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02e_TFP for US_A-DEA_crs.R
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02e_TFP for US_A-DEA_crs.R
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# --------------------------------------- #
# #
# This program estimates the additive TFP #
# index under CRS using shadow prices as #
# weights for the US state-level data #
# #
# --------------------------------------- #
# Open packages
library(tidyverse) #for data manipulation
library(dplyr) #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 required source codes
source('R_aux/aux_lowe.R')
source('R_aux/auxiliaries.R')
source('R_aux/lowe.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 US state-level data
load("R_output/USdata.Rda")
# ------------------------------------ #
#### Estimation of reference prices ####
# ------------------------------------ #
# Estimation of each observations' shadow prices
# Input and output vectors
XREF <- t(as.matrix(USdata[, c("x_capital", "x_land", "x_labor", "x_interm")]))
YREF <- t(as.matrix(USdata[, c("q_livestock", "q_crops", "q_otheroutp")]))
# Output distance function
outShad <- foreach(dmu = 1:dim(USdata)[1], .combine = rbind) %do%
{
DO.shdu(XOBS = XREF[, dmu], YOBS = YREF[, dmu], XREF = XREF,
YREF = YREF, rts = "crs")
}
# Input distance function
inpShad <- foreach(dmu = 1:dim(USdata)[1], .combine = rbind) %do%
{
DI.shdu(XOBS = XREF[, dmu], YOBS = YREF[, dmu], XREF = XREF,
YREF = YREF, rts = "crs")
}
# Use mean shadow prices as reference prices
# Compute means
meanY <- apply(outShad, 2, FUN = function(x) mean(x))
meanX <- apply(inpShad, 2, FUN = function(x) mean(x))
# Add reference prices to data frame
USdata$p0_livestock <- meanY[1]
USdata$p0_crops <- meanY[2]
USdata$p0_otheroutp <- meanY[3]
USdata$w0_capital <- meanX[1]
USdata$w0_land <- meanX[2]
USdata$w0_labor <- meanX[3]
USdata$w0_interm <- meanX[4]
# -------------------------------------------- #
#### Calculate and decompose TFP under CRS ####
# -------------------------------------------- #
# Subsets for each region
Pacific <- subset(USdata, state == "CA" | state == "OR" | state == "WA")
Mountain <- subset(USdata,state == "AZ" | state == "CO" | state == "ID" |
state == "MT" | state == "NM" | state == "NV" |
state == "UT" | state == "WY")
NPlains <- subset(USdata, state == "KS" | state == "ND" | state == "NE" |
state == "SD" )
SPlains <- subset(USdata, state == "OK" | state == "TX")
Cornbelt <- subset(USdata, state == "IA" | state == "IL" | state == "IN"|
state == "MO" | state == "OH")
Southeast <- subset(USdata, state == "AL" | state == "FL" | state == "GA" |
state == "SC")
Northeast <- subset(USdata, state == "CT" | state == "DE" | state == "MA" |
state == "MD" | state == "ME" | state == "NH" |
state == "NJ" | state == "NY"| state == "PA" |
state == "RI" | state == "VT")
Lake <- subset(USdata,state == "MI" | state == "MN" | state == "WI")
Appalacian <- subset(USdata, state == "KY" | state == "NC" | state == "TN"|
state == "VA" | state == "WV")
Delta <- subset(USdata, state == "AR" | state == "LA" | state == "MS")
