Introduction

Real economic growth is different from the increase in GDP that comes from monetary expansion. The 12-year min-max method that I have come up with is a way of calculating a country’s GDP growth rate over a 12-year period that aims to cancel out “fake” growth caused by monetary expansion and boom-bust cycles by taking recent economic crashes into account.

To find out China’s real economic growth from 2004 to 2015 (inclusive), find the year with the highest economic growth and the year with the lowest economic growth over the 12 year period, then average the two numbers. Over the 2004-2015 period, China’s highest economic growth was 14.7%, in 2007. Her lowest economic growth was 6.9%, in 2015. To find her real economic growth:

14.7 + 6.9
__________ = 10.8
    2

China’s GDP in 2004 was $1.9417 trillion. According to the above calculation, her economy grew at an annual rate of 10.8%, meaning that in 2015, her true GDP would be $5.999 trillion.

According to the above, China’s real economy in 2015 was three times as large as its 2004 economy. This finding is remarkably close to the 3.3 times growth of GDP from 2004 to 2015 predicted by PPP calculations ($5.7 trillion to 19.4 trillion).

Since the dollar is not a reliable store of value, the $5.999 trillion number for 2015 doesn’t mean much of anything, unless we compare it to the 2004 number of $1.94. The result, as mentioned, is that the 2015 number is three times larger, meaning the 2015 economy was three times as large as it used to be in 2004.

Another way of thinking of the above graph is to draw a rectangle with the country’s highest and lowest economic growth rates at opposing corners, then taking the midpoint of the height of the rectangle:

Extending the calculation to other countries, I will start with India:

Over the 2004-2015 period, India’s lowest growth rate was in 2008 at 3.89%. Its highest growth was in 2010 at 10.26%. Averaging the two results in 7.075%.

In 2004, India’s GDP was $0.7215 trillion. An annual growth rate of 7.075% results in a 2015 real GDP of $1.53 trillion, meaning its economy grew by a factor of 2.12. Both nominal and PPP calculations say its economy grew by a factor of 2.7. The 2.12 is quite believable to me, the truth probably lies somewhere in between.

Moving on to the United State:

America’s highest GDP growth was in 2004, at 3.79%. Its lowest growth was in 2009, at -2.78%. Averaging the two, the result is a miserable annual growth rate of half of a percent. America’s GDP in 2004 was $12.27 trillion. According to this calculation, its real 2015 GDP was $12.97 trillion, which fits the feelings of America’s population. America’s 2015 economy was only 5% larger than its 2004 economy from an honest, Smithian point of view. During the same period, its population grew 9.77% larger, meaning the wealth or income of America’s average citizen actually fell during the period. The pie grew by 5%, the pie eaters grew by 9.77%.

Predictably, Japan is even more of a trainwreck than the United States.

Over the 2004-2015 period, Japan’s high point of growth was in 2010 at 4.71%. Its low point was in 2009, at -5.53%. Averaging the two, the result is an annual growth rate of -0.41%, meaning its economy actually shrunk by that amount every year. This is well in keeping with a stagnant economy and an aging and shrinking population count. Japan’s GDP in 2004 was $4.65 trillion. According to this economic shrink rate, Japan’s GDP in 2015 was $4.45 trillion, 4.3% smaller than in 2004.

Why 12 Years

The average length of a central-bank induced asset bubble cycle is 8 years (think the late 2000 and 2008 crashes). 12 years is a cycle and a half. The 2004 to 2015 window, as an example, captures the recovery from the last crash, and our current “recovery”, providing a rich picture of what brought us here. If we look at two cycles (2000-2015 inclusive), the data will be skewed by the previous cycle’s peak, which is quite irrelevant today. If we look at only one cycle (2008-2015), we will lose historical context that is still relevant today (the period that lead to the 2008 crash).

A market cycle and a half makes for a relevant and informative picture of most periods of a country’s economic history. The idea of using a market cycle and a half comes from John P. Hussman, manager of the billion-dollar Hussman Funds mutual fund.

Why It Works

The 12-year min-max average takes a realistic look at the “fakeness” factor within an economy and takes it out. It shows how much of an economy is a house of cards built on debt and investor euphoria, and how much of it is real and of lasting value.

The basic idea is that it measures the psychology of an economy’s participants. Crashes happen when a very large number of investors realize it wasn’t such a smart idea to buy those overpriced assets, thinking that they would be able to sell them for even higher. This moment of realization happened in 1929, 2000 and 2008.

