Decaying economic growth in EU as an economic argument for Brexit

In our previous post, we demonstrated the fall in the rate of economic growth observed in the USA since 1960. Overall, there exists a strong negative trend expressing the fundamental property of economic growth – constant annual increment. Explained in simple words, an economy goes up with steps on constant height, like stairs. This was an example of benign growth, however: the rate of growth follows theoretical predictions. For Europe, however, everything is much worse. Some of the biggest economies demonstrate an accelerated decay.

In the past, we published a number of papers [1, 2, 3] and a book on the evolution of real GDP per capita and explained what does mean inertial economic growth, i.e. the growth with constant steps in real GDP per capita. We have proven that developed economies grow with a constant step, not at a constant rate. Theoretically, the rate of economic growth has to be inversely proportional to the attained level of GDP per capita. Here, we use the estimates of real GDP per capita as listed in the Total Economy Database managed by the Conference Board.

Two figures below update our previous results obtained for Germany, France, Italy, Switzerland, and the UK. Four countries (France, Italy, Spain, and Switzerland) demonstrate extremely poor performance during the past decades. Germany is on par with theoretical predictions and the UK is slightly over the predicted growth rate but still on the negative trends since 1960. The EU is under severe stress in the years to come since the future of economic growth is likely dark. Germany follows its cruise speed and unlikely to spill economic growth over the other biggest economies.

It might be good time for the UK to think about the traction forces associated with the clear features of economic stagnation in EU.

Fig. 1. Evolution of annual increment of GDP per capita, i.e. the difference of the current GDP per capita and that one year ago.

Fig. 2. The rate of growth of the real GDP per capita.

On the decaying rate of economic growth in the USA

We have published a few papers [1, 2, 3] and a book on the evolution of real GDP per capita. The message is very simple – economies grow with a constant step, not a constant rate. Therefore, the rate of economic growth is inversely proportional to the attained level of GDP per capita. Three figures below just update our previous results obtained for the USA using new estimates for the past three years. How dare economists confuse people and authorities with fairy tales that the rate of economic growth will return to that observed in the 1960s. The average rate of 3% per year will never happen again, short term excursions are possible, although. In the long run, the rate of growth will fall from the current 1.6% to 1% per year in 2035.

Fig. 1. Evolution of annual increment of GDP per capita, i.e. the difference of the current GDP per capita and that one year ago.

Fig. 2. Annual increment of the GDP per capita as a function of real GDP per capita.

 

Fig. 3. The rate of growth of the real GDP per capita. Currently, it is about 1.6% per year and will be above 1% another 20 years.

Modeling economic success of Poland and failure in Russia and Ukraine. The 2015 model revision

In 2005, I developed and presented at the Fourth Annual EEFS Conference (Faculty of Economics, University of Coimbra, Portugal 19-22 May 2005) a paper “Modelling the transition from a socialist to capitalist economic system” (Slide Share). An extended and updated version was published in the JAES in 2009.  

My model predicts the measured evolution of real GDP per capita and is based on two fundamental physical laws - the decay of socialist system is similar to radioactive decay and the growth of capitalist system is similar to the process of exponential saturation. The predictive power of the model was extremely high even for the realm of physics and accurately described the transition from socialist to capitalist economic system from 1989 (East Europe) and 1991 (Former Soviet Union) to 2003-2007. Ukraine was an excellent example of the model success. Figure 1 shows the observed and predicted curves with the extension to data available in 2016 (we use the Total Economy database from the Conference Board as data source). Two curves are very close between 1991 and 2006.  In 2007, a slight deviation from the predicted curve starts, which then developed in a catastrophic fall in GDP to the level of 0.802 relative to 1991. Essentially, the level of GDP per capita in Ukraine now resides in the earlier 1970s, i.e. half a century in the past.  The predicted curve implies that the rate of growth had to be 3.65% per year (as observed from 1995) and thus had to be in 2015 at 1.84 (relative to 1991).  Missed opportunity.

Russia (Figure 2) presents an intermediate case with a healthy evolution before 2008, recession in 2009-2010, quick recovery and then stagnation at the level of 1.3 relative to 1991 instead of permanent growth at a rate of 3.5% per year. It has to be at 1.8 in 2015. 

Poland is likely the best performer between the former socialist countries. Poland fully uses its growth potential –  real GDP per capita grows at a rate of 3% per year since 1991. Our model cannot predict total (Ukraine) or partial (Russia) economic failures, which are likely in tight connection with national and international political turmoil, but our model accurately predicts the evolution of GDP per capita when growth potential is used in full. Poland has been chasing developed countries and it is the best example for Ukraine and Russia (and for other former socialist countries), which are hardly to be able to return to the full growth potential any time soon. The gap between them and developed countries rises at a threatening rate. 

Figure 1. Comparison of the observed and predicted evolution of real GDP per capita in Ukraine.

Figure 2. Comparison of the observed and predicted evolution of real GDP per capita in Russia.

 Figure 3. Comparison of the observed and predicted evolution of real GDP per capita in Poland.  The transition started in 1989.