# Note: We use the code from the Lowe index but use the above estimated
# shadow prices to obtain the additive TFP index with shadow prices
# as weights.
#Pacific
ADEA_pacific <- lowe(data = Pacific, id.var = "state", time.var = "year",
x.vars = c("x_capital", "x_land","x_labor","x_interm"),
y.vars = c("q_livestock", "q_crops", "q_otheroutp"),
w.vars = c("w0_capital", "w0_land","w0_labor","w0_interm"),
p.vars = c("p0_livestock", "p0_crops", "p0_otheroutp"),
tech.change = T, tech.reg = T, rts = "crs",
orientation = "out", cores = 8, scaled = F,
window = c(5), by.year = 1)
#Mountain
ADEA_mountain <- lowe(data = Mountain, id.var = "state", time.var = "year",
x.vars = c("x_capital", "x_land","x_labor","x_interm"),
y.vars = c("q_livestock", "q_crops", "q_otheroutp"),
w.vars = c("w0_capital", "w0_land","w0_labor","w0_interm"),
p.vars = c("p0_livestock", "p0_crops", "p0_otheroutp"),
tech.change = T, tech.reg = T, rts = "crs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#NPlains
ADEA_nplains <- lowe(data = NPlains, id.var = "state", time.var = "year",
x.vars = c("x_capital", "x_land","x_labor","x_interm"),
y.vars = c("q_livestock", "q_crops", "q_otheroutp"),
w.vars = c("w0_capital", "w0_land","w0_labor","w0_interm"),
p.vars = c("p0_livestock", "p0_crops", "p0_otheroutp"),
tech.change = T, tech.reg = T, rts = "crs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#SPlains
ADEA_splains <- lowe(data = SPlains, id.var = "state", time.var = "year",
x.vars = c("x_capital", "x_land","x_labor","x_interm"),
y.vars = c("q_livestock", "q_crops", "q_otheroutp"),
w.vars = c("w0_capital", "w0_land","w0_labor","w0_interm"),
p.vars = c("p0_livestock", "p0_crops", "p0_otheroutp"),
tech.change = T, tech.reg = T, rts = "crs",
orientation = "out", cores = 8, scaled = F,
window = c(8), by.year = 1)
#Cornbelt
ADEA_cornbelt <- lowe(data = Cornbelt, id.var = "state", time.var = "year",
x.vars = c("x_capital", "x_land","x_labor","x_interm"),
y.vars = c("q_livestock", "q_crops", "q_otheroutp"),
w.vars = c("w0_capital", "w0_land","w0_labor","w0_interm"),
p.vars = c("p0_livestock", "p0_crops", "p0_otheroutp"),
tech.change = T, tech.reg = T, rts = "crs",
orientation = "out", cores = 8, scaled = F,
window = c(3), by.year = 1)
#Southeast
ADEA_southeast <- lowe(data = Southeast, id.var = "state", time.var = "year",
x.vars = c("x_capital", "x_land","x_labor","x_interm"),
y.vars = c("q_livestock", "q_crops", "q_otheroutp"),
w.vars = c("w0_capital", "w0_land","w0_labor","w0_interm"),
p.vars = c("p0_livestock", "p0_crops", "p0_otheroutp"),
tech.change = T, tech.reg = T, rts = "crs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#Northeast
ADEA_northeast <- lowe(data = Northeast, id.var = "state", time.var = "year",
x.vars = c("x_capital", "x_land","x_labor","x_interm"),
y.vars = c("q_livestock", "q_crops", "q_otheroutp"),
w.vars = c("w0_capital", "w0_land","w0_labor","w0_interm"),
p.vars = c("p0_livestock", "p0_crops", "p0_otheroutp"),
tech.change = T, tech.reg = T, rts = "crs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#Lake States
ADEA_lake <- lowe(data = Lake, id.var = "state", time.var = "year",
