FAO COMMODITY AND TRADE POLICY RESEARCH WORKING PAPER No. 14

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ABSTRACT The paper analyzes the domestic regional maize markets in the United Republic of Tanzania as far as their spatial integration is concerned, as well as their influence from world prices. It is found that the domestic maize markets are integrated, despite the fact that several are remote and isolated. It is found that domestic Tanzania maize prices are not related to the main international reference maize prices, but they seem to be related to the SAFEX prices. Hedging rules utilizing SAFEX futures prices are defined, and it is shown that they are effective in reducing the unanticipated variation in the cost of maize imports.

RÉSUMÉ Ce document analyse les marchés du maïs dans les différentes régions de la Tanzanie du point de vue de leur intégration spatiale ainsi que de l'impact qu'ont sur ces marchés les prix mondiaux. La conclusion est que les marchés nationaux du maïs sont intégrés alors même que plusieurs d'entre eux sont éloignés et isolés. L'on constate que les prix intérieurs du maïs en Tanzanie ne sont pas liés aux principaux prix internationaux de référence mais plutôt aux prix cotés à la Bourse SAFEX des marchés à terme. Le document présente un certain nombre de méthodes de couverture fondées sur les prix des marchés à terme cotés à la SAFEX et montre que de telles opérations peuvent beaucoup réduire les variations imprévues du coût des importations de maïs.

RESUMEN El documento analiza los mercados internos regionales en Tanzanía por lo que se refiere a su integración espacial, tanto como a su influencia generada por los precios mundiales. Se encuentra que los mercados internos del maíz están integrados, a pesar del hecho que algunos se encuentran en zonas remotas e aisladas. Se observa que los precios internos del maíz en Tanzanía no están relacionados con los principales precios de referencia internacional del maíz, sino parecen estar relacionados con los precios SAFEX. Las reglas de protección que utilizan los precios futuros de SAFEX son definidas, y se demuestra que éstos son eficaces en reducir la imprevisible variación del costo de importación del maíz.

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CONTENTS

ABSTRACT/RESUME/RESUMEN....................................................................................................... i 1 INTRODUCTION.......................................................................................................................... 1 2 BACKGROUND............................................................................................................................ 3 3 DATA AND VARIABLES UTILIZED......................................................................................... 3 4 METHODOLOGY FOR ASSESSING MAIZE MARKET INTEGRATION AND TRANSMISSION .......................................................................................................................... 5 5 DOMESTIC MARKET INTEGRATION...................................................................................... 8 6 INTERNATIONAL PRICE TRANSMISSION............................................................................. 9 7 HEDGING STRATEGIES AND RESULTS............................................................................... 10 8 CONCLUDING REMARKS ....................................................................................................... 13 REFERENCES..................................................................................................................................... 14 FIGURES .......................................................................................................................................... 15 TABLES .......................................................................................................................................... 19

A. Sarris is Director and E. Mantzou consultant in the Commodities and Trade Division of the Food and Agriculture Organization (FAO) of the United Nations. This paper was prepared in the context of a World Bank-FAO project on “Devising policy instruments to allow vulnerable and food importing developing countries to cope better with uncertainty in international and domestic food markets”. The paper has benefited from comments received in presentations at a World Bank workshop on “Managing Food Price Risks and Instability in the Context of Market Liberalization” held in Washington D.C. in February 2005, and at the International Task Force on Commodity Risk Management at Interlaken, Switzerland in May 2005. The authors would like to thank for comments and assistance with some of the data organization Piero Conforti, Adam Prakash and George Rapsomanikis.

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1 INTRODUCTION Food security looms large in the domestic political and economic objectives of most developing countries (DCs) and especially least developed countries (LDCs) in Africa. Upheavals in domestic food markets are both undesirable and of critical concern to governments, and a variety of policies have been attempted over the years, both at national and international levels, to deal with domestic food risks in low income countries, and to assure that all citizens and especially those that are most vulnerable have access to minimum levels of basic food at all times. The issues of food availability as well as access to food by all inhabitants are crucial in this respect.

Domestic food market price instability is a long standing issue in Africa in this respect, and policies to deal with this type of instability have long been considered as integral parts of national food security strategies. Most countries in Africa, for instance, had instituted domestic grain marketing boards, or other monopolies during the early periods after their independence, notably in the 1960s, 1970s, and 1980s. In the 1980s a wave of structural adjustment programmes to promote market-based development was introduced. It was believed that once government intervention was phased out, the market mechanisms would be set into motion to provide undistorted price incentives for producers and to boost GDP growth. Empirical evidence, however, does not support a smooth short-term transition process after liberalization policies were introduced (Kherallah et al, 2002, Stiglitz, 2002, Sechamani, 1998). The process has been accompanied by increased domestic market instability.

Domestic market liberalization in many LDCs has been accompanied by increasing trade liberalization in food products, and the recent two decades have seen an increase in food imports by low-income food-deficit countries (LIFDCs), including those in Africa. A recent FAO study (Gürkan et al, 2003) indicated that between the mid-1980s and the 1990s, the food imported by LIFDCs gradually reached, on average, about 12 percent of their apparent food consumption by the end of the millennium. The study showed that throughout this period, the growth in these countries’ commercial food import bills consistently outstripped the growth of their GDP as well as total merchandise exports. The study also revealed that these countries faced large and unanticipated price ‘spikes’ that exacerbated their already precarious food security situation. Indeed, it was discovered that variations in import unit costs of many important food commodities contributed to around two-thirds of the variation in their commercial food import bills. Coupled with substantial declines in food aid flows over the same period, these developments have meant significant increases in the vulnerability of these countries.

Given the above developments, it seems that the problem of managing the risks of food imports, in light of increasing domestic market instability, is increasing in importance, and is already a major issue for several LIFDCs. The purpose of this paper is to explore how a low-income food-deficit African developing country, the United Republic of Tanzania in this case, can manage some of the basic food import risks facing its economy, due to fluctuating and unpredictable world prices for its basic staple food, and in light of the nature of its domestic market in this staple.

Two issues are relevant in this context. The first concerns the integration of domestic food markets. The effectiveness of imports to smooth out domestic market shocks depends on whether domestic markets are well connected. Secondly, if international markets are to be utilized to hedge domestic market shocks, then these markets must be related to the domestic markets.

In Africa agricultural production often takes place in remote rural areas while demand is largely determined in densely populated areas far away from the producers. Large distances, poor infrastructure and inadequate price transmission mechanisms result in large and unstable price margins across space and time. The lack of adequate risk sharing mechanisms across the trading chain favours wholesalers and other market agents who can reap the benefits from excess demand often to the detriment of producers who are uninsured against the vagaries of agricultural production. As Ravallion (1986) points out, the degree to which specific regions can cope vis-à-vis weather-induced supply shocks largely depends on the reliability of trade linkages with other regions, and this is even more important when imports are envisioned as stabilizing mechanisms.

Considerable theoretical and empirical research has been devoted to market integration issues in the context of the market efficiency hypothesis. Fackler and Goodwin (2001) distinguish between spatial price efficiency and spatial market integration. Market integration is a measure of the degree to which demand and supply shocks arising in one region are transmitted to another region, with perfect market

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integration implying the Law of One Price, as postulated by standard static point-location models (Enke, 1951; Samuelson, 1952; Takayama and Judge, 1972). However, the Law of One Price implies spatial market integration only when there is physical interregional trade, which is not undermined by excessive transportation costs. Spatial arbitrage, which represents the weak form of the Law of One Price, implies that the price of a homogeneous good at any two locations will differ by at most the transportation cost between the two sites. Spatial market efficiency is then based on the extension of the notion that efficient markets ensure that the allocation of resources maximizes utility of output, allowing for short-run inefficiencies if their elimination (e.g. need of infrastructure) is prohibitively costly (Buccola, 1989).

Tradability alone does not ensure that equilibrium prices within and across borders will only differ by the transportation costs. Empirical research on spatial market integration analysis has ranged from static analyses using correlations and simple regressions to using impulse response analysis and co-integration tests allowing for variable and/or exogenously determined transport costs, risk management in the process of the trading chain, switching regimes and asymmetric price transmission mechanisms (for a recent survey see Fackler and Goodwin, 2001).

Many countries intervene in domestic commodity markets with stabilization policies; however, the overall benefits from reduced price variability from these policies tend to be small (Larson and Coleman, 1993, p. 69). The major problem of LIFDCs is not price or quantity variations per se, but rather major unforeseen and undesirable departures from expectations, which may induce unintended adjustments in other sectors of the economy. Hedging has therefore been used at times to reduce the risks of such stabilization policies. The development of a variety of risk management instruments in international markets, such as futures and options for basic food commodities, can serve for hedging against unpredictable foreign exchange costs. Faruqee et al (1997) and Sarris et al (2005) have simulated various hedging strategies with futures and options on wheat and maize in the Chicago Board of Trade (CBOT) stock exchange, to examine their effect in reducing import bill volatility. Their simulations in different countries suggest that market based risk management strategies can prove beneficial in the long run.

Hedging in the context of an LIFDC under a private import trading system, can be envisioned as relevant to three types of agents. The first is the large import traders themselves (private or publicly owned), who have enough resources and international connections to do the hedging themselves. The second type includes the local banks that finance import trade. Their problem is the risk of non-repayment by the food importers who have obtained loans to import basic food. The third type of agent is the central bank of a country, which may need to plan on allocations of scarce foreign exchange, including external loans, well in advance of the requirements of the various domestic banks or other agents. Given that food imports are a crucial part of an LIFDC’s imports, related to domestic food security, unexpected price variations may influence adversely the allocation of foreign exchange to other sectors, and create problems in other productive sectors. Hence this unpredictability may need to be managed actively.

