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This study analyzed the economic welfare implications of the 1990 intraregional trade liberalization scheme within the Economic Community of West African States (ECOWAS) on member country producers and consumers. Four countries were chosen as a point of focus: Burkina Faso, Cote d'Ivoire, Ghana and Mali, and two commodities: millet and sorghum. The supply and demand functions were estimated using time series data from 1970 to 1990 obtained at the level of administrative regions within each of the four countries. Optimal production, consumption, trade quantities and trade flows were determined using the REACTT model, a spatial price equilibrium solution algorithm. Two trade scenarios were simulated. The first examined trade flows under the 1990 tariff structures and the second examined trade flows under the proposed zero tariff rates. / The REACTT model results showed that removal of the tariffs would increase the crossborder trade flows between the four countries by about 12% for millet and 38% for sorghum. The welfare calculations showed that in the case of millet, all four countries would have net positive gains to the tune of $4.6 million in total. For sorghum, Burkina Faso, Ghana and Mali would have net positive gains, C ote d'Ivoire would have a net welfare loss, and the net impact on all four countries would be a positive gain of about $9.3 million. The results of the REACTT model and the welfare calculations suggest that intra-ECOWAS trade liberalization would increase total trade flows and total economic well being of the member countries.

Graduation date: 1982

Binder's title on spine: United States Department of Agriculture grain commodities programs. / Typescript. / Thesis (Ph. D.)--University of Hawaii at Manoa, 1986. / Bibliography: leaves 100-102. / Photocopy. / x, 102 leaves 29 cm

Syftet med denna uppsats är att undersöka de ideologiska motiven bakom frisläppandet av spannmålshandeln 1775 och 1780. Utgångspunkten är det funktionalistiska synssättet med handlingsaxiom inom det praxeologiska läran som komplement för att undersöka individernas situation. Tillvägagångssättet blir att undersöka böndernas och statens ekonomiska situation och syn på marknaden före och efter tidigare nämnda årtal.    Under tidiga 1700-talet var Sverige ett samhälle med starkt statlig kontroll av marknaden med inrikes tullar, premier och fördelar för vissa personer i samhället. Samtidigt under upplysningstiden träder tänkare som Adam Smith, Anders Chydenius och även fysiokrater fram och hävdar att en fri marknad skulle vara till fördel för både landet och individerna, något som Johan Liljencrantz delvis var influerad av. Resultatet av denna uppsats visar att upplysningstiden och fysiokratism låg bakom frisläppandet av spannmålshandeln då fokus centrerades till bondens positiva ekonomiska utveckling. / The main purpose in this essay is to research the ideological motives behind the extrication of the grain trade in 1775 and 1780. To do this research I have to see the Swedish state and farmers economic situation before 1775 and after 1775, but also what time Liljencrantz lived in and how relevant the time was to the reform. The starting point for this essay is using the functionalism analysis with action axiom of praxeology in mind to see the relationship between the state and the individual.    During the early 18th century, Sweden had a mercantile system where the industry had to be protected with high tariffs and a strong nobility owning them. During the enlightenment thinkers like Adam Smith, Anders Chydenius and to some extent physiocrats sought a society with free market that would have the result of more wealth, to the nation as well as to the citizen. The result of this essay is that Liljencrantz reform were the causes of his time and political view, the time of enlightenment and physiocracy to make the farmer more free and prosperous. It was also the start of the more liberal economy that would dominate the Swedish society in the 1800 hundreds.

