README

This Github repository contains R code and survey data to reproduce results in the book Making Democracy Safe for Business: Corporate Politics During the Arab Uprisings by Robert Kubinec (Cambridge, 2023). If you are not familiar with how to download (or “clone”) the files in this repository, I encourage you to consider using the free Github Desktop software to enable you to do so.

The repository contains one R script for each empirical chapter in the book in the rscripts folder. These scripts were generated from the underlying Rmarkdown files used to create the book. However, because the text of the book cannot be released due to copyright issues, the code is included without the text. The data folder contains necessary survey and ancillary data for the code to run. All survey responses have been anonymized by removing identifiers and any variables that could indicate the location of respondents. All reproduced figures from the book are saved in the figures folder.

This data and code is released under the MIT license (see included file LICENSE).

Requirements to run the code

This repository uses the R package packrat to manage package dependencies. All of the packages used to run the code are included in the repo as source files with the versions used when the code was last run by the author. When R is started in the root directory (or via a project in Rstudio), packrat will set up a package library in the root directory and install any necessary packages from included source package files, which includes the ones listed below and all of their dependencies. If you are having trouble installing source packages on Mac OS X, try running R from the terminal in the project folder (see Github issue here: rstudio/rstudio#10883).

Specifically, the following package libraries need to be installed to run the code:

tidyverse
ggplot2
Hmisc
ggthemes
qualtRics
lubridate
stringr
googlesheets4
haven
forcats
cjoint
binom
readr
brms
kableExtra
boot
mirt
WDI
remotes
vdemdata
knitr
rmarkdown

If packrat is not available or you do not wish to you use it, simply install the packages above from CRAN. Note that package vdemdata is not available on CRAN but can be installed from the Github repo site with the following code:

remotes::install_github("vdeminstitute/vdemdata")

The session info of the machine last used to run the code is as follows:

sessionInfo()

## R version 4.2.2 (2022-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.6.1
## 
## Matrix products: default
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/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] parallel  stats4    grid      stats     graphics  grDevices utils     datasets  methods  
## [10] base     
## 
## other attached packages:
##  [1] knitr_1.43          ggrepel_0.9.3       patchwork_1.1.2     vdemdata_13.0      
##  [5] WDI_2.7.8           mirt_1.39           lattice_0.20-45     boot_1.3-28.1      
##  [9] kableExtra_1.3.4    brms_2.19.0         Rcpp_1.0.10         binom_1.1-1.1      
## [13] cjoint_2.1.0        survey_4.1-1        survival_3.4-0      Matrix_1.5-1       
## [17] lmtest_0.9-40       zoo_1.8-12          sandwich_3.0-2      haven_2.5.3        
## [21] googlesheets4_1.1.1 qualtRics_3.1.7     ggthemes_4.2.4      lubridate_1.9.2    
## [25] forcats_1.0.0       stringr_1.5.0       dplyr_1.1.0         purrr_1.0.1        
## [29] readr_2.1.4         tidyr_1.3.0         tibble_3.2.0        ggplot2_3.4.2      
## [33] tidyverse_2.0.0    
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.4.1      Hmisc_5.1-0          systemfonts_1.0.4    plyr_1.8.8          
##   [5] igraph_1.4.1         GPArotation_2023.3-1 splines_4.2.2        crosstalk_1.2.0     
##   [9] rstantools_2.3.0     inline_0.3.19        digest_0.6.31        htmltools_0.5.4     
##  [13] fansi_1.0.4          magrittr_2.0.3       checkmate_2.1.0      cluster_2.1.4       
##  [17] tzdb_0.3.0           RcppParallel_5.1.7   matrixStats_0.63.0   vroom_1.6.1         
##  [21] xts_0.13.0           svglite_2.1.1        timechange_0.2.0     prettyunits_1.1.1   
##  [25] colorspace_2.1-0     rvest_1.0.3          mitools_2.4          textshaping_0.3.6   
##  [29] xfun_0.39            callr_3.7.3          crayon_1.5.2         jsonlite_1.8.4      
##  [33] glue_1.6.2           gtable_0.3.1         gargle_1.5.1         webshot_0.5.4       
##  [37] distributional_0.3.1 pkgbuild_1.4.0       rstan_2.21.8         dcurver_0.9.2       
##  [41] abind_1.4-5          scales_1.2.1         mvtnorm_1.1-3        DBI_1.1.3           
##  [45] miniUI_0.1.1.1       viridisLite_0.4.1    xtable_1.8-4         htmlTable_2.4.1     
##  [49] foreign_0.8-83       bit_4.0.5            Formula_1.2-5        StanHeaders_2.21.0-7
##  [53] DT_0.27              htmlwidgets_1.6.1    httr_1.4.5           threejs_0.3.3       
##  [57] posterior_1.4.0      ellipsis_0.3.2       pkgconfig_2.0.3      loo_2.5.1           
##  [61] farver_2.1.1         nnet_7.3-18          utf8_1.2.3           tidyselect_1.2.0    
##  [65] labeling_0.4.2       rlang_1.1.1          reshape2_1.4.4       later_1.3.0         
##  [69] munsell_0.5.0        cellranger_1.1.0     tools_4.2.2          cli_3.6.0           
##  [73] generics_0.1.3       sjlabelled_1.2.0     evaluate_0.20        fastmap_1.1.1       
##  [77] yaml_2.3.7           ragg_1.2.5           processx_3.8.0       bit64_4.0.5         
##  [81] fs_1.6.1             packrat_0.9.1        pbapply_1.7-0        nlme_3.1-160        
##  [85] mime_0.12            xml2_1.3.3           compiler_4.2.2       bayesplot_1.10.0    
##  [89] shinythemes_1.2.0    rstudioapi_0.14      stringi_1.7.12       ps_1.7.2            
##  [93] highr_0.10           Brobdingnag_1.2-9    markdown_1.5         permute_0.9-7       
##  [97] vegan_2.6-4          shinyjs_2.1.0        tensorA_0.36.2       vctrs_0.5.2         
## [101] pillar_1.9.0         lifecycle_1.0.3      bridgesampling_1.1-2 data.table_1.14.8   
## [105] insight_0.19.1       httpuv_1.6.9         R6_2.5.1             promises_1.2.0.1    
## [109] gridExtra_2.3        codetools_0.2-18     MASS_7.3-58.1        colourpicker_1.2.0  
## [113] gtools_3.9.4         withr_2.5.0          Deriv_4.1.3          shinystan_2.6.0     
## [117] mgcv_1.8-41          hms_1.1.2            rpart_4.1.19         coda_0.19-4         
## [121] rmarkdown_2.23       googledrive_2.1.1    shiny_1.7.4          base64enc_0.1-3     
## [125] dygraphs_1.1.1.6

