My replication code and data can be accessed on Harvard Dataverse.

My other code can be accessed on GitHub, including the packages I’ve written (described below).

Python packages

get_files automates downloading files from a website using web scraping when you provide it with a url and the file extensions to scrape.

word2pdf automates Microsoft Word document to pdf conversion.

R packages

tabulator efficiently tabulates and produces Stata tabulate-like output.

To install tabulator directly through R:

devtools::install_github("skhiggins/tabulator")

tabulator includes the following functions:

  • tab() efficiently tabulates based on a categorical variable, sorts from most common to least common, and displays the proportion of observations with each value, as well as the cumulative proportion.
  • tabcount() counts the unique number of categories of a categorical variable or formed by a combination of categorical variables.
  • quantiles() produces quantiles of a variable. It is a wrapper for base R quantile() but is easier to use within data.tables or tibbles.
  • quo_to_chr() works under the hood, converting a quosure to a character string.

Stata packages

To install directly through Stata:

ssc install <package_name>, replace

ceq is a suite of commands to estimate fiscal incidence following the Commitment to Equity framework.

exampleobs prints (randomly selected) example observations and optionally stores the values in a local macro. This is useful to explore possible values of a variable in your data set without being biased by the ordering of the data.

fiscal_impoverishment includes commands to estimate fiscal impoverishment (FI) and fiscal gains to the poor (FGP), which are measures of how much the poor benefit from or are hurt by the tax and transfer system from Higgins and Lustig (2016). Additional commands graph FI and FGP curves.

head prints the head observations (first observations in data set) and mimics the head() function in R and head command in Linux.

randomselect randomly selects observations and marks them with a dummy variable. It differs from sample in that it does not drop the non-selected observations from the data set, and that either individual observations or other units, defined by a variable in the data set, can be randomly selected.

tail prints the tail observations (last observations in data set) and mimics the tail() function in R and tail command in Linux. ​