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Real GDP | GDP Nominal vs GDP PPP
Investment Perspective
In evaluating and analyzing global investment opportunities and, also in the business decision making process we need to have some fundamental data and facts available to us. Most important of these facts and data are the actual comparative state of the economies and GDPs of various countries and regions in the world.
We need to know how fast the individual economies are growing relative to other economies? For example; is China’s GDP larger than that of the US GDP? And, what is the real rate of growth of their respective economies?
On the surface it seems quite simple; just look at the official release of data on the respective economies and GDP? But thera is one major obvious problem – beside many other underlying and ‘not-so-obvious’ issues, in understanding and decoding the real relative state of various economies is the matter of currency conversion rates.
When it comes to comparing the national economies of different countries by their Nominal GDP, converting its value into a common currency—typically the US dollar, presents a very ’unrealistic’ picture, since all other currencies in the world relate to the US dollar—and, the US dollar itself is not measured in any tangible manner.
Further, currency conversions rates – or the market rates as they are commonly described, are ‘fixed’ to gain an edge in global commerce. And, in case of the US Dollar—which is the dominant global trade settlement currency, and also the dominant global reserve currency, its value is just the matter of perception!
Therefore, in reality, when we look at the GDP of ‘non-US Dollar’ economies, we are looking at—somewhat, a manipulated and distorted image of the real GDP.
The problem is that a distorted view of the global relative economies and GDP is not very conducive in the decision making process—especially when the large sums of capital investment or business prospects are involved.
It is therefore pertinent that businesses have in place a mechanism to filter out the distortions and be able to see and understand the real state of the GDPs of the target economies, their various components and the inclusions and exclusions etc.
Nominal vs Purchasing Power Parity Methods
By definition, GDP is the total market value of all final goods and services produced in an economy in a given period – normally, a year. Value of GDP is given in an economy’s own currency. To compare the GDP of various countries, the value their GDP is converted into a common currency—typically the US dollar.
There are two methods to convert GDP into a common currency: Nominal and Purchasing Power Parity (PPP).
Nominal GDP computation method translates a non-US$ country’s economy in terms of the current conversion rate of that country’s currency. PPP GDP, on the other hand, uses the method that is based on the real value of a country’s production.
The following description taken from the IMF will help elaborate the effect of Nominal GDP vs GDP:
The PPP exchange rate is the rate at which the currency of one country would have to be converted into that of another to purchase the same amount of goods and services in each country
Nominal GDP, as we can see, presents quite a ’muddy’ comparative picture of the countries’ economies. For example: If we compare the GDP on a Purchasing Power Parity (PPP) basis, Chinese Economy is bigger than the US economy.
China GDP PPP surpassed that of the US during 2014. China GDP PPP had reached $18.3 Trillion in 2014, whereas US GDP PPP was $17.5 Trillion at that time. China’s GDP (for the year 2018) in terms of Purchasing Power Parity was US$27,449 Trillion against the US’ 21,482 Trillion!
Further, with China Riding high on the fast tracks of the Belt and Road Initiative & Made in China 2025 projects, the gap between the GDPs of the US and China is expected to increase at a faster pace. But, the real picture of the two economies will not be visible by looking at the nominal GDPs of the two.
Per the IMF forecasts, China’s GDP PPP will reach $37.2 Trillion in comparison to US’ $24.7 Trillion by 2023.
✍ The point that should also be noted is that beside just looking at the GDP, there are other factors to be considered in making investment/business decisions like, the state of a target economy’s infrastructure and politico-social environment. But, that is a separate topic in itself and is covered separately.
✍ The study of various computing methodologies—and their impact on the declared nominal GDPs, is an interesting subject worth delving into. It is covered separately under its own topic: ‘GDP—Computing Methodologies’.
The tables below present the GDP of Top 50 Economies of the World—Calculated separately under the Nominal GDP method and PPP GDP method. Together, these tables serve to highlight the contrast in the economic outlook of economies under these two methodologies.