In the following discussion, we will not be concerned with the particular institutional character of the agent that does the physical importing or financing of imports. We will refer to an “agent” as the institution that does both the actual importing as well as the hedging, knowing full well that in an actual country situation, the relevant import-related and financing functions may be split among various institutions. This assumption is made, in order to concentrate on the hedging strategies, rather than on the specific institutional arrangements in any food importing country. Nevertheless, it is assumed that the agent will need to plan for imports (in physical or financial terms) ahead of the actual time that imports need to be ordered.

In the next section we examine the characteristics of Tanzania maize markets and the pricing and trade reforms that have taken place during the last twenty years. In Section 3 we describe the data that we have compiled and used for the subsequent analysis. Section 4 presents the methodology utilized for the market integration analysis. Section 5 presents the empirical results of the tests for domestic maize market integration. Section 6 presents the empirical results of the tests for transmission of prices from international to domestic markets. Section 7 discusses the hedging rules of maize imports and presents simulation results for a variety of hedging rules. Section 8 summarizes the main results.

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2 BACKGROUND Tanzania is a large country covering 945 087 square kilometres including the islands of Mafia, Pemba and Zanzibar. It is among the world’s poorest countries with a per capita income of about US$280. During most of its post-independence history, Tanzania pursued socialist policies which resulted in extended periods where economic performance was clearly below Tanzania’s potential. In the mid-1980s, Tanzania embarked on economic reforms which were not sustained, and after an initial period of economic growth in the late 1980s, the early 1990s were again characterized by macro-economic disequilibria and poor economic growth. Domestic food markets were gradually liberalized between 1986 and 1989. Price controls were radically reduced and a series of devaluations aimed at promoting exports, and reducing the need for import controls were undertaken in the late 1980s and early 1990s.

White maize is produced in most of the 21 regions1 of the country. Arusha in the Northern Zone, Shinyanga in the Lake Victoria area and most of the regions in the Southern Highlands have the highest levels of white maize production in the country. On the other hand, the Central Zone, the North and the South Coast have limited production (Table 1). The poor road network however inhibits at times the transfer of white maize from the remote surplus areas to the deficit areas. Normally, the Southern Highlands supply parts of the Northern Coast, the Central Zone covers its deficit from the Northern Zone and the Southern Highlands, whereas the Southern Coast is supplied by Ruvuma of the Southern Highlands. The West Zone is very remote and most probably it only trades with regions from the Lake Victoria area or engages in cross-border informal trading. A high portion of the production of Lake Victoria is absorbed by the local population since the region is highly populated, whereas some regions might engage in informal cross-border trade with Uganda and Kenya. Consequently the main maize supply areas for the densely populated urban centres of the country are the regions of Iringa, Mbeya, Rukwa and Ruvuma in the Southern Highlands and Arusha in the Northern Zone region.

The contribution of maize to aggregate agricultural GDP in the mid 1990s was 23 percent. In addition maize comprises one third of the daily nutritional intake. White maize is therefore one of the main staple foods which means that most of it is locally traded, rendering it at times a non-tradable good in remote areas. In isolated markets maize prices are driven by the local forces of demand and supply, whereas in some dominant markets prices are affected by world prices and/or real exchange rates. Maize production has increased throughout the years at a slow pace. Despite increased production, real maize prices more than doubled during the first years of market liberalization, namely between 1991 and 1993, and declined sharply thereafter, reaching pre-liberalization levels by 1998 (Figure 1).

Maize import volumes have varied over time while food aid has been substantial in times of bad harvests (Table 2). According to data compiled by FAOSTAT, domestic production has often been supplemented by substantial amounts of food aid, especially in the early 1980s, although this has declined in importance recently. Table 2 shows that food aid does not always seem to follow a similar pattern to that of commercial imports or net imports (see also Figures 2 and 3). A similar trend of food aid and net imports is more evident after the liberalization era. Tanzania has not always been a net importer of maize, as shown in Figure 3. Following years of high production levels, Tanzania became a net exporter of maize, especially in the late 1980s. In addition, the exact amount of international trading cannot be known with certainty given unofficial reports of informal trade across borders with neighbouring countries, especially Uganda and Kenya in the North, and Zambia and Mozambique in the South. In any case, imports comprise a small portion of total domestic consumption.

3 DATA AND VARIABLES UTILIZED To examine the extent of spatial integration of maize markets across the country we have used maize price data from monthly surveys of 44 markets within seven broadly defined geographical zones. Retail food prices were collected over the period 1983-2002 by the Marketing Development Bureau (MDB), and complemented by the Famine Early Warning System (FEWS) project office in Dar-es-Salaam. All reported prices are in Tanzanian shillings per Debe (the equivalent of 20 kilograms). Data on the monthly Consumer Price Index, which was available up to the end of 1997, was combined with the Consumer Price Index, reported in the IMF-IFS country database to serve as the basis for deflation

1 There were 20 regions up to 2002, when the Arusha Region was split into Arusha and Manyara.

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of all price series. In subsequent analysis of domestic spatial market integration these real price series were used in levels or in logs.

For the purposes of data analysis, Tanzania was divided into seven geographical zones, the Northern Zone (4 markets surveyed), the Northern Coast (7 markets surveyed), Lake Victoria (12 markets surveyed), the Western Zone (4 markets surveyed), the Central Zone (5 markets surveyed), the South Highlands (8 markets surveyed) and the Southern Coast (4 markets surveyed).

One of the problems encountered in the handling of the data was the varying and scattered number of missing values across the domestic markets. To avoid distorted results based on price series largely resulting from a linear method of filling-in missing values, the pattern and the extent of the missing values in all the domestic price series was examined. As a rule, the markets that had more than five missing values in a row were excluded. However, in order to avoid bias by excluding rural markets that were more likely to have more missing values in the time series, we ensured that more than one market from each geographical area was included in the sample. In the Western Zone only Kigoma (urban area) out of the four markets qualified to enter the sample, so we decided to include the market in the region with the next fewer reported number of missing values, Kibondo. Given the landlocked nature of the Central Zone and the fact that the northern regions within the Central Zone will trade with the markets in the Northern Zone, whereas the southern and western regions will mostly trade with the surplus markets of the Southern Highlands, we decided to include Dodoma (a medium producer of maize which had not originally qualified) in the sample. In the process of filling in the missing values2 the market prices converged quite early in the Kibondo market whereas prices in the Dodoma market began stabilizing only after the 70th regression.

After reviewing the maize market situation in Tanzania, we also decided to include in the sample the markets of Iringa in the region of Iringa, and Sumbawanga in the region of Rukwa, both surplus maize producing regions that were not originally represented in the sample. Iringa is the main producing market in the Iringa region, which in turn is one of the main producing regions in the Southern Highlands, for which no values were missing. Sumbawanga is the only market in the Rukwa region in the Southern Highlands, and again a main producing market. Using this process we reduced the sample to 27 markets for which we have (originally or by construction) complete time series data.

Table 3 shows the markets for which some price data is available, the degree of market isolation as indicated in the United Republic of Tanzania, World Bank and IFPRI (2000), and which markets were utilized according to the above criteria. The markets included are classified as non-isolated and isolated markets depending on their proximity to a rail link to Dar-es-Salaam or Tanga (important coastal ports) or to a major all weather road to Dar-es-Salaam or Tanga. From Table 3, it is evident that all the Regions that are main suppliers and main consumers of white maize have accessible markets and one expects to find correlation not only between these domestic market prices but also between prices in these markets and international prices. The only Regions that seem to be fairly isolated are the South Coast and to a lesser extent the Western Zone.

Given the variability of the markets within each region, coupled with the geographical, morphological and climatic diversity and heterogeneity of the regions across the country, we constructed regional domestic price indices. To avoid outliers, these indices used the median price across markets within each region for the regional price indices. We also constructed a national maize price index as the mean of the regional price indices, following the methodology suggested in Dana et al (2005).

In order to examine the transmission mechanism across borders, we converted the regional domestic price indices, as indicated above, into dollars per tonne using the IMF-IFS reported exchange rate for Tanzania. Dollar prices for US number 2 yellow maize fob Gulf, Chicago Board of Trade (CBOT) 2 The missing values of each market were filled in with the predicted values of an autoregression model with two lags. The autoregression was repeated as many times as necessary in order for the predicted values to converge at a specific value, i.e. to stabilize in each successive autoregression using each time the original values and the predicted new values. The original prices were kept and the stabilized predicted values were inserted in the final time series. Some market series stabilized after the 10th to the 15th iteration, whereas others converged after the 40th, the 50th or even the 70th regression. The number of autoregressions before convergence was not related to the number of missing values in the series.

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number 2 yellow maize futures prices, as well as SAFEX white and yellow maize spot and futures prices, were all utilized (in dollars per tonne) as reference international prices. For SAFEX and CBOT futures prices, we used as spot prices the monthly average of the daily nearest to expiration contract price.3

4 METHODOLOGY FOR ASSESSING MAIZE MARKET INTEGRATION AND TRANSMISSION

To explore the domestic spatial market integration hypothesis and the international price transmission mechanism in the maize market of Tanzania we employed various methods.

We first examined the characteristics of the time series data at hand. By plotting the international prices and the average white maize (WM) Tanzanian price index (Figure 4) as well as all the constructed regional price indices (Figure 5), against time, we checked the existence of any spikes or structural breaks throughout the period under examination. In addition, such plots could serve to indicate whether or not there was a co-movement in domestic and international prices.

Given that, at a more aggregate level, the regions are implicitly divided up according to whether they are more or less easily connected with dominant maize markets depending on the characteristics of the majority of regional markets, we examined the intraseasonal behaviour of domestic prices by running the following regression:

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0 12

, 1,...,7it i i ij j ij

p T M u iα α α=

= + + + =∑ (1)

where itp is the domestic regional price index for region i , T is a monthly trend variable, jM are

monthly dummies and iu is i.i.d. with zero mean and constant variance. These regressions are expected to show the general trend of domestic prices, as well as the seasonal pattern in different regions.

