GGDC10S: Groningen Growth and Development Centre 10-Sector Database

In collapse: Advanced and Fast Data Transformation

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")

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.

See Also

wlddev, Collapse Overview

Examples

namlab(GGDC10S, class = TRUE)
# aperm(qsu(GGDC10S, ~ Variable, ~ Variable + Country, vlabels = TRUE))

library(data.table)
library(magrittr)
library(ggplot2)

## World Regions Structural Change Plot

GGDC10S %>%
  fmutate(across(AGR:OTH, `*`, 1 / SUM),
          Variable = fifelse(Variable == "VA","Value Added Share", "Employment Share")) %>%
  replace_outliers(0, NA, "min") %>%
  collap( ~ Variable + Region + Year, cols = 6:15) %>% qDT() %>%
  melt(1:3, variable.name = "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 = fifelse(Variable == "VA","Value Added Share", "Employment Share")) %>%
  replace_outliers(0, NA, "min") %>% qDT() %>%
  melt(1:2, variable.name = "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")

collapse documentation built on Nov. 13, 2023, 1:08 a.m.