The main objective of this study is to investigate the nature of price relationships existing amongst geographically separated agricultural commodity markets in Malawi. Market liberalisation provides the overarching framework on which this investigation is based. The liberalisation of commodity markets is assumed to facilitate the integration of geographically separated markets, increase gains from trade and the general welfare of the cOuntry. The investigation is performed using modern cointegration techniques. The cOintegration techniques applied in this thesis are based on the concept of spatial price equilibrium (SPE) initially proposed by Samuelson in 1952 and threshold cointegration techniques. The two categories of cointegration techniques, linear and non-linear, have been ) applied to the analysis of dry beans and maize markets in Malawi. For linear cointegration techniques, the Johansens' (1988) model and the Engle-Granger (1987) model have been applied (see chapters 6 to 8); while for non-linear model techniques, two types of threshold cOintegration (TAR and M-TAR) models have been applied (see chapter 8). Both linear and non-linear models investigate different aspects of price behaviour in the dry beans and maize markets. The analysis uses monthly price averages for the January 2000 and January 2012 period. The dry beans and maize markets have been selected for analysis because of their distinct characteristics. The most prominent distinguishing characteristic between them is that dry beans markets are fully liberalised while maize markets are partially liberalised. The dry beans market fully satisfies the key assumptions of the market liberalisation concept. Therefore, the a priori expectation is that the dry beans market is perfectly integrated. The principal maize market is not expected to be perfectly integrated because it is not fully liberalised. These distinctions between the two markets provide some sort of a 'natural experiment' on which the assumption that market liberalisation facilitates market integration can be tested.

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Photocopy of typescript. / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Thesis (M.S.)--Pennsylvania State University, 1981. / Includes bibliographical references.

Absorption of iron from foods has been reported to be inhibited by various factors including some diet components. Fiber from different sources has been observed to bind ferrous iron. This may be the reason for iron deficiencies observed in populations that consume diets high in non-digestible materials. The effect of different levels of fiber from corn tortillas and cooked beans on iron availability in the rat was studied. Iron-depleted rats were fed test diets containing different levels of iron and fiber and the diets were tested for their hemoglobin repletion ability. Estimation of the neutral detergent fiber (NDF) in corn tortillas and cooked beans and the soluble and ionizable iron in the test foods and diets were also determined. Corn tortillas contained 6.53% NDF. Raw beans contained 5.80% NDF and increase to 15.75% upon cooking. In cooked beans, 60.16% of the total iron is in the insoluble form while only 20.3% of the total iron in corn tortillas is insoluble. The mean relative biological value (RBV) for the test diets was 55.0 ± 19.0% with values ranging,from 40.41 ± 12.50% to 64.34 ± 11.10% compared to that of ferrous sulfate (100% available). Fiber or iron level had no significant effect on iron availability, however, a ratio of 1.78 of corn tortilla to cooked beans showed the highest degree of iron availability. Percent efficiency of the test diets to incorporate iron into hemoglobin ranged from 11.70 ± 1.4% for a diet containing 25 ppm iron and 15% NDF to 25.61 ± 6.10% for 35 ppm iron, 10% NDF diet. The control diet containing 30 ppm ferrous sulfate had a 43.0 ± 8.7% efficiency. Percent efficiency seems to show less variability in the determination of iron availability from foods than RBV. A significant correlation (p < 0.01) between soluble iron at pH 1.35 in vitro and percent efficiency was observed. This method could be used to predict iron availability for different foods. / Graduation date: 1982

Market information services established in 1999 were aimed at the promotion of market efficiency through provision of information across the nation. While the responsible bodies have improved the knowledge of prices, information exchange and flow, as a result of competition between markets, is not known and questions of market effectiveness still stand. This study examines market efficiency based upon response to price signals across Ugandan markets. We focus on information exchange for maize and beans among 16 key markets. We study weekly price data from the first week of 2000 to the last week of 2003 from each of the sixteen markets. Each commodity is studied separately using Vector Autoregessions (VARs) and Directed Acyclic Graphs (DAGs). The two techniques are widely used to show market risk and causal relations in time series data. While results are presented individually for each commodity, the markets are comparable. In determining market efficiency, we test for stationarity of the data, explore the magnitude of forecast error decompositions over time across markets, and observe the patterns of communication based on DAGs. We find that markets are more efficient in exchanging information on maize than beans. Communication of data is mostly between markets in eastern, western, and central parts of Uganda. Overall, markets are very slow in reacting to information in the short run.Information from the Mbale and Iganga markets, which are located in areas of high production, is very valuable in the maize trade. However, of the two markets, it is data from the Mbale market, located near the border with Kenya, which is of paramount importance. Specifically, price is discovered in Mbale in the maize trade. Our results also show the Gulu market, which is situated in an insecure zone, to be very responsive to price signals over the long run. In the case of beans, it is the price signals from Tororo and Jinja that cause more disruption in most of the markets. Price is discovered in these two markets. A majority of the markets is more affected by data from Jinja than Tororo. This segmentation in market price discovery suggests an existing market failure. Arua and Gulu are found to be the least responding markets in regards to price signals for beans. We do not find information from the Kampala market to be important in either the maize or beans trade.

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