The on-line magazine Aeon today published an article of mine on why economic inequality tends to wax and wane in very long (‘secular’) cycles, and what consequences it has for the society.
One of the central ideas in the article was that general well-being (that is, of the overwhelming majority of population) tends to move in the opposite direction from inequality: when inequality grows, well-being declines, and vice versa. To illustrate this idea I put together an ‘infographic,’ which was later modified by the Aeon’s graphic designer. The result was visually pleasing, but I felt that the changes obscured certain features of the graph that I felt were important. I did not press the point, because generally the editors at the Aeon made excellent suggestions, and greatly improved my text. Also, I am a techno-geek when it comes to the analysis (graphical and statistical) of dynamics – I’ve been doing it for nearly 30 years and wrote two technical books on it, so what I see is very different from what the regular reader sees. I generally prefer austere black-and-white graphs, and use colors only when it is necessary to make a point. A good scientific graph should be clear, not pretty.
A popular article, like the one on the Aeon, is not a place to provide the ‘gory details’ of the analysis underlying the infographic. Because the topic is quite controversial, I am sure my critics will want to know these details so that they can rebut me. Eventually these details will be published as part of the book that I am working on, but the graph has been published now, so I will use this blog to provide the background to the figure. Many of my readers may also be interested in the ‘view from the kitchen’ of where curves come from.So welcome to the kitchen.
First, here is the infographic in the form that I prefer.
The red curve shows the peaks and valleys of economic inequality, while the blue curve depicts the ups and downs of popular well-being. Here’s a very important point: the curves reflect not absolute levels of these two variables, but deviations around a trend. We all know that the United States changed dramatically between 1800 and 2000 – population grew by orders of magnitude, GDP and GDP per capita expanded, life expectancies increased, and the quality of life generally improved. Generally speaking, the causes of these changes are quite well understood. But it does not mean that the change has occurred smoothly. Many variables of interest to the structural-demographic theory (which explains the dynamics of inequality and well-being, among other things) have grown rapidly for some decades and then stagnated, or even declined in subsequent periods. Then they resumed growing, and so on. I am interested in capturing these oscillations around the rising trend, and the standard way of focusing on such deviations, called ‘detrending,’ is to subtract the trend from the data.
Here’s an illustration using the average age of first marriage as a ‘proxy’ (indicator) of social mood. Generally speaking, when people feel optimistic about their future economic prospects they tend to get married early. If, on the other hand, they are unsure that they will have a well-paying job next year, they tend to delay marriage until they work up to a more secure position, or save some money. However, age of first marriage is only imperfectly correlated with social optimism, because it is also affected by other factors. For example, today people who are completely secure in their economic prospects tend to marry later than people in similar position who lived two centuries ago. For a variety of reasons, as societies modernized, people tend to marry later.
Here’s what the actual data for the average age of first marriage of American women looks like:
The top half shows that the basic pattern is one of up, down, and again up around a rising mean. In the bottom half I subtracted the trend, so now the numbers are fluctuating around the zero line.
There could be other reasons why the average age of marriage is an imperfect indicator of social optimism. For example, changes in tax laws that affect marriage penalty (or, conversely, marriage advantage) may result in many people delaying marriage (or deciding to marry earlier). Additionally, while being able to marry when you found the love of your life (instead of waiting for years until you can afford it) is certainly a good thing, it’s just one thing of many that makes us happy. Thus, if we want to get at such a generic parameter as ‘well-being’ it’s best to approach it with several proxies.
This is why I used four different indicators to approximate the generalized well-being curve. In addition to social optimism, proxied by marriage age, I also looked at an economic indicator and two biological (health) indicators. The economic indicator is the wage of production workers divided by the GDP per capita. Basically it tells us how the fruits of economic growth are distributed – actually paid as wages to workers, or paid out as dividends to share-holders or as compensation to CEOs.
The health aspect of well-being is captured with two proxies: life expectancy and the average stature (height). Life expectancy is an obvious measure of the quality of life, and so is average stature as is documented in voluminous literature (including writings by the Nobel laureate Robert Fogel).
