A test of factors affecting the U.S. rice export: An econometric modeling using U.S. world partners and one major competitor
Notice bibliographique
Résumé
ABSTRACT The purpose of this study was primarily assessing how major U.S. exchange partners in rice export namely: Indonesia, Republic of Korea, Nigeria, and Canada affect the overall U.S. rice export. Thailand was also included because of its position as a major competitor of the United States. A two-Stage Least Square method (2SLS) was applied to the U.S. sub-model using historical data. Because of a simple recursive model reflected by its only major competitor namely Thailand, an ordinary-Least Square method (OLS) was applied to each relationship. In the U.S. model, wholesale price of milled rice was an important factor affecting the quantity of milled rice demanded for both food use and brewer. Price of wheat, price of corn, and per capita income were the other factors, as indicated by their significant coefficients. Estimated elasticities of milled rice were inelastic for both rice for food use and beer processing. While caution must be exercised in applying and interpreting the theory, this information is useful in international business, export opportunities and price competitiveness. INTRODUCTION Rice and wheat are the most important cereals used as food. In the Third World, the demand for rice has often exceeded the available domestic supply. To contend with such continued deficit, increasing quantities have to be imported from abroad. In the United States, even though rice is not a major crop for consumption, it is a major crop in international trade. Twenty-one per cent of all rice traded in the international market comes from the United States, despite a low share (1.46%) of the world production. The world's major rice producers are China, India, Bangladesh, Thailand, Burma, and Japan, all accounting for more than 78% of the total world's production (USDA reports, 1997-2002). In the United States, there are three basic groups of rice produced; long grain (58%), medium grain (32%), and short grain (10%). Sixty percent of U.S. rice production goes to export and the remaining forty percent goes to domestic consumption. Rice for food use, one type of domestic consumption in the United States, may either be used directly or indirectly. When used directly, it includes precooked, parboiled, and brown rice. When used indirectly, it includes cereal, soup, baby food and package mixes. Rice is also used in beer brewing and livestock feeds preparation as well. U.S. rice exports are of two types; first, there are commercial rice shipments that go through business channels. Others operate through the Commodity Credit Corporation (CCC), a commercial financing program for U.S. agricultural commodities. Second, there is the U.S. government assistance program, PL 480. Rice shipments under this program (by far the most important of the government export program) was intended to help countries lacking foreign exchange to purchase U.S. agricultural commodities and to facilitate U.S. foreign aid effort by providing donation or low interest, long term credits (USDA reports, 1997-2002). The Third World, especially Asia has been the largest market for U.S. rice export. The U.S. grain industry is part of a worldwide system. It has always faced stiff competition from another important rice exporter, Thailand. At some point before 1972, the domestic wholesale price of U.S. rice as measured at the mills was above the world price as represented by the export price for Thai rice. In an effort to export U.S. rice to the world markets at a competitive price, the U.S. Department of Agriculture began an export plan in December 1958, through export payments. These payments represented the difference between the U.S. support price and the expected world price level. Export payments were suspended when the world price level approached the U.S. support level in 1966. They were reinstated in March 1969 and continued until December 1972. No export payment has been made since 1972. (USDA reports, 1960-1977). …
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».