Top 50 Economies By GDP Nominal
[Data are in Billions of US Dollars]
Rank | Country/Economy | 2019 | % Share | Diff. | 2023 |
---|---|---|---|---|---|
1 | United States | 21,482.41 | 24.4 | – – | 24,671 |
2 | China | 14,172.20 | 16.1 | 7,310 | 19,581 |
3 | Japan | 5,220.57 | 5.93 | 8,952 | 5,908 |
4 | Germany | 4,117.07 | 4.67 | 1,104 | 4,937 |
5 | India | 2,957.72 | 3.36 | 1,159 | 4,330 |
6 | France | 2,844.70 | 3.23 | 113 | 3,364 |
7 | UK | 2,809.91 | 3.19 | 34.8 | 3,257 |
8 | Italy | 2,112.80 | 2.40 | 697 | 2,396 |
9 | Brazil | 1,929.71 | 2.19 | 183 | 2,351 |
10 | Canada | 1,820.36 | 2.07 | 109 | 2,322 |
11 | S. Korea | 1,699.68 | 1.93 | 121 | 2,055 |
12 | Russia | 1,649.21 | 1.87 | 50.5 | 1,818 |
13 | Spain | 1,474.12 | 1.67 | 175 | 1,758 |
14 | Australia | 1,464.41 | 1.66 | 9.71 | 1,794 |
15 | Mexico | 1,242.39 | 1.41 | 222 | 1,527 |
16 | Indonesia | 1,066.84 | 1.21 | 176 | 1,446 |
17 | Netherlands | 933.18 | 1.06 | 134 | 1,107 |
18 | Saudi Arabia | 795.58 | 0.903 | 138 | 889.5 |
19 | Switzerland | 731.14 | 0.830 | 64.4 | 873.6 |
20 | Turkey | 631.16 | 0.717 | 100.0 | 958.3 |
21 | Taiwan (China) | 626.72 | 0.712 | 4.44 | 774.0 |
22 | Poland | 581.29 | 0.660 | 45.4 | 787.7 |
23 | Sweden | 563.24 | 0.639 | 18.1 | 669.1 |
24 | Belgium | 545.19 | 0.619 | 18.1 | 637.9 |
25 | Thailand | 524.25 | 0.595 | 20.9 | 652.2 |
26 | Austria | 469.66 | 0.533 | 54.6 | 559.3 |
27 | UAE | 455.59 | 0.517 | 14.1 | 533.6 |
28 | Norway | 448.46 | 0.509 | 7.12 | 503.5 |
29 | Nigeria | 447.01 | 0.508 | 1.45 | 736.5 |
30 | Argentina | 408.03 | 0.463 | 39.0 | 540.7 |
31 | South Africa | 385.53 | 0.438 | 22.5 | 456.4 |
32 | Hong Kong-SAR | 380.86 | 0.432 | 4.67 | 479.2 |
33 | Ireland | 379.80 | 0.431 | 1.06 | 469.9 |
34 | Israel | 376.13 | 0.427 | 3.67 | 468.7 |
35 | Malaysia | 372.63 | 0.423 | 3.50 | 498.5 |
36 | Denmark | 362.15 | 0.411 | 10.5 | 438.1 |
37 | Singapore | 359.62 | 0.408 | 2.53 | 434.9 |
38 | Colombia | 355.16 | 0.403 | 4.46 | 422.6 |
39 | Philippines | 354.31 | 0.402 | 0.85 | 510.9 |
40 | Iran | 333.60 | 0.379 | 20.7 | 382.4 |
41 | Bangladesh | 313.51 | 0.356 | 20.1 | 445.6 |
42 | Chile | 305.56 | 0.347 | 7.95 | 377.7 |
43 | Pakistan | 298.31 | 0.339 | 7.25 | 407.3 |
44 | Egypt | 298.15 | 0.338 | 0.15 | 414.8 |
45 | Finland | 282.01 | 0.320 | 16.1 | 331.1 |
46 | Vietnam | 266.24 | 0.302 | 15.8 | 376.2 |
47 | Czech Republic | 264.50 | 0.300 | 1.74 | 342.9 |
48 | Iraq | 250.07 | 0.284 | 14.4 | 299.0 |
49 | Romania | 248.84 | 0.283 | 1.23 | 334.4 |
50 | Portugal | 242.83 | 0.276 | 6.01 | 283.8 |
Rank | World | 2019 | 88,081 | Diff. | 108,712 |
Top 50 Economies By GDP Purchasing Power Parity
[Data are in Billions of US Dollars]