To combine the four variables into a single index, I did the following. First, I log-transformed each variable to make peaks and valleys more symmetric. Then I detrended them, as with the age of marriage above. Finally I divided them by the standard deviation, which brings them all to the same scale. Here’s what the four curves look like when plotted together after detrending and scaling (note that marriage age was flipped upside down, because it is earlier age that correlates with well-being):
It is clear that there is a general tendency for these variables to move up and down together. However, this correlation is by no means perfect. The erratic fluctuations are partly due to what is known as ‘measurement noise.’ This is particularly important for earlier periods, when collecting national statistics has not yet been perfected. But, in addition, fluctuations also reflect genuinely different dynamics of these proxies for well-being. In my tax law example, such legislation could affect marriage age, but not life expectancy, while the introduction of penicillin will affect life expectancy, but not marriage age. By averaging the four curves (the thick line) we smooth out those erratic fluctuations, and bring out the cyclic component. This average is then my best estimate of the generalized well-being curve, and it is the blue curve plotted in the main graph.
The red curve is easier to explain. It is based on the idea of Kevin Philips (as explained in the Aeon article) to measure inequality by the ratio of the largest private fortune to the wealth of a typical (median) household:
|
year |
Largest Fortune ($$mln) |
Median Wealth ($$) |
Ratio (×1000) |
|
1803 |
3 |
300 |
10 |
|
1830 |
6 |
350 |
17 |
|
1848 |
20 |
400 |
50 |
|
1868 |
40 |
500 |
80 |
|
1875 |
105 |
500 |
210 |
|
1890 |
200 |
540 |
370 |
|
1912 |
1000 |
800 |
1250 |
|
1921 |
1000 |
1250 |
800 |
|
1940 |
1500 |
1750 |
857 |
|
1962 |
1000 |
7200 |
139 |
|
1982 |
2000 |
33,300 |
60 |
|
1992 |
8000 |
43,200 |
185 |
|
1999 |
85,000 |
60,000 |
1417 |
|
2005 |
46,500 |
102,844 |
452 |
|
2010 |
54,000 |
66,740 |
809 |
‘Ratio’ is log-transformed, detrended, and scaled in the same way as other variables, after which it becomes the red curve in the main graph.
It’s pretty obvious that the red and blue curves are close to being mirror opposites of each other. During the integrative phases of the secular cycles well-being is high and inequality low. During the disintegrative phases well-being is low and inequality is high.
This does not mean that there is a direct causal connection, that inequality directly depresses quality of life for the majority of population. Or that quality of life directly depresses inequality. Rather, these two variables are different facets of some integrated whole. The Aeon article traces out the interconnections between these and other structural-demographic variables (in dynamical systems there is no cause and effect, each variable is both a cause and an effect).
In particular, if you look closer, you can see that trend reversals of the two curves are slightly out of phase: inequality tends to turn the corner after well-being.
The main graph also lists some iconic events that illustrate the back-and-forth swings of American history. The events on the left hand side, coded with red, are typical disintegrative phase occurrences. Mostly I am showing such instances of political instability as riots, violent labor strikes, and, of course, the American Civil War. Note how they tend to bunch up during the periods of growing inequality. I have also added Social Darwinism and ‘Greed is Good’ on the left side of the ledger, for reasons explained in the Aeon article.
On the right hand side and coded blue, I list some of the more important integrative occurrences. Unlike internal wars (such as the American Civil War that divided the nation) external wars (War of 1812, World War II) are listed on the right side of the ledger, because they were powerful unifying (and therefore integrative) events.
Two questions:
1) What is the data base for changing height stature of Americans?
2) Do you believe that the rise in average-age-married during the last 30 years to be a reflection of uncertainty in the future, or as is the standard interpretation, a direct result of the sexual revolution? If it is the second, wouldn’t that tend to skew the data somewhat?
