EpisodeR

An R package to load, explore, and work with any index variable in the V-Dem data set - a project of the V-Dem Institute - to built episodes of growth and decline in a specific V-Dem index variable. This package was used for the Academic Freedom Growth and Decline Episodes data set introduced in Lott (2023).

This package uses code parts of the ERT package (V-Dem Institute (2023)). All copied and adapted code parts are marked accordingly in the R code of this package. However, unlike the ERT package, this package uses a different approach to identify episodes of change in a variable. These differences are documented below.

Acknowledgements

I would like to thank the authors of the ERT package, namely Seraphine Maerz, Amanda Edgell, Joshua Krusell, Laura Maxwell, and Sebastian Hellmeier, who have licensed the ERT package under the GPL-3 license. This package (EpisodeR) is also licensed under the GPL-3 license. Without the excellent documentation and work of these colleagues, this package would not have been possible.

The work of the Episodes of Regime Transformation team has published as Maerz et al. (2023), Edgell et al. (2022), Wilson et al. (2023), Boese et al. (2021), among others. The package development has been funded by a Volkswagen Foundation [grant number A138109, PI: Katrin Kinzelbach and Staffan I. Lindberg] and is part of the Academic Freedom Index project.

EpisodeR package

Load, explore, and work the episodes data sets

• Note: for non-R users I provide the Academic Freedom Growth and Decline Episodes data set in this GitHub project as .csv files.
• RELEASE: Academic Freedom Growth and Decline Episodes data set 14.0 is based on the V-Dem dataset v14. In the following years, updated Academic Freedom Growth and Decline Episodes data set will be provided once a year when updated V-Dem is published.

Functions

• get_episode: Identify episodes of growth and decline in an index variable in which this index variable systematically grows or declines in respective and connected country-years. This functions controls for overlapping uncertainty intervals, “var_codelow” and “var_codehigh”, as suggested by Pelke and Croissant (2021). A growth episode in an index variable is defined as a cumulative increase of 0.1 or more on any index variable. A decline episode is defined as a a cumulative drop of 0.1 or more on any index variable.
• get_episode_wo_CI: Identify episodes of growth and decline in an index variable in which this index variable systematically grows or declines in respective and connected country-years. This functions does not control for overlapping uncertainty intervals, “var_codelow” and “var_codehigh”, as suggested by Pelke and Croissant (2021). A growth episode in an index variable is defined as a cumulative increase of 0.1 or more on any index variable. A decline episode is defined as a a cumulative drop of 0.1 or more on any index variable.
• plot_episodes: Plot growth and decline episodes over time for a selected country.
• plot_all_episodes: Plot share or absolute number of all countries in growth and decline episodes of a specific index variable over time.

Differences between ERT package and EpisodeR package

The ERT package differs from this package in important ways. The ERT package is computationally more efficient and more flexible in what constitutes an episode of change in a variable. It was one important source of code for this package it inspired my work for this package. However, two drawbacks come with the ERT package:

• It does not enable users to search for episode of regime transformation / change other than the Electoral Democracy Index.
• Secondly, the ERT package does not control for overlapping uncertainty intervals (before the start of an episode and at the end of an episode), as suggested by Pelke and Croissant (2021).

These drawbacks are partially resolved in this package, while it comes with other important drawbacks: It is computationally inefficient and less flexible in terms of episode termination and tolerance for temporary stagnation.

The EpisodeR package copied and adapted some code parts of the ERT package but does not use the most important part of the ERT package (C++ code for finding episodes) to estimate episodes of change data. It differs most substanially here:

• The ERT package uses C++ language to find episodes of regime transformation, while this package uses some loops in R for finding these episodes of change in an index variable. The EpisodeR package is computationally more inefficient compared to the C++ solution from the ERT package. To find episodes of change in a variable, this package uses code originally used by Pelke and Croissant (2021).
• The ERT package enables users to consider a specific number of years as tolerance for stasis or a gradual movement in the opposite direction. By default it is set to five years. In the EpisodeR package users cannot change the tolerance parameter and there is a temporary stasis on the specific variable with no further increase/decline of start_incl points in four years.

Installation

You can install the development version of EpisodeR from GitHub with:

# Install the development version of the EpisodeR package 
# (since this package is still an ongoing project, 
# keep checking for updates, new functions, etc.!)

# First, you need to have the devtools package installed
install.packages("devtools")
# now, install the EpisodeR package directly from GitHub
devtools::install_github("https://github.com/larslott/EpisodeR")

# installed. If you have troubles with the installation 
# write to the package maintainer Lars Lott (lars.lott@fau.de).

Example

This is a basic example which shows you how to use the EpisodeR package:

library(EpisodeR)
## basic example code

You can use get_episode to return a data.frame identifying decline and growth episodes of an index variable in which this variable systematically grow or decline in respective and connected country-years.

df <- get_episode(data = EpisodeR::vdem,
                  start_incl = 0.01,
                  cum_incl = 0.1,
                  year_turn = 0.03,
                  variable = "v2xca_academ")

Vignettes explaining the different functions and how they differ from the ERT package are available in the Vignettes folder in the GitHub repository.

References

Boese, Vanessa A., Amanda B. Edgell, Sebastian Hellmeier, Seraphine F. Maerz, and Staffan I. Lindberg. 2021. “How Democracies Prevail: Democratic Resilience as a Two-Stage Process.” Democratization 28 (5): 885–907. https://doi.org/10.1080/13510347.2021.1891413.

Edgell, Amanda B., Vanessa A. Boese, Seraphine F. Maerz, Patrik Lindenfors, and Staffan I. Lindberg. 2022. “The Institutional Order of Liberalization.” British Journal of Political Science 52 (3): 1465–71. https://doi.org/10.1017/S000712342100020X.

Lott, Lars. 2023. “Academic Freedom Growth and Decline Episodes.” Higher Education, December. https://doi.org/10.1007/s10734-023-01156-z.

Maerz, Seraphine F., Amanda B. Edgell, Matthew C. Wilson, Sebastian Hellmeier, and Staffan I. Lindberg. 2023. “Episodes of Regime Transformation.” Journal of Peace Research. https://doi.org/10.1177/00223433231168192.

Pelke, Lars, and Aurel Croissant. 2021. “Conceptualizing and Measuring Autocratization Episodes.” Swiss Political Science Review 27 (2): 434–48. https://doi.org/10.1111/spsr.12437.

V-Dem Institute. 2023. “Vdeminstitute/ERT.” https://github.com/vdeminstitute/ERT.

Wilson, Matthew C., Juraj Medzihorsky, Seraphine F. Maerz, Patrik Lindenfors, Amanda B. Edgell, Vanessa A. Boese, and Staffan I. Lindberg. 2023. “Episodes of Liberalization in Autocracies: A New Approach to Quantitatively Studying Democratization.” Political Science Research and Methods 11 (3): 501–20. https://doi.org/10.1017/psrm.2022.11.