To examine the evolution of cross-market price variation over time, we used the disaggregated data of all the uninterrupted market price series. We constructed the intra-market coefficient of variation of all domestic prices and then regressed it on monthly dummies, trend variables and a set of other explanatory variables. The intra-market coefficient of variation of prices in period t ( tCV ) is defined as:

* 2

1*

1 ( )1

n

it ti

tt

p pnCV

p=

−−

=∑

(2)

where

* 1

n

iti

t

pp

n==∑

(3)

and n is the number of markets observed (27 in this case, as indicated earlier).

We then ran the following regression:

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2 30 1 2 3

1 1

m

t j j i it tj i

CV M T T T Xβ β γ γ γ δ ε= =

= + + + + + +∑ ∑ (4)

3 SAFEX data was downloaded from the official web site of SAFEX, whereas CBOT data was purchased from the CBOT.

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where the variable T represents a time trend and the variable jM is a monthly dummy variable. The square and cubic nature of the time trend was aimed at capturing non-linear trends in the marketing structures and policies. In the 1m× vector X we included a proxy of the real exchange rate (RER) calculated as the ratio of the product of the monthly exchange rate of the Tanzanian shilling with the United States Consumer Price Index (CPI), to the national CPI. To allow for marketing costs we also included the estimated real rate of interest (RINT) derived by subtracting the monthly inflation rate - calculated by the change of the CPI - from the nominal interest rate. As a nominal interest rate we used different interest rates in successive regressions to check the robustness of the results. We used the Savings Interest rate, the 3-6 months Deposit Rate, the Treasury Bill rate and the Lending Upper Margin Rate, as these are reported in the IFS-IMF database. In the following section we present only the results derived by using the 3-6 months Deposit Rate, as results using the other interest rates were very similar.

Finally, we included a variable designed to measure the changing degree of foreign exchange risk (FRISK) which should affect the overall risk faced by traders in the economy. This was estimated by running a GARCH(1,1) regression on the real exchange rate:

0 1 1t t tRER a a RER v−= + + (5)

and then using the estimated conditional variance of the errors 20 1 1 2 1t t th b b v b h− −= + + as the value for

the FRISK variable.

All necessary data was drawn from the International Monetary Fund database. All the exogenous variables are expected to show the influence of the marketing costs on the variation of prices across the country. An increase in RER due to a real depreciation (as was the case in Tanzania before 1993) is expected to increase marketing costs and thus interregional variation of prices. Increases in marketing costs may also result from increased interest rates (RINT) and/or increased risk faced by traders (FRISK). Consequently the sign of all these variables is expected to be positive.

It is clear that the above test will not reveal whether markets are efficient or integrated, assuming tradability of maize across the country. Rather, it will show the pattern of imperfection or integration and whether they are is stable over time. If marketing margins between different locations change over time, then the CV should also change accordingly.

As a first step towards examining the extent of market efficiency, we ran simple pairwise correlations among all regional price indices. Despite the limitations of this methodology due to the fact that common exogenous influences throughout the country could dictate a common trend, comparing pairwise correlations can give some indication of which markets might be more closely connected. In the case of Tanzania, we believe that these correlations are not biased by non-competitive practices, since merchants and producers are involved in small-scale interregional trade.

Cointegration analysis has been used to determine the existence of market integration, and we also used it. Before proceeding to check the cointegration relation among the prices of the regional markets, some basic analysis of the time-series properties of the data was carried out. The Augmented Dickey Fuller (ADF) test and the Phillips-Perron tests with and without a trend were used to examine the order of integration of the regional price indices time series. The number of lags in these tests was chosen according to the Akaike Information Criterion. If the series was found to be non-stationary, an ADF test was carried out on each first difference to check if it was an (1)I process. The stationarity tests were carried out for all price series in lags and in levels. As the order of integration was not clear using the tests in the price levels, we chose to work with the log price series. We then ran pairwise cointegration tests between any two price series that shared the same order of integration.

Given n spatially separated markets where a homogeneous commodity is traded, with a corresponding 1n× non-stationary price vector [ ]1 2 ...t t ntP P P′ =P , itP being the log price of a homogeneous commodity at time t in market i , they are said to be integrated if:

(i) each price in the vector can be decomposed as it i t itP a g P−

= + 1....i n= ,

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where tg is the cointegration relation that characterizes the long run equilibrium process and

itP−

is the transitory, short run adjustment process,

(ii) for all i , 0ia ≠ , and

(iii) the 'iP s are co integrated with exactly 1n − co-integrating vectors.

If the n markets are cointegrated, then the vector tP has a vector error correction representation

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k

t t i t i ti

P a P P ε− −=

∆ = +Π + Γ ∆ +∑ (6)

where Π and Γ are n n× matrices of coefficients and k is the appropriate lag length. The second term on the right hand side is a linear combination of (1)I variables which gives the long-run relationship of the variables. The hypothesis of cointegration is ( ) :H r αβ′Π = , where r is the rank of Π and α and β are n r× matrices of full rank. α is the matrix of weights with which the cointegrating vector enters the n equations of the vector autoregression model and β is the matrix of cointegrating parameters. The cointegrating vector gives the number of linear relations that exist among the markets, whereas the matrix of cointegrating parameters gives the speed of adjustment.

The maxλ and the traceλ values derived from the above cointegration tests were used to test the hypothesis for the rank order of the Π matrix to determine whether there is a cointegration relation between markets. The number of lags used for the tests was determined using Akaike’s Information Criterion. Results on theβ were not taken at face value and are therefore not reported.

Correlation and cointegration tests were also run in order to examine the price transmission mechanism of international prices into the domestic markets.

To further analyze the existence of a relation between domestic and international prices, in the absence of cointegration relations - which implies long-term Granger causality - we looked at the short-run Granger causality between prices. The hypothesis that one price Granger-causes another and vice versa can be assessed within a Vector Autoregression (VAR) framework, by testing the null hypothesis that the coefficients of the lagged values of one price are jointly equal to zero in the equation of the other price. The VAR that has been used is the following:

01

k

t i t i ti

P a a P ε−=

= + +∑ (7)

where the same vector notation used above stands. In our case, where we consider pairwise price relations, tP and 0a are 2 1× vectors, the first encompassing the domestic and the international price and ia being a 2 2× vector of coefficients.

In addition to cointegration tests we ran simple regressions of the average Tanzanian WM prices on lagged values of international prices, and domestic production:

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0 01 1

*l

D i I mark D ji j

p p D q T Mδ δ θ θ υ= =

= + + + + +∑ ∑ (8)

where Dp and Dq stand for average domestic price and annual quantity produced respectively, Ip is the reference international price (US YM gulf prices, CBOT YM futures prices, SAFEX WM and YM spot and futures prices), markD is a dummy for the main marketing months of the year, i.e. June, July and August, T represents a time trend and the variable jM is a monthly dummy variable.

The above regression gave some indication of the time lapse before international prices are felt domestically and the relation of domestic prices to production, allowing for seasonal effects.

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Finally, we sought to examine the relation of annual net imports of maize to domestic production of staple foods and lagged international prices.

2

, 11

t Dt i it I t ti

m q x p uα β γ δ −=

= + + + +∑ (9)

where tm are the net imports of maize (including and excluding food aid), Dtq is the annual production of maize, tix is the annual production of competitive staple foods (rice and wheat in the case of Tanzania), and 1Itp − is the lagged international price. The relation of international prices to net imports plus food aid can indicate whether increased Tanzanian food import needs can be explained in the light of expectations for international price increases, in order to sustain food security levels.

5 DOMESTIC MARKET INTEGRATION Figure 1 indicates that the average real price of white maize was relatively higher in the period before liberalization. Prices soared during the transition period, whereas once the free-market mechanism was set into place prices of white maize started to gradually decline, but with a substantial spike in year 1999/2000 when there was a poor harvest. Variations in prices after 1993/94 were primarily due to seasonal effects. Table 4 presents the regressions indicated in (1). Examining the results in Table 4, it can be seen that seasonal patterns are evident in all regions, but less so in the main consuming region, the North Coast and the main producing one, the Southern Highlands. Markets in the land-locked regions of Lake Victoria and West Zone, and those in the South Coast region, which have no easy access to excess supply markets, exhibit stronger seasonal variations in prices. It is interesting that producers in the Southern Highlands, the main producing region, do not seem to face substantial decreases in their prices during peak production periods.

The above results are further reinforced by looking at the intertemporal standard deviation and coefficient of variation of the levels of regional price series (Table 5). The coefficient of variation is highest in the isolated South Coast followed by Lake Victoria and the West Zone, while lower in the Southern Highlands and Central regions. It thus appears that the Southern Highlands and the Central zones are better able to smooth variations in excess demand of maize.

The simple regressions of real market prices on trend and seasonal variables shown in Table 4 indicate a downward overall trend in white maize prices.

Using all 27 selected markets, we constructed the intra-market coefficient of variation and regressed it against a number of determinants, as indicated in the previous section, in order to examine whether there is a pattern of spatial dispersion that varies over time. In Table 6 we report the regression results of the coefficient of variation of prices in levels and in logs, using the 3-6 months deposit rate of the IMF-IFS database.

We ran all the regressions in levels and in logs for the whole period and for the period before and after 1994. We chose 1994 as a cut-off point, because after 1993 there was a real appreciation of the national exchange rate. In addition, the availability of data on interest rates in the IFS-IMF database was interrupted for the year 1993. In the regressions encompassing the whole period, we included a dummy for the period after January 1994 multiplied by each of the exogenous variables. These new variables served to examine whether there was a change in the influence of these factors on the intra-market coefficient of variation after the appreciation of the exchange rate. Given that these all turned out to be insignificant, they are not shown in the reported results.

The intra-market coefficient of variation was not substantially affected either by the exogenous variables introduced in the regression, or by the seasonal variables. However, both regressions in levels and in logs showed a strong non-linear trend in the variation of prices across the country, indicating in all significant cases, that the intramarket coefficient of variation increased over time, also after 1993. During the transition period before and after liberalization took place, the coefficient of variation was relatively constant throughout the period, declining in the first years after liberalization. However, once the market mechanisms were set in motion, a higher variation of prices throughout the country was observed.

