Information Flows on Mobiles

The idea to use mobile phones (here and here) to help economic development in the most remote corners of the world is fascinating and definitely smart. For one thing, mobile phones have already reached the Bottom Billion. In 2007 there were 45 subscribers per 100 inhabitants in the developing countries. That means that we can now expect to have one mobile in every family. Everywhere. As well in communities where services like water, electricity, hospitals, schools or transportation are still far away.

What poor people mostly need are functioning institutions. And market is one of these. If market is not working, farmers will pay higher prices for what they buy and got less money for what they sell.  Moreover they could buy or sell at the wrong time and possibly in the wrong place. In the words of the government of Rwanda,

the success of these farmers has been greatly affected by lack of access to pricing information. Many times, farmers speculate what crops to grow and what prices to charge at harvest. Some farmers depend on middlemen to dictate the prices and in most cases the latter exploit the former. For any farmer to earn a decent living from agriculture, easy access to information on market prices is of paramount importance.

Making information flows on mobile phones could

empower farmers to enable them make more informed market pricing decisions and ultimately more successful farming.

The idea of mobile banking goes in the same direction: making a  service so critical for development accessible to almost everyone. That will not end poverty, but  will probably make the task easier.

Thursday, 16 April 2009

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

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RSS r-bloggers.com

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    Introduction Making Multiple Plots on the Same Subject Preparing the Data Writing Functions to Generate Multiple Plots Making Custom Plot Themes Updating Plot Themes Introduction There are often situations when you need to perform repetitive plotting tasks. For example, you’d like to plot the same kind of data (e....
  • Expand broom::tidy() output for categorical parameter estimates
    Introduction The tidycat package includes the tidy_categorical() function to expand broom::tidy() outputs for categorical parameter estimates. Documentation For full documentation, see the package vignette: The tidycat package: expand broom::tidy() output for categorical parameter estimates Hello World The tidy() function in the broom package takes the messy output ...
  • RcppSimdJson 0.1.0: Now on Windows, With Parsers and Faster Still!
    A smashing new RcppSimdJson release 0.1.0 containing several small updates to upstream simdjson (now at 0.4.6) in part triggered by very excisting work by Brendan who added actual parser from file and string—and together with Daniel upstream worked...
  • Drunk-under-the-lamppost testing
    I’m writing a response here to Abraham Mathews’s post, Best practices for code review, R edition, because my comment there didn’t show up and I think the topic’s important. Mathews’s post starts out on the right track, then veers away from best practices in the ...
  • xspliner: An R Package to Build Explainable Surrogate ML Models
    This talk was presented virtually at eRum 2020 by Appsilon engineer Krystian Igras. Here is a direct link to the video. Why Should We Explain Black Box ML Models? A vast majority of state-of-the-art ML algorithms are black boxes, meaning it is difficult to understand their inner workings. The more that ...

RSS Simply Statistics

  • Asymptotics of Reproducibility
    Every once in a while, I see a tweet or post that asks whether one should use tool X or software Y in order to “make their data analysis reproducible”. I think this is a reasonable question because, in part, there are so many good tools out there! This is undeniably a good thing and […]
  • Amplifying people I trust on COVID-19
    Like a lot of people, I’ve been glued to various media channels trying to learn about the latest with what is going on with COVID-19. I have also been frustrated - like a lot of people - with misinformation and the deluge of preprints and peer reviewed material. Some of this information is critically important […]
  • Is Artificial Intelligence Revolutionizing Environmental Health?
    NOTE: This post was written by Kevin Elliott, Michigan State University; Nicole Kleinstreuer, National Institutes of Health; Patrick McMullen, ScitoVation; Gary Miller, Columbia University; Bhramar Mukherjee, University of Michigan; Roger D. Peng, Johns Hopkins University; Melissa Perry, The George Washington University; Reza Rasoulpour, Corteva Agriscience, and Elizabeth Boyle, National Academies of Sciences, Engineering, and Medicine. […]

RSS Statistical Modeling, Causal Inference, and Social Science

  • Drunk-under-the-lamppost testing
    I’m writing a response here to Abraham Mathews’s post, Best practices for code review, R edition, because my comment there didn’t show up and I think the topic’s important. Mathews’s post starts out on the right track, then veers away from best practices in the section “What code should be reviewed?” where he says, …In […]
  • “Time Travel in the Brain”
    Natalie Biderman and Daphna Shohamy wrote this science article for kids. Here’s the abstract: Do you believe in time travel? Every time we remember something from the past or imagine something that will happen in the future, we engage in mental time travel. Scientists discovered that, whether we mentally travel back into the past or […]
  • Statistical controversy on estimating racial bias in the criminal justice system
    1. Background A bunch of people have asked me to comment on these two research articles: Administrative Records Mask Racially Biased Policing, by Dean Knox, Will Lowe, and Jonathan Mummolo: Researchers often lack the necessary data to credibly estimate racial discrimination in policing. In particular, police administrative records lack information on civilians police observe but […]