James S. Eales

Bio: James S. Eales is an academic researcher from Purdue University. The author has contributed to research in topics: Demand curve & Demand management. The author has an hindex of 19, co-authored 59 publications receiving 2024 citations. Previous affiliations of James S. Eales include University of Alberta & Agricultural & Applied Economics Association.

The relation between dietary change and rising US obesity.

Abstract: OBJECTIVE: To determine if the source from which food is obtained has contributed to the increased obesity of the US population, while controlling for demographic, lifestyle and regional factors. METHODS: Multiple regression was used to estimate the effect of food source on body mass index (BMI) while accounting for other factors which have been shown to affect obesity in a nationally representative sample of the US population. SAMPLE: This study used secondary data from the 1994–1996 Continuing Survey of Food Intake by Individuals (CSFII). The CSFII is a nationally representative sample of 16,103 individuals, obtaining for each respondent 24 h recalls of all food intake on two nonconsecutive days as well as demographics and information on lifestyle choices. RESULTS: For a large number of demographic and lifestyle factors, our results support those which have previously been found to contribute to increased overweight. Our contribution is to examine whether the source from which food is obtained also contributes to increased overweight. Our evidence suggests that this is the case. The average height for males in our sample was 1.77 m. For two such males, one who ate food away from home (FAFH) during the previous 24 h period and the other who did not, results suggest that the first will be about 1 kg heavier, all other factors being equal. For two females of average height (1.63 m) the same is true for those who ate fast food, but not at restaurants. In all cases, except females who ate at restaurants, the effects are significant in the regression (P<0.05). CONCLUSION: The trends in both increased US obesity and in increased consumption of FAFH are unlikely to be coincidental. FAFH, and particularly fast food consumption, are likely to be contributing factors to increased obesity.

An Examination of Fishing Location Choice in the Pink Shrimp Fishery

Abstract: This article analyzes fishing location choices made by pink shrimp (Pandalus jordani) fishermen fishing off the coast of northern California. Data were gathered for 17 commercial vessels making 3000 net sets over a season. A simple multiplechoice logit model was used to examine whether recent information on success in various regions aids in explaining location choice. Results suggest that fishermen do account for economic factors in a manner consistent with economic theories of choice.

Generalized Models of Japanese Demand for Fish

Abstract: Given a relative lack of knowledge about Japanese consumer preferences for fish, Japanese fish demand is modeled using both Marshallian (ordinary) and inverse demand systems, each of which nests a number of competing specifications. Results indicate that the inverse demand systems dominate the ordinary demand systems in forecasting performance and in nonnested tests. The inverse system suggests that Japanese fish prices are less responsive to changes in consumption than found in previous studies.

Convenience, accessibility, and the demand for fast food

Abstract: This study explores the growth in demand for fast food. A distinguishing characteristic of fast food is its convenience; in today's pervasive marketplace, consumers need not travel far to find a fast food outlet. This greater availability translates into a decrease in the full price of obtaining a meal, which contributes to greater consumption. Market-level data are used to estimate demand equations in two time periods, incorporating changes in availability as well as prices, income, and various demographic characteristics. Our findings show that greater availability has led to increased consumption. Failure to account for these types of marketplace changes could lead to incorrect inferences regarding the factors responsible for the industry growth.

Estimation and Inference in Econometrics

Abstract: Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.