The order of economic success and influence in the 21st century

 The real GDP per capita is the best measure of economic success. It can be an expression of various driving forces behind the growth, however. The largest (practically full spectrum of industries and services) economies are competing with each other in all political (i.e., economic and military) aspects. The military power is very important to protect the non-equivalent exchange with smaller economies. The overall economic influence (the margin of non-equivalent exchange) of a given country can be described by three parameters – the increment of real GDP per capita, military power, and total population. The smallest countries can be extremely successful in a few specific activities, however. Bigger countries have to fight.

Clearly, the military interaction in the key trade focus points (Suez canal, Panama canal, Malacca strait) grows in intensity and probably in the next decade may come closer to the global war: China squeezes the USA out of the strait of Malacca droplet by droplet, Russia has a military base in Syria and will get a new technical base for warships in Sudan (Red Sea); China and Russia both invest in Venezuela and provide military support. Yet, Russia and China does not control these focus points but can ruin them and stop the trade at any moment. In all the strategic points, the West loses control gained after the Former Soviet Union closed its own military bases worldwide.  

Below I present a table listing the real GDP per capita annual increments in the 21^st century and total population (2018), as presented in the Maddison Project Database. Position #5 is occupied by Saudi Arabia. Its wealth is based on oil and military protection by the USA.  Position #10 belongs to Switzerland. It is difficult to call this country “small” in terms of economy, but it has a few very specific businesses likely subject to increasing influence from abroad. The Russian Federation is number 20: the unbeaten military power with nuclear weapon and the largest territory is well supported by the sixth place in the real GDP (PPP) list. The growth rate in real GDP per capita was extremely healthy ($784 per year on average) after the recovery from the dramatic fall associated with the transition to capitalism from socialism.  Germany occupies position 27 and likely is the major beneficiary of the EU. The success of East European countries is related to the EU subvention, which is close to the end, however. This is the reason for the two-speed Europe – almost all east European countries lost their economic power.  The UK (73), France (83), Italy (92), Spain (89) are almost the worst performers in the EU – lost their power relative to Germany. The USA is #41 (close to the "Ultimate Question of Life, the Universe, and Everything"). Let’s see what happens in 2020 and 2021. China is 47, but its economic and military power is based on the largest population despite lower real GDP per capita.

