Groningen Growth and Development Centre 10-Sector Database

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")

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

Source

https://www.rug.nl/ggdc/productivity/10-sector/

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

namlab(GGDC10S, class = TRUE)
#>      Variable     Class                                                 Label
#> 1     Country character                                               Country
#> 2  Regioncode character                                           Region code
#> 3      Region character                                                Region
#> 4    Variable character                                              Variable
#> 5        Year   numeric                                                  Year
#> 6         AGR   numeric                                          Agriculture 
#> 7         MIN   numeric                                                Mining
#> 8         MAN   numeric                                         Manufacturing
#> 9          PU   numeric                                             Utilities
#> 10        CON   numeric                                          Construction
#> 11        WRT   numeric                         Trade, restaurants and hotels
#> 12        TRA   numeric                  Transport, storage and communication
#> 13       FIRE   numeric Finance, insurance, real estate and business services
#> 14        GOV   numeric                                   Government services
#> 15        OTH   numeric               Community, social and personal services
#> 16        SUM   numeric                               Summation of sector GDP
# aperm(qsu(GGDC10S, ~ Variable, ~ Variable + Country, vlabels = TRUE))
# \donttest{
library(ggplot2)

## World Regions Structural Change Plot

GGDC10S |>
  fmutate(across(AGR:OTH, `*`, 1 / SUM),
          Variable = ifelse(Variable == "VA","Value Added Share", "Employment Share")) |>
  replace_outliers(0, NA, "min") |>
  collap( ~ Variable + Region + Year, cols = 6:15) |> qDT() |>
  pivot(1:3, names = list(variable = "Sector"), na.rm = TRUE) |>

  ggplot(aes(x = Year, y = value, fill = Sector)) +
    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("#00FF66", "#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"))


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

plotGGDC <- function(ctry) {

  GGDC10S |>
  fsubset(Country == ctry, Variable, Year, AGR:SUM) |>
  fmutate(across(AGR:OTH, `*`, 1 / SUM), SUM = NULL,
          Variable = ifelse(Variable == "VA","Value Added Share", "Employment Share")) |>
  replace_outliers(0, NA, "min") |> qDT() |>
  pivot(1:2, names = list(variable = "Sector"), na.rm = TRUE) |>

  ggplot(aes(x = Year, y = value, fill = Sector)) +
    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("#00FF66", "#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("BWA")


# }