Thinking

Global Rankings by Net Nutritional Status

Global Ranking by Effective Stature
Ranking Country Effective stature (cm) Height (cm) BMI Life Expectancy (years) Population (million)
200 Ethiopia 157 166 20 63 64
199 Eritrea 158 168 20 63 4
198 Timor-Leste 159 160 21 67 1
197 Rwanda 160 163 21 NA 8
196 Bangladesh 160 164 21 71 137
195 Madagascar 161 162 22 64 16
194 DR Congo 161 167 21 59 52
193 Zambia 161 167 21 60 10
192 Uganda 161 166 22 59 24
191 Niger 161 168 21 59 11
190 Viet Nam 162 164 22 72 79
189 India 162 165 22 67 1,004
188 Cambodia 162 163 22 67 12
187 Burundi 162 167 22 58 7
186 Sierra Leone 162 164 22 49 5
185 Mozambique 163 165 22 57 18
184 Central African Republic 163 167 22 51 4
183 Myanmar 163 165 22 65 44
182 Nepal 164 162 23 69 25
181 Lao PDR 164 161 23 64 5
180 Malawi 164 162 23 60 12
179 Indonesia 164 164 23 68 NA
178 Tanzania 164 165 22 61 34
177 Chad 164 170 22 52 8
176 Senegal 164 173 22 65 11
175 Burkina Faso 164 169 22 59 12
174 Kenya 164 170 22 64 31
173 Equatorial Guinea 164 167 22 58 0
172 Angola 164 167 22 60 10
171 Congo 164 167 22 63 3
170 Sri Lanka 164 166 23 73 19
169 Zimbabwe 164 169 22 59 12
168 Afghanistan 164 165 23 62 24
167 Somalia 164 167 23 54 7
166 Guinea 164 168 22 58 8
165 Nigeria 165 166 23 54 123
164 Philippines 165 163 23 66 81
163 Lesotho 165 166 23 51 2
162 Benin 165 167 23 60 7
161 Togo 165 168 23 60 5
160 Ghana 165 169 23 62 20
159 Guinea Bissau 165 168 23 58 1
158 Botswana 166 172 23 62 2
157 Namibia 166 167 23 61 2
156 Liberia 166 164 23 58 3
155 Mauritania 166 163 23 63 3
154 Yemen 166 160 24 64 17
153 Mali 166 171 23 57 10
152 Sudan 166 167 23 63 NA
151 Comoros 166 166 23 62 1
150 Gambia 166 165 24 61 1
149 Pakistan 167 167 23 66 146
148 Bhutan 167 165 24 70 1
147 Cote d’Ivoire 167 167 23 53 17
146 Djibouti 167 167 24 62 0
145 Cameroon 167 168 24 56 15
144 Sao Tome and Principe 168 167 24 67 0
143 Japan 168 171 24 81 126
142 Thailand 168 169 24 72 62
141 Gabon 169 168 24 64 1
140 Cabo Verde 169 173 24 71 0
139 China 169 172 24 75 1,263
138 Swaziland 170 168 25 55 1
137 North Korea 170 172 24 68 21
136 Maldives 170 168 25 77 0
135 Singapore 170 173 24 81 4
134 Papua New Guinea 170 164 25 64 5
133 Mauritius 170 170 25 72 1
132 Algeria 170 170 25 76 30
131 Bahrain 170 168 25 78 1
130 South Africa 170 167 25 60 45
129 South Korea 170 175 24 79 47
128 China (Hong Kong SAR) 171 174 25 NA NA
127 Bolivia 171 167 25 69 8
126 Malaysia 171 168 25 73 22
125 Guyana 171 170 25 64 1
124 Guatemala 171 163 26 70 11
123 Solomon Islands 171 164 26 70 0
122 Taiwan 172 175 25 NA NA
121 Morocco 172 170 26 75 30
120 Cuba 172 172 25 77 11
119 Vanuatu 172 168 26 70 0
118 Honduras 172 166 26 73 6
117 Dominica 172 176 25 NA 0
116 Peru 172 165 26 74 26
115 Iran 172 174 25 75 63
114 Suriname 172 173 25 69 0
113 Mongolia 172 169 26 66 3
112 Colombia 172 169 26 72 40
111 Grenada 172 177 25 71 0
110 Armenia 172 172 26 71 3
109 Dominican Republic 173 173 26 71 8
108 Tajikistan 173 171 26 69 6
107 Antigua and Barbuda 173 173 26 73 0
106 Tunisia 173 174 26 74 10
105 Seychelles 173 174 26 69 0
104 Panama 173 168 26 75 3
103 Azerbaijan 173 170 26 70 8
102 Kyrgyzstan 173 171 26 67 5
101 Nicaragua 173 167 27 73 5
100 Uzbekistan 173 169 26 70 25
99 Ecuador 173 167 27 74 12
98 Jamaica 173 175 26 74 3
97 Oman 173 169 27 75 3
96 Brunei Darussalam 173 165 27 76 0
95 Kazakhstan 173 171 26 66 15
94 Saint Vincent and the Grenadines 174 173 26 70 0
93 Brazil 174 174 26 72 176
92 Costa Rica 174 169 27 77 4
91 Russian Federation 174 176 26 66 147
90 Greenland 174 175 26 NA NA
89 Portugal 174 173 26 79 10
88 Turkmenistan 174 172 27 64 4
87 Haiti 174 173 27 61 8
86 El Salvador 174 170 27 69 6
85 Paraguay 174 173 27 73 6
84 Venezuela 174 172 27 70 23
83 Trinidad and Tobago 174 174 27 69 1
82 Barbados 174 176 27 73 0
81 Fiji 174 174 27 67 1
80 Egypt 175 167 28 69 71
79 Belize 175 169 28 68 0
78 France 175 180 26 80 61
77 Mexico 175 169 28 74 100
76 Moldova 175 175 27 68 4
75 Libya 175 174 27 71 5
74 Albania 175 173 27 75 3
73 Syrian Arab Republic 175 170 28 55 16
72 Bahamas 175 173 27 73 0
71 Marshall Islands 175 163 29 NA NA
70 Saudi Arabia 175 168 28 74 23
69 Austria 175 177 27 80 8
68 Romania 175 175 27 72 22
67 Belarus 175 178 27 68 10
66 Turkey 175 174 27 73 67
65 Iraq 175 170 28 67 23
64 Italy 175 178 27 81 58
63 Netherlands 175 183 26 81 16
62 Uruguay 175 173 28 73 3
61 Denmark 175 181 26 79 5
60 Bosnia and Herzegovina 175 181 27 75 4
59 Micronesia (Federated States of) 175 169 28 69 0
58 Finland 175 180 27 79 5
57 Switzerland 175 178 27 82 7
56 Bermuda 175 173 28 NA NA
55 Slovenia 175 180 27 79 2
54 Saint Kitts and Nevis 176 170 28 NA 0
53 United Arab Emirates 176 170 28 77 3
52 Chile 176 172 28 77 15
51 Georgia 176 174 28 69 5
50 Spain 176 177 27 81 40
49 Serbia 176 181 27 74 10
48 Ukraine 176 178 27 66 49
47 Sweden 176 180 27 81 9
46 Montenegro 176 178 27 74 1
45 Occupied Palestinian Territory 176 172 28 NA NA
44 Bulgaria 176 178 27 72 8
43 Jordan 176 171 29 73 5
42 Luxembourg 176 178 27 81 0
41 Andorra 176 176 27 NA NA
40 Malta 176 173 28 80 0
39 Cyprus 176 175 28 79 1
38 Lithuania 176 179 27 69 4
37 Poland 176 177 28 74 39
36 Belgium 176 182 27 79 10
35 Argentina 176 175 28 74 37
34 Macedonia (TFYR) 176 178 27 74 2
33 Lebanon 176 174 28 75 4
32 Puerto Rico 176 172 28 NA NA
31 United Kingdom 176 177 27 80 60
30 Qatar 176 170 29 78 1
29 Kiribati 176 169 29 64 0
28 Palau 176 168 30 NA 0
27 Iceland 176 180 27 81 0
26 Canada 176 178 27 81 31
25 Israel 176 177 28 81 6
24 Estonia 176 182 27 73 1
23 Greece 176 177 28 79 11
22 Latvia 176 181 27 70 2
21 Slovakia 176 180 28 74 5
20 Norway 176 180 27 81 4
19 Germany 177 180 27 79 82
18 Nauru 177 168 32 NA NA
17 Kuwait 177 172 29 74 2
16 Australia 177 179 28 81 19
15 Tuvalu 177 170 30 NA NA
14 Saint Lucia 177 172 30 73 0
13 Hungary 177 177 28 73 10
12 New Zealand 177 178 28 81 4
11 Ireland 177 179 28 80 4
10 Czech Republic 177 180 28 76 10
9 Croatia 177 181 28 75 4
8 United States of America 178 177 29 77 282
7 Cook Islands 178 175 33 NA NA
6 American Samoa 178 176 33 NA NA
5 Samoa 178 174 31 72 0
4 Tokelau 178 175 32 NA NA
3 French Polynesia 178 177 30 NA NA
2 Niue 178 176 32 NA NA
1 Tonga 178 177 31 71 0

