Merge pull request #2 from deleip/gdp_update

update to 2017 as currency base year
pik-piam · Aug 15, 2024 · 4b0ae2d · 4b0ae2d
2 parents af78a7a + a5fc43d
commit 4b0ae2d
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Showing 23 changed files with 121 additions and 111 deletions.
diff --git a/R/calcAgEmplILO.R b/R/calcAgEmplILO.R
@@ -7,7 +7,7 @@
 #' @param inclFish boolean: should employment in fisheries be included?
 #' @param inclForest boolean: should emplyoment in forestry be included?
 #' @param dataVersionILO which version of the ILO input data to use. "" for the oldest version and
-#' old regression, or "monthYear" (e.g. "Aug23") for newer data
+#' old regression, or "monthYear" (e.g. "Aug24") for newer data
 #' @return List of magpie objects with results on country level, weight on country level, unit and description.
 #' @importFrom stringr str_split
 #' @author Debbora Leip
@@ -16,7 +16,7 @@
 #' calcOutput("AgEmplILO")
 #' }
 
-calcAgEmplILO <- function(subsectors = TRUE, inclFish = FALSE, inclForest = FALSE, dataVersionILO = "Aug23") {
+calcAgEmplILO <- function(subsectors = TRUE, inclFish = FALSE, inclForest = FALSE, dataVersionILO = "Aug24") {
 
   # read original employment dataset from ILO (convert from thous. to mil.)
   dataType <- ifelse(dataVersionILO == "", "EmplByActivityModelled",
@@ -33,7 +33,7 @@ calcAgEmplILO <- function(subsectors = TRUE, inclFish = FALSE, inclForest = FALS
   subtype <- ifelse(dataVersionILO == "", "AgEmplShare", paste("AgEmplShare", dataVersionILO, sep = "_"))
   regCoeff <- readSource("RegressionsILO", subtype)
 
-  gdpPPPpc <- calcOutput("GDPpcPast", GDPpcPast = "WDI-MI", unit = "constant 2005 Int$PPP", aggregate = FALSE)
+  gdpPPPpc <- calcOutput("GDPpcPast", GDPpcPast = "WDI-MI", unit = "constant 2017 Int$PPP", aggregate = FALSE)
 
   estShare <- (regCoeff[, , "slope", drop = TRUE] * log10(gdpPPPpc) + regCoeff[, , "intercept", drop = TRUE]) ** 2
   const <- (regCoeff[, , "slope"] * log10(regCoeff[, , "threshold"]) + regCoeff[, , "intercept"]) ** 2

diff --git a/R/calcFacReq.R b/R/calcFacReq.R
@@ -52,8 +52,8 @@ calcFacReq <- function(splitSectors = FALSE) {
   }
 
   return(list(x = facReq,
-              unit = "USD05MER per tDM",
+              unit = "USD2017MER per tDM",
               weight = weight,
-              description = "Factor requirements for different crops (USD05MER per tDM) at regional level",
+              description = "Factor requirements for different crops at regional level",
               isocountries = FALSE))
 }
diff --git a/R/calcFacReqGLO.R b/R/calcFacReqGLO.R
@@ -37,6 +37,6 @@ calcFacReqGLO <- function() {
 
   return(list(x = x,
               weight = NULL,
-              unit = "USD05MER per tDM",
-              description = "Factor requirements for different crops (USD05MER per tDM) at global level"))
+              unit = "USD2017MER per tDM",
+              description = "Factor requirements for different crops at global level"))
 }
diff --git a/R/calcFactorCostsCrops.R b/R/calcFactorCostsCrops.R
@@ -11,11 +11,11 @@
 #' }
 #' @importFrom magclass setNames dimSums time_interpolate
 
-calcFactorCostsCrops <- function(datasource = "USDA", unit="constant 2017 US$MER") {
+calcFactorCostsCrops <- function(datasource = "USDA", unit = "constant 2017 US$MER") {
 
   if (datasource == "USDA") {
     # Value of Production for livestock in US$MER2017 (including FAO livst categories not mapped to MAgPIE categories)
-    vopCrops <- calcOutput("VoPcrops", fillGaps = TRUE, aggregate = FALSE, unit="constant 2017 US$MER") 
+    vopCrops <- calcOutput("VoPcrops", fillGaps = TRUE, aggregate = FALSE, unit = "constant 2017 US$MER") 
 