9

The coefficient of the real exchange rate is significant only in the regressions in levels, but negative, although small - contrary to what the marketing margins theory predicts. On the other hand, when running the regression in logs, we obtained a significant, small but negative coefficient for the real interest rate, again contrary to what theory predicts. The negative but small coefficients of these variables might suggest low intra-regional trade in maize, which implies that marketing margins are not very important for explaining intra-market price variations. The coefficient of the proxy of the foreign exchange risk variable is zero and not significant. The low coefficient of the real exchange rate and the zero coefficient of the foreign exchange risk variable may also be due to the limited trade across regions.

Turning to time series analysis, Table 7 indicates pairwise correlations between regional prices. Correlation coefficients between any pair of regional indices apart from the South Coast range from 0.63 to nearly 0.9, whereas the South Coast markets show lower correlation with all other markets. This is consistent with the earlier observation that the South Coast is more isolated and exhibits a higher overall coefficient of price variation.

For a more elaborate examination of the relation between regional prices, we ran cointegration tests as outlined in the previous section. All the regional price indices series were integrated by order one (Table 8), which allowed us to examine the existence of cointegration relationships between all pairs of markets. All cointegration tests were run with and without a trend and with the lags suggested by the Akaike Information Criterion. Results are reported in Table 9 and suggest that there is a cointegration relation between all pairs of regional markets. To save space, the coefficients of the cointegration relation and the speed of adjustment are not reported, but it appears that prices are in most cases transmitted within a month, corresponding to the time unit of our dataset.

6 INTERNATIONAL PRICE TRANSMISSION The graphs in Figure 4, showing the average Tanzanian WM price index against various international prices, do not reveal a common pattern. The domestic price is subject to considerable fluctuations, while international price variations are smoother throughout the period under consideration. There seems to be more visual relation with the SAFEX white and yellow maize prices (computed as the monthly average of the daily prices of the nearest futures). Given that SAFEX introduced agricultural commodity financial products in 1996, results are less generalizable. After 1999 there would seem to be a somewhat common pattern of prices, but data is too short and this common trend might be due to seasonality patterns. SAFEX spot prices were not utilized or plotted since they are available for only three years.

Table 10 exhibits pairwise correlation coefficients of selected domestic regional price indices and the average price index with all available international prices, There seems to be a consistently higher correlation of domestic prices with the US Gulf and the CBOT prices, whereas the Southern Highlands regional price index (the main national supplier market of maize) exhibits a higher correlation with the SAFEX futures prices. Negative correlations of domestic prices with the SAFEX spot prices are very small and should be attributed to the short timespan of the SAFEX data. All correlation coefficients appear to be small, with none exceeding 0.4, suggesting that there might not be much transmission.

Table 11 reports regressions of the national price index against lagged international prices and the dummy of the main marketing months (June, July and August) multiplied by annual production. The results show that SAFEX futures and spot prices affect domestic Tanzania prices after a month has passed and then the effect fades away. US Gulf and CBOT prices on the other hand will affect domestic prices after two months have elapsed, whereas after 1993 this effect is non-significant for US Gulf prices. The negative trend suggests that domestic prices have fallen over time. Seasonal effects suggest that prices tend to fall in September and October, when the peak marketing period is over. These results suggest transmission from international to domestic maize prices.

A more robust analysis was given by the cointegration tests. We first established that all domestic and international price series (all expressed in US$/tonne) are integrated by the same order. Tables 12 and 13 indicate the relevant tests that show that all relevant series are I(1). Next we ran a series of pairwise cointegration tests of all domestic price indices and international prices examined. Cointegration tests were run with and without trend, isolating monthly spikes and accounting for seasonality. The lags used in the cointegration tests were chosen according to the Akaike Information Criterion. Table 14

10

summarizes the results for the two major domestic regional price indices and the average national price index. There seems to be no evidence of the existence of a cointegration relation between any domestic and any international price. The exception is the existence of a cointegrating relation between the SAFEX white maize spot prices and all domestic prices. However, as these results are based on observations from only three years, they should be interpreted with caution.

Table 15 reports the results of tests for short-run Granger causality among domestic and international prices. The results reinforce the conclusion that international maize prices are not transmitted domestically, with the exception of SAFEX spot prices. Given that the imported volume of maize is low relative to the volume that is domestically traded, the results of low transmission as well as non-causality should perhaps not come as a surprise.

7 HEDGING STRATEGIES AND RESULTS The final type of analysis involves hedging strategies for Tanzania imports. The previous analysis demonstrated that domestic markets are not much affected by international markets, and this may be the result of the fact that imports, even including food aid, have been a small share of total Tanzanian maize consumption, and hence have little affect on markets. As can be seen from Table 2, total maize imports have never exceeded 10 percent of domestic production.

To obtain an idea of how Tanzania trade in maize is related to domestic production, we regressed maize net imports with and without food aid on domestic staple food production and lagged values of the US Gulf and the CBOT prices4 (Tables 16 and 17). We ran the regression successively using the US Gulf yellow maize spot price and the CBOT yellow maize near futures prices (SAFEX annual data was not sufficient). We ran the regressions first with the current world prices and then with the first lagged values of the world prices. We ran these regressions using net imports as the dependent variable (commercial imports – exports) and repeated them using net imports plus maize food aid as the dependent variable. Since we do not know whether maize substitutes for wheat and rice in consumer preferences, this regression can also serve to examine this substitutability.

We found that in only one case was the coefficient of domestic production significant and with the correct sign (negative) at the 10 percent level in the regression which does not include food aid. Informal imports might partly account for this result. The coefficients for the other staple foods were also insignificant, suggesting the absence of substitutability among staple foods, although this might not be the case given the low and very variable volume of maize imports. The coefficient of the lagged value of world prices was significant in both regressions, though marginally more significant when taking food aid into account.

Despite the low volume of maize imports compared to domestic consumption levels, foreign exchange constraints and the gradual reduction of food aid supplies provide scope for hedging against the import bill risk. We will therefore now examine the importance of designing appropriate hedging strategies in order to reduce the risk of international price fluctuations. Our analysis will try to simulate past imports in the presence of hedging rules.

We assume that large import traders in Tanzania need to plan imports of maize. Since there is no clear evidence as to whether there is any import substitution between various types of staple crops imported, we will confine ourselves to actual maize imports realized in the past, assuming that hedging practices would not have affected short-term import quantities of maize. We also assume that import traders know several months in advance the exact amounts of maize to be imported in every subsequent month. This is not unrealistic given that domestic production can be anticipated several months before harvesting, although the exact amounts of imports needed will only be known at the time of ordering (and after food aid deliveries have been decided upon).

Tanzania has sourced on average 28 percent of its maize imports over the period 1995-2003 from South Africa, 40 percent from the US and the rest from other countries. Given, however, that we have found that the SAFEX prices are the ones most likely affecting or correlated with Tanzanian domestic maize prices it seems best to utilize SAFEX for any hedging strategies. 4 We have not run the regressions for SAFEX prices, given that annual figures for SAFEX are only available for 7 years.

11

To describe the hedging rules that we simulate, assume that the import trader knows at month t the volumes of maize mt+k+1 that have to be imported (actually delivered) at month t+k+1, and therefore they have to be ordered at time t+k5. At period t the international price is tp whereas the price the trader will have to pay when actually ordering the imports will be t kp + . The agent can choose to hedge with futures6 contracts or call options contracts7, both risk management instruments. Let t k

tfε+ + be the

price at time t for the contract expiring at t k ε+ + , where ε indicates the period from t+k to the closest fixed futures contract after t k+ 8. The price of the same contract at time t k+ is t k

t kf ε+ ++ . In the

case that the contract expires this same month, this is equivalent to t kt kf ε

ε+ ++ + . The call option contract is

written on the same futures contract expiring soonest after period t k+ and stipulates that if t k t k

t k tf Xε ε+ + + ++ > , where t k

tX ε+ + is the strike price at the time of the purchase of the option, then the owner of the call option will choose to exercise it (buy the asset at the lower strike price and sell the futures at the highest price) and make profit equal to t k t k

t t k tf Xε επ + + + ++= − . Otherwise, he will not

exercise it and he will make 0 profit. In either case, however, he will have incurred the cost of the price of the call t k

trε+ + at time t . ty and tz are the amount of futures and option contracts purchased

respectively.

We shall postulate that the agent minimizes the variance (risk) of his foreign exchange bill, expressed as follows:

( ) ( )t k t k t kt k t k t k t t t k t tM p m f f y r zε ε επ+ + + + + ++ + + += × − − − − (10)

The agent will have to decide on the hedging ratio that he will use, i.e. what ty and tz will be as a share of the import volume.

Theory (see Sarris, Conforti and Prakash (2005), for the theory and relevant references) suggests that the optimal futures hedge ratio is β, the regression coefficient in the linear relation between spot and futures price, namely p=α+ βf+u, where p and f are the spot and futures prices respectively at time t, and u is a zero mean serially uncorrelated disturbance term and represents the part of the spot price that cannot be fully predicted by observing movements in futures prices (the basis risk). 9 The assumptions compatible with this rule are that that import volumes are known with certainty at the time of hedging, the current futures price is unbiased, i.e. the currently observed futures price at t is the conditional expected value of the futures price at t k+ and options are fairly priced, i.e. the call option price tr is the expected value of profit t kπ + .

