Yet there has been minimal research linking community-level food prices to individual-level dietary behaviors and overall food consumption patterns. The earlier USDA surveys as well as the National Health and Nutrition Examination Survey do not allow food price data to be linked to individual level data at small geographic levels (e.g., many deal with state-level prices). Yet, data from experimental studies show that food price is one of the most influential factors in determining food choice, second only to taste(Shannon et al., 2002, Glanz et al., 1998, French, 2003, Epstein et al., 2006, Epstein et al., 2007).

Further, there is a large agricultural economics literature that addresses the association between food price data with dietary behavior, food production, and overall food expenditure and consumption, yet this work is focused on commodity- and brand-level demand(Frazao, 1999, Timmer et al., 1984). Of particular relevance is the differential association between food prices and dietary intake across income levels as individuals of low income are typically more sensitive to changes in food price(Chung and Myers, 1999a, MacDonald and Nelson Jr., 1991, Stewart et al., 2004, Stewart et al., 2003, Popkin, 2008a) and tend to have less varied, lower quality diets compared to higher income consumers(Hulshof et al., 2003). A range of small studies suggest that food expenditure patterns for high-priced healthier products is associated with reductions in fruit, vegetable, and dairy consumption among low income families(Chung and Myers, 1999b, Kirkpatrick and Tarasuk, 2003, Guo et al., 1999). However, there are still major gaps in the literature and a recent review called for studies estimating price effects and responsiveness among at risk populations, particularly low-income and ethnic minority populations.(Andreyeva et al., 2009)

Negative binomial models predicting the number of fast food meals per week show statistically greater intake among males than females, and significantly lower intake for Asians, Hispanics and non-Hispanic blacks relative to whites (Table 2) even after controlling for wave, age, and community. Further there were differential relationships between community-level food prices of our key target foods, soda and burgers and individual-level fast food intake across race/ethnic groups and by income.

To aid interpretation of results for our key findings (burger and soda), Figure 1 shows predictions based on the model coefficients from Table 2. The relatively stronger association between community-level food prices and individual-level fast food intake for males relative to females is clearly evident. For example, in the group with strongest associations (black males), we found a 20% increase in price of soda was associated with a decrease of a 0.25 visits to a fast food restaurant per week.

Further, there is variation in the association by food type and across race/ethnicity, with greatest association between food prices and fast food intake observed for price of soda in black males.

A recent review suggests that away-from-home foods and sodas are among the most responsive to price changes(Andreyeva et al., 2009), which is confirmed in the present study. In general, the current literature has largely included cross-sectional, descriptive analyses of ecological variations in income, energy-density of diet, and obesity(Maillot et al., 2007, Drewnowski and Darmon, 2005b, Drewnowski and Specter, 2004). Thus, few studies have examined food prices and how they might underlie health disparities. In fact, a recent review called for studies estimating price effects and responsiveness among at risk populations.(Andreyeva et al., 2009) The current research demonstrates variation in price sensitivity across race/ethnicity and income, with greater price sensitivity for soda versus burger prices and relatively higher change in individual-level fast food intake with community-level soda prices for blacks. A similar relationship was found among adults followed 20 years over the 20–34 to 40–54 age period(Duffey et al., 2010). Further, there seems to be greater price sensitivity for soda for individuals of low income and an intervention study suggests responsivity to soda price manipulation.(Block et al., 2010) In contrast, Powell(Powell, 2009, Powell et al., 2010) using longitudinal, self-reported height and weight data (but no data on fast food intake) found no such association between fast food prices and obesity.

