GGDC10S: Groningen Growth and Development Centre 10-Sector Database

Description

The GGDC 10-Sector Database provides a long-run internationally comparable dataset on sectoral productivity performance in Africa, Asia, and Latin America. Variables covered in the data set are annual series of value added (in local currency), and persons employed for 10 broad sectors.

Usage

data("GGDC10S")

Arguments

Format

A data frame with 5027 observations on the following 16 variables.

Country: char: Country (43 countries)
Regioncode: char: ISO3 Region code
Region: char: Region (6 World Regions)
Variable: char: Variable (Value Added or Employment)
Year: num: Year (67 Years, 1947-2013)
AGR: num: Agriculture
MIN: num: Mining
MAN: num: Manufacturing
PU: num: Utilities
CON: num: Construction
WRT: num: Trade, restaurants and hotels
TRA: num: Transport, storage and communication
FIRE: num: Finance, insurance, real estate and business services
GOV: num: Government services
OTH: num: Community, social and personal services
SUM: num: Summation of sector GDP

References

Timmer, M. P., de Vries, G. J., & de Vries, K. (2015). "Patterns of Structural Change in Developing Countries." . In J. Weiss, & M. Tribe (Eds.), Routledge Handbook of Industry and Development. (pp. 65-83). Routledge.

Examples

Run this code

# NOT RUN {
namlab(GGDC10S, class = TRUE)
qsu(GGDC10S, ~ Variable, ~ Variable + Country, vlabels = TRUE)

library(data.table)
library(ggplot2)

# }
# NOT RUN {
## World Regions Structural Change Plot

dat <- GGDC10S
get_vars(dat, 6:15) <- replace_outliers(sweep(get_vars(dat, 6:15), 1, dat$SUM, "/"), 0,
                                        single.limit = "min")
names(dat) <- stringr::str_wrap(vlabels(dat), width = 20)
dat$Variable <- Recode(dat$Variable,"VA"="Value Added Share","EMP"="Employment Share")
dat <- collap(dat, ~ Variable + Region + Year, cols = 6:15)
dat <- melt(qDT(dat), 1:3, variable.name = "Sector")

ggplot(aes(x = Year, y = value, fill = Sector), data = dat) +
  geom_area(position = "fill", alpha = 0.9) + labs(x = NULL, y = NULL) +
  theme_linedraw(base_size = 14) + facet_grid(Variable ~ Region, scales = "free_x") +
  scale_fill_manual(values = sub("#00FF66FF", "#00CC66", rainbow(10))) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 7), expand = c(0, 0))+
  scale_y_continuous(breaks = scales::pretty_breaks(n = 10), expand = c(0, 0),
                     labels = scales::percent) +
  theme(axis.text.x = element_text(angle = 315, hjust = 0, margin = ggplot2::margin(t = 0)),
        strip.background = element_rect(colour = "grey30", fill = "grey30"))
# }
# NOT RUN {

# A function to plot the structural change of an arbitrary country

plotGGDC <- function(ctry) {
dat <- qDT(GGDC10S)[Country == ctry]
dat <- cbind(get_vars(dat, c("Variable","Year")),
             replace_outliers(sweep(get_vars(dat, 6:15), 1, dat$SUM, "/"), 0, NA, "min"))
dat$Variable <- Recode(dat$Variable,"VA"="Value Added Share","EMP"="Employment Share")
dat <- melt(dat, 1:2, variable.name = "Sector")

ggplot(aes(x = Year, y = value, fill = Sector), data = dat) +
  geom_area(position = "fill", alpha = 0.9) + labs(x = NULL, y = NULL) +
  theme_linedraw(base_size = 14) + facet_wrap( ~ Variable) +
  scale_fill_manual(values = sub("#00FF66FF", "#00CC66", rainbow(10))) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 7), expand = c(0, 0)) +
  scale_y_continuous(breaks = scales::pretty_breaks(n = 10), expand = c(0, 0),
                     labels = scales::percent) +
  theme(axis.text.x = element_text(angle = 315, hjust = 0, margin = ggplot2::margin(t = 0)),
        strip.background = element_rect(colour = "grey20", fill = "grey20"),
        strip.text = element_text(face = "bold"))
}
plotGGDC("TZA")

# }