Experimental analysis of the effect of taxes and subsides on calories purchased in an on-line supermarket

Taxes and subsidies are a public health approach to improving nutrient quality of food purchases. While taxes or subsidies influence purchasing, it is unclear whether they influence total energy or overall diet quality of foods purchased. Using a within subjects design, selected low nutrient dense foods (e.g. sweetened beverages, candy, salty snacks) were taxed, and fruits and vegetables and bottled water were subsidized by 12.5% or 25% in comparison to a usual price condition for 199 female shoppers in an experimental store. Results showed taxes reduced calories purchased of taxed foods (coefficient = -6.61, Cl = -11.94 to -1.28) and subsidies increased calories purchased of subsidized foods (coefficient = 13.74, Cl = 8.51 to 18.97). However, no overall effect was observed on total calories purchased. Both taxes and subsidies were associated with a reduction in calories purchased for grains (taxes: coefficient = -6.58, Cl = -11.91 to -1.24, subsidies: coefficient = -12.86, Cl = -18.08 to -7.63) and subsidies were associated with a reduction in calories purchased for miscellaneous foods (coefficient = -7.40, CI = -12.62 to -2.17) (mostly fats, oils and sugars). Subsidies improved the nutrient quality of foods purchased (coefficient = 0.14, Cl = 0.07 to 0.21). These results suggest that taxes and subsidies can influence energy purchased for products taxed or subsidized, but not total energy purchased. However, the improvement in nutrient quality with subsidies indicates that pricing can shift nutritional quality of foods purchased. Research is needed to evaluate if differential pricing strategies based on nutrient quality are associated with reduction in calories and improvement in nutrient quality of foods purchased

Women who are motivated to eat and discount the future are more obese

OBJECTIVE: Food reinforcement and delay discounting (DD) predict Body Mass Index (BMI), but there is no research studying whether these variables interact to improve prediction of BMI. DESIGN AND METHODS: BMI, the relative reinforcing value of high (PMAX(HED)) and low (PMAX(LED)) energy dense food, and DD for $10 and$100 future rewards (DD(10), DD(100)) were measured in 199 adult females. RESULTS: PMAX(HED) (p = 0.017), DD(10) (p = 0.003) and DD(100) (p = 0.003) were independent predictors of BMI. The interaction of PMAX(LED) X DD(10) (p = 0.033) and DD(100) (p = 0.039), and PMAX(HED) X DD(10) (p = 0.041) and DD(100) (p = 0.045) increased the variance accounted for predicting BMI beyond the base model controlling for age, education, minority status, disinhibition and dietary restraint. Based on the regression model, BMI differed by about 2 BMI units for low versus high food reinforcement, by about 3 BMI units for low versus high DD, and by about 4 BMI units for those high in PMAX(HED) but low in DD versus high in PMAX(HED) and high in DD. CONCLUSIONS: Reducing DD may help prevent obesity and improve treatment of obesity in those who are high in food reinforcement