Patents Per Capita and Hofstede's Cultural Dimensions

Thinking about social dimensions and innovation, it occurred to me that there might be a relationship with masculinity, but then quickly dismissed it, considering it much more likely to be predicated on science/math education. Even then, other cultural elements might be more likely correlated. What follows is an exploration of various correlations with patents per capita.

Although Hofstede's Cultural Dimensions did have a significant correlation with patents per capita, somewhat surprisingly, PISA scores by country, education, nor average IQ, had a strong relationship with patent production, although if Asia was included, statistically it would.

Notes:

• I often exclude Asia from analyses, as the initial driver of this work was looking at cultures that are similar, to tease out social effects.
• That is also why I ignore looking at all countries, as some relationships across the entire world disappear when limited to just developed economies. As an example, the value of work and its benefits to social welfare are quite high when considered globally, but are almost non-existent, within my study parameters, for the developed world.
• This looks at only patents per capita, to find effects unrelated to sheer size, or which countries like the US and Japan dominate.

An explanation of dimensions can be found on Hofstede's site and source data is here.

Loading Data Vectors

In retrospect, I could have done this in a matrix, and maybe should have, but I am just getting back into coding and did not start this as a clearly-defined project, but as a simple exploration.

 # LM - Multiple Regression - Patents  
 # Clear workspace  
 rm(list = ls())  

 # Set working directory  
 setwd("../Data")  
 oecdData <- read.table("OECD - Quality of Life - Minus Asia.csv", header = TRUE, sep = ",")  
 print(names(oecdData))  

 # Set Vectors  
 # Primary focus  
 vPatents <- oecdData$Patents  
 vPatentsPerCapita <- oecdData$PatentsPerCapita  

 # Hofstede  
 vPowerDx <- oecdData$HofstederPowerDx  
 vMasculinity <- oecdData$HofstederMasculinity  
 vIndividuality <- oecdData$HofstederIndividuality  
 vUncertaintyAvoidance <- oecdData$HofstederUncertaintyAvoidance  
 vLTO <- oecdData$HofstederLongtermOrientation  
 vIndulgence <- oecdData$HofstederIndulgence  

 # Education  
 vPisaScience <- oecdData$PISAScience  
 vPisaMath <- oecdData$PISAMath  
 vPisaReading <- oecdData$PISAReading  
 vIQ <- oecdData$IQ  
 vTertiaryEdu <- oecdData$TertiaryEdu  
 vEduReading <- oecdData$EduReading  
 vEduScience <- oecdData$EduScience  

 # Social welfare  
 vGini <- oecdData$Gini  
 vLifeExpectancy <- oecdData$LifeExpectancy  
 vObesity <- oecdData$Obesity  
 vInfantDeath <- oecdData$InfantDeath  
 vHoursWorked <- oecdData$HoursWorked  

Cultural Dimensions and Patents per Capita

The two (2) dimensions with the highest correlations, and probability under .01, were Uncertainty Avoidance and Individuality. Translated into common speech, cultures that tolerate ambiguity and are least rule-based, along with having high individuality, produce large number of patents.

 # Patents per Capita ~ Hofstede  
 Hofstede_PatentsPerCapita <- lm(vPatentsPerCapita ~ vPowerDx + vIndividuality + vMasculinity + vUncertaintyAvoidance + vLTO + vIndulgence)  
 print(Hofstede_PatentsPerCapita)  
 print(summary(Hofstede_PatentsPerCapita))  
 print(anova(Hofstede_PatentsPerCapita))  

 cor.test(vPatentsPerCapita, vIndividuality)  
 plot(vPatentsPerCapita, vIndividuality, col = "blue", main = "vPatentsPerCapita ~ vIndividuality", abline(lm(vIndividuality ~ vPatentsPerCapita)), cex = 1.3, pch = 16, xlab = "Patents per Capita", ylab = "Individuality")  



Result: r-squared = 0.5463338, p-value = 0.008522,  df = 20


cor.test(vPatentsPerCapita, vUncertaintyAvoidance)  
plot(vPatentsPerCapita, vUncertaintyAvoidance, col = "blue", main = "vPatentsPerCapita ~ vUncertaintyAvoidance", abline(lm(vUncertaintyAvoidance ~ vPatentsPerCapita)), cex = 1.3, pch = 16, xlab = "Patents per Capita", ylab = "Uncertainty Avoidance")  



Result: r-squared = -0.5548058, p-value =  0.007364,  df = 20