     # no VoP data before 1991, data for 2019 incomplete
     years <- setdiff(getYears(vopCrops, as.integer = TRUE), c(1960:1990, 2019))
@@ -56,9 +56,9 @@ calcFactorCostsCrops <- function(datasource = "USDA", unit="constant 2017 US$MER
 
   if(unit != "constant 2017 US$MER"){
     out <- convertGDP(out,
-                         unit_in = "constant 2017 US$MER",
-                         unit_out = unit,
-                         replace_NAs = "no_conversion")  
+                      unit_in = "constant 2017 US$MER",
+                      unit_out = unit,
+                      replace_NAs = "no_conversion")  
   }   
 
 

diff --git a/R/calcFactorCostsLivst.R b/R/calcFactorCostsLivst.R
@@ -17,7 +17,8 @@ calcFactorCostsLivst <- function(datasource = "USDA", otherLivst = FALSE, unit =
 
   if (datasource == "USDA") {
     # Value of Production for livestock in US$MER2005 (including FAO livst categories not mapped to MAgPIE categories)
-    vopLivst <- calcOutput("VoPlivst", fillGaps = TRUE, aggregate = FALSE, other = otherLivst, unit = "constant 2017 US$MER") 
+    vopLivst <- calcOutput("VoPlivst", fillGaps = TRUE, aggregate = FALSE, other = otherLivst,
+                           unit = "constant 2017 US$MER")
 
     # no VoP data before 1991, data for 2019 incomplete
     years <- setdiff(getYears(vopLivst, as.integer = TRUE), c(1960:1990, 2019))

diff --git a/R/calcFactorIntensity.R b/R/calcFactorIntensity.R
@@ -10,7 +10,7 @@
 #' For the "CapitalStock" method only "intensities" and "requirements" outputs supported.
 #' @param method "USDA" or "CapitalStock"
 #' @param unit output currency unit based on the convertGDP function from the  GDPuc library
-#' @return magpie object of the factor requirements intensity or factor intensity in 05USDppp/tDM per crop,
+#' @return magpie object of the factor requirements intensity or factor intensity in USD/tDM per crop,
 #' or capital share fraction.
 #' @author Edna J. Molina Bacca
 #' @importFrom luscale speed_aggregate
@@ -22,15 +22,15 @@
 #' a <- calcOutput("FactorIntensity")
 #' }
 #'
-calcFactorIntensity <- function(output = "intensities", method = "USDA", unit="constant 2017 US$MER") {
+calcFactorIntensity <- function(output = "intensities", method = "USDA", unit = "constant 2017 US$MER") {
 
 
   if (method == "USDA") { # using USDA method
 
     # Production of crops. mio. ton
       cropProdDMall  <- collapseDim(calcOutput("Production", products = "kcr", aggregate = FALSE, attributes = "dm"))
 
-      vopCrops <- calcOutput("VoPcrops", aggregate = FALSE, unit="constant 2017 US$MER")
+      vopCrops <- calcOutput("VoPcrops", aggregate = FALSE, unit = "constant 2017 US$MER")
 
       gnames <- intersect(getNames(vopCrops), getNames(cropProdDMall))
       gyears <- intersect(getYears(vopCrops), getYears(cropProdDMall))
@@ -127,7 +127,7 @@ calcFactorIntensity <- function(output = "intensities", method = "USDA", unit="c
   }
 
    units <-
-   if (output %in% c("intensities", "requirements")) paste0("mio ",unit) else if (output == "CapitalShare") "fraction"
+   if (output %in% c("intensities", "requirements")) paste0("mio ", unit) else if (output == "CapitalShare") "fraction"
 
    if(unit != "constant 2017 US$MER" & output %in% c("intensities", "requirements")){
      x <- convertGDP(x,

