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02b_TFP for US_Lowe_vrs_different_windows.R
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02b_TFP for US_Lowe_vrs_different_windows.R
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# ------------------------------------- #
# #
# This program estimates the LOWE TFP #
# index for different windows using the #
# state-level US 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 source codes for the Lowe index
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")
# Calculate reference prices for the entire sample
USdata$p0_livestock <- mean(USdata$p_livestock)
USdata$p0_crops <- mean(USdata$p_crops)
USdata$p0_otheroutp <- mean(USdata$p_otheroutp)
USdata$w0_capital <- mean(USdata$w_capital)
USdata$w0_land <- mean(USdata$w_land)
USdata$w0_labor <- mean(USdata$w_labor)
USdata$w0_interm <- mean(USdata$w_interm)
# 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")
# ---------------------- #
#### Original windows ####
# ---------------------- #
#Pacific
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(5), by.year = 1)
#Mountain
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#NPlains
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#SPlains
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(8), by.year = 1)
#Cornbelt
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(3), by.year = 1)
#Southeast
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#Northeast
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#Lake States
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(5), by.year = 1)
#Appalacian
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(3), by.year = 1)
#Delta States
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(5), by.year = 1)
#merge all results
lowe_pacific <- lowe_pacific$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_mountain <- lowe_mountain$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_nplains <- lowe_nplains$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_splains <- lowe_splains$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_cornbelt <- lowe_cornbelt$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_southeast <- lowe_southeast$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_northeast <- lowe_northeast$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_lake <- lowe_lake$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_appalacian <- lowe_appalacian$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_delta <- lowe_delta$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
Lowe.level <- rbind(lowe_pacific, lowe_mountain, lowe_nplains,
lowe_splains, lowe_cornbelt, lowe_southeast,
lowe_northeast, lowe_lake, lowe_appalacian,
lowe_delta)
# Order results by state
Lowe.level <- Lowe.level[order(Lowe.level$state),]
# Calculate changes (worig=windows original)
Lowe_worig <- data.frame(matrix(ncol = 5, nrow = 48))
x <- c("state", "windows", "DTFP", "DMP", "DTFPE")
colnames(Lowe_worig) <- x
Lowe_worig$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"))
lowe1960 <- subset(Lowe.level, year==1960)
lowe2004 <- subset(Lowe.level, year==2004)
# DTFP
Lowe_worig$DTFP <- (lowe2004[,"TFP"] / lowe1960[,"TFP"])
# DMP
Lowe_worig$DMP <- (lowe2004[,"MP"] / lowe1960[,"MP"])
# DTFPE
Lowe_worig$DTFPE <- (lowe2004[,"TFPE"] / lowe1960[,"TFPE"])
# Add US-average as geometric mean
Lowe_worig <- Lowe_worig %>%
add_row(state="US48",
DTFP=exp(mean(log(Lowe_worig$DTFP))),
DMP=exp(mean(log(Lowe_worig$DMP))),
DTFPE=exp(mean(log(Lowe_worig$DTFPE))))
#Windows Pacific
Lowe_worig$windows[Lowe_worig$state=="CA" |
Lowe_worig$state=="OR" |
Lowe_worig$state=="WA" ] <- "5 (original)"
#Windows Mountain
Lowe_worig$windows[Lowe_worig$state=="AZ" |
Lowe_worig$state=="CO" |
Lowe_worig$state=="ID" |
Lowe_worig$state=="MT" |
Lowe_worig$state=="NM" |
Lowe_worig$state=="NV" |
Lowe_worig$state=="UT" |
Lowe_worig$state=="WY" ] <- "2 (original)"
#Windows Northern Plains
Lowe_worig$windows[Lowe_worig$state=="KS" |
Lowe_worig$state=="ND" |
Lowe_worig$state=="NE" |
Lowe_worig$state=="SD"] <- "4 (original)"
#Windows Southern Plains
Lowe_worig$windows[Lowe_worig$state=="OK" |
Lowe_worig$state=="TX"] <- "8 (original)"
#Windows Corn Belt
Lowe_worig$windows[Lowe_worig$state=="IA" |
Lowe_worig$state=="IL" |
Lowe_worig$state=="IN" |
Lowe_worig$state=="MO" |
Lowe_worig$state=="OH"] <- "3 (original)"
#Windows Southeast
Lowe_worig$windows[Lowe_worig$state=="AL" |
Lowe_worig$state=="FL" |
Lowe_worig$state=="GA" |
Lowe_worig$state=="SC"] <- "4 (original)"
#Windows Northeast
Lowe_worig$windows[Lowe_worig$state=="CT" |
Lowe_worig$state=="DE" |
Lowe_worig$state=="MA" |
Lowe_worig$state=="MD" |
Lowe_worig$state=="ME" |
Lowe_worig$state=="NH" |
Lowe_worig$state=="NJ" |
Lowe_worig$state=="NY" |
Lowe_worig$state=="PA" |
Lowe_worig$state=="RI" |
Lowe_worig$state=="VT" ] <- "2 (original)"
#Windows Lake States
Lowe_worig$windows[Lowe_worig$state=="MI" |
Lowe_worig$state=="MN" |
