The effect of eating restaurant prepared food on BMI: evidence from China during the COVID-19 pandemic

ABSTRACT

Leveraging the unexpected variation in the frequency of eating restaurant prepared food due to the COVID-19 pandemic, we seek to identify and estimate the causal relationship between the frequency of eating restaurant prepared food and people’s BMI. We use first-differencing and instrumental variable approaches to correct for potential endogeneity bias due to both the time-invariant and time-varying unobserved factors. Our results show eating more restaurant prepared food has a positive and statistically significant effect on BMI, and in addition to other channels that have been identified in the literature, mood-boosting is another channel through which eating restaurant prepared food leads to weight gain. Heterogeneous effect analysis further shows that eating restaurant prepared food is more likely to have an impact on those people who lead a more stressful lifestyle before the pandemic.

KEYWORDS:

• Obesity
• Restaurant prepared food
• BMI
• COVID-19

JEL CLASSIFICATION:

• I12
• I18
• L51
• L66

Acknowledgments

We thank the Editor, two anonymous referees, Alejandro Gutierrez-Li, Chengfang Liu, Scott Rozelle, and Yuqing Zheng for helpful comments and suggestions. All remaining errors are our own. Juan He gratefully acknowledges financial support from the Fundamental Research Funds for the Central Universities in China grant 2662020JGPYG12.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ One exception in the US is the Flexible Consumer Behavior Survey USDA-ERS added to the Health and Nutrition Examination Survey (NHANES). However, in that survey, only the frequency of food away from home, which does not distinguish between food prepared at restaurants and food prepared at grocery stores, vending machines and dining halls, is asked.

² To save space, only those questions that are relevant to this study are presented.

³ Wjx.cn has been used by many other studies to collect survey data. For a recent example, see Zhou et al. (2017).

⁴ Although in principle, variables such as education, family size and occupation can change over time, the two periods in our data are just two months apart and it is a low probability event for our surveyed individuals to experience a change in these variables.

⁵ More details and related references can be found in Section II.

⁶ More details and related references can be found in Section II on the discussion of ‘grid management’..

⁷ One boiled egg contains 78 calories. Source: FoodData Central, USDA. https://fdc.nal.usda.gov/.

⁸ The average number of times of eating restaurant prepared food per week in December 2019 is calculated from our survey data.

⁹ The eastern region covers the eastern China (including Shandong, Jiangsu, Anhui, Jiangxi, Zhejiang, Fujian, Shanghai), the southern China (including Guangdong, Guangxi, and Hainan) and the middle China (including Hubei, Hunan and Henan). The northern region covers the northern China (including Beijing, Tianjin, Hebei, Shanxi, and Inner Mongolia) and the northeastern China (including Liaoning, Jilin, and Heilongjiang). The western region covers southwest China (including Sichuan, Yunan, Guizhou, Tibet, and Chongqin) and northwest China (including Ningxia, Xinjiang, Qinhai, Shaanxi, and Gansu).

¹⁰ Among the nine provincial-level regions where incomes per person were higher than the national income in 2019 (income data are from National Bureau of Statistics of China), seven of them are from the east region.

¹¹ Many researchers recommend using checks in surveys to screen out careless respondents (e.g. Berinsky, Margolis and Sances, 2014; Curran, 2016).

¹² See the 7^th row in question 21 of the survey in the Appendix A.

Additional information

Funding

The work was supported by the the Fundamental Research Funds for the Central Universities in China [2662020JGPYG12].