mean GDP pc increment since 2000 (2011 prices)	Country	population, x1000
5999	Qatar	2606
1702	Singapore	5996
1697	Norway	5313
1649	Kuwait	4229
1528	United Arab Emirates	9619
1467	Saudi Arabia	33344
1438	Ireland	4859
1249	Bahrain	1584
1230	Turkmenistan	5410
1007	Switzerland	8668
997	China, Hong Kong SAR	7350
993	Taiwan, Province of China	23324
920	Lithuania	2799
889	Kazakhstan	18323
867	Equatorial Guinea	797
842	Seychelles	95
823	Republic of Korea	51635
818	Poland	38371
790	Czechoslovakia	16015
784	Russian Federation	146476
761	Czech Republic	10605
748	Montenegro	614
735	Australia	24855
732	Slovakia	5409
724	Romania	19191
722	Latvia	1927
712	Germany	84152
708	Panama	3801
694	Hungary	9774
690	Azerbaijan	9648
683	Oman	4725
644	Malta	487
632	Malaysia	31143
630	Sweden	10175
589	Estonia	1292
580	Mongolia	3103
574	Iceland	353
557	Bulgaria	7167
534	Belarus	9528
532	Netherlands	17232
525	United States	327835
521	Trinidad and Tobago	1215
510	Iraq	40012
507	Dominican Republic	10385
487	Croatia	3861
473	New Zealand	4840
465	China	1385439
455	Austria	8892
449	Former Yugoslavia	21424
448	Serbia	7078
440	Canada	37206
439	Iran (Islamic Republic of)	82239
433	Botswana	2263
430	Slovenia	2067
427	Puerto Rico	3295
411	Algeria	41490
409	Turkey	85935
409	Luxembourg	610
405	Denmark	5794
396	Uruguay	3396
394	Georgia	4922
390	Thailand	67515
388	Gabon	2083
383	Israel	8460
383	Chile	18676
364	Uzbekistan	30023
359	Indonesia	259494
351	Armenia	2907
350	Albania	3063
348	Peru	31382
345	Finland	5516
340	United Kingdom	66746
335	Belgium	11429
326	Mauritius	1364
323	Sri Lanka	21732
320	Angola	22638
304	Japan	125848
298	Costa Rica	4994
298	Egypt	102184
291	Libya	6688
286	Lebanon	7708
284	France	67029
280	Colombia	48128
277	TFYR of Macedonia	2119
270	Cyprus	1022
258	Ukraine	43952
255	South Africa	55341
250	Spain	47380
247	Paraguay	7099
240	Cuba	11116
233	Brazil	211308
233	Lao People's DR	7234
233	Argentina	44695
228	Philippines	112106
225	India	1298136
225	Viet Nam	97076
222	Myanmar	54400
216	Jordan	9116
216	Mexico	121656
211	Tunisia	11478
209	Republic of Moldova	3453
207	Ecuador	16499
205	Morocco	35496
204	Portugal	10221
188	Bosnia and Herzegovina	3947
185	Dominica	74
182	Saint Lucia	166
180	Cabo Verde	568
177	El Salvador	6368
176	Swaziland	1087
172	Nigeria	202534
171	Namibia	2554
151	Tajikistan	8604
145	Bangladesh	167475
140	Bolivia (Plurinational State of)	11306
138	Greece	10662
131	Pakistan	219964
120	Ghana	27858
117	Zambia	17255
116	Nicaragua	6085
109	Cambodia	16409
107	Kyrgyzstan	5718
96	U.R. of Tanzania: Mainland	56367
95	Honduras	9002
92	Italy	60447
86	Congo	5165
86	Guatemala	17232
85	Mauritania	3840
84	Sao Tome and Principe	204
81	Kenya	51372
80	Afghanistan	34941
79	Sudan (Former)	41774
79	Djibouti	884
76	Côte d'Ivoire	27623
74	Chad	15253
59	Ethiopia	106468
58	Nepal	29718
51	Cameroon	26374
50	Rwanda	11916
48	Sierra Leone	6312
47	Uganda	42331
42	Lesotho	1962
41	Jamaica	2672
35	Senegal	16356
31	Guinea	11855
29	Comoros	821
29	Mali	16507
25	D.R. of the Congo	95239
25	Gambia	2093
17	Burkina Faso	19700
17	Benin	12018
17	Togo	8176
15	Niger	21234
15	Guinea-Bissau	1833
12	Haiti	10788
10	Madagascar	25684
8	Malawi	20217
-3	D.P.R. of Korea	25381
-4	Mozambique	25998
-5	Burundi	11229
-9	Liberia	4810
-9	State of Palestine	4615
-19	Central African Republic	5745
-33	Zimbabwe	14097
-34	Barbados	293
-107	Yemen	30217
-182	Venezuela (Bolivarian Republic of)	28890
-240	Syrian Arab Republic	16931

 

 

 

The real GDP in the USA is driven by population pyramid: statistics of the economic bubble 2003-2007

In our previous post, we presented Figure 1, which displays two variables – the measured number of 9-year-olds, N9, and the same number predicted from the GDP growth rate as based on the link between real GDP per capita and N9; it was tested statistically (cointegration tests). There is a spike between 2003 and 2009 which introduce a significant disturbance in the statistical estimates of the link (both cointegration tests are still valid and Rsq=0.9.).  

The years between 2003 and 2007 are well known as the “sub-prime bubble”, i.e. the economically unsupported increase in the house price, and thus, in the imputed  rent, which gives 8% of the GDP. Figure 2 splits the GDP into 4 major components as presented by the BEA. This is the input of 4 components to the (nominal) GDP growth in a given year. The largest input between 2003 and 2007 comes from “Personal consumption expenditures” (PCE) and “Gross private domestic investment”. The latter also leads in a deep fall in 2008 and 2009, when the predicted curve comes back to the measured N9. Figure 3 splits the PCE into the components with the largest positive input. 

It is hard to distinguish between real economic growth and “bubble” in measurable parameters. However, the discrepancy between the measured and predicted N9 in Figure 1 is likely related to the growth in fictitious GDP components and the fall to the measured curve manifests the fact that the long–term GDP evolution is defined by economic factors and short-term deviations may be real or related to accounting tricks. The latter always return (with pain) to economic "normality" 

Figure 1. Lower panel: Same as in the upper panel for the period between 1960 and 2019.

 

Figure 2. Four major components of the GDP of growth. The largest input between 2003 and 2007 comes from “Personal consumption expenditures” and “Gross private domestic investment”

Figure 3. Major components of the PCE growth.

