Abstract
This study documents prevalence rates, trends in, and determinants of body mass index (BMI), outcomes related to obesity, and proximate inputs into obesity such as caloric intake for panels of countries, categorized by geographical regions and levels of development for the time period 1980–2008. Our estimates inform the nature and scope of obesity on a global scale, and contribute toward an understanding of the drivers of the upward trend in obesity and toward identifying effective public policy responses. The cross-national trends, across countries spanning the spectrum of economic development and geographic regions, suggest that obesity is not a problem limited to the United States and other developed countries, but rather a global concern. With the exception of Sub-Saharan Africa and the group of low-income countries, average BMI levels for all other country groupings (based on geographic regions and level of economic development) had reached the overweight/pre-obese range by 2008. Concurrently, we also observe an increase in glucose levels. We further find that higher caloric intake globally over the past three decades may be a direct driving factor to the increase in BMI. Fixed effects regression analyses reveal that changes in real GDP per capita and labor force participation can partly explain the increase in BMI through their impact on caloric intake and physical inactivity. The commonality in the rising trends in BMI and obesity around the world is suggestive of common underlying causes, although substantial heterogeneity remains, as well as marked differences by gender.
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Notes
In recent years, obesity appears to have plateaued at these high rates, and there is some indication that childhood obesity may have declined very slightly among low-income families [CDC 2013].
This does not preclude interaction effects between genes and changes in the environment.
These trends are generally occurring not only in the United States but worldwide. Smoking rates, for example, have declined around the world. Global prevalence of daily tobacco smoking decreased from 41.2 percent (10.6 percent) to 31.1 percent (6.2 percent) from 1980 to 2012 for men (women), although the raw number of smokers has risen [Ng et al. 2014].
See Cawley and Burkhauser [2008] for a discussion of the limitations of BMI in capturing obesity.
Furthermore, we estimate models that control for country fixed effects, which will account for any unobserved but stable cross-country differences in BMI measurements.
The Hausman test rejects a random effects model in favor of fixed effects. Conceptually, a random effects model is also difficult to a priori justify in the context of studying cross-national differences in obesity-related outcomes since one would expect the country-level fixed effects (unobservables) to be correlated with included country-specific factors such as GDP, LFP rate, caloric intake, and physical inactivity. We therefore estimate and report fixed effects specifications in lieu of random effects.
The World Bank classifies countries by income according to GNI per capita. The groups are: low income, $1,035 or less; lower-middle income, $1,036–4,085; upper-middle income, $4,086–12,615; and high income, $12,616 or more. The countries are divided according to 2012 GNI per capita, calculated using the World Bank Atlas method. More information can be found at http://data.worldbank.org/about/country-classifications.
Models of physical inactivity are estimated for a cross-section of countries, and include region fixed effects.
These data are publicly available at https://apps.who.int/infobase/Comparisons.aspx, http://faostat.fao.org/default.aspx, and http://databank.worldbank.org/ddp/home.do, respectively.
See, for instance, the WHO’s STEPS instrument, the tool used to collect data on non-communicable disease risk factors, for further details, (http://www.who.int/chp/steps/instrument/en/index.html).
The total quantity of food commodities produced is adjusted for the total quantity imported or exported. A distinction is also made between food commodities fed to livestock and/or used for seed, losses during storage and transportation, and net supplies available for human consumption.
This information in turn is based on self-reported physical activity from the GPAQ (Global Physical Global Physical Activity Questionnaire), the IPAQ (International Physical Activity Questionnaire), or an equivalent questionnaire. See http://apps.who.int/gho/indicatorregistry/App_Main/view_indicator.aspx?iid=2381.
These values are similar to those recommended around the world. The United Kingdom’s National Health Service, for instance, suggests that “a man needs around 10,500 kJ (2,500 kcal) a day to maintain his weight. For a woman, that figure is around 8,400 kJ (2,000 kcal) a day.” (See http://www.nhs.uk/chq/pages/1126.aspx?categoryid=51.) The Australian Government Department of Health and Ageing National Health and Medical Research Council establishes guidelines based on level of activity and basal metabolic rate (BMR), defined as the minimal rate of energy expenditure compatible with life, that are similar to those established by the FAO and WHO. (See https://www.nhmrc.gov.au/_files_nhmrc/publications/attachments/n35.pdf.) The Food and Agricultural Organization, World Health Organization, and United Nations University set clear guidelines in a 1985 report, updated in 2001, based on BMR, level of activity, weight, and height. For males 18–30 years old with an average height of 1.7 m with a BMI of 21 who are moderately active, the recommended energy intake would be around 2,800 calories. For females of the same age, height, BMI, and level of activity, the recommended energy intake would be around 2,400 calories [FAO/WHO/UNU 2001].
On the basis of data from the FAO food balance sheets, the overall net increase in world caloric consumption is attributed to an increase in caloric intake from the consumption of vegetables (38.12 percent), meat (13.28 percent), cereal (3.62 percent), fruits (7.77 percent), and maize (1.80 percent) and a decrease in caloric intake from the consumption of starchy foods (7.28 percent) and sugar (2.85 percent). It should be noted that while calories consumed from vegetables increased at such a high rate, the relative contribution of vegetables to total caloric intake remains very low; in 2007, vegetables accounted for only about 2 percent of total caloric intake, and fruits accounted for an additional 3.8 percent. The largest share of caloric intake in 2007 can be attributed to cereal (39.7 percent), followed by sugar (10.3 percent), meat (7.9 percent), maize (6.9 percent), and starch (6.4 percent). In contrast, for the United States, cereal accounted for 23.5 percent of total calories while sugar accounted for almost 17 percent in 2007.
The decline in caloric consumption may also therefore reflect a shift in the composition of an EECA country’s population due to this emigration. However, we do not explicitly measure these factors in our study.
This estimation utilizes the elasticity of BMI relative to caloric intake of 0.33 from Model (2) in Table 4. If the elasticity is 0.25, the increase in calories can explain about 37 percent of the growth in BMI, and if the lowest elasticity of 0.09 is utilized, about 13 percent of the observed growth in BMI in the United States is driven by increase in calories consumed. However, given the inherent measurement error in daily caloric intake data, these estimates may be biased downwards and thus likely reflect a conservative accounting of how much the increase in calories consumed may have driven the increase in observed BMI.
At the same time, it should be noted, as discussed earlier, that there is considerable heterogeneity in growth rates across countries and regions. For instance, even among high-income countries, there is a large variation in trends between the US and Western European nations. With respect to the growth in BMI, glucose levels, and caloric intake, and with respect to the decrease in cholesterol levels, the United States consistently exhibits more adverse trends compared with Western Europe. Such heterogeneity also points to country-specific factors, for instance related to the health-care system, agricultural policies, variation in taxes and prices, employment-based policies, and differences in the labor markets, which may underlie the differential trends.
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Acknowledgements
The authors are very grateful to Muzhe Yang, Susan Averett, session participants at the 2013 Eastern Economic Association Conference, and three anonymous referees for valuable comments. The authors alone are responsible for errors.
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Doytch, N., Dave, D. & Kelly, I. Global Evidence on Obesity and Related Outcomes: An Overview of Prevalence, Trends, and Determinants. Eastern Econ J 42, 7–28 (2016). https://doi.org/10.1057/eej.2014.37
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DOI: https://doi.org/10.1057/eej.2014.37