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Religious statistics.

Education, Intellect, Economy, Crime and Life Expectancy Population Mixer
Education, Intellect, Economy, Crime and Life Expectancy

CSV document-table: IQ_and_Countries.csv

# Code Description Why here (causes → consequences)
1 AT average annual temperature of the country climate / sun exposure — the deepest geographic input
2 AT → IQ intelligence quotient downstream of climate, upstream of every human-capital outcome below
3 IQ ↔ EDU education, literacy cognitive ability enables learning and an educated society lifts measured IQ
4 IQ → GDP gross domestic product educated workforce builds the economy
5 IQ → IM infant mortality per 1 000 births wealth funds neonatal medical care
6 IQ → LE average life expectancy wealth funds healthcare across all ages
7 IQ → FR birth rate (fertility) demographic transition follows health, wealth, education
8 IQ → NB non-believers (percentage) secularisation follows education
9 IQ → CPI corruption perception index clean governance follows wealth + education
10 IQ → CI crime index crime drops with good governance
11 IQ → IH homicides per 100 000 inhabitants homicide is the worst-case crime outcome

1. IQ and average annual temperature (°C). Climate is the deepest geographic input: countries near the optimal ~22 °C band tend to register higher average IQs.

IQ ≈ −1.04·AT + 101.4
Linear fit, R² = 0.38 (n = 186 countries). Cubic R² = 0.39 — no improvement.
The data does not support a 22 °C optimum: country-level IQ falls roughly 1 point per 1 °C of mean annual temperature with no peak. The cubic adds two parameters and gains 0.5 percentage points of R² — not enough to justify the complexity. Linear is the honest fit.

2. IQ of country starts to rise quickly if EDU is higher than 85%. Lower EDU doesn’t have big impact on IQ.

IQ ≈ 2.07×10−4·EDU3 − 0.033·EDU2 + 1.81·EDU + 32.46
Cubic fit, R² = 0.54 (n = 194 countries).
Cubic captures the article's claim of an inflection near EDU = 85 %: predicted IQ rises from ~75 at EDU = 85 to ~91 at EDU = 100, while sitting almost flat (≈ 70–75) for EDU below 70 %. A linear fit (R² = 0.47) misses this S-shape; quadratic (R² = 0.52) captures most of it but loses the third-derivative detail.

3. The higher IQ - the higher GDP. To raise economy quickly, country must have not less than 85 IQ - support of poor countries without investing in education is waste of money.

GDP ≈ −0.313·IQ3 + 97.1·IQ2 − 8,889·IQ + 256,050
Cubic fit, R² = 0.44 (n = 194 countries).
GDP grows super-linearly with IQ. The cubic captures the "knee" near IQ = 85 the article mentions: predicted GDP is ≈ $3 K at IQ = 70, ≈ $9 K at IQ = 85, ≈ $25 K at IQ = 100, ≈ $40 K at IQ = 110. Quadratic is essentially as good (R² = 0.43) but the cubic better matches the sub-$5 K floor that low-IQ countries cluster at.

4. Infant mortality and IQ.

IM ≈ 8.6×10−4·IQ3 − 0.19·IQ2 + 11.73·IQ − 128.6
Cubic fit, R² = 0.53 (n = 194 countries).
Strong monotonic decline. The cubic flattens at high IQ — it has to, since infant mortality cannot drop below zero. A linear fit (R² = 0.52) is almost as good but predicts negative IM above IQ ≈ 102, which is physically impossible. Cubic respects the floor.

5. To live 60-80 years, country must have at least 80 IQ average. To live more than 80 years, country must have 95 IQ average. Life expectancy rises with IQ, but this rise is not significant when IQ reaches 100.

LE ≈ −2.6×10−4·IQ3 + 0.054·IQ2 − 2.92·IQ + 88.2
Cubic fit, R² = 0.62 (n = 194 countries) — the best fit in this set.
A near-saturating S-curve. Predicted LE: ≈ 50 yrs at IQ = 65, ≈ 60 yrs at IQ = 75, ≈ 75 yrs at IQ = 90, ≈ 81 yrs at IQ = 100, then plateaus around 83–84. Both of the article's thresholds (≥ 80 IQ for 60–80 yrs life, ≥ 95 IQ for 80+ yrs) are visible in the curve. Quadratic (R² = 0.62) gives the same fit with one fewer parameter and could be substituted.

