Senarath Dharmasena

Intended and unintended consequences of a proposed national tax on sugar‐sweetened beverages to combat the U.S. obesity problem

Monthly data derived from the Nielsen Homescan Panel for calendar years 1998 through 2003 are used to estimate the effects of a proposed tax on sugar-sweetened beverages (SSBs). Most arguments in describing the ramifications of a tax fail to consider demand interrelationships among various beverages. To circumvent this shortcoming we employ a variation of Quadratic Almost Ideal Demand System (QUAIDS) model. The consumption of isotonics, regular soft drinks and fruit drinks, the set of SSBs, is negatively impacted by the proposed tax, while the consumption of fruit juices, low-fat milk, coffee, and tea is positively affected. Diversion ratios are provided identifying where the volumes of the SSBs are directed as a result of the tax policy. The reduction in the body weight as a result of a 20% tax on SSBs is estimated to be between 1.54 and 2.55 lb per year. However, not considering demand interrelationships would result in higher weight loss. Unequivocally, it is necessary to consider interrelationships among non-alcoholic beverages in assessing the effect of the tax.

Unraveling Demand for Dairy-Alternative Beverages in the United States: The Case of Soymilk

Soymilk is one of the fastest growing categories in the U.S dairy alternative functional beverage market. Using household-level purchase data from Nielsens 2008 Homescan panel and the Tobit econometric procedure, we estimate conditional and unconditional own-price, cross-price, and income elasticities for soymilk, white milk, and flavored milk. Income, age, employment status, education level, race, ethnicity, region, and presence of children in a household are significant drivers of demand for soymilk. White milk and flavored milk are competitors for soymilk, and soymilk is a competitor for white milk. Strategies for pricing and targeted marketing of soymilk are also discussed.

Ascertaining the Impact of the 2000 USDA Dietary Guidelines for Americans on the Intake of Calories, Caffeine, Calcium, and Vitamin C from At-Home Consumption of Nonalcoholic Beverages

Obesity is one of the most pressing and widely emphasized health problems in America today. Beverage choices made by households have impacts on determining the intake of calories, calcium, caffeine, and vitamin C. Using data from the Nielsen Homescan Panel over the period 1998–2003, and a two-way random-effects Fuller-Battese error components procedure, we estimate econometric models to examine economic and demographic factors affecting per-capita daily intake of calories, calcium, caffeine, and vitamin C derived from the consumption of nonalcoholic beverages. Our study demonstrates the effectiveness of the USDA 2000 Dietary Guidelines in reducing caloric and nutrient intake associated with nonalcoholic beverages.

On the Evaluation of Probability Forecasts: An Application to Qualitative Choice Models

Using data from Nielsen HomeScan scanner panel for calendar year 2003, we develop binary choice models to focus on the decision made by a sample of U.S. households to purchase various non-alcoholic beverages. We evaluate the probabilities generated through those qualitative choice models using an array of techniques such as expectation-prediction success tables; receiver operating characteristics (ROC) curve, Kullback-Leibler Information criteria; calibration; resolution (sorting); the Brier score; and the Yates partition of the Brier score. In using expectation-prediction success tables, we paid attention to sensitivity and specificity. Use of a naive 0.50 cut-off to classify probabilities resulted in the over or under estimation of sensitivity and specificity values compared to the use of the market penetration value. Area under the ROC curve is suggested as an alternative to the use of 0.5 cut-off as well as cut-off at market penetration level to classify probabilities, because this method treats a wide range of cut-off probabilities to come up with a coherent measure in classifying probabilities. The area under the ROC was highest for coffee for with-in-sample probabilities while it was highest for fruit juice model for out-of-sample probabilities. Kullback-Leibler Information Criteria which selects the model with the highest log-likelihood function value observed at out-of-sample observations (OSLLF) to evaluate probabilities show “closeness” or deviation of model generated probabilities to the true data generating probability overall, although this method does not offer classification of probabilities for events that occurred versus that did not. Again, with respect to OSLLF value, probabilities associated with fruit juice model outperform all other beverages. Forecast probabilities with respect to most of the beverage purchases were well calibrated. All resolution graphs were almost flat against a 45-degree perfect resolution graph, indicative of poor sorting power of choice models. The Brier score was lowest for fruit juices and the highest for low-fat milk. According to the calculated Brier score, probability forecasts for fruit juices outperformed other non-alcoholic beverages. Although the Brier score gave an overall indication of the ability of a model to forecast accurately, the components of the Yates decomposition of the Brier score provided a clearer and broader indication of the ability of the model to forecast. With-in-sample probabilities generated through logit model for coffee outperforms probabilities generated for other beverages based on area under the ROC curve, covariance between probabilities and outcome index and slope of covariance. Out-of-sample probabilities generated through logit model for fruit juice performs better than any other beverage category based on area under the ROC curve, Brier Score, and OSLLF value. In the event where researchers are confronted with alternative models that issue probability forecasts, the accuracy of probability forecasts in determining the best model can be measured through myriad of metrics. Even though traditional measures such as expectation-prediction success tables, calibration and log-likelihood approaches are still used, ROC charts, resolution, the Brier score and the Yates partition of the Brier score to evaluate probabilities generated through alternative models are highly recommended.