After a crash, central banks use monetary easing to motivate investors to bid up the prices of assets. This creates a growing atmosphere of euphoria that increases spending in the economy and creates years of apparent growth. When the next crash inevitably comes, the atmosphere changes from one of euphoria to one of fear and panic. The market sobers up. In this year of sobriety, investors often underestimate the market’s worth. This acts as an antidote to the overestimation of the market during euphoric years. This is what happened in March 2009.

The momentum of an economy that’s truly expanding cannot be reversed by a financial crisis. It can slow a little, but it will continue growing, as China, India and Bangladesh’s numbers show. When we average out the years of most euphoria and least euphoria in a fast-growing economy, we still get a high rate of growth, because the growth is real, it is not entirely based on the feelings of investors.

But how does investor feeling affect GDP? The blaze of euphoria caused by monetary easing and fanned by complicit mass media makes it easier, and more likely, for businesses to borrow and expand their activities, regardless of supply-and-demand considerations. Overvalued corporations find it easy to convince lenders to give them large loans. The wealthy from around the country open their wallets and outbid each other in investing their wealth in all kinds of business activities in fear of being left behind.

In stagnant economies (where standards of living for the average citizen are no longer improving appreciably) like the those of the US, Japan and Western Europe, it is by and large the feelings of investors that drive economic expansion and recession.

Measuring Crashiness

One obvious difficulty with choosing only two points on a 12-year growth graph is that growth is cumulative. A 3% growth at the beginning of the period could mean a trillion dollars increase, while at the end of the period it could mean two or three trillions.

Regardless of where the low point is on the graph, it shows that it is possible, within that economy, to lose this much value. It shows that a great amount of volatility exists within the economy, so that even if in recent years the growth numbers have all been great, if there is one big crash year on the 12-year graph, it tells us that a similar crash is in the future.

The cumulative effect doesn’t take away from the idea, the idea contains it within itself. No matter how much growth we’ve had in recent years, a crash can take most of it away, as it is farther in the future, so that its percentage effect is going to be larger than the effect of the growth of the previous years.

The PPP GDP per capita of the United States over the 12-year period  (denominated in 2005 international dollars) illustrates this. In 2004 it was $48,597. Five years of “growth” later, it was $48,557 in the 2009 crash. The crash easily outweighed all the previous years of growth. In 2015, the number had grown to $52,549, and the coming crash can easily take it back close to square one again.

The 12-year min-max average shows the “crashiness” of an economy. And since there is always a bigger crash in the future, measuring a country’s greatest year of growth against its worst year of growth makes a lot sense.

Honesty requires that I admit I came up with these reasonings for why the 12-year min-max average works after I had discovered it. I needed a reliable way of comparing the true economic momentums of different countries for the HQI, the Human Genetic-Cultural Quality Index that I will announce later, and among the methods I tried out, this one brilliantly fit the true states of many economies I have studied.

List of Countries by Real 12-Year Annual GDP Growth

Below is a list of 203 sovereign states along with their real economic growth from 2004 to 2015 according to the 12-year min-max average. The countries are sorted by population.

The number I’m most skeptical of is Germany’s. Germany has appeared to grow fast in recent years. The 12-year min-max average says its growth has actually been negative 0.77% (i.e. that the next crash will wipe out all of its recent growth). This is possible, and only history will tell.

Russia gets a 0.36% growth rate. I expect its rate will be shown to be quite higher after the next crash, as it puts the 2008 crash behind, and what remains will be its recent real economic growth rate that cannot be wiped out by a crash.

Please see the fine print at the end of the table regarding certain corrections I’ve made.

