EXTERIOR ASSESSMENT OF COUNTRY SIZES AND GROWTH RATES

So far we had almost no theoretical and methodological background for our decisions on country sizing. This changes now.

The whole documentation, including the datasets, articles, and other materials, can be found here > https://shorturl.at/iAHS5

DIFFUSION CAPACITY

Correlation Matrix for Diffusion Capacity

Based on the theoretical background, we need we need to use certain sources of external data in order to find about the diffusion capacity scenario.

But there are tons of different variables, so which ones we should use? The answer can be found if we try to correlate these variables - the less correlated the more explanatory power the variable has for the diffusion model.

In order to see this, I put together all of the variables I have downloaded so far, and create a correlation matrix based on 2022 only (as some of the variables are not available in the past).

Z-SCORE E_GOVERNMENT INDEX

Z-SCORE GITHUB COMMITS PER MILLION PEOPLE

Z-SCORE ICT SKILLS IN EDUCATION

Z-SCORE MOBILE CONNECTIVITY INDEX

Z-SCORE ADOPTION OF EMERGING TECHNOLOGIES

Z-SCORE SHARE OF ICT TECHNOLOGIES

Label e.Government.Development.index GitHub.commits ICT.skills Mobile.connectivity Share.of.ICT.goods
United Arab Emirates 1.45 0.15 1.11 1.58 1.61
Saudi Arabia 1.11 -0.38 1.04 0.55 -0.43
Israel 1.36 3.57 1.62 1.01 3.02
Turkey 0.70 -0.11 -1.33 0.44 -0.31
South Africa 0.25 -0.24 -1.05 -0.18 -0.36
Qatar 0.09 -0.30 1.29 1.19 -0.53
Egypt -0.82 -0.34 -0.15 -0.30 0.20
Nigeria -1.82 -0.27 -1.25 -1.30 -0.51
Kenya -1.04 -0.08 -0.08 -1.43 -0.46
Kuwait 0.34 -0.38 -1.31 0.81 -0.49
Morocco -0.81 -0.32 -0.13 -0.57 0.01
Oman 0.59 -0.40 0.58 0.14 -0.40
Algeria -1.03 -0.42 -0.11 -1.21 -0.52
Bahrain 0.50 -0.15 0.55 0.64 -0.30
Ghana -0.87 -0.32 -0.76 -1.36 -0.53

INNOVATION CAPACITY

Correlation Matrix for Innovation Capacity

Z-SCORE PATENT APPLICATIONS

Z-SCORE RESEARCH & DEVELOPMENT SHARE OF NATIONAL GDP

# Z-SCORE VENTURE CAPITAL INVESTMENTS

Label Patent_applications RD_percentage VC_investment
United Arab Emirates -0.14 0.40 0.21
Saudi Arabia 0.28 -0.32 0.82
Israel 1.80 3.46 3.27
Turkey 1.50 0.31 0.38
South Africa 2.17 -0.25 -0.11
Qatar -0.59 -0.19 -0.58
Egypt -0.20 -0.02 0.02
Nigeria -0.52 -0.48 -0.29
Kenya -0.74 -0.39 -0.37
Kuwait -0.80 -0.55 -0.55
Morocco -0.04 -0.17 -0.56
Oman -0.65 -0.48 -0.58
Algeria -0.57 -0.30 -0.58
Bahrain -0.71 -0.61 -0.57
Ghana -0.79 -0.41 -0.51

IDC Share vs GDP Share

Computing the Software Revenue Share

based on Diffusion and Innovation parameters

So now our goal is to build a new software revenue share based on the data we put together.

Let:

S = Original GDP Share, D = Normalized Diffusion Score, I = Normalized Innovation Score, Md = Max Diffusion Impact (e.g. 33%), Mi = Max Innovation Impact (e.g. 15%), A = Adjustment Factor.

Then:

Diffusion Adjustment (Da) = D * Md

Innovation Adjustment (Ia) = I * Mi

Combined Adjustment (Ca) = Da + Ia

Mean Adjustment (Ma) = Average(Ca)

Centered Adjustment (Aa) = Ca - Ma

Adjusted Share (Sa) = S * (1 + Aa)

Finally, normalize the adjusted shares (Sa) to sum to 100%.

In words:

Scale the diffusion and innovation scores by their max impacts. Add them to get a combined adjustment. Subtract the mean combined adjustment to center it around 0. Multiply the GDP share by 1 plus the centered adjustment. Normalize the adjusted shares to retain total