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

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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 […]