Given higher prices of healthy foods (i.e. fresh fruits and vegetables)(Drewnowski and Darmon, 2005a, Drewnowski and Darmon, 2005d), public health professionals, politicians and others have suggested that foods high in calories, saturated fat, or added sugar be subject to added taxes and/or that healthier foods be subsidized(Cash et al., 2005, Chouinard et al., 2007, Jacobson and Brownell, 2000, Popkin, 2008a). Such manipulation of food prices, through subsidies and other methods, has been a mainstay of global agricultural and food policy(Popkin, 2008b, von Braun, 2008) employed as a means to increase availability of animal foods and basic commodities, but have not been readily employed as a mechanism to promote public health and chronic disease prevention efforts.(Popkin, 2008b, WHO, 2000, WHO/FAO, 2003) The current results suggest that increased soda prices may have associated decreases in fast food intake. While the size of the estimated impact of shifts in community-level prices on individual-level fast food consumption is relatively low, at the aggregate level, shifts in one or more meals eaten away from home at fast food establishments per week translates to significant impact on daily caloric intake as well as many important minerals(Guthrie et al., 2002, Paeratakul et al., 2003).

While other studies have examined the association between consumption of away-from-home foods, such as fast food intake, and adverse health outcomes(Duffey et al., 2007, Popkin et al., 2005, Prentice and Jebb, 2003, Burdette and Whitaker, 2004, Currie et al., 2009, Duffey et al., 2009), these studies generally ignore self-selection of fast food eating behaviors as well as consumption of other related food substitutions. In this study, we estimate the effect of community-level prices on individual-level fast food consumption. While, we face the issue of self-selection from the point of residential selection, we control for this with our use of indicator variables for school.

While major strengths of this study include our ability to address differential associations by race/ethnicity and income, the current analysis is limited by its focus on a small number of food and beverage groups – albeit the most important foods in terms of price sensitivity(Andreyeva et al., 2009). Additional and important substitution and complementary foods and beverages may exist and should be examined in future studies. The relationship between community-level price and individual-level consumption of “healthy” food items (i.e. raw fruits and vegetables) should also be examined; the C2ER price data do not include full detail on such foods. Ideally, the current research would benefit from inclusion of a full set of prices and food groups, as would the use of the demand approach frequently employed by economists, the Almost Ideal Demand System(Huang, 1997, Huang and Bouis, 1996, Wu et al., 1995). It is also possible that some of these paired changes, i.e. the price and consumption of soda, are parallel trends over time which are associated with other unobserved factors, and are not necessarily related. There are also limitations in the C2ER data. First, the C2ER price data are available at participating metropolitan and nonmetropolitan areas; these data sources provide the only current and historical price data available at the smallest geographic unit in the US. While these price data are only available for larger county-level units, it is possible that within-county price variations are important, however there are no national food price datasets at smaller than county level. Further, there is no way to obtain retrospective price data other than the C2ER data. Given that we follow individuals over time regardless of residential movement and a portion of the sample moves residences over time, some of the changes in prices may reflect changes in community of residence over time, rather than true decreases in prices (note that the number of counties where respondents lived doubled between waves II and III). As such, our analysis is individual-based in that we estimate the effect of community-level food prices on individual-level behavior, as opposed to following specific communities over time. There are simply no national, community-level, longitudinal samples with food price and individual-level behavior data. Our findings have clear implications for how food prices influence individual behavior. There is a clear need for longitudinal, national research in this area, and currently the C2ER data provide the only option. Yet even so, the datasets include some missing temporal and geographic data. In the current study this limitation was overcome by using imputation strategies widely used in the literature(Grossman and Chaloupka, 1997, Grossman, 1989, Chou et al., 2004, Duffey et al., 2010, Raper, 1999). We are also limited by the choice of data collected by the C2ER. Whereas the price of soda is reported per 2-liter size, this size is not the usual size to be purchased at fast food restaurants. Nonetheless, the pricing of soda will still impact the relative price of soda regardless of purchase size. It is also possible for some degree of county mismatch in individuals who moved residences shortly before measurement.

In sum, using national, longitudinal data including community-level food prices, observed increases in community-level prices of fast food and soda were associated with reductions in individual-level fast food consumption. Further, there was variation in these associations by race/ethnicity and income, with generally greater price sensitivity for soda versus burgers. The current findings contribute to the recent national discussion regarding economic incentives for reducing obesity(Brownell and Frieden, 2009).

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