A recent Huffington Post article discusses one of my papers with Michael Lovenheim at Cornell University. In this paper we analyze a series of unique Big Data sources tracking over 123 million food purchases over the period 2002-2007 in order to create a detailed model of food demand in the US. Understanding food demand is important as obesity is a major public health concern world wide. Obesity kills more than 2.8 million people every year, according to the WHO. Today, over 2/3 of Americans are overweight and over 36% are obese. Estimates also suggest that about 30% of children are obese or overweight. The increases in obesity have been more pronounced among those with lower income, especially for women, as well as among non-Asian minorities. Obesity has been linked to a higher prevalence of chronic diseases, such as arthritis, diabetes and cardiovascular disease and the associated cost to the U.S. medical system has been estimated at about $147 billion per year, with Medicare and Medicaid financing approximately half such costs.
Using Big Data we simulate the role of product taxes on soda, sugar-sweetened beverages, packaged meals, and snacks, and nutrient taxes on fat, salt, and sugar. We find that nutrient taxes (e.g. on sugar) has a significantly larger impact on nutrition than an equivalent product tax (e.g. a soda tax), due to the fact that nutrient taxes are broader-based taxes. However, the costs of these taxes in terms of consumer utility are not higher. A sugar tax in particular is a powerful tool to induce healthier nutritive bundles among consumers, and appears to be more effective than other product or nutrient taxes.
Harding, M. and Lovenheim, M. (2013) “The Effect of Prices on Nutrition: Comparing the Impact of Product- and Nutrient-Specific Taxes”, NBER WP 19781.
Graphical representation of food expenditures on the 14 major product categories in the sample.
The size of the squares is proportional to the budget share of the corresponding product. The budget share is given in % under each product category name. The color shading of each rectangle corresponds to the price per ounce of products in each of the categories. The price per ounce in $ is also reported under the budget share for each category.