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04b_TFP for EU_GlobMalm.R
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04b_TFP for EU_GlobMalm.R
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# ------------------------------------------- #
# #
# This program estimates the Global Malmquist #
# TFP index for the EU country-level data #
# #
# ------------------------------------------- #
# Open packages
library(tidyverse) #for data manipulation
library(reshape2) #to reshape the data
library(tikzDevice) #to save ggplot object in latex format
library(ggpubr) #to combine plots
library(kableExtra) # to convert data frames to Latex
# More packages (productivity decomposition)
library(doFuture) #for DEA
library(progressr) #for DEA
library(lpSolveAPI) #for DEA
library(plm) #for DEA (The source codes rely on is.pbalanced)
# Load the source codes for the Lowe index
source('R_aux/aux_globalmalm.R')
source('R_aux/auxiliaries.R')
source('R_aux/globalmalm.R')
# Set path to Latex compiler if figures should be stored in Latex format
options("tikzLatex"='C:/Program Files/MiKTeX/miktex/bin/x64/pdflatex.exe')
# Load the EU country-level data and arrange by country name
load("R_output/EUdata.Rda")
EUdata <- EUdata[order(EUdata$country),]
# Create variable with the clusters
EUdata$cluster <- case_when(EUdata$country == "BEL" | EUdata$country == "FRA" | EUdata$country == "GBR" |
EUdata$country == "IRL" | EUdata$country == "NLD" ~ "Cluster1",
EUdata$country == "EST" | EUdata$country == "FIN" | EUdata$country == "LTU" |
EUdata$country == "LVA" | EUdata$country == "SWE" ~ "Cluster2",
EUdata$country == "AUT" | EUdata$country == "CZE" | EUdata$country == "DEU" |
EUdata$country == "DNK" | EUdata$country == "HUN" | EUdata$country == "LUX" |
EUdata$country == "POL" | EUdata$country == "ROU" | EUdata$country == "SVN"|
EUdata$country == "SVK" ~ "Cluster3",
EUdata$country == "ESP" | EUdata$country == "GRC" | EUdata$country == "ITA" |
EUdata$country == "MLT" |EUdata$country == "PRT" ~ "Cluster4")
table(EUdata$cluster)
# Calculate and decompose the index
globmalm <- globalmalm(data = EUdata, id.var = "country", time.var = "year",
x.vars = c("x_capital", "x_land","x_labor","x_interm"),
y.vars = c("q_animals", "q_crops", "q_other"),
g.var = ("cluster"),
orientation = c("out"), parallel = FALSE, scaled = FALSE,
window = 3)
GlobMalm.levels <- globmalm$Levels
GlobMalm.levels <- GlobMalm.levels[order(GlobMalm.levels$country),]
# ---------------------------------- #
#### Create table for the results ####
# ---------------------------------- #
# Prepare data for the tables
data_GlobMalm <- data.frame(matrix(ncol = 13, nrow = 25))
colnames(data_GlobMalm) <- c("country",
"TFP2000", "TFP2019", "DTFP",
"OTE2000", "OTE2019", "DOTE",
"BPG2000", "BPG2019", "DBPG",
"TGR2000", "TGR2019", "DTGR")
data_GlobMalm$country <- rep(unique(EUdata$country))
# TOTAL FACTOR PRODUCTIVITY
# TFP2000
data_GlobMalm$TFP2000 <- GlobMalm.levels$DOGt[GlobMalm.levels$year==2000]
# TFP2019
data_GlobMalm$TFP2019 <- GlobMalm.levels$DOGt[GlobMalm.levels$year==2019]
# DTFP
data_GlobMalm$DTFP <- (data_GlobMalm$TFP2019 / data_GlobMalm$TFP2000)
# OUTPUT TECHNICAL EFFICIENCY
# OTE2000
data_GlobMalm$OTE2000 <- GlobMalm.levels$DOt[GlobMalm.levels$year==2000]
# OTE2019
data_GlobMalm$OTE2019 <- GlobMalm.levels$DOt[GlobMalm.levels$year==2019]
# DOTE
data_GlobMalm$DOTE <- (data_GlobMalm$OTE2019 / data_GlobMalm$OTE2000)
# BEST PRACTICE GAP (BPG)
# BPG2000
data_GlobMalm$BPG2000 <- GlobMalm.levels$BPGt[GlobMalm.levels$year==2000]
# BPG2019
data_GlobMalm$BPG2019 <- GlobMalm.levels$BPGt[GlobMalm.levels$year==2019]
# DBPG
data_GlobMalm$DBPG <- (data_GlobMalm$BPG2019 / data_GlobMalm$BPG2000)
# TECHNOLOGY GAP RATIO (TGR)
# TGR2000
data_GlobMalm$TGR2000 <- GlobMalm.levels$TGRt[GlobMalm.levels$year==2000]
# TGR2019
data_GlobMalm$TGR2019 <- GlobMalm.levels$TGRt[GlobMalm.levels$year==2019]
# DTGR
data_GlobMalm$DTGR <- (data_GlobMalm$TGR2019 / data_GlobMalm$TGR2000)
# Add EU-average as geometric mean
data_GlobMalm <- data_GlobMalm %>%
add_row(country="EU25",
TFP2000=NA, TFP2019=NA, DTFP=exp(mean(log(data_GlobMalm$DTFP))),
OTE2000=NA, OTE2019=NA, DOTE=exp(mean(log(data_GlobMalm$DOTE))),
BPG2000=NA, BPG2019=NA, DBPG=exp(mean(log(data_GlobMalm$DBPG))),
TGR2000=NA, TGR2019=NA, DTGR=exp(mean(log(data_GlobMalm$DTGR))))
# Write Table: "TFP decomposition for EU agriculture (2000-2019) using the Global Malmquist index.
# Set global option to produce latex output
options(knitr.table.format = "latex", knitr.kable.NA = '')
# Create table
Tab_GlobMalm_EU_TFPDecomp <- kable(data_GlobMalm, booktabs = T,
digits = 2,
row.names = FALSE,
escape = FALSE,
linesep = "",
caption = "TFP decomposition for EU agriculture (2000--2019) using the Global Malmquist index.",
label = "Tab_GlobMalm_EU_TFPDecomp",
col.names = c('Country',
'2000', '2019', "$\\Delta$",
'2000', '2019', "$\\Delta$",
'2000', '2019', "$\\Delta$",
'2000', '2019', "$\\Delta$")) %>%
add_header_above(c("", "TFP" = 3, "OTE" = 3, "BPG" = 3, "TGR" = 3)) %>%
row_spec(25, hline_after=T) %>%
footnote(general = "TFP is total factor productivity, OTE is output-oriented
technical efficiency, BPG is the best practice gap, and
TGR is a technology gap ratio capturing technical and environmental
differences across the four biogeographical regions.",
footnote_as_chunk = T,
threeparttable = T,
general_title = "Notes:",
escape=F) %>%
kable_styling(latex_options = c("scale_down", "HOLD_position"))
# Print Latex file
writeLines(Tab_GlobMalm_EU_TFPDecomp, "Tables/Tab_GlobMalm_EU_TFPDecomp.tex")