Using quite involved mathematics one can derive the optimum hedging strategy using both futures and call options, which are rather difficult to implement. Here, in the absence of actual data on call option

5 Since it normally takes a month from the ordering of imports to actual delivery. 6 Futures contracts are legally binding commitments to purchase or sell a specified asset of a given quality at a specified price on a specified date. If a country foresees that it will need to make imports in the future to sustain its domestic consumption of agricultural commodities, it can purchase futures contracts for importing a specific commodity, e.g. a main staple crop at a specified price. If the price of this commodity goes up then the price of the futures contract will also go up so it can sell the futures contracts at a higher price and offset the extra cost of importing the commodity. Conversely, if prices fall, then the lower costs of importing will help to offset the cost incurred by selling the futures contracts at a lower price. 7 A call option is the right to purchase a certain asset at a preset price (the strike price). This may be exercised on (European type options) or before (American type options) the specified date. Hedging works in the same manner as in the case of futures contracts. 8 In the SAFEX stock exchange, which will be used as a source for our hedging simulations there are 5 standard agricultural commodity futures contracts for March, May, July, September and December. 9 If the price of an asset goes up then the price of the futures contract on this is also expected to go up. The possibility that spot and futures prices will not move together is called basis risk. This may occur as a result of exchange rate fluctuations or basis fluctuations.

12

prices from the SAFEX stock exchange from which we draw data, we will run simulations of hedging with futures contracts only or with call option contracts only. In both cases we used a hedging ratio equal to one (β=1), which turns out to be the proper hedging ratio under the above assumptions.

For our hedging simulations we used the monthly maize import volumes of Tanzania from FAO data. Where monthly data was missing, we assumed that the pattern of imports, namely the shares of total annual imports in each month was the same as the average pattern of monthly imports in the years for which we have monthly observations. Import orders for maize are placed one month in advance of actual delivery. This implies that the prices at which maize imports are valued and eventually paid are prices for one month ahead of the actual physical arrival at the border. Our actual futures data was obtained from the SAFEX website and is daily from 1996 for the prices of the 5 standard futures contracts (March, May, July, September, December). South African spot prices were reported in the SAFEX website from 1996 to 1999. Since March 1999, SAFEX has introduced a futures contract that expires in the following month, as well as the standard 5 futures contracts. Since our simulations use data from 1999 onwards10, this quoted price was used as the equivalent current international spot price. In addition, we assumed that contracts were bought in the middle of the current month, so we used quoted prices on the 15th of each month or the closest trading day.

Call options prices were calculated for different strike prices using the Black and Scholes formula, assuming that the option was exercised at the expiration date or not at all11 . Agents hedge for k months ahead and simulations were run for k=4, 6 and 1012 months ahead. A hedge ratio of 1 was assumed in all simulations. In hedging with call options we calculated the strike price as (1 ) fα+ , with f being the equivalent futures price and α taking the values 0.05, 0.10, 0.15 and 0.20 in subsequent simulations.

Given that the objective of hedging is to reduce the conditional variance of the import bills, we computed the change in the value of imports with and without hedging. Assuming the cash market to be efficient, the unanticipated change at time t in the cost or value of imports between period t and period t k+ is:

( )t k t t kp p m+ +− (11)

To examine if hedging can reduce the variance of the above import bill, we simulate the following hedging rules:

Rule 1

The agent decides to fully hedge with futures. He thus buys futures expiring at t k ε+ + months, in month t . It is assumed that the agent can buy futures contracts for the exact amount of the product that he will hedge. This is not unrealistic given that it is possible to get futures for whatever amount the agent wishes at a low fee through brokers, even though SAFEX contracts are for 1 000 tonnes. In our case, assuming a hedge ratio of 1, this amount is the imports that have to be ordered in time t k+ so that they are delivered in the following month 1t k+ + . It is assumed that the cost of trading futures ( )fτ is 0.15$ per tonne. Given that futures contracts are bought and then sold this cost is incurred twice when hedging.

10 We ran all the simulations from March 1999, since from that date onwards we had the reported volatility of option prices and could thus estimate call option prices using the Black and Scholes formula. For simulation results with futures and options to be comparable, we ran the simulations for the same time period. 11 The Black and Scholes Formula is used for European type options. It cannot be used to accurately price options of the American type as it calculates the option price at only one point in time – at expiration. SAFEX options are of the American type and can therefore be exercised at any time. Under the assumption that a call option is on an asset that does not have any payouts, the American call price is the same as the European one at the expiration date. However for the months for which no futures contract expires, we consider the price of the call option at expiration date. In any case, given the absence of actual call option prices, the results should be interpreted as indicative. 12 In the case of 10 months we had futures contract prices but did not always have reported volatility and could therefore not calculate call option prices.

13

If all imports (hedge ratio equal to 1) are hedged with futures, the actual change in the import cost will be:

[( ) ( )]t k t kt k t t k t f t kp p f f mε ε τ+ + + ++ + +− − − − (12)

Rule 2

The agent decides to fully hedge with call options. He follows the same hedging strategy as with futures, only this time he buys call options at various strike prices. Depending on the price of the underlying futures contract he will make positive or zero profit. He nevertheless has to incur the transaction cost for buying a call ( )οτ , which is 4.5 percent of the calculated call option price and is calculated as 0.045 tr× .

If all imports are hedged only with call options, the actual change in the import cost will be:

[( ) ( )]t k t kt k t t k t t t kp p r r mε ε

οπ τ+ + + ++ + +− − − − (13)

To examine if the variation of the import bill is reduced with hedging, we compare the normalized variances of expressions (11), (12) and (13). The normalization is obtained by dividing each of the standard deviations of the above expressions by the average unhedged import bill (average t tp m ) over the whole period of the simulation13.

Results are reported in Table 18. It is apparent that hedging with futures is the best strategy to follow assuming that the alternative is to buy all maize imports in cash from South Africa. On the other hand, the results for hedging with call options indicate that hedging with a short time horizon is preferable to hedging with a longer one. Hedging with options should be treated with caution, given that we have constructed the price of the call option using the Black and Scholes formula which does not account for the complexity of the American type call options. In any case given that the cost of the call option is small relative to the costs of the transaction, accounting for the real price of the options might not reverse the results.

8 CONCLUDING REMARKS The analysis reported here has highlighted the following points. First domestic maize markets in Tanzania seem to be well integrated with the main producing and consuming centres, albeit some of the more remote markets are less so. Nevertheless spatial price dispersion seems to have increased in recent years, and this could be the result of increases in marketing costs that we have not been able to capture. For instance a decrease in the quality of roads would automatically increase the cost of transport, widening spatial price differences.

The second main point is that the domestic maize markets in Tanzania do not seem to be affected much by the major international maize markets. However, they appear to be affected by the South African market, despite the fact that the bulk of maize imports are sourced from the United States. This could be due to the fact that, as South Africa seems to be the main maize producer in Southern Africa, SAFEX may be the main organized market for the region, and its prices reflect the maize market conditions in all countries in southern Africa. Given that there are reports of considerable informal and unrecorded trade between Tanzania and its neighbours, the markets in southern Africa could well be much more tied to the Tanzanian internal markets.

Thirdly it appears that there is considerable scope for hedging Tanzanian maize import risks in SAFEX. The hedging is especially effective when done with futures, rather than with call options. However, this result is conditioned by the particular assumptions on pricing of the options imposed by the lack of data, as well as by the inability to apply more complicated compound hedging strategies that would utilize both futures and options. Nevertheless, it appears that there is now a well organized exchange where traders in southern African countries could hedge their import cost risks.

13 Given that we missed some observations when hedging for 10k = , we have averaged across the months for which we have data for all hedging strategies, in order to obtain comparable results.

14

REFERENCES Buccola, S.T. 1989. Pricing Efficiency in Agricultural Markets: Issues, Methods and Results. Western Journal of Agricultural Economics, 14: 111-121.

Dana, J., Gilbert, C.L. & Shim, E. 2005. Hedging Grain Price Risks in the SADC: Case Studies of Malawi and Zambia. Unpublished report World Bank.

Delgado, C. Minot, N. & Tiongco, M. 2004. Evidence and Implications of non-tradability of food staples in Tanzania 1983-1998. MTID Discussion Paper No. 72. International Food Policy Research Institute: 36 pp.

Enke, S. 1951. Equilibrium among spatially separated markets: solution by electrical analogue”, Econometrica, 19: 40-47.

Fackler, P.L. & Goodwin, B.K. 2001. Spatial Price Analysis, in B. L. Gardner and G. C. Rausser (eds.) Handbook of Agricultural Economics: vol. 1B: Marketing Distribution and Consumers. Amsterdam: Elsevier.

Faruqee, R., Coleman, J. R. & Scott, T. 1997. Managing price risk in the Pakistan wheat market. The World Bank Economic Review, 11(2): 263-292.

Goodwin, B.K. & Schroeder, T.C. 1991. Cointegration tests and spatial market linkages in regional cattle markets. American Journal of Agricultural Economics, 73: 452-464.

Gürkan, A.A, Balcombe, K. & Prakash, A. 2003. Food import bills: Experiences, factors underpinning changes, and policy implications for food security of least developed food-importing developing countries. Commodity Market Review 2003-2004, FAO, Rome.

Kherallah, M., Delgado, C., Gabre-Madhin, N., Minot, E. & Johnson, M. 2002. Reforming Agricultural Markets in Africa. Baltimore: The Johns Hopkins University Press.

Larson, D.F. & Coleman, J. R. 1993. The effects of option hedging on the costs of domestic price stabilization schemes. In S. Claessens and R. C. Duncan (eds.) Managing Commodity Price Risk in Developing Countries, Baltimore: The John Hopkins University Press.

Ravallion, M. 1986. Testing market integration. American Journal of Agricultural Economics, 68: 102-109.

Samuelson, P.A. 1952. Spatial price equilibrium and linear programming. American Economic Review, 42: 560-580.

Sarris, A., Conforti, P. & Prakash, A. 2005. The Use of Organized Commodity Markets to Manage Food Import Price Instability and Risk. Unpublished paper, FAO.

Schroeter, J. & Azzam, A. 1991. Marketing margins, market power and price uncertainty. American Journal of Agricultural Economics, 73(5): 990-999.