Notes. Effective stature is an actuarially fair measure of net nutritional status. It combines anthropometric and actuarial information and is meant to be interpreted as a general measure of living standards. Simply put, it is the combination of mean population height and mean population BMI (linear in the former and quadratic in the latter) that best tracks life expectancy. Effective stature contains roughly the same information as the Waaler Index. Our estimates are based on the Waaler surface we extracted from panel data. We have rescaled and recentered our estimates to match the mean and variance of average height across nations. One can thus think of it as effective or risk-adjusted stature.

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An Ultra-Brief History of Living Standards, 10000BC-Present

Here’s what I see going on with polarization since the human career began. The orthodoxy has it that the Neolithic Revolution and the Industrial Revolution demarcate stadial social evolution. Both the Neolithic Revolution and the Industrial Revolution are either overrated or misdated. The former is dated to ten or twelve centuries before Christ and said to last until the birth of civilization; the latter to 1760-1830 Britain. If we buy the anthropometric evidence, the second is bunk. There is also good reason to believe that Andrew Sheratt is right on the money so that what really mattered in the former case was not the Neolithic Revolution but the Secondary Products Revolution which began at Uruk and spread out from there between 4000 and 3000 BCE.

What the anthropometric evidence points to is that the premodern pattern prevailed until the late-nineteenth century. Indeed, it was not until the mid-century passage, 1920-1960, that the hockey stick is hit in earnest. I want to argue that it was the Second Industrial Revolution, 1860-1960, that made the hockey stick in living standards possible in the temperate zone; that it was the Secondary Industrial Revolution that repealed the Malthusian law; just as Sheratt’s Secondary Products revolution made Uruk the first central (and then the only existing) power. The issue is that there were indeed two general revolutions in the material possibility frontier. These were the secondary revolutions that revolutionized the value of generalized domestications in the first case and in the second case, generalized industrial production with fossil fuel-powered machinery in agriculture, industry, and transport, thus revolutionizing the thermodynamic basis of civilization.

There is indeed some truth to the stadial frame. The secondary revolutions made possible hitherto unimaginable living standards. In the first case, this began at Uruk sometime during the Ubaid period. From there on Babylonia was a part of the Central Civilization—the longest continuously urbanized macro-region is the zone at the intersection of Europe, Africa, and Asia. This is of course no coincidence. What happened in Babylonia is that a major climate shock made the lower Euphrates extraordinarily fertile. At Uruk in the fourth millennium BCE, agricultural yields, pottery manufacturing, metallurgy took off; they invented writing, administration, the potter’s wheel, the plow and the wagon; domesticated oxen for traction, cattle for milk products, and the donkey for transport; and mastered micro-domestication (leavened bread, cheese, beer and wine). The ‘early high civilization’ is an extraordinary case of polarization. The Sumerians played an extraordinary role as the founders of the Central Civilization when they had the field to themselves in the fourth millenium BCE. It was the transmission of Babylonian wagon technology at the very edge of the Uruk world-system, the steppe (southern Russia) that triggered the very formation of the Yamanaya (Proto-Indo European speakers). The wagon allowed the Yamanaya to tame the steppe since they could exploit the otherwise meager open steppe by near constant movement, as David Antony has argued so forcefully. In the history of the peopling of the world by west eurasians, Uruk thus plays the dramatic role usually assigned to alien civilizations in fiction.

