Brain matters

Saying that education strengths economic growth sounds good old common sense. But proving and measuring this relation is not immediate and therefore interesting. A reasearch, published last year, does it. Eric Hanushek, Dean T. Jamison, Eliot A. Jamison and Ludger Woessmann estimate that

each additional year of average schooling in a country increased the average 40-year growth rate in GDP by about 0.37 percentage points. That may not seem like much, but consider the fact that since World War II, the world economic growth rate has been around 2 to 3 percent of GDP annually. Lifting it by 0.37 percentage points is a boost to annual growth rates of more than 10 percent of what would otherwise have occurred, a significant amount.

Nonetheless, the research suggests that what really matters for economic growth is the quality of education. In other words it is not enough to send children to school: you have to teach them something. Using test-score performances around the world to measure the cognitive skills of students appears

that countries with higher test scores experienced far higher growth rates. If one country’s test-score performance was 0.5 standard deviations higher than another country during the 1960s (…) the first country’s growth rate was, on average, one full percentage point higher annually over the following 40-year period than the second country’s growth rate. Further, once the impact of higher levels of cognitive skills are taken into account, the significance for economic growth of school attainment, i.e., additional years of schooling, dwindles to nothing. A country benefits from asking its students to remain in school for a longer period of time only if the students are learning something as a consequence.

These results are extremely important especially for the countries of the Bottom Billion. What they are saying is that it is better to invest on the quality of the education (where rate of return is much higher) rather than spending to keep students in schools longer.

Tuesday, 17 March 2009

tweets


Twitter: frbailo

links


blogroll


RSS r-bloggers.com

  • Lecture slides: Real-World Data Science (Fraud Detection, Customer Churn & Predictive Maintenance)
    These are slides from a lecture I gave at the School of Applied Sciences in Münster. In this lecture, I talked about Real-World Data Science and showed examples on Fraud Detection, Customer Churn & Predictive Maintenance. Real-World Data Scie...
  • Use foreach with HPC schedulers thanks to the future package
    The future package is a powerful and elegant cross-platform framework for orchestrating asynchronous computations in R. It's ideal for working with computations that take a long time to complete; that would benefit from using distributed, parallel frameworks to make them complete faster; and that you'd rather not have locking up your interactive R session. You […]
  • Feature Selection using Genetic Algorithms in R
    From a gentle introduction to a practical solution, this is a post about feature selection using genetic algorithms in R.
  • Using clusterlab to benchmark clustering algorithms
    Clusterlab is a CRAN package (https://cran.r-project.org/web/packages/clusterlab/index.html) for the routine testing of clustering algorithms. It can simulate positive (data-sets with __1 clusters) and negative controls (data-sets with 1 cluster). Why test clustering algorithms? Because they often fail in identifying the true K in practice, published algorithms are not always well tested, and we need to know […]
  • Selecting ‘special’ photos on your phone
    At the beginning of the new year I always want to clean up my photos on my phone. It just never happens. So now (like so many others I think) I have a lot of photos on my phone from … Continue reading →

RSS Simply Statistics

  • How Data Scientists Think - A Mini Case Study
    In episode 71 of Not So Standard Deviations, Hilary Parker and I inaugurated our first “Data Science Design Challenge” segment where we discussed how we would solve a given problem using data science. The idea with calling it a “design challenge” was to contrast it with common “hackathon” type models where you are presented with […]
  • The Netflix Data War
    A recent article in the Wall Street Journal, “At Netflix, Who Wins When It’s Hollywood vs. the Algorithm?” by Shalini Ramachandran and Joe Flint details some of the internal debates within Netflix between the Los Angeles-based content team, which is in charge of developing and marketing new content for the streaming service, and the data […]
  • The Role of Theory in Data Analysis
    In data analysis, we make use of a lot of theory, whether we like to admit it or not. In a traditional statistical training, things like the central limit theorem and the law of large numbers (and their many variations) are deeply baked into our heads. I probably use the central limit theorem everyday in […]

RSS Statistical Modeling, Causal Inference, and Social Science