood over 33 years (1970 – 2003) using the long run restricted ECM. The study proved the lagged values of the official exchange rate to be one of the most important factors determining the quantities of export of sawn wood from Nigeria. Amin (1996) estimated the effects of exchange rate policies on Cameroon’s agricultural export competitiveness. His estimates show that a 10 percent depreciation of real exchange rate stimulates about one percent increase in cocoa.

Rudaheranwa, et al. (2003), in their study of supply response of selected export commodities in Uganda, got the following elasticity 0.09 for maize and beans, 0.67 for cotton and tobacco and 0.03 for coffee and tea. The results confirmed that the inelastic nature of Uganda’s agricultural commodities renders the exchange rate depreciation ineffective in terms of improving the competitiveness of agricultural products in external markets.

The gravity model has been extensively used in analyzing the pattern and the determinants of trade flows of countries particularly in Europe, Latin America, and Asia. For example, Gani (2008) applied the gravity model to examine the factors influencing trade between Fiji and its Asian partners, using a panel data for the period 1985 to 2002. The results suggested that Fiji’s exports are significantly influenced by Fiji’s infrastructure, the distance to export markets, and the real exchange rate.

According to Adubi and Okunadewa (1999), changes in international prices of exports and exchange rates may lead to major decline in future output if they are erratic. Their results indicate that exchange rate volatility influences exports negatively, while export price volatility affects exports positively. However, such fluctuations whether positive are not desirable as it increases risks and uncertainty in international transactions and thus discouraging trade.

Studies by Friedman (1953) and Johnson (1969) indicate that exchange rate has an effect on volume of trade and that flexible exchange rates promote trade and macroeconomic stability, however, in the short run fluctuations in rates do not affect volume of trade. While findings by Sangita (2000) indicated that exchange rate depreciation promotes increased export volumes. Additionally, an increase in RER means a real appreciation of the domestic currency, which makes exportable items more costly. If the RER appreciates, the demand for exports is likely to fall, and the reverse is likely to occur if the RER depreciates. On the other hand, the study by Ajab (1996) indicated that favourable exchange rates for the export sub sector made imports more expensive thus encouraging the production of import-substitutes and services in Uganda. He adds that overvaluation of exchange rate discourages domestic production of exports making imports cheap and exports non-competitive. But despite the importance of an appropriated real exchange rate, it is easy to maintain a favourable real exchange rate as it involves also the macro-level management of the economy (Ajab, 1996).

Studies by Ayudin.et al., (2004) also indicate that real exchange rate is a significant determinant of imports and the trade deficit, but not of exports and that it exhibits a negative relationship. However, Rodrick (1998) in his study on Trade Policy and Economic Performance in Sub- Saharan Africa pointed out that overvalued currencies were due to inward oriented policies, which reduced openness.

According to Qian and Varangis (1994) exchange rate volatility increases risks and uncertainty in international transactions and thus discouraging trade. This has an implication to a risk adverse exporter to incur added costs to avoid the risks leading to the exporter supplying less at a higher price in presence of exchange rate volatility. Additionally exchange rate variability may lead to significant changes in exports and imports as well as investments in a country in general. Studied by Friedman (1953), Johnson (1969), Kihangire (2004) and Joordan et al, (2007); argued that adopting a flexible exchange rate would reduce protectionism and promote trade. On the other hand Kihangire (2007) argues that exchange rate volatility is associated with floating exchange rate, which was presumed not to affect international trade.

2.1.2 Coffee prices and coffee exports

From 1991 to 1998, coffee exports increased mainly due to fair prices on the international market. Thereafter, coffee exports declined almost every subsequent year. This is mainly due to adverse prices on the international market, and there exists a huge value gap between the global revenues generated from coffee and what producing countries earn, due to a long supply chain with very many participants. For instance, in the year 2006/2007, the global coffee revenues were US$90 billion but farmers in producing countries all combined including Brazil earned only US$9 billion which is 10 percent of the global value share (UCDA, 2009).

Coffee farmers in African producing countries all combined earned less than US$2 billion, which is about 22 percent of the total value share that producing countries earned when all combined. Africa’s total value share to global value was only about two percent. Uganda earned about US$170 million and the coffee farmers altogether earned less than US$90 million which is about 53 percent value share, while middlemen (occasional traders) and exporters earned 38 percent and nine percent respectively. This clearly shows that coffee farmers have to upgrade and increase their value share (UCDA, 2009).

The secular decline in real commodity prices and large price fluctuations have direct consequences for earnings and poverty levels, since farmers cannot generate the surplus needed to invest in measures to raise productivity through more intensive and appropriate use of capital and inputs, or to diversify production for export. Managing large fluctuations in commodity prices is a formidable task not only for farmers but also for Governments and enterprises. In addition, observing the large risks in agriculture and lacking the know-how for dealing with these, financiers have generally been reticent in providing the necessary seed and working capital. This is further complicated by the emergence of increasingly concentrated market structures at the international level and stringent standards and requirements in developed country markets. If present trends continue, a large number of commodity-dependent developing countries risk being excluded from the dynamic segments of the world economy, with serious implications for their export performance, sustainable development and poverty levels.

According to Edwards and Alves (2006) a one percent increase in the relative price of exports is estimated to raise average manufacturing export volumes by 0.99 percent to 2.33 percent in the long-run. The very elastic response of export volumes to changes in relative prices found in these estimates, suggests that much of the improvement in export performance during the 1990s can be attributed to the real depreciation of the currency during this period.