The real GDP in the USA is driven by population pyramid

 We often discuss the evolution of real GDP (per capita) in developed countries and built a quantitative model, which includes two terms – inertial growth with constant annual increment of GDPpc and the change in a specific age population. We have presented a number of growth models for various developed counties and validated them with new data.  The last revision was around ten years ago, and it is a good opportunity to look at the model with data for the previous decade. It is also important that all relevant data undergo regular revision back into in the past, actually decades.

 

The original model for the U.S. links the change rate of real GDP per capita, dlnG/dt, to the change in the number of 9-year-olds, dlnN9/dt, and the reciprocal value of the attained level of GDP per capita, A/G: 

dlnG/dt= A/G + 0.5dlnN9/dt     (1) 

where A is an empirically derived constant.  One can rewrite (1) relative to N9 and obtain the following equation in a discrete form: 

N9(t) = N9(t-1)[2.0( dlnG - A/G) + 1] (2) 

where dt=1 year. 

The upper panel of Figure 1 presents the result of the N9 modeling between 1960 and 2005.  The agreement between the measured and predicted N9 is excellent, and we have shown that these time series are cointegrated.  Our model has passed all rigorous econometric (Johansen and Engle-Granger) tests and can be used for GDP forecasts when the quality of population estimates is good enough. Here, we update the data set using the most recent release of population and real GDP data. The lower panel of Figure 1 shows the updated curves. The predicted number of 9-year-olds has a short but intensive spike between 2003 and 2008, which dropped to the predicted level in the 2008 recession. The same Johansen and Engle-Granger cointegration tests applied to the new measured and predicted time series of 9-year-olds. Both tests confirm that these two time series are cointegrated, and thus, the linear regression is valid and defines the link between them. Simple linear regression is not the best choice for the predicted and measured number of 9-year-olds in Figure 1. One has to retain in mind the measurement errors in both time series. Considering the fact that the GDP difference test in (2) is very sensitive to the accuracy of annual GDP pc estimates and often revisions to the population estimates, we could give a conservative estimate of the measurement error is 20% of the annual GDP pc estimate. For this error, the Rsq=0.91. It is worth noting, that the predicted N9(t) value is the integral of the GDPpc-related difference, while the measured N9 is an instant measurement and the next measured N9 does not depend on the previous one.  Therefore, the number of 9-year-olds is a fully independent variable, and it defines the long-term (integral) behaviour of the GDP per capita.

We do not know the reason behind the absence of the spike in the measured population data, but it is clear that the postcensal data for a single-year-of-age population are smoothed. The raw population data is not available. The measured GDP growth between 2003 and 2007 in the USA was intensive, and we have no indication that these data are somehow corrupted. 

The updated model indicates that the deviations from the inertial growth in the USA (and likely in all developed countries) are related to the change in 9-year-olds. This might be the age when some financial operations are started by parents in view of future education. This might be the age when additional saving starts and the economy gets a changing fresh portion.  

 

Figure 1. Upper panel: Measured number of 9-year-olds (open circles) in the U.S. and that predicted from real GDP per capita (solid circles) for the period between 1960 and 2005. Lower panel: Same as in the upper panel for the period between 1960 and 2019.

Growth rate of the GDP per capita revisited. 4. Arab Spring – an economic disaster

I intentionally avoid any economic discussion in this section and just present data on the annual GDPpc increment. Here we see the influence of noneconomic factors on economic development. These factors are rarely positive – almost two decades of healthy economic growth in the presented countries were suddenly stopped (except Morocco).

Figure 1. Algeria. Annual GDPpc the increment between 1961 and 2018 with the average value for the studied period of $188 (2011 prices). Notice the fall in 2011 and following poor performance

Figure 2. Same as in Figure 1 for Egypt.

Figure 3. Same as in Figure 1 for Iran.

Figure 4. Same as in Figure 1 for Iraq.

Figure 5. Same as in Figure 1 for Libya.

Figure 6. Same as in Figure 1 for Morocco

Figure 7. Same as in Figure 1 for Tunisia.

Figure 8. Same as in Figure 1 for Bahrain.

 

Figure 9. Same as in Figure 1 for Oman.

  

Figure 10. Same as in Figure 1 for Qatar.

 

Figure 11. Same as in Figure 1 for Saudi Arabia.