diff --git a/R/calcHourlyLaborCosts.R b/R/calcHourlyLaborCosts.R
@@ -3,7 +3,7 @@
 #' @param datasource either raw data from "ILO" (agriculture+forestry+fishery) or data calculated based on total labor
 #' costs from "USDA_FAO" (crop+livestock production).
 #' @param dataVersionILO Which version of ILO data to use (for hourly labor costs if source is ILO, for ag empl. if
-#' source is USDA_FAO). "" for the oldest version, or "monthYear" (e.g. "Aug23") for a newer version)
+#' source is USDA_FAO). "" for the oldest version, or "monthYear" (e.g. "Aug24") for a newer version)
 #' @param sector should average hourly labor costs be reported ("agriculture"), or hourly labor costs specific to
 #' either "crops" or "livestock" production. For ILO only the aggregate hourly labor costs are available.
 #' @param fillWithRegression boolean: should missing values be filled based on a regression between ILO hourly labor
@@ -25,7 +25,7 @@
 #' @importFrom stringr str_split str_to_title
 
 
-calcHourlyLaborCosts <- function(datasource = "USDA_FAO", dataVersionILO = "Aug23", # nolint: cyclocomp_linter
+calcHourlyLaborCosts <- function(datasource = "USDA_FAO", dataVersionILO = "Aug24", # nolint: cyclocomp_linter
                                  sector = "agriculture", fillWithRegression = TRUE, calibYear = 2010,
                                  cutAfterCalibYear = TRUE, projection = FALSE) {
 
@@ -40,7 +40,7 @@ calcHourlyLaborCosts <- function(datasource = "USDA_FAO", dataVersionILO = "Aug2
       items <- c("ISIC_Rev31: A Agriculture, hunting and forestry", "ISIC_Rev31: B Fishing",
                  "ISIC_Rev4: A Agriculture; forestry and fishing")
       hourlyCosts <- readSource("ILOSTAT", datasource)[, , items]
-      hourlyCosts <- hourlyCosts[, , "US$MER2005", drop = TRUE]
+      hourlyCosts <- hourlyCosts[, , "US$MER2017", drop = TRUE]
 
       # aggregate within rev 3.1
       mapping <- data.frame(from = c("ISIC_Rev31: A Agriculture, hunting and forestry",
@@ -68,6 +68,10 @@ calcHourlyLaborCosts <- function(datasource = "USDA_FAO", dataVersionILO = "Aug2
       hourlyCostsIndia <- c(0.2063, 0.2080, 0.2015, 0.1970, 0.1936, 0.1964, 0.2014, 0.2350, 0.2520,
                             0.2672, 0.3097, 0.3336, 0.3568, 0.3795, 0.3903, 0.3956, 0.4008)
       hourlyCosts["IND", setdiff(2000:2017, 2006), ] <- hourlyCostsIndia
+      hourlyCosts["IND", , ] <- convertGDP(hourlyCosts["IND", , ],
+                                           unit_in = "constant 2005 US$MER",
+                                           unit_out = "constant 2017 US$MER",
+                                           replace_NAs = c("linear", "no_conversion"))
 
       # add data for China, aggregated using production as weight (provided by Xiaoxi)
       hourlyCostsChina <- readSource("HourlyLaborCostsChina", convert = FALSE)
@@ -84,12 +88,16 @@ calcHourlyLaborCosts <- function(datasource = "USDA_FAO", dataVersionILO = "Aug2
 
       years <- intersect(getYears(hourlyCosts), getYears(hourlyCostsChina))
       hourlyCosts["CHN", years, ] <- hourlyCostsChina[, years, ]
+      hourlyCosts["CHN", , ] <- convertGDP(hourlyCosts["CHN", , ],
+                                           unit_in = "constant 2005 US$MER",
+                                           unit_out = "constant 2017 US$MER",
+                                           replace_NAs = c("linear", "no_conversion"))
 
