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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
# Please put your title here to include it in the file below.
Title <- "NGO Repression as a Predictor of Worsening Human Rights Abuses"
Authors <- "Suparna Chaudhry and Andrew Heiss"
Year <- "2022"
```
# `r Title`
[Suparna Chaudhry](http://www.suparnachaudhry.com/) • Department of International Affairs • Lewis \& Clark College
[Andrew Heiss](https://www.andrewheiss.com/) • Andrew Young School of Policy Studies • Georgia State University
---
[](https://dx.doi.org/10.17605/OSF.IO/MTR6X) [](https://doi.org/10.5281/zenodo.5715402)
> `r Authors`. `r Year`. ["`r Title`,"](https://doi.org/10.1177/0899764020971045) *Journal of Human Rights* (forthcoming).
**All this project's materials are free and open:**
- [Download the data](#data)
- [See the analysis notebook website](https://stats.andrewheiss.com/cautioning-canary/)
   
---
## Abstract
An increasing number of countries have recently cracked down on non-governmental organizations (NGOs). Much of this crackdown is sanctioned by law and represents a bureaucratic form of repression that could indicate more severe human rights abuses in the future. This is especially the case for democracies, which unlike autocracies, may not aggressively attack civic space. We explore if crackdowns on NGOs predict broader human rights repression. Anti-NGO laws are among the most subtle means of repression and attract lesser domestic and international condemnation compared to the use of violence. Using original data on NGO repression, we test whether NGO crackdown is a predictor of political terror, and violations of physical integrity rights and civil liberties. We find that while de jure anti-NGO laws provide little information in predicting future repression, their patterns of implementation—or de facto civil society repression—predicts worsening respect for physical integrity rights and civil liberties.
---
This repository contains the data and code for our paper. Our pre-print is online here:
> `r Authors`. `r Year`. "`r Title`"". Accessed `r format(Sys.time(), '%B %e, %Y')`. Online at <https://dx.doi.org/10.17605/OSF.IO/MTR6X>
## How to download and replicate
You can either [download the compendium as a ZIP file](/archive/master.zip) or use GitHub to clone or fork the compendium repository (see the green "Clone or download" button at the top of the GitHub page).
We use the [**renv** package](https://rstudio.github.io/renv/articles/renv.html) to create a stable version-specific library of packages, and we use the [**targets** package](https://docs.ropensci.org/targets/) to manage all file dependencies and run the analysis. ([See this for a short helpful walkthrough of **targets**.](https://books.ropensci.org/targets/walkthrough.html)).
To reproduce the findings and re-run the analysis, do the following:
1. Download and install these fonts (if you’re using Windows, make sure you right click on the font files and choose “Install for all users” when installing these fonts):
- [Cochineal](https://fontesk.com/cochineal-typeface/)
- [Inter](https://fonts.google.com/specimen/Inter)
- [Linux Libertine O](https://www.cufonfonts.com/font/linux-libertine-o) (also [here](https://sourceforge.net/projects/linuxlibertine/))
- [Libertinus Math](https://github.com/alerque/libertinus)
- [InconsolataGo](https://github.com/ryanoasis/nerd-fonts/tree/master/patched-fonts/InconsolataGo)
2. [Install R](https://cloud.r-project.org/) (and preferably [RStudio](https://www.rstudio.com/products/rstudio/download/#download)).
- If you're using macOS, [install XQuartz too](https://www.xquartz.org/), so that you have access to the Cairo graphics library
- If you’re using Windows, [install RTools too](https://cran.r-project.org/bin/windows/Rtools/) and add it to your PATH so that you can install packages from source if needed
3. Open `cautioning-canary.Rproj` to open an [RStudio Project](https://r4ds.had.co.nz/workflow-projects.html).
4. Make sure you have a working installation of LaTeX:
- *Easy-and-recommended way*: Install the [**tinytex** package](https://yihui.org/tinytex/) by running `install.packages("tinytex")` in the R console, then running `tinytex::install_tinytex()`
- *Easy-but-requires-huge-4+-GB-download way*: Download TeX Live ([macOS](http://www.tug.org/mactex/); [Windows](https://miktex.org/))
5. If it's not installed already, R *should* try to install the **renv** package when you open the RStudio Project for the first time. If you don't see a message about package installation, install it yourself by running `install.packages("renv")` in the R console.
6. Run `renv::restore()` in the R console to install all the required packages for this project.
7. Run `targets::tar_make()` in the R console to automatically download all data files, process the data, run the analysis, and compile the paper and appendix.
Running `targets::tar_make()` will create several helpful outputs:
1. All project data in `data/`
2. An analysis notebook website in `analysis/_site/index.html`
3. PDF, HTML, and Word versions of the manuscript in `manuscript/output/`
## Data
<div style="color:#FF4136">
**NB**: If you're reproducing this project, it is best if you rely on the **targets** package and all the functions in `R/funs_data-cleaning.R` to handle the cleaning, processing, tidying, and merging. All the analysis in the project depends on having a **targets**-created object named `panel`.
