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13b_Bootstrap_wosel.R
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13b_Bootstrap_wosel.R
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#-----------------------------------------------------#
# #
# This program estimates the model without non-random #
# crop selection with bootstrapped standard errors. #
# #
# Note: The bootstrap program (Section 1: Bootstrap #
# program) cannot be run because farm-level data are #
# confidential. The results of the bootstrap are #
# stored in lines 700 and 715, allowing to #
# create the tables in Section 2 (Presentation of #
# results). #
# #
#-----------------------------------------------------#
# load packages
library(dplyr)
library(writexl)
library(systemfit)
library(doFuture)
library(doRNG)
# load data and selection terms
load("rOutput/farm_ready.Rda")
# keep only variables needed
df_farm <- df_farm %>% select(key, year, nuts2,
iCereals, iProtein, iOilseed, iRoots, iCorn,
np_cereals, np_protein, np_oilseed, np_roots, np_corn, nw_fert,
k_land, k_labor, k_capital, trend, trend2,
lsh_cereals, lsh_protein, lsh_oilseed, lsh_roots,
gdd_1to3, prec_1to3, gddHigh_1to3, dd_1to3,
gdd_4to10, prec_4to10, gddHigh_4to10, dd_4to10,
np_cereals_fm, np_protein_fm, np_oilseed_fm, np_roots_fm, np_corn_fm, nw_fert_fm,
k_land_fm, k_labor_fm, k_capital_fm, trend_fm, trend2_fm,
lsh_cereals_fm, lsh_protein_fm, lsh_oilseed_fm, lsh_roots_fm,
gdd_1to3_fm, prec_1to3_fm, gddHigh_1to3_fm, dd_1to3_fm,
gdd_4to10_fm, prec_4to10_fm, gddHigh_4to10_fm, dd_4to10_fm,
qq_cereals, qq_protein, qq_oilseed, qq_roots, qq_corn, nx_fert, x_fert, x_otherinp,
gdd_obs, prec_obs, gddHigh_obs, dd_obs,
gdd_obs_1to3, prec_obs_1to3, gddHigh_obs_1to3, dd_obs_1to3,
gdd_obs_4to10, prec_obs_4to10, gddHigh_obs_4to10, dd_obs_4to10,
gdd_obs_fm, prec_obs_fm, gddHigh_obs_fm, dd_obs_fm,
gdd_obs_1to3_fm, prec_obs_1to3_fm, gddHigh_obs_1to3_fm, dd_obs_1to3_fm,
gdd_obs_4to10_fm, prec_obs_4to10_fm, gddHigh_obs_4to10_fm, dd_obs_4to10_fm)
#----------------------------#
#### 1) Bootstrap program ####
#----------------------------#
my.boot <- function(data, nrep, cluster, nCores, actual) {
# Set up the bootstrap
B <- nrep #Number of draws
registerDoFuture() # Initialize parallel computing
plan(multisession, workers = nCores) # Define parallel computing plan with number of cores
res <- foreach(1:B, .combine = rbind) %dorng% {
if (actual==FALSE) {
cluster_id <- unique(cluster)
sb_ID <- sample(cluster_id, replace = TRUE) # For clustering at farm-level, select "ID_farm". Otherwise, select "ID_nuts2"
data_boot <- list()
for (j in 1:length(sb_ID)) {
data_boot[[j]] <- df_farm[which(cluster == sb_ID[j]), ]
}
data_boot <- do.call(rbind, data_boot)
} else if (actual==TRUE) {
data_boot <- df_farm
}
tryCatch( { # in case there is an optimization error in one of the draws:
# -------------------- #
# START OF ESTIMATIONS #
# -------------------- #
library(dplyr)