       # remove outliers
       hourlyCosts[hourlyCosts > 100] <- 0 # unreasonable high values
       gdpMERpc <- calcOutput("GDPpc",
                              scenario = "SSPs",
-                             unit = "constant 2005 US$MER",
+                             unit = "constant 2017 US$MER",
                              average2020 = FALSE,
                              naming = "scenario",
                              aggregate = FALSE)[getItems(hourlyCosts, dim = 1), getItems(hourlyCosts, dim = 2), "SSP2"]
@@ -138,10 +146,10 @@ calcHourlyLaborCosts <- function(datasource = "USDA_FAO", dataVersionILO = "Aug2
     hourlyCosts <- calcOutput("HourlyLaborCosts", dataVersionILO = dataVersionILO, datasource = datasource,
                               sector = sector, fillWithRegression = FALSE, aggregate = FALSE)
 
-    # calculate GDPpc [USD05MER] for regression
+    # calculate GDPpc [USD2017MER] for regression
     gdpMERpc <- calcOutput("GDPpc",
                            scenario = "SSPs",
-                           unit = "constant 2005 US$MER",
+                           unit = "constant 2017 US$MER",
                            average2020 = FALSE,
                            naming = "scenario",
                            aggregate = FALSE)
@@ -257,6 +265,6 @@ calcHourlyLaborCosts <- function(datasource = "USDA_FAO", dataVersionILO = "Aug2
 
   return(list(x = hourlyCosts,
               weight = weight,
-              unit = "US$05MER",
+              unit = "US$2017MER",
               description = "Mean nominal hourly labour cost per employee in agriculture"))
 }
diff --git a/R/calcIFPRIsubsidy.R b/R/calcIFPRIsubsidy.R
@@ -2,7 +2,7 @@
 #' @description Adds non-allocated subsidies to crop subsidies (as most subsidies are linked to land area), and
 #' excludes NRP subsidies (as those are border measures, which are already reflected in ag. prices)
 #' @param fillGaps boolean, should gaps in the dataset be filled using interpolation?
-#' @return magpie object. in mio. USDMER05
+#' @return magpie object. in mio. USD
 #' @author Debbora Leip
 #'
 #' @seealso [calcOutput()]
@@ -35,6 +35,6 @@ calcIFPRIsubsidy <- function(fillGaps = TRUE) {
 
   return(list(x = subsidies,
               weight = NULL,
-              unit = "mio. USDMER05",
+              unit = "mio. USDMER2017",
               description = "Agricultural subsidies"))
 }
diff --git a/R/calcLaborCosts.R b/R/calcLaborCosts.R
@@ -4,7 +4,7 @@
 #' @param datasource data source on which the labor costs should be based. Available are ILO, USDA (which also uses data
 #' on VoP from FAO), and GTAP.
 #' @param dataVersionILO If source is ILO, the version can be chosen. "" for the oldest version, or "monthYear" (e.g.
-#' "Aug23") for a newer version)
+#' "Aug24") for a newer version)
 #' @param subsectors boolean: should output be aggregated or split into available subsectors (crops, livst, forestry,
 #' fishery)
 #' @param inclFish boolean: should fish labor costs be included?
@@ -24,7 +24,7 @@
 #' @importFrom GDPuc convertGDP
 #' @importFrom stringr str_split
 
-calcLaborCosts <- function(datasource = "ILO", dataVersionILO = "Aug23", subsectors = TRUE, inclFish = FALSE,
+calcLaborCosts <- function(datasource = "ILO", dataVersionILO = "Aug24", subsectors = TRUE, inclFish = FALSE,
                            inclForest = FALSE, otherLivst = TRUE, gtapVar = "NVFA", addSubsidies = FALSE) {
 
   # get data from specified source
@@ -42,26 +42,26 @@ calcLaborCosts <- function(datasource = "ILO", dataVersionILO = "Aug23", subsect
     iloEmpl <- iloEmpl[, years, ]
     iloWeeklyHours <- iloWeeklyHours[, years, ]
 
-    # mean nominal hourly labor cost per employee in agriculture (US$05MER/h) based on USDA_FAO calculation
+    # mean nominal hourly labor cost per employee in agriculture (US$2017MER/h) based on USDA_FAO calculation
     # and regression based on ILO raw data
     iloLaborCosts <- calcOutput("HourlyLaborCosts", dataVersionILO = dataVersionILO, datasource = datasource,
                                 fillWithRegression = TRUE, projection = "SSP2", # scenario has no influence until 2020
                                 aggregate = FALSE)[, years, ]
 