x.vars = c("x_capital", "x_land","x_labor","x_interm"),
y.vars = c("q_livestock", "q_crops", "q_otheroutp"),
w.vars = c("w0_capital", "w0_land","w0_labor","w0_interm"),
p.vars = c("p0_livestock", "p0_crops", "p0_otheroutp"),
tech.change = T, tech.reg = T, rts = "crs",
orientation = "out", cores = 8, scaled = F,
window = c(5), by.year = 1)
#Appalacian
ADEA_appalacian <- lowe(data = Appalacian, id.var = "state", time.var = "year",
x.vars = c("x_capital", "x_land","x_labor","x_interm"),
y.vars = c("q_livestock", "q_crops", "q_otheroutp"),
w.vars = c("w0_capital", "w0_land","w0_labor","w0_interm"),
p.vars = c("p0_livestock", "p0_crops", "p0_otheroutp"),
tech.change = T, tech.reg = T, rts = "crs",
orientation = "out", cores = 8, scaled = F,
window = c(3), by.year = 1)
#Delta States
ADEA_delta <- lowe(data = Delta, id.var = "state", time.var = "year",
x.vars = c("x_capital", "x_land","x_labor","x_interm"),
y.vars = c("q_livestock", "q_crops", "q_otheroutp"),
w.vars = c("w0_capital", "w0_land","w0_labor","w0_interm"),
p.vars = c("p0_livestock", "p0_crops", "p0_otheroutp"),
tech.change = T, tech.reg = T, rts = "crs",
orientation = "out", cores = 8, scaled = F,
window = c(5), by.year = 1)
#All states (to obtain the true monetary outcomes)
ADEA_all <- lowe(data = USdata, id.var = "state", time.var = "year",
x.vars = c("x_capital", "x_land","x_labor","x_interm"),
y.vars = c("q_livestock", "q_crops", "q_otheroutp"),
w.vars = c("w_capital", "w_land","w_labor","w_interm"),
p.vars = c("p_livestock", "p_crops", "p_otheroutp"),
tech.change = T, tech.reg = T, rts = "crs",
orientation = "out", cores = 8, scaled = F,
by.year = 1) # no window here
#Merge monetary with TFP results
ADEA_pacific <- right_join(ADEA_all$Levels[ ,1:10],
ADEA_pacific$Levels[,c(1,2,11:20)],
by=c("state","year"))
ADEA_mountain <- right_join(ADEA_all$Levels[ ,1:10],
ADEA_mountain$Levels[,c(1,2,11:20)],
by=c("state","year"))
ADEA_nplains <- right_join(ADEA_all$Levels[ ,1:10],
ADEA_nplains$Levels[,c(1,2,11:20)],
by=c("state","year"))
ADEA_splains <- right_join(ADEA_all$Levels[ ,1:10],
ADEA_splains$Levels[,c(1,2,11:20)],
by=c("state","year"))
ADEA_cornbelt <- right_join(ADEA_all$Levels[ ,1:10],
ADEA_cornbelt$Levels[,c(1,2,11:20)],
by=c("state","year"))
ADEA_southeast <- right_join(ADEA_all$Levels[ ,1:10],
ADEA_southeast$Levels[,c(1,2,11:20)],
by=c("state","year"))
ADEA_northeast <- right_join(ADEA_all$Levels[ ,1:10],
ADEA_northeast$Levels[,c(1,2,11:20)],
by=c("state","year"))
ADEA_lake <- right_join(ADEA_all$Levels[ ,1:10],
ADEA_lake$Levels[,c(1,2,11:20)],
by=c("state","year"))
ADEA_appalacian <- right_join(ADEA_all$Levels[ ,1:10],
ADEA_appalacian$Levels[,c(1,2,11:20)],
by=c("state","year"))
ADEA_delta <- right_join(ADEA_all$Levels[ ,1:10],
ADEA_delta$Levels[,c(1,2,11:20)],
by=c("state","year"))
ADEA.level_crs <- rbind(ADEA_pacific, ADEA_mountain, ADEA_nplains,
ADEA_splains, ADEA_cornbelt, ADEA_southeast,
ADEA_northeast, ADEA_lake, ADEA_appalacian,
ADEA_delta)
#replace TT
ADEA.level_crs$TT <- ADEA.level_crs$PROF / ADEA.level_crs$TFP
