Money on My Mind: How Financial Scarcity Affects Dietary Decisions
Teresa Rufín, Princeton Class of 2017
ABSTRACT
Obesity is one of the top public health concerns in the United States. Despite being caused by excessive caloric intake, obesity disproportionately affects low-income areas of the country at a higher rate. Research has revealed the role of environmental factors such as availability of supermarkets and access to cheap, healthy foods, but few studies have investigated how living in poverty directly impacts eating behaviors and dietary choices. My thesis aimed to investigate if mental strain caused by financial concerns would have a negative effect on nutritional decision-making. Visitors to a food pantry in Trenton, New Jersey, were presented with a series of either easy or difficult financial questions, and then offered a choice of either low-fat or high-fat snacks to consume during the study. Consistent with my predictions, participants who had to make more difficult financial decisions were more likely to choose high-fat snacks than participants who made easy financial decisions. The results of this study provide a possible additional explanation for the relationship between poverty and obesity, as well as justification for interventions that focus on not just increasing access to healthy food options, but making nutritional decisions an easier and less cognitively taxing task.
INTRODUCTION
According to the 2015 State of Obesity report[1], over 33% of adults who earned less than $15,000 per year were obese, compared to 24% ofadults who earned at least $50,000 per year. Similar disparities in obesity rates are also seen between adults who did not graduate high school and adults who graduated college. While these statistics might support the stereotypes of poor and obese individuals as lazy, shortsighted, and impulsive, the urban environments of low-income areas of the United States limit access to affordable, healthy foods. The U.S. Department of Agriculture defines “food deserts” as areas that have a poverty rate of at least 20% and 33% of the population living over a mile from a supermarket or large grocery store[2]. Whereas accessibility to supermarkets is linked to a decrease in obesity rates, the opposite effect is seen with higher accessibility to convenience stores, which are more prevalent in food deserts. Additionally, energy-dense food, such as those made up of refined grains, added sugars, or fats, are often lower-cost options than foods higher in nutritional value[3]. As such, many junk foods provide more dietary energy at a lower cost than fruits and vegetables.
Apart from environmental influences, the effect that financial scarcity has on decision-making may also impede one’s ability to consider the nutritional value of the food they consume on a day-to-day basis. Research on the psychological burden of poverty has shown that living in a state of scarcity impedes mental abilities, including self-control and decision-making[4]. The theory behind this research posits that having to do constant mental accounting to make ends meet on a tight budget consumes cognitive resources needed for other tasks, such as long-term planning. Under this state of mental constraint, individuals are more likely to use automatic, unconscious decision-making processes in other aspects of their life, such as purchasing and eating food. Living in poverty is also strongly linked to high levels of prolonged stress, which not only impedes mental functioning, but also induces food cravings and increases fat storage. Despite the plausibility of a causal relationship between mental scarcity under poverty and weight gain, no research has directly explored how these psychological mechanisms might influence dietary decisions. My thesis aimed to answer the question of how the psychological burden of poverty affects nutritional choices as a way of offering a possible additional explanation for the poverty-obesity relationship.
METHOD
The study was conducted at Arm in Arm, a food pantry in Trenton, NJ. Trenton exemplifies a typical food desert in the United States, with an obesity rate of 39% and a poverty rate of 28%[5]. One hundred visitors to Arm in Arm participated in the study. The majority of participants were between the ages of 45 and 64 (73%), African American (73%), and had an annual income of $10,000 or less (82%)[6].
Participants were randomly assigned to one of two groups: an “easy” financial condition and a “difficult” financial condition. Each participant was given a questionnaire with a series of hypothetical financial scenarios in which they were asked to consider spending a certain amount of money. After each scenario, participants were asked what decision they would make and why. Participants in the difficult conditions had to consider spending significantly larger amounts of money for each scenario than participants in the easy condition. While participants completed this questionnaire, they were offered two bowls of individually wrapped bags of chips as a snack: one was labeled as a “low-fat” option, and the other was labeled as a “regular” option. The “low-fat” bowl contained chips that were baked or popped to have lower fat content than the chips in the “regular” bowl.
After answering the financial questions, participants were given a series of Raven’s Progressive Matrices (RPM) puzzles to complete. These puzzles were meant to measure participants’ mental capacity after making financial decisions. The RPM is a 3x3 matrix of patterns in which the last box is left blank, and participants are to choose which of the options best completes the blank box according to the pattern (Fig. 1).
Figure 1. Example of a Raven’s Progressive Matrices puzzle
In the above example, the correct response would be #3.
After completing the RPM task, participants were given a demographic survey to complete and then compensated for their participation. For each participant, we recorded which condition they were in, how many snack bags they chose of each type, and how many of the RPM puzzles they completed correctly.
RESULTS
My primary hypothesis was that participants in the difficult financial condition would consume more high-fat snacks than participants in the easy condition. The results seen in Figure 2seem to support this hypothesis: whereas participants in the easy condition chose about equal amounts of low-fat and high-fat snacks, participants in the difficult condition were significantly likely to choose high-fat snacks over low-fat snacks.
Figure 2: Nutritional consumption by condition
The blue bars indicate participants in the easy financial decision while the orange bars indicate participants in the difficult financial condition.