#calculate sample mean for elasticity evaluation
dat_sm <- dplyr::summarise_all(data_boot, mean)
#Define structural equations
eqQQcereals <- qq_cereals ~ np_cereals + np_protein + np_oilseed + np_roots + np_corn + nw_fert +
k_land + k_labor + k_capital + trend + trend2 +
gdd_obs + prec_obs + gddHigh_obs + dd_obs +
gdd_1to3 + prec_1to3 + gddHigh_1to3 + dd_1to3 +
gdd_4to10 + prec_4to10 + gddHigh_4to10 + dd_4to10 +
gdd_obs_1to3 + prec_obs_1to3 + gddHigh_obs_1to3 + dd_obs_1to3 +
gdd_obs_4to10 + prec_obs_4to10 + gddHigh_obs_4to10 + dd_obs_4to10 +
np_cereals_fm + np_protein_fm + np_oilseed_fm + np_roots_fm + np_corn_fm + nw_fert_fm +
k_land_fm + k_labor_fm + k_capital_fm + trend_fm + trend2_fm +
gdd_obs_fm + prec_obs_fm + gddHigh_obs_fm + dd_obs_fm +
gdd_1to3_fm + prec_1to3_fm + gddHigh_1to3_fm + dd_1to3_fm +
gdd_4to10_fm + prec_4to10_fm + gddHigh_4to10_fm + dd_4to10_fm +
gdd_obs_1to3_fm + prec_obs_1to3_fm + gddHigh_obs_1to3_fm + dd_obs_1to3_fm +
gdd_obs_4to10_fm + prec_obs_4to10_fm + gddHigh_obs_4to10_fm + dd_obs_4to10_fm
eqQQprotein <- qq_protein ~ np_cereals + np_protein + np_oilseed + np_roots + np_corn + nw_fert +
k_land + k_labor + k_capital + trend + trend2 +
gdd_obs + prec_obs + gddHigh_obs + dd_obs +
gdd_1to3 + prec_1to3 + gddHigh_1to3 + dd_1to3 +
gdd_4to10 + prec_4to10 + gddHigh_4to10 + dd_4to10 +
gdd_obs_1to3 + prec_obs_1to3 + gddHigh_obs_1to3 + dd_obs_1to3 +
gdd_obs_4to10 + prec_obs_4to10 + gddHigh_obs_4to10 + dd_obs_4to10 +
np_cereals_fm + np_protein_fm + np_oilseed_fm + np_roots_fm + np_corn_fm + nw_fert_fm +
k_land_fm + k_labor_fm + k_capital_fm + trend_fm + trend2_fm +
gdd_obs_fm + prec_obs_fm + gddHigh_obs_fm + dd_obs_fm +
gdd_1to3_fm + prec_1to3_fm + gddHigh_1to3_fm + dd_1to3_fm +
gdd_4to10_fm + prec_4to10_fm + gddHigh_4to10_fm + dd_4to10_fm +
gdd_obs_1to3_fm + prec_obs_1to3_fm + gddHigh_obs_1to3_fm + dd_obs_1to3_fm +
gdd_obs_4to10_fm + prec_obs_4to10_fm + gddHigh_obs_4to10_fm + dd_obs_4to10_fm
eqQQoilseed <- qq_oilseed ~ np_cereals + np_protein + np_oilseed + np_roots + np_corn + nw_fert +
k_land + k_labor + k_capital + trend + trend2 +
gdd_obs + prec_obs + gddHigh_obs + dd_obs +
gdd_1to3 + prec_1to3 + gddHigh_1to3 + dd_1to3 +
gdd_4to10 + prec_4to10 + gddHigh_4to10 + dd_4to10 +
gdd_obs_1to3 + prec_obs_1to3 + gddHigh_obs_1to3 + dd_obs_1to3 +
gdd_obs_4to10 + prec_obs_4to10 + gddHigh_obs_4to10 + dd_obs_4to10 +
np_cereals_fm + np_protein_fm + np_oilseed_fm + np_roots_fm + np_corn_fm + nw_fert_fm +
k_land_fm + k_labor_fm + k_capital_fm + trend_fm + trend2_fm +
gdd_obs_fm + prec_obs_fm + gddHigh_obs_fm + dd_obs_fm +
gdd_1to3_fm + prec_1to3_fm + gddHigh_1to3_fm + dd_1to3_fm +
gdd_4to10_fm + prec_4to10_fm + gddHigh_4to10_fm + dd_4to10_fm +
gdd_obs_1to3_fm + prec_obs_1to3_fm + gddHigh_obs_1to3_fm + dd_obs_1to3_fm +
gdd_obs_4to10_fm + prec_obs_4to10_fm + gddHigh_obs_4to10_fm + dd_obs_4to10_fm
