The Science of 70%

A technique that is integral to the science of Transition Economics’ (TE) Proof Charts (TEPs), is The Science of 70%.

Similar to the Pareto 80/20 Rule and Six Sigma quality control sciences, The Science of 70% is an easy approach to finding important measures within very large data sets.

The science of 70% permits us to determine causality from a very large, complex dataset by taking the value at the 70%-value of a known-important indicator’s data, and comparing the rankings of the amplitudes of charts created in frequency distributions.

If this sounds complicated, it’s not; in fact, it’s a very simple technique. It just takes a few steps to mine value from even the largest dataset using this approach.

To explain how this works, let’s take a real-world example. You can follow along with a Jupiter Notebook session or use a spreadsheet as well if you like. I assume here that you have a pretty good understanding of spreadsheets, so look for tutorials online if you need spreadsheet help …

Part 1: Determining the 70% value for National Savings %GDP

  1. First, let’s download a causal indicator’s data to make this easy. Go to the Data Science page/tab at the WAOH Library and search for “Gross domestic savings (% of GDP)”. Click on the hyperlink presented – (, and it will take you to a Sheet of TEP charts that are all drawn based on the Science of 70%.
  2. Next, let’s download the data that comprises this chart. Search google for “World Bank NY.GDS.TOTL.ZS
  3. From the Data tab we want the most recent data, so build a list of most-recent data:
    Country Name 2017 2018 2019
    Aruba 22.51873515            —>  22.51873515
    Angola 29.88168324 33.16398899 32.04094604
    Albania 8.893553742 9.818071383 8.231647145
    Arab World 30.87885253 33.90737467 32.82091963
    United Arab Emirates 49.48114701 49.87897504 47.80923381
    Argentina 15.56353276 17.77413958 19.61479264
    Armenia 7.651454985 8.712420363 4.088341312
    American Samoa      
    Antigua and Barbuda 26.28307018 27.43485333 27.43485333
    Australia 24.64969067 24.90992989 25.70835979
  4. Sort the most recent data values (in the 2019 column in this example) in ascending order, and next locate the value that is located at 70% in the list. You should have approximately 215 values, so you are looking for the value at cell #149   (215 x .70 = 149). This 2019 column cell should show a value  of approximately 27.5 – and you might also want to notice if neighbouring cells have values that are the same, close, or quite different.
  5. With this threshold value, now we are going to assume that those nations that have Gross National Savings by %GDP – greater than or equal to 27.5 – are Advancing; and nations with Savings less than 27.5% are trending toward collapse.

That’s it, you’re done. By the Science of 70%, you have just created a new way to confirm the causality of any national indicator.

I chose a Causal indicator (Domestic Savings) – Causal indicators advance nations that have high values 100% of the time, and collapse nations with low values 100%. The Domestic Savings indicator was located as a causal indicator by using another Science of 70% frequency distribution for the indicator, Trade Balance – as a % of GDP.

How did I know that Trade Balance was an important measure? I observed that it was a factor in other evidence-based reports  … Notice in these two charts how Income Equality reduced Health and Social Problems in nations with positive trade balances, where trade deficit nations were highest in Health and Social Problems.

Inequity creates Social Problems

By using Trade Balance <> greater or less than zero (0)  – “Advancing” was assigned to all nations with trade balances greater-than zero, and “Collapsing” was assigned to nations with trade balances less-than zero. We  decided that Germany and Japan are “Advancing”, but that Canada and the U.S. are “Collapse Trending”.

Transition Economics calls the Trade Balance TEP-Chart’s red line “Advancing Economies”, but any Causal indicator would let you build new criteria to determine Advance or Collapse.

Now we have a table of chart-able values for 187 countries.

In line 1 of the chart we see that Canada is a collapse-trending nation with a Gross Domestic Savings value of 21.5% of its GDP

Nation              Status (based on Trade <> 0.0)            Gross Domestic Savings (%GDP)

Canada             Collapsing       21.5
United States  Collapsing       18.2
Germany          Advancing       27.2
Japan                Advancing       24.5
… for 187 countries

This frequency distribution TEP Chart now shows at left that 30 nations with Savings less than -13% were 100% collapsing; while 15 nations at right had Savings greater than 43% (to 79%) and were 100% advancing. 

What does this data tells us?

Note from the TEP Chart’s frequency distribution of 187 nations, that 100% of high-Domestic Savings nations are advancing and 100% of low-savings nations are collapsing. The amplitude of this chart is 100% from bottom to top; we can calculate this because it has a maximum value of 100% (= 1.0) minus a minimum value of 0.0 – to equal the highest amplitude possible – 1.0.

This would rank Domestic Savings as either #1, or as one of the highest-amplitude indicators, because the very great majority of measures – for other economic and social indicators – rank much lower than 1.0.

This TEP Chart report is now either telling us that Savings is a causal indicator, or that high-savings nations simply export more than import – as a coincidence.

Here is where science, correlating reports, and observation come into play. Is the standard of living and production in Germany higher than counterparts in Japan, Canada, and the U.S. by observation? Perhaps a comparison between highest savings nations (Ireland, Qatar, Brunei, Luxemburg, and UAE) versus lowest-saving nations (Somalia, Haiti, West Bank and Gaza, Zimbabwe, Central African Republic) confirms coincidence or causality.

Trade Surplus nations have higher Social Contracts (lower social problems and inequity) similarly – shown here by a report created by an Edinburgh University Research Team in 2013. Individuals and nations with more savings and fewer social problems have the potential to be more productive, and almost all of the countries on the lower half of this list have trade surpluses consistently.

Inequity creates Social Problems

On the TEP Sheet for Domestic Savings below, we see how Domestic Savings %GDP looks based on a frequency distribution TEP Chart of SCP values >5.0 (5.0 is a value determined by the Science of 70%). The TEP  Chart’s amplitude is .7 (70% minus 0) so it’s not as high as the amplitude above, therefore its  lower-ranking TEP Chart but still holds valuable information.

A tutorial for reading TEP Charts is located under the Data Science tab at the WAOH library and also in the Transition Economics info webpage.

I use TE’s data science tool MEMS to find highest-ranking TEP Charts and then the WAOH library posts this as searchable sheet scores.

Part 2: Building “National Savings” TEP Charts

  1. To build this TEP Chart yourself, you can start with a basic excel template or upload a much larger sample pack on the WAOH “About” page – under Contributions (click here). There are a lot of examples in the large pack and you can create your own frequency distribution charts as well.
  2. Here in the spreadsheet is a TEP chart that I created using the process described above
  3. As we work with the final charts produced, we might decide to tweak the calculations to use 65% or 75%. This chart has a too-low “24% Advancing” and so I reduced the Domestic Savings value to 25 (from 27.5) but the Science of 70% is usually very useful for the analysis of all 220 nations

For quickly analyzing 2,000 indicators, we configure our professional MEMS tool to do this heavy number-crunching and chart work.

As you can see, this can be a time-consuming one-chart-at-a-time analysis, but the Science of 70% works well to find causality within a very large dataset using this testing method.

By measuring the amplitudes of TEP Charts created by National Savings, we can easily rank the top correlations to the lowest rank using this causal indicator now as well. 

©2022 All rights Reserved, CSQ Research

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