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

tweets


Twitter: frbailo

links


blogroll


RSS r-bloggers.com

  • Model-Based Causal Forests for Heterogeneous Treatment Effects
    A new arXiv paper investigates which building blocks of random forests, especially causal forests and model-based forests, make them work for heterogeneous treatment effect estimation, both in randomized trials and observational studies. ... Continue reading: Model-Based Causal Forests for Heterogeneous Treatment Effects
  • A Major Contribution to Learning R
    Prominent statistician Frank Harrell has come out with a radically new R tutorial, rflow. The name is short for “R workflow,” but I call it “R in a box” –everything one needs for beginning serious usage of R, starting from little or no background. By serious usage I mean real ... Continue reading: A Major […]
  • Evaluating GitHub Activity for Contributors
    Say you have a bug report or feature request to make to a package. How can you use information on GitHub to manage your expectations (will there be a quick fix) and actions (should you go ahead and fork the repository)? In this post, we shall go over ... Continue reading: Evaluating GitHub Activity for […]
  • Developing React Applications in RStudio Workbench
    Introduction RStudio Workbench provides a development environment for R, Python, and many other languages. When developing a performant web application you may progress from Shiny towards tools li... Continue reading: Developing React Applications in RStudio Workbench
  • Food Crisis Analysis and, Forecasting with Neural Network Autoregression
    The war between Russia and Ukraine has affected the global food supply other than many vital things. Primarily cereal crop products have been affected the most because the imports have been provided to the world mainly through Ukraine and Russia. Let’s check the situation we’ve mentioned for G20 ... Continue reading: Food Crisis Analysis and, […]

RSS Simply Statistics

RSS Statistical Modeling, Causal Inference, and Social Science