I. INTRODUCTION Average adult body mass index (BMI: weight in kilograms divided by height in meters squared) in the United States increased approximately 12% between the 1960s and 2004 (Regal et al. 2009) and approximately one third of the U.S. adults were estimated to be obese in 2007-2008 (Flegal et al. 2010). At the same time, the proportion of expenditure on food away from home out of total food expenditure more than doubled between 1960 and 2008 from 19.7% to 41.4% (USDA 2011). In particular, the proportion of sales of food away from home at limited service restaurants increased from 9.7% to 37.1% between 1963 and 2008 (USDA 2011). Data on nationwide surveys of food consumption patterns and household expenditures also show a marked upward trend in total energy intake derived from away-from-home sources, in particular for fast food outlets (Guthrie, Lin, and Frazao 2002: Nielsen, Siega-Riz, and Popkin 2002; Stewart et al. 2004). Fast food consumption was found to be associated with higher total energy intake and higher intake of fat, saturated fat, carbohydrates, sugar, and carbonated soft drinks and lower intake of micronutrients and fruit and vegetables (Binkley. Eales, and Jekanowski 2000; Bowman et al. 2004; Bowman and Vinyard 2004; French et al. 2001; French, Hamack, and Jeffery 2000; Paeratakul et al. 2003) and has been associated with higher body weight (Binkley, Eales, and Jekanowski 2000; Bowman and Vinyard 2004; Guthrie, Lin, and Frazao 2002). Recently, researchers have explored the importance of food prices as a modifiable economic contextual factor to combat the obesity epidemic given that food prices have fallen, whereas the opportunity cost of physical activity has risen over time (Cutler, Glaeser, and Shapiro 2003; Lakdawalla, Philipson, and Bhattacharya 2006). Particularly, understanding the effect of fast food prices on body weight is important because it provides evidence on the extent to which food pricing policies such as taxes on energy dense foods may be effective policy tools to reverse such an epidemic. Lower fast food prices have been reported to be statistically significantly associated with higher weight outcomes among adolescents (Auld and Powell 2009; Powell 2009; Powell et al. 2007) and children in low-socioeconomic families (Powell and Bao 2009). Particularly, the price elasticity of body weight outcomes with regard to fast food prices was found to be heterogeneous over the conditional distribution of BMI for adolescents in a cross-sectional analysis with larger associations at higher BMI quantiles (Auld and Powell 2009). However, the evidence is not as consistent for adults. Chou, Grossman, and Saffer (2004) reported that higher prices of full-service restaurants and fast food restaurants (BMI only) were statistically significantly associated with lower BMI and obesity prevalence among adults. However, another study found that the association between fast food prices and adult weight was statistically insignificant (Beydoun. Powell, and Wang 2008). This study builds on the previous studies and examines the heterogeneous relationship between fast food prices as a proxy for energy dense food away from home and adult body weight outcomes in the United States using data for adults drawn from a nationally representative longitudinal study. We estimate an individual fixed effect quantile regression model of the linear measure of BMI on food prices to estimate changes in the nature, direction, and magnitude of the relationship between fast food prices and body weight outcomes over the conditional distribution of BMI. The quantile regression model allows capturing any changes in the conditional dispersion of BMI associated with fast food prices, whereas typical mean estimation such as ordinary least squares (OLS) only accounts for changes in the mean value. If, for example, fast food prices are associated with individuals' weight only in the top or bottom of the distribution of BMI, the conditional distribution of BMI from OLS estimation would become thinner and wider without any changes in the mean value. …