Total Economy Database estimates of real GDP per capita in Switzerland must be a joke

This is an extension of the previous post comparing the real GDP per capita (GDPpc) estimates obtained from various sources. We demonstrated that the GDPpc estimates for a given country for the same period from three major sources (the OECD, Total Economy Database, and Maddison Project Database) may give quite different total growth. In other words, the history of real economic development depends on the source, and the overall deviation is extremely large. One cannon consider this strong deviation as a stochastic bias. In this post, we extend the list by 6 countries: Australia, New Zealand, Italy, Spain, Switzerland, and Russia. Switzerland and Russia are likely the most striking examples of the difference between sources so far.

Figure 1 presents the case of Australia with the GDPpc estimates spanning the period between 1959 and 2018. The MPD data provides the largest total growth of 3.62. This is likely due to the underestimation of the initial value in 1959 since the current GDPpc levels are close. The OECD and TED report very similar estimates. The lower panel in Figure 1 illustrates the fact that the difference between the OECD and TED is likely stochastic with a small negative trend. All three time series were close between 1960 and 1970, and then started to slightly deviate. The biggest break was observed in 1990 when the MPD started to demonstrate a much higher rate of growth. The Maddison Project Database (University of Groningen) likes Australia and the other two sources are less benevolent.  

Figure 2 illustrates the case of New Zealand. The MPD and TED give the best result since 1970 – the real GDP per capita grew by a factor of 1.95. The OECD is less generous and gives the factor of 1.86. The difference between the sources is low and one can suggest that New Zealand is not an economic rival for any major economy, and thus, the estimates of real economic growth are not biased. One can trust these data.

Figures 3 and 4 present Italy and Spain (both European countries) with the MPD providing the highest total growth estimates since 1970. The second best source is the OECD for Italy and the TED for Spain.

The case of Switzerland must be a joke. The total increase in real GDP per capita reported by the MPD is 2.56, and the other two sources give approximately 1.6. The difference is related to the very low MPD estimate for 1970 - $23,459. It is not clear how the MPD estimates are so different if all economic agencies use the same original data and methodologies. In my view, such differences are not acceptable. One cannot assess the statistical performance of the real economic growth models using fully incompatible estimates of basic economic variables.

The positive side of the observed differences is that one can judge the implicit relationships between countries, i.e. who is who in this world. The closest US allies can be easily revealed as well as the friends of Germany or France.   

Finally, Figure 6 presents Russia. The MPD and TED estimates between 1960 and 1990 are identical and then the MPD (Europe) reports faster real economic growth than the TED (USA). The difference between the TED and MPD has a linear dependence on the time between 1990 and 2010. No difference is observed since 2010.

 

Figure 1. Upper panel: The evolution of real GDP per capita estimates in Australia as borrowed from the 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 1959. Lower panel: Two pair-wise ratios revealing the relative differences in the three time series.

Figure 2. Same as in Figure 1 for New Zealand

 

Figure 3. Same as in Figure 1 for Italy

 

Figure 4. Same as in Figure 1 for Spain

Figure 5. Upper panel: GDPpc estimates from three sources. Middle panel: the curves in the upper panel are normalized to their respective levels in 1970. Lower panel: pair-wise ratios of the normalized curves.

 

Figure 6. Same as in Figure 1 for Russia

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.    

Figure 5. Same as in Figure 1 for the UK

 

Figure 6. Same as in Figure 1 for Japan

 

Figure 7. Same as in Figure 1 for China

 

Figure 8. Same as in Figure 1 for Austria

Figure 9. Same as in Figure 1 for Canada

 

The link between unemployment and real economic growth in Australia

We revisit our 10-year-old models linking real GDP per head and the rate of unemployment in developed countries. We have already validated the modified Okun’s law using new data for the USA, Canada, France, and Germany.  For the USA, we had more sources of data: the estimates from the BEA and BLS were used in addition to the OECD and Maddison Project Database. The new GDP and unemployment data covered the years between 2010 and 2019. Excellent model performance was achieved with just a few breaks in the linear link between the change rate in the GDP per capita and the change in the rate of unemployment. The years of these breaks correspond to the breaks in the GDP per head time series caused by revisions to real GDP definition.   

In this post, we apply the same approach to Australia. According to the established procedure, we first present the breaks in the GDP deflator, dGDP. The difference between the CPI and dGDP clearly reveals the definitional breaks in the dGDP estimates.  In the upper panel of Figure 1, we present the evolution of the cumulative inflation (the sum of annual inflation estimates) as defined by the CPI and dGDP between 1962 (we use the OECD data for the unemployment rate since 1961) and 2018. Both variables are normalized to their respective values in 1960. In the middle panel, the inflation rate is presented for both indices, and the lower panel displays the differences of the curves in the upper and middle panel. The difference between cumulative price change estimates has a complex structure with many pivot points. In such a complex structure, the estimated break years might be no so reliable due to larger uncertainty in the modeled parameters.

