Figures for “Closing Space and the Restructuring of Global Activism”

Lewis & Clark College

Georgia State University

library(tidyverse)
library(scales)
library(sf)
library(patchwork)
library(countrycode)
library(readxl)
library(validate)
library(pander)
library(here)

source(here("lib", "graphic-functions.R"))
# qwraps2::lazyload_cache_dir("figures_cache/html")

options(dplyr.summarise.inform = FALSE)

CIVICUS map

# Load CIVICUS data
# Downloaded from HTML page at https://monitor.civicus.org/
civicus <- read_csv(here("data", "civicus_monitor_2017.csv"), na = "Null") %>%
  mutate(Rating = factor(Rating, levels = c("Open", "Narrowed", "Obstructed", 
                                            "Repressed", "Closed"), 
                         ordered = TRUE),
         iso3 = countrycode(Country, "country.name", "iso3c"))

# Load global shapefiles
# Get Admin 0 file from http://www.naturalearthdata.com/downloads/110m-cultural-vectors/
world_shapes <- st_read(here("data", "ne_110m_admin_0_countries",
                             "ne_110m_admin_0_countries.shp"),
                        quiet = TRUE) %>% 
  # Ignore Antarctica
  filter(ISO_A3 != "ATA")

# Use the Robinson map projection
projection = "ESRI:54030"

# Join CIVICUS data with map data
map_with_civicus <- world_shapes %>% 
  # Fix some Natural Earth ISO weirdness
  mutate(ISO_A3 = ifelse(ISO_A3 == "-99", as.character(ISO_A3_EH), as.character(ISO_A3))) %>% 
  mutate(ISO_A3 = case_when(
    .$ISO_A3 == "GRL" ~ "DNK",
    .$NAME == "Norway" ~ "NOR",
    TRUE ~ ISO_A3
  )) %>% 
  left_join(civicus, by = c("ISO_A3" = "iso3"))

plot_civicus_map <- ggplot() +
  geom_sf(data = map_with_civicus, aes(fill = Rating), size = 0.15, color = "black") +
  coord_sf(crs = st_crs(projection), datum = NA) +
  scale_fill_manual(values = c("grey90", "grey70", "grey45",
                               "grey20", "black"),
                    na.translate = FALSE, name = "Civic space") +
  theme_ngo_map(9)
plot_civicus_map

# Save the map
ggsave(plot_civicus_map, filename = here("output", "civicus_map.pdf"), 
       width = 5.5, height = 3, units = "in", device = cairo_pdf)

ggsave(plot_civicus_map, filename = here("output", "civicus_map.png"), 
       width = 5.5, height = 3, units = "in", dpi = 300, type = "cairo")

# ggsave(plot_civicus_map, filename = here("output", "civicus_map.tiff"), 
#        width = 5.5, height = 3, units = "in", type = "cairo", dpi = 600)

Legal barriers to NGO activity

DCJW

# https://darinchristensen.com/replication/JoD_Replication.zip

dcjw_indexes <- tribble(
  ~barrier,   ~question, ~question_clean,
  "Entry",    "q_2b",    "NGO registration burdensome",
  "Entry",    "q_2d",    "Registration barriers different if foreign funds involved",
  "Funding",  "q_3b",    "Prior approval required\nfor foreign funds",
  "Funding",  "q_3c",    "Foreign funds channeled\nthrough government",
  "Funding",  "q_3d",    "Foreign funds restricted",
  "Funding",  "q_3e",    "Foreign funds prohibited",
  "Funding",  "q_3f",    "Foreign funds prohibted\nfor some types of NGOs",
  "Advocacy", "q_4a",    "NGOs restricted from politics",
  "Advocacy", "q_4c",    "Political barriers different\nif foreign funds involved"
)

# Original DCJW data
dcjw_raw <- read_excel(here("data", "dcjw-ngo-laws", "DCJW_NGO_Laws.xlsx"))[,1:50] %>%
  select(-c(contains("source"), contains("burden"), contains("subset"), Coder, Date))