The important role of world coffee prices to a coffee exporting country brings up the empirical question of how coffee prices are determined (cash Prices) and discovered (futures prices). According to Catlett and Libbin (1999), Cash and futures markets are two separate, yet very related, markets that trade commodities. Whereas the cash market refers to the actual buying and Selling of physical commodities in the present, the futures market deals with the buying or selling of future obligations to make or take delivery of the underlying asset.

Branchi, et al. (1999) analyzed the impact of price variable on coffee production and exports in a selected group of developing countries, with particular focus on a subgroup of Sub-Saharan 29 countries. They tested the long-run impact of policies on producer’s behavior by means of a cross country linear regression model. About one third of cross-country variability in planted areas is found to be attributable to exchange rate and, to a lesser extent, taxation policies. However, price policies do not appear to exert any significant impact on yields.

Edwards and Golub (2004) in their study of export performance of manufacturing sector in South Africa, using time series data got a significant positive coefficient on foreign prices. Foreign prices appear to have a more significant impact with a one percent rise in foreign prices resulting in a positive impact of about 0.5 percent in the short run and 3.2 percent in the long run. Morrissey and Andrew (2006) analyzed Africa’s export performance using estimates of volume of exports, available from UNCTAD, to explain African trade performance. Using a dynamic panel data analysis for 48 African countries over the period 1987-2002, the key determinants of export performance were ascertained. These include; the unit price of exports with elasticity of 0.93, gross fixed capital formation with elasticity of 0.15, foreign direct investment with elasticity of 0.10 and real effective exchange rate with elasticity of 0.02. All these were significant, implying that exports respond to changes in these variables. Their analysis put center stage on the issue of commodity prices. They therefore, concluded that finding a solution for the problem of low commodity prices is thus more urgent than ever.

Maitha (1975) re-estimated the supply response of Kenyan coffee. Using changes in productivity as the dependent variable rather than the acreage. He used an aggregate production function of the constant elasticity of substitution (CES) and a fisher distributed lag to derive his productivity equation. The acreage productivity index was the dependent variable while the lagged price (derived through the fisher distributed lag method) and a time trend were his independent 30 variables. The results indicated that under the estate, the short-run elasticity was 0.657 and the long-run elasticity was 0.985.This implies that price has a more significant impact with a one percent rise in price resulting in a positive impact of about 0.7 percent in the short run and 0.98 percent in the long run.

Gbetnkom and Sunday (2002) investigated the determinants of three agricultural exports from Cameroon between 1971/72 and 1995/1996. Export supply functions were specified and estimated for the three export crops chosen: cocoa, coffee and banana. Quantitative estimates obtained from the ordinary least squares (OLS) estimation procedure indicate that the response of export supply of all the crops to relative price changes is positive, but fairly significant (elasticity of relative price was 0.14 for cocoa and 0.32 for coffee). This implies that an increase in the relative prices does not lead to a proportionate increase in export supply of agricultural products.

Jebuni, et al. (1991) found the elasticity of the international price to be positive but insignificant (0.1924). this means that, export unit values based on world market prices did not have a significant effect on export volumes. This suggests that in a regulated market system favourable world market prices may not be passed on to the producer.

Old and Prizzon (2010) show that the performance of Export volume is highly ambiguous with respect to price: positive, but not significant. This was based on the price elasticity of 0.09 which was insignificant. This means that a one percent increase in the international price marginally increases exports by about 0.1 percent.

Jaeger (1992) carried out an ambitious econometric study on 21 SSA countries in order to estimate the price responsiveness of total agricultural supply and of a few key crops taken individually. In the case of coffee, he found a positive short-run elasticity of 0.237 for SSA producers as a whole, but almost no significant results when examining each country separately. This implies that a one percentage increase in price leads to 0.2 percent increase in the supply of Sub-Saharan agricultural exports.

In another study (Gabriele, 1994) tried to estimate the price elasticity of traditional primary Exports in four Central American countries over the 1960-1990 periods. Short-term price elasticity varied between 0.08 and 0.19. This implies that traditional primary exports of Central American countries do not respond highly to changes in price.

Ezekiel (1938) which states that output is determined by the level of price in the previous period. All price focused models specified supply as function of current and lagged prices, exchange rate and a supply shock indicator (Alemayehu, 2002). Heterodox model, on the other hand, specify supply function as a function of farmer’s income and price. The mixed (heterodox) factor based model provides insight into some important points in specifying export functions of primary commodities. Firstly, factors other than price are found to be important determinants of commodity supply. Secondly, a distinction across commodities, especially between annual and perennial crops, is essential in specifying supply functions.

According to Ackah and Morrissey (2005), factors external to an individual country, such as world prices are typically more important determinants of the volume and value of exports than a country’s own trade policies. This is because small country producers have no capacity to determine these prices on their own.

A set of theoretical models by Dixit (1989), Krugman (1989) and others suggest that hysteresis in exports may be due to the sunk costs in entering the export market at firm level. The underlying theory is that there are fixed costs of exporting that deter those firms operating below a threshold level of efficiency because their prospective profits from exporting do not compensate for additional costs (Roberts and Tybout, 1997). Sunk costs may include expenses related to establishing a distribution channel and modification of commodities to foreign tastes. These costs may vary with the skill of staff, firm age, firm size and ownership structure of the firm (Graner and Isaksson, 2002)

Mbowa (1992) noted that cotton, coffee and tea exports increased with depreciation in the Ugandan shilling. This implied that improving price incentives through exchange rate adjustment was effective in boosting traditional export growth. While Orcutt (1950) indicated that trade flows respond positively or negative to small and temporary changes in prices on the international market. According to Morrissey et al., (2003), export revenues are largely determined by world prices and the prices play an important role in the determination of imports and exports in developing countries.

