Temporary changes in biological state, such as hunger, can impact decision making differently for men and women. Food scarcity is correlated with a host of negative economic outcomes. Two explanations for this correlation are that hunger affects economic preferences directly or that hunger creates a mindset that focuses on scarcity management to the detriment of other decisions. To test these predictions, we conduct a lab-in-the-field experiment in a health screening clinic in Shanghai, recruiting participants who finish their annual physical exam either before or after they have eaten breakfast. We compare the hungry and sated groups on their risk, time and generosity preferences as well as their cognitive performance. Our results show that men and women respond to hunger in opposite directions, thus hunger reduces the gender gap in decision quality, risk aversion and cognitive performance, but creates one in generosity. Finally, we examine several biomarkers and find that higher blood lipid levels are correlated with greater choice inconsistency, risk aversion and generosity. We contribute to emerging insights on the biological foundations for economic preferences and outcomes.
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Using micro-level data, Kearney (2005) demonstrates that household lottery spending is financed primarily by a reduction in non-gambling expenditures. In particular, low-income households experience significant declines in expenditures on food, rent, mortgage, and other bills.
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Another possible mechanism for why hunger could impact decision quality is that hunger creates stress. This stress, in turn, lowers decision quality (Haushofer and Fehr 2014). In this paper, we explore the mind-set hypothesis in several ways but do not directly measure stress.
When we review the literature, we specify a study’s sample size when it is below 100, an admittedly arbitrary cutoff.
The body’s quantity of glucose increases with eating, and as such, we include glucose manipulations as part of our review. Here, we focus on those studies where glucose levels are manipulated experimentally with food or liquid intake.
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One of the unique features of China’s health insurance system is that annual health checkups and other preventive health care procedures are not covered by the national health insurance. Instead, they are provided by employers as part of an employee’s benefit package. On the supply side, private health clinics provide these annual health checkups. The largest of these private health clinic chains is our research partner, MeiNian.
Previous work testing for glucose tolerance among active and sedentary people, finds that the impact of ingesting glucose is present at 30 min after ingestion and as much as 2 h after ingestion (Heath et al. 1983). Further, that this pattern of impact (30 min to 2 h after) is not affected by the activity level of the individual, but the levels of impact are. Because the time between getting breakfast and participating in a session did not exceed 2 h, we can conclude that the biological impact of eating breakfast was present during our sated treatments.
The exchange rate at the time of the experiment was $1 \(= 6.25\) CNY.
A bat and a ball cost 1.10 in total. The bat costs 1.00 more than the ball. How much does the ball cost? If it takes 5 machines 5 min to make 5 widgets, how long would it take 100 machines to make 100 widgets? In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half of the lake?
According to the Shanghai Municipal Human Resources and Social Security Bureau, in 2014, the average monthly salary in Shanghai was 5451 CNY. Source: http://www.12333sh.gov.cn/201412333/xxgk/flfg/gfxwj/ldbc/bcfp/201504/t20150401_1199449.shtml, last retrieved on July 16, 2015.
The URL for the ICPSR repository is https://www.openicpsr.org/openicpsr/.
Our proportion of participants exhibiting inconsistent responses is in line with that obtained in studies using non-college-student samples in developing countries, e.g., in Rwanda and Peru (55% and 52% for risk measures) (Jacobson and Petrie 2009; Ashraf et al. 2006). In comparison, the proportion of individuals displaying inconsistent behavior in US populations ranges from 8 to 30% (Holt and Laury 2002; Prasad and Salmon 2013; Dave et al. 2010).
Researchers using the CTB methodology typically find very few subjects who choose an option that implies an upward-sloping demand curve. For example, Andreoni and Sprenger (2012) find that only 8/97 subjects did so. However, they caution that this relatively small percentage should be understood within the context of the fact that a high fraction of subjects (37% in the 2012 study) have no interior choices. Cheung (2015), Ashton (2014) and Andreoni et al. (2015) have similar findings.
Several comments have been published regarding this measure (Cheung 2015; Harrison et al. 2013; Epper and Fehr-Duda 2015; Miao and Zhong 2015). These authors note that one major empirical challenge with CTB is that the majority of observations are at the edges of the choice set. This response pattern is not well suited to the nonlinear least squares modeling approach used for data analysis. It also suggests that subjects may not fully comprehend the task.
Recall that the price of giving is defined as the ratio of one’s own token value to the other’s token value.
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We would like to thank Colin Camerer, Soo Hong Chew, Uri Gneezy, Muriel Niederle, Georg Weizsäcker, Maytal Shabat-Simon, and participants at Michigan (Ross), the Jerusalem Conference on the Typologies of Bounded Rationality (2015), the International ESA meetings (Sydney, Australia, 2015) for helpful comments, Jim Andreoni, Peter Kuhn and Charles Sprenger for sharing their data and code with us, as well as Carrie Wenjing Xu, Linfeng Li and Kurtis Drodge for excellent research assistance. We thank two anonymous referees and the Editor, Lata Gangadharan, for their thoughtful and constructive comments which significantly improved the paper. The financial support from the National Science Foundation through Grant No. BCS-1111019 to Chen is gratefully acknowledged. The research has been approved by the University of Michigan IRB.
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Chen, Y., Jiang, M. & Krupka, E.L. Hunger and the gender gap. Exp Econ 22, 885–917 (2019). https://doi.org/10.1007/s10683-018-9589-9
- Risk preference