homesteads

This repository provides data and code for reproducing "State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction".

Please cite the paper if you use this code for academic research:

@ARTICLE{2019arXiv190308028P,
       author = {{Poulos}, Jason},
        title = "{State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction}",
      journal = {arXiv e-prints},
     keywords = {Economics - General Economics, Economics - Econometrics, Statistics - Applications},
         year = "2019",
        month = "Mar",
          eid = {arXiv:1903.08028},
        pages = {arXiv:1903.08028},
archivePrefix = {arXiv},
       eprint = {1903.08028},
 primaryClass = {econ.GN},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190308028P},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Prerequsites

• R (tested on 4.0.1)
• Required packages located in package-list.R
  • The packages libgdal-dev and libproj-dev are required prior to installing the rgdal R package:

$ sudo apt-get install gdal-bin proj-bin libgdal-dev libproj-dev

• The package mdbtools is required prior for the mdb.get function of the HMisc R package:

$ sudo apt-get install gdal-bin proj-bin libgdal-dev libproj-dev

Install the forked MCPanel repo:

install.packages("devtools")
library(devtools) 
install_github("jvpoulos/MCPanel")

Note: fitting the matrix completion model with covariates (mcnnm_wc_cv) is computationally expensive and will likely make a laptop crash. The code below is run on a single node with 30G RAM and 6 CPU-cores on a high-performance compute cluster.

Instructions for data assembly

• Clone a copy of the repository to your working directory with the command

$ git clone https://github.com/jvpoulos/homesteads

• Download and extract patents data (2.8 GB uncompressed) to data/ directory:

  • patents.tar.xz

• Make shell file code/main.sh executable from the Linux/Unix command line:

$ chmod +x code/main.sh

• Execute the file:

$ ./code/main.sh > main.txt

Run order for experiments and causal estimates

1. For simulated data experiments, run with command line argument Rscript code/mc-simulation.R [arg1], where [arg1] is a number specifying the estimator and simulation setting

• set flag doMPI to FALSE to run serially
• code/mc-simulation-plot.R # plot results for simulated data experiments

2. Repeat the last step for code/mc-synth.R, code/mc-capacity.R, and code/mc-simulation.R to run experiments on synthetic control, state government finance datasets, and simulated data, resp.

• set flag doMPI to FALSE to run serially
• code/mc-synth-simulation-plot.R, code/mc-capacity-simulation-plot.R, and code/mc-simulation-plot.R to plot results

3. For MC placebo estimates on state government finances: Rscript code/mc-capacity-placebo.R [arg1], where [arg1] is a number specifying the estimator and outcome of interest

• set flag doMPI to FALSE to run serially

4. For causal estimates on state government finances via MC and comparison estimators: Rscript code/mc-capacity-estimates.R [arg1], where [arg1] is a number specifying the estimator and outcome of interest

• code/mc-capacity-plots.R # to plot results

5. For causal estimates on state government finances and land inequality via continuous treatment DID: Rscript code/dd-capacity.R [arg1], where [arg1] is a number specifying the imputation method, and Rscript code/dd-inequality.R