6. Fertility rate, total (births per woman):

FR ≈ −0.063·IQ + 7.70
Linear fit, R² = 0.51 (n = 194 countries). Cubic R² = 0.52 — no real improvement.
The classic demographic transition: every 10 IQ points cuts births per woman by ≈ 0.6. Linear is the right model — predicted FR is 3.3 at IQ = 70, 2.7 at IQ = 80, 2.0 at IQ = 90, 1.4 at IQ = 100. The cubic adds parameters but only gains 1 % of R²; not worth the complexity.

7. Non believers and IQ - Religion is a side effect of poor education.

NB ≈ 2.97×10−4·IQ3 − 0.058·IQ2 + 3.42·IQ − 64.0
Cubic fit, R² = 0.34 (n = 194 countries).
Non-believers are nearly 0 % below IQ ≈ 85, then climb sharply. The cubic captures this "knee"; a linear fit (R² = 0.22) badly underfits. The modest R² reflects strong country-specific factors (state religion, history, political regime) that aren't in the data — at any given IQ level, the spread is wide.

8. Corruption Perception Index. Points: 0 - absolutely corrupt country, 100 - very transparent country. Source: transparency.org (...)

CPI ≈ 8.0×10−4·IQ3 − 0.173·IQ2 + 12.13·IQ − 252.1
Cubic fit, R² = 0.44 (n = 177 countries with Transparency International data).
Transparency rises sharply with IQ, especially above ~ 90. The cubic captures the late acceleration: predicted CPI ≈ 25 at IQ = 80, ≈ 45 at IQ = 95, ≈ 75 at IQ = 105. Below IQ 80, virtually all countries score under 40 (corrupt). Linear (R² = 0.34) badly underfits the elbow.

9. Crime index. Crime index is an estimate of the overall level of crime in a country. Crime level scores: 0-20 - very low, 20-40 - low, 40-60 medium, 60-80 high, 80-100 very high. Source: numbeo.com

CI ≈ −6.3×10−4·IQ3 + 0.134·IQ2 − 10.6·IQ + 340.1
Cubic fit, R² = 0.27 (n = 136 countries — only those with a Numbeo crime index).
Modest negative correlation. The cubic captures slight curvature (crime drops faster as IQ approaches 100) but the wide spread at every IQ level confirms that crime is mostly explained by enforcement, inequality, and history rather than IQ alone.

10. Intentional homicides (per 100,000 people):

IH ≈ 2.7×10−4·IQ3 − 0.068·IQ2 + 5.69·IQ − 143.3
Cubic fit, R² = 0.11 (n = 186 countries) — the weakest correlation in the set.
Country-level homicide rates are dominated by local factors (gun policy, gang activity, history of conflict) that overpower any IQ signal. The cubic captures only 11 % of variance — read this trend cautiously.

What could be done to make sure that people in power are smarter?

It is logical for people to want smarter decisions to succeed. Slightly modified voting system, which takes into account experience and knowledge, could be used for that. Each citizen, at the age of 16, will receive ability to cast 1.00 vote. On reaching certain age (experience) and education degree (knowledge), person would get ability to cast small amount of bonus vote points:

Nr. Education
(Knowledge)		 Study
Years		 Age
Exp		 Average
IQ		 Voting
Points
1. Early 5 5 30 -
2. Preschool 1 6 49 -
3. General (Initial) 4 10 55 -
4. General (Basic) 4 14 74 -
5. General (Secondary) 2 16 88 1.00
6. General (Higher) 2 18 100 1.14
7. Higher (Bachelor) 4 22 114 1.30
8. Higher (Master's) 2 24 122 1.39
9. Higher (Doctor) 3 27 127 1.44

This system could be used on large-scale anonymous elections i.e., where individual vote doesn't change a lot. People must agree on bonus points formula, which must be small enough for everybody to agree and to make sure that amount can’t be used to gain unfair advantage with bonus points.

Calculations from Lithuanian census in 2001, based on achieved education level by religious and non-religious groups. Census shows that less educated areas are filled with more religious people.