Oskooee (1986) argued that trade flows are more responsive to changes in the relative prices than to changes in the exchange rates in the long-run. In addition Goldstein and Khan (1985) reported that for perfect substitutes, price respond negatively and so export volumes shrink as relative export price increases and expand as relative prices of substitutes decrease, which is an important assumption in the perfect elasticities of export supplies. However, all the above studies were assessing the impact of export prices of a country’s general trade volumes not isolating the fast growing export commodities like the organics.

On the other hand, Vijaya (2007) argued that the drivers of price volatility for cotton and other agricultural exports are at the production, processing, exporter and macro levels summarized in Table 3.1. Although this may also be true to the Ugandan context, other factors like high certification costs, quality inconsistencies, absence or limited support policies are likely to affect the entire organic sub-sector in Uganda.

2.1.3 Coffee quality and coffee exports

(Adams, F.G. and J.R. Behrman 1982, p. 3) Clearly, the role of primary commodity exports as the main source of foreign exchange for LOCs is of vital importance. For Ethiopia, about 90 percent of its exports are agricultural primary products, and as has been discussed in the preceding chapter, coffee is the single most important crop contributing 60-65 percent, on average, of the total foreign exchange earnings of the country.

Quality is a key factor for Uganda’s access to the world coffee market and for the price obtained for its coffee. The price paid for different coffee qualities depends on the type of coffee (Arabica/Robusta), bean size (screen), processing (dry/wet), color, taste (cup), and the reputation of the country of origin (Belling, 2002). In addition, western consumers are increasingly willing to pay a higher price for coffee produced in socially and environmentally responsible ways, and in reputable localities or appellations. Obtaining a price premium thus depends as much on the ability to sell a story as on the intrinsic qualities of the coffee (Ponte, 2001).

A spectacular growth in the specialty coffee niche markets in North America and Western Europe in the 1990s (Ponte 2002b) provides the Ugandan coffee industry with an opportunity to increase its revenues from this market segment and so partly compensate for revenue losses in the industrial blends markets. Specialty coffee here includes all coffees that are not traditional industrial blends, notably single-origin and high-grade Arabica coffee grown in certified, environmentally friendly ways (organic, shade-grown, bird-friendly), and coffee sold through Fair Trade organizations, which guarantee a higher compensation to producers and workers (Ibid).

The North American specialty coffee market is expanding by around 20% per year in retail sales terms and similar growth rates are observed in Europe (COMPETE 2002, Ponte 2002b). Globally, sales of so-called sustainable coffee (certified organic, fair trade, and shade-grown coffee) currently represented slightly less than one percent of the coffee market (Ponte 2002b). The global retail values of certified organic and fair trade coffee have been estimated at US$223 and US$393 million, respectively, in 2000 (Ibid). A relatively large share (46% in 2001) of Ugandan Arabic coffee is dry processed (drugar) and obtains a price which is 26% lower than the best wet processed qualities (Bugisu). Larger beans attract a higher price and may be attained through improved farm practices and planting of Clonal coffee varieties. In 2001, the export price of the highest screen (18) was 51% higher than the lowest but accounted for only 9.4% of volume (from 6.5% in 1999).

Quality attributes such as aroma, taste, cleanness, and so on may be improved through proper production and processing methods, Systematic pruning of coffee trees is a good example of a low-cost method, which can improve bean size and taste, while also reducing pests and diseases and prolonging the productive life of the tree (Belling 2002:12). Except for wet processing, these quality improvement methods do not require complex technologies or institutional arrangements, something that place then within reach of resource-poor farmers. While there are many technical possibilities of raising the intrinsic qualities of coffee beans, the transmission of quality-based price incentives to the producer remains a major constraint to a general improvement in quality.

Quality regulation at the local level is expensive due to weak infrastructure and low output per producer, among other factors. The dry processing method further hinders effective quality control at the farm level since it implies that the dry pulp covers the bean at the time of delivery (Ponte 2001). Given such constraints for quality regulation to be cost-effective small producers must sell their coffee collectively (horizontal integration), something which they are reluctant to do after the poor performance of cooperatives in the past (Ibid; UCDA, pers. comm.). This may be combined with contract farming-type arrangements whereby the exporter or roaster controls both production and handling conditions (vertical integration). Establishing these forms of integration is severely constrained by the weak legal system at the local level, however (IITA & NRI 2002). On the positive side, it is noteworthy that quality control is less important in the Robusta than in the mild Arabica market, and that the neutral taste of Ugandan Robusta, related to the high altitude of cultivation, is by far its most important intrinsic quality (Ponte 2001). As for the latter, because dry processing is relatively easy (compared to wet processing), it is more difficult to ruin a natural Robusta bean than a mild Arabica bean. (The coffee yearbook: 2004/05).

The decline of Arabica coffee forests however is difficult to predict as many factors influence their existence. Coffee is an under storey species, and future investigations should examine the likely impacts of climatic change on the primary forest trees.

Climatic change is expected to result in a change of forest type and species (Bezabih et al, 2010). Depending upon the resilience of the forest trees to climatic change, the impact on Arabica coffee may be less than expected, or more if the forest stand is expected change significantly, as climate changes. Forests can provide a more sheltered environment and more protective atmosphere than field grown crops , and forests may provide a greater resilience to climatic change through changed water holding dynamics, interception of heavy rainfall , and shaded environments (Senbeta et al ., 2007).

