This report presents the data analysis for “Studying Policy Design Quality in Comparative Perspective”, published in the American Review of Political Science.
First it contains a brief chapter with the description of the data, and reproduces the figures in the article and the online appendix.
Then in contains two chapters on the analysis of environmental performance, one with the main model and another one with the robustness check from a variation of the main model.
Later, it contains four chapters on the analysis of portfolio diversity, one with the main model and three more with various robustness checks for different specifications.
Finally, one chapter compares the different robustness models of portfolio diversity against the main model.
library(PolicyPortfolios)
data(consensus)Show the portfolios of selected countries / years for illustration purposes.
D <- consensus %>%
filter(Sector == "Environmental") %>%
droplevels() %>%
pp_measures()
consensus.without.labels <- consensus %>%
mutate(Target = as.factor(as.numeric(Target))) %>%
mutate(Instrument = as.factor(as.numeric(Instrument)))
rM <- pp_array(filter(consensus, Sector == "Environmental" & Country == "France" & Year == 1976), return_matrix = TRUE)
manual.aid.fr.1976 <- round(D$value[D$Country == "France" & D$Year == 1976 & D$Sector == "Environmental" & D$Measure == "Div.aid"], 3)
manual.aid.fr.2005 <- round(D$value[D$Country == "France" & D$Year == 2005 & D$Sector == "Environmental" & D$Measure == "Div.aid"], 3)
manual.aid.us.1976 <- round(D$value[D$Country == "United States" & D$Year == 1976 & D$Sector == "Environmental" & D$Measure == "Div.aid"], 3)
manual.aid.us.2005 <- round(D$value[D$Country == "United States" & D$Year == 2005 & D$Sector == "Environmental" & D$Measure == "Div.aid"], 3)
# France: growth in size and diversity
f1 <- pp_plot(droplevels(filter(consensus.without.labels, Sector == "Environmental")),
id = list(Country = "France", Year = 1976),
subtitle = FALSE, caption = "") +
labs(subtitle = paste0("AID: ", manual.aid.fr.1976)) +
theme(plot.subtitle = element_text(size = 3.0))
f2 <- pp_plot(droplevels(filter(consensus.without.labels, Sector == "Environmental")),
id = list(Country = "France", Year = 2005),
subtitle = FALSE, caption = "") +
labs(subtitle = paste0("AID: ", manual.aid.fr.2005)) +
theme(plot.subtitle = element_text(size = 3.0))
#grid.arrange(f1, f2, ncol = 2)
f3 <- pp_plot(droplevels(filter(consensus.without.labels, Sector == "Environmental")),
id = list(Country = "United States", Year = 1976),
subtitle = FALSE, caption = "") +
labs(subtitle = paste0("AID: ", manual.aid.us.1976)) +
theme(plot.subtitle = element_text(size = 3.0))
f4 <- pp_plot(droplevels(filter(consensus.without.labels, Sector == "Environmental")),
id = list(Country = "United States", Year = 2005),
subtitle = FALSE, caption = "") +
labs(subtitle = paste0("AID: ", manual.aid.us.2005)) +
theme(plot.subtitle = element_text(size = 3.0))
#grid.arrange(f1, f2, ncol = 2)
grid.arrange(f1, f2, f3, f4, ncol = 2)