## 11/24/18

### Liberalism, conservatism, fairness and free riders

Trying to develop a simple math model for the most recent events in western democracies I took some findings in neurosciences (e.g. psychology of moral)  as a set of statistical links between variables. Specifically, there is a book “The Righteous Mind: Why Good People are Divided by Politics and Religion” (2012) written by J. Haidt, which stresses the importance of fairness for both conservative and liberal minds (in sense of moral prejudice). All people have own understanding of fairness, having some core values, however. It was also found that people cooperate much better when unfairness in cooperation is punished. When free-riders pay for the absence of fair behavior for other participants (it is not the absence of fairness for the free riders but rather fair-not-ness for the others). The whole ensemble of current fairness/unfairness judgements in the entire population defines net morality in the society. Majority is always around the net value. However, extreme deviations from this net value can be large and such people are most interested in shifting the equilibrium to their side (e.g., preachers, hippie, priests, criminals, etc.). They all proved to be able to influence individuals and groups shifting moral norms, including the understanding of personal and universal fairness. These are examples of inherent moral influence because such preachers do believe in their versions of fairness.
Another category of people trying to change the net value of moral judgements at large and using the outcome of such shifting is politicians. One can look at the map of the most recent elections in Austria or in the USA, or any other western country, and observe the distribution of moral predominance in big cities and the rest. It looks like that the whole territory is covered by the same color. And this is usually not the color of the winning party (except the USA).  This is great division between conservative and liberal moral, both are intrinsic to people (at least, it is my interpretation of the Haidt’s book). For us, the most important is that this division is dynamic and subject to influence of propaganda (in Bernays terms). We suggest that such influence and dynamics are not random and can be described by simple mathematical model based on laws stationary evolution of an ensemble under endogenous and exogenous forces.
Now, we get back to free riders. One part of population considers another part as free riders on average in terms that they obtain more than deserved in a not fair way. The outcome is simple – the absence of cooperation with the other part and cooperation enhancement within the same moral norms group. The division has been growing with time together with the growth in economic (and likely social) inequality. Trump got it (as well as right-wing politicians in Europe) first and sent to the conservative part of population a message that unfairness will be punished (make the US great again!) This is direct message which actually make two major effects – increasing cooperation in both liberal and conservative cores of population and increasing deviations of the average conservative and liberal understanding of fairness from the net value.
We develop a math model of moral evolution, which includes the intrinsic (basic) and current distribution of fairness understanding, the dynamic development under endogenous (and likely exogenous in satellite countries of Western Europe) forces depending on the evolution of economic (income) inequality observed during the past 30 to 50 years. The latter is described by the evolution of Gini ratio, the share of labor in GDP (GDI) and the mean personal income. The sensitivity of a person to external influence is also distributed over the population and depends on the intensity of external influence. In other words, increasing economic inequality made many people to feel unfairness and become more sensitive to external propaganda which, in turn, makes people more sensitive on average.

# Abstract

We have estimated the performance of discrimination criterion based on the P/S spectral amplitude ratios obtained from six underground tests conducted by the DPRK since October 2006 and six aftershocks induced by the last two explosions. Two aftershocks were detected in routine processing at the IDC. Three aftershocks were detected by a prototype waveform cross correlation procedure with explosions as master events, and one aftershock was found with the aftershocks as master event. Two seismic arrays USRK and KSRS of the IMS and two non-IMS 3-C stations SEHB (South Korea) and MDJ (China) were used. With increasing frequency, all stations demonstrate approximately the same level of deviation between the Pg/Lg spectral amplitude ratios belonging to the DPRK explosions and their aftershocks. For a single station, simple statistical estimates show that the probability of any of six aftershocks not to be a sample from the explosion population is larger than 99.996% at the KSRS and even larger at USRK. The probability of any of the DPRK explosion to be a representative of the aftershock population is extremely small as defined by the distance of 20 and more standard deviations to the mean explosion Pg/Lg value. For network discrimination, we use the Mahalanobis distance combining the Pg/Lg estimates at three stations: USRK, KSRS and MDJ. At frequencies above 4 Hz, the (squared) Mahalanobis distance, D2, between the populations of explosions and aftershocks is larger than 100. In the frequency band between 6 and 12 Hz at USRK, the aftershocks distance from the average explosion D2>21,000. Statistically, the probability to confuse explosions and aftershocks is negligible. These discrimination results are related only to the aftershocks of the DPRK tests and cannot be directly extrapolated to the population of tectonic earthquakes in the same area

### Probability of the North Korea nuclear testing mountain collapse is increasing

Seismic stations in South Korea, China and Russia detected increasing aftershock activity within the mountain where the biggest DPRK nuclear test was conducted on September 3, 2017. A few aftershock events occur three months after the test and about two months after the previous aftershock on October 12. This activity might be related to a complete collapse of the mountain or chimney collapse with opening of a direct access from the explosion cavity filled with radioactive debris to the atmosphere. This is the worst case scenario, but one cannot exclude this effect especially in view of high international tension in this region. China, Russia and South Korea are just in tens of kilometers from the test site, and Japan is not too far away. Seismologists continue detailed study without interruption and delay.

