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

Selva Amazónica, More Valuable Standing Than Felled

An article published on Science this week analyzes the development of the region across the Amazon deforestation frontier. In three words: boom and bust. It means that comparing the Human Development Index of different classes of Brazilian municipalities, from prefrontier municipalities to heavily post frontier deforested municipalities, you can see how the HDI relatively grows in the first phase of the deforestation (on the frontier line) and relatively declines when deforestation is completed.  In other words,

when the median HDI of each class is plotted against deforestation extent, a boom-and-bust pattern becomes apparent, which suggests that relative development levels increase rapidly in the early stages of deforestation and then decline as the frontier advances. Hence, although municipalities with active deforestation had development levels that approached the overall Brazilian median, pre- and postfrontier HDI values were substantially lower and statistically indistinguishable from each other (P > 0.9). These results are robust to the particular thresholds used to define the frontier classes. A boom-and-bust pattern is also found for each of the HDI subindices: standard of living, literacy, and life expectancy.

This strongly suggests that the poor have no choice but to exploit every resource available. It is difficult to think that farmers or loggers do not see that they are compromising their very own future. They simply have no choice. The challenge is giving them a choice.

Friday, 12 June 2009

On the Evolution of Thinking

What if we are becoming the very same Artificial Intelligence that we are trying to design? The doubt has has been raised by Nicholas Carr in an article published one year ago on The Atlantic and now published on Le Monde. The theory is intriguing and the discourse goes, in the words of developmental psychologist Maryanne Wolf, more or less in this direction:

We are not only what we read, We are how we read.

So, learning directly from the voice of Socrates is not the same as learning from the Internet. The way we approach new ideas and knowledge influences how we assimilate them and how we develop our thinking. The risk is that our mind might find so attractive the effectivness of the Google’s algorithm to try to replicate it forgetting all the ambiguity that has made us what we are. What we are so far.

Update: Have a look at this article on Le Monde about the influence of the new information technologies on culture.

Friday, 5 June 2009

tweets


Twitter: frbailo

links


blogroll


RSS r-bloggers.com

  • Backcast a Time Series for Covid-19 Truths
    A couple of months ago, Turkey’s Health Minister announced that the positive cases showing no signs of illness were not included in the statistics. This statement made an earthquake effect in Turkey, and unfortunately, the articles about covid-19 I have wrote before came to nothing. The reason for this ... The post Backcast a Time […]
  • The Impact of the COVID-19 Pandemic on My Walking Behavior in 2020
    In this post, we will take a look back at 2020, and analyze my step count data to understand some of the impacts that the COVID-19 crisis had on my walking behavior during that crazy year. The Data Step Counts & Measurement Devices The step count data come from 2 sources in 2020 - ... […]
  • Share R shiny apps with brightRserver: 70-second sneak-peek
    Building, maintaining, and improving interactive R web apps has never been easier. YakData’s brightRserver seamlessly combines the best-in-class R editor and R web app server with Secure FTP publishing and synchronization. The post Share R shiny apps with brightRserver: 70-second sneak-peek first appeared on R-bloggers.
  • Making a Solar Insolation Map for Alberta (For novices!)
    Been a while since I've blogged here; wrapping up an MSc and moving continents from Europe to North America is all the excuse I need. This blog post is not going to be revolutionary, and obviously it builds on a lot of what others have done before (see... The post Making a Solar Insolation Map […]
  • Counting Missing Values (NA) in R
    To check for missing values in R you might be tempted to use the equality operator == with your vector on one side and NA on the other. Don’t! If you insist, you’ll get a useless results. x The post Counting Missing Values (NA) in R first appeared on R-bloggers.

RSS Simply Statistics

  • 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 […]
  • 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 […]

RSS Statistical Modeling, Causal Inference, and Social Science

  • Hierarchical stacking
    (This post is by Yuling) Gregor Pirš, Aki, Andrew, and I wrote: Stacking is a widely used model averaging technique that yields asymptotically optimal predictions among linear averages. We show that stacking is most effective when the model predictive performance is heterogeneous in inputs, so that we can further improve the stacked mixture by a […]
  • The norm of entertainment
    Someone pointed me to a comment that a psychology researcher wrote that he almost never reads our blog and that it “too quickly bores me.” That’s ok. I’m sure that lots of people have stumbled upon our blog, one way or another, and have been bored by it. We don’t have a niche audience, exactly; […]
  • Tessa Hadley on John Updike
    Lots to think about here. To start with, this is the first New Yorker fiction podcast I’ve heard where they actually criticize the author instead of just celebrating him and saying how perfect the story is. This time, they went right at it, with the interviewer, Deborah Treisman, passing along some criticisms of Updike and […]