Oops, sorry, scratch the second question. Posted these after reading the Aoen essay. I ought ot have paid a bit closer attention to the blog post itself!
The stature data is from the Historical Statistics of the United States, Millennial Edition (Table Bd653-687. Selected anthropometric measurements-height, weight, and body mass index: 1710-1989). There is more up-to date data in papers by anthropometricists like John Komlos, but I am still analyzing those data. The problem is that until humans reach the age of the early 20s we don’t really know what their height will be. So this makes it difficult to find out what stature did in the last 20 years. However, Komlos made much progress looking at the body length of infants, so we should be able to get a handle on it.
In any case, for the period up to 2000 (which is when the curve ends) we are on solid grounds.
Stature as a measure of general nutrition level is a good choice, but it has a lag built in, and with the very low recent price of food may not be as meaningful now as it it was 100 years ago.
Very interested and nice tutorial for me on detrending. Have you published more formally on the idea of using marriage age as an important component of optimism/well being or can you point me to other such discussions? Have you considered whether male marriage age indexes other information than female marriage age? – Thanks
The curve of male ages of marriage essentially parallels that for females. On average, men tend to marry a few years later, but the ups and downs are the same. So it really has something to do with generalized optimism. A fun book to read about social moods is John Casti’s “Mood Matters”:
http://www.amazon.com/Mood-Matters-Rising-Lengths-Collapse/dp/364204834X
And I will be making a more extended argument in my forthcoming book, to which the Aeon article refers.
Perhaps it’s relevant to this question that my wife and I happened to get married at the same time? The only way any difference in marriage age between the sexes can exist is by virtue of age differences within each marrying couple.
The pendulum swings, but sometimes the clock breaks.
The kondratiev cycle is a subset of the demographic cycle that you present so nicely in your books and articles. The K cycle is basically driven debt/demograpy/innovation which has been significant since the birth of the industrial world. Peopl who follow the kcycle also believe that we are entering a period of stress from now right through till about 2013, when hope fully the debt has its jubilee and the innovation that is accumulating in PV energy, Nanotechnology, biotechnology can be applied.
However there are major discontinuities that are approaching. Firstly the hostile elite that rules the USA whose plans appear to be to extract maximum financial wealth and displace the Wasps, are in such a position to change the whole political game in the USA. Obama has been given the power to kill on a whim…a reversal of everthing that america stood for. The declining power of the west means that they can no longer impose order on many parts of the world particularly the middle east. This is occuring at the time when conventional crude production has already peaked and small disturbances can create immense volatility. Lastly the USA was always an island continent effectively free of outside influences. Now with the rise of China and Islamic power will have to start facing fundamental changes in its power relationships and take second place in obtaining resources. There is a significant chance that if not in the current downside, then in the K-summer (c 2040) during a time when resources become tight and whites become a minority, that there will be fundamental and dramatic changes in the whole world system. will it be from republic to principate or from Romulus Augustus to Odoacer is an interesting question
“Nevertheless, when Communism collapsed, its significance was seriously misread. It’s true that the Soviet economy could not compete with a system based on free markets plus policies and norms that promoted equity. Yet the fall of the Soviet Union was interpreted as a vindication of free markets, period. The triumphalist, heady atmosphere of the 1990s was highly conducive to the spread of Ayn Randism and other individualist ideologies. The unwritten social contract that had emerged during the New Deal and braved the challenges of the Second World War had faded from memory.”
A simpler explanation is that the skilled US worker gained post WWII due to the lack of worldwide competition, this began to erode as more women and minorities and competitors from other free nations entered the system. With the fall of Communism and the expansion of technology, the competition among laborers became substantially more fierce. Capital has flourished by capitalizing on a surplus of labor.
On a broader scale, this has been the best period ever for workers worldwide. The important dynamic today is not between some imaginary zero sum battle between the wealthiest capitalists and workers, but between advantaged first world workers finally being placed on equal footing with third world workers, minorities and women. More people have emerged from poverty in past decade than ever before, and it is because the playing field for labor has become more fair, not less.