dcjw_tidy <- dcjw_raw %>% 
  gather(key, value, -Country) %>%
  separate(key, c("question", "var.name"), 4) %>%
  filter(!is.na(Country)) %>%
  mutate(var.name = ifelse(var.name == "", "value", gsub("_", "", var.name))) %>%
  spread(var.name, value) %>%
  # Get rid of rows where year is missing and regulation was not imposed
  filter(!(is.na(year) & value == 0)) %>%
  # Some entries have multiple years; for now just use the first year
  mutate(year = str_split(year, ",")) %>% unnest(year) %>% 
  group_by(Country, question) %>% slice(1) %>% ungroup() %>%
  mutate(value = as.integer(value), year = as.integer(year)) %>%
  # If year is missing but some regulation exists, assume it has always already
  # existed (since 1950, arbitrarily)
  mutate(year = ifelse(is.na(year), 1950, year))

dcjw_potential_panel <- dcjw_tidy %>% 
  filter(question %in% dcjw_indexes$question) %>% 
  expand(Country, question, year = min(.$year, na.rm = TRUE):2015)

dcjw_full_panel <- dcjw_potential_panel %>% 
  left_join(dcjw_tidy, by = c("Country", "question", "year")) %>% 
  # Bring most recent legislation forward
  group_by(Country, question) %>%
  mutate(value = zoo::na.locf(value, na.rm = FALSE)) %>%
  ungroup() %>% 
  mutate(value = ifelse(is.na(value), 0, value)) %>% 
  mutate(value = case_when(
    # Recode 0-2 questions as 0-1
    .$question == "q_3e" & .$value == 1 ~ 0.5,
    .$question == "q_3e" & .$value == 2 ~ 1,
    .$question == "q_3f" & .$value == 1 ~ 0.5,
    .$question == "q_3f" & .$value == 2 ~ 1,
    .$question == "q_4a" & .$value == 1 ~ 0.5,
    .$question == "q_4a" & .$value == 2 ~ 1,
    TRUE ~ .$value
  )) %>% 
  spread(question, value) %>% 
  mutate(
    entry = rowSums(
      select(., one_of(filter(dcjw_indexes, barrier == "Entry")$question))),
    funding = rowSums(
      select(., one_of(filter(dcjw_indexes, barrier == "Funding")$question))),
    advocacy = rowSums(
      select(., one_of(filter(dcjw_indexes, barrier == "Advocacy")$question)))
    ) %>% 
  # Lop off the ancient observations
  filter(year >= 1980)

n_countries <- dcjw_full_panel %>% distinct(Country) %>% nrow()

df_barriers_summary <- dcjw_full_panel %>% 
  group_by(Country, year) %>% 
  summarize_at(vars(one_of(dcjw_indexes$question)), list(~. > 0)) %>% 
  group_by(year) %>% 
  summarize_at(vars(-Country), list(~sum(.))) %>% 
  gather(question, value, -year) %>% 
  left_join(dcjw_indexes, by = "question") %>% 
  arrange(desc(value)) %>% 
  mutate(barrier = paste0("Barriers to ", str_to_lower(barrier))) %>% 
  mutate(question_clean = fct_inorder(question_clean, ordered = TRUE))

plot_entry <- ggplot(
  filter(df_barriers_summary, barrier == "Barriers to entry"), 
  aes(x = year, y = value, color = question_clean, linetype = question_clean)
) +
  geom_line(size = 0.5) +
  expand_limits(y = c(0, 60)) +
  scale_y_continuous(sec.axis = 
                       sec_axis(~ . / n_countries,
                                labels = percent_format(accuracy = 1)),
                     expand = c(0, 0)) +
  scale_colour_manual(values = c("black", "grey80", "grey50"), name = NULL) +
  scale_linetype_manual(values = c("solid", "solid", "21"), name = NULL) +
  guides(color = guide_legend(nrow = 2)) +
  labs(x = NULL, y = "Number of countries") +
  theme_ngo() + 
  theme(legend.justification = "left") +
  facet_wrap(~ barrier)

plot_funding <- ggplot(
  filter(df_barriers_summary, barrier == "Barriers to funding"), 
  aes(x = year, y = value, color = question_clean, linetype = question_clean)
) +
  geom_line(size = 0.5) +
  expand_limits(y = c(0, 35)) +
  scale_y_continuous(sec.axis = 
                       sec_axis(~ . / n_countries,
                                labels = percent_format(accuracy = 1)),
                     expand = c(0, 0),
                     breaks = seq(0, 35, 5)) +
  scale_colour_manual(values = c("black", "grey80", "grey50", "black", "grey80"), name = NULL) +
  scale_linetype_manual(values = c("solid", "solid", "solid", "21", "21"), name = NULL) +
  guides(color = guide_legend(nrow = 3),
         linetype = guide_legend(nrow = 3)) +
  labs(x = NULL, y = "Number of countries") +
  theme_ngo() + 
  theme(legend.justification = "left") +
  facet_wrap(~ barrier)