# Order results by state
ADEA.level_crs <- ADEA.level_crs[order(ADEA.level_crs$state),]
# Save results for comparison in "03h_TFP for US_comparison"
save(ADEA.level_crs, file="R_output/ADEA.level_crs.Rda")
# Normalize with Base = AL 1960
ADEA.level_crs_norm <- ADEA.level_crs %>%
mutate_each(funs(./.[1]), setdiff(names(.), c("state","year")))
# ---------------------------------------------------- #
#### Replicate tables 2 and 3 from O'Donnell (2012) ####
# ---------------------------------------------------- #
# Prepare data for the tables
data_tables <- data.frame(matrix(ncol = 28, nrow = 48))
colnames(data_tables) <- c("state", "PROF1960", "PROF2004", "DPROF",
"TT1960", "TT2004", "DTT",
"TFP1960", "TFP2004", "DTFP",
"MP1960", "MP2004", "DMP",
"TFPE1960", "TFPE2004", "DTFPE",
"OTE1960", "OTE2004", "DOTE",
"OSE1960", "OSE2004", "DOSE",
"OME1960", "OME2004", "DOME",
"OSME1960", "OSME2004", "DOSME")
data_tables$state <- rep(c("AL","AR","AZ","CA","CO","CT","DE","FL",
"GA","IA","ID","IL","IN","KS","KY","LA",
"MA","MD","ME","MI","MN","MO","MS","MT",
"NC","ND","NE","NH","NJ","NM","NV","NY",
"OH","OK","OR","PA","RI","SC","SD","TN",
"TX","UT","VA","VT","WA","WI","WV","WY"))
# PROFITABILITY
# PROF1960
data_tables$PROF1960 <- ADEA.level_crs$PROF[ADEA.level_crs$year==1960]
# PROF04
data_tables$PROF2004 <- ADEA.level_crs$PROF[ADEA.level_crs$year==2004]
# DPROF
data_tables$DPROF <- (data_tables$PROF2004 / data_tables$PROF1960)
# TERMS OF TRADE
# TT1960
data_tables$TT1960 <- ADEA.level_crs$TT[ADEA.level_crs$year==1960]
# TT04
data_tables$TT2004 <- ADEA.level_crs$TT[ADEA.level_crs$year==2004]
# DTT
data_tables$DTT <- (data_tables$TT2004 / data_tables$TT1960)
# TOTAL FACTOR PRODUCTIVITY
# TFP1960
data_tables$TFP1960 <- ADEA.level_crs$TFP[ADEA.level_crs$year==1960]
# TFP04
data_tables$TFP2004 <- ADEA.level_crs$TFP[ADEA.level_crs$year==2004]
# DTFP
data_tables$DTFP <- (data_tables$TFP2004 / data_tables$TFP1960)
# MAXIMM TOTAL FACTOR PRODUCTIVITY
# MP1960
data_tables$MP1960 <- ADEA.level_crs$MP[ADEA.level_crs$year==1960]
# MP04
data_tables$MP2004 <- ADEA.level_crs$MP[ADEA.level_crs$year==2004]
# DMP
data_tables$DMP <- (data_tables$MP2004 / data_tables$MP1960)
# TFP EFFICIENCY
# TFPE1960
data_tables$TFPE1960 <- ADEA.level_crs$TFPE[ADEA.level_crs$year==1960]
# TFPE04
data_tables$TFPE2004 <- ADEA.level_crs$TFPE[ADEA.level_crs$year==2004]
# DTFPE
data_tables$DTFPE <- (data_tables$TFPE2004 / data_tables$TFPE1960)
# OUTPUT TECHNICAL EFFICIENCY
# OTE1960
data_tables$OTE1960 <- ADEA.level_crs$OTE[ADEA.level_crs$year==1960]
# OTE04
data_tables$OTE2004 <- ADEA.level_crs$OTE[ADEA.level_crs$year==2004]
# DOTE
data_tables$DOTE <- (data_tables$OTE2004 / data_tables$OTE1960)
# OUTPUT SCALE EFFICIENCY
# OSE1960
data_tables$OSE1960 <- ADEA.level_crs$OSE[ADEA.level_crs$year==1960]
# OSE04
data_tables$OSE2004 <- ADEA.level_crs$OSE[ADEA.level_crs$year==2004]
# DOSE
data_tables$DOSE <- (data_tables$OSE2004 / data_tables$OSE1960)
# OUTPUT MIX EFFICIENCY
# OME1960
data_tables$OME1960 <- ADEA.level_crs$OME[ADEA.level_crs$year==1960]
# OME04
data_tables$OME2004 <- ADEA.level_crs$OME[ADEA.level_crs$year==2004]
