Increased male bargaining power in households causes greater expenditure on food, an improvement in Weight-for-Age Z-scores in young children, and a deterioration in Height-for-Age Z-scores in very young children, as observed in the context of South Africa’s 2010 state pension expansion for males. In 2010 the male eligibility age for the South-African state pension was brought to par with female eligibility age (60, previously 65). I exploit this policy change in order to estimate the effect of the increased male bargaining power in the household, on growth of young children living in the same household, as well as food expenditure. The policy change took place shortly after the completion of the first wave of South Africa’s National Income Dynamics Survey and shortly before the start of the second wave, which lends itself well for a Difference-in-Differences approach on the right hand side. On the left hand side I use z-scores of growth anthropometrics of young children in the household (against WHO standards) as well as food expenditure.

A new R package gvc is now available on CRAN. The package implements several global value chain indicators Importing to Export (i2e(), a.k.a. vertical specialization) Exporting to Re-export (e2r()) New Revealed Comparative Advantage (nrca()) As well as several other tools. The gvc package can now be install directly from R using: {% highlight r %} install.packages(“gvc”) {% endhighlight %} In addition to this, a development version is available on GitHub, this version is to be used at your own risk, it can be install using:

I am proud to announce that after a few emails back and forth with Prof. Brian Ripley, which consisted mostly of me appologising for not following the proper procedure for submission, I received an email announing that my decompr package is now available on CRAN. The package can now easily be installed using: {% highlight r linenos %} install.packages(“decompr”) {% endhighlight %} The version published contains several updates, most importantly, I used a regional input-output table from the WIOD project, which is substantially smaller and makes the decompositions significantly faster.

Yesterday the Johns Hopkins School of Public Health published a post about their Data Science Specialisation on the online MOOC platform Coursera. The post metiones the first batch 266 students finishing the specialisation (among them, me :-) ). In total more than 800,000 people have registerd for one of the courses, out of which 14,000 finished at least one. The Specialisation The Data Science Specialisation consists of nine courses and a capstone project (which is was announced, but is yet to open for registration). The courses are:

In yesterday’s post I discussed some of the principles I use to make my work replicable and - to an extent - reproducible. In this post I want to collect some resources which I think will be very helpful in getting a grasp of the basics of R and Git. R An easy way to start with R without having to install it is using the online tutorial Try R by Code School.