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SpatialRDD

Travis build status

This repository hosts the code underlying the R package SpatialRDD. The workhorse functions in a nutshell are:

  • assign_treated()
  • border_segment()
  • discretise_border()
  • spatialrd()
  • plotspatialrd()
  • printspatialrd()
  • shift_border()
  • cutoff2polygon()

The package can estimate heterogenous treatment effects alongside an RD cutoff. Moreover it provides powerful spatial functions to carry out placebo exercises (move borders and reassign (placebo) treatment status). These functionalities are also useful for different empirical identification strategies that rely on flexibly changing geographic boundaries.

For full guidance check out the different vignettes in the vignettes folder here on github or with

  • vignette(spatialrdd_vignette)
  • vignette(shifting_borders)

in the R console.

Installation

install.packages("devtools")
devtools::install_github("axlehner/SpatialRDD")

What you need to run you own spatial RD with SpatialRDD:

  1. An RD boundary as a single line (the simplest way is to cut this by hand in e.g. ArcGIS or QGIS by just splitting a line off a polygon for example - also make sure to merge all features together in case there are multiple left, this will be important and prevent annoying troubles later on)
  2. The data frame containing the columns with x- and y-coordinates. Read in with read.csv() or with readstata13::read.dta13(). Make them an sf object with st_as_sf(data, coords = c("x", "y"), crs = 4326) if you have longitude/latitude as coordinates (which is reflected by the 4326 EPSG). If this is the case use st_transform() on all your objects into a local UTM projection (not necessary but recommended for several reasons). Note: In case your data comes as a shapefile/geopackage/etc. directly, just read it in with st_read("path/to/file.shp"). If these are polygons it is advised to work with the centroids straightaway (extract with st_centroid()). If you need zonal statistics (on elevation/ruggednes etc.) for e.g. checking identifying assumptions, do these before converting to centroids.
  3. Ideally also a polygon that covers the treated areas (this could be created within the package with cutoff2polygon though)

You could verify the “geographic validity” of your objects with e.g. mapview::mapview().

Quick Guide

Adding border segment for transparent fixed effect category creation (for the non-parametric specification that is just OLS with lm() or lfe::felm()):

points_samp.sf$segment5 <- border_segment(points_samp.sf, cut_off.sf, 5)
#> Starting to create 5 border segments with an approximate length of 26 kilometres each.
tm_shape(points_samp.sf) + tm_dots("segment5", size = 0.1) + tm_shape(cut_off.sf) + tm_lines()

Create points alongside border and run GRD to explore heterogeneity:

borderpoints.sf <- discretise_border(cutoff = cut_off.sf, n = 50)
#> Starting to create 50 borderpoints from the given set of borderpoints. Approximately every 3 kilometres we can run an estimation then.
results <- spatialrd(y = "education", data = points_samp.sf, cutoff.points = borderpoints.sf, treated = "treated", minobs = 10)
#> We have 1000 observations of which 215 are treated observations.
#> We are iterating over 50 Boundarypoints.
#> The dependent variable is education .
plotspatialrd(results, map = T)

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