eqQQroots <- qq_roots ~ np_cereals + np_protein + np_oilseed + np_roots + np_corn + nw_fert +
k_land + k_labor + k_capital + trend + trend2 +
gdd_obs + prec_obs + gddHigh_obs + dd_obs +
gdd_1to3 + prec_1to3 + gddHigh_1to3 + dd_1to3 +
gdd_4to10 + prec_4to10 + gddHigh_4to10 + dd_4to10 +
gdd_obs_1to3 + prec_obs_1to3 + gddHigh_obs_1to3 + dd_obs_1to3 +
gdd_obs_4to10 + prec_obs_4to10 + gddHigh_obs_4to10 + dd_obs_4to10 +
np_cereals_fm + np_protein_fm + np_oilseed_fm + np_roots_fm + np_corn_fm + nw_fert_fm +
k_land_fm + k_labor_fm + k_capital_fm + trend_fm + trend2_fm +
gdd_obs_fm + prec_obs_fm + gddHigh_obs_fm + dd_obs_fm +
gdd_1to3_fm + prec_1to3_fm + gddHigh_1to3_fm + dd_1to3_fm +
gdd_4to10_fm + prec_4to10_fm + gddHigh_4to10_fm + dd_4to10_fm +
gdd_obs_1to3_fm + prec_obs_1to3_fm + gddHigh_obs_1to3_fm + dd_obs_1to3_fm +
gdd_obs_4to10_fm + prec_obs_4to10_fm + gddHigh_obs_4to10_fm + dd_obs_4to10_fm
eqQQcorn <- qq_corn ~ np_cereals + np_protein + np_oilseed + np_roots + np_corn + nw_fert +
k_land + k_labor + k_capital + trend + trend2 +
gdd_obs + prec_obs + gddHigh_obs + dd_obs +
gdd_1to3 + prec_1to3 + gddHigh_1to3 + dd_1to3 +
gdd_4to10 + prec_4to10 + gddHigh_4to10 + dd_4to10 +
gdd_obs_1to3 + prec_obs_1to3 + gddHigh_obs_1to3 + dd_obs_1to3 +
gdd_obs_4to10 + prec_obs_4to10 + gddHigh_obs_4to10 + dd_obs_4to10 +
np_cereals_fm + np_protein_fm + np_oilseed_fm + np_roots_fm + np_corn_fm + nw_fert_fm +
k_land_fm + k_labor_fm + k_capital_fm + trend_fm + trend2_fm +
gdd_obs_fm + prec_obs_fm + gddHigh_obs_fm + dd_obs_fm +
gdd_1to3_fm + prec_1to3_fm + gddHigh_1to3_fm + dd_1to3_fm +
gdd_4to10_fm + prec_4to10_fm + gddHigh_4to10_fm + dd_4to10_fm +
gdd_obs_1to3_fm + prec_obs_1to3_fm + gddHigh_obs_1to3_fm + dd_obs_1to3_fm +
gdd_obs_4to10_fm + prec_obs_4to10_fm + gddHigh_obs_4to10_fm + dd_obs_4to10_fm
eqNXfert <- nx_fert ~ np_cereals + np_protein + np_oilseed + np_roots + np_corn + nw_fert +
k_land + k_labor + k_capital + trend + trend2 +
gdd_obs + prec_obs + gddHigh_obs + dd_obs +
gdd_1to3 + prec_1to3 + gddHigh_1to3 + dd_1to3 +
gdd_4to10 + prec_4to10 + gddHigh_4to10 + dd_4to10 +
gdd_obs_1to3 + prec_obs_1to3 + gddHigh_obs_1to3 + dd_obs_1to3 +
gdd_obs_4to10 + prec_obs_4to10 + gddHigh_obs_4to10 + dd_obs_4to10 +
np_cereals_fm + np_protein_fm + np_oilseed_fm + np_roots_fm + np_corn_fm + nw_fert_fm +
k_land_fm + k_labor_fm + k_capital_fm + trend_fm + trend2_fm +
gdd_obs_fm + prec_obs_fm + gddHigh_obs_fm + dd_obs_fm +
gdd_1to3_fm + prec_1to3_fm + gddHigh_1to3_fm + dd_1to3_fm +
gdd_4to10_fm + prec_4to10_fm + gddHigh_4to10_fm + dd_4to10_fm +
gdd_obs_1to3_fm + prec_obs_1to3_fm + gddHigh_obs_1to3_fm + dd_obs_1to3_fm +
gdd_obs_4to10_fm + prec_obs_4to10_fm + gddHigh_obs_4to10_fm + dd_obs_4to10_fm
# Define system of equations
system <- list( QQcereals = eqQQcereals,
QQprotein = eqQQprotein,
QQoilseed = eqQQoilseed,
QQroots = eqQQroots,
QQcorn = eqQQcorn,
NXfert = eqNXfert)
## restrictions
restrict <- c( "QQcereals_np_protein - QQprotein_np_cereals = 0",