The Secondary Industrial Revolution identified by Gordon as the Second Industrial Revolution, 1870-1970, made both living standards and war potential higher than hitherto imagined. (See the evidence from stature.) More generally, the evidence from anthropometry cannot be reconciled with the orthodox narrative. Let’s not forget that the hockey stick adds a dynamic element to the global condition. Interestingly, latitude is not priced in c. 1880. Far from being a “constant,” latitude’s influence was weak in the premodern era when stature was a function of the local availability of protein. It is the hockey stick that radically polarizes the world in terms of living standards. Latitude itself has a history that has yet to be told. But then so do both the secondary revolutions.

Any credible deep history of human welfare must put the secondary revolutions at the center of the narrative frame.

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Estimation of Waaler Surfaces from Panel Data

In the international cross-section everything we care about is correlated. It is difficult if not impossible to nail down the causal channels at play. For instance, disease burden is a very strong correlate of labor productivity. Is it because higher disease burdens cause productivity to be lower or is it because disease burdens are correlated with other variables such thermal burdens which in turn directly suppress productivity? That was my reading of the cross-section. In this particular case the logic is compelling since both are functions of latitude. Yet, the argument is very far from water tight. Recent research has focused on identifying the causal channels in play by looking at controlled settings and exploiting natural experiments. It has yielded compelling evidence in favor of the Heliocentric theory.

I have been trying to interrogate the information embedded in anthropometric measures of everyday living standards. They are a compelling alternative to economic measures such as per capita GDP and real wages. Moreover, they bring attention to the nexus between human health and living standards. The picture that is emerging suggests that hot nations are poor because they are stuck in a nutrition-health-productivity trap. The polarization of the world is a function of the spread of the Second Industrial Revolution. The latter is a function of latitude—the temperate world industrialized; the tropical did not. This divergence obtained because heat, malnutrition and disease burdens suppress productivity; and because productivity is low, the expansion of the formal sector is limited by the international division of labor (for an open economy) or macroeconomic stability and the balance of payments constraint (under relative autarchy); in turn, the small size of the formal sector reinforces the vicious nutrition-health-productivity trap.

If one is interested in understanding global polarization, Waaler surfaces are a good place to begin. This is essentially any plot that graphs risk as a function of height and weight. For starters, we would like to know how life expectancy relates to height and weight (equivalently, height and BMI). We can do that with a glance at a Waaler surface.

The method proposed and implemented by Kim (1996) requires mortality tables arrayed by height and weight. These are very difficult to find. That may be a reason why Waaler surfaces have not been taken up by researchers and actuaries. We propose a straightforward method to estimate Waaler surfaces from panel data.

We begin with the obvious linear model. Suppose we were to posit that life expectancy is a linear function of population height and weight.

naive_model

The naive linear model.

 

We are then immediately faced with the elementary problem that life expectancy has been trending upwards. And so has stature and weight.

 

 

One could add TIME as a variable but there is an easier way to control for underlying trends in our three variables. Namely, we use deviations from global means instead. We then have Model 1. (See next figure.) Since all three variables are expressed as deviations from global means, they are automatically detrended.

model(2)

Model 1.

Alternately, we could just add mean life expectancy by year as a covariate for then that would absorb the trend. Happily, both strategies yield very similar estimates for height and BMI. We use the dataset for 200 countries compiled by NCD-RisC. We find that the gradient of height is 0.25 (so that a population that is 10cm taller enjoys a 2.5 year premium in life expectancy) and that for BMI is 2.34. Both are significant at the 1 percent level. Height explains 8 percent of the variation and BMI explains 42 percent; together they explain precisely half the variation in the panel data.

However, this is not quite right. For we know from Kim that mortality and mobility risk are quadratic in BMI. This observation motives Model 2.

quadratic_model

Model 2. Linear in height and quadratic in BMI.

Coefficient estimates and fit statistics can be read off the following figure. We find that the quadratic term is significant although it explains only 5 percent of the variation. (Height explains 7 percent and BMI explains 39 percent.)

Model

Model 2 estimates.

Model 2 is a basic 4-parameter model that captures the essentials of the trade-off between height and BMI at the heart of the Waaler surface approach. We can now plot our baseline estimate of the Waaler surface. The next figure displays isorisk curves, ie lines of constant life expectancy. The numbers displayed on the isorisk curves are an affine function of estimated life expectancy; they represent the ordering not the actual values.

Waaler_surface

Waaler surface constructed from Model 2 estimates.

Now that we have figured out how to obtain a Waaler surface from panel data, what can it tell us about living standards across the world? The next figure shows the location of selected countries in 2000 and 2014. We see that the US has lower life expectancy than the northern Europeans both because of lower stature and higher-than-optimal BMI. India, Bangladesh, and Viet Nam are at the other extreme. They have much lower predicted life expectancy both because they are shorter and because their BMI is too low.

Countries

Evolution of selected countries on the Waaler surface.

More generally, this technique can be used to construct Waaler surfaces for other measures of risk. Doing that for a number of risks should be illuminating. Moreover, with Waaler surfaces handy we can better examine the progress made by countries and macroregions or the lack thereof. Because half of the variation in risk is due to movements across the Waaler surface (this is the modern pattern; in the premodern period essentially all of the variation was captured by movements across the Waaler surface) we obtain a much finer resolution on the economic information embedded in anthropometric data.


 

Waaler_all

The contemporary location of all 200 countries in the data set on the Waaler surface for life expectancy.

 

 

 

 

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The geometry of Waaler surfaces

Waaler (1984) wondered whether looking at both and weight would be more informative than a single measure of nutritional status such as stature or BMI (the ratio of weight in kilograms to squared height in meters). He suggested that one could plot mortality risk (or any other measure of mortality or morbidity risk) as a function of height and weight. This suggestion was realized by Kim (1996) in his doctoral dissertation who named the resulting graphical survival table representation a Waaler surface.

