Will Obesity Rates in Africa Increase as it Becomes More Wealthy?
Obesity is now an epidemic. The number of obese people has rapidly increased leading to a whole range of issues including rising rates of cardiovascular diseases. Part of the blame for this sudden increase goes to the media and the progressive left who have turned obesity into an identity; people can now identify as obese and feel good about it. However, my observation in this essay is that obesity is not inevitable in all societies. Some societies are more prone to obesity than others even after we control for income, physical inactivity, and cultural practices.
One way to understand obesity is by looking at the mean difference in obesity rates by world regions. The Wikipedia has a list of Obesity Rates for all the countries in the world. From the list, you notice neighboring countries tend to have obesity rates within one to two percent of one another. For example, Eswatini has an obesity rate of 16.50 which is closer to Lesotho with a rate of 16.60. Namibia has an obesity rate of 17.20 and Botswana 18.90. All these countries have obesity rates that cluster within the same range. The same is true of a country like Egypt with an obesity rate of 32.00 and ranks closer to its neighbors like Libya with a rate of 32.50, Turkey 32.01, Lebanon 32.00, and United Arab Emirates 31.70. Clearly, there must be something going on. I created a chart showing the mean obesity rates by world region.
The chart provides a good visual which helps understand obesity in different regions of the world. The highest mean obesity rates are in Australia and Oceania (42%) followed by North America (31.5%), Middle East and North Africa (26.51%), Central America and Caribbean (23.5%), South America (23.4%), Europe (22.6%), Sub Saharan Africa (11%), and Asia (9.0%). These means even though revealing by themselves can also mask huge differences in some regions. The United States, for example, looks like an outlier in North America with an obesity rate of 36.2% compared to Canada with 29.4% and Mexico 28.9%. Australia is also ranked together with the Polynesian Islands which cumulatively have an mean obesity rate of 42%, Australia and New Zealand each have much lower obesity rates of 29% and 30.8% respectively. These regional variances might have pushed the means up or down. This problem could have been solved by dividing these 8 regions into even smaller regions. The gist, however, is that obesity rates vary by world region and the differences are huge.
A visual showing obesity by world region leads us to further question what similarities within regions and differences between regions accounts for the differences. My first assumption was that maybe higher incomes and wealth in each region had something to do with obesity rates. We live in a consumer world where high income countries indulge more in food stuffs, have higher number of KFC and MacDonald's per block, and generally, enjoy food with more calories. The relationship between GDP (PPP) per Capita and obesity rates sounds intuitive. Comparing GDP or GDP per Capita with obesity rates would have missed differences in living standards between countries. I opted for Purchasing Power Parity as the more accurate metric provided by the World Bank and alternatively the CIA where data was missing. The data is of the most recent estimates as presented here. The chart below shows how different regions of the world compare in mean GDP per Capita.
The chart is counterintuitive because it almost tells the opposite story compared to the chart depicting obesity rates by world region. Oceania which has the highest mean obesity rate is relatively poor compared to the rest of the world. Only Sub- Saharan Africa is poorer. The two wealthiest regions in the world North America and Europe were second and sixth respectively in mean obesity rates. Even without further analysis, it becomes markedly clear wealth and incomes might not be the true drivers of increasing obesity rates. I did a correlation between obesity rates and GDP (PPP) per capita and the results were just as revealing.
Results from the analysis were significant and the correlation between the rate of obesity and GDP (PPP) Per Capita was r = 0.25. This is a positive but low correlation which means more wealth and incomes do not markedly increase obesity rates. The scatter plot shows higher obesity rates were more likely found in poor countries with per capita GDPs below $ 20,000 while higher income countries tended to have obesity rates between 20% and 40%. Most of these poor but obese countries are the oceanic islands. There is also a large concentration of poor and less obese countries probably from Africa and Asia at the lower left.