Using mean annual temperatures and total annual precipitation as predictors of climatic suitability has limitations. Crops do not respond directly to the amount of precipitation but, instead, to water availability. Evapotranspiration, soil water capacity and management systems affect water availability, along with other factors that are not modeled or accounted for in this study, while precipitation may act as a good proxy for water availability, especially in large parts of East Africa where Arabica coffee is most often farmed as a rain-fed crop, (Willson, 1985).

Mean annual temperature and precipitation are not the only variables that will affect the distribution and success of Arabica coffee. Atmospheric composition affects yields and plant health and some studies suggest that yields of C plants such as rice, wheat and soybean could rise by 10% -20% under increased CO concentrations (Tubiello, 2007). Arabica coffee is a C plant and should benefit from yield increases through increased CO2 atmospheric concentrations.

Arabica coffee requires slightly acidic soils and a good supply of nutrients, especially potassium (Willson, 1985). Nutrient availability may be affected by future climatic change, through changing interactions between minerals, water supply and above ground versus below ground processes (Montoya and Raffaelli, 2010).

Gregory et al.(2009), concluded that the number of soil dwelling weevil larvae Sitonia spp, which attacks legume root nodules, will increase under higher CO2 concentrations, reducing the rate of nitrogen fixation (Gregory et al., 2009).

Arabica coffee is susceptible to a number of pests and diseases. The coffee berry borer, Hypothenemus hampei, is the most devastating pest of coffee in the world, and as mean annual temperatures increase across East Africa the incidence of H. hampei will increase , as the insect develops fastest at temperatures between 27°C -30°C (Jaramillo et al., 2009).

Currently, mean temperatures across East Africa are around 22°C but this will increase to 26C by 2080. Before 1984, the region of Jimma, Southwest Ethiopia was too cold for H. hampei to develop; today there are at least one -two cycles of generation each year (Jaramillo et al .2009). Coffee Berry Disease, Cooletotrichum coffeanum, attacks young coffee berries and thrives in wet conditions as rainfall is required for spore production and dispersal (Waller, 1985). The future climate in East Africa may become more favourable for a variety of pests and diseases, decreasing the productivity of Arabica coffee.

Disease may burden the local population, as malaria is predicted to expand into new areas within East Africa and water -borne sanitation diseases such as cholera thrive in warm water environments (Hay et al., 2002; Lippet al., 2002).

As East Africa becomes warmer and wetter, new environmental conditions may favour the development of human diseases. Increasing illness and mortality amongst the populations of East Africa limits the workforce available to manage farmland.

Coffee is a labour intensive crop, requiring pruning on a regular basis to maintain the highest quality and quantity yields (Gay et al., 2006). Malnutrition and hunger is expected to remain high in large parts of East Africa, and some regions are expected to endure higher rates of malnutrition than the present day (Liuet al., 2008).

CHAPTER THREE

METHODOLOGY

3.0 Introduction

This chapter presents the methodology which consists of the research design, data types and source, tools of data collection, and data analysis.

3.1 Research design

This study adopted a cross sectional survey design. This design was preferred because it enabled collecting data in a short time (Creswell 2003 and Koul 2005). Quantitative approach was also used because of its flexibility to form multiple scale and indices focused on the same construct (Ahunja 2005).

3.2 Study population

The researcher used secondary data obtained from the national income accounts of Uganda as prepared and compiled by the Ministry of Finance and Economic Development (MOFED), Department of National Accounts, UBOS, UCDA and World Bank Africa database for the period between 1991 and 2010.

3.3 Sample size and selection technique

The study focused on determinants of export volumes of Uganda’s coffee covering 20 years that was from 1991 to 2010. The period was chosen because reforms in the coffee export sub-sector were introduced in 1991. These were expected to have contributed to a sustained increase in coffee exports.

3.4 Data type and sources

Source of data was from secondary sources. The main source of data for this study was the national income accounts of Uganda as prepared and compiled by the Ministry of Finance and Economic Development (MOFED), Department of National Accounts, UBOS, UCDA. In addition World Bank Africa database was used. The data was for the period 1991-2010.

Data on quantity of coffee exports and coffee prices was got from the Uganda coffee development authority (UCDA), Uganda Bureau of statistics, journals, newspapers, text books and while data on interest rates was got from Bank of Uganda and UBOS. Secondary data was sourced because it yielded more accurate information than obtained through primary data, and it was also cheaper.

3.5 Tools of data collection

The data was got by presenting an introduction letter given to me by the head of department Economics and Statistics. This clearly presented my purpose to the different organizations where I was legible to collect the necessary data for my analysis.

3.6 Data Analysis

The time series data was analyzed using Ordinary Least Squares. Unit root tests was conducted using ADF to find out the existence of unit root. The variables were then tested to determine the presence or absence of cointegration. Error Correction Model was used to capture the short run effects. There after a short-run parsimonious model was estimated after isolating the statistically insignificant variables from the error correction model. This was followed by performing diagnostic tests of serial correlation, stability and heteroskedasticity. The instruments of data analysis will be SPSS, EVIEWS.

