On China’s Economic Potential

The best functional equivalent of today’s China isn’t India or the E.U. or the United States. It’s the Roman Empire, were it still around today. Both have ancient origins. Both are fairly diverse both climatically and demographically, but not as diverse as the post-1492 European empires. The Roman Empire region’s population today is half that of China (at the Roman Empire’s height, its population was basically equal to that of China). Like China today, the Roman Empire was a nominal Republic that was in practice a despotism. Both empires were similarly technologically advanced when the Roman Empire was at its height.

This comparison is extremely useful today in understanding where China will end up over the next few decades, first economically and secondly in terms of national power. China’s current per capita GDP is similar to Mexico’s. The best predictor of per capita GDP in a country is its human capital. Both China and the Roman Empire region contain areas of very high and very unimpressive human capital. As a result, we should expect China’s GDP per capita to end up around where Roman Empire’s would be were it a modern-day Mediterranean state.

All evidence shows math is a special strength of the Chinese (especially Southeastern Chinese, e.g., Hakka and Fujianese), but math scores as not a useful predictor of economic development once one already has Verbal/Science scores. Korea and Japan are basically as rich as Britain and France, even though their math scores are obviously superior. Beijing-Shanghai-Jiangsu-Guangdong’s 2015 smart fraction for PISA Science was right between Britain and Belgium (verbal scores were relatively worse). The population of B-S-J-G is around 240 million. Half of this would be 120 million -precisely as one would expect if China were comparable to the Roman Empire region and England, France, and the Netherlands were comparable to B-S-J-G. The 2018 numbers released last year were clearly gamed (as the Chinese leadership is wont to do) and are, thus, worthless for analysis. Fujian and Zhejiang are, I presume, comparable in non-math human capital to Switzerland, Northern Italy, and the formerly Roman-occupied parts of Germany.

So where is China’s equivalent of the Muslim Mediterranean? One would expect it to exist. China is, after all, the fairly recent origin of the Filipinos/Maori/Polynesians, as well as of the Thais and Laotians. None of these groups have large smart fractions. And, indeed, though evidence is far from conclusive, there are strong indications that China’s equivalent of the Muslim Mediterranean does exist in the regions of Guangxi, Guizhou, Yunnan, Jiangxi, Anhui, Hebei, and Sichuan. Though the test used in the paper linked to isn’t particularly predictive of national outcomes, and the idea intellectual will and ability in these provinces are actually the lowest in the world seems extremely doubtful, the assumption that the state of human capital in Guizhou and Jiangxi is not much different from that in -and these regions are not richer than- Indonesia and Egypt seems a fairly safe one to make.

Since China for obvious reasons cannot hope to economically surpass the most successful post-Communist countries -Slovenia and Czechia- and since it is already almost at Bulgaria’s (≈Mexico’s) level of GDP per capita, a reasonable observer should conclude China will probably stop its above-trend growth with its current institutions at a level of GDP per capita somewhere in between these -say at around that of Croatia, Latvia, or even Hungary. Given China’s not as impressive as advertised human capital state, this indicates a rather positive assessment of current Chinese Communist economic institutions- that they are at least as good as those that can be expected from the post-Communist European Union. Further institutional reform (since all agree China’s economic institutions are far from ideal -an identical Chinese worker will never earn as much in real terms in a comparable part of China as in Taiwan, and especially not under current Chinese Communist economic institutions) would thus surely guarantee China’s economy being at least as large as the U.S. by exchange rates, and more than twice the size of the U.S. by PPP.

