A technique and “neat trick” that evolved from the analysis of Transition Economics Proof (TEP) Charts, was the Science of 70%.
The science of 70% says that you can determine causality from a very large complex dataset by taking the value at the 70%-value of a causal indicator’s data and comparing the rankings of the amplitudes of charts created by frequency distributions.
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
- First, let’s download a causal indicator to make this easy. Go to the Data Science page at the WAOH Library and search for “Gross domestic savings (% of GDP)”. Click on the link at WAOH (https://csq1.org/info/NY.GDS.TOTL.ZS.htm) and it will take you to a Sheet of TEP charts that are all drawn based on the Science of 70%.
- Next, let’s download the data that comprises this chart. Search google for “World Bank NY.GDS.TOTL.ZS“
- 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 Afghanistan Angola 29.88168324 33.16398899 32.04094604 Albania 8.893553742 9.818071383 8.231647145 Andorra 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
- Sort the most recent data values in ascending order, and 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 & .70 = 149). This cell shows a value that is close to other values and is approximately 27.5
- So, now we are going to assume that those nations that have Gross National Savings by %GDP – greater than 27.5 are Advancing; and that 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 (National Savings) that was located using another Science of 70% frequency distribution – Trade Balance (%GDP) (greater or less than 0.0 – the following chart simply calls the red line chart “Advancing Economies”), but any causal indicator like it would let you build new criteria to determine Advance or Collapse.
Note how 100% of high-Savings nations are advancing and 100% of low-savings nations are collapsing? The amplitude of this chart is 100% from bottom to top – or we simply say it has a value of 1.0 so that we can compare it to other TEP chart results. This report is either a causal and predictive indicator, or the other option is that high-savings nations simply export more than import as a coincidence. Here is where science, correlating reports, and observation come into play.
Trade Surplus nations also have higher Social Contracts (lower social problems and inequity) – shown here by a report created by an Edinburgh University Research Team. 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 side of this list have trade surpluses.
From the TEP Sheet above, here is how National Savings %GDP looks based on an SCP>5.0 (where 5.0 is a Science of 70% value as well). Its amplitude is .7 so not as high as above. I use MEMS to find highest-combinations of TEP Charts and WAOH posts highest sheetscores.
Circle back as we’ll shortly build a TEP Chart using this new “National Savings > 27.5” as a measure of advance or collapse next …
Part 2: Building “National Savings” TEP Charts
- To build this yourself, you can start with the 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.
- Here is an initial chart that I create as an analysis of Trade as a %GDP (https://data.worldbank.org/indicator/NE.TRD.GNFS.ZS) measured by National Savings > 27.5
- As we work with the final charts produced, we might decide to tweak the calculations to use 65% or 75%, but the Science of 70% is usually very useful for the analysis of all 220 nations and also 70 high-income nations too.
For quickly analyzing 2,000 indicators, we configure our professional MEMS tools to do the heavy number and chart work. As you can see, this can be a time-consuming one-off analysis, but the Science of 70% works well to find causality within a large data set using this method.
By measuring the amplitudes of TEP Charts created by National Savings, we can easily rank the top correlations to this causal indicator as well. I’ll update this article as we add it to MEMS over time.Tags: Data Science econometrics Statistics