# DOME
data_tables$DOME <- (data_tables$OME2004 / data_tables$OME1960)
# OUTPUT SCALE MIX EFFICIENCY
# OSME1960
data_tables$OSME1960 <- ADEA.level_crs$OSME[ADEA.level_crs$year==1960]
# OSME04
data_tables$OSME2004 <- ADEA.level_crs$OSME[ADEA.level_crs$year==2004]
# DOSME
data_tables$DOSME <- (data_tables$OSME2004 / data_tables$OSME1960)
# Add US-average as geometric mean
data_tables <- data_tables %>%
add_row(state="US48",
PROF1960=NA, PROF2004=NA, DPROF=exp(mean(log(data_tables$DPROF))),
TT1960=NA, TT2004=NA, DTT=exp(mean(log(data_tables$DTT))),
TFP1960=NA, TFP2004=NA, DTFP=exp(mean(log(data_tables$DTFP))),
MP1960=NA, MP2004=NA, DMP=exp(mean(log(data_tables$DMP))),
TFPE1960=NA, TFPE2004=NA, DTFPE=exp(mean(log(data_tables$DTFPE))),
OTE1960=NA, OTE2004=NA, DOTE=exp(mean(log(data_tables$DOTE))),
OSE1960=NA, OSE2004=NA, DOSE=exp(mean(log(data_tables$DOSE))),
OME1960=NA, OME2004=NA, DOME=exp(mean(log(data_tables$DOME))),
OSME1960=NA, OSME2004=NA, DOSME=exp(mean(log(data_tables$DOSME))))
# Save US-average for comparison in 03h_TFP for US_comparison.R
Summary_adea_US_crs <- list(data_tables$DTFP[data_tables$state=="US48"],
data_tables$DMP[data_tables$state=="US48"],
data_tables$DTFPE[data_tables$state=="US48"])
names(Summary_adea_US_crs) <- c("TFP", "TC", "TFPE")
save(Summary_adea_US_crs, file = "R_output/Summary_adea_US_crs.Rda")
# Write Table: "Profitability, TFP, and efficiency change in US agriculture (1960--2004) using the A-DEA index)
# Set global option to produce latex output
options(knitr.table.format = "latex", knitr.kable.NA = '')
# Select data
data_ADEA_ProfDecomp <- subset(data_tables, select = c(1:16))
# Write table
Tab_ADEA_ProfDecomp_US_crs <- kable(data_ADEA_ProfDecomp, booktabs = T,
digits = 2,
row.names = FALSE,
escape = FALSE,
linesep = "",
caption = "Profitability, TFP, and efficiency change in US agriculture (1960--2004) using the A-DEA index under CRS.",
label = "Tab_ADEA_ProfDecomp_US_crs",
col.names = c('State',
'1960', '2004', "$\\Delta$",
'1960', '2004', "$\\Delta$",
'1960', '2004', "$\\Delta$",
'1960', '2004', "$\\Delta$",
'1960', '2004', "$\\Delta$")) %>%
add_header_above(c("", "PROF" = 3, "TT" = 3,
"TFP" = 3, "TFP*" = 3,
"TFPE" = 3)) %>%
row_spec(48, hline_after=T) %>%
kable_styling(font_size = 12,
latex_options = c("scale_down", "HOLD_position"))
# Print Latex file
writeLines(Tab_ADEA_ProfDecomp_US_crs, "Tables/Tab_ADEA_ProfDecomp_US_crs.tex")
# Write Table: "Output-oriented components of efficiency change in US agriculture (1960--2004) using the A-DEA index"
# Set global option to produce latex output
options(knitr.table.format = "latex", knitr.kable.NA = '')
# Select data
data_ADEA_TPFEDecomp <- subset(data_tables, select = c(1,14:28))
# Write table
Tab_ADEA_TFPEDecomp_US_crs <- kable(data_ADEA_TPFEDecomp, booktabs = T,
digits = 2,
row.names = FALSE,
escape = FALSE,
linesep = "",
caption = "Output-oriented components of efficiency change in US agriculture (1960--2004) using the A-DEA index under CRS.",
label = "Tab_ADEA_TFPEDecomp_US_crs",
col.names = c('State',
'1960', '2004', "$\\Delta$",
'1960', '2004', "$\\Delta$",
'1960', '2004', "$\\Delta$",