Predictions for the 2020s

1. China’s growth will be slower than in the 2010s (60%)

2. Russia’s growth will be faster than in the 2010s (70%)

3. Indonesia’s growth will be slower than in the 2010s (70%)

4. India’s growth will be slower than in the 2010s (90%)

5. U.S. forces will withdraw from Syria (70%)

6. Idlib will be fully terrorist-free (95%)

7. The Communist Party will continue to rule in China (70%)

8. Kim Jong-Un will be ruler of North Korea (80%)

9. The Saudi monarchy will remain in power (95%)

10. Trump will lose in 2020 (80%)

11. Democracy will not come to Venezuela (60%)

12. The U.S. economy will fall into recession in 2020 (70%)

13. Japan’s population will fall (95%)

14. There will be no major (unemployment 8% or higher) U.S. recessions in the 2020s (95%)

15. The president’s party will have less than 200 seats in the House in 2023 (80%)

16. Democracy will not come to Cuba (90%)

17. The machine will retain power in Russia (70%)

18. The machine will retain power in Turkey (50%)

19. The machine will lose power in Bangladesh (60%)

20. Africa will grow in population (100%)

21. New Cold War continues (90%)

22. More states will recognize Crimea as part of Russia (80%)

23. The Iran Deal will be restored (70%)

24. The machine will retain power in Iran (90%)

25. There will be no major anti-establishment left elected officials in the United States at the end of the decade (95%)

26. There will be no major anti-establishment left elected officials in the United States at any point in the decade (90%)

27. There will be no FUNDAMENTAL changes in Republican Party ideology (70%)

28. Trump’s approval rating will be above water at the end of the decade (80%)

29. Marijuana will be legalized nationwide in the United States (80%)

30. Religion in the United States will continue to decline (90%)

31. The U.S. Non-Hispanic White population will continue to decline (80%)

32. No resolution of the Palestinian question (95%)

33. No resolution of the Kashmiri question (100%)

34. No Russian military incursion into Ukraine (70%)

35. No major (1 million+ dead) wars in Africa (70%)

36. No major (100,000+ dead) wars in Asia outside the Middle East (Middle East includes Afghanistan and Central Asia) (70%)

37. No public option or Medicare For All in the United States (80%)

38. Pakistan’s per capita GDP grows faster in the 2020s than in the 2010s (70%)

39. No third Iraqi Civil War (70%)

40. No military invasion of Taiwan (95%)

41. Labour recovery in the next election (this means by seats) (80%)

42. At least one major U.S.-based social network will decline to insignificance in the coming decade (60%)

Some Remarks on Premodern GDP

I have seen these misinterpretations more times than I can possibly count:

1. Confusion of premodern real GDP per capita with living standards. No such thing as a necessary lower bound on GDP to prevent widespread destitution exists. If everyone is a subsistence farmer with high labor productivity, but nobody sells or buys anything from anyone else, that’s a society with a GDP of $0, but with fairly high living standards historically. If the share of output which is not for sale is highly variable and is only weakly correlated with real GDP per capita between societies, real GDP per capita will often severely underestimate actual output per capita and generally be a poor measure of living standards. And if the statistical agencies did count household production in GDP, the world would be a lot different.

2. Confusion of premodern inequality with real GDP per capita. More luxury goods and services may simply be a result of a higher rate of exploitation by the elites of the commoners, rather than a higher real GDP per capita.

3. Confusion of premodern economic complexity with real GDP per capita. Living standards and real GDP per capita are absolutely not measures of economic complexity. High productivity due to gifts of nature is no substitute for high productivity due to the gifts of the human mind. Korea was at least as poor as Ghana back when Ghana became independent. That absolutely does not mean Korea’s economic complexity was even remotely comparable to that of Ghana (see also previous link). Likewise, in a Malthusian environment, increased population may result in simultaneously falling real GDP per capita as a result of falling per capita agricultural output, but rising economic complexity as a result of growing division of labor and easier ability to create goods and services with high fixed costs. Economic complexity is in many cases much more useful to analyze than real GDP per capita. Much easier to analyze, as well. You and I admire the extent of division of labor in the Empire of Rome much more than its per capita agricultural output.

The Ramesses III Sea Peoples Reliefs

Whenever you search Ramesses III Sea Peoples you ALWAYS get a depiction of the relief showing the Battle of the Delta. You never see a depiction of the relief showing the Battle of Djahay. I have sought here to remedy this.
The depiction of the Battle of Djahy:
djahyfull.png

The depiction of the Battle of the Delta:

From here.
Translations here.