I would recommend using R version 4.2 or greater to run the code.

Running the code

The R script master_script.R in the root directory will run each script in the rscripts folder and recompile the README file with the latest session info. Note that the code reproduces some figures from image files rather than from raw data. These are usually descriptive graphics and if there is any question about these files, please email the author at rmk7@nyu.edu.

The two scripts in rscripts with statistical models (06- and 07-) have an option run_code that is set by default to TRUE because the first time the code is run it will save fitted model objects for Bayesian regression models in data. Setting this option to FALSE in the script after running it will save significant time at reproducing the figures. All reproduced figures are saved by the code in the figures folder.

The individual R scripts are as follows:

04a-Case-Study-Egypt.R : code for Chapter 2: The Egyptian Military as the Gatekeeper
05a-Case-Study-Tunisia.R : code for Chapter 3: Broad Rent-seeking and the Collapse of Tunisia’s Anti-Democratic Coalition
06-Quantitative-Surveys.R : code for Chapter 4: Experiments in Business and Political Connections
07-Quantitative-Surveys-Other.R : code for Chapter 5: Crony Capitalism in International Comparison

Important data files are as follows:

qual_data_new.rds: original survey data for 2017 surveys of business employees in Egypt, Algeria and Tunisia
egypt_mil_survey.csv: 2018 survey of Egyptian military officers and enlisted personnel and Tunisian businesspeople
all_imp_jn_m.rds : imputed datasets for the 2018 Jordan and Morocco survey
all_imp_eg_vn.rds : imputed datasets for the 2020 Egypt, Ukraine and Venezuela surveys
all_imp_eg_tn.rds : imputed datasets for the 2017 Egypt and Tunisia survey

A brief list of questions in the survey and their meaning:

rank_eg The rank of a military officer
Q14 / registration type of firm
Q74 / ceo whether the respondent is a CEO of the firm
Q13 / sector_1 sector of the firm
Q38 / bribe_increase whether bribes paid by the firm have increased since the Arab Spring
Q30_2 / supply_2 rank of military-owned companies as a supplier to the firm (1 = highest)
Q28_2 / cust_2 rank of military-owned companies as a customer of the firm (1 = highest)
Q8 / firm_size Number of firm employees
Q8_1 / conglomerate Whether or not the company is a part of a conglomerate
Q37 / bribe_income How much does the company pay in bribes as a percentage of its income?
Q9 / position What is the status of the respondent (i.e. are they an employee or manager in the company?)
ResponseId ID of the respondent in the survey (anonymized)
Q52_1 / firm_pol Did the firm contribute funds to a candidate in the elections?
Q52_2 / firm_pol Did the firm distribute campaign literature to employees?
Q52_3 / firm_pol Did the firm instruct employees to vote for a specific candidate?
Q52_4 / firm_pol Did the firm host party rallies?
Q33_1 / inspect_1_1 How many times was the company inspected by government regulators in the past year?

Note that there is substantial additional data available in the surveys, but this has not been released to protect the anonymity of respondents. It is possible to share this additional data for research purposes. If you are interested in additional data, please contact the author of the survey at rmk7@nyu.edu.

saudiwin/crony_book

README

Requirements to run the code

Running the code