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.

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.

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.


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.

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.

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

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

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.

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?

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.

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.

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.

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.

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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