20180714_173107

Figure 1. A Waaler surface.

The geometry of Waaler surfaces turns out to be particularly simple for stable populations. Kim (1996) observed that the mortality risk information encoded in isorisk curves tend to satisfy three empirical regularities. First, risk is monotonically decreasing in stature; taller populations have a higher nutritional status for the same BMI as shorter populations. Second, optimal BMI is a decreasing function of stature; taller populations need to have smaller BMIs to face the same mortality risk. Third, risk is quadratic in weight; at any given height, health status is a function of the squared distance from optimal BMI.

Kim (1996) defined the Waaler Index as the difference in overall risk between a comparison and a reference population attributable to differences in shifts along the Waaler surface; that is, the component explained by  differences in the frequency distributions of height and weight. Let A be the reference population and B the comparison population. Let f denote the joint density of height and weight and R the survival table arranged by height and weight. Then the Waaler Index is given by,

Waaler_Index.png

We can define an equivalent measure as follows. Notice that under the three regularity assumptions noted above, Waaler surfaces are effectively one-dimensional. Specifically, we can map all points on an isorisk curve to the point where the isorisk curve intersects the optimal weight line. In Figure 2, for instance, B is risk-equivalent to D. This means that the Waaler Index captures exactly the same information as what we will call effective stature: the mean stature of a population facing the same average risk as B but with optimal BMI. This single overall measure captures all the actuarially-relevant information contained in the Waaler surface.

20180714_172758

Figure 2. Risk measures with Waaler surfaces.

Under the same regularity conditions as above, we obtain a separation of variables. Even though risk is a joint function of height and weight, we can decompose the effect of BMI and stature geometrically. In Figure 2, B and C have the same stature. The difference in their mortality risk is the BMI risk premium, the greater risk incurred due to a suboptimal BMI. The difference in risk between A and C is then due solely due to the lower stature of population C and not due to BMI. We call this the stature risk premium. We can measure both in either centimeters or survival probabilities. We shall use the former.

Note that BMI is a measure of short-term health status while stature is a long-term measure of health status. This difference is extremely important for it allows us to think of the stature risk premium (the difference in stature between the comparison population and the reference population) as reflecting steady-state differences between populations since stationary populations sit on the optimal weight curve by construction. In economic history applications then, it is this latter measure that has pride of place. For instance, the settler premium in the early modern era that we isolated should be thought of in terms of the stature risk premium. Similarly, one can think of the latitude premium that exists due to differential protein availability at different latitudes (say as captured by cattlehead per capita).

20180714_183623.jpg

Figure 3. Settler premium and latitude premium.

So we obtain two different anthropometric measures of health status. Stature risk premium is suitable for questions of long-term socio-economic polarization. Effective stature is an actuarially fair measure of health status suitable for overall contemporaneous evaluations. The two charts after the references are from Kim (1996).


References
Waaler, Hans Th. “Height. Weight and mortality the Norwegian experience.” Acta medica scandinavica 215.S679 (1984): 1-56.
Kim, John M. Waaler surfaces: the economics of nutrition, body build, and health. Diss. PhD dissertation, University of Chicago, USA, 1996.
globalNorway_France_England

 

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Latitude and World Order: Evidence from the Cross-Section of Human Stature

Adam Tooze​ wrote recently about the challenge posed by economic statistics both to historical actors and to historians. The upshot is that all national economic statistics must be taken with a pinch of salt. This concern has prompted me to focus on human stature instead of per capita income and output per worker as the carrier of information on global polarization. What I have discovered has astonished me. The picture that emerges suggests that latitude was not in fact all that important until the passage to modernity. Indeed, the empirical evidence reveals that the world got polarized along latitude only in the twentieth century. It suggests that a satisfactory history of global polarization has yet to be written; one that ties the polarization of global living standards to the Second Industrial Revolution.

Gradients

Figure 1. Transition to modernity in global stature. Our reported gradient is the product of (1) the slope coefficient in a simple linear regression with both variables standardized to have mean 0 and variance 1 (also called beta) and (2) 1-pValue of said slope coefficient. This means that insignificant coefficients are mechanically attenuated.

The basic outline of human statures over the past three hundred years is that until about the mid-nineteenth century the classic premodern pattern held—everywhere we had medium-term cycles characteristic of the Malthusian Trap and a significant settler colonial premium in stature. In 1860-1890, the settler colonial premium vanishes, but global polarization was still modest. It is not until 1920-1960 that we hit the hockey stick in earnest. In the century 1860-1960, Western heights grew 10cm on average; 7cm in 1920-1960 alone. Elsewhere heights grew much more modestly. By 1960, global statures stabilized in the modern pattern—highly polarized along latitude with the Dutch leading the way. Everyday living standards were revolutionized by the Second Industrial Revolution because it was broad-based enough to repeal the Malthusian Law. That’s why the settler premium vanished and we hit the hockey stick. Any explanation of global polarization must take latitude as the point of departure because thermal burdens dictate the work intensity that can be sustained on the factory floor and therefore the cross-section of output per worker.

latitude

Figure 2. Latitude and stature.

With this interpretation in mind (premodern 1700-1890, hockey stick 1920-1960, modern regime 1960-) the following estimates make for very interesting reading. Turns out, latitude was not the strongest correlate of stature in premodern 1880; it was cattlehead per capita. (As it presumably had been since the Secondary Products Revolution.) To be sure, latitude was definitely priced in as well. But latitude explained less than 10 percent of the variation. (The percentage of the variation in Y explained by X is just the square of the gradient once we standardize both X and Y to have mean 0 and variance 1.)  Meanwhile, cattle per head explained 37 percent of the cross-sectional variation in 1880. This is evidently the premodern pattern. Given the extraordinary cost of bulk transport, the governing variable for stature was the local availability of protein (meat and secondary). It is astonishing that this premodern pattern persists until as late as 1880. The exit from the Malthusian Trap was indeed very, very slow.

transition_to_modernity

Figure 3. Transition to modernity. Latitude, cattlehead, and stature.