EU, Syria, migrants and Russia

There are some speculations on the effect of  the Russian campaign in Syria on migration in EU.
The EU statistics gives actual numbers to illustrate this effect  - Russia has helped to reduce the monthly rate  of first time asylum applicants.

How universal is the law of income distribution? Cross country comparison (post #777)

This paper could be absolutely amazing  for physicists. It shows that income distribution in four (English Speaking) countries follows a universal law. Incomes are driven by only one(!) external variable - real GDP per capita. All differences in income distribution are defined by the gap in GDP: Canada, New Zealand and the UK exactly follow steps of the USA with a time delay of 15 to 25 years! This is definitely a fundamental result for physics of income evolution as described by our model in the previous post.  

Luckily, this is post #777 in this blog. 

How universal is the law of income distribution? Cross country comparison

Ivan O. Kitov  and Oleg I. Kitov (link to full text on arxiv.org via IDEAS)

The evolution of personal income distribution (PID) in four countries: Canada, New Zealand, the UK, and the USA follows a unique trajectory. We have revealed precise match in the shape of two age-dependent features of the PID: mean income and the portion of people with the highest incomes (2 to 5% of the working age population). Because of the U.S. economic superiority, as expressed by real GDP per head, the curves of mean income and the portion of rich people currently observed in three chasing countries one-to-one reproduce the curves measured in the USA 15 to 25 years before. This result of cross country comparison implies that the driving force behind the PID evolution is the same in four studied countries. Our parsimonious microeconomic model, which links the change in PID only with one exogenous parameter - real GDP per capita, accurately predicts all studied features for the U.S. This study proves that our quantitative model, based on one first-order differential equation, is universal. For example, new observations in Canada, New Zealand, and the UK confirm our previous finding that the age of maximum mean income is defined by the root-square dependence on real GDP per capita.

Gender income disparity in the USA: analysis and dynamic modelling

We have published principal  results of this study as a working paper on arxiv.org. Our model provides an accurate quantiative description of how the difference between men and women evolves since 1962.

Gender income disparity in the USA: analysis and dynamic modelling

Ivan O. Kitov  and Oleg I. Kitov 
(link to full text on arxiv.org via IDEAS)

We analyze and develop a quantitative model describing the evolution of personal income distribution, PID, for males and females in the U.S. between 1930 and 2014. The overall microeconomic model, which we introduced ten years ago, accurately predicts the change in mean income as a function of age as well as the dependence on age of the portion of people distributed according to the Pareto law. As a result, we have precisely described the change in Gini ratio since the start of income measurements in 1947. The overall population consists of two genders, however, which have different income distributions. The difference between incomes earned by male and female population has been experiencing dramatic changes over time. Here, we model the internal dynamics of men and women PIDs separately and then describe their relative contribution to the overall PID. Our original model is refined to match all principal gender-dependent observations. We found that women in the U.S. are deprived of higher job positions. This is the cause of the long term income inequality between males and females in the U.S. It is unjust to women and has a negative effect on real economic growth. Women have been catching up since the 1960s and that improves the performance of the U.S. economy. It will take decades, however, to full income equality between genders. There are no new defining parameters included in the model except the critical age, when people start to lose their incomes, was split into two critical ages for low-middle incomes and the highest incomes, which obey a power law distribution. Such an extension becomes necessary in order to match the observation that the female population in the earlier 1960s was practically not represented in the highest incomes.

Gender income inequality: final discussion of modelling results

Our model stems from extensive physical intuition and is supported by direct comparison of income observations with closed-form solutions of simple differential equations describing fundamental physical processes. The set of equations describing the growth and fall of incomes is fully borrowed from physics, with the empirically estimated constant of dissipation and the distribution of sizes of personal capabilities and instruments. The transition to the power law distribution of the highest incomes is also a physical process, which can be qualitatively described by the concept of self-organized criticality. In that sense, the dynamics of the highest incomes is not governed by simple physical relationships – the power law distribution is a purely statistical description rather than a solution of a system of differential equations. Nevertheless, all properties in the super-critical regime are defined by two parameters – the number of people above the Pareto threshold and the power law index. The former parameter is exactly predicted by our model as a function of time and age for males and females. The index has to be empirically estimated, as in other physical cases like for the slopes of earthquake recurrence curves in various seismic regions [Kitov et al., 2011]. Overall, the system of personal income distribution is fully and accurately described by physical equations. In that sense, it is a physical system.

We introduced the microeconomic model of personal income distribution a decade ago and used the CPS historical data to calibrate all defining parameters. The March CPS reports aggregate incomes in five-year age cells since 1993. Ten-year cells are used between 1947 and 1992, with sporadic appearance of shorter cells for the youngest population. The income data and the U.S. age pyramids between 1947 and 2011 published by the U.S. Census Bureau were used as they are without gender separation. The IPUMS income microdata not only make it possible to distinguish between males and females but also provide various estimates in one-year cells. In this study, we use the advantage of income microdata and model two specific age-dependent features of personal income distribution: mean income and portion of people in the Pareto distribution. These two features are most sensitive to the influence of time and age. A correct income distribution model must accurately describe the dynamics of secular and age-dependent changes observed in actual data. Any model not predicting the dynamics of actual changes should be disregarded. Our model successfully predicts all principal changes in both features observed between 1962 and 2014 for males and females separately.