Analytical Procedure

This study used annual data to examine the determinants of coffee export in Uganda. The co-integration procedure requires time series in the system to be non-stationary in their levels. Moreover, it is imperative that all time series in the co-integrating equation have the same order of integration. Thus, the study will first ascertains the time series properties of coffee export and other explanatory variables by using the augmented Dickey-Fuller (ADF) and Philips-Perron test for stationarity (Dickey and Fuller, 1979 and 1981). The equation estimated for the ADF test is stated as follows:

Where, for example Xt=LCX is the coffee export supply in natural logarithmic, D is the first difference operator, t is the time trend, β, δ and θ are parameters, Ɛ is the stationary random error and n is the maximum lag length. The null hypothesis is that the series contains a unit root which implies that β₁=0 the null hypothesis is rejected if β₁ is negative and statistically significant. To determine the long run relationship between coffee export supply and explanatory variables, the Johansen co-integration procedure is used (Johansen and Juselius, 1990 and Johansen, 1991). The procedure involves the estimation of a VECM. The VECM used in the study is as follows:

Where, Yt is the dependent variable, Zt is the explanatory variables, Xt is exogenous variable, Yt-1 – θZt-1 is the error correction and D is represents the difference operator. Furthermore, ε represents the vector of white noise process. The VECM allows causality to emerge even if the coefficients of the lagged differences of the explanatory variable are not jointly significant.

(Granger, 1983; Engle and Granger, 1987; Miller and Russek, 1990; Miller, 1991; Dawit, 2003). In this study, an attempt is made to specify the coffee export supply function of Ethiopia following Alemayehu (2002) and UNCTAD (2005). The hypothesized variables in this study are rainfall, relative domestic price, labour employed in agriculture, real exchange rate, domestic interest rate, foreign capital inflow, capacity utilization rate, real income, and term of trade. All variables are in natural logarithmic forms and β’s are parameters to be estimated which are elasticities.

3.7 Limitations of the study

The researcher faced financial constraint in terms of transport, stationery, research assistants, printing and binding services during the research process.
The time available for the research was limited to balancing time between research and other responsibilities was hectic.

CHAPTER FOUR

FINDINGS, PRESENTATIONS AND DISCUSSIONS

4.0 Introduction

This chapter presents the data analyzed from secondary data sources on the determinants of coffee exports in Uganda between 1991 and 2010. The data was tabulated to give a meaningful presentation and interpretation. Presentation and interpretation were based on the specific objectives to address the research problem.

This section further reports the estimates for Uganda’s coffee export function. In order to detect the long-run co-movement among the variables, the cointegration procedure developed by Johansen (1991) and Juselius (1990) was employed. An error correlation model for the determinants of coffee exports was used.

4.1 Tests

The researcher had to first run some tests before analyzing his data and these included the following:

4.1.1 Test for normality

Figure 2: The histogram-normality test

A regression was run and on clicking on the view-residual test-histogram-normality test, the histogram is bell-shaped, suggesting a normal curve shape, and the jarque-bera statistics has high p-value of 0.628520 indicating that the errors in the regression are normal that is to say; the jarque-bera statistics probability of 0.628520 is greater than zero and it has a percentage of 63% greater than 10% (63%>10%) thus the errors in the regression are normal.

4.1.2 Test for the presence of heteroskedasticity

Figure 3: Residual plot

The residual plot clearly shows that the variance of the error term was not constant hence there is heteroskedasticity. The researcher therefore transformed the model by introducing logs so as to remove heteroskedasticity. This was done in Eviews and the logs for the dependent and independent variables were obtained.

4.1.3 Test for the constancy of parameters

Ho: There is perfect parameter stability

H_A_:There is no perfect parameter stability

Figure 4: Recursive estimates to test for parameter stability

The above is a graph of recursive estimates to test for parameter stability

From the graph above, the null is not rejected since the CUSUM of squares plot strays within the band hence concluding that there is perfect parameter stability.

4.1.4 Test for omitted variables

Table 1: Chow Breakpoint Test

The null hypothesis is rejected and we conclude that there is a structural break in the data.

4.2 Basic exploratory data analysis

This involved establishing the basic descriptive statistics and the correlation matrix. The descriptive statistics of all the variables in logarithms are displayed in table 4.2, while the correlation matrix in table 4.3 demonstrates the relationship between quantity of coffee exported (LQs) and the other variables used in the study.

Table 2: Descriptive statistics of the series, sample period 1991-2010

The Jarque-Bera tests the hypothesis that the series is normal. Since the probability value for price (LOGPRICE), exchange rate (LOGER), dummy for quality (DUMMYQ), and quantity of coffee exported (LOGQ) is greater than five percent significant level, the null cannot be rejected meaning the series is normal.

Table 3 : Correlation matrix

From table 4.3, the correlation coefficient 0.217899 means quantity of coffee (LOGQ) is positively and weakly correlated with price (LOGPRICE). On the other hand the correlation coefficient -0.099234 implies that quantity of coffee (LOGQ) is negatively and weakly correlated with exchange rate (LOGER) and the correlation coefficient -0.763450 implies that quantity of coffee (LOGQ) is negatively and strongly correlated with quality of coffee (DUMMYQ). The correlation coefficients – 0.07130 and -0.131278 implies that Price (LOGPRICE) is negatively and weakly correlated with exchange rate (LOGER) and quality of coffee (DUMMYQ) respectively. Finally, the correlation coefficient 0.084663 implies that there is a weak positive relationship between exchange rate (LOGER) and quality of coffee (DUMMYQ)

4.2.1 Unit root test results

Unit root tests were carried out using the augmented Dickey-Fuller test statistic. This was carried out to check whether the series were stationary (integrated) or not. This is because standard inference procedures do not apply to regressions which contain an integrated dependent variable or integrated regressors. The test statistic tested the null hypothesis that the time series has a unit root against the alternative that there is no unit root. The test statistic values are compared to the critical values at five percent significant level. The test statistic values less than the critical values at five percent level of significance indicate that the series are non-stationary otherwise they are stationary.