     # combine data sets to get total labor costs
     iloTotalHours <- 52.1429 * iloWeeklyHours[, , , drop = TRUE] * iloEmpl # mio. hours
-    out <- iloTotalHours * iloLaborCosts[, , , drop = TRUE] # mio. US$MER05
+    out <- iloTotalHours * iloLaborCosts[, , , drop = TRUE] # mio. US$MER2017
 
   } else if (datasource == "USDA") {
 
     if (isTRUE(inclForest)) stop("Forest labor costs not available for this datasource")
 
-    # Value of Production for livestock in US$MER2005 (including FAO livst categories not mapped to MAgPIE categories)
-    vopLivst <- calcOutput("VoPlivst", other = otherLivst, fillGaps = TRUE, aggregate = FALSE) # mio. US$MER05
+    # Value of Production for livestock in US$MER2017 (including FAO livst categories not mapped to MAgPIE categories)
+    vopLivst <- calcOutput("VoPlivst", other = otherLivst, fillGaps = TRUE, aggregate = FALSE) # mio. US$MER2017
     vopLivst <- setNames(dimSums(vopLivst, dim = 3), "Livestock")
 
-    # Value of Production for crops in US$MER2005
-    vopCrops <- calcOutput("VoPcrops", fillGaps = TRUE, aggregate = FALSE) # mio. US$MER05
+    # Value of Production for crops in US$MER2017
+    vopCrops <- calcOutput("VoPcrops", fillGaps = TRUE, aggregate = FALSE) # mio. US$MER2017
     vopCrops <- setNames(dimSums(vopCrops, dim = 3), "Crops")
 
     # no VoP data before 1991, data for 2019 incomplete (using fillGaps in calcVoP reduces years to 1991:2013 anyway)
@@ -180,16 +180,16 @@ calcLaborCosts <- function(datasource = "ILO", dataVersionILO = "Aug23", subsect
     # aggregate if subsectors == FALSE
     if (isFALSE(subsectors)) laborCosts <- dimSums(laborCosts, dim = 3)
 
-    # convert to USDMER05 (for countries missing the inflation factor, we assume no inflation)
+    # convert to USDMER2017 (for countries missing the inflation factor, we assume no inflation)
     out <- convertGDP(laborCosts, unit_in = "current US$MER",
-                      unit_out = "constant 2005 US$MER", replace_NAs = "no_conversion")
+                      unit_out = "constant 2017 US$MER", replace_NAs = "no_conversion")
 
   } else {
     stop("Data source not available")
   }
 
   return(list(x = out,
               weight = NULL,
-              unit = "mio. USD05MER",
+              unit = "mio. USD2017MER",
               description = "labor costs in agriculture"))
 }
diff --git a/R/calcNonMAgPIEFactorCosts.R b/R/calcNonMAgPIEFactorCosts.R
@@ -7,7 +7,7 @@
 #' @param aggSubsidies boolean: if subtype is "subsidies", should crop and livestock subsidies be reported separately
 #' or as aggregate?
 #' @param extrapolate boolean: should values be extrapolate (by keeping constant) until 2150?
-#' @return magpie object. in mio. USDMER05
+#' @return magpie object. in mio. USD
 #' @author Debbora Leip
 #' @seealso [calcOutput()]
 #' @examples
@@ -24,7 +24,7 @@ calcNonMAgPIEFactorCosts <- function(subtype = "subsidies", aggSubsidies = FALSE
   fractionLivst <- setNames(dimSums(fractionLivst[, , c("Labor", "Capital")], dim = 3), "Livestock")
   fractions <- mbind(fractionCrops, fractionLivst)
 
-  if (subtype == "subsidies") { # ag. subsidies in mio.USD05MER
+  if (subtype == "subsidies") { # ag. subsidies in mio.USD2017MER
     out <- calcOutput("IFPRIsubsidy", aggregate = FALSE, fillGaps = TRUE)
 