Rank | Country/Economy | 2019 | % Share | Diff. | 2023 |
---|---|---|---|---|---|
1 | China | 27,449.05 | 19.2 | – – | 37,198 |
2 | United States | 21,482.41 | 15.0 | 5,967 | 24,671 |
3 | India | 11,412.97 | 7.98 | 10,069 | 16,575 |
4 | Japan | 5,806.72 | 4.06 | 5,606 | 6,380 |
5 | Germany | 4,555.47 | 3.18 | 1,251 | 5,184 |
6 | Russia | 4,345.36 | 3.04 | 210 | 4,966 |
7 | Indonesia | 3,753.20 | 2.62 | 592 | 4,969 |
8 | Brazil | 3,524.06 | 2.46 | 229 | 4,149 |
9 | UK | 3,144.55 | 2.20 | 380 | 3,609 |
10 | France | 3,081.00 | 2.15 | 63.6 | 3,541 |
11 | Mexico | 2,696.45 | 1.88 | 385 | 3,256 |
12 | Italy | 2,474.39 | 1.73 | 222 | 2,748 |
13 | Turkey | 2,372.53 | 1.66 | 102 | 2,808 |
14 | Korea | 2,241.56 | 1.57 | 131 | 2,690 |
15 | Spain | 1,949.68 | 1.36 | 292 | 2,249 |
16 | Saudi Arabia | 1,942.56 | 1.36 | 7.12 | 2,277 |
17 | Canada | 1,930.68 | 1.35 | 11.9 | 2,227 |
18 | Iran | 1,627.14 | 1.14 | 304 | 1,885 |
19 | Thailand | 1,403.55 | 0.981 | 224 | 1,741 |
20 | Egypt | 1,396.98 | 0.976 | 6.57 | 1,897 |
21 | Australia | 1,383.93 | 0.967 | 13.1 | 1,654 |
22 | Taiwan (China) | 1,306.75 | 0.913 | 77.2 | 1,525 |
23 | Poland | 1,270.56 | 0.888 | 36.2 | 1,532 |
24 | Nigeria | 1,221.05 | 0.853 | 49.5 | 1,452 |
25 | Pakistan | 1,219.72 | 0.852 | 1.33 | 1,493 |
26 | Malaysia | 1,068.11 | 0.746 | 152 | 1,390 |
27 | Philippines | 1,041.13 | 0.728 | 27.0 | 1,458 |
28 | Netherlands | 1,018.97 | 0.712 | 22.2 | 1,191 |
29 | Argentina | 922.95 | 0.645 | 96.0 | 1,109 |
30 | Bangladesh | 829.27 | 0.580 | 93.7 | 1,172 |
31 | South Africa | 819.09 | 0.572 | 10.2 | 948.1 |
32 | Colombia | 792.00 | 0.553 | 27.1 | 983.8 |
33 | UAE | 775.88 | 0.542 | 16.1 | 947.2 |
34 | Vietnam | 769.67 | 0.538 | 6.21 | 1,067 |
35 | Iraq | 733.93 | 0.513 | 35.7 | 878.6 |
36 | Algeria | 693.11 | 0.484 | 40.8 | 778.9 |
37 | Singapore | 582.55 | 0.407 | 111 | 698.4 |
38 | Switzerland | 573.36 | 0.401 | 9.19 | 660.1 |
39 | Belgium | 570.10 | 0.398 | 3.26 | 651.9 |
40 | Sweden | 566.77 | 0.396 | 3.33 | 660.5 |
41 | Romania | 543.17 | 0.380 | 23.6 | 663.5 |
42 | Kazakhstan | 534.67 | 0.374 | 8.50 | 663.8 |
43 | Hong Kong-SAR | 508.83 | 0.356 | 25.8 | 618.6 |
44 | Chile | 507.94 | 0.355 | 0.89 | 617.2 |
45 | Peru | 487.42 | 0.341 | 20.5 | 615.6 |
46 | Austria | 484.07 | 0.338 | 3.35 | 553.2 |
47 | Czech Republic | 417.21 | 0.292 | 66.9 | 496.9 |
48 | Norway | 415.13 | 0.290 | 2.07 | 481.3 |
49 | Ukraine | 410.81 | 0.287 | 4.32 | 502.9 |
50 | Ireland | 402.08 | 0.281 | 8.73 | 487.7 |
Rank | World | 143,089 | % Share | Diff. | 177,424 |
[Source of Data: International Monetary Fund (IMF)]
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