By 1920, the relative positions of latitude and cattlehead per capita had reversed. The latter fell into insignificance. But latitude explained no more than 18 percent of the cross-sectional variation so that everyday living standards were still only modestly polarized. It is only in 1960 (and thereafter) that the coefficient of latitude becomes 0.68, meaning that it singlehandedly explained 46 percent of the cross-sectional variation in stature. This corresponds to the considerably heightened polarization in everyday living standards in the modern era (1960-). Cattlehead meanwhile continues to be priced in (even after controlling for latitude) but explains only a modest 5 percent of the variation. See the estimates reported in Table 1-3 below and Figure 3 above.

income

Figure 4. Per capita income and stature.

burden

Figure 5. Disease burden and stature.

Before the turn of the century, latitude was priced into the cross-section of stature but not after controlling for cattlehead per capita. Astonishingly, even per capita income is not a significant correlate of stature (in 1920 and 1960—we don’t have sufficient observations to test this in 1880) once we control for latitude or disease burdens (infant mortality). The evidence can be read off Table 1-3.

Table 1. Stature, 1880
Standard errors below estimates. Estimates in bold font are significant at 5 percent.
Latitude 0.32 0.25 0.32
0.14 0.13 0.25
Cattle 0.61 0.51
0.15 0.16
Infant mortality -0.29 -0.34
0.11 0.14
Income 0.54 0.43 -0.04
0.17 0.19 0.19
N 42 43 11 18 39 18 18
R^2 0.12 0.30 0.42 0.39 0.36 0.45 0.45
adj R^2 0.09 0.28 0.36 0.35 0.32 0.37 0.37
Table 2. Stature, 1920
Standard errors below estimates. Estimates in bold font are significant at 5 percent.
Latitude 0.43 0.39 0.54
0.12 0.13 0.22
Cattle 0.36 0.23
0.12 0.13
Infant mortality -0.36 -0.23
0.17 0.23
Income 0.85 0.53 -0.12
0.17 0.20 0.43
N 58 61 13 23 55 23 23
R^2 0.19 0.13 0.28 0.53 0.25 0.64 0.64
adj R^2 0.18 0.11 0.21 0.51 0.22 0.60 0.60
Table 3. Stature, 1960
Standard errors below estimates. Estimates in bold font are significant at 5 percent.
Latitude 0.68 0.67 0.58
0.10 0.10 0.20
Cattle 0.33 0.21
0.12 0.11
Infant mortality -0.72 -0.74
0.10 0.19
Income 0.66 0.18 -0.04
0.10 0.20 0.19
N 58 61 43 58 55 51 51
R^2 0.45 0.11 0.58 0.42 0.50 0.49 0.49
adj R^2 0.44 0.09 0.57 0.41 0.49 0.47 0.47

The empirical case for the Heliocentric model becomes even stronger once we observe that per capita income and disease burdens (and cattlehead until 1920) are themselves functions of latitude. See Table 4.

Table 5. Functions of latitude.
Standard errors below estimates. Estimates in bold font are significant at 5 percent.
Infant Income Cattle
1880 1920 1960 1880 1920 1960 1880 1920 1960
Latitude -0.19 -0.34 -0.84 0.48 0.70 0.84 0.41 0.34 0.25
0.64 0.45 0.09 0.27 0.18 0.09 0.13 0.13 0.13
N 11 13 39 20 23 52 55 56 56
R^2 0.01 0.05 0.71 0.15 0.42 0.65 0.16 0.11 0.06
adj R^2 -0.10 -0.04 0.71 0.10 0.39 0.65 0.14 0.09 0.05

 

Table 6. Gain in stature, 1920-1960
Standard errors below estimates. Estimates in bold font are significant at 5 percent.
Latitude 0.56
0.12
Cattlehead per capita (change) -0.08
0.13
Per capita income (change) 0.33
0.19
Infant mortality (change) -0.64
0.23
N 58 61 23 13
R^2 0.29 0.01 0.13 0.41
adj R^2 0.27 -0.01 0.08 0.36

What is especially striking is that gain in per capita income is a poor predictor of gain in stature. Change in per capita income in 1920-1960 does not explain gain in stature in the same period (although admittedly, the sample is small). What explains the cross-section of the gain in stature is again latitude. See Table 5. Also compare the bottom-right graphs in Figure 2, Figure 4, and Figure 5.

So the question is not whether but why global living standards are polarized along latitude. Moreover, the weight of the empirical evidence suggests a very late date for polarization in living standards. Before the onset of the global condition living standards were not radically different across the world. As late as 1920, the gradients were modest. Polarization in global stature really and truly obtains only during the mid-century passage, 1920-1960. This is consistent with the modern understanding of global living standards we find in the work of David Edgerton and Adam Tooze. Fascinating that one can read it off the cross-section of stature so clearly.

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An Illustrated Guide to Western Stature, 1700-2000

It has recently been rediscovered that stature contains information about the populace’s health, well-being and standard of living. Now that we are all sick of national economic statistics, perhaps it is time to examine the evidence from human biology. It is known that mean national height is a strong correlate of per capita income, life expectancy, infant mortality, disease burdens, latitude and mean temperatures in the cross-section. We shall however concentrate on the dispersion and evolution of Western stature in this dispatch, for as we shall see, this variable contains very interesting information on historical polarization within the Western world.

Stature

Figure 1. Western stature, 1710-1990. Source: Clio Infra.

Figure 1 displays the mean heights in eight rich, Western nations. In the eighteenth century, the Americans towered over the Europeans. In 1710-1790, they were on average around 5cm taller than Britons, Swedes and Dutch, 6cm taller than the Italians, and 7cm taller than the French. In the nineteenth century, we see Americans and Canadians towering over the Europeans. This supremacy was not confined to North America. Figure 2 shows stature in Anglo-Saxon settler colonies and Britain. We see that there existed a significant setter colonial premium in stature that did not vanish until the end of the nineteenth century. Despite the British Industrial Revolution in 1780-1830, Britain did not close the gap with the Americans, Canadians, Australians, and the Kiwis until the turn of the century.

Gap

Figure 2. Anglo-Saxon stature. Source: Clio Infra.