Country	Population	Real 12-Year Annual GDP Growth Rate (2004-2015)
China	1,401,586,609	10.545
India	1,282,390,303	7.075
United States	325,127,634	0.505
Indonesia	255,708,785	5.49
Brazil	203,657,210	3.7
Pakistan	188,144,040	4.64
Nigeria	183,523,432	18.195
Bangladesh	160,411,249	6.055
Russian Federation	142,098,141	0.36
Japan	126,818,019	-0.41
Mexico	125,235,587	0.205
Philippines	101,802,706	4.39
Ethiopia	98,942,102	11.11
Viet Nam	93,386,630	6.4
Egypt	84,705,681	4.485
Germany	82,562,004	-0.77
Iran	79,476,308	5.02
Turkey	76,690,509	2.265
Democratic Republic of the Congo	71,246,355	5.915
Thailand	67,400,746	3.385
France	64,982,894	-0.075
United Kingdom	63,843,856	-0.595
Italy	61,142,221	-1.735
Myanmar	54,164,262	10.415
South Africa	53,491,333	2.025
Tanzania	52,290,796	6.56
South Korea	49,750,234	3.605
Colombia	49,529,208	4.275
Spain	47,199,069	0.3
Kenya	46,748,617	4.315
Ukraine	44,646,131	-1.35
Argentina	42,154,914	2.17
Algeria	40,633,464	3.75
Uganda	40,141,262	7.025
Sudan	39,613,217	4.655
Poland	38,221,584	4.23
Canada	35,871,283	0.13
Iraq	35,766,702	27.77
Morocco	33,955,157	4.995
Afghanistan	32,006,788	11.27
Venezuela	31,292,702	7.545
Peru	31,161,167	5.095
Malaysia	30,651,176	2.95
Saudi Arabia	29,897,741	5.895
Uzbekistan	29,709,932	8.46
Nepal	28,440,629	4.73
Mozambique	27,121,827	8.06
Ghana	26,984,328	8.965
Yemen	25,535,086	4.38
North Korea	25,155,326	1.85
Madagascar	24,235,390	1.56
Australia	23,923,101	2.985
Cameroon	23,393,129	4.085
Taiwan	23,381,038	4.53
Angola	22,819,926	12.5
Syrian Arab Republic	22,264,996	5.065
Sri Lanka	21,611,842	6.27
Romania	21,579,201	0.695
Côte d’Ivoire	21,295,284	3.16
Niger	19,268,380	18.195
Chile	17,924,062	2.5
Burkina Faso	17,914,625	5.81
Malawi	17,308,685	5.745
Netherlands	16,844,195	-0.035
Kazakhstan	16,770,447	5.95
Mali	16,258,587	3.18
Guatemala	16,255,094	3.415
Ecuador	16,225,691	4.25
Cambodia	15,677,059	6.67
Zambia	15,519,604	6.76
Zimbabwe	15,046,102	-2.88
Senegal	14,967,446	4.22
Chad	13,605,625	16.855
Rwanda	12,428,005	7.92
Guinea	12,347,766	2.35
Cuba	11,248,783	6.76
Tunisia	11,235,248	2.165
Belgium	11,183,411	0.675
Greece	11,125,833	-1.67
Bolivia	11,024,522	5.08
Benin	10,879,828	4.295
Burundi	10,812,619	1.42
Czech Republic	10,777,060	1.02
Dominican Republic	10,652,135	5.805
Portugal	10,610,014	-0.77
Haïti	10,603,731	0.01
Hungary	9,911,396	-0.81
Sweden	9,693,883	0.405
Azerbaijan	9,612,580	17.285
United Arab Emirates	9,577,128	2.3
Serbia	9,424,030	2.965
Belarus	9,259,666	3.78
Tajikistan	8,610,384	7.05
Austria	8,557,761	-0.09
Honduras	8,423,917	2.07
Switzerland	8,238,610	1.005
Israel	7,919,528	3.705
Jordan	7,689,760	5.45
Papua New Guinea	7,631,819	6.48
Hong Kong	7,313,557	3.12
Togo	7,170,797	3.56
Bulgaria	7,112,641	1.73
Paraguay	7,032,942	5.035
Laos	7,019,652	7.81
Eritrea	6,737,634	3.855
El Salvador	6,426,002	0.39
Sierra Leone	6,318,575	6.195
Libya	6,317,080	21.205
Nicaragua	6,256,510	1.735
Kyrgyzstan	5,707,529	5.225
Denmark	5,661,723	-0.645
Singapore	5,618,866	7.32
Finland	5,460,592	-1.545
Slovakia	5,457,889	2.67
Turkmenistan	5,373,487	9.85
Norway	5,142,842	1.17
Lebanon	5,053,624	5.6
Costa Rica	5,001,657	3.88
Central African Republic	4,803,082	3.45
Ireland	4,726,856	1.085
Congo	4,671,142	3.585
New Zealand	4,596,396	1.25
Palestine (West Bank & Gaza)	4,548,815	6.175
Liberia	4,503,439	4.915
Georgia	4,304,540	4.28
Croatia	4,255,374	-1.115
Oman	4,157,783	3.555
Mauritania	4,080,224	8.915
Panama	3,987,866	8.46
Bosnia and Herzegovina	3,819,684	2.945
Puerto Rico	3,680,058	-0.785
Kuwait	3,583,399	1.84
Moldova	3,436,828	1.705
Uruguay	3,429,997	4.39
Albania	3,196,981	4.32
Lithuania	2,998,969	-1.86
Armenia	2,989,467	-0.14
Mongolia	2,923,050	8.01
Jamaica	2,813,276	-0.765
Namibia	2,392,370	6.15
Qatar	2,350,549	14.875
Lesotho	2,120,116	5.09
Macedonia	2,109,251	3.005
Slovenia	2,079,085	-0.43
Botswana	2,056,370	1.105
Latvia	2,031,361	-1.225
Gambia	1,970,081	1.36
Guinea-Bissau	1,787,793	3.765
Gabon	1,751,199	1.715
Bahrain	1,359,726	5.195
Trinidad and Tobago	1,346,697	5.025
Swaziland	1,285,519	2.375
Estonia	1,280,227	-2.225
Mauritius	1,253,581	3.565
Timor-Leste	1,172,668	4.245
Cyprus	1,164,695	-0.505
Djibouti	899,658	4.585
Fiji	892,727	1.945
Guyana	807,611	2.53
Equatorial Guinea	799,372	12.905
Bhutan	776,461	10.035
Comoros	770,058	1.95
Montenegro	621,556	2.5
Solomon Islands	584,482	4.1
Macao	584,420	3.2
Suriname	548,456	5.38
Luxembourg	543,261	1.51
Cape Verde	508,315	6.95
Malta	431,239	0.75
Brunei Darussalam	428,539	1.03
Bahamas	387,549	-0.39
Maldives	357,981	7.57
Belize	347,598	2.69
Iceland	336,728	2.405
Barbados	287,482	0.825
French Polynesia	282,764	0.11
Vanuatu	263,888	4.84
New Caledonia	263,147	3.75
Sao Tome and Principe	202,781	6.185
Samoa	193,228	0.755
Saint Lucia	184,937	3.25
Guam	169,885	3
Saint Vincent and the Grenadines	109,374	2.145
Grenada	106,694	3.33
Tonga	106,379	-0.2
Kiribati	105,555	1.505
Federated States of Micronesia	104,460	-0.505
Aruba	103,889	-2.26
Seychelles	93,754	3.785
Antigua and Barbuda	91,822	0.67
Andorra	80,950	-0.26
Dominica	72,680	2.895
Bermuda	65,578	0.13
Cayman Islands	59,967	-0.2
Greenland	57,275	0.05
Saint Kitts and Nevis	55,376	1.83
Marshall Islands	52,993	0.875
Monaco	38,320	1.64
Liechtenstein	37,461	-0.4
San Marino	31,802	3.255
Palau	21,291	-0.22
Tuvalu	9,916	2.01