Lowe_worig$state=="WI"] <- "5 (original)"
#Windows Appalacian
Lowe_worig$windows[Lowe_worig$state=="KY" |
Lowe_worig$state=="NC" |
Lowe_worig$state=="TN" |
Lowe_worig$state=="VA" |
Lowe_worig$state=="WV"] <- "3 (original)"
#Windows Delta States
Lowe_worig$windows[Lowe_worig$state=="AR" |
Lowe_worig$state=="LA" |
Lowe_worig$state=="MS"] <- "5 (original)"
#Windows US48
Lowe_worig$windows[Lowe_worig$state=="US48"] <- "(original)"
# save these results
save(Lowe_worig, file="R_output/Lowe_worig.Rda")
# --------------------- #
#### 2-years windows ####
# --------------------- #
#Pacific
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#Mountain
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#NPlains
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#SPlains
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#Cornbelt
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#Southeast
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#Northeast
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#Lake States
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#Appalacian
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#Delta States
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(2), by.year = 1)
#merge all results
lowe_pacific <- lowe_pacific$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_mountain <- lowe_mountain$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_nplains <- lowe_nplains$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_splains <- lowe_splains$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_cornbelt <- lowe_cornbelt$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_southeast <- lowe_southeast$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_northeast <- lowe_northeast$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_lake <- lowe_lake$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_appalacian <- lowe_appalacian$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_delta <- lowe_delta$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
Lowe.level <- rbind(lowe_pacific, lowe_mountain, lowe_nplains,
lowe_splains, lowe_cornbelt, lowe_southeast,
lowe_northeast, lowe_lake, lowe_appalacian,
lowe_delta)
Lowe.level <- Lowe.level[order(Lowe.level$state),]
# Calculate changes (w2=2-years windows)
Lowe_w2 <- data.frame(matrix(ncol = 5, nrow = 48))
x <- c("state", "windows", "DTFP", "DMP", "DTFPE")
colnames(Lowe_w2) <- x
Lowe_w2$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"))
Lowe_w2$windows = 2
lowe1960 <- subset(Lowe.level, year==1960)
lowe2004 <- subset(Lowe.level, year==2004)
# DTFP
Lowe_w2$DTFP <- (lowe2004[,"TFP"] / lowe1960[,"TFP"])
# DMP
Lowe_w2$DMP <- (lowe2004[,"MP"] / lowe1960[,"MP"])
# DTFPE
Lowe_w2$DTFPE <- (lowe2004[,"TFPE"] / lowe1960[,"TFPE"])
# Add US-average as geometric mean
Lowe_w2 <- Lowe_w2 %>%
add_row(state="US48",
DTFP=exp(mean(log(Lowe_w2$DTFP))),
DMP=exp(mean(log(Lowe_w2$DMP))),
DTFPE=exp(mean(log(Lowe_w2$DTFPE))))
Lowe_w2$windows[Lowe_w2$state=="US48"] <- 2
# save these results
save(Lowe_w2, file="R_output/Lowe_w2.Rda")
# --------------------- #
#### 4-years windows ####
# --------------------- #
#Pacific
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#Mountain
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#NPlains
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#SPlains
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#Cornbelt
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#Southeast
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#Northeast
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#Lake States
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#Appalacian
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#Delta States
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(4), by.year = 1)
#merge all results
lowe_pacific <- lowe_pacific$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_mountain <- lowe_mountain$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_nplains <- lowe_nplains$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_splains <- lowe_splains$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_cornbelt <- lowe_cornbelt$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_southeast <- lowe_southeast$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_northeast <- lowe_northeast$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_lake <- lowe_lake$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_appalacian <- lowe_appalacian$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_delta <- lowe_delta$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
Lowe.level <- rbind(lowe_pacific, lowe_mountain, lowe_nplains,
lowe_splains, lowe_cornbelt, lowe_southeast,
lowe_northeast, lowe_lake, lowe_appalacian,
lowe_delta)
Lowe.level <- Lowe.level[order(Lowe.level$state),]
# Calculate changes
Lowe_w4 <- data.frame(matrix(ncol = 5, nrow = 48))
x <- c("state", "windows", "DTFP", "DMP", "DTFPE")