The Myth of Desperation

One narrative that’s been floating around the lyin’ press throughout the past two years is that that Trump and Sanders voters were mainly driven by desperation -that one wouldn’t vote for a candidate of dramatic change if one was perfectly satisfied with one’s affairs.

Perhaps the perfect counterexample to that is the county in Michigan with the highest median household income and lowest poverty rate in the state -Livingston.

Livingston County is many things, but it ain’t desperate. It’s rich, very Republican -it went for John McCain with 55% of the vote in November 2008, and 61% of the vote for Mitt Romney in 2012- and is not the place where one would find out-of-work factory workers or coal miners discontented with their economic situation, because there aren’t much of them. And, during the 2016 primaries, the candidate there who got the most votes was Donald Trump. The candidate who got the second-most votes there was Bernie Sanders (indeed, Livingston County had a higher Bernie share in the Democratic primary than all the counties surrounding it). The candidate who got the third-most votes there was John Kasich -this county isn’t as socially conservative as the western part of the state. Nor did woke neocon Marco Rubio appeal there much -he got a lower share of the Republican vote there than in the rest of the state, and Rubio and Kasich’s vote share combined would not have sufficed to prevent Trump from winning it in the primary.

Now, before 2016, Michigan hadn’t had a real Democratic primary for ages. But it did have real Republican primaries in 1996, 2000, 2008, and 2012. And guess who won the vote in Livingston County (a solidly Republican county, it must be remembered) each time? Mitt Romney by double digits in 2012, Mitt Romney by double digits in 2008, George W. Bush by single digits in 2000 [most MI counties went for McCain at the time], and Bob Dole by double digits in 1996 (Buchanan did well in Lapeer and St. Clair, though, and nearly won the famous Macomb). Not Ron Paul. Not Mike Huckabee. Not Alan Keyes. Rich guy Mitt Romney and establishment candidate George W. Bush.

There are other examples of this. Nevada’s third congressional district. Long Island. In the general election only, Minnesota’s sixth and second congressional districts (though Trump did far worse than Rubio there in the caucuses, he did better than Romney there in the general election).

Now, yes, Trump and Sanders really did appeal more to those among the really desperate who are White, at least, relative to Ted Cruz and Hillary Clinton. The results of the 2016 primaries in the poorest non-Hispanic White majority congressional district in the country (KY-05) are enough to prove this. But that does not mean economic or social desperation was either a necessary or sufficient condition for Trump or Sanders support (many Whites in desperate rural areas in the South also voted for HRC in the primary).

Calculating Partisan Gerrymandering (Part III of a III-part series)

Transforming a percentage into a probability of victory is fairly easy. Convert a percentage into the log odds of the percentage, multiply that by some integer, and convert that back into a percentage.

By what integer should I multiply the log-odds(percentage)? The answer varies.

I first tried this out with Michigan’s presidential vote in 2012. Michigan is known, after all, to be a high-quality gerrymander on the federal level. The result, somewhat surprisingly, was that given low enough number the log-odds(percentage) is multiplied by [i.e., given high enough values of voter swinginess] it was the Democrats who were favored under that House map (i.e., the 2011 House map in MI was a dummymander), due to the safety of the Dem seats and the complete lack of safety of the Republicans’ seats (or so it appeared) that year.

The first row in the below table is the number the log-odds (percentage) was multiplied by to produce the estimated probabilities of victory in the below rows.

However, by the 2016 presidential election numbers, the Republicans became clearly favored due to the newfound safety of their seats and a newfound danger to the Dem seats:

Note: the two-party HRC percentage is listed as over 50% in the above table due to more Democratic districts having lower voter turnout, and each district being counted equally during averaging the vote.

The number one should multiply the log-odds percentage by remains to be debated with historical statistical evidence; but I would be surprised if it were not within the range of three to twenty.