For Iran, ignored the 2012 crash, as it was a political event. Used the 2008 crash instead, as it was a shared world event. For Brazil too due to 2015. For Iraq 2014 due to CIA-ISIS. For Venezuela, 2009 was used due to recent mismanagement. For Syria, 3.23 lowest growth from 2010 was used, due to start of the Syria v. CIA war in 2011. For Yemen, ignored numbers from 2011 due to the crisis, used lowest number from 2006.

Data for New Caledonia taken from Trading Economics (World Bank data missing).

Monaco’s highest growth rate taken from World Bank (14.58 for 2007). Lowest number (-11.3 for 2009) missing from World Bank, taken from Trading Economics.

Data for Greenland after 2009 is missing. Used 2009 number as lowest growth rate (-5.41), and 2007 number as highest growth rate (5.51).

No reliable annual growth data for French Polynesia. Assumed a top growth of 4.42 and a bottom growth of -4.2, as hinted at by the CIA World Factbook.

For Sierra Leone, ignored 2013 and 2015 numbers, as they were aberrations caused by the mining sector’s boom and collapse.

For Liechtenstein, top and bottom growth numbers were taken from Trading Economics as World Bank seemed inaccurate and missed the years after 2009.

Guadelope, Cook Islands, Montserrat, Netherlands Antilles, Anguilla, Martinique, Réunion, Gibraltar, American Samoa, US and British Virgin Islands, Northern Mariana Islands and French Guiana not included as GDP growth rate is not. It is possible that the World Bank counts them toward their parent states, couldn’t verify this.

Faroe Islands not included as GDP growth rate data is unavailable.

Data for North Korea, Guam and Cayman Islands’ GDP growth rate taken from Trading Economics as World Bank doesn’t provide them.

For the Central African Republic, numbers after 2012 are ignored due to the civil war there. Used 2004’s GDP growth rate as the top number (5.99) and 2005 as the bottom (0.91).

For Eritrea, ignored 2008 GDP growth low as it was caused by US sanctions. Used 2006 low instead (-0.97).

Somalia is not included in the World Bank data, and other available data is none too reliable due to the fact that the country is a war zone. Decided not to include the country as no useful conclusions can be drawn from the data.

No reliable data found for Turks and Caicos Islands, Wallis and Futuna, Nauru, Mayotte, and Western Sahara, so omitted them.