Seshamani, V. 1998. The Impact of Market Liberalization on Food Security in Zambia. Food Policy, 23: 539-551.

Stiglitz, J.E. 2002. Globalization and It’s Discontent. New York and London: W.W. Norton & Company Inc.

Takayama, T. & Judge, G.G. 1971. Spatial and Temporal Price Allocation Models. Amsterdam: North Holland.

United Republic of Tanzania, World Bank & IFPRI. 2000. Agriculture in Tanzania since 1986: Follower or Leader of Growth. Washington, D.C.: World Bank.

15

FIGURES Figure 1. Average Tanzanian white maize (WM) real price index from 1985 onwards. (TZ

shillings/Mt)

1020

3040

50Ta

nzan

ia P

rice

Inde

x

1984m11986m11988m11990m11992m11994m11996m11998m12000m12002m1Time

Note: In these figures Mt = tonne(s) Source. FAO

Figure 2. Tanzania, maize commercial imports and food aid (in Mt left scale) and maize production (‘000 Mt right hand scale)

500

1000

1500

2000

2500

3000

Mai

ze p

rodu

ctio

n (1

000

Mt)

050

000

1000

0015

0000

2000

0025

0000

1970 1980 1990 2000 2010Year...

Maize Commercial Imports (Mt) Maize Food Aid (Mt)

Maize production (1000 Mt)

Source. FAO

16

Figure 3. Tanzania: Maize net commercial imports (imports-exports) and food aid (Mt left scale, and maize production (‘000 Mt right hand scale)

500

1000

1500

2000

2500

3000

Mai

ze p

rodu

ctio

n (1

000

Mt)

-100

000

010

0000

2000

0030

0000

1970 1980 1990 2000 2010Year...

Maize Net Commercial Imports (Mt) Maize Food Aid (Mt)

Maize production (1000 Mt)

Source. FAO

Figure 4. Average Tanzanian white maize (WM) price , SAFEX WM near futures price, US Gulf yellow maize (YM) spot price and maize import unit value from 1972 (US$/Mt)

020

040

060

080

0

1970 1980 1990 2000 2010Year

Tanzania Average WM Price ($/Mt) SAFEX WM Futures Prices ($/Mt)

US Gulf YM Spot Prices ($/Mt) Maize Import Unit Values ($/Mt)

Source. Authors’ calculations

17

Figure 5. Regional price indices for WM from 1985 onwards. (TZ Shillings/Mt)

020

4060

80

1984m11986m11988m11990m11992m11994m11996m11998m12000m12002m1newt

Price Index of North Zone Price Index of North Coast

Price Index of Lake Victoria Price Index of West Zone

Price Index of Central Zone Price Index of Southern Highlands

Price Index of South Coast

Source. Authors’ calculations

Figure 6. Intra-Market Coefficient of Variation of domestic WM prices in levels

0.5

11.

5In

tra-m

arke

t coe

ffici

ent o

f var

iatio

n of

pric

es

1988m1 1990m1 1992m1 1994m1 1996m1 1998m1 2000m1 2002m1Trend

Source. Authors’ calculations

18

Figure 7. Intra-Market Coefficient of Variation of WM prices in logs

.05

.1.1

5.2

.25

Intra

-mar

ket c

oeffi

cien

t of v

aria

tion

of lo

g pr

ices

1988m1 1990m1 1992m1 1994m1 1996m1 1998m1 2000m1 2002m1Trend

Source. Authors’ calculations

19

TABLES Table 1. Area and production of maize in Tanzania in 2000/2001 by region

Region

Area ‘000 Ha

Production ‘000 tonnes

Northern Zone Arusha 147.9 177.5 Kilimanjaro 99.5 159.2 Northern Coast Coast/DSM 67.6 40.6 Morogoro 81.4 162.9 Tanga 128.7 154.5 Lake Victoria Kagera 45.1 103.6 Mara 47.5 95.0 Mwanza 84.8 152.7 Shinyanga 134.0 201.0 Western Zone Kigoma 56.3 129.4 Central Zone Dodoma 59.1 94.6 Singida 56.1 61.7 Tabora 67.2 121.0 Southern Highlands Mbeya 101.8 234.1 Iringa 121.3 315.5 Rukwa 97.6 224.5 Ruvuma 90.3 162.5 Southern Coast Lindi 60.5 72.6 Mtwara 25.5 30.6 Total 1 581.5 2 693.4

Source. Tanzania. Crop Monitoring and Early Warning Unit, Ministry of Agriculture and Food Security.

Table 2. Maize import volumes and food aid (tonnes)

Years Food Aid Commercial Imports

Total Imports Exports Production

1986 34 731 0 34 731 0 2 211 000 1987 0 31 000 31 000 90 000 2 359 000 1988 9 000 0 9 000 18 711 2 339 000 1989 12 68 80 30 347 3 128 000 1990 2 000 208 2 208 57 039 2 445 000 1991 0 1 651 1 651 7 000 2 331 800 1992 370 43 630 44 000 4 141 2 226 424 1993 7 453 41 547 49 000 9 637 2 282 200 1994 56 641 136 359 193 000 0 2 158 800 1995 43 917 0 43 917 0 2 874 400 1996 0 50 575 50 575 0 2 648 200 1997 10 444 2 545 12 989 16 185 1 831 200 1998 43 219 226 396 269 615 20 2 684 600 1999 13 317 22 268 35 585 15 808 2 451 700 2000 751 48 702 49 453 16 871 2 551 160 2001 45 878 0 45 878 26 386 2 698 000 2002 16 334 47 039 63 373 152 310 2 704 849 2003 45 280 32 711 77 991 156 192 2 430 000

Source. FAO

20

Table 3. Maize markets in Tanzania that are included in the analysis, and markets with few data, according to degree of isolation

Close to rail line or major road Isolated Market name Utilized in the analysis Market name Utilized in the analysis Northern Zone Arusha Yes Mbulu No Moshi Yes Gonja (Same) Yes Northern Coast Dar-es-Salaam Yes Mafia No Bagamoyo No Kisarawe No Morogoro Yes Tanga Yes Lushoto Yes

Lake Victoria Mwanza Yes Bukoba No Magu Yes Geita Yes Kwimba Yes Ukerewe No Musoma No Sengerema Yes Tarime No Maswa Yes Shinyanga No Kahama No

Western Zone Kigoma Yes Kasulu No Kibondo Yes Mpanda No

Central Zone Mpwapwa Yes Singida Yes Dodoma Yes Tabora Yes Urambo Yes

South Highlands Mbeya Yes Sumbawanga Yes Iringa Yes Njombe Yes Mafinga Yes Songea No Mbinga No Tonduru No

South Coast Mtwara No Lindi Yes Newala No Masasi Yes

Source. Adapted from Table 3.3 of United Republic of Tanzania, World Bank & IFPRI (2000), pp. 28-29.

21

Table 4. Regression of regional domestic median white maize (WM) prices on monthly dummies and time trend

Dependent variable

Log of median price of North Coast Markets

Log of median price

of Lake Victoria Markets

Log of median price of West Zone Markets

Log of median prices of Southern Highlands Markets

Log of median price of South Coast

Markets

Trend -0.005*** -0.005*** -0.005*** -0.003*** -0.006*** [15.676] [14.027] [18.661] [11.426] [13.567] January 0.142 0.000 0.047 0.082 0.173 [1.344] [0.004] [0.479] [0.820] [1.076] February 0.170 0.050 0.080 0.152 0.118 [1.615] [0.383] [0.815] [1.515] [0.735] March 0.167 -0.060 0.063 0.139 0.075 [1.598] [0.463] [0.641] [1.402] [0.471] April 0.198* -0.230* -0.103 0.152 -0.036 [1.898] [1.775] [1.061] [1.538] [0.228] May 0.189* -0.329** -0.343*** 0.146 -0.147 [1.810] [2.535] [3.529] [1.476] [0.923] June 0.127 -0.392*** -0.412*** 0.075 -0.352** [1.217] [3.021] [4.244] [0.758] [2.212] July 0.097 -0.297** -0.350*** -0.009 -0.277* [0.930] [2.287] [3.607] [0.096] [1.743] August -0.087 -0.185 -0.336*** -0.025 -0.368** [0.834] [1.428] [3.461] [0.251] [2.310] September -0.298*** -0.296** -0.396*** -0.183* -0.314** [2.860] [2.276] [4.081] [1.849] [1.972] October -0.135 -0.143 -0.162 -0.073 -0.245 [1.274] [1.088] [1.645] [0.732] [1.519] November -0.054 -0.102 -0.075 -0.039 -0.110 [0.509] [0.773] [0.758] [0.394] [0.685] Constant 4.036*** 4.254*** 4.187*** 3.556*** 4.420*** [48.298] [40.882] [53.711] [44.868] [34.641] Observations 235 235 234 235 235 R-squared 0.574 0.503 0.663 0.416 0.494

Source. Computed by authors

* Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level

22

Table 5. Intertemporal standard deviation and coefficient of variation of regional price indices and the Tanzania average price (prices in levels)

North Zone Standard Deviation Mean Coefficient of Variation

0.4367321 3.443943 0.1268117North Coast

Standard Deviation Mean Coefficient of Variation0.4855979 3.490735 0.1391105

Lake Victoria Standard Deviation Mean Coefficient of Variation

0.5600032 3.430321 0.1632509West Zone

Standard Deviation Mean Coefficient of Variation0.508418 3.357156 0.1514431

Centre Zone Standard Deviation Mean Coefficient of Variation

0.4319736 3.284731 0.1315096South Highlands

Standard Deviation Mean Coefficient of Variation0.3932097 3.183379 0.1235196

South Coast Standard Deviation Mean Coefficient of Variation

0.6802624 3.516311 0.1934591Average Tanzania Price

Standard Deviation Mean Coefficient of Variation0.4379623 3.432998 0.1275743

Source. Authors’ calculations

23

Table 6. Regression of the intra-market coefficient of variation prices using Central Bank’s 3-6 Months Deposit Rate

