Eyes on Guatemala

The Economist has published an article on malnutrition in Guatemala. Hunger is not new in the country, with half of the children population not eating enough Guatemala is the six-worst country in the world, but in some Maya communities children chronic malnutrition can reach 75% (the Economist says 80%). These figures are astonishing, especially because the problem is not food scarcity.

But this as well is hardly new. It was 1981 when Amartya Sen published his Poverty and Famines: An Essay on Entitlement and Deprivation demonstrating that hunger is mostly caused by inequality rather than scarcity. There is no lack of food in Guatemala if you have the money to buy it. In Guatemala City is taking place, as we speak, the 14th Festival Gastronómico Internacional so it seems difficult to talk about a famine or about an emergency (according to the Longman Dictionary an emergency is “an unexpected and dangerous situation that must be dealt with immediately”). The problem is the lack of a functioning state. Because a state cannot function with tax revenues estimated at just 10% of GDP.

Democracy is highly unrepresentative in Guatemala. Who should push for a better redistribution of resources has no voice. National newspapers point constantly the finger at the government (presidency, parliament, judiciary) in a impressive campaign of delegitimation. The Rosenberg tape was just part of it. I’m not defending the government, but saying that criticising it and attempting to systematically destroy its credibility are not quite the same thing. While the headlines cover crime, corruption and hunger the real battle within the country is on the tax reform. A battle that so far every government has badly lost.

Friday, 28 August 2009

tweets


Twitter: frbailo

links


blogroll


RSS r-bloggers.com

  • Angela Bassa discusses managing data science teams and much more.
    Hugo Bowne-Anderson, the host of DataFramed, the DataCamp podcast, recently interviewed Angela Bassa, the Director of Data Science at iRobot. Here is the podcast link. Introducing Angela Bassa Hugo: Hi there Angela, and welcome to DataFramed. Angela: Thanks, thanks for having me. Hugo: It's a great pleasure to have you on the show, and I'm […]
  • Preview my new book: Introduction to Reproducible Science in R
    I’m pleased to share Part I of my new book “Introduction to Reproducible Science in R“. The purpose of this …Continue reading →
  • How to de-Bias Standard Deviation Estimates
    This note is about attempting to remove the bias brought in by using sample standard deviation estimates to estimate an unknown true standard deviation of a population. We establish there is a bias, concentrate on why it is not important to remove it for reasonable sized samples, and (despite that) give a very complete bias […]
  • Data Science With R Course Series – Week 9
    There are only two more weeks in the course! This week will extend what you learned from the Expected Value by performing an optimization and sensitivity analysis. The optimization and sensitivity analysis will teach you how to identify the maximum bu...
  • RATest. A Randomization Tests package is available on CRAN
    This blog post introduces the RATest package we released a while back on CRAN with my colleague and good friend Mauricio Olivares-Gonzalez. The package contains a collection of randomization tests, data sets and examples. The current version focuses on two testing problems and their implementation in empirical work, mostly related to economics. First, it facilitates […]

RSS Simply Statistics

  • The role of academia in data science education
    I was recently asked to moderate an academic panel on the role of universities in training the data science workforce. I preceded each question with opinionated introductions which I have fused into this blog post. These are weakly held opinions so please consider commenting if you disagree with anything. To discuss data science education we […]
  • Guest Post: Galin Jones on criteria for promotion and tenture in (bio)statistics departments
    Editor’s Note: I attended an ASA Chair’s meeting and spoke about ways we could support junior faculty in data science. After giving my talk Galin Jones, Professor and Director of Statistics at University of Minnesota, and I had an interesting conversation about how they had changed their promotion criteria in response to a faculty candidate […]
  • The economic consequences of MOOCs
    tl;dr check out our new paper on the relationship between MOOC completion and economic outcomes! Last Monday we launched our Chromebook Data Science Program so that anyone with an internet connection, a web browser, and the ability to read and follow instructions could become a data scientist. Why did we launch another MOOC program? Aren’t […]

RSS Statistical Modeling, Causal Inference, and Social Science