## 10/31/17

### Did North Korea test tectonic weapon?

The Democratic People's Republic of Korea (DPRK) conducted 6 underground test, with  the last 5 from 6 in the same mountain. The biggest (sixth) event conducted on September 3, 2017 (DPRK6) had magnitude (mb=6) and resulted in visible landslides and also was followed by a few aftershocks with magnitudes between 2.4 and 3.4. Two of them occurred on September 23 (around 4:40 and 8:30 UTC) and are likely were reported today as the cause tunnel collapse and casualties. The test conducted on September 9, 2016 was also followed by a small aftershock well described in our paper (https://arxiv.org/abs/1611.03055 or https://link.springer.com/article/10.1134/S1028334X17030011 ), which is very similar to the aftershocks of the DPRK6.
Among many challenges in the analysis of various physical measurements related to the DPRK test is the absence of measurable levels of radioactivity after 4 from 6 events and just minor traces of radioactivity after the other two.  Very deep placement of warheads can prevent radioactive gas venting and  thus provide effective containment of radioactive debris,  Let's consider two possibilities alternative to nuclear testing.
Having the last event equivalent to about 100,000 tons of TNT, one can reject the hypothesis that this DPRK test was a chemical blast. An alternative explanation would be testing of tectonic weapon, as introduced by Russian geophysicist V. Nikolayev in 1992. There are several physical mechanisms that can be used to facilitate effective release of pre-existing tectonic energy and generation of seismic waves. In any case, the mountain is exhausted after five tests and no more tectonic release is possible.  I do not believe that tectonic weapon can be as efficient as we observed in six DPRK events. In case it does exist this is an additional threat for the peaceful world.

## 2/16/17

### On decreasing rate of economic growth in the USA

We published a book and a few papers [e.g., 1, 2, 3] on economic growth. There are numerous plots demonstrating the decelerating rate of real economic growth in developed countries. The long-term component of real GDP increase is very simple – annual increment in real GDP per capita, rGDPpc, is a constant, which does not change with time since the late 1940s. This makes the rate of growth to be inversely proportional to the current level of rGDPpc: dln(rGDPpc(t)) = A/rGDPpc(t).  The population component is large in the USA – approximately 1% per year since the 1950s, but negative in Japan. Therefore, we use rGDPpc instead of rGDP, which is rather misleading.
In the first paper published in 2005, we used data between 1950 and 2003 and found that the rGDPpc annual increment has a positive slope. It was considered as an indication of future problems between 2004 and 2016, which was actually observed as the Great Recession and further sluggish recovery. Figure 1 displays the original time series between 1947 and 2003 (black circles) extended by the estimated made since 2004. Instead of time, we use rGDPpc as argument. For recession periods, when the rGDPpc falls, one observes loops in the curve. Our prediction was right – the slope of the regression line has dropped from 0.02 (black linear trend line) to 0.007 (red trend line). The average increment in rGDPpc was $551 as measured in 2009US$.

Figure 1. Annual increment in real GDP per capita in the USA since 1947. Black circles show the period between 1947 and 2003, and red circles show the period extended into 2016. Linear trends are shown in black and red, respectively. Two regression equations demonstrate the change in slope since 2003.

There is another problem with the original rGDPpc time series borrowed from the BEA web site. It is calculated as a ratio of rGDP and total population, while only economically active population matters. Therefore, we have to correct the original  rGDPpc for the ratio shown in Figure 2. When the rGDPpc multiplied by the population correction factor, one obtains a better view on the long-term growth rate, as depicted in Figure 3. The trend in the rGDPpc annual increment is absent. This observation means that the rate of real GDP growth in the USA (and other developed countries) is decaying inversely proportional to rGDPpc. Leading economic countries will be growing at a rate of about 1.5% per year in the next 20 years. In 2016, the rate of rGDPpc growth was 0.9% per year. This mediocre growth will be accompanied by decreasing rate of population growth as observed since the earlier 2000s.