We can calculate the settler colonial premium more broadly as the mean difference with American heights. Figure 3 displays this measure. We see that the premium broadly vanished over the course of the late-19th century. Note that the US-Canadian differential is bounded by (-2cm,+2cm), which we can think of as containing information on the underlying volatility of the error term. With this ballpark in mind, we can be confident that the ~6cm premium during the ancien régime, 1700-1860, is significant. Note that 6cm is the central tendency until the mid-nineteenth century. But the range is wide. Americans were more than 10cm taller than Germans in 1840, but only 4cm taller than Swedes. Britons came within reach of a 2cm premium in 1750 but diverged again, not to close the gap until the turn of the century. And it was really only in 1930 that Britons became taller than Americans for the first time.

 

SettlerPremium

Figure 3. Settler colonial premium in Western stature, 1710-1980. Source: Clio Infra.

More broadly, the settler colonial premium vanished over the course of the late-nineteenth century. But the transition to modern stature does not take place until the mid-20th century. Go back to Figure 1. As late as 1920, we had no observations over 174cm. By 1960, they are all above 174cm. So we have two different transformations. First, the settler colonial premium vanishes in 1860-1890. Then, after 1920 but continuing a movement that started decades earlier in many countries, mean heights increase rapidly until they stabilize by 1960.

In the modern regime, 1960-1980, the Dutch have enjoyed an extraordinary primacy. They are about 2-3cm taller than the Germans and the Swedes, 3-4cm taller than Americans and Canadians, 6cm taller than Britons and the French, and an extraordinary 8cm taller than the Italians. This polarization is suggestive. Might the naive Heliocentric theory explain it? It is indeed known that latitude is priced into the cross-section of stature. Since we don’t have a large enough sample we cannot try to replicate that result. Still, Figure 4 suggests a ballpark estimate of 20mm per degree so that a 20 degree difference in latitude predicts a 4cm difference in stature. That’s a very impressive gradient for what is dismissed as a “Tropical issue.” And the earlier estimates for the gradient are much larger. The truth is likely closer to the latter since even after the vanishing of the settler colonial premium, the United states is an outlier. The problem is of course that the US is so large that even though it has a mean latitude of 37º, much of the country lies in the temperate zone. So the true gradient is probably closer to 30-40mm per degree than 20mm, implying that a 20 degree increase in latitude would predict a 6-8cm gain in stature with all that implies about everyday living standards.

CrossSection

Figure 4. Latitude and stature in selected Western countries. Source: Clio Infra, author’s computations. Small sample estimates. Note that we drop the United States and Canada from our 1820 and 1860 samples to avoid the distortion induced by the setter premium.

How do we make sense of the panel evidence in Figure 1? We suggest the following periodization: premodern era 1700-1860, transition period 1860-1960, and modern era 1960-1980. A significant setter colonial premium of around 4-8cm was the invariant of the premodern era. European stature was always below 170cm, roughly around 167cm, while the Anglo-Saxon setters were all above 170cm, roughly around 172cm. National stature went up and down in medium-term cycles characteristic of the Malthusian trap. Multiple cycles can be discerned in Figure 2. As late as the mid-19th century Americans were getting shorter by the decade. Things got better for two or three generations, then they got worse for a while. Stature fell together with real wages and life expectancy. Repeat ad infinitum … or more precisely, until the exit from the Malthusian trap. That exit did not obtain until after 1900. See Figure 5.

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Figure 5. Decadal growth in stature in the US, Britain, and Canada. Source: Clio Infra.

In the modern era, 1960-1980, Western heights were distributed around 180cm, a full 10cm above the Malthusian boundary between the settler colonies and the continent. They are ordered roughly by latitude with the Dutch leading the way. Dutch primacy is an invariant of the modern regime. But the broader Heliocentric polarization is a much older story. It holds before and after the exit from the Malthusian trap.

During the transition era, we first see the collapse of the settler colonial premium in 1860-1990, and then the beginnings of a major upward movement in 1890-1920. But it is only in the course of the transformative mid-century passage, 1920-1960, that all previous records for mean stature are broken. As late as 1920, the Western average was still 171cm; high for a premodern European country but not for the settler colonies. But by 1960, the Western average jumps to 178cm. Over the whole century of the transition to modernity, 1860-1960, Western stature increased by 10cm; clocking an astonishing rate of increase of 1cm per decade.

The evidence from human stature suggests that the physical environment dictated everyday basic living standards in the ancien régime. This meant that there was a significant settler premium. Anglo-Saxon setters dwarfed Europeans. The vanishing of this setter premium in 1860-1890 suggests an earlier data for transition to modern living standards than the period of major growth in Western stature, 1930-1960. But these suggested dates are in fact consistent. What we have here is this: The Second Industrial Revolution, which unlike the more limited British revolution, 1760-1830, was broad-based enough to repeal the Malthusian Law. The vanishing of the setter colonial premium attests to this fundamental transformation in the nature of the game. It is only then, after the turn of the century, that everyday living standards are revolutionized. We emphasize that the revolution in Western living standards obtained very, very slowly. It is not until the mid-century passage that we hit the hockey stick in earnest.

The revolution in stature was in no way confined to the Western world. Figure 7 shows that the transition to the modern pattern was global in 1860-1960. The global median rose from 165cm in 1860 to 170cm in 1960, exactly where it is now. Although patchy coverage suggests caution, there seems to have been a decline in global stature in the neoliberal era. Median global height fell 2cm in 1980-1990, then recovered half the loss in 1990-2000.

Neoliberal collapse in global stature

Figure 7. Evolution of global stature. Source: Clio Infra.

The broad historical pattern of Western and global stature suggests that the transition to modernity took place in 1860-1960. This was accompanied by a Great Divergence in living standards. Western stature rose roughly 10cm from around 170cm to 180cm, while world-wide (Western and non-Western) median stature rose by only 5cm from 165cm to 170cm, implying that the rise in non-Western standards was much more modest.


Bonus chart.

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Figure 8. Setter colonial premium in Anglo-Saxon stature. Source: Clio infra.