Questions on the inequality Graph
I am a newbie to Cliodynamics and I have questions on the methodology of Kevin Philips’ graph of inequality using ratios of the highest net worth to the median net worth. It would seem to me that we should be using the median of all the highest net worth fortunes rather than choosing a single high net worth fortune as an index point. I just finished Secular Cycles and you chose the quantity of the elites as a factor in the cycles. It seems to me your approach makes more sense than Phillips. The tacit assumption in my comment is that I think inequality is understated by Philips. I wonder how you would redraw the graph which I find less readable on a vertical axis than the more traditional horizontal.
The problem with estimating the median of the highest fortunes is that it is not clear how to define this class unambiguously. What is the cut-off point? In medieval France there was a categorical legal distinction between noblemen and commoners. So it would make sense to calculate the median or the mean of noble fortunes. But in America there is no such categorical distinction. The top 0.1 percent grades into the top 1 percent who grade into the top 10 percent, and so on.
Thank you for your explanation. Clear enough . It seems we are dealing with a Gaussian distribution function which would be superior to point function analysis. . Contemporary data would be no problem, but distant data……? My point is that do you think that current income inequality is understated by the graph? If this is such an important variable in cliodynamics, than accurate quantification is essential, especially in view of the commonly repeated meme of vastly increasing income inequality in the US, especially since 1980.
Remember that the graph was detrended. Its purpose is to identify the turning points, not the relative height of peaks and troughs. Also, the curve is based on wealth, not income inequality. However, both wealth and income inequality turned a corner in the late 1970s and has been rapidly increasing. So the increase since 1980 is real enough.
Hi Peter,
Great blog post and article in Aeon. Just one question – it seems to me that there are two distinct and different factors driving the upward trends in the well-being measure in your models – the threat of revolution in this period from the US, while the supply of labor is the one that you focus on for the older societies. I don’t think that this is problematic per se – it is a reasonable conjecture that the threat of revolution was what prevented the modern examples from requiring a huge population decline in order to turn the corner. If this is indeed the case then the differentiating factor would be the pre-existing widespread belief in a revolutionary alternative (i.e. socialism) in the modern examples, which the elites were sufficiently afraid of to usher in reforms that caused the cycle to turn the corner without any huge population cull.
The problem is, however, that we currently no longer have such a widespread belief in an alternative and there is little or no prospect of a generalised labour shortage in the West given automation, productivity improvements, etc. This would suggest to me that the next trough in the well-being curve will require cataclysm for the corner to be turned.
Am I reading this right or am I missing something?
I think of these two mechanisms as working together in an interactive way. Greatly simplifying, the threat of revolution caused a shift of social mood among the elites that caused them to adopt a number of reforms. One of them was immigration reform that essentially shut down immigration and decreased the supply of labor. Things are more complex, of course. Immigration Acts were not the only reforms that were adopted in the Progressive Era and later New Deal, and in fact most historians don’t consider them as a major part of action duirng this period.
On the effects of technlological change on labor supply one of the best articles is by Randall Collins. If you can’t find it, I’ll try to dig up the link later.
Would it be useful to measure wealth inequality by using the Gini coefficient and income inequality by the difference between mean and median income?
These are valid ways of measuring inequality, but I prefer not to use them, because they are difficult to interpret. Opaque. When Gini goes from 0.6 to 0.65, what does it really mean? So I prefer to look at such measures as the median to the lowest 10 %, the upper 10% (>90%) to median, and upper 1%, 0.1% and 0.001% to median. Such numbers are much more directly understandable.
I was thinking about how age of first marriage indirectly measures social and economic optimism, and started wondering if marriage itself might also be a measure of asabiya. Specifically, could a breakdown of marriage be an indicator of general individualism and mistrust? Lower asabiya might percolate down to the individual levels. Thus, I figured that a breakdown in families might be an indication of moralists (social conservatives) losing out to saints (social liberals) and knaves.