           

Figure 1. Upper panel: The evolution of the cumulative inflation (the sum of annual inflation estimates) as defined by the CPI and dGDP between 1961 and 2018. Both variables are normalized to their respective values in 1961.  Middle panel: The dGDP and CPI inflation estimates. Lower panel: The difference between the CPI and the dGDP curves in the upper and middle panels.

 

In our model, we are looking for breaks near the years and obtain the following intervals and coefficients: 

du_p = -0.76dlnG + 1.50,     1993>t≥1977

du_p = -0.35dlnG + 0.75,      2006≥t≥1993     

du_p = -0.76dlnG + 1.25,      2013≥t≥2007              

du_p = -0.36dlnG + 0.25,               t≥2014    (1)

 where du_p – one-year change in the (OECD) the unemployment rate , G – real GDP per capita (2011 prices). The break years are slightly different from those estimated from the inflation curves in Figure 1. This is likely due to much the higher sensitivity of the predicted unemployment rate to the coefficients in (1). As could be expected, there are 3 pivot points (breaks) in the linear dependence: 1993, 2006, and 2013. Nevertheless, the overall fit is relatively good (Rsq=0.87) as the lower panel in Figure 2 demonstrates. The revised model for Australia is successful.

 

Figure 2. Upper panel: The measured rate of unemployment in Australia between 1977 and 2018, and the rate predicted by model (1) with the real GDP per capita published by the MPD and the unemployment rate reported by the OECD. Middle panel: The model residual: stdev=2.5%. Lower panel: Linear regression of the measured and predicted time series. Rsq. = 0.87. 

Validation of the modified Okun's law for Germany

In the upper panel of  figure 1, we present the evolution of the cumulative inflation (the sum of annual inflation estimates) in Germany. There are two curves as defined by the CPI and dGDP between 1970 and 2018. Both variables are normalized to their respective values in 1970. Since 1996, the dGDP curve is above the CPI one and this configuration we interpret as economic super-performance. This effect is likely related to the EU financial rules with the ECB in Frankfurt. In the past, Germany has weaker performance and the EU leadership made it a super-economy. In the middle panel, the inflation rates are shown for both variables. In the lower panel, we present the difference between the CPI and dGDP curves in the upper and middle panels. One can see that the difference between the cumulative curves has several quasi-linear segments. The change in the slope between these segments in most likely related to the multiple revision to the dGDP definition. We have already used this observation of the segmented character of the real GDP estimates in order to assess our Okun’s-law-like model of the link between the change in unemployment and the change in real GDP per capita. The years of breaks in the dGDP time series are not easy to estimate from the lower panel of Figure 1 and we allow the LSQR method to find these years when minimizing the RMS residuals.

  

Figure 1. Upper panel: The evolution of the cumulative inflation (the sum of annual inflation estimates) as defined by the CPI and dGDP between 1970 and 2018. Both variables are normalized to their respective values in 1970.  Middle panel: The dGDP and CPI inflation estimates. Lower panel: The difference between the CPI and dGDP curves in the upper and middle panels.    

 

As for other countries, we minimize the model residuals, i.e. determine the break years together with the regression coefficients. For Germany, the best fit model between 1971 and 2018 is as follows:

           du_p = -0.42dlnG + 1.50, 1970>t≥1984

du_p = -0.555dlnG + 0.700,  1985≥t≥1992

du_p = -0.450dlnG + 1.300,  1993≥t≥2006

du_p = -0.450dlnG + 0.400,            t≥2007     (1)

where du_p – one-year change in the (OECD) the unemployment rate, G – real GDP per capita (2011 prices). The break years are determined automatically. Figure 2 presents the measured and predicted rate of unemployment (upper panel), the model residual error (middle panel), and the regression of the measured and predicted time series. The overall fit (Rsq.=0.88) is more when excellent with the break years close to those expected from Figure 1. One of the largest model errors in the residual time series was observed in 1990. This is most likely related to the reunification and merging of two time series belonging to different economies. When this spike is excluded, the standard deviation falls from 0.99% to 0.76%, and Rsq increases from 0.88 to 0.92. 

 The modified Okun’s law linking the change in the unemployment rate and the change in the real GDP per capita is validated by new data for Germany for the period between 2010 and 2019.