Table 4: Unit root tests of the series 1991-2010

	Variable in level		DW	Variable in 1^st difference		DW
	ADF	Critical value (5%)	DW	ADF	Critical value (5%)	DW
LOGER	-1.9972	-3.029	1.143	-0.344	-3.0400	1.528
LOGPRICE	-1.264	-3.029	1.313	-1.860	-3.0400	1.701

Note: lag length for the Augmented Dickey-Fuller test statistic

In the table 4, the quantity of coffee (LOGQ), Exchange rate (LOGER) and Price (LOGPRICE) are stationary in the levels and after the first difference since there ADF statistic are greater than the critical values.

4.2.2 Cointegration tests

Among the variables that are integrated of order 1(1), an attempt was made to check whether Cointegration holds. The purpose of the Cointegration tests was to determine whether a linear combination of a group of non-stationary series is stationary. Engle and Granger (1987) pointed out that a linear combination of two or more non- stationary series may be stationary. The linear combination of quantity of coffee, real exchange rate, and prices was checked to find out whether the residuals were stationary. The static equation, whose residuals were modeled, tested for stationarity and thereafter formed the error correction term after the first lagging as presented in table 4.5

Table 5: Cointegration tests output

Variable	Coefficients	Std.Error	t-Statistics	Prob
Price	102602	168975.6	0.6072	0.5522
Exchange rate	-172.5716	232.0187	-0.743784	0.4678
Quality of coffee	-845481.8	191905.2	-4.405726	0.0004
C	3475187	432765.6	8.030183	0.0000

The next attempt involved testing the residuals for the order of integration. The application of the Augmented Dickey Fuller test statistic revealed that the residuals are stationary in levels (table 4). This confirmed that the linear combination of quality of coffee, price, and exchange rate formation is indeed stationary.

4.3 Multivariate analysis

Table 6: Regression analysis

Dependent variable: LOGQUANTITY

Variable	Coefficient	t-values	Probability
C	15.14451	19.54967	0.0000
Logexchange rate	-0.018595	-0.174567	0.8636
Logprice	0.050706	0.736831	0.4719
Logquality of coffee	-0.296218	-4.647847	0.0003
R squared	0.597713
Adjusted R squared	0.522284
Durbin- Watson stat	1.563469

The results indicate that the p-value (0.8636) is greater than 0.05 thus we accept the null and conclude that exchange rate has a significant influence on coffee exports. The p-value (0.4719) results into accepting the null hypothesis since it is greater than 0.05 and hence we conclude that prices have a significant influence on coffee exports. The p-value (0.0003) is less than 0.05 the level of significance, we reject the null hypothesis and conclude that quality of coffee is statistically insignificant. That is it has no significant influence on coffee exports.

The results indicate that the t-statistic │-0.174567│ is less than 2 hence do not reject the null hypothesis thus concluding that exchange rate has a significant influence on coffee exports.

The results also indicate that the t-statistic │0.736831│ is less than 2 hence do not reject the null hypothesis thus concluding that price has a significant influence on coffee exports.

The results further indicate that the t-statistic │-4.647847│ is greater than 2 hence the null hypothesis is rejected thus concluding that quality of coffee is statistically insignificant.

Basing on R-squared (0.597713), the model is a fairly good fit. This implies that 59.7% of the variations in quantity of coffee exported is explained by exchange rate, prices. The remaining 40.3% is explained by other variables.

The Durbin-Watson value (1.563469) implies that the time series data is free from autocorrelation since it is approximately 2 (1.5634692)

The coefficient of real exchange rate (-0.018595) implies that a one percent increase in exchange rate is likely to cause a decline in the volume of coffee exports by about 10%( in the short run). The coefficient of real effective exchange rate is negative and statistically significant both in the short run and in the long run. This agrees with the findings of Frauk and Yavuz (2007) that the real exchange rate is statistically significant and negative.

Looking at the coefficient of price (0.050706), results indicate that it is positive and statistically significant at 5% level. This means that a one percent increase in price of coffee leads to an increase in volume of coffee exports by about 0.05%. The results conform to that of Oyejide (1986) who contend that high and attractive prices are an incentive to producers and exporters to increase the volume of agricultural exports in Nigeria and Jebuni,et al.(1991) who found out that the elasticity of international price is positive and significant.

The coefficient of quality of coffee (-0.296218) is negative and statistically insignificant and agrees with the theory of apriori. This implies that a one percent increase in quality of coffee is likely to cause about 0.296 percent decline in the volume of coffee exports.

Substituted Coefficients:

=====================

LOGQUANTITY = 15.1445113 + 0.0507055676*LOGPRICE – 0.01859476562*LOGEXCHANGERATE – 0.2962180921*DUMMYFORQUALITY

This implies that a unit increase in the log of price by 1% on average increases the quantity of coffee exported by approximately 0.0507055676 keeping other factors constant.

A unit increase in the log of exchange rate by 1% on average reduces the quantity of coffee exported by approximately 0.01859476562 keeping other factors constant.

Also a unit increase in the log of dummy for quality by 1% on average reduces the quantity of coffee exported by approximately 0.2962180921 keeping other factors constant.

CHAPTER FIVE

SUMMARY, CONCLUSIONS AND POLICY RECOMMENDATIONS

5.1 Introduction

The chapter presents the discussion of findings, conclusions and recommendations for solutions to be undertaken as a result of the study and the areas seemed important for further study. The discussions, conclusions and recommendations are presented in reference to the study objectives.