So, I looked for data, and found this: http://www.census.gov/prod/2011pubs/p70-126.pdf
It’s interesting, because there is a very clear cut drop in percent of children in two-children households starting with the sexual revolution in the 60s, which goes along perfectly with your double helix. On the other hand, the previous data is not as clear cut. There is a small rise in percent of children living in two-parent households starting around 1900 and extending for a few decades, which coincides with the integrative phase. But I could just as easily argue that this may be due to increases in life expectancy (i.e. it might not have been that children were born less out of wedlock, but that both parents tended to be alive). However, looking at the trends in the difference between the percent of children living with father only and the percent of children living with mother only suggests otherwise (we wouldn’t expect fathers to start dying less but mothers to start dying more, so the difference would likely be due to single motherhood).
I’ll see if I can try to dig up data prior to 1880. If my hypothesis is correct, we would have seen an increase in the difference between the percent of children living with mother only and the percent of children living with father only.
I have been aware of these trends, but haven’t seen the data. I need to think about the significance of this for structural-demographic processes, though. Thanks very much for bringing these data up!
Living in two parent households does not mean birth parents. Many children were sent West to be adopted by farm families where labor was needed. I do not know if this was enough to affect the gross statistics.
I all the time emailed this weblog post page to all my associates, since if like to
read it then my links will too.
The best theory of history I’m aware of before yours is R. Buckminster Fuller’s theory of the “Great Pirates”, although the latter is not at all rigorous in any mathematical sense. It seems that your elite class roughly corresponds, in principle at least, to the “Great Pirates” or their modern equivalent, whose real power is cyclical. These cycles would seem to correspond to an increase in power for the elite originating in and supported by the general well-being of the society in question during inclusive economic phases until the sense of entitlement this fosters in the elite (modern equivalent of the divine right of kings) rises to the point of self-destructive feedback in extractive economic phases. A virtual penny for your thoughts (grossly under-priced, I’m sure).
By the way, Peter, are you familiar with this relatively recent Swiss study uncovering the network topology of financial control in transnational corporations: “The Network of Global Corporate Control”? Here’s the link:
http://arxiv.org/PS_cache/arxiv/pdf/1107/1107.5728v2.pdf
If you can possibly find the time, you might also find these articles, with links to various pertinent studies, interesting:
http://writerbeat.com/articles/592-Practical-Reality-Check-I
http://writerbeat.com/articles/709-Practical-Reality-Check-II
Interesting, and probably true.
This same/similar idea was put forward in the Stanford Research Institute report “The Changing Images of Man” (1982).
It seems reasonable to assume we all know that some amount of income inequality is natural, since we as individuals have varying abilities to contribute productively to the economy. This begs the question of at what level income inequality is natural and/or (perhaps also) optimal. If we look at income inequality as a causal factor with respect to well being, as your model as I understand it assumes, this seems to imply that well being is maximized at some optimum income inequality level that we could possibly deduce from this data and use to guide policy decisions.
Judging from the very few cycles available in our history as displayed in the “double helix” graphic, it looks like well being peaks a few years earlier than the income inequality bottoms. If we assume some time delay between a hypothetical optimal level of income inequality and maximum well being, this appears to imply that some level well before the minimums of income inequality is ideal. In other words, too much distance in either direction from a hypothetical ideal level of income inequality reduces overall well being.
This looks to be rather strongly reinforced not only by the earlier peak, but by the substantial decreases in well being preceding the minimum points of income inequality. Since this graphic is detrended, it is not possible to deduce anything reliable about this from the graphic, since no ratio representing income equality is consistent along the time axis. In fact, the fairly radical changes in ratios over time for the same distance from the central axis raises the doubt that any long term absolute value could ever be deduced from the available data (detrended or not). However, there still might exist some way to derive a short term optimum value within a given economy. I would love to know what consideration you have given this or your current thoughts about it if not.