</div>
For reference, though, we export CSV and RDS files of the final dataset:
- [**`data/derived_data/panel.csv`**](data/derived_data/panel.csv) and [**`data/derived_data/panel.rds`**](data/derived_data/panel.rds): CSV and RDS versions of our final combined dataset
- [**`data/derived_data/panel_lagged.csv`**](data/derived_data/panel_lagged.csv) and [**`data/derived_data/panel_lagged.rds`**](data/derived_data/panel_lagged.rds): CSV and RDS versions of our final combined dataset with lagged and leaded versions of variables
This data is derived from many different original data sources. See the ["Process and merge data" page of our analysis notebook](https://stats.andrewheiss.com/cautioning-canary/01_data-overview.html) for details about both the original data and the merging process.
- **Chaudhry NGO restrictions**: We use counts of anti-NGO legal barriers from [the replication data](https://doi.org/10.7910/DVN/JHOGNX) for Suparna Chaudhry's "The Assault on Civil Society: Explaining State Repression of NGOs" (*International Organization*, 2022).
- `data/raw_data/Chaudhry restrictions/SC_Expanded.dta`
- **Political Terror Scores**: We use data from the [Political Terror Scale (PTS) project](http://www.politicalterrorscale.org/) to measure state repression. This project uses reports from the US State Department, Amnesty International, and Human Rights Watch and codes political repression on a scale of 1-5.
- `data/raw_data/Political Terror Scale/PTS-2019.RData`, v2019
- **Latent Human Rights Protection Scores**: We use Chris Fariss's [Latent Human Rights Protection Scores](https://doi.org/10.7910/DVN/RQ85GK), which are estimates from fancy Bayesian models that capture a country's respect for physical integrity rights.
- `data/raw_data/Latent Human Rights Protection Scores/HumanRightsProtectionScores_v4.01.csv`, v4.01
- **Varieties of Democracy data**: We use a bunch of variables from the [Varieties of Democracy (V-Dem) project](https://www.v-dem.net/en/).
- `data/raw_data/Country_Year_V-Dem_Full+others_R_v10/V-Dem-CY-Full+Others-v10.rds`, v10
- **UN data**: We use [the **WDI** package](https://vincentarelbundock.github.io/WDI/) to collect data from the World Bank. However, we don't use WDI data for GDP and % of GDP from trade because the WDI data is incomplete (especially pre-1990, but that's not an issue in this project) To get around that, we create our own GDP and trade measures using data directly from the UN (at [UNData](https://data.un.org/)). They don't have a neat API like the World Bank, so you have to go to their website and export the data manually. We collect three variables:
- [GDP at constant 2015 prices](http://data.un.org/Data.aspx?q=gdp&d=SNAAMA&f=grID%3a102%3bcurrID%3aUSD%3bpcFlag%3a0) (`data/raw_data/UN data/UNdata_Export_20210118_034054729.csv`)
- [GDP at current prices](http://data.un.org/Data.aspx?q=gdp&d=SNAAMA&f=grID%3a101%3bcurrID%3aUSD%3bpcFlag%3a0) (`data/raw_data/UN data/UNdata_Export_20210118_034311252.csv`)
- [Population](https://population.un.org/wpp/Download/Standard/Population/) (`data/raw_data/UN data/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx`)
- **UCDP/PRIO Armed Conflict Data**: We use [UCDP/PRIO Armed Conflict data](https://ucdp.uu.se/downloads/index.html#armedconflict) to create an indicator marking if a country-year was involved in armed conflict that resulted in at least 25 battle-related deaths.
- `data/raw_data/UCDP PRIO/ucdp-prio-acd-191.csv`, v19.1
- **Natural Earth shapefiles**: We use the ["Admin 0 - Countries" 1:110m cultural shapefiles](https://www.naturalearthdata.com/downloads/110m-cultural-vectors/) for maps.
- `data/raw_data/ne_110m_admin_0_countries/`
- **2020 Civicus Monitor ratings**: CIVICUS rates countries [using a 5-item scale of civic space openness](https://monitor.civicus.org/widgets/world/), but getting their data in a machine-readable format is a little tricky. We downloaded [the standalone embeddable widget](https://monitor.civicus.org/widgets/world/) as an HTML file with `wget https://monitor.civicus.org/widgets/world/` and saved it as `index_2021-03-19.html`. We then extracted the `COUNTRIES_DATA` variable embedded in a `<script>` tag (`xpath = /html/body/script[5]`), which is JSON-ish, but not quite. The **jsonlite** R package couldn't parse it for whatever reason, but some random online JSON formatter and validator could, so we ran it through that and saved the resulting clean file.
- `data/raw_data/Civicus/index_2021-03-19.html`
- `data/raw_data/Civicus/civicus_2021-03-19.json`
**BUT AGAIN** we *highly* recommend just relying on the **targets** pipeline to construct this data rather than doing it all yourself! ([See this for a short helpful walkthrough.](https://books.ropensci.org/targets/walkthrough.html))
## `r emoji::emoji_glue(":warning::bird:")`: "Cautioning canary" project name
Because project titles change all the time with revisions, rewriting, and peer review, we use "cautioning canary" as an internal-to-us project name that won't change. (We chose this name before the [**codename**](http://svmiller.com/codename/) was invented—had it existed already, we would have used that to generate the name.)
## Licenses
**Text and figures:** All prose and images are licensed under Creative Commons ([CC-BY-4.0](http://creativecommons.org/licenses/by/4.0/))
**Code:** All code is licensed under the [MIT License](LICENSE.md).
## Contributions
We welcome contributions from everyone. Before you get started, please see our [contributor guidelines](CONTRIBUTING.md). Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.