"QQcereals_np_oilseed - QQoilseed_np_cereals = 0",
"QQcereals_np_roots - QQroots_np_cereals = 0",
"QQcereals_np_corn - QQcorn_np_cereals = 0",
"QQcereals_nw_fert - NXfert_np_cereals = 0",
"QQprotein_np_oilseed - QQoilseed_np_protein = 0",
"QQprotein_np_roots - QQroots_np_protein = 0",
"QQprotein_np_corn - QQcorn_np_protein = 0",
"QQprotein_nw_fert - NXfert_np_protein = 0",
"QQoilseed_np_roots - QQroots_np_oilseed = 0",
"QQoilseed_np_corn - QQcorn_np_oilseed = 0",
"QQoilseed_nw_fert - NXfert_np_oilseed = 0",
"QQroots_np_corn - QQcorn_np_roots = 0",
"QQroots_nw_fert - NXfert_np_roots = 0",
"QQcorn_nw_fert - NXfert_np_corn = 0")
#--------------------------------#
#### Run Structural equations ####
#--------------------------------#
## Regression with iterated SUR estimation
model_linear <- systemfit::systemfit( formula = system, method = "SUR",
data = data_boot, restrict.matrix = restrict,
maxit = 100 )
coef_linear <- coef(model_linear)
# -------------------------------- #
#### Compute price elasticities ####
# ---------------------------------#
#set-up a data frame to store the results
elast_prices <- data.frame("Variables"=c("P Cereals", "P Protein", "P Oilseed", "P Roots", "P Corn", "W Fertilizer", "W Others"),
"Q Cereals"=rep(NA,7),
"Q Protein"=rep(NA,7),
"Q Oilseed"=rep(NA,7),
"Q Roots"=rep(NA,7),
"Q Corn"=rep(NA,7),
"X Fertilizer"=rep(NA,7),
"X Others"=rep(NA,7))
# ------------------------------ #
# Price elasticities for outputs #
# ------------------------------ #
#set-up the loop over crops
list_qcrops <- data.frame(crops=c("cereals","protein","oilseed","roots","corn"),
probit=c("iCereals","iProtein","iOilseed","iRoots","iCorn"),
struct=c("QQcereals","QQprotein", "QQoilseed", "QQroots", "QQcorn"))
n_qcrops <- list_qcrops %>% count() %>% as.numeric() # number of crops
list_prices <- data.frame(prices=c("np_cereals", "np_protein", "np_oilseed", "np_roots", "np_corn", "nw_fert"),
endswith=c("np_cereals)", "np_protein)", "np_oilseed)", "np_roots)", "np_corn)", "nw_fert)"))
n_prices <- list_prices %>% count() %>% as.numeric()
# Loop over all crops
for (i in 1:n_qcrops) {
coefs_struct_crop <- dplyr::as_tibble(t(coef_linear)) %>% select(starts_with(list_qcrops[i,"struct"]))
mean_quantity <- dat_sm %>% select(paste0("qq_",list_qcrops[i,"crops"]))
for (j in 1:n_prices) {
coef_struct_p <- coefs_struct_crop %>% select(ends_with(list_prices[j,"prices"]))
mean_price <- dat_sm %>% select(list_prices[j,"prices"])
elast_prices[j,1+i] <- (coef_struct_p) * (mean_price / mean_quantity )
}
# Price elasticity for numeraire adds up to 0
elast_prices[j+1,1+i] <- 0 - elast_prices[1,1+i] - elast_prices[2,1+i] - elast_prices[3,1+i] - elast_prices[4,1+i] - elast_prices[5,1+i] - elast_prices[6,1+i]
}
# ----------------------------- #
# Price elasticities for inputs #
# ----------------------------- #
# Fertilizer demand (note: There is no selection equation for fertilizer --> use regular derivative)
coef_fert <- dplyr::as_tibble(t(coef_linear)) %>% select(starts_with("NXfert"))