5.2 Summary of findings

Based on the econometric analysis, the results show that depreciation of the exchange rate reduces coffee export volumes.

Increase in coffee price leads to a great increase in coffee export volumes. This is because increase in price acts as an incentive to producers and exporters to increase production and exports of coffee.

A decrease in the quality of coffee leads to a great depreciation of coffee exports.

The model was a fairly good fit for the data as shown by the coefficient of determination. Some tests were run on the data and it was found that the data was normal.

There was autocorrelation and heteroskedasticity and thus the model as transformed by introducing logs to remove heteroskedasticity. The parameters were also found to be perfectly stable.

5.3 Conclusions

In conclusion, the results from the study indicated that exchange rate had a significant effect on the quantity of coffee exported. An increase in the volume of coffee exports can therefore be attained by reducing the exchange rate since quantity of coffee exported has an inverse relationship with exchange rate.

Since its inception as a commercial crop, coffee has dominated and continues to dominate as the most single important cash/export crop. Its contribution stands at around 20% of total exports, but is threatened by a number of issues; Lack of a national coffee policy, Lack of farmer ownership in the coffee value chain, Lack of a national law that addresses all industry issues from research, production, marketing, processing and exporting, Lack of a law on coffee research and its funding, Deteriorating funding for research, Domination of coffee exports by poor grade coffees, Absence of an industry specific advisory services to farmers, Public and private institutions in the industry are not streamlined on their respective responsibilities and coordination of such responsibilities.

The empirical results based on Cointegration analysis show that the coffee export volumes have a long-run relationship with exchange rate, coffee prices and quality of coffee.

From the results, it can be concluded that real effective exchange rate depreciation leads to a big reduction in the coffee export volumes while an increase in coffee price greatly increases coffee export volumes. On the other hand, the decrease in coffee quality leads to a reduction in the coffee export volumes. In order to increase the coffee export earnings, the government should embark on exporting high quality coffee which can fetch high prices on the international market.

5.4 Policy recommendations

The findings of the study led to the following policy recommendations necessary to ensure steady and sustainable increase in the coffee export volumes. The policy proposals are as follows:

In view of the statistical significance of coffee price, the exporters should initiate the establishment of agreements with international coffee buyers. This will help in increasing coffee prices thereby encouraging coffee production and increase in coffee export volumes. Also, the exchange rate should not be allowed to depreciate to avoid reduction in coffee export volumes.

The government should sensitize people on better farming methods, avail them with fertilizers, disease resistant coffee tress and better farming tools to help in improving coffee bean quality before export.

For the government to reap from its coffee exports, it should regulate its exports on grounds of quality. Once quality coffee is put on market, it fetches good prices whereas poor quality coffee may spoil the country’s marketability in regards to coffee output.

Open policies on export activities that is to say; free trade and free foreign exchange regimes to maximize economies of scale.

There should be stronger oversight mechanisms by MAAIF and MOFPED with regard to how UCDA deploys its revenues so that the bulk of the resources are spent on developing the sector,

Review the regulatory framework for support institutions such as NARS/COREC, NAADS to make them responsive to the research and extension needs of the sector. Research should be demand driven.

5.5 Areas for further study

The government needs to embark on more research within these areas to help in granting more opportunities to get ideas and insight on quality improvement among others.

Effect of trade liberalization on coffee exports in Uganda.

The impact of the government fiscal policy on coffee exports in Uganda.

The researcher only looked at the coffee export supply function. The coffee demand function also need be tackled and then the equilibrium position established by use of simultaneous equations. It is therefore suggested that a future study focuses on this kind of analysis so as to drive improved results.

References

Dawit Sheggu, 2003. Real Effective Exchange Rate Misalignment, Volatility and Their Impact on Macroeconomic Performances of Ethiopia: An Empirical Investigation, Unpublished M.Sc. Thesis, Department of Economics, Addis Ababa University.

Dickey, D.A. and W.A. Fuller, 1981. Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49:1057-1072.

Engel, R.F. and C.W.J. Granger, 1987. Co-integration and Error-Correction: Representation, Estimation and Testing. Econometrica, 55:251-76.

Ezekiel, M, 1938. The Cobweb Theorem, Quarterly Journal of Economics, 52:255-280. Granger, C.W.J., 1983. Co-Investigated Variables and Error-Correction Models, Working Paper, 83-13. University of California, San Diego.

Johansen, S., 1991. Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models”. Econometrica, 59(6): 1551-1580.

Miller, M., 1991. Money Dynamics: An Application of Co-integration and Error-Correction Modeling, Journal of Money, Credit, and Banking, 23:139-54.

Miller, S. and F.S. Russek, 1990. Co-integration and Error-Correction Models, The Temporal Causality between Government Taxes and Spending, Southern Economic journal,5:221-29.

International Monetary Fund, 2005, “Uganda- Ex Post Assessment of Performance under Fund-Supported Programs’ SM/05/223 (Washington: International Monetary Fund).

International Monetary Fund, 1991c, “Uganda-Background Paper and Statistical Appendix” SM/91/72 (Washington: International Monetary Fund).

Harberger, A. (2004), “Economic Adjustment and the Real Exchange Rate”, in S. Edwards and L.Ahamed (eds.), “Economic Adjustment Exchange Rates in Developing Countries”, University of Chicago Press, 10, 308-321.

International Coffee Organization. (2011). Coffee quality Improvement program

Goldstein, M. & Khan, M., S. (1985). Income and Price Effects in Foreign Trade: in Handbook of International Economics (2nd Ed). Amsterdam: Elsevier Science Publishers.