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Thinking

Isotherms and Regional Polarization: Evidence from Italy, US, Chile, China, and India

It was argued in the previous dispatch that the international cross-section of productivity and hence per capita income is explained by the distribution of heat on the surface of the planet. More precisely, productivity is a function of the intensity and tempo of work performed in a factory. And the intensity and tempo of factory work is a function of the thermal environment. That the human thermal balance is a binding constraint on work intensity in countries across the globe is clear from known facts about global temperatures (countries face wildly different thermal regimes) together with known facts about the human physiological response to heat (the human thermal balance equation).

Wet bulb temperature

Figure 1. Work performance and heat. Source: Wyndham (1969).

Figure 1 shows the fall-off in work performance in a South African mine where precise control of wind velocity and wet bulb temperature allowed Wyndham to carry out scientific controlled experiments. As a physiologist in Apartheid South Africa, Wyndham was able to test racial differences under controlled conditions. What he found was that the fall-off in productivity as a result of heat was more or less uniform across the races. The most important factor in work intensity was acclimatization. Between acclimatized Bantu and South African White men, the differences in heat tolerance and productivity were minor compared to the importance of wind velocity, which is already a second-order correction to the contribution of humidity-adjusted temperature (“wet bulb temperature”).

The Heliocentric model has a specific implication for the regional polarization of countries straddling isotherms. Namely, cooler regions should be richer than warmer regions. We can test this hypothesis by visual inspection of per capita income and temperatures. We begin with Italy.

Italy

Figure 2. Isotherms and per capita income distribution in Italy.

Figure 2 displays the mean temperatures and per capita incomes in Italy. This is the  pattern we expect to find if the Heliocentric Hypothesis is true. The cool, northern extremity had a mean per capita income above 30,000 euros in 2010; the hot, southern half of the Italian peninsula had a mean per capita income less than 18,000 euros. The former is close in per capita income to the countries of northwestern Europe; the latter to the Mediterranean region.

If the Heliocentric model is correct, mean temperatures should be “priced in” the cross-section of per capita income across regions. By that we mean that the gradient in a simple linear regression of per capita income onto mean temperatures should be economically and statistically significant. This is easy enough to check by hand.

Italy.png

Figure 3. The Italian cross-section.

Figure 3  displays the mean urban temperatures and per capita incomes of the Italian administrative regions. We obtain a statistically significant gradient of 1,683 euros per degree Celsius. That is, a six degree difference, such as that which exists between Sicily and Lombardy, translates into a difference of nearly 11,000 euros in per capita income. The scatter plot in fact suggests that there are two quite different clusters with their own gradients: Rich Italy, Poor Italy. Sardinia has the highest income of the latter but is still shy of 20,000 euros. Liguria has the lowest income of Rich Italy, 27,200 euros. There is a “gap” of 7000 euros where we have no observation.

Figure 4 displays Rich Italy and Poor Italy data two side-by-side. The gradient of mean temperature for rich Italy is a statistically significant 927 euros per degree Celsius; that of the poor Italy is not statistically significant but it has the right sign. The thermal variable explains 30 percent of the variation in the full sample, and 47 percent of the variation once we restrict the sample to the rich regions. That’s very high.

 

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Figure 4. Rich Italy, Poor Italy.

Table 1 displays the mean temperature and per capita incomes of the 15 administrative regions of Italy.

Table 1. Regional polarization in Italy.
Region Per capita income Mean temperature (Celsius)
Sicily 16,600 18.4
Apulia 17,100 15.8
Campania 16,000 15.7
Lazio 29,900 15.7
Liguria 27,200 14.7
Emilia-Romagna 32,100 14.0
Tuscany 28,200 13.6
Sardinia 19,700 13.5
Piedmont 28,200 12.6
Calabria 16,400 12.4
Lombardy 33,900 11.9
Veneto 30,200 11.9
Basilicata 18,300 11.7
Trentino-Alto Adige 34,450 10.0
Aosta Valley 33,700 9.7

We move on to the United States. Figure 3 displays the cross-section of per capita income for US states. When we project per capita income onto mean temperatures we obtain a gradient of $740 per degree Celsius (statistically significant at 5 percent). That is, if the mean temperature of a state is just 5 degrees Celsius higher, we expect its per capita income to be $3,700 lower. To be sure, the temperature gradient only explains 12 percent of the interstate variation in per capita income, so this is obviously an inadequate theory of regional polarization in the United States. But the gradient is priced in. And that is really remarkable. The United States is the most powerful and capable state in the world. If even the US cannot counter the polarization induced by the heat, we ignore the thermal variable at our own peril.

States

Figure 3. Regional polarization in the United States.

A very interesting case is that of Chile. It extends more than 4000 kilometers on a roughly north-south axis but is nowhere more than 200 kilometers wide. It hugs the Andes for almost the entire length with the result that elevation plays a very significant role in governing the isotherms (lines of equal temperature). The northern bit is the Atacama desert; sparsely populated and with significant mining wealth. The southern bit is again sparsely populated forest merging into tundra as one goes further south. The central zone is the bread-basket where two-thirds of the population lives. We have to keep these observations in mind when we examine Chile’s regional polarization.

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Figure 4. Chile, population density and per capita GDP.

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Figure 5. Chile, rainfall, temperature, and per capita GDP.

 

Half the Chilean population lives in Santiago and Valparaíso where the per capita GDP is in the range $20,000-$25,000. The Köppen climate classification of these two regions is Cfc meaning that they have a temperate climate with a dry, cold summer. This is very attractive territory and the concentration of population here is no coincidence. By contrast, Maule, Bío Bío and Araucanía, where a quarter of the Chilean population lives, are classified as Csb, meaning that they have a temperate climate with a dry, warm summer. Their per capita income is in the range $11,000-$16,000. So the difference between a temperate climate with dry, cold summer and a temperate climate with dry, warm summer translates into a $9,000 advantage. That’s roughly the difference is per capita income between Wisconsin and Kentucky.

Though it has a low population density, O’Higgins is exceptional. Despite hot summers, the million odd people of O’Higgins have an average income of $21,500. (Geography is not destiny.) The high per capita incomes in some of the sparsely populated northern and southern regions is a function of mining activity that is quite pronounced particularly in the Atacama desert.

