If the Asian Growth Model is not Working Anymore

In 1981 poverty rate in China was 64% of the population, in 2004 the rate was 10%: it means that 500 million people stepped out of poverty (look here and here). China and South-East Asia economies were propelled by export demand and by someone else’s debt. What now? In the words of FT columnist Michael Pettis

The assumption that implicitly underlay the Asian development model – that US households had an infinite ability to borrow and spend – has been shown to be false. This spells the end of this model as an engine of growth.

It seams like bad news for economists pointing at free trade and export-led growth as a practical receipt for development.  It seams like bad news for everybody. People in developing countries need to increase their income, and it is difficult to think how they could find the money in their neighborhoods.

Tuesday, 19 May 2009

Brain matters

Saying that education strengths economic growth sounds good old common sense. But proving and measuring this relation is not immediate and therefore interesting. A reasearch, published last year, does it. Eric Hanushek, Dean T. Jamison, Eliot A. Jamison and Ludger Woessmann estimate that

each additional year of average schooling in a country increased the average 40-year growth rate in GDP by about 0.37 percentage points. That may not seem like much, but consider the fact that since World War II, the world economic growth rate has been around 2 to 3 percent of GDP annually. Lifting it by 0.37 percentage points is a boost to annual growth rates of more than 10 percent of what would otherwise have occurred, a significant amount.

Nonetheless, the research suggests that what really matters for economic growth is the quality of education. In other words it is not enough to send children to school: you have to teach them something. Using test-score performances around the world to measure the cognitive skills of students appears

that countries with higher test scores experienced far higher growth rates. If one country’s test-score performance was 0.5 standard deviations higher than another country during the 1960s (…) the first country’s growth rate was, on average, one full percentage point higher annually over the following 40-year period than the second country’s growth rate. Further, once the impact of higher levels of cognitive skills are taken into account, the significance for economic growth of school attainment, i.e., additional years of schooling, dwindles to nothing. A country benefits from asking its students to remain in school for a longer period of time only if the students are learning something as a consequence.

These results are extremely important especially for the countries of the Bottom Billion. What they are saying is that it is better to invest on the quality of the education (where rate of return is much higher) rather than spending to keep students in schools longer.

Tuesday, 17 March 2009

Crisis from the South

According to Banco de Guatemala, for the first time since 1999, in January 2009 remittances from abroad decreased by 7.75% compared to the same month in 2008. In Guatemala remittance  flows represent 11.89% of GDP. According to the World Bank remittances can represent more than 50% of rurally-based family income and for the International Organization for Migration 30.4% of the population receives money from abroad.

Causes? Probably economic crisis and deportation of  illegal immigrants from the US.

Something is certain: remittances are a strong factor in reducing poverty in Guatemala. If flows continue to decrease is more than probable that poverty will rise in a country where, according to the government, 45,6% of children are already underweight.

Monday, 16 February 2009

tweets


Twitter: frbailo

links


blogroll


RSS r-bloggers.com

  • COVID-19 shiny / plotly dashboard
    Governments and COVID-19: Which one stops it faster, better, has fewer people dying? These questions get answered with my dashboard. A contribution to the shiny-contest: https://community.rstudio.com/t/material-design-corona-covid-19-dashboard-2020-shiny-contest-submission/59690 Intro How did Corona spread? Using the animation feature of R-shiny this can be easily tracked.COVID-19 is the major topic in all news channels. The place I live in […]
  • RcppSimdJson 0.0.4: Even Faster Upstream!
    A new (upstream) simdjson release was announced by Daniel Lemire earlier this week, and my Twitter mentions have been running red-hot ever since as he was kind enough to tag me. Do look at that blog post, there is some impressive work in there. We wr...
  • C is for coalesce
    For the letter C, we'll talk about the coalesce function. If you're familiar with SQL, you may have seen this function before. It combines two or more variables into a single column, and is a way to deal with missing data. When you give it a list of va...
  • Introductory videos for Explanatory Model Analysis with R
    Remote teaching at my university encouraged me to prepare some video materials for Explanatory Model Analysis techniques, i.e. techniques of exploration, explanation and visualisation of predictive models.The pyramid for Explanatory Model Analysis. Lef...
  • Custom Power BI visual for Line chart with two Y-Axis
    Power BI support certain type of visuals that are by default available in the document. These are absolutely great and work perfectly fine, have a lot of capabilities to set properties and change the settings. But every so often in…Read more ›

RSS Simply Statistics

  • Is Artificial Intelligence Revolutionizing Environmental Health?
    NOTE: This post was written by Kevin Elliott, Michigan State University; Nicole Kleinstreuer, National Institutes of Health; Patrick McMullen, ScitoVation; Gary Miller, Columbia University; Bhramar Mukherjee, University of Michigan; Roger D. Peng, Johns Hopkins University; Melissa Perry, The George Washington University; Reza Rasoulpour, Corteva Agriscience, and Elizabeth Boyle, National Academies of Sciences, Engineering, and Medicine. […]
  • You can replicate almost any plot with R
    Although R is great for quickly turning data into plots, it is not widely used for making publication ready figures. But, with enough tinkering you can make almost any plot in R. For examples check out the flowingdata blog or the Fundamentals of Data Visualization book. Here I show five charts from the lay press […]
  • So You Want to Start a Podcast
    Podcasting has gotten quite a bit easier over the past 10 years, due in part to improvements to hardware and software. I wrote about both how I edit and record both of my podcasts about 2 years ago and, while not much has changed since then, I thought it might be helpful if I organized […]

RSS Statistical Modeling, Causal Inference, and Social Science

  • Noise-mining as standard practice in social science
    The following example is interesting, not because it is particularly noteworthy but rather because it represents business as usual in much of social science: researchers trying their best, but hopelessly foiled by their use of crude psychological theories and cruder statistics, along with patterns of publication and publicity that motivate the selection and interpretation of […]
  • Conference on Mister P online tomorrow and Saturday, 3-4 Apr 2020
    We have a conference on multilevel regression and poststratification (MRP) this Friday and Saturday, organized by Lauren Kennedy, Yajuan Si, and me. The conference was originally scheduled to be at Columbia but now it is online. Here is the information. If you want to join the conference, you must register for it ahead of time; […]
  • More coronavirus research: Using Stan to fit differential equation models in epidemiology
    Seth Flaxman and others at Imperial College London are using Stan to model coronavirus progression; see here (and I’ve heard they plan to fix the horrible graphs!) and this Github page. They also pointed us to this article from December 2019, Contemporary statistical inference for infectious disease models using Stan, by Anastasia Chatzilena et al. […]