The difference in income dynamics demonstrated by two genders represents enormous challenge for quantitative modelling. A model unifying (at first glance) incompatible results for two genders has to be parsimonious and include only parameters common for both cases. The dynamic discrepancy between male and female incomes has to be explained only by values of defining parameters: constants and variables controlled by exogenous measurable forces represented by continuous time series. The evolution of gender-dependent income features together with all changes in the difference between them should be driven by the same driving forces. In our model, the only force moving personal income distribution along predefined trajectories is real economic growth as expressed by GDP per capita calculated for working age population.

Dynamic behavior of the difference in income distribution between males and females requires a special approach in quantitative models of income distribution. The original version of KKM made no difference between men and women. Here, we extend the KKM by introducing two independent populations with different features of income distribution as reported by the CPS and IPUMS. Since gender divides U.S. population in approximately equal proportions over time and age the gender-related income effects do improve the KKM predictive power upon the original version. In other words, females have sizeable contribution to the total income. The next step to a more precise model might be the introduction of race differences of income distribution. The income difference between white males and black females is much more dramatic than income difference between two sexes considered in this study. This is a real challenge to our income distribution model.

All in all, we have demonstrated in this paper that the refined KKM accurately explains a number of common and gender-specific features. The principal finding of this study is that female population in the U.S. has the same distribution of the capability to earn money (notation similar but not equivalent to human capital) and consistently lower sizes of work instruments (work capital) compared to those for men. The income gap between women and men has been closing since 1960 and currently an average female has work capital making 65% of that available for an average man. It was only 45% in the 1960s. Considering the same capability to earn money for females, one can conclude that the relatively lower work capitals (e.g., job positions, assets, …) are controlled by external force. A fair distribution has not been achieved yet. It will likely take decades.

The relatively lower instrument sizes available for females make the proportion of female above the Pareto threshold lower. In turn, this effect lowers the mean income for the same age since a relatively lower number of rich females occurs in all age groups. However, the lack of rich women is partially compensated by the effect of lowered Pareto threshold for females, which is most prominent in the 1960s and 1970s. The coherent increase in the instrument size and Pareto threshold for women has been incorporated into our model. As a result, the model accurately predicts the early growth trajectory, which is most sensitive to the size of work instrument, and the number of females above their own Pareto threshold. As in the original model, both parameters increase with time as the square root of real GDP per capita. For women, we have introduced a specific option as revealed from observations - the relative instrument size and the Pareto threshold both follow linear time trends with different slopes.

The female mean income shows a very specific feature – it is practically constant during an extended period spanning the ages between ~30 and ~60. In our model, this feature results from the fast growth of all personal incomes to their peak values, which are then retained at the same level. The expedite rise in all incomes is induced by the lowered sizes of work instruments available for women. In turn, the lower instruments do not allow personal incomes to reach the Pareto threshold and there are almost no rich women by male standards in the 1960s and 1970s. Therefore, the disparity in work capitals affects the low-middle incomes and higher incomes together. Such a shelf is absent in the overall mean income curve because of larger instrument sizes available for males.

The shelf in the females’ mean income curves has also revealed the difference between critical times for the low-middle (in physical notation - sub-critical) and high (super-critical) incomes, the latter governed by the Pareto distribution. Equation (13) describing the sub–critical regime is valid from the start of work experience to the age of retirement. Then incomes fall along an exponential trajectory described by equation (17). The actual age of retirement varies in a narrow band between ~60 and ~65 years and is embedded into the model as constant. The fall is described by an exponential function with a negative index. This is a new feature of the upgraded model. In the original model, the critical age, T_c, was the same for low-middle and high incomes. The input of rich men in the overall PID masked the presence of the low-middle income critical age. Instructively, the mean income measured for males supports the existence of two critical ages.

The refined model includes several new features not compromising the underlying physical concept of saturation growth and the transition from sub-critical to super-critical regime of income distribution. The extended version of the original model accurately predicts the PID evolution for males and females in the U.S. from 1962 to 2014, i.e. where the IPUMS data are available. Since the GDP estimates are available from the U.S. Bureau of Economic Analysis since 1929 we start our model in 1930 for males. Actually, the model spans the period since 1870, i.e. the year when started their work people who reached the age of 75 in 1930. For females, the start year is shifted to 1960 because of changing relative size of work instrument and Pareto threshold.

Forced deprivation of higher job positions (work capital) is the cause of the observed long term income inequality between male and female in the U.S. It is not only unjust to women but has a negative effect on real economic growth. The replacement of highly capable women with less capable men results in lower total income, which is an equivalent to real GDP. Women have been catching up since the 1960s and that improves the performance of the U.S. economy. It will take decades, however, to full income equality between genders. The problem of race income disparity will take longer time to full resolution, however.