Addendum: The assumption of some hypothetical ideal or “natural” level of income inequality seems to explain why economic systems like Marxism or heavily socialist systems that work to eliminate inequality or reduce it too much are doomed to failure. If we assume that there is a theoretical ideal for income inequality that perfectly rewards with absolute fairness each individual’s true economic contribution to society, this could well explain why too much or too little net income inequality at a societal level would reduce the well being of that society.
I would think that there would be some psychological nuance that factors into this simple and perhaps overly simplistic view. It seems likely that having some safety net would remove counterproductive anxiety that could otherwise reduce productivity by reducing individual resilience or recuperative potential. This is obviously one possible example of an exception to what an overly literal interpretation of how “a theoretical ideal for income inequality that perfectly rewards with absolute fairness each individual’s true economic contribution to society” might actually translate to optimum economic function.
My personal interest in what I regard as your very important work is inspired by how it applies to good government. By good government I refer to how we might structure government and positively constrain how it legislates with the aim to optimize economic function and its fairness. I suspect optimum economic function and fairness tempered by compassion for the genuinely incapacitated are in practice equivalent terms. I don’t pretend that we could structure to fully conform to such an ideal, but I suspect we could greatly reduce the bumps in the road with an adequate governmental and legal suspension system, if you will indulge the metaphor.
Peter, I’m realizing I was wrong in stating that you assume a causal relationship between income inequality and societal well being. The following are my admittedly highly speculative thoughts in reaction to your research:
Income inequality that is too low or excessively reduced politically (Marxism) fails to reward the economic merit of individual contributions adequately to motivate overall productivity and resulting societal well being while rewarding too much those who fail to contribute. That’s the essence of why fully Marxist economies guarantee failure.
Income inequality that is too high rewards the financial elite too much for too little genuine economic merit in their contributions to society while rewarding the less wealthy too little for their valuable work. That is the essence of what threatens to turn highly extractive economies into failed states. It’s not as if we had no examples of any of this.
This should make it very clear why the financial elite who promote conditions that favor high income inequality use the clear failure of Marxism to support their schemes. It’s an easy game to play, since history has already demonstrated the utter failure of Marxism while the evils at the opposite extreme are less obvious, making it a simple matter to misdirect attention by inflating Marxist tendencies and blaming all the wrong things. But either excessively high or low income inequality excessively rewards the wrong people for their contributions. Very high income inequality excessively rewards the wealthy while very low income inequality excessively rewards unproductive slackers and fails to motivate productive enterprise.
These factors motivate less productive workers to favor socialist tendencies while motivating those with an aristocratic sense of entitlement to favor laissez-faire (unpoliced) capitalism. I see this as the essence of political conflict in any society. The ideal situation would be willing, productive economic contributors who sincerely desire fair compensation to everyone for the genuine economic merit in their individual contributions.
However, selfish desire for unearned advantage is a common human flaw as abundantly illustrated in gambling addictions, a sense of unmerited wealth as one’s due by virtue of mere social status (aristocratic entitlement, divine right of kings, etc.), exploitation of others for personal gain justified only by the power to do so, theft, fraud, abuse of government safety nets like welfare and food stamps, etc. Selfish desire for unearned advantage is common to all these social ailments at both ends of the social/financial spectrum.
I see this is as the root cause of the excessive oscillations back and forth between the extremes of high and low income inequality around whatever level at which it would naturally settle if these human flaws were absent. I suspect that just as law and law enforcement ideally and fortunately often successfully reduce criminal behavior in decently well-ordered societies and so increase protection for law abiding citizens, intelligent legal and economic structures based on this understanding could also potentially reduce substantially the wild extremes of income inequality generated by the flawed human tendency to selfishly seek unmerited rewards at the expense of society.
The failure to recognize the dual nature, the double-edged sword of selfish interest is in my opinion the great gaping hole in Ayn Rand’s thinking. She praises it in entrepreneurs, ignoring the ethics or lack of it in the means and pretending that efficient markets abound and automatically self-regulate. Meanwhile, she despises precisely the same selfishness as it manifests in free-loaders at the other end of the spectrum. Ill-gotten gains for the wealthy are just as despicable as free-loading on welfare, food stamps, or disability by someone perfectly capable of earning an adequate living.