Uganda Bureau of Statistics, (1993, 1996, 1999, 2001, 2002, 2004, 2005, 2008) Uganda Statistical Abstract

Uganda Coffee Development Authority, (2007) Annual Report 2006/07

Uganda Coffee Development Authority,(2009) monthly Report for January 2009.

Uganda Export Promotion Board, (2006) Export Performance Analysis 2005.

Anderson K (2004). Agriculture Trade Reform and Poverty Reduction: Implications for Sub-Saharan Africa. UNCTAD Policy Issues in International Trade and Commodities No. 22.

Bacchetta M and Jansen M (2003). Adjusting to Trade Liberalization: The Role of Policy Institutions and WTO Disciplines. Special Studies No. 7, World Trade Organization, Geneva.

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Buchinski M (1998). Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research. Journal of Human Resources, 33(1), 88-126.

Appendices

Appendix 1: Dependent and independent variables

Table 7: Showing the quantity of coffee exported in tons, price per kilogram, exchange rates, logs for the dependent and independent variables quality of coffee

Appendix 2: Unit root tests of the series 1991-2010

At level with intercept

At level with intercept, differenced once

At level with intercept

At level 1^st difference

Appendix 3: Breusch-Godfrey Serial Correlation LM Test

Appendix 4: Regression analysis

Appendix 5: White Heteroskedasticity test

Appendix 6: Ramsey RESET Test

Ramsey RESET Test:

F-statistic	0.040408	Probability		0.843385
Log likelihood ratio	0.053805	Probability		0.816570


Test Equation:
Dependent Variable: LOGQ
Method: Least Squares
Date: 10/18/15 Time: 16:43
Sample: 1991 2010
Included observations: 20

Variable	Coefficient	Std. Error	t-Statistic	Prob.

C	-212.5603	1132.760	-0.187648	0.8537
LOGPRICE	-1.449441	7.463098	-0.194214	0.8486
LOGER	0.529607	2.729342	0.194042	0.8487
DUMMYQ	8.452609	43.52272	0.194211	0.8486
FITTED^2	0.993056	4.940142	0.201018	0.8434

R-squared	0.598794	Mean dependent var		14.87086
Adjusted R-squared	0.491805	S.D. dependent var		0.203806
S.E. of regression	0.145289	Akaike info criterion		-0.807869
Sum squared resid	0.316632	Schwarz criterion		-0.558936
Log likelihood	13.07869	F-statistic		5.596810
Durbin-Watson stat	1.564348	Prob(F-statistic)		0.005820

Appendix 7: Coffee exports by type and grade for the period 2000/01-2005/06

Table 8: Coffee exports by type and grade.

From table 8, Uganda’s coffee exports are dominated by poor quality coffee grades as highlighted in red. Low quality coffee grades fetch low prices on the world market which affects the value of coffee exports.

Appendix 8: Latent Losses in Uganda’s Coffee exports

Using information in table 7, tables 8(a) and 8(b) were derived. The latter two tables indicate hidden losses to Uganda’s coffee industry through exports of low quality coffees.

Table 8 (a): Latent Loss from Robusta (screens 15 and 12) Exports at 2005/06 Screen 18 prices

Table 8 (b): Latent Losses from Drugar Arabicas Exports at 2005/06 Wugar average prices

Tables 8(a) and 8(b), give comparisons, for both Robusta and Arabica coffee, if Uganda were to export low and high quality coffee grades at 2005/06 average prices. The values in the tables were derived from table 2.2 using average prices of 2005/06. The differential columns in tables 8(a) and 8(b) indicate values that Uganda would loose if it exported poor quality against high quality coffee at 2005/06 prices. It is appreciated that some of the factors that affect bean quality (husbandry practices, weather, age of tree) are beyond the control of coffee stakeholders, but Uganda should strategize to increase its share of good quality coffees and reduce poor quality coffee so as to realise better values from a given volume of coffee. Latent losses also impact on the volume of cess, since cess is an ad valorem levy.

ACRONYMS

LIST OF TABLES

LIST OF FIGURES

ABSTRACT

CHAPTER ONE

1.0 Introduction

1.2 Problem statement

1.3.2 Specific objectives

1.5 Scope of the study

1.5.1 Content scope.

1.6 Significance of the study

CHAPTER TWO

LITERATURE REVIEW

2.0 Introduction

2.1 Determinants of coffee exports

2.1.1 Real exchange rate and export performance

2.1.3 Coffee quality and coffee exports

CHAPTER THREE

METHODOLOGY

3.0 Introduction

3.1 Research design

3.2 Study population

3.3 Sample size and selection technique

3.4 Data type and sources

3.5 Tools of data collection

3.6 Data Analysis

3.7 Limitations of the study

CHAPTER FOUR

FINDINGS, PRESENTATIONS AND DISCUSSIONS

4.0 Introduction

4.1 Tests

The researcher had to first run some tests before analyzing his data and these included the following:

4.1.1 Test for normality

4.1.2 Test for the presence of heteroskedasticity

4.1.3 Test for the constancy of parameters

Ho: There is perfect parameter stability

HA: There is no perfect parameter stability

The above is a graph of recursive estimates to test for parameter stability

From the graph above, the null is not rejected since the CUSUM of squares plot strays within the band hence concluding that there is perfect parameter stability.

4.1.4 Test for omitted variables

The null hypothesis is rejected and we conclude that there is a structural break in the data.

4.2 Basic exploratory data analysis

4.2.1 Unit root test results

H_A_:There is no perfect parameter stability