Other causes of obesity
Wealth is just one of many ways in which countries can be similar or different. Similar cultures could also account for region wide similarities in obesity rates. Some of these cultural practices revolve around culinary habits and eating. A good example is Mauritania which has a fattening culture. Women in Mauritania are fattened by their mothers beginning middle childhood until they are ready for marriage. The food mostly comprises of fatty animal suet mixed with animal milk. Even when satiated, the are forced to take even more food. Force is used when women refuse to eat or start vomiting. According to Mauritanians, women have to be fat to be marriageable. Men consider fat to be beautiful and petite women don’t have a chance in marriage. Despite that, obesity rate in Mauritania is 12.7%. I am not sure whether neighboring countries have a fattening culture. Consider the obesity rates in Mali and Senegal are 8.6% and 8.8% respectively. Mauritania is 4 parentage points fatter than its neighbors which I believe is mostly explained by that culture.
Other than Mauritania which has a fattening culture, some countries might also share cuisines and foods likely to lead to more weight. Wheat based diets and other carbohydrates are argued to be the leading cause of obesity across the world. Carbohydrates are also cheaper than proteins meaning they are more affordable to the poor. This has been called Engel’s law which states that “the poorer a family, the larger the share of its income is spent on food.” Gregory Clark in A Farewell to Alms, further expounds a variant of Engel’s law stating that:
When people are very poor, so that hunger is ever present, they consume the cheapest forms of calories available — grains such as wheat, rice, rye, barley, oats, or maize, and beans or potatoes — consumed in the cheapest possible way as porridge, mush, or bread.
Essentially, if obesity rates were a product of higher consumption of carbs such as wheat, rice, potatoes, and maize then we would expect that the poor would be the most obese. In Kenya, most poor families spend a significant amount of their incomes buying maize flour which they stir in boiled water to form a solid mass called ugali. Ugali is a Kenyan staple prominent in poor households where it is eaten with cheap vegetables like kale or sukuma wiki. The word sukuma wiki means push the week. It is a vegetable eaten throughout the week meaning ugali is also eaten in most Kenyan households throughout the week. Other than ugali, Kenyan households eat a lot of wheat based diets including bread and chapati which is either taken with tea or grains such as beans and green grams. Like Engel’s law states, the poor will spend a larger portion of their incomes on cheap carbs and grains. The table below shows how much money was spent on food in most households before the 19th century.
If consumption of carbs and cheap grains correlated with higher obesity rates, then we would expect poor countries, especially in Africa, to be the most obese. Evidence from most African countries suggests that is not the case; Africa has a mean obesity rate of 11%. The Polynesian Islands of Tonga, Maori, Nauru, Palau, Marshall Islands, Samoa, and Kiribati apparently do not consume large amounts of carbs despite being poor. Understandably, as Oceanic Islanders, these countries mostly consume seafood including fish and shell fish. Their menus have fewer carbs or grains. The lingering question is what made them so fat? Asians also have lower rates of obesity even though they eat a lot of rice and wheat based products. If there was data for wheat/carbs consumption per capita for every country, I would probably have run the correlation. I did not find any. The largest wheat importers in the world include countries like China and Nigeria which says very little about per capita consumption. Carbohydrate consumption, therefore, does not tell us much about obesity rates across the world.
Physical Activity and obesity
Higher physical activity makes people fit and less likely to become obese. For individuals, at least, higher levels of activity helps burn excess calories keeping weight in check. This is true considering the mounting evidence showing increased levels of obesity among sedentary groups. In Africa, the Maasai are known to be thin and tall because of their nomadic lifestyles. Their Bantu counterparts, like the Kikuyu and Luhya, who settled in fertile highlands seem to have higher rates of obesity. However, what I was interested in was global data on physical activity. The chart below shows physical inactivity as recorded from 73 countries across the world. It provides rates of physical inactivity for both men and women.