Worse, there is a vast difference in their proportion of the economy, making the free-loading (exploitative, predatory, crony) component of the self-entitled wealthy by far the biggest potential threat to societal well being as long as we avoid a Marxist ideal that pretends to eliminate the natural inequality of income and wealth. (Precisely because income inequality is natural and so in practice impossible to eliminate, Marxism guarantees frightfully enormous levels of political hypocrisy, as if politics were not already intrinsically hypocritical.) The current paranoia regarding Marxism on the current far right is just the result of the entitled wealthy successfully enlisting grass roots political help in their fight against anything that would reduce their ability to suck up unearned money from the economy at large…in other words, from all the rest of us.
Currently I see most if not virtually all conservatives as completely clueless regarding both these issues and which end of the swing we’re now experiencing. They keep trying to justify with Ayn Rand or whomever else a situation that is economically unsustainable in any practical economic scenario.
Peter, I think you will find this research on the psychological effect on ordinary subjects when made to feel even briefly rich with play money very interesting in light of your observations of elite behaviors:
http://planetsave.com/2013/12/23/a-rigged-game-of-monopoly-reveals-how-feeling-wealthy-changes-our-behavior-ted-video/
Hello Dr. Turchin.
I am interested in historical cycles as sort of a hobby and have dabbling in it for about 15 years. I happened upon your book Secular Cycles and found it intriguing. I first encountered the English secular cycle originally in Fisher’s The Great Wave My interest is in the shorter (but still long) cycles, specifically the 50-year oscillations you noted in Secular Cycles. For example his is an English price series over 1162-1800.
http://mikebert.neocities.org/Price-fig.gif
I detrend the price series by dividing by the long-term trend. The long term trend are the successive great waves that correspond to your secular cycles. I divided the raw data by the trend and subtract the minimum value of this ratio to give a set of residuals I call price distress. A moving average is included in the figure to visualize the 50-year cycles.
I relate the price distress oscillations to frequency of unrest, and founding dates of monasteries and cathedrals in article #2 here:
http://mikebert.neocities.org/home.htm
I then move forward and summarize cycle work for America in article #1.
I present my paradigm model (article #3) as an explanation for the American cycles. What needs to be explained is why the cycle timing has changed. For example the 50-years cycles noted above are now 80 years long. And that’s not a guess, in 2000 I believed the cycle was 60 years long, in 2002 I thought it was 70 years long as was awaiting confirmation in the form of event I predicted who happen around 2000 based on that ca. 70 years length. I thought it was underway in 2001, it was not. It finally happened in 2008, establishing an 80-year cycle length.
This model is going to be tested experimentally by the electoral outcomes of the next two presidential elections. Two elections and two parties mean four possible outcomes. The model appears to exclude two of them. We will see,
There is a period in between the medieval-early modern cycles, and the post-18th century cycles. I have some comments on that period in the War Cycles article (article #4)
The paradigm model is actually a externalized conception of the Strauss and Howe generational model. Article #5 are some comments on the generation model and the American cycles..
One big difference between my work and yours about the American cycle is that according to your conception the Age of Jackson is a low-unrest period. It does not seem to map out that way;. I see more unrest, and more religious/spiritual events during this time.
The other is in your view most of American history falls into a single iteration of your cycle, which makes it difficult to make explicit predictions that allow the model to be tested experimentally. The shorter cycle times I use allow comparisons to be made to previous eras of the same type. These periods correspond to what S&H call a secular crisis, that is, one of the Kondratiev-spaced high unrest/stress periods that doesn’t have an elevated frequency of religious events. Or, if one is focused on high-unrest periods like the 1970’s, one would consider the Kondratiev-spaced high unrest/stress periods that do have an elevated frequency of religious events, that is, what Strauss and Howe call a spiritual awakening.
My e-mail address is at mikebert@yahoo.com