In levels In logs Prices in levels

from 1983 Prices in levels from

September 1994 Prices in logs

from 1983 Prices in logs

from September 1994

Trend 0.069*** -0.008 0.084** 0.002** [3.721] [0.521] [2.058] [2.045] Trend^2 -0.001*** -0.001** [3.994] [2.066] Trend^3 0.000*** 0.000** [4.243] [2.095] Real Exchange Rate -0.001*** 0.002 -0.799 -0.040 [2.951] [1.240] [1.625] [0.159] Real Interest Rate 0.001 -0.008 -0.009** -0.019*** [0.726] [1.010] [2.685] [4.894] Foreign Exchange Risk 0.000 0.000 0.081 0.177 [0.096] [1.421] [0.877] [0.608] January 0.005 0.053 0.000 0.000 [0.081] [0.527] [.] [.] February -0.010 0.056 0.000 -0.018 [0.147] [0.494] [.] [0.087] March -0.020 0.280 0.026 0.000 [0.299] [1.389] [0.131] [.] April 0.012 0.369 0.168 0.199 [0.171] [1.260] [0.912] [1.507] May 0.073 0.469 0.149 0.039 [1.046] [1.407] [0.806] [0.285] June -0.007 0.423 0.193 0.203 [0.097] [1.475] [1.061] [1.533] July 0.081 0.366* 0.226 0.159 [1.139] [1.911] [1.196] [1.185] August 0.022 0.368* 0.159 0.074 [0.309] [2.027] [0.860] [0.589] September 0.097 0.308 0.304 0.157 [1.425] [1.449] [1.619] [1.217] October -0.003 0.365 0.000 0.000 [0.049] [1.327] [.] [.] November -0.010 0.308 0.000 0.000 [0.150] [1.394] [.] [.] Constant -1.798*** -0.837* 1.841 -0.165

[2.922] [1.981] [0.892] [0.098] Observations 138 24 54 47 R-squared 0.358 0.849 0.498 0.533

Source. Authors’ calculations.

* Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level

24

Table7. Simple correlation coefficients between the regional price indices (prices in levels) North Zone North Coast Lake Victoria West Zone Centre

Zone South

Highlands North Zone North Coast 0.898 Lake Victoria 0.735 0.706 West Zone 0.630 0.675 0.849 Central Zone 0.782 0.822 0.804 0.795 South Highlands 0.874 0.892 0.715 0.677 0.865 South Coast 0.469 0.606 0.512 0.537 0.654 0.575

Source. Authors’ calculations.

Table 8. Unit root tests of the regional price indices (prices in logs) in Tanzania shillings

Order of Integration With a drift With a trend First DifferenceNorth Zone I(1) with a drift ADF test -2.753 -4.269 -13.147Phillips Perron test Zρ -13.087 -25.912 Phillips Perron test Zt -2.455 -3.75 North Coast I(1) with a drift and a trend ADF test -0.769 -2.49 -10.038Phillips Perron test Zρ -5.672 -33.846 Phillips Perron test Zt -0.633 -2.597 Lake Victoria I(1) with a drift and a trend ADF test -0.315 -1.991 -9.662Phillips Perron test Zρ -2.268 -19.735 Phillips Perron test Zt -0.302 -1.922 West Zone I(1) with a drift and a trend ADF test -0.938 -3.3 -10.909Phillips Perron test Zρ -8.373 -41.735 Phillips Perron test Zt -0.987 -3.206 Centre Zone I(1) with a drift ADF test -2.656 -4.576 -15.844Phillips Perron test Zρ -16.164 -37.188 Phillips Perron test Zt -2.69 -4.421 South Highlands I(1) with a drift ADF test -1.745 -3.68 -9.23Phillips Perron test Zρ -15.075 -37.421 Phillips Perron test Zt -1.603 -3.161 South Coast I(1) with a drift ADF test -1.748 -3.617 -21.357Phillips Perron test Zρ -42.486 -109.66 Phillips Perron test Zt -4.123 -7.565 Critical values for the ADF and Phillips Perron tests

With a drift 1 percent Critical value 5 percent Critical Value 10 percent Critical Value ADF test -3.466 -2.881 -2.571Phillips Perron test Zρ -20.247 -13.968 -11.179Phillips Perron test Zt -3.465 -2.881 -2.571 With a drift and a trend 1 percent Critical value 5 percent Critical Value 10 percent Critical Value ADF test -3.996 -3.432 -3.132Phillips Perron test Zρ -28.293 -21.236 -17.947Phillips Perron test Zt -3.995 -3.432 -3.132

Source. Authors’ calculations.

25

Table 9. Pairwise cointegration test results among the regional price indices (prices in logs) North Zone North Coast Lake Victoria West Zone Centre Zone South Highlands North Zone North Coast Yes Lake Victoria No Yes West Zone With a trend only Yes Yes Centre Zone Yes Yes Yes Yes South Highlands Yes Yes With a trend only Yes Yes South Coast Yes Yes Yes Yes Yes Yes

Source. Authors’ calculations.

Table 10. Simple correlation coefficients of selected regional median prices and the average Tanzania WM price with international prices (All prices in $/tonne)

Median North Coast Price

Median South Highlands Price

Average TZ Price

(Levels) SAFEX Futures WM Price 0.0954 0.3103 0.1150 SAFEX Futures YM Price 0.1285 0.2187 0.1207 SAFEX Spot WM Price* -0.1893 -0.1397 -0.0441 SAFEX Spot YM Price* -0.0087 -0.1294 -0.0807 US Gulf YM Spot Price 0.3268 0.3247 0.3447 CBOT Futures YM Price 0.3550 0.3547 0.3888 (Logs) SAFEX Futures WM Price 0.0122 0.2877 0.0761 SAFEX Futures YM Price 0.0296 0.2168 0.0924 SAFEX Spot WM Price* -0.2164 -0.1392 -0.0248 SAFEX Spot YM Price* -0.0296 -0.1190 -0.0456 US Gulf YM Spot Price 0.2724 0.2992 0.3215 CBOT Futures YM Price 0.2320 0.2583 0.2893

* Calculations with the SAFEX spot prices have been made based on monthly data from March 1996 to April 1999.

Source. Authors’ calculations.

26

Table 11. Regression of average Tanzanian WM price on international prices and production multiplied by a dummy for the marketing months (June, July, August) (2 lags used – all prices in levels)

SAFEX WM

Futures Prices

SAFEX YM

Futures Prices

US Gulf YM Spot Prices -

after 1983

US Gulf YM Spot Prices -

after 1993

CBOT YM Futures Prices

SAFEX WM Spot

Prices

SAFEX YM Spot

Prices

Lagged World Price (1 lag) ($/tonnes) 0.926** 1.002* 0.794* 1.353* [2.264] [1.933] [1.740] [2.058] Lagged World Price (2 lags) ($/tonnes) -1.280*** -1.696*** 2.266*** 0.173 4.665*** -1.243** -0.301 [3.030] [3.190] [4.905] [1.327] [7.341] [2.715] [0.350] Dummy on marketing month times annual production (1 000 tonnes): -0.030 -0.046* -0.089 -0.049** -0.062 -0.051** -0.067** [1.237] [1.974] [1.464] [2.403] [1.069] [2.241] [2.582] January 11.532 16.089 44.386 12.925 45.980 -2.177 -1.502 [0.658] [0.963] [0.871] [0.864] [0.955] [0.096] [0.062] February 17.929 24.143 50.548 20.134 57.368 11.952 2.225 [1.019] [1.441] [0.992] [1.346] [1.192] [0.537] [0.090] March 24.005 34.478** 47.295 18.837 49.056 16.070 -0.015 [1.360] [2.014] [0.928] [1.259] [1.019] [0.724] [0.001] April 19.986 27.500 24.142 8.727 24.804 2.391 -22.902 [1.125] [1.587] [0.473] [0.583] [0.515] [0.104] [0.819] May 19.012 22.900 12.881 -5.856 10.594 0.700 -16.294 [1.035] [1.245] [0.252] [0.391] [0.220] [0.031] [0.647] June 69.898 104.843* 179.058 96.741* 108.595 102.848* 129.450* [1.127] [1.729] [1.165] [1.836] [0.745] [1.764] [1.945] July 62.281 98.981 166.106 94.255* 97.608 86.316 129.875* [0.995] [1.626] [1.081] [1.789] [0.670] [1.472] [1.940] August 48.632 91.194 152.267 87.858* 84.597 73.230 130.161* [0.777] [1.497] [0.991] [1.668] [0.581] [1.240] [1.985] September -28.983* -30.376* -49.533 -38.212** -46.960 -49.917** -28.296 [1.686] [1.831] [0.971] [2.553] [0.975] [2.270] [1.144] October -33.422* -37.126** -40.031 -27.911* -33.610 -62.354** -39.925 [1.903] [2.213] [0.775] [1.819] [0.690] [2.732] [1.561] November -13.695 -17.147 -9.756 -13.411 -7.664 -24.593 -18.127 [0.781] [1.027] [0.189] [0.874] [0.157] [1.113] [0.758] Trend -1.236*** -1.428*** -1.760*** -0.072 -1.803*** 0.578 4.078* [6.628] [7.436] [11.073] [0.723] [12.170] [0.802] [2.003] Constant 591.557*** 693.517*** 429.810*** 143.052*** 213.085** 62.369 -1,227.337 [8.396] [8.931] [5.561] [3.551] [2.544] [0.244] [1.669] Observations 75 75 237 117 237 36 36 R-squared 0.594 0.633 0.487 0.358 0.543 0.764 0.732

Source. Authors’ calculations.