Table 2. Regional polarization in Chile.
Name Latitude rank Area Population Population density per capita income Climate
Arica and Parinacota 1 16,873 239,126 14 13,268 Cold arid desert
Tarapacá 2 42,226 336,769 8 27,100
Antofagasta 3 126,049 622,640 5 60,439
Atacama 4 75,176 312,486 4 29,278
Coquimbo 5 40,580 771,085 19 14,355
Valparaíso 6 16,396 1,825,757 111 20,223 Temperate, dry cold summer
Santiago 7 15,403 7,314,176 475 25,328
O’Higgins 8 16,387 918,751 56 21,501 Temperate, without dry season, hot summer
Maule 9 30,296 1,042,989 34 13,971 Temperate, dry warm summer
Bío Bío 10 37,069 2,114,286 57 15,633
Araucanía 11 31,842 989,798 31 11,574
Los Ríos 12 18,430 404,432 22 14,623 Temperate, without dry season, warm summer
Los Lagos 13 48,584 841,123 17 16,277
Aysén 14 108,494 108,328 1 26,949
Magallanes 15 132,291 164,661 1 27,968 Temperate, without dry season, cold summer

We turn now to Asia where the real action is. At least if the following “high-pass” population density map is interpreted strategically. Germany, the Gangetic plain and the region between the Yangtze and the Yellow Sea emerge as the core regions of the globe.

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Figure 6. A “high-pass” world map of population density.

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Figure 7. A “high-pass” map of population density in eastern Eurasia.

The “high pass” density map in Figure 7 shows a finer resolution of the core regions of eastern Eurasia. This is the key figure to keep in mind. On the subcontinent, population densities are very high along a vast belt stretching from Punjab to Bengal. This is good alluvial soil constantly replenished by the floodwaters of the Ganges. The same riverine detail is behind the Chinese distribution. (Note the shape of the high density zone in China.) Indeed, Asian population density maps onto the river systems originating in the Tibetan plateau. See Figure 8.

rivers-tibet

Figure 8. The Tibetan origins of the river systems of eastern Eurasia.

We have to keep these in mind as we look at India and China. The two have been dealt very unequal hands by the dealer. Recall the thermal map of the globe (Figure 9 below) that displays the number of days of the year with temperatures above 20 degrees Celsius. We see that India is hot, while China is cool. However, southern China is hotter than northern China. Might this explain the density map of China in Figure 7?

Days exceeding heat load

Figure 9. Number of days above 20 degrees Celsius.

Let’s take a closer look at China’s geography. Figure 10 displays density by administrative region. We see that the core region is a triangle with vertices in Beijing, Henan, and Zhejiang. The center of mass is Jiangsu, and of course, Shanghai. Call it “the Han triangle.” In southeastern China, only Guandong exceeds 400 persons per square kilometer.

map-china-provinces-population_density

Figure 10. Population densities in China.

Keeping that Han triangle in mind, observe the location of the high income provinces in Figure 11. The northern coastal provinces, Beijing, Tianjin, Jiangsu, Shanghai, and Zhejiang, are not only more populous but richer than the southern coastal Guangdong and Fujian. Even Shandong is more populous and as rich as Guangdong. Only Henan is more populous but poorer; but it is inland. Beyond river basins and coastlines, Can isotherms explain this over-weighting of wealth and population in Jiangsu and Shanghai?

China_gdp_map

Figure 11. China’s regional polarization.

Figure 12 suggests that isotherms may be a factor. The cool provinces lying across the Yellow Sea (Beijing, Tianjin, Jiangsu and Shanghai) are favored over the warm provinces lying across the East China Sea (Zhejiang), which are in turn favored over the still warmer provinces facing the Taiwan Strait (Fujian), and all of them are trailed by the hot provinces facing the South China Sea (Guangdong and Hainan). Before the Yangtze bends upwards towards Shanghai, the temperature gradient does not turn favorable until well beyond the northern shores of the Yangtze. Riches and people are overweight Jiangsu relative to Guangdong, despite the latter being the center of gravity of maritime trade, because the former is cooler. More generally, the northern bias of the coastal core of China has the definite imprint of the macroclimate.

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Figure 12. Chinese isotherms.

We turn last to India before gathering our results. As we saw in Figure 7, the Indian population is packed into the Gangetic plain stretching from Punjab to Bengal. There is an independent high density zone in the extreme south.

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Figure 13. Indian population density by state.

Figure 14 shows that mean average temperatures on the vast bulk of the subcontinent, including the Gangetic belt, range over 20-30 degrees Celsius. By comparison, the region with the highest mean annual temperature among Italian administrative regions is Sicily at 18 degrees Celsius. Lombardy and Veneto have a mean of 12 degrees. If the Heliocentric model is right, India is twice as disfavored as Sicily compared to Lombardy. Unfortunately, because it is so hot everywhere in India except the northern and northeastern extremities, we should not expect the thermal variable to be priced in the cross-section of state-level per capita income. The Heliocentric model’s main implication for the subcontinent would be higher expected incomes in the regions that are cooler—J&K, Himachal, Uttarakhand, Nepal, Sikkim, Bhutan, Meghalaya and Arunachal.

india-map-annualtemperature.jpg

Figure 14. Indian isotherms.

The evidence from Figure 15 is not altogether unfavorable. Sikkim and Uttarakhand do have high incomes; Himachal and Arunachal also have modestly higher incomes than average. But Meghalaya and J&K do not; although that comes as no surprise since both are sites of violent insurgency and counter-insurgency. The odds of observing this configuration by chance are not terribly low, so we should take this evidence with a pinch of salt.

India-per-capita.png

Figure 15. Per capita GDP, Indian states.

Time then to gather our results. We documented that the thermal variable is priced into the cross-section of per capita income across US states and Italian administrative regions. We documented evidence of climate-related polarization of the core regions of Chile and China. We also documented some evidence of an income advantage in exceptional Indian states with cooler climates. The weight of the qualitative and quantitative evidence marshaled so far is consistent with the hypothesis that the distribution of heat on the surface of the earth is an important cause of regional polarization. It would be ideal to test the Heliocentric model at a finer resolution, say county level, and for as many nations and macroregions straddling isotherms as possible. It would also be useful to flesh out the geoeconomic, geopolitical and grand-strategic implications of the Heliocentric model. We’ll leave these two tasks to future work.

 

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