'1960', '2004', "$\\Delta$",
'1960', '2004', "$\\Delta$")) %>%
add_header_above(c("", "TFPE" = 3, "OTE" = 3,
"OSE" = 3, "OME" = 3,
"OSME" = 3)) %>%
row_spec(48, hline_after=T) %>%
kable_styling(font_size = 12,
latex_options = c("scale_down", "HOLD_position"))
# Print Latex file
writeLines(Tab_ADEA_TFPEDecomp_US_crs, "Tables/Tab_ADEA_TFPEDecomp_US_crs.tex")
# --------------------------------------------- #
#### Replicate Table 4 from O'Donnell (2012) ####
# --------------------------------------------- #
data_table <- data.frame(matrix(ncol = 17, nrow = 48))
colnames(data_table) <- c("state",
"TFP60-70", "MP60-70", "OTE60-70", "OSME60-70",
"TFP70-80", "MP70-80", "OTE70-80", "OSME70-80",
"TFP80-90", "MP80-90", "OTE80-90", "OSME80-90",
"TFP90-02", "MP90-02", "OTE90-02", "OSME90-02")
data_table$state <- rep(c("AL","AR","AZ","CA","CO","CT","DE","FL",
"GA","IA","ID","IL","IN","KS","KY","LA",
"MA","MD","ME","MI","MN","MO","MS","MT",
"NC","ND","NE","NH","NJ","NM","NV","NY",
"OH","OK","OR","PA","RI","SC","SD","TN",
"TX","UT","VA","VT","WA","WI","WV","WY"))
ADEA1960 <- subset(ADEA.level_crs, year==1960)
ADEA1970 <- subset(ADEA.level_crs, year==1970)
ADEA1980 <- subset(ADEA.level_crs, year==1980)
ADEA1990 <- subset(ADEA.level_crs, year==1990)
ADEA2002 <- subset(ADEA.level_crs, year==2002)
#1960-1970
data_table$`TFP60-70` <- log(c(ADEA1970[,"TFP"])/c(ADEA1960[,"TFP"])) / (1970-1960) * 100
data_table$`MP60-70` <- log(c(ADEA1970[,"MP"])/c(ADEA1960[,"MP"])) / (1970-1960) * 100
data_table$`OTE60-70` <- log(c(ADEA1970[,"OTE"])/c(ADEA1960[,"OTE"])) / (1970-1960) * 100
data_table$`OSME60-70` <- log(c(ADEA1970[,"OSME"])/c(ADEA1960[,"OSME"])) / (1970-1960) * 100
#1970-1980
data_table$`TFP70-80` <- log(c(ADEA1980[,"TFP"])/c(ADEA1970[,"TFP"])) / (1980-1970) * 100
data_table$`MP70-80` <- log(c(ADEA1980[,"MP"])/c(ADEA1970[,"MP"])) / (1980-1970) * 100
data_table$`OTE70-80` <- log(c(ADEA1980[,"OTE"])/c(ADEA1970[,"OTE"])) / (1980-1970) * 100
data_table$`OSME70-80` <- log(c(ADEA1980[,"OSME"])/c(ADEA1970[,"OSME"])) / (1980-1970) * 100
#1980-1990
data_table$`TFP80-90` <- log(c(ADEA1990[,"TFP"])/c(ADEA1980[,"TFP"])) / (1990-1980) * 100
data_table$`MP80-90` <- log(c(ADEA1990[,"MP"])/c(ADEA1980[,"MP"])) / (1990-1980) * 100
data_table$`OTE80-90` <- log(c(ADEA1990[,"OTE"])/c(ADEA1980[,"OTE"])) / (1990-1980) * 100
data_table$`OSME80-90` <- log(c(ADEA1990[,"OSME"])/c(ADEA1980[,"OSME"])) / (1990-1980) * 100
#1990-2002
data_table$`TFP90-02` <- log(c(ADEA2002[,"TFP"])/c(ADEA1990[,"TFP"])) / (2002-1990) * 100
data_table$`MP90-02` <- log(c(ADEA2002[,"MP"])/c(ADEA1990[,"MP"])) / (2002-1990) * 100
data_table$`OTE90-02` <- log(c(ADEA2002[,"OTE"])/c(ADEA1990[,"OTE"])) / (2002-1990) * 100
data_table$`OSME90-02` <- log(c(ADEA2002[,"OSME"])/c(ADEA1990[,"OSME"])) / (2002-1990) * 100
# Add US48-average as arithmetic average (because values are in logs)
data_table <- data_table %>%
add_row(state = "US48",
`TFP60-70` = mean(data_table$`TFP60-70`),
`MP60-70`=mean(data_table$`MP60-70`),
`OTE60-70`=mean(data_table$`OTE60-70`),
`OSME60-70`=mean(data_table$`OSME60-70`),