colnames(Lowe_w4) <- x
Lowe_w4$state <- rep(c("AL","AR","AZ","CA","CO","CT","DE","FL",
"GA","IA","ID","IL","IN","KS","KY","LA",
"MA","MD","ME","MI","MN","MO","MS","MT",
"NC","ND","NE","NH","NJ","NM","NV","NY",
"OH","OK","OR","PA","RI","SC","SD","TN",
"TX","UT","VA","VT","WA","WI","WV","WY"))
Lowe_w4$windows = 4
lowe1960 <- subset(Lowe.level, year==1960)
lowe2004 <- subset(Lowe.level, year==2004)
# DTFP
Lowe_w4$DTFP <- (lowe2004[,"TFP"] / lowe1960[,"TFP"])
# DMP
Lowe_w4$DMP <- (lowe2004[,"MP"] / lowe1960[,"MP"])
# DTFPE
Lowe_w4$DTFPE <- (lowe2004[,"TFPE"] / lowe1960[,"TFPE"])
# Add US-average as geometric mean
Lowe_w4 <- Lowe_w4 %>%
add_row(state="US48",
DTFP=exp(mean(log(Lowe_w4$DTFP))),
DMP=exp(mean(log(Lowe_w4$DMP))),
DTFPE=exp(mean(log(Lowe_w4$DTFPE))))
Lowe_w4$windows[Lowe_w2$state=="US48"] <- 4
# save these results
save(Lowe_w4, file="R_output/Lowe_w4.Rda")
# --------------------- #
#### 8-years windows ####
# --------------------- #
#Pacific
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(8), by.year = 1)
#Mountain
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(8), by.year = 1)
#NPlains
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(8), by.year = 1)
#SPlains
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(8), by.year = 1)
#Cornbelt
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(8), by.year = 1)
#Southeast
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(8), by.year = 1)
#Northeast
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(8), by.year = 1)
#Lake States
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(8), by.year = 1)
#Appalacian
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(8), by.year = 1)
#Delta States
lowe_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 = "vrs",
orientation = "out", cores = 8, scaled = F,
window = c(8), by.year = 1)
#merge all results
lowe_pacific <- lowe_pacific$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_mountain <- lowe_mountain$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_nplains <- lowe_nplains$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_splains <- lowe_splains$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_cornbelt <- lowe_cornbelt$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_southeast <- lowe_southeast$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_northeast <- lowe_northeast$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_lake <- lowe_lake$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_appalacian <- lowe_appalacian$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
lowe_delta <- lowe_delta$Levels[,c(1,2,11:13)] %>%
filter(year==1960 | year==2004)
Lowe.level <- rbind(lowe_pacific, lowe_mountain, lowe_nplains,
lowe_splains, lowe_cornbelt, lowe_southeast,
lowe_northeast, lowe_lake, lowe_appalacian,
lowe_delta)
Lowe.level <- Lowe.level[order(Lowe.level$state),]
# Calculate changes
Lowe_w8 <- data.frame(matrix(ncol = 5, nrow = 48))
x <- c("state", "windows", "DTFP", "DMP", "DTFPE")
colnames(Lowe_w8) <- x
Lowe_w8$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"))
Lowe_w8$windows = 8
lowe1960 <- subset(Lowe.level, year==1960)
lowe2004 <- subset(Lowe.level, year==2004)
# DTFP
Lowe_w8$DTFP <- (lowe2004[,"TFP"] / lowe1960[,"TFP"])
# DMP
Lowe_w8$DMP <- (lowe2004[,"MP"] / lowe1960[,"MP"])
# DTFPE
Lowe_w8$DTFPE <- (lowe2004[,"TFPE"] / lowe1960[,"TFPE"])
# Add US-average as geometric mean
Lowe_w8 <- Lowe_w8 %>%
add_row(state="US48",
DTFP=exp(mean(log(Lowe_w8$DTFP))),
DMP=exp(mean(log(Lowe_w8$DMP))),
DTFPE=exp(mean(log(Lowe_w8$DTFPE))))
Lowe_w8$windows[Lowe_w8$state=="US48"] <- 8
# save these results
save(Lowe_w8, file="R_output/Lowe_w8.Rda")
# ------------------------------------ #
#### Create a table for all results ####
# ------------------------------------ #
load("R_output/Lowe_worig.Rda")
load("R_output/Lowe_w2.Rda")
load("R_output/Lowe_w4.Rda")
load("R_output/Lowe_w8.Rda")
TFPwindows <- rbind(Lowe_worig,Lowe_w2, Lowe_w4, Lowe_w8)
TFPwindows <- TFPwindows[order(TFPwindows$state),]
# Set global option to produce latex output
options(knitr.table.format = "latex",
knitr.kable.NA = '')
# Select subset of states
TFPwindows <- TFPwindows %>%
filter(state == "CA" |
state == "AZ" |
state == "KS" |
state == "OK" |
state == "IA" |
state == "AL" |
state == "CT" |
state == "MI" |
state == "KY" |
state == "AR" |
state == "US48")
# Create table
Tab_TFPwindows <- kable(TFPwindows, booktabs = T,
digits = 2,
row.names = FALSE,
escape = FALSE,
linesep = "",
caption = "TFP and efficiency change in US agriculture (1960--2004) using the Lowe index with different windows.",
label = "Tab_TFPwindows",
col.names = linebreak(c('State', 'Window length \n in years',
"$\\Delta$TFP",
"$\\Delta$TFP*",
"$\\Delta$TFPE"))) %>%
row_spec(c(4,8,12,16,20,24,28,32,36,40), hline_after=T) %>%
kable_styling(font_size = 10,
latex_options = c("HOLD_position"))
# Print Latex file
writeLines(Tab_TFPwindows, "Tables/Tab_TFPwindows.tex")