for (j in 1:n_prices) {
coef_struct_p <- coef_fert %>% select(ends_with(list_prices[j,"prices"]))
mean_price <- dat_sm %>% select(list_prices[j,"prices"])
elast_prices[j,7] <- coef_struct_p * (mean_price/dat_sm$nx_fert)
}
elast_prices[j+1,7] <- 0 - elast_prices[1,7] - elast_prices[2,7] - elast_prices[3,7] - elast_prices[4,7] - elast_prices[5,7] - elast_prices[6,7]
# Other input demand
list_prices <- data.frame(prices=c("np_cereals", "np_protein", "np_oilseed", "np_roots", "np_corn", "nw_fert"),
endswith=c("np_cereals)", "np_protein)", "np_oilseed)", "np_roots)", "np_corn)", "nw_fert)"),
quantities=c("qq_cereals", "qq_protein", "qq_oilseed", "qq_roots", "qq_corn", "x_fert")) # Note: Here I use x_fert instead of nx_fert so that I do not have to add a negative sign to X.Others_W.Fert
n_prices <- list_prices %>% count() %>% as.numeric()
for (j in 1:n_prices) {
elast_sum <- sum(elast_prices[1:n_prices,j+1]) # sum of all elasticities except for w_others
mean_quantity <- dat_sm %>% select(list_prices[j,"quantities"])
mean_price <- dat_sm %>% select(list_prices[j,"prices"])
elast_prices[j,8] <- (mean_quantity * mean_price) / dat_sm$x_otherinp *
(elast_sum)
}
elast_prices[j+1,8] <- 0 - elast_prices[1,8] - elast_prices[2,8] - elast_prices[3,8] - elast_prices[4,8] - elast_prices[5,8] - elast_prices[6,8]
# ------------------------------------- #
# Store price elasticities in bootstrap #
# --------------------------------------#
#q_cer
el_qcer_pcer <- c('el_qcer_pcer' = as.numeric(elast_prices[1,2]))
el_qcer_pprot <- c('el_qcer_pprot' = as.numeric(elast_prices[2,2]))
el_qcer_poil <- c('el_qcer_poil' = as.numeric(elast_prices[3,2]))
el_qcer_proots <- c('el_qcer_proots' = as.numeric(elast_prices[4,2]))
el_qcer_pcorn <- c('el_qcer_pcorn' = as.numeric(elast_prices[5,2]))
el_qcer_wfert <- c('el_qcer_wfert' = as.numeric(elast_prices[6,2]))
el_qcer_wotherinp <- c('el_qcer_wotherinp' = as.numeric(elast_prices[7,2]))
#q_prot
el_qprot_pcer <- c('el_qprot_pcer' = as.numeric(elast_prices[1,3]))
el_qprot_pprot <- c('el_qprot_pprot' = as.numeric(elast_prices[2,3]))
el_qprot_poil <- c('el_qprot_poil' = as.numeric(elast_prices[3,3]))
el_qprot_proots <- c('el_qprot_proots' = as.numeric(elast_prices[4,3]))
el_qprot_pcorn <- c('el_qprot_pcorn' = as.numeric(elast_prices[5,3]))
el_qprot_wfert <- c('el_qprot_wfert' = as.numeric(elast_prices[6,3]))
el_qprot_wotherinp <- c('el_qprot_wotherinp' = as.numeric(elast_prices[7,3]))
#q_oil
el_qoil_pcer <- c('el_qoil_pcer' = as.numeric(elast_prices[1,4]))
el_qoil_pprot <- c('el_qoil_pprot' = as.numeric(elast_prices[2,4]))
el_qoil_poil <- c('el_qoil_poil' = as.numeric(elast_prices[3,4]))
el_qoil_proots <- c('el_qoil_proots' = as.numeric(elast_prices[4,4]))
el_qoil_pcorn <- c('el_qoil_pcorn' = as.numeric(elast_prices[5,4]))
