Back into Poverty

Increase in food prices has pushed back into poverty at least 100 million people in 2008 and, according to the United Nations Standing Committee on Nutrition (here, p. 60),

erase at least four years of progress towards the Millennium Development Goal (MDG) 1 target for the reduction of poverty. The household level consequences of this crisis are most acutely felt in LIFDCs [Low-Income Food-Deficit Countries] where a 50% rise in staple food prices causes a 21% increase in total food expenditure, increasing these from 50 to 60% of income. In a high income country this rise in prices causes a 6% rise in retail food expenditure with income expenditure on food rising from 10 to 11%. FAO estimates that food price rises have resulted in at least 50 million more people becoming hungry in 2008, going back to the 1970 figures.

According to the World Bank (here) this means that between 200,000 and 400,000 more children will died every year for malnutrition until 2015.

Thursday, 18 June 2009

WikiPrices

Erik Hersman, has recently created a site called Africa Signals. And it is not just a site: it is a wiki site. The site aims to collect and share mobile phone and Internet rates across Africa. (I have found about this site here)

Now. In my experience, one of the many reasons that makes poor a poor farmer is coping with a non-functioning market (I said it two posts ago). So I can just imagine how helpful would be to have a tool to make market work better.

Creating a wiki page to collect and share the price of one particular agricultural product in one particular time in one particular place would be great. But succeeding in integrating such a site with the mobile phone network would be even better.  How to do this? The government of Rwanda is moving in the very same direction without creating a wiki site. (It is difficult to imagine a government managing wikis). But bureaucracy is not something we usually associate with the words efficiency and effectiveness, especially in poor countries. And in any case we do not really need a government to make a site like this work.

Just think about a wiki site collecting and sharing data through sms. Actually Twitter, without the wiki interface, is doing it right now. So, think about a farmer receiving a message with updated price information the night before market day and, on this information, taking his/her decisions. And think about a farmer sending via sms the price information to the wiki site after leaving the market.

We can imagine the farmer to pay for the sms he or she receives and, on the contrary, we can imagine sending sms back to the site to be completely free.  And we can imagine some volunteers to be the administrators of the site (just like Wikipedia).

Tuesday, 28 April 2009

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

  • New Course Available Now: Machine Learning with Tidymodels
    New Course Available Now: Machine Learning with Tidymodels The ever increasing application of machine learning models in industry and academia requires tools which are easy to use and ensure a reliable model fitting process. The R package universe cov... The post New Course Available Now: Machine Learning with Tidymodels first appeared on R-bloggers.
  • Cluster Analysis in R
    Cluster Analysis in R, when we do data analytics, there are two kinds of approaches one is supervised and another is unsupervised. Clustering is... The post Cluster Analysis in R appeared first on finnstats. The post Cluster Analysis in R first appeared on R-bloggers.
  • Recidivism: Identifying the Most Important Predictors for Re-offending with OneR
    In 2018 the renowned scientific journal science broke a story that researchers had re-engineered the commercial criminal risk assessment software COMPAS with a simple logistic regression (Science: The accuracy, fairness, and limits of predicting recidivism). According to this article, COMPAS uses 137 features, the authors just used two. In this post, I ... The post […]
  • Webscraping Tables in R: Datapasta Copy-and-Paster
    This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. Here are the links to get set up. 👇 Get the Code YouTube Tutorial (Click image to play tutorial) ... The post Webscraping Tables in R: Datapasta Copy-and-Paster first appeared on R-bloggers.
  • SwimmeR goes to the Para Games and other Updates – v0.9.0
    There’s a new version of SwimmeR available, v0.9.0. It follows v0.8.0, which I didn’t like and didn’t write about. I’ve made some improvements though and here we are. Rather than just telling you what’s in v0.9.0 I’m going to indulge myself and approach this ... The post SwimmeR goes to the Para Games and other […]

RSS Simply Statistics

  • Streamline - tidy data as a service
    Tldr: We started a company called Streamline Data Science https://streamlinedatascience.io/ that offers tidy data as a service. We are looking for customers, partnerships and employees as we scale up after closing our funding round! Most of my career, I have worked in the muck of data cleaning. In the world of genomics, a lot of […]
  • The Four Jobs of the Data Scientist
    In 2019 I wrote a post about The Tentpoles of Data Science that tried to distill the key skills of the data scientist. In the post I wrote: When I ask myself the question “What is data science?” I tend to think of the following five components. Data science is (1) the application of design […]
  • Palantir Shows Its Cards
    File this under long-term followup, but just about four years ago I wrote about Palantir, the previously secretive but now soon to be public data science company, and how its valuation was a commentary on the value of data science more generally. Well, just recently Palantir filed to go public and therefore submitted a registration […]

RSS Statistical Modeling, Causal Inference, and Social Science

  • Can you trust international surveys? A follow-up:
    Michael Robbins writes: A few years ago you covered a significant controversy in the survey methods literature about data fabrication in international survey research. Noble Kuriakose and I put out a proposed test for data quality. At the time there were many questions raised about the validity of this test. As such, I thought you […]
  • We’re hiring (in Melbourne)
    Andrew, Qixuan and I (Lauren) are hiring a postdoctoral research fellow to explore research topics around the use on multi-level regression and poststratification with non-probability surveys. This work is funded by the National Institutes of Health, and is collaborative work with Prof Andrew Gelman (Statistics and Political Science, Columbia University) and Assoc/Prof Qixuan Chen (Biostatistics, […]
  • Hierarchical modeling of excess mortality time series
    Elliott writes: My boss asks me: For our model to predict excess mortality around the world, we want to calculate a confidence interval around our mean estimate for total global excess deaths. We have real excess deaths for like 60 countries, and are predicting on another 130 or so. we can easily calculate intervals for […]