GDPuc (a.k.a. the GDP unit-converter) provides a simple function to convert GDP time-series data from one unit to another. All common GDP units are supported, i.e.:
Here “YYYY” is a placeholder for a year, e.g. “2010” or “2015”, and “LCU” stands for Local Currency Unit.
There currently only exists the development version on github.
Open a R session on your machine and use the remotes package to install GDPuc.
# Install remotes if necessary:
install.packages("remotes")
# Install GDPuc from github:
remotes::install_github("johanneskoch94/GDPuc")Load the package.
The main function of the package is convertGDP.
Here, the gdp argument takes a tibble or a data-frame that contains, at least:
A corresponding magpie object, see the magclass package, is also accepted.
Use the source argument to control the source of the underlying conversion factors (GDP deflators, MERs and PPPs). There are two source options shipped with the package, wb_wdi and wb_wdi_linked (with wb_wdi set as the default), but custom sources are possible. A custom source can be any tibble with columns:
convertGDP(
gdp = gdp,
unit_in = "constant 2005 LCU",
unit_out = "constant 2017 Int$PPP",
source = "wb_wdi"
)Set the verbose argument to TRUE to print out the underlying conversion steps and factors. The verbosity can also be controlled via the option "GDPuc.verbose".
convertGDP(
gdp = gdp,
unit_in = "constant 2005 LCU",
unit_out = "constant 2017 Int$PPP",
verbose = TRUE
)Use the with_regions argument to convert aggregated GDP data, e.g. regional-level data. The default value is NULL, but if passed a data-frame with a country-to-region mapping, then custom regional codons will be recognized. The data-frame should have two columns, one named “iso3c” with iso3c country codes, and one named “region” with the corresponding region codes. The conversion of regional values is then undertaken by disaggregating the regions to a country level (using the mapping and weighed by the GDP shares of countries within that region in the base year of unit_in).
convertGDP(
gdp = gdp,
unit_in = "constant 2005 LCU",
unit_out = "constant 2017 Int$PPP",
with_regions = my_mapping_data_frame
)Use the replace_NAs argument to replace missing conversion factors; either with 1 (so essentially no conversion) or with regional GDP-weighted averages. The default value is NULL, and missing conversion factors will lead to NAs. If set to 1, then no conversion will take place, but no NAs will be created either. To use regional GDP-weighted averages, set the argument to “regional_averages”.
convertGDP(
gdp = gdp,
unit_in = "constant 2005 LCU",
unit_out = "constant 2017 Int$PPP",
replace_NAs = 1
)
convertGDP(
gdp = gdp,
unit_in = "constant 2005 LCU",
unit_out = "constant 2017 Int$PPP",
with_regions = my_mapping_data_frame,
replace_NAs = "regional_averages"
)Use print_source_info to print information on a specific, or all available sources.
Use the ::: operator to take a closer look at the sources shipped with GDPuc.
library(GDPuc)
gdp <- tibble::tibble(
iso3c = "USA",
year = 2010:2014,
value = 100:104
)
print(gdp)
#> # A tibble: 5 × 3
#> iso3c year value
#> <chr> <int> <int>
#> 1 USA 2010 100
#> 2 USA 2011 101
#> 3 USA 2012 102
#> 4 USA 2013 103
#> 5 USA 2014 104
convertGDP(
gdp = gdp,
unit_in = "constant 2005 LCU",
unit_out = "constant 2017 Int$PPP"
)
#> # A tibble: 5 × 3
#> iso3c year value
#> <chr> <int> <dbl>
#> 1 USA 2010 123.
#> 2 USA 2011 125.
#> 3 USA 2012 126.
#> 4 USA 2013 127.
#> 5 USA 2014 128.Setting verbose = TRUE will additionally print the following information to the console:
convertGDP(
gdp = gdp,
unit_in = "constant 2005 LCU",
unit_out = "constant 2017 Int$PPP",
verbose = TRUE
)
#> ℹ Converting GDP with conversion factors from wb_wdi:
#> constant 2005 LCU → constant 2017 LCU
#> 2017 value of base 2005 GDP deflator in (constant 2017 LCU per constant 2005
#> LCU) used:
#> USA: 1.23304244543521
#> constant 2017 LCU → constant 2017 Int$PPP
#> 2017 PPP conversion factor in (LCU per international $) used:
#> USA: 1
#> # A tibble: 5 × 3
#> iso3c year value
#> <chr> <int> <dbl>
#> 1 USA 2010 123.
#> 2 USA 2011 125.
#> 3 USA 2012 126.
#> 4 USA 2013 127.
#> 5 USA 2014 128.Print out information on the underlying wb_wdi source.
print_source_info("wb_wdi")
#> ── wb_wdi ──────────────────────────────────────────────────────────────────────
#> → Origin: The World Bank's World Development Indicator Database
#> → Date: Downloaded on the 6 of May 2021
#> → Html: https://databank.worldbank.org/source/world-development-indicators
#> → Note: Uses the standard deflator.
#> ────────────────────────────────────────────────────────────────────────────────This package makes us of country-specific GDP deflators, Market Exchange Rates (MER), and Purchasing Power Parity (PPP) conversion factors to convert GDP values. Setting the verbose argument to TRUE should make the conversion process transparent and allow you to analyze the individual steps taken. All conversion functions were successfully tested on the World Bank’s World Development Indicator (WDI) data: given any 2 WDI GDP series, convertGDP will reliably convert the one to the other.
That being said, converting GDP series can be complex and the use of this package should not absolve one of thinking carefully on what conversion is being done and how. When using the provided wd_wdi source, this specifically concerns conversions using PPPs and MERs together.
That is because the PPP conversion factors provided by the World Bank are based off of linked GDP deflators, while the MERs are not. That means, that converting the World Bank’s current international dollar PPP series into LCU, will result in the “GDP: linked series (current LCU)”, while converting the current US dollar MER series into LCU, will result in the “GDP (current LCU)” series. Therefore, when linked and non-lined GDP deflators differ (which they do more often for developing countries, and in general the further into the past one looks), converting from current IntPPP to current USMER will not result in the exact same series as given in the WDI data.