Economics as Classical Mechanics: Economic data as a weapon in the economic war between developed countries. This is a shame

Economic data analysis is a hobby rather than a duty. My professional occupation is geophysics with an emphasis on the effects of nuclear explosions in three media – solid earth, underwater, and atmosphere. After the Comprehensive Nuclear-test-ban Treaty was signed, my interests and activity have been focused on monitoring nuclear explosions at a global level. The essence of monitoring is not to miss the event of interest (e.g. the DPRK tests) in the intensive flux of similar events (e.g. hundreds of earthquakes per day). Such a requirement creates professional deformation related to data quality and consistency. The CTBTO uses only authenticated and quality checked data obtained by the International Monitoring System. In routine data processing carried out by the International Data Centre for the State Parties of the CTBTO, it is not allowed to use unauthenticated national or international data. In economic data, I found an extremely strong argument against the use of data provided by developed countries in any independent activity (Technical Secretariat of the CTBTO is supposed to be an independent actor as well as other UN-related organizations in economic data published by several “independent” sources). In case the countries controlling these sources or any other country will provide data to the CTBTO or like organizations it should be denied. This data is highly likely biased in favor of the providers.

We have been studying statistical links between various economic parameters since 2003. In December 2020, the COVID-19 limitation to remote work was a good argument in favor of revisiting all studies conducted before 2013 and validation of the models by adding new data between 2010 and 2020. The most recent set of posts in this blog was associated with models similar to Okun’s law. The modification used in our approach is just conversion of the link between the rate of unemployment and real GDP per capita (as the measure of output gap) into a differential form. Then the integral change in the unemployment rate is predicted by the GDP per capita growth.

For this reason, the real GDP per capita data are needed as the major term of the differential equation. We have already reported on the definitional revisions (problems) to the GDP deflator which make our model piece-wise in accordance with these revisions. However, we have also found another problem – the real GDP per capita estimates provided by various sources (BEA, OECD, Total Economy Database, Maddison Project Database) are quite different. There was no reason to classify these differences in a conspiratorial sense and we used them without prejudice as fully interchangeable. There are some features, however, that made us think in non-economic vocabulary.

The sources of economic data are highly biased in favor of the sponsoring countries.

In this post, we just present examples of the biased estimates. The reference years in economic time series are related to real GDP change to later dates with the major/comprehensive revisions. One cannot directly compare the real GDP per capita estimates from two sources when the reference years are different. Therefore, we normalize all time series to the same year, usually to the start year of the shortest time series. Obviously, the relative change in the real GDP per capita has to be the same, when all time series are normalized to the same year and we can consider any difference as related to definitions used in the corresponding estimation procedure. Economics is a developing science in both theoretical and experimental (measurement) parts and we understand the necessity of different approaches as an important methodological aspect of the overall progress. However, the differences between the normalized time series reveal high bias in the estimation of real GDP growth specifically in the countries sponsoring these estimates. It is unacceptable in any science and this is a shame. We start with the USA.

The upper panel in Figure 1 displays the evolution of real GDP per capita estimates borrowed from four sources: the Bureau of Economic Analysis (BEA - USA), Organization of Economic Cooperation and Development (OECD, Headquarters – Paris), Maddison Project Database (MPD – Netherlands), and Total Economy Database (TED – USA, China, …). In the past, the MPD was also controlled by the Conference Board publishing the TED. Currently, MPD and TED are two different databases. As described in the previous paragraph, all time series are normalized to their respective values in 1970 (OECD’s start point). One can see that the TED gives the highest growth in the GDP per capita since 1970. The MPD provides the lowest estimates. In the lower panel, several pair-wise ratios are presented in order to illustrate the relative differences in the four time series. It is worth noting that the BEA and OECD provide the same estimates except for the most recent period, which is subject to further revisions, however. The BEA provides data only for the USA and is not used in further comparison.

Figure 1. Upper panel: The evolution of real GDP per capita estimates borrowed from the Bureau of Economic Analysis (BEA - USA), Organization of Economic Cooperation and Development (OECD, Headquarters – Paris), Maddison Project Database (MPD – The Netherlands), and Total Economy Database (TED – USA, China, …). All time series are normalized to their respective values in 1970 (OECD start point). Lower panel: Several pair-wise ratios revealing the relative differences in the four time series.

Figure 2 is similar to Figure 1 and illustrates the case of Germany. The best result to Germany is given by the MPD – the total growth in the real GDP per capita since 1970 is 2.67. The OECD is less generous and gives the factor of 2.41. The TED gives the worst estimate – 2.09. The US-based source with tight connections to China does not see Germany as a country with healthy economic growth. The MPD is a part of University of Groningen and Figure 3 presents the Netherlands. Again, the MPD gives the highest growth and the TED is not nice to the Netherlands. The connection between the TED and MPD is expressed in straight lines in the lower panel displaying the ratios.

Figure 2. Same as in Figure 1 for Germany

Figure 3. Same as in Figure 1 for the Netherlands

The OECD headquarters resides in Paris, France. Figure 4 displays the real GDP per capita estimates and their ratios. It proves the assumption that the OECD is in favor of France in terms of the rate of economic growth since 1950. The OECD estimate is a factor of 4.93 between 1950 and 2018, which is much higher than 4.66 from the MPD and 4.69 from the TED. The OECD curve is above the other two sources from the very beginning.

Figure 4. Same as in Figure 1 for France

Finally, we report potential bias in the real GDP per capita estimates for the UK. Figure 5 shows that the largest growth is estimated by the Office of national statistics (ONS). The ONS is a national source and its bias is not unexpected. The OECD gives almost the same estimates as the ONS. The TED is in favor of modest economic growth in the UK, and the MPD is the least generous.

Summarizing the observations in Figures 1 through 5, one can conclude that the data origin (sponsor or country) defines the method of real GDP estimation (i.e. definition of nominal GDP and GDP deflator) most appropriate for the sponsor/country real economic growth to be the largest. Such an approach definitely introduces a serious bias in the estimates of various economic variables used for quantitative analysis. The latter becomes vulnerable to non-economic forces and likely suffers larger problems with statistical estimates in the mainstream economic models. We do not know the decisions and reasons behind this bias, but one cannot deny the fact that this bias is always in favor of the source controlling the country. The advantage of the formally higher economic growth is likely related to the attractiveness of a country for investors and the likes. In that sense, the biased estimates of real economic growth is a weapon in the fight for international finances. And this fight seems to be nasty and without rules. This is called civilization – all means are good.

We add Japan, China, Austria, and Canada (Figure 6 through 9) to the main set. One can judge who is who in this world: easily find who the US allies are, and who has better relations with Germany.