I am a newfound advocate of using R for data analysis, and using R Markdown and GitHub for writing reproducible research, revision control, and managing my workflow. I occasionally blog here about using these tools in my research and teaching.
Copyright laws traditionally attempt to incentivize expression and minimize free rider problems through legal restrictions, at the expense of closing off access to cultural history. However, entrepreneurial changes to institutions and the creation of alternative governance structures can allow for spaces that facilitate expression without resorting to the copyright approach. Wikipedia, the free online encyclopedia, stands as a highly visible example of such institutional entrepreneurship, leveraging copyright law against its intended purpose. This paper uses the Bloomington School’s IAD framework to explain the success of Wikipedia’s alternative model of managing a common resource of free encyclopedia articles, and suggests a roadmap for understanding the role of institutional entrepreneurship in crafting alternative governance structures to foster expression.
The most robust framework for understanding the evolution and consequences of copyright statutes in the United States is the dynamics of interventionism. I apply the framework of Kirzner’s (1985) perils of regulation to the general revision of copyright law in 1976, and explore its effects on entrepreneurship and discovery processes. Critics of copyright commonly recognize the distortions of rent-seeking, but I emphasize the utility of interventionism to explain the “unsimulated” and the “stifled” discovery processes set in motion by copyright interventions, which use legal processes to allocate resources, and deter future discovery by raising transaction costs.
One of the advantages of using Stata for linear regression is that it can automatically use heteroskedasticity-robust standard errors simply by adding , r to the end of any regression command. Anyone can more or less use robust standard errors and make more accurate inferences without even thinking about what they represent or how they are determined since it’s so easy just to add the letter r to any regression.
As I have mentioned in other posts, this semester I trying to maximize the number of intuitive visualizations for my students to master economic and mathematical concepts more in my teaching, as well as develop my own R skills. For my intermediate microeconomics course, I recently put together an interactive Shiny App with R that demonstrates the effect of a tax on consumers and producers.
Simply input your own (inverse) demand and supply functions and the size of the tax, and the graph and summary will update with the market equilibrium and the surpluses lost by the tax.
For my econometrics course this semester, I have been using R to help students visualize linear regression models. Running a regression in R is quite simple, as is intepretting the results, with a little bit of training. However, I emphasize that I want students to understand what is happening “inside the black box” of regression. I discourage blindly trusting R’s opaquely simple input and output, and get students to learn what R is doing under the hood, even if they will never have to manually estimate the model themselves.
This semester, I am posting my lecture slides for my Econometrics class on GitHub. While I usually post my lecture slides for my courses on my website or link to my Dropbox, those are the final PDF documents. On GitHub, I am posting both the final PDFs as well as the source .rmd files. I am primarily doing this for my econometrics students, who will be learning R and R Markdown for their assignments (which is how I write my slides), but this is also open to anyone.
With the new school year in full swing, I have redesigned my personal website, and plan to make occasional posts on the tools I use in my research and teaching. Over the summer, I made the full conversion to using R, R Markdown, and Github for nearly everything I do in my professional life (including managing my website with Hugo/Academic).
This semester, I am teaching econometrics to my students using R for the first time, and optionally nudging them to use R Markdown for their homeworks and paper assignment.