Mauritania, the country with the bride-fattening culture called leblouh, stands out. The rate of physical inactivity among women there is above 70%, higher than that of Mauritanian men with physical inactivity rates of 52%. Is it that fattening causes lower physical activity among Mauritanian women or low physical activity is utilized to contribute to the fattening process? What explains the low physical activity among men? Most of these Mauritanians are Moors, Barbers, and nomadic Muslims of the Sahara. My hypothesis at this point is that desert and semi-desert climatic conditions increase physical inactivity. Physical Inactivity in Namibia is also very high from the chart above probably due to the Namib desert. The mode of transport in deserts like the Sahara is camels and men might spend a lot of time riding them while women stay at home. Using data from Dumith et al. (2011), where the chart above comes from, I tried to correlate physical inactivity with rates of obesity. The data set only provides figures for 73 countries which is a huge limitation. Despite that, there was a positive correlation between physical inactivity and obesity r = 0.28. Obese people are very likely to be physically inactive.
The data set was small and did not include rates of physical inactivity in Oceania where we have a large number of obese people. Comparisons by region would have been appropriate if all countries had been included in the data set. Mean rates of physical inactivity by region reveal Middle East (31.9%), a mostly arid region, as the most inactive region of the world followed by South America (26.7%). Middle East, however, had a lower mean rate of obesity than Oceania and North America. The chart below shows the distribution of mean physical inactivity rates by world region. Worth mentioning is that there were huge variances within regions when it comes to physical inactivity. Data points were not as tightly packed as we saw with rates of obesity.
Obesity and genetics
We have seen that there is only a slight positive relationship between living standards and obesity rates. There is also a small relationship between physical inactivity and obesity. The last reason for regional similarities in obesity rates might be genetics. Neighboring countries not only share cultures and habits but also genetic ancestry. Most neighboring countries have similar ancestries meaning they are closer genetically than countries far away from them. It is, therefore, essential to establish whether obesity rates within regions are subject to shared genes.
Robert Plomin writes in the Blueprint: How DNA makes us who we are that “weight is the result of behavior — What we eat and how much we eat and how much we exercise — and psychology is the science of behavior. In many ways, the obesity epidemic is a psychological problem.” We need to look at genetics to understand obesity and why it mostly appears in certain regions of the world and not others regardless of what they eat, how much income they make, and their levels of physical inactivity. Within families, there is a high likelihood that obese parents will tend to have obese children. De-linking genetics from the environment in this case can be difficult but its possible. Arguably, one could say that the reason obesity runs in families is because people of the same family eat the same things. A Mauritanian family with two girls undergoing Leblouh will be fattened not because of genetics but because of the parents’ deliberate attempts to fatten the girls.
In behavior genetics, de-linking genes from the environment requires twins who share 100% of their genes or separately adopted siblings. Same genes are tossed into different environments and the effects recorded. If the twins turn out differently, then the environment is said to have exerted some influence. If the twins turn out the same, then the strong influence of genetics is justified. Plomin summarizes the experiment simply:
Weight can run in families for reasons of nature (genetics) or nurture (environment). For a century, genetic research has relied on two methods to disentangle nature and nurture: the adoption method and the twin method. The two methods have different assumptions, strengths and weaknesses. Despite the great differences in the two methods, the results of adoption and twin studies converge on the same conclusion about the importance of inherited DNA differences in the origins of psychological traits.
From analysis of these types of experiments, the heritability of obesity converges at 70%. A significant amount of variance in a population’s weight is due to genetics. Comparing why some people in a population are fat and others thin, it is safe to assume genetics played a role. Note that we are talking about populations and not individuals. The theme of this article to this end has been regional evaluation of obesity and not individual differences in obesity. Like the Mauritanian case, environmental habits such as force feeding brides can make them obese. Similarly, sedentary habits like sitting in an office whole day or working at home where one has easy access to snacks and beverages can make a person fat. One thing we should, however, take from Plomin is that genes can also influence our environmental predispositions. He argues that our tendencies, either towards food or exercise, are behavioral and our genes can have a role to play too.
The Polynesian Islands share similar ancestries which might explain their higher rates of obesity due to shared genetics. The same can explain why obesity rates in Asia and Africa are quite low. Asian countries have similar ancestries too and their behavioral outcomes are likely to converge. If incomes were to explain differences in obesity between regions, we would expect Europe, East Asia, and North America to top the list with high obesity rates. If it was all about the carbohydrates, the poor who survive on cheap carbs and grains especially in Africa would top the list. More incomes in Africa will very likely not bulge obesity rates.