Introduction: the empirical workflow Econometrics is much easier without the data—Marno Verbeek
The quote above does not only apply to economics and econometrics, but to all of the social sciences in general. Empirical research—that is, dealing with data in all its forms—requires a rigorous approach, even more so, with the increasing emphasis on openness and reproducibility of all kinds of scientific research. Therefore, it is strange that in academic education there is not much guidance in choosing which research tools to use and in the philosophy behing choosing an efficient and reproducable workflow.
My second Ph.D. student got her Ph.D. degree! With an almost flawless defense and a very good Ph.D. thesis Zhiling obviously deserves all the credit. However, I could not resist to post this picture. Somehow, I resemble Jose Mourinho, the angry looking football trainer.
Factor mobility and welfare For educational purposes we teach in the second year’s course regional and urban economics students the Edgeworth-Bowley box. At first sight the concept is quite simple, but because there are restrictions for the total amount of both labour and capital in both regions or countries, the intuition behind the model and especially the drawing of the box is rather complex. Therefore, I once wrote a straightforward but elaborate LaTeX script invoking the Tikz package.
Drawing the diagram of a stylized version of Krugman’s Increasing Returns and Economic Geography For educational purposes we teach in the second year’s course regional and urban economics a simplified version of Krugman’s model in his paper titled Increasing Returns and Economic Geography. The model we have adopted goes as follows:
We consider a simplified economy with two regions and 1 (million) workers ( $L=1$ ) in total. Region 1 is inhabited by 100,000 farmers (bound to their land so immobile), while in Region 2 there are 200,000 farmers.
With Daniel Arribas-Bel
This resource describes WooW-II, a two-day workshop on open workflows for quantitative social scientists. The workshop is broken down in five main parts, where each of them typically consists of an introductionary tutorial and a hands-on assignment. The specific tools discussed in this workshop are Markdown, Pandoc, Git, Github, R, and Rstudio, but the theoretical approach applies to a wider range of tools (e.g., LaTeX, and Python).