     # get factor cost share of subsidies
@@ -71,6 +71,6 @@ calcNonMAgPIEFactorCosts <- function(subtype = "subsidies", aggSubsidies = FALSE
 
   return(list(x = out,
               weight = NULL,
-              unit = "mio. USDMER05",
+              unit = "mio. USDMER2017",
               description = "Factor costs affecting employment but not included in MAgPIE factor costs"))
 }
diff --git a/R/calcPricesProducer.R b/R/calcPricesProducer.R
@@ -23,7 +23,7 @@ calcPricesProducer <- function(products = "kcr", calculation = "VoP", weighting
   if (products == "kcr") {
   if (calculation == "FAO") {
     # items for aggregation
-    pricesProdFAO <- readSource("FAO_online", "PricesProducerAnnual") # USD05MER per ton wet matter
+    pricesProdFAO <- readSource("FAO_online", "PricesProducerAnnual") # USD2017MER per ton wet matter
 
 
     getNames(pricesProdFAO)[getNames(pricesProdFAO) == "254|Oil palm fruit"] <- "254|Oil, palm fruit"
@@ -128,7 +128,7 @@ stop("only production and consumption weights")
 
     if (calculation == "FAO") {
 
-    pricesProdFAO <- readSource("FAO_online", "PricesProducerAnnual") # USD05 per ton
+    pricesProdFAO <- readSource("FAO_online", "PricesProducerAnnual") # USD2017 per ton
 
     # get mapping
     mappingFAO <- toolGetMapping("FAOitems_online.csv", type = "sectoral", where = "mappingfolder") # Reads mapping
@@ -203,11 +203,11 @@ stop("invalid type")
     }
   }
 
-  units <- "05USDMER/tDM"
+  units <- "2017USDMER/tDM"
 
   return(list(x = x,
               weight = weight,
               mixed_aggregation = NULL,
               unit = units,
-              description = "Producer prices in 05USDMER/tDM"))
+              description = "Producer prices in 2017USDMER/tDM"))
 }
diff --git a/R/calcProductionCosts.R b/R/calcProductionCosts.R
@@ -9,7 +9,7 @@
 #' calcOutput("ProductionCosts", source = "Vittis")
 #' }
 #'
-calcProductionCosts <- function(datasource = "Vittis", unit="constant 2017 US$MER") {
+calcProductionCosts <- function(datasource = "Vittis", unit = "constant 2017 US$MER") {
   if (datasource == "Vittis") {
     costs <- readSource("Vittis")                                        # US$17/ha
     areas <- calcOutput("Croparea", sectoral = "kcr", aggregate = FALSE) # "million ha"
@@ -21,15 +21,15 @@ calcProductionCosts <- function(datasource = "Vittis", unit="constant 2017 US$ME
     stop("Source not available")
   }
 
-  if(unit !="constant 2017 US$MER"){
+  if (unit != "constant 2017 US$MER") {
 
     costs<-convertGDP(costs,
                          unit_in = "constant 2017 US$MER",
                          unit_out = unit,
                          replace_NAs = "no_conversion")
   }
 
-  units <- paste0("mio ",unit)
+  units <- paste0("mio ", unit)
 
   return(list(x = costs,
               weight = NULL,

diff --git a/R/calcRegFactorShare.R b/R/calcRegFactorShare.R
@@ -56,7 +56,7 @@ calcRegFactorShare <- function(datasource = "USDA", factor = "cap") {
     usdaShares <- usdaShares / dimSums(usdaShares, dim = 3)
 
     # GDP (US$PPP) per capita as independent variable
-    gdp <- calcOutput("GDPpc", naming = "scenario", aggregate = FALSE)[, , "SSP2"]
+    gdp <- calcOutput("GDPpc", unit = "constant 2017 Int$PPP", naming = "scenario", aggregate = FALSE)[, , "SSP2"]
     country <- intersect(getCells(gdp), unique(mapping$ISO))
     mapping2 <- mapping[mapping$ISO %in% country, ]
     years <- intersect(getYears(gdp), getYears(weight))