  

Figure 2. Upper panel: The measured rate of unemployment in Germany between 1970 and 2018, and the rate predicted by model (1) with the real GDP per capita and the unemployment rate published by the MPD. Middle panel: The model residual: stdev=0.99%. When the 1991 reading is excluded, stdev=0.76%.  Lower panel: Linear regression of the measured and predicted time series. Rsq. = 0.88.  

 

Modern professional sport is an ultimate expression of gender and race segregation

I had a post on the Winter Olympics as an example of hostile racial segregation. The winners of the Olympics have the same fame and honor as the winners of the (summer) Olympic Games. They almost all belong to one race and this predominance cannot be resolved - no natural snow and ice for the majority of UN countries. This segregation is based on historical prejudice.  It is even more outrageous because modern sport is professional and the absence of equal conditions is the worst expression of all kinds of inequality, including economic. 

Another example of professional segregation is the gender split in all major competitions. I have passed a few programs and exams on gender equality which definitely proved that the professional quality of males and females as well as of people of all races and sexual orientations is at least equal. As mentioned above - modern sport is a paid professional activity and thus there should be no gender/race segregation in sport. I have written a few papers on gender and racial income inequality in the USA and UK (e.g. 1, 2, 3)  which has to eliminated by all means. Income inequality in sports is based on one principle - competitions are split into two gender groups. 

Our duty is to fight against gender and racial segregation in sport. All sportspersons have a natural right to compete in equal conditions - no gender segregation. 

The modified Okun's law for France. Model validation with Rsq=0.98

 In the upper panel of Figure 1, we present the evolution of the cumulative inflation (the sum of annual inflation estimates) in France. There are two curves as defined by the CPI and dGDP between 1955 and 2018. Both variables are normalized to their respective values in 1955. Since 1985, the dGDP curve is above the CPI one and this configuration we interpret as economic underperformance. Also, we reported in this post that France has very low annual increment of the real GDP per capita. This effect is likely related to the EU financial rules. In the past, France and other European countries with underperforming economics forced price inflation in order to make exports more attractive due to lowering the exchange rate. In the middle panel, the inflation rates are shown for both variables. In the lower panel, we present the difference between the CPI and dGDP curves in the upper and middle panels. One can see that the difference between the cumulative curves has several quasi-linear segments. The change in the slope between these segments in most likely related to the multiple revision to the dGDP definition (e.g., imputed rent). We have already used this observation of the segmented character of the real GDP estimates in order to assess our Okun’s-law-like model of the link between the change in unemployment and the change in real GDP per capita. The years of breaks in the dGDP time series are not easy to estimate from the lower panel of Figure 1 and we allow the LSQR method to find these years when minimizing the RMS residuals.           

Figure 1. Upper panel: The evolution of the cumulative inflation (the sum of annual inflation estimates) as defined by the CPI and dGDP between 1955 and 2018. Both variables are normalized to their respective values in 1961.  Middle panel: The dGDP and CPI inflation estimates. Lower panel: The difference between the CPI and dGDP curves in the upper and middle panels.    

As in the previous posts, we minimize the model residuals, i.e. determine the break years together with the regression coefficients. For France, the best fit model between 1962 and 2018 is as follows: 

du_p = -0.134dlnG + 0.750, 1962>t≥1984

du_p = -0.255dlnG + 0.620,  1985≥t≥1999     

du_p = -0.520dlnG + 0.355,            t≥2000     (1) 

where du_p – one-year change in the (OECD) the unemployment rate, G – real GDP per capita (2011 prices). The break years are determined automatically. Figure 2 presents the measured and predicted rate of unemployment (upper panel), the model residual error (middle panel), and the regression of the measured and predicted time series. The overall fit (Rsq.=0.98) is more when excellent with the break years close to those expected from Figure 1. One of the possible reasons is that France has a good set of methods and procedures to measure/estimate economic parameters. This approach does not avoid data incompatibility problems, however, and statistical analysis needs extra efforts to distinguish between actual economic structural breaks and ignorance of basic procedures. The importance of data quality is best illustrated by an example in Figures 3 and 4, where two GDP per capita estimates from the OECD and MPD are compared. One can see that these two agencies provide quite different estimates. The use of the MPD estimates would change the statistical model. We do not know whose estimates are more accurate, but the OECD time series gives excellent results.

  

Figure 2. Upper panel: The measured rate of unemployment in France between 1960 and 2018, and the rate predicted by model (1) with the real GDP per capita and the unemployment rate published by the OECD. Middle panel: The model residual: stdev=0.50%. Lower panel: Linear regression of the measured and predicted time series. Rsq. = 0.98. 

