NOTE: this repo has moved to https://github.com/vdeminstitute/demspaces

The Demoractic Spaces Barometer forecasts significant changes, both democratizing and autocratizing, for six facets of democratic governance for all major countries in the world 2 years ahead. The current forecasts cover 2020-2021 and can be explored with the dashboard at https://www.v-dem.net/en/analysis/DemSpace/.

This repo contains the code and data needed to reproduce the forecasts and dashboard. It is organized into three self-contained folders:

• create-data/: combine V-Dem and other data sources into the historical data that is used to code the dependent variables and estimate the forecast models
• modelrunner/: contains the random forest forecast models and will generate both the test and live forecasts
• dashboard/: the R Shiny dashboard at the V-Dem website

The folders are self-contained in the sense that each has a copy of the inputs it needs to run, and will not reach into other folders to pull code or data. For example, modelrunner saves the forecasts to output/fcasts-rf.csv, and dashboard contains a copy of these forecasts in Data/fcasts-rf-2020.csv. (This also means that if there are any changes in relevant data, they need to be manually copied over.)

The forecasts/ folder contains a record of the forecasts we made in 2019 and updated forecasts from March 2020, when V-Dem version 10 was released.

The R packages needed to run all code in this repo are listed in required-packages.txt. Two packages need special treatment:

• demspaces is not on CRAN. This package contains some custom model wrappers to make it easier to work with the 12 outcome variables we are modeling. It can be installed with:

  remotes::install_github("andybega/demspaces")

• states needs to be at least verion 0.2.2.9007, which by the time you are reading this may be met by the version on CRAN. If not, you can also install the development version from GitHub:

  # Check the package version
  packageVersion("states")
  if (packageVersion("states") < "0.2.2.9007") {
    remotes::install_github("andybega/states")
  }

To check for and install the remaining packages, try:

packs <- readLines("required-packages.txt")
need  <- packs[!packs %in% rownames(installed.packages())]
need
install.packages(need)

1. In create-data/, run the data munging scripts in the scripts/ folder to recreate the final data, output-data/states.rds. See create-data/README.md for more details.
2. In modelrunner/, run R/rf.R to run the forecast models and create the test and live forecasts. See modelrunner/README.md for more details.
3. Update the forecast data in dashboard.

Note: the v9 to v10 update process was before some file changes and cleaning, so probably future updates will require adjusting and updating the steps below.

To update the forecasts:

1. Update the input data sources in create-data/input as needed.
2. Re-run each data munging script in create-data/scripts. This will probably require some changes here and there, e.g. if sets of missing values have changed, and thus should be done interactively (i.e. don’t source the scripts and assume everything is ok).
3. Copy the updated states.rds data to modelrunner/input/.
4. Adjust the end year in modelrunner/R/rf.R line 56 and re-run.
5. Copy modelrunner/output/fcasts-rf.csv to dashboard/data/.

Andreas Beger, Richard K. Morgan, and Laura Maxwell, 2020, “The Democratic Spaces Barometer: global forecasts of autocratization and democratization”, https://www.v-dem.net/en/analysis/DemSpace/

@misc{beger2020democratic,
  author = {Beger, Andreas and Morgan, Richard K. and Maxwell, Laura},
  title = {The Democratic Spaces Barometer: Global Forecasts of Autocratization and Democratization},
  year  = {2020},
  url   = {https://www.v-dem.net/en/analysis/DemSpace/},
}

We welcome any error and bug reports dealing with mistakes in the existing code and data. Please open an issue or email Andreas Beger at [email protected].

This repo is not under active development and mainly serves for the sake of transparency and to allow reproduction of the forecasts and dashboard. There is no plan for continuing development aside from, potentially, annual forecast updates in the future. It is thus unlikely that more substantive feedback, like suggestions about additional features/predictors or alternative models, would be incorporated unless you do most of the legwork and can clearly demonstrate improved performance. This is not meant as discouragement, we simply don’t have the resources to put more time in this and want to prevent disappointment.

The Democratic Space Barometer is the product of a collaboration between Andreas Beger (Predictive Heuristics), Richard K. Morgan (V-Dem), and Laura Maxwell (V-Dem).

The six conceptual dimensions we focus on come from the International Republican Institute’s Closing Space Barometer, which includes an analytical framework for assessing the processes that facilitate a substantial reduction (closing events) within these six spaces. This framework was developed based on a series of workshops conducted with Democracy, Human Rights, and Governance (DRG) donors and implementing partners in 2016 and represent the conceptual features of democratic space which are most amenable to DRG assistance programs.

We adapted these conceptual spaces, expanded the scope to include substantial improvements (opening events), and developed an operationalization method to identify opening and closing events within each space. This dashboard, and the forecast that drive it, is the output of these efforts.

sessionInfo()

## R version 4.0.0 (2020-04-24)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.4
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] compiler_4.0.0  magrittr_1.5    tools_4.0.0     htmltools_0.4.0
##  [5] yaml_2.2.1      Rcpp_1.0.4.6    stringi_1.4.6   rmarkdown_2.1  
##  [9] knitr_1.28      stringr_1.4.0   xfun_0.13       digest_0.6.25  
## [13] rlang_0.4.6     evaluate_0.14

packs <- readLines("required-packages.txt")
installed <- as.data.frame(installed.packages(), stringsAsFactors = FALSE)
packs <- installed[installed$Package %in% packs, c("Package", "Version")]
rownames(packs) <- NULL
packs

##      Package    Version
## 1   doFuture      0.9.0
## 2      dplyr      0.8.5
## 3     future     1.17.0
## 4     glmnet      3.0-2
## 5       here        0.1
## 6   jsonlite      1.6.1
## 7    leaflet      2.0.3
## 8        lgr      0.3.4
## 9   magrittr        1.5
## 10    ranger     0.12.1
## 11     readr      1.3.1
## 12     rgeos      0.5-2
## 13        sf      0.9-2
## 14     shiny    1.4.0.2
## 15    states 0.2.2.9008
## 16   stringr      1.4.0
## 17    tibble      3.0.1
## 18     tidyr      1.0.2
## 19 tidyverse      1.3.0
## 20       zoo      1.8-7