plot_advocacy <- ggplot(
  filter(df_barriers_summary, barrier == "Barriers to advocacy"), 
  aes(x = year, y = value, color = question_clean)
) +
  geom_line(size = 0.5) +
  expand_limits(y = c(0, 25)) +
  scale_y_continuous(sec.axis = 
                       sec_axis(~ . / n_countries,
                                labels = percent_format(accuracy = 1)),
                     expand = c(0, 0)) +
  scale_colour_manual(values = c("black", "grey80"), name = NULL) +
  guides(color = guide_legend(nrow = 1)) +
  labs(x = NULL, y = "Number of countries") +
  theme_ngo() + 
  theme(legend.justification = "left") +
  facet_wrap(~ barrier)

V-Dem

When loading the V-Dem CSV, readr::read_csv() chokes on a bunch of rows for whatever reason and gives a ton of warnings, but it still works. Loading the Stata version of V-Dem doesn’t create the warnings, but it’s slower and it results in the same data. So I just load from CSV and make sure it has the right number of rows and columns in the end.

# V-Dem data
vdem_raw <- read_csv(here("data", "Country_Year_V-Dem_Extended_CSV_v8",
                          "V-Dem-CY+Others-v8.csv"))

# Check that the data loaded correctly
vdem_raw %>% 
  check_that(n_rows = nrow(.) == 26537, n_cols = ncol(.) == 4641) %>%
  summary() %>% 
  mutate(expression = expression %>% map_chr(pandoc.verbatim.return)) %>% 
  pandoc.table()

name	items	passes	fails	nNA	error	warning	expression
n_rows	1	1	0	0	FALSE	FALSE	nrow(.) == 26537
n_cols	1	1	0	0	FALSE	FALSE	ncol(.) == 4641

vdem_small <- vdem_raw %>% 
  select(country_name, year, ccode = COWcode, v2x_regime, v2csreprss, v2cseeorgs) %>% 
  # Autocracy = closed autocracies and electoral autocracies with V-Dem's RoW index
  mutate(autocracy = v2x_regime <= 1,
         csre = v2csreprss + v2cseeorgs)

generally_autocracies <- vdem_small %>% 
  filter(year > 1980, year <= 2018) %>% 
  group_by(ccode) %>% 
  summarize(prop_autocracy = sum(autocracy, na.rm = TRUE) / n(),
            generally_autocracy = prop_autocracy >= 0.5)

df_csre_summary <- vdem_small %>%
  filter(!is.na(autocracy), year >= 1980, year <= 2018) %>% 
  left_join(generally_autocracies, by = "ccode") %>% 
  group_by(year, generally_autocracy) %>%
  nest() %>% 
  mutate(cis = data %>% map(~ mean_cl_normal(.$csre))) %>% 
  unnest(cis) %>% 
  mutate(fake_facet_title = "Civil society regulatory environment",
         generally_autocracy = factor(generally_autocracy, levels = c(FALSE, TRUE),
                                      labels = c("Democracies", "Autocracies"), ordered = TRUE))

plot_csre <- ggplot(df_csre_summary, aes(x = year, y = y)) +
  geom_ribbon(aes(ymin = ymin, ymax = ymax, fill = generally_autocracy), alpha = 0.2) +
  geom_line(aes(color = generally_autocracy), size = 0.5) +
  annotate(geom = "text", x = 2013, y = -2.2, hjust = "right", size = 2,
           label = "Larger values = more open civil society") +
  scale_colour_manual(values = c("black", "grey75"), name = NULL) +
  scale_fill_manual(values = c("black", "grey75"), name = NULL) +
  scale_linetype_manual(values = c("solid", "solid", "21")) +
  labs(y = "Average CSRE", x = NULL) +
  theme_ngo() +
  theme(legend.justification = "left") +
  facet_wrap(~ fake_facet_title)

De jure and de facto anti-NGO barriers

barriers_summary <- 
  ((plot_entry + plot_funding) / (plot_advocacy + plot_csre)) &
  theme(legend.text = element_text(size = rel(0.8)),
        axis.title.y = element_text(margin = margin(r = 3), size = rel(0.8)),
        legend.box.margin = margin(t = -0.5, unit = "lines"))

barriers_summary