Age at which education was officially finished - average IQ

22yr - 114 IQ
18yr - 100 IQ
16yr -  88 IQ
14yr -  74 IQ
10yr -  55 IQ

Average IQ of non-believers - 89.31
Average IQ of believers - 83.93
IQ difference - 5.38
Average IQ of population is actually higher - 94 (2007).

Author Dr Pat Mottram said: "Overall our findings show that the average IQ of the prison population is 13 below the national average of 100. Source: news.bbc.co.uk (...)

Difference between prison inmates and population average is bigger (-17 IQ) for serious offenders and smaller (-1 IQ) for non-serious offenders (Criminology: an interdisciplinary approach" by Anthony Walsh, Lee Ellis).

With help of education IQ can be improved:

COUNTRY      (YEAR) IQ (YEAR)  IQ (+IQ)

West Germany (1954) 78 (2002) 102 (+24)
France       (1949) 73 (2002)  98 (+25) 
Netherlands  (1952) 78 (2002) 102 (+24) 
Japan        (1950) 79 (2002) 105 (+26) 
USA          (1918) 72 (2002)  98 (+26)
UK           (1900) 70 (2002) 100 (+30)

Data Sources:

  • GDP: GDP per capita (current US$). 190 cells updated. World Bank Open Data — NY.GDP.PCAP.CD (latest 2018–2024).
  • LE: Life expectancy at birth (years). 192 cells updated. World Bank Open Data — SP.DYN.LE00.IN (latest 2018–2024).
  • IM: Infant mortality rate (per 1,000 live births). 183 cells updated. World Bank Open Data — SP.DYN.IMRT.IN (latest 2018–2024).
  • FR: Total fertility rate (births per woman). 187 cells updated. World Bank Open Data — SP.DYN.TFRT.IN (latest 2018–2024).
  • Pop: Total population. 0 cells updated — file was already on 2024 figures. World Bank Open Data — SP.POP.TOTL (2024).
  • EDU: Adult literacy rate (% of people ages 15+). 106 cells updated; 88 high-income countries do not report. World Bank Open Data — SE.ADT.LITR.ZS (latest 2018–2024).
  • IH: Intentional homicides (per 100,000 population). 140 cells updated. UNODC via World Bank — VC.IHR.PSRC.P5 (latest 2018–2023).
  • CPI: Corruption Perceptions Index (0 = highly corrupt, 100 = clean). 149 cells updated. Transparency International — CPI 2024 (annual XLSX report).
  • CIA — The World Factbook. General country information (geography, demographics, economy) used to cross-check edge cases. cia.gov/the-world-factbook.

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Only individuals at or above the threshold may immigrate (default 114 ≈ Bachelor's-degree level). Qualifying pool size is computed from a Normal(μ_source, σ=15) bell curve.

⚠ Uncontrolled migration warning. Averaging in lower-IQ source populations drags the host nation's IQ down. A nation typically gains 1 IQ point per 130–150 years; a 4-point loss sets a society back roughly 600 years. Enable the checkbox above to filter migrants by IQ and rebase their contributed metrics against the target country's IQ-trend curve.
 
Sources - mix from
Target — mix into
 
Stat Source Target Target
(after mix)		 Δ Δ%
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Each numeric metric is recomputed as a population-weighted average: (target × target_pop + source × take) / (target_pop + take). Pop is summed; Region/Tier are kept from the target.
FR* N — target (native) population responds to native ΔGDP (β=0.3). FR* M — source (migrant) population responds to GDP gap from origin (β=0.3).
Controlled migration: assumes IQ within each source country is Normal(μ = source IQ, σ = 15). The qualifying pool is the area under that bell curve above the chosen threshold (114 ≈ Bachelor's-degree level — see the IQ/education table later in this article). When enabled, each source's effective take is capped at its qualifying pool size, and the source's contributed IQ is the conditional mean μ + σ·φ(z)/Φ̄(z) (the inverse-Mills-ratio shift). Other migrant metrics are rebased to the target's environment with a simple productivity multiplier p = max(1, migrantIQ / targetIQ): GDP, LE scale up by p; IM, IH, CI scale down by 1/p; EDU, CPI, NB, FR adopt the target's value (institutional / cultural — they don't change because new arrivals show up). Bounds applied: LE ≤ 95, EDU/CPI ≤ 100, bad metrics ≥ 0.

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