el_qoil_wfert <- c('el_qoil_wfert' = as.numeric(elast_prices[6,4]))
el_qoil_wotherinp <- c('el_qoil_wotherinp' = as.numeric(elast_prices[7,4]))
#qroots
el_qroots_pcer <- c('el_qroots_pcer' = as.numeric(elast_prices[1,5]))
el_qroots_pprot <- c('el_qroots_pprot' = as.numeric(elast_prices[2,5]))
el_qroots_poil <- c('el_qroots_poil' = as.numeric(elast_prices[3,5]))
el_qroots_proots <- c('el_qroots_proots' = as.numeric(elast_prices[4,5]))
el_qroots_pcorn <- c('el_qroots_pcorn' = as.numeric(elast_prices[5,5]))
el_qroots_wfert <- c('el_qroots_wfert' = as.numeric(elast_prices[6,5]))
el_qroots_wotherinp <- c('el_qroots_wotherinp' = as.numeric(elast_prices[7,5]))
#qcorn
el_qcorn_pcer <- c('el_qcorn_pcer' = as.numeric(elast_prices[1,6]))
el_qcorn_pprot <- c('el_qcorn_pprot' = as.numeric(elast_prices[2,6]))
el_qcorn_poil <- c('el_qcorn_poil' = as.numeric(elast_prices[3,6]))
el_qcorn_proots <- c('el_qcorn_proots' = as.numeric(elast_prices[4,6]))
el_qcorn_pcorn <- c('el_qcorn_pcorn' = as.numeric(elast_prices[5,6]))
el_qcorn_wfert <- c('el_qcorn_wfert' = as.numeric(elast_prices[6,6]))
el_qcorn_wotherinp <- c('el_qcorn_wotherinp' = as.numeric(elast_prices[7,6]))
#xfert
el_xfert_pcer <- c('el_xfert_pcer' = as.numeric(elast_prices[1,7]))
el_xfert_pprot <- c('el_xfert_pprot' = as.numeric(elast_prices[2,7]))
el_xfert_poil <- c('el_xfert_poils' = as.numeric(elast_prices[3,7]))
el_xfert_proots <- c('el_xfert_proots' = as.numeric(elast_prices[4,7]))
el_xfert_pcorn <- c('el_xfert_pcorn' = as.numeric(elast_prices[5,7]))
el_xfert_wfert <- c('el_xfert_wfert' = as.numeric(elast_prices[6,7]))
el_xfert_wotherinp <- c('el_xfert_woth' = as.numeric(elast_prices[7,7]))
#xother
el_xotherinp_pcer <- c('el_xotherinp_pcer' = as.numeric(elast_prices[1,8]))
el_xotherinp_pprot <- c('el_xotherinp_pprot' = as.numeric(elast_prices[2,8]))
el_xotherinp_poil <- c('el_xotherinp_poil' = as.numeric(elast_prices[3,8]))
el_xotherinp_proots <- c('el_xotherinp_proots' = as.numeric(elast_prices[4,8]))
el_xotherinp_pcorn <- c('el_xotherinp_pcorn' = as.numeric(elast_prices[5,8]))
el_xotherinp_wfert <- c('el_xotherinp_wfert' = as.numeric(elast_prices[6,8]))
el_xotherinp_wotherinp <- c('el_xotherinp_wotherinp' = as.numeric(elast_prices[7,8]))
# ---------------------------------- #
#### Compute weather elasticities ####
# ---------------------------------- #
# ------------------------------ #
# Weather elasticities for crops #
# ------------------------------ #
#set-up a data frame to store the results
elast_weather <- data.frame("Variables"=c("GDD_obs", "PREC_obs", "GDDHigh_obs", "DD_obs", "GDD_past", "PREC_past", "GDDHigh_past", "DD_past"),
"Q Cereals"=rep(NA,8),
"Q Protein"=rep(NA,8),
"Q Oilseed"=rep(NA,8),
"Q Roots"=rep(NA,8),
"Q Corn"=rep(NA,8),
"X Fertilizer"=rep(NA,8))
# Weather vars
list_weather <- data.frame(weather_obs= c("gdd_obs","prec_obs","gddHigh_obs","dd_obs"),
weather_1to3= c("gdd_1to3","prec_1to3","gddHigh_1to3","dd_1to3"),
