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

  • r/finance, 1 year later
    The prominent conference R/Finance, held annually in Chicago, had a great program yesterday and today. As I wrote following last year’s conference, the organizers were criticized for including no women in its speaker lineup. The problem was that no women had submitted papers for consideration; no input, thus no output. I’m a member of the […]
  • A new package for panel data analysis in R
    It has been a long time coming, but my R package panelr is now on CRAN. Since I started work on it well over a year ago, it has become essential to my own workflow and I hope it can be useful for others. panel_data object class One key contribution, that I hope can help […]
  • The never-ending editor war (?)
    The creation of this blog post was prompted by this tweet, asking an age-old question: @spacemacs— Bruno Rodrigues (@brodriguesco) May 16, 2019 This is actually a very important question, that I have been asking myself for a long time. An IDE, and plain text editors, are a very important tools to anyone writing code. Most […]
  • Earthquake Analysis (4/4): Cluster Analysis
    Are you interested in guest posting? Publish at DataScience+ directly from your editor (i.e., RStudio). Category Basic Statistics Tags Data Visualisation Maps R Programming This is the fourth part of our post series about the exploratory analysis of a publicly available dataset reporting earthquakes and similar events within a specific 30 days time span. In […]
  • Mapping Tornado Alley with R
    I caught a re-tweet of this tweet by @harry_stevens: THREAD: I wrote a post on @observablehq about a map I made today. It shows a typical day in the life of a graphics journalist: You never know what problems you'll have to solve on deadline! https://t.co/yRhW1wbLxN #d3js #dataviz 1/7 pic.twitter.com/7N6mmK0nz3 — Harry Stevens (@Harry_Stevens) May... […]

RSS Simply Statistics

  • Generative and Analytical Models for Data Analysis
    Describing how a data analysis is created is a topic of keen interest to me and there are a few different ways to think about it. Two different ways of thinking about data analysis are what I call the “generative” approach and the “analytical” approach. Another, more informal, way that I like to think about […]
  • Tukey, Design Thinking, and Better Questions
    Roughly once a year, I read John Tukey’s paper “The Future of Data Analysis”, originally published in 1962 in the Annals of Mathematical Statistics. I’ve been doing this for the past 17 years, each time hoping to really understand what it was he was talking about. Thankfully, each time I read it I seem to […]
  • Interview with Abhi Datta
    Editor’s note: This is the next in our series of interviews with early career statisticians and data scientists. Today we are talking to Abhi Datta about his work in large scale spatial analysis and his interest in soccer! Follow him on Twitter at @datta_science. If you have recommendations of an (early career) person in academics […]

RSS Statistical Modeling, Causal Inference, and Social Science

  • Vigorous data-handling tied to publication in top journals among public heath researchers
    Gur Huberman points us to this news article by Nicholas Bakalar, “Vigorous Exercise Tied to Macular Degeneration in Men,” which begins: A new study suggests that vigorous physical activity may increase the risk for vision loss, a finding that has surprised and puzzled researchers. Using questionnaires, Korean researchers evaluated physical activity among 211,960 men and […]
  • Hey, people are doing the multiverse!
    Elio Campitelli writes: I’ve just saw this image in a paper discussing the weight of evidence for a “hiatus” in the global warming signal and immediately thought of the garden of forking paths. From the paper: Tree representation of choices to represent and test pause-periods. The ‘pause’ is defined as either no-trend or a slow-trend. […]
  • Data quality is a thing.
    I just happened to come across this story, where a journalist took some garbled data and spun a false tale which then got spread without question. It’s a problem. First, it’s a problem that people will repeat unjustified claims, also a problem that when data are attached, you can get complete credulity, even for claims […]