`TFP70-80` = mean(data_table$`TFP70-80`),
`MP70-80`=mean(data_table$`MP70-80`),
`OTE70-80`=mean(data_table$`OTE70-80`),
`OSME70-80`=mean(data_table$`OSME70-80`),
`TFP80-90` = mean(data_table$`TFP80-90`),
`MP80-90`=mean(data_table$`MP80-90`),
`OTE80-90`=mean(data_table$`OTE80-90`),
`OSME80-90`=mean(data_table$`OSME80-90`),
`TFP90-02` = mean(data_table$`TFP90-02`),
`MP90-02`=mean(data_table$`MP90-02`),
`OTE90-02`=mean(data_table$`OTE90-02`),
`OSME90-02`=mean(data_table$`OSME90-02`))
# Write Table: Average annual rates of growth in TFP and efficiency in US agriculture (1960--2002) using the A-DEA index
# Create table
Tab_ADEA_AvgRates_US_crs <- kable(data_table, booktabs = T,
digits = 2,
row.names = FALSE,
escape = FALSE,
linesep = "",
caption = "Average annual rates of growth (\\%) in TFP and efficiency in US agriculture (1960--2002) using the A-DEA index under CRS.",
label = "Tab_ADEA_AvgRates_US_crs",
col.names = c('State',
'TFP', 'TFP*', 'OTE', 'OSME',
'TFP', 'TFP*', 'OTE', 'OSME',
'TFP', 'TFP*', 'OTE', 'OSME',
'TFP', 'TFP*', 'OTE', 'OSME')) %>%
add_header_above(c("", "1960-1970" = 4, "1970-1980" = 4,
"1980-1990" = 4, "1990-2002" = 4)) %>%
row_spec(48, hline_after=T) %>%
kable_styling(latex_options = c("scale_down", "HOLD_position"))
# Print Latex file
writeLines(Tab_ADEA_AvgRates_US_crs, "Tables/Tab_ADEA_AvgRates_US_crs.tex")
# ------------------------------------- #
#### Create figures for US aggregate ####
# ------------------------------------- #
# Prepare the data
# Compare every level to the state's *own* 1960 level --> obtains cumulative growth
idstates <- unique(ADEA.level_crs$state)
ADEA.cumulative_crs <- list()
for (i in idstates) {
ADEA.cumulative_crs[[which(i==idstates)]] <- cbind(state = i, year=1960:2004,
sweep(ADEA.level_crs[ADEA.level_crs$state==i,-c(1:2)],MARGIN = 2,
STATS = as.numeric(ADEA.level_crs[ADEA.level_crs$state==i,-c(1:2)][1,]),FUN = "/")) # for myself: 2 refers to column
}
ADEA.cumulative_crs <- do.call(rbind, ADEA.cumulative_crs) #turns the list into a data frame
# Calculate US average using the geometric mean
ADEA.US48_crs <- list()
for (yr in 1960:2004) {
ADEA.US48_crs[[which(yr == 1960:2004)]] <- c(year = yr, apply(ADEA.cumulative_crs[ADEA.cumulative_crs$year==yr,-c(1:2)],2,FUN = function(x) exp(mean(log(x)))))
}
ADEA.US48_crs <- as.data.frame(do.call(rbind, ADEA.US48_crs)) #turns the list into a data frame
# For the efficiencies (last figure), we need arithmetic averages
ADEA.US48_crs_eff <- ADEA.level_crs[,c("state", "year","TFPE", "OTE", "OME", "ROSE", "OSME")]
ADEA.US48_crs_eff <- ADEA.US48_crs_eff %>%
group_by(year) %>%
summarise_at(.vars = vars(TFPE,OTE,OME,ROSE,OSME),
.funs = c(mean="mean"))
#-------------------------------------------------------------#
# Panel A: Profitability, quantity, and price change (1960=1) #
#-------------------------------------------------------------#
# Prepare data for the plot
data_plot <- ADEA.US48_crs %>%
select("year", "W", "P", "TFP", "PROF", "TT") %>%
reshape2::melt(id.vars="year")
# plot
panel_A <- ggplot() +
geom_line(data = data_plot, aes(x = year, y = value, color = variable, linetype = variable)) +