weather_4to10=c("gdd_4to10","prec_4to10","gddHigh_4to10","dd_4to10"),
weather_obs_1to3 = c("gdd_obs_1to3","prec_obs_1to3","gddHigh_obs_1to3","dd_obs_1to3"),
weather_obs_4to10 = c("gdd_obs_4to10","prec_obs_4to10","gddHigh_obs_4to10","dd_obs_4to10"),
weather_obs_endwith= c("_gdd_obs","_prec_obs","_gddHigh_obs","_dd_obs"),
weather_1to3_endwith= c("_gdd_1to3","_prec_1to3","_gddHigh_1to3","_dd_1to3"),
weather_4to10_endwith=c("_gdd_4to10","_prec_4to10","_gddHigh_4to10","_dd_4to10"),
weather_obs_1to3_endwith = c("_gdd_obs_1to3","_prec_obs_1to3","_gddHigh_obs_1to3","_dd_obs_1to3"),
weather_obs_4to10_endwith = c("_gdd_obs_4to10","_prec_obs_4to10","_gddHigh_obs_4to10","_dd_obs_4to10"))
n_weather <- list_weather %>% count() %>% as.numeric()
# Loop over all crops
for (i in 1:n_qcrops) {
coefs_struct_crop <- dplyr::as_tibble(t(coef_linear)) %>% select(starts_with(list_qcrops[i,"struct"]))
mean_quantity <- dat_sm %>% select(paste0("qq_",list_qcrops[i,"crops"]))
# Loop over all weather variables
for (j in 1:n_weather) {
# Weather-interaction terms
coef_struct_weather_obs_1to3 <- coefs_struct_crop %>% select(ends_with(list_weather[j,"weather_obs_1to3_endwith"]))
coef_struct_weather_obs_4to10 <- coefs_struct_crop %>% select(ends_with(list_weather[j,"weather_obs_4to10_endwith"]))
# Mean weather for derivatives
mean_weather_obs <- dat_sm %>% select(list_weather[j,"weather_obs"])
mean_weather_1to3 <- dat_sm %>% select(list_weather[j,"weather_1to3"])
mean_weather_4to10 <- dat_sm %>% select(list_weather[j,"weather_4to10"])
# Observed weather (note: observed weather only affects structural equations, not crop selection; also note that PHI_* is treated like a constant term )
coef_struct_weather <- coefs_struct_crop %>% select(ends_with(list_weather[j,"weather_obs_endwith"]))
elast_weather[j,i+1] <-(coef_struct_weather + coef_struct_weather_obs_1to3*mean_weather_1to3 + coef_struct_weather_obs_4to10*mean_weather_4to10) * (1/mean_quantity)
# Weather_1to3
coef_struct_weather <- coefs_struct_crop %>% select(ends_with(list_weather[j,"weather_1to3_endwith"]))
elast_weather_1to3 <- (coef_struct_weather + coef_struct_weather_obs_1to3*mean_weather_obs) * (1 / mean_quantity )
# Weather_4to10
coef_struct_weather <- coefs_struct_crop %>% select(ends_with(list_weather[j,"weather_4to10_endwith"]))
elast_weather_4to10 <- (coef_struct_weather + coef_struct_weather_obs_4to10*mean_weather_obs) * (1 / mean_quantity )
# Weather_past
elast_weather[j+4,i+1] <- elast_weather_1to3 + elast_weather_4to10
}
}
# ----------------------------------- #
# Weather elasticities for fertilizer #
# ----------------------------------- #
# Weather vars
list_weather <- data.frame(weather_obs= c("gdd_obs","prec_obs","gddHigh_obs","dd_obs"),
weather_1to3= c("gdd_1to3","prec_1to3","gddHigh_1to3","dd_1to3"),
weather_4to10=c("gdd_4to10","prec_4to10","gddHigh_4to10","dd_4to10"),
weather_obs_1to3 = c("gdd_obs_1