Least Squares and Entropy: A Penalty Function Perspective

Mathematical measures of entropy as defined by Shannon and cross entropy as defined by Kullback and Leibler are currently in vogue in the field of econometrics, primarily due to the comprehensive work of Golan, Judge, and Miller. An alternative interpretation of the entropy measure as a penalty function over deviations is presented, and a number of parallels are drawn with least squares estimators. It is demonstrated that both approaches may be applied to the general linear model. The causes of differences in estimated parameter values are described, and some suggestions for the formulation of entropy-based econometric problems are presented.     

Modeling the impact of technological change on nutrition and marketed surplus

This study develops and demonstrates procedures for modeling the impact of agricultural technology adoption decisions on consumption and nutrition in a subsistence‐farming context. The method is based on expected direct utility maximization (EDUM) formulation and incorporates subsistence quantities for broad aggregates of protein, calories, and other consumption goods. The method is applied to a hillside farming system of southern Honduras where new sorghum cultivars and erosion control techniques are being introduced. The expected direct utility maximization model allows the estimation of the effects of new technology on consumption and marketed surplus in situations where marginal values of products vary by state of nature and are affected by consumption and production choices. The introduction of the new technologies in southern Honduras results in improved nutrition and substantial increases in marketed surplus. These effects are due to simultaneous changes in output and consumption patterns. This work extends the subject of household modeling to problems with risk, and thus complements prior work in both the integrated analysis of production/consumption decisions and stochastic decision analysis.     

Bridging the Gap between Partial and Total Factor Productivity Measures Using Directional Distance Functions

Technical dependencies as well as data constraints limit our ability to allocate inputs across sectors and hence our ability to measure sectoral productivity. We adapt a directional measure of efficiency to the measurement of sector-specific productivity that does not require allocating all inputs across sectors. Applied to the agricultural sector of a group of countries, the results show important differences in livestock and crops productivity growth. Commonly used partial factor productivity measures for livestock and crops tend to overestimate productivity growth in most developing countries while underestimating it in European countries.     

Predicting within country household food expenditure variation using international cross-section estimates

International cross-section demand systems have not been tested on their ability to accurately predict within country consumption expenditures. This paper reports on such a validation exercise for the case of food budget share predictions across expenditure percentiles within Bangladesh.     

Model selection when estimating and predicting consumer demands using international,cross section data

This paper assesses the ability of five structural demand systems to predict demands when estimated with cross sectional data spanning countries with widely varying per capita expenditure levels. Results indicate demand systems with less restrictive income responses are superior to demand systems with more restrictive income effects. Among the least restrictive demand systems considered, An Implicitly, Directly Additive Demand System (AIDADS) and Quadratic Almost Ideal Demand System (QUAIDS) seem roughly tied for best, while the Quadratic Expenditure System (QES) is a close second. Given differences in the characteristics of AIDADS and QUAIDS, it is concluded the former is better suited to instances where income exhibits wide variation and the latter to cases when prices exhibit considerable variation. First Version Received: November 2000/Final Version Received: February 2002 RID="*" ID="*"  The authors acknowledge the insightful comments of two journal referees and Baldev Raj. Bettina Aten kindly provided the data used in this study. Any errors or omissions remain the responsibility of the authors. Partial financial support of the United States Department of Agriculture – National Research Initiative Grant #97-35400-4752 and the Purdue Research Foundation is gratefully acknowledged. An expanded version of this paper is available from the authors upon request. RID="**" ID="**"  Contact author     

Productivity growth and convergence in crop, ruminant, and nonruminant production: measurement and forecasts

Projections of future productivity growth rates in agriculture are an essential input for a great variety of tasks, ranging from development of an outlook for global commodity markets to the analysis of interactions between land use, deforestation, and ecological diversity. Yet solid projections for these variables have proven elusive—particularly on a global basis. This is due, in no small part, to the difficulty of measuring historical total factor productivity growth. Consequently, most productivity projections are based on partial factor productivity measures that can be quite misleading. The purpose of this work is to provide worldwide forecasts of agricultural productivity growth till the year 2040 based on the latest time series evidence on total factor productivity growth for crops, ruminants, and nonruminant livestock. The results suggest that most regions in the sample are likely to experience larger productivity gains in livestock than in crops. Within livestock, the nonruminant sector is expected to continue to be more dynamic than the ruminant sector. Given the rapid rates of productivity growth observed recently, nonruminant and crop productivity in developing countries may be converging to the productivity levels of developed countries. For ruminants, the results show that productivity levels in developing countries are likely to be diverging from those in developed countries.