Figure 2. The ratio of total population and working age population (16 and above). The rGDPpc is corrected (multiplied) by this ratio.

Figure 3. Same as in Figure 1, but for the population corrected rGDPpc.

## 1/21/17

### Trump and real economy

I have no doubt that real economic growth in the USA  does not depend on presidential opinion and action. On average, the US will be growing at a rate of about 2% in the long run. In 2017, a recession is possible, but it has no connection to Trump.
What Trump can change then? This is the question from both sides of the split nation. The complete answer is difficult to present in a post. I'll try to formulate some predictions, which are most relevant to my study.

1. Oil price depends on global processes and hardly to be corrected by Trump's energy policy. Hydrofracturing might become a cheaper technology because of lower expenses for environment protection.
2. Most of commodities (iron, non-ferrous metals, grain, etc.)  are low now and will likely grow in the near future. It is good time to renew the US production capabilities. His plan to return production to the US is wise.
3. Competition with China is getting hot. China is a bigger US factory with cheap labor. All profit of cheaper labor is privatized by the top 1% of the US population. China cannot resist because its economy is not able to grow without the US market and investment.
4. The richest 1%  is a big threat to Trump, however. Some of them are inside his administration. Bees against honey. Such conflicts are always resolved in favor of money, as Marx said.
5. Inflation will be low with low volatility. US administration does not affect prices.
6. Stock market will fall in 2017.
7. Labor force participation will be falling and the labor force participation will not be growing  fast.

## 1/15/17

### Recession may hit New Zealand in 2017, but at a five-year horizon real economic growth is about 2% per year

In 2010, we published a paper in the Journal of Applied Economic Sciences, which predicted real GDP per capita, rGDPpc, in several developed countries. Corresponding working paper was published in 2009 and covered the period before 2007. The evolution of rGDPpc in New Zealand was also presented in this blog in 2011.
Here, we revisit the 2010 model for New Zealand. It is important to stress that all defining parameters, which were estimated by the LSQ method from the data before 2008, are retained in the revisited model. Therefore, this is an out-of-sample test. The test result shows that our model accurately predicts the evolution of real GRP in New Zealand at an 8-year horizon. As predicted in 2009, in the next few years the growth rate will be increasing, except a deep fall in 2017, as we also expect in the USA.  Since the full prediction horizon is 14 years, we will be reporting on the model prediction in the future, but not often – the change in real economic growth is a slow process.
The original macroeconomic model for real GDP growth in developed countries was formulated in 2006 in the paper “GDP growth rate and population” published in the ECINEQ WPS. The model links the rate of growth in rGDPpc, g(t) = dln(rGDPpc)/dt, with the attained level of the rGDPpc and the rate of growth in population of a coutry-specific age.

g(t) = dln rGDPp(t)/dt  = A/rGDPpc(t) + 0.5dlnNs(t)/dt     (1)

where empirical constant A and the specific age, Ns, are estimated from data. To obtain the model parameters, we used rGDPpc time series borrowed from the Total Economy Database.   The best fit annual increment value is A=$420 (notice that we used the EKS US$, as published by the Conference Board in 2016, while the GK 1990US\$ were used before).  The term A/rGDPpc(t)  corresponds to inertial economic growth, which is observed when there is no change in the Ns.  The specific age population in New Zealand is 14 years, as in the previous versions. To describe the change in Ns, we used the age pyramid obtained in the 2006 census and extrapolated it in the past and in the future. The precision of Ns predictions decreases with the difference between the predicted year and 2006. We do not use fresher censuses because the goal of this study is to prove the model and to assess the accuracy of prediction at various time horizons. The largest time horizon for the 2006 census is 2021.
Figure 1 presents the observed and predicted GDP growth rates for New Zealand as obtained in 2008. Both curves are characterized by high-amplitude oscillations likely associated with measurement errors. Therefore, in Figure 2 we present both annual curves smoothed with MA(5) and MA(3), respectively. One can conclude that our prediction from 2008 was correct and real GDP per capita in New Zealand follows the predicted curve. This is the best validation of our model for NZ and the driving force of real economic growth in developed countries.

Figure 1. Observed and predicted growth rate of real GDP per capita in New Zealand between 1980 and 2015.
Figure 2. The observed curve in Figure 1  is smoothed with a five-year moving average. The predicted rate is smoothed with MA(3). One can observe an outstanding accuracy of GDP prediction for 2009 and 2015 (between the smoothed curves).