Figure 3. Comparison of the real GDP per capita estimates reported by the OECD and Maddison Project Database. Both time series are normalized to their respective levels in 1960. 

Figure 4. The ratio of the OECD and MPD real GDP per capita estimates between 1960 and 2018.

The modified Okun's law for Canada. Model validation

 In our previous post, we revisited and validated our version of Okun’s law for the USA with new GDP and unemployment data for the years between 2010 and 2019. The revised model accurately describes the new data and three quarters of 2020, i.e. the original model is validated. In order to reach the best fit between the measured and predicted unemployment rates, we introduced a structural break in 2010 as related to the change in real GDP definition. 

In this post, we apply the same approach to Canada and start with the CPI and GDP deflator difference, which is used to reveal definitional breaks in the dGDP estimates. Obviously, such breaks in the dGDP creates breaks in the real GDP per capita estimates, and thus, in the statistical estimates associated with our model. One needs to find such breaks and allow the model to compensate for corresponding disturbances. At this stage, we ignore well-known steps in the unemployment rate estimates (see, TP-66 – CPS Design and Technology) related to the change in the population controls after the decennial censuses, e.g. the 2010 census. Such steps could be accurately compensated by dummy variables. More efforts are needed to investigate this problem and find the years when such steps were introduced in the labor force statistics.

 In the upper panel of Figure 1, we present the evolution of the cumulative inflation (the sum of annual inflation estimates) as defined by the CPI and dGDP between 1962 (we use the OECD data for the unemployment rate since 1961) and 2018. Both variables are normalized to their respective values in 1961. From the very beginning, the dGDP curve is above the CPI one and this configuration we interpret as economic underperformance. Another indicator of underperformance is the average annual increment of the real GDP per capita (from the Maddison Project Database) of $533 (2011 prices) compared to $643 in the USA – the biggest trade partner. In the middle panel, the inflation rates are shown for both variables. In the lower panel, we present the fit between the CPI and the dGDP cumulative inflation curves after correction of the latter in 1962 (coefficient 0.8), 1977 (0.8*1.4=1.12) and 2003(0.8*1.4*0.77=0.86). There was a period between 1977 and 2003 when the CPI grew faster than the dGDP.

  

Figure 1. Upper panel: The evolution of the cumulative inflation (the sum of annual inflation estimates) as defined by the CPI and dGDP between 1961 and 2018. Both variables are normalized to their respective values in 1961.  Middle panel: The dGDP and CPI inflation estimates. Lower panel: The fit between the CPI and the dGDP cumulative inflation curves after correction of the latter in 1962, 1977 and, 2003 (see text).   

 

In our model, we are looking for breaks near the years and obtain the following intervals and coefficients: 

du_p = -0.270dlnG + 1.130, 1977>t≥1970

du_p = -0.281dlnG + 0.303,  2000≥t≥1978     

du_p = -0.280dlnG + 0.505,  2009≥t≥2001              

du_p = -0.350dlnG + 0.180,           t≥2010     (1)

 

where du_p – one-year change in the (OECD) unemployment rate, G – real GDP per capita (2011 prices). The break years are slightly different from those estimated from the inflation curves in Figure 1. This is likely due to the higher sensitivity of the predicted unemployment rate to the coefficients in (1). The cumulative inflation curves in the upper panel of Figure 1 are both synchronously corrected in many revisions through their whole length. The estimates of the unemployment rate are obtained in the Current Population Surveys and represent independent estimates. The unemployment values are also corrected in the revisions to unemployment definition and when new population controls estimated after the decennial censuses. The original estimates cannot be changed but rather synchronously corrected. The rate of unemployment is an independent economic variable consisting of independent measurements. The predicted rate of unemployment depends on the integral value of the real GDP per capita. This makes the predicted value to be very sensitive to the GDPpc evolution. In other words, the current prediction, u_p, depends on the initial value, u(t₀), and the whole path of the GDPpc between t₀ and the current time. This is 49 years for Canada and 68 for the USA. The new readings of the unemployment rate and GDPpc (2011 to 2019) validate the model, which links the change in the rate of unemployment and the relative growth rate of the real GDP per capita in Canada.

 

Figure 2. Upper panel: The measured rate of unemployment in Canada between 1970 and 2019, and the rate predicted by model (1) with the real GDP per capita published by the MPD and the unemployment rate reported by the OECD. Middle panel: The model residual: stdev=0.62%. Lower panel: Linear regression of the measured and predicted time series. Rsq. = 0.87.