In several countries around the world, grain commodities are traded as assets on stock exchanges. This indicate that the market and effectively the prices of the grain commodities in such countries, are controlled by several local and international economic, political and social factors that are rapidly changing. As a result, the prices of some grain commodities are volatile and trading in such commodities are prone to price-related risks. There are different trading strategies for minimising price-related risks and maximising profits. But empirical research suggests that making the right decision for effective grain commodities trading has been a difficult task for stakeholders due to high volatility of grain commodities prices. Studies have shown that this is more challenging among grain commodities farmers because of their lack of skills and the time to sift through and make sense of the datasets on the plethora of factors that influence the grain commodities market. This thesis focused on providing an answer for the main research problem that grain farmers in South Africa do not take full advantage of all the available strategies for trading their grain commodities because of the complexities associated with monitoring the large datasets that influence the grain commodities market. The main objective set by this study is to design a framework that can be followed to collect, integrate and analyse datasets that influence trading decisions of grain farmers in South Africa about grain commodities. This study takes advantage of the developments in Big Data and Data Science to achieve the set objective using the Design Science Research (DSR) methodology. The prediction of future prices of grain commodities for the different trading strategies was identified as an important factor for making better decisions when trading grain commodities and the key factors that influence the prices were identified. This was followed by a critical review of the literature to determine how the concepts of Big Data and Data Science can be leveraged for an effective grain commodities trading decision support. This resulted in a proposed framework for grain commodities trading. The proposed framework suggested an investigation of the factors that influence the prices of grain commodities as the basis for acquiring the relevant datasets. The proposed framework suggested the adoption of the Big Data approach in acquiring, preparing and integrating relevant datasets from several sources. Furthermore, it was suggested that algorithmic models for predicting grain commodities prices can be developed on top of the data layer of the proposed framework to provide real-time decision support. The proposed framework suggests the need for a carefully designed visualisation of the result and the collected data that promotes user experience. Lastly, the proposed framework included a technology consideration component to support the Big Data and Data Science approach of the framework. To demonstrate that the proposed framework addressed the main problem of this research, datasets from several sources on trading white maize in South Africa and the factors that influence market were streamed, integrated and analysed. Backpropagation Neural Network algorithm was used for modelling the prices of white maize for spot and futures trading strategies were predicted. There are other modelling techniques such as the Box-Jenkins statistical time series analysis methodology. But, Neural Networks was identified as more suitable for time series data with complex patterns and relationships. A demonstration system was setup to provide effective decision support by using near real-time data to provide a dynamic predictive analytics for the spot and December futures contract prices of white maize in South Africa. Comparative analysis of predictions made using the model from the proposed framework to actual data indicated a significant degree of accuracy. A further evaluation was carried out by asking experienced traders to make predictions for the spot and December futures contract prices of white maize. The result of the exercise indicated that the predictions from the developed model were much closer to the actual prices. This indicated that the proposed framework is technically capable and generally useful. It also shows that the proposed framework can be used to provide decision support about trading grain commodities to stakeholders with lesser skills, experience and resources. The practical contribution of this thesis is that relevant datasets from several sources can be streamed into an integrated data source in real-time, which can be used as input for a real-time learning algorithmic model for predicting grain commodities prices. This will make it possible for a predictive analytics that responds to market volatility thereby providing an effective decision support for grain commodities trading. Another practical contribution of this thesis is a proposed framework that can be followed for developing a Decision Support System for trading in grain commodities. This thesis made theoretical contributions by building on the information processing theory and the decision making theory. The theoretical contribution of this thesis consists of the identification of Big Data approach, tools and techniques for eradicating uncertainty and equivocality in grain commodities trading decision making process.

Grain shippers are constantly faced with making merchandising and logistical decisions while trying to achieve a positive net margin. They have to decide how much grain to sell and when the most opportune time to do so occurs. In addition, decisions regarding how much freight should be acquired and where grain should be shipped need to be addressed. These decisions are met by several sources of risk such as futures spreads, basis levels, transit times, equipment placements, and farmer deliveries. The primary objective of this thesis was to develop a model to determine both the optimal amount of grain that should be sold in the pipeline and the optimal amount of freight that should be hedged by grain shippers through the use of forward shipping mechanisms. Certificates of Transportation (COTs) offered by the Burlington Northern Santa Fe (BNSF) Railway were used to represent forward shipping mechanisms in this thesis. A stochastic simulation model of a prototypical grain shipper containing three country elevators and two export facilities was developed. A sensitivity analysis was conducted on merchandising and logistical variables to evaluate different scenarios. The analysis revealed that committing to too many shuttle COTS limited the shipper's flexibility, forced sales to be made in suboptimal periods, and significantly increased the level of demurrage. The type of freight ordering strategy implemented by each elevator ultimately determined the overall sustainability of the firm; shippers need to diversify the type of freight they commit to because ordering too much long-term freight can result in bad sales decisions, whereas relying only on short-term freight is costly and inefficient. Not being able to quickly adapt to volatile market conditions can result in making bad selling decisions and untimely freight purchases which can hinder the longevity of a firm.

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