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

Understanding Capitalism

Nobel-winning economist Amartya Sen argues, in an article published on The New York Review of Books, that the way out from the crisis passes through a better understanding of the ideas that contributed to build the actual economic system. Adam Smith, John Maynard Keynes, Arthur Cecil Pigou, should be read, not just quoted. And I quote

Smith viewed markets and capital as doing good work within their own sphere, but first, they required support from other institutions—including public services such as schools—and values other than pure profit seeking, and second, they needed restraint and correction by still other institutions—e.g., well-devised financial regulations and state assistance to the poor—for preventing instability, inequity, and injustice. If we were to look for a new approach to the organization of economic activity that included a pragmatic choice of a variety of public services and well-considered regulations, we would be following rather than departing from the agenda of reform that Smith outlined as he both defended and criticized capitalism.

We must understand how institutions work and make them work better. But not just aiming at economic growth.

There is a critical need for paying special attention to the underdogs of society in planning a response to the current crisis, and in going beyond measures to produce general economic expansion.

A crisis not only presents an immediate challenge that has to be faced. It also provides an opportunity to address long-term problems when people are willing to reconsider established conventions. This is why the present crisis also makes it important to face the neglected long-term issues like conservation of the environment and national health care, as well as the need for public transport (…).

Sunday, 22 March 2009

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RSS r-bloggers.com

  • Monitoring Website SSL/TLS Certificate Expiration Times with R, {openssl}, {pushoverr}, and {DT}
    macOS R users who tend to work on the bleeding edge likely noticed some downtime at this past weekend. Part of the issue was an SSL/TLS certificate expiration situation. Moving forward, we can monitor this with R using the super spiffy {openssl} and {pushoverr} packages whilst also generating a daily report with {rmarkdown} and... Continue […]
  • Evaluate your R model with MLmetrics
    This post will explore using R’s MLmetrics to evaluate machine learning models. MLmetrics provides several functions to calculate common metrics for ML models, including AUC, precision, recall, accuracy, etc. Building an example model Firstly, we need to build a model to use as an example. For this post, we’ll be using a dataset on pulsar […]
  • Data re-Shaping in R and in Python
    Nina Zumel and I have a two new tutorials on fluid data wrangling/shaping. They are written in a parallel structure, with the R version of the tutorial being almost identical to the Python version of the tutorial. This reflects our opinion on the “which is better for data science R or Python?” They both are […]
  • Does Australia need More Fires (but the Right Kind)? A Multi-Agent Simulation
    We have all watched with great horror the catastrophic fires in Australia. Over many years scientists have been studying simulations to understand the underlying dynamics better. They tell us, that “what Australia needs is more fires, but of the right kind”. What do they mean by that? One such simulation of fire is based on […]
  • Some everyday data tasks: a few hints with R (revisited)
    One year ago, I published a post titled ‘Some everyday data tasks: a few hints with R’. In that post, I considered four data tasks, that we all need to accomplish daily, i.e. subsetting sorting casting melting In that post, I used the methods I was more familiar with. And, as a long-time R user, […]

RSS Simply Statistics

  • 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. […]
  • You can replicate almost any plot with R
    Although R is great for quickly turning data into plots, it is not widely used for making publication ready figures. But, with enough tinkering you can make almost any plot in R. For examples check out the flowingdata blog or the Fundamentals of Data Visualization book. Here I show five charts from the lay press […]
  • So You Want to Start a Podcast
    Podcasting has gotten quite a bit easier over the past 10 years, due in part to improvements to hardware and software. I wrote about both how I edit and record both of my podcasts about 2 years ago and, while not much has changed since then, I thought it might be helpful if I organized […]

RSS Statistical Modeling, Causal Inference, and Social Science

  • Are GWAS studies of IQ/educational attainment problematic?
    Nick Matzke writes: I wonder if you or your blog-colleagues would be interested in giving a quick blog take on the recent studies that do GWAS (Genome-Wide-Association Studies) on “traits” like IQ, educational attainment, and income? Matzke begins with some background: The new method for these studies is to claim that a “polygenic score” can […]
  • Causal inference in AI: Expressing potential outcomes in a graphical-modeling framework that can be fit using Stan
    David Rohde writes: We have been working on an idea that attempts to combine ideas from Bayesian approaches to causality developed by you and your collaborators with Pearl’s do calculus. The core idea is simple, but we think powerful and allows some problems previously that only had known solutions with the do calculus to be […]
  • My review of Ian Stewart’s review of my review of his book
    A few months ago I was asked to review Do Dice Play God?, the latest book by mathematician and mathematics writer Ian Stewart. Here are some excerpts from my review: My favorite aspect of the book is the connections it makes in a sweeping voyage from familiar (to me) paradoxes, through modeling in human affairs, […]