The results of the RPM task also supported my secondary hypothesis, which stated that participants in the easy condition would score a higher average of correct responses than participants in the difficult condition. Participants in the easy condition scored an average of 2.4 out of 10 correct, while participants in the difficult condition scored an average of 1.6 out of 10 correct. However, these results are limited by the difficulty that many participants had in completing the puzzles, having never been exposed to a test such as the RPM.
DISCUSSION & RECOMMENDATIONS
Despite the promising results that this study revealed, it still contained a number of limitations. For example, the majority of participants were extremely low-income and food insecure, meaning that many found even the easy financial scenarios to be mildly stressful. In addition, many participants took bags of chips home with them in addition to eating during the study, not wanting to waste an opportunity for free food. Perhaps with a slightly higher income population, we might see a more robust effect as participants feel less incentivized to take free food and are less concerned about the amounts of money proposed in the easy financial questions. This study also does not fully explain what psychological mechanism is causing individuals under financial strain to pay less attention to their nutritional decisions. Are they stressed, mentally taxed, or both?
Even with the above limitations, this study offers a promising preliminary approach to a novel way of understanding the relationship between poverty and obesity. In an effort to increase the accessibility of affordable healthy food options in food deserts, programs have been introduced to provide tax incentives and bring farmer’s markets to these low socioeconomic areas. However, the programs have been largely ineffective so far, mostly due to the strength that food preferences and nutritional education have in dietary decisions over availability of options[7]. If stress and cognitive load impede an individual’s ability to consider a variety of healthy options and make a deliberate decision to eat healthy, then accessibility to options will not be a productive solution. Combining accessibility with an ease of decision-making, however, could produce favorable results. Behavioral interventions in supermarkets such as placing healthier items at the front of the store, or at eye level on shelves, have shown modest promise in influencing shopping decisions[8]. As a way of mitigating a lack of nutritional education, other solutions have involved designing nutritional labels that more visually salient an easier to understanding, such as using a “traffic light” coloring pattern to indicate healthy vs. unhealthy foods[9]. Many of these solutions do not show drastic effects, but they also are exceptionally low cost and low effort to implement, and could be useful in combination with existing interventions. While it would be naïve to assume that behavioral psychology is the magic solution to the United State’s obesity crisis, for individuals who are under significant mental strain already, making healthy eating simpler and cheaper certainly can’t hurt.
BIBLIOGRAPHY
Cohen, D. A., & Babey, S. H. (2012). Candy at the cash register—a risk factor for obesity and
chronic disease.New England Journal of Medicine,367(15), 1381-1383.
Drewnowski, A., & Specter, S.E. (2004). Poverty and obesity: the role of energy density and
energy costs. The American Journal of Clinical Nutrition, 79(1): 6-16.
Levi, J., Segal, L., St. Laurent, R., & Rayburn, J. (2015). The State of Obesity: Better Policies for
a Healthier America. Trust for America’s Health.
Mani, A., Mullainathan, S., Shafir, E., & Zhao, J. (2013). Poverty Impedes Cognitive Function.
Science, 341(6149): 976-980.
Maubach, N., Hoek J, & Mather D. (2014). Interpretive front-of-pack nutrition labels.
Comparing competing recommendations. Appetite 82(1): 67-77.
Sanger-Katz, M. (2015, May 8). Giving the Poor Easy Access to Healthy Food Doesn’t Mean
They’ll Buy It. The New York Times. Retrieved from
Teresa Rufín, “Money on My Mind: How Financial Scarcity Affects Dietary Decisions.” Senior
Thesis, Princeton University, 2017.
Trenton Health Team. (2013). Community Health Needs Assessment Report. Trenton, New
Jersey: KallaGervasio & Carol McAloon.
U.S. Department of Agriculture Economic Research Service. (2016). Food Security in the U.S.
Retrieved from
in-the-us/measurement/.
[1] Levi, J., Segal, L., St. Laurent, R., & Rayburn, J. (2015). The State of Obesity: Better Policies for a Healthier America. Trust for America’s Health.
[2] U.S. Department of Agriculture Economic Research Service. (2016). Food Security in the U.S. Retrieved from
[3]Drewnowski, A., & Specter, S.E. (2004). Poverty and obesity: the role of energy density and energy costs. The American Journal of Clinical Nutrition, 79(1): 6-16.
[4] Mani, A., Mullainathan, S., Shafir, E., & Zhao, J. (2013). Poverty Impedes Cognitive Function. Science, 341(6149): 976-980.
[5] Trenton Health Team. (2013). Community Health Needs Assessment Report. Trenton, New Jersey: KallaGervasio & Carol McAloon.
[6] Teresa Rufín, “Money on My Mind: How Financial Scarcity Affects Dietary Decisions.” Senior Thesis, Princeton University, 2017.
[7] Sanger-Katz, M. (2015, May 8). Giving the Poor Easy Access to Healthy Food Doesn’t Mean They’ll Buy It. The New York Times. Retrieved from
[8] Cohen, D. A., & Babey, S. H. (2012). Candy at the cash register—a risk factor for obesity and chronic disease.New England Journal of Medicine,367(15), 1381-1383.
[9]Maubach, N., Hoek J, & Mather D. (2014). Interpretive front-of-pack nutrition labels. Comparing competing recommendations. Appetite 82(1): 67-77.