27

Table 12. Unit root tests of the regional price indices in dollars (prices in logs)

Order of Integration With a drift With a trend First DifferenceNorth Zone I(1) with a drift and a trend ADF test -2.195 -2.973 -12.649Phillips Perron test Zρ -8.217 -14.267 Phillips Perron test Zt -2.02 -2.678 North Coast I(1) with a drift and a trend ADF test -1.502 -3.064 -9.197Phillips Perron test Zρ -6.958 -23.265 Phillips Perron test Zt -1.049 -2.551 Lake Victoria I(1) with a drift and a trend ADF test -0.56 -1.986 -9.512Phillips Perron test Zρ -4.035 -16.065 Phillips Perron test Zt -0.694 -2.016 West Zone I(1) with a drift and a trend ADF test -0.983 -2.739 -10.064Phillips Perron test Zρ -7.869 -26.448 Phillips Perron test Zt -1.221 -2.838 Centre Zone I(1) with a drift ADF test -2.785 -3.74 -13.72Phillips Perron test Zρ -12.547 -23.184 Phillips Perron test Zt -2.559 -3.453 South Highlands I(1) with a drift and a trend ADF test -1.667 -3.021 -8.595Phillips Perron test Zρ -11.033 -23.806 Phillips Perron test Zt -1.67 -2.836 South Coast I(1) with a drift ADF test -2.185 -3.902 -20.418Phillips Perron test Zρ -27.445 -83.595 Phillips Perron test Zt -3.246 -6.525 Average TZ Price I(1) with a drift and a trend ADF test -1.534 -2.756 -13.708Phillips Perron test Zρ -6.122 -18.173 Phillips Perron test Zt -1.359 -2.752

Source. Authors’ calculations.

28

Table 13. Unit root tests for international prices in dollars (prices in logs)

Order of Integration With a drift With a trend First DifferenceSAFEX WM Futures Prices* I(1) with a drift and a trend ADF test -3.074 -3.063 -6.537Phillips Perron test Zρ -11.623 -11.568 Phillips Perron test Zt -2.477 -2.466 SAFEX YM Futures Prices* I(1) with a drift and a trend ADF test -2.906 -2.961 -6.473Phillips Perron test Zρ -10.692 -10.742 Phillips Perron test Zt -2.4 -2.43 SAFEX WM Spot Prices* I(1) with a drift and a trend ADF test -2.025 -2.340 -6.681Phillips Perron test Zρ -7.114 -9.296 Phillips Perron test Zt -2.318 -2.066 SAFEX YM Spot Prices* I(1) with a drift and a trend ADF test -1.771 -1.931 -7.384Phillips Perron test Zρ -4.615 -8.483 Phillips Perron test Zt -1.893 -1.690 US Gulf YM Spot Price I(1) with a drift and a trend ADF test -2.299 -2.583 -7.747Phillips Perron test Zρ -7.788 -9.569 Phillips Perron test Zt -2 -2.265 CBOT YM Futures Price I(1) with a drift and a trend ADF test -2.531 -2.693 -14.291Phillips Perron test Zρ -10.525 -11.811 Phillips Perron test Zt -2.28 -2.394

* Critical values for SAFEX prices are different due to the smaller number of observations.

Critical values for the ADF and Phillips Perron tests (relevant for domestic prices and the US Gulf and CBOT prices)

With drift 1 percent Critical Value 5 percent Critical Value 10 percent Critical Value ADF test -3.466 -2.881 -2.571Phillips Perron test Zρ -20.253 -13.972 -11.181Phillips Perron test Zt -3.465 -2.881 -2.571With drift and trend 1 percent Critical Value 5 percent Critical Value 10 percent Critical Value ADF test -3.996 -3.432 -3.132Phillips Perron test Zρ -28.307 -21.244 -17.953Phillips Perron test Zt -3.995 -3.432 -3.132Critical values for the ADF and Phillips Perron tests (relevant for SAFEX prices only)

With drift* 1 percent Critical Value 5 percent Critical Value 10 percent Critical Value ADF test -3.511 -2.891 -2.58Phillips Perron test Zρ -19.803 -13.702 -11.001Phillips Perron test Zt -3.51 -2.89 -2.58With drift and trend* 1 percent Critical Value 5 percent Critical Value 10 percent Critical Value ADF test -4.042 -3.451 -3.151Phillips Perron test Zρ -27.407 -20.704 -17.503Phillips Perron test Zt -4.04 -3.45 -3.15

Source. Authors’ calculations.

29

Table 14. Cointegration relations of domestic prices with world prices

SAFEX WM Near Futures Prices

SAFEX YM Near Futures Prices

US Gulf YM Spot Price (1993-2002)

CBOT YM Near Futures Prices (1993-2002)

SAFEX WM Spot Prices

SAFEX YM Spot Prices

North Coast No No No No Yes with an intercept and a trend in the CE*

No

South Highlands No No No No Yes with an

intercept in CE* No

TZ Average Price No No No No Yes with an

intercept in CE* No

* CE stands for the cointegration relation part of the VECM implicitly estimated when running the Johansen cointegration test. However, the results are based on the observations of 3 years (March 1996-April 1999) and are not sufficient to deduce any rigid conclusions.

Source. Authors’ calculations.

Table 15. Short-run Granger causality between the average Tanzanian WM price and international prices (all prices in $/tonnes)

H0: The lagged values of the quoted international price in the

average TZ Price equation are simultaneously zero

H0: The lagged values of the average TZ WM prices in the

quoted international price equation are simultaneously zero

chi2 0.02 chi2 3.13 SAFEX WM Futures Price Prob > chi2 0.9924 Prob > chi2 0.2086

chi2 0.94 chi2 0.23 SAFEX YM Futures Price Prob > chi2 0.6258 Prob > chi2 0.8916

chi2 6.63 chi2 1.04 SAFEX WM Spot Price Prob > chi2 0.0363 Prob > chi2 0.5953

chi2 0.58 chi2 0.82 SAFEX YM Spot Price Prob > chi2 0.7468 Prob > chi2 0.6640

chi2 0.87 chi2 3.32 Gulf YM Spot Prices Prob > chi2 0.6475 Prob > chi2 0.1898

chi2 2.35 chi2 0.57 CBOT YM Futures Prices Prob > chi2 0.3087 Prob > chi2 0.7521

Source. Authors’ calculations.

30

Table 16. Regression of maize net imports including food aid on production of staple foods and world prices (prices in levels – 1 lag used)

World price used US Gulf YM Spot Prices CBOT YM Futures Prices Maize production (1000 tonnes) -0.058 -0.084 [1.059] [1.550] Wheat production (1000 tonnes) 1.582 1.750 [1.177] [1.354] Rice production (1000 tonnes) -0.071 -0.005 [0.441] [0.033] Lagged World Prices (1 lag) ($/tonnes) 1.969** 2.901*** [2.261] [2.786] Constant -115.357 -188.170 [0.786] [1.264] Observations 31 31 R-squared 0.315 0.369

Source. Authors’ calculations.

Table 17. Regression of maize net imports excluding food aid on production of staple foods and world prices (prices in levels – 1 lag used)

World price used US Gulf YM Spot Prices CBOT YM Futures Prices Maize production (1000 tonnes) -0.081 -0.099* [1.515] [1.810] Wheat production (1000 tonnes) 1.793 1.909 [1.367 [1.471] Rice production (1000 tonnes) 0.043 0.086 [0.274] [0.539] Lagged World Prices (1 lag) ($/tonnes)

1.501* 2.059*

[1.765] [1.968] Constant -121.032 -161.239 [0.845] [1.078] Observations 31 31 R-squared 0.248 0.267

Source. Authors’ calculations.

Table 18. Unanticipated normalized standard deviations of monthly import bill changes with and without hedging with futures and call options

Months in advance of actual import orders

Without Hedging

Hedging with

futures

Hedging with call options

(a=0.05)

Hedging with call options

(a=0.10)

Hedging with call options

(a=0.15)

Hedging with call options (a=0.20)

k=4 1.025 0.139 0.859 0.913 0.960 1.004k=6 1.381 0.118 1.089 1.157 1.215 1.270k=10 1.400 0.387 N/A N/A N/A N/A

Source. Authors’ calculations.

FAO COMMODITY AND TRADE POLICY RESEARCH WORKING PAPERS

2006 16 The use of organized commodity markets to manage food import price instability and risk. Alexander Sarris, Piero Conforti and Adam Prakash 15 The impact of domestic and international commodity price volatility on agricultural income instability in Ghana, Vietnam and Peru. George Rapsomanikis and Alexander Sarris 14 Linkages between domestic and international maize markets, and market based strategies for hedging maize import price risks in Tanzania. Alexander Sarris and Ekaterini Mantzou 13 Food import risk in Malawi: simulating a hedging scheme for Malawi food imports using historical data. Wouter Zant 2005 12 The effect of direct payments of the OECD countries in world agricultural markets. Evidence from partial and general equilibrium frameworks Piero Conforti 11 The impact of import surges: country case study results for Senegal and Tanzania Ramesh Sharma, David Nyange, Guillaume Duteutre and Nancy Morgan 2004 10 Agricultural trade liberalization in the Doha round. Alternative scenarios and strategic interactions between developed and developing countries Piero Conforti and Luca Salvatici 9 The EU cotton policy regime and the implications of the proposed changes for producer welfare Giannis Karagiannis 8 The impact of domestic and trade policies on the world cotton market Daneswar Poonyth, Alexander Sarris, Ramesh Sharma and Shangnan Shui 7 Price transmission in selected agricultural markets Piero Conforti 6 The marketing potential of date palm fruits in the European market Pascal Liu 5 World markets for organic citrus and citrus juices: Current market situation and medium-term prospects Pascal Liu 4 Agricultural Policy Indicators Timothy Josling and Alberto Valdés (also issued as ESA Working Paper No. 2004/4) 2003 3 Quantifying appropriate levels of the WTO bound tariffs on basic food products in the context of the Development Box proposals Ramesh Sharma 2 The WTO and environmental and social standards, certification and labelling in agriculture. Cora Dankers 1 The Brazilian ethanol programme: impacts on world ethanol and sugar markets Tatsuji Koizumi