labs(
x = "",
y = "",
linetype = "") +
scale_color_manual(name = "",
breaks=c("W", "P", "TFP", "PROF", "TT"),
labels=c("dW", "dP", "dTFP", "dPROF", "dTT"),
values=c("#0a0a0a","#0a0a0a","#0a0a0a","#9E9E9E","#0a0a0a")) +
scale_linetype_manual(name = "",
breaks=c("W", "P", "TFP", "PROF", "TT"),
labels=c("dW", "dP", "dTFP", "dPROF", "dTT"),
values=c("12", "42", "solid", "solid", "1141")) +
theme_bw() +
scale_x_continuous(breaks = seq(1960, 2004, by = 3)) +
scale_y_continuous(breaks = seq(0, 7, by = 1), limits=c(0,7.5)) +
theme(axis.text.x = element_text(angle=90)) +
theme(legend.position="bottom",
legend.margin=margin(t = -0.7, unit='cm'),
legend.text=element_text(size=6))
# --------------------------------- #
# Panel B: Components of TFP change #
# --------------------------------- #
# Prepare data for the plot
data_plot <- ADEA.US48_crs %>%
select("year", "TFP", "MP", "TFPE") %>%
reshape2::melt(id.vars="year")
# plot
panel_B <- ggplot() +
geom_line(data = data_plot, aes(x = year, y = value, linetype = variable, color = variable)
) + labs(
x = "",
y = "",
linetype = ""
) + scale_linetype_manual(name ="",
breaks=c("TFP", "MP", "TFPE"),
labels=c("dTFP", "dTFP*", "dTFPE"),
values=c("solid", "42", "12")) +
scale_color_manual(name ="",
breaks=c("TFP", "MP", "TFPE"),
labels=c("dTFP", "dTFP*", "dTFPE"),
values=c("#0a0a0a","#0a0a0a","#0a0a0a")) +
theme_bw() +
scale_x_continuous(breaks = seq(1960, 2004, by = 3)) +
scale_y_continuous(breaks = seq(0, 2.5, by = 0.5), limits=c(0,2.8)) +
theme(axis.text.x = element_text(angle=90)) +
theme(legend.position="bottom",
legend.margin=margin(t = -0.7, unit='cm'),
legend.text=element_text(size=6))
# -------------------------- #
# Panel C: Efficiency levels #
# -------------------------- #
# Prepare data for the plot
data_plot <- ADEA.US48_crs_eff %>%
reshape2::melt(id.vars="year")
# plot
panel_C <- ggplot() +
geom_line(data = data_plot, aes(x = year, y = value, linetype = variable, color = variable)
) + labs(
x = "",
y = "",
linetype = ""
) + scale_linetype_manual(name ="",
breaks=c("OTE_mean", "OME_mean", "ROSE_mean", "OSME_mean", "TFPE_mean"),
labels=c("OTE", "OME", "ROSE", "OSME", "TFPE"),
values=c("solid", "42", "1141", "solid", "12")) +
scale_color_manual(name ="",
breaks=c("OTE_mean", "OME_mean", "ROSE_mean", "OSME_mean", "TFPE_mean"),
labels=c("OTE", "OME", "ROSE", "OSME", "TFPE"),
values=c("#0a0a0a","#0a0a0a","#0a0a0a","#9E9E9E","#0a0a0a")) +
theme_bw() +
scale_x_continuous(breaks = seq(1960, 2004, by = 3)) +
scale_y_continuous(breaks = seq(0, 1.2, by = 0.2), limits=c(0,1.3)) +
theme(axis.text.x = element_text(angle=90)) +
theme(legend.position="bottom",
legend.margin=margin(t = -0.7, unit='cm'),
legend.text=element_text(size=6))
# -------------------------------------------------- #
# Combine panels to one figure and save as tex file #
# -------------------------------------------------- #
# set filepath for latex output
tikz(file = "Figures/Fig_ADEA_US48_crs.tex", width = 7, height = 7)
plot <- ggarrange(panel_A, panel_B, panel_C,
labels = c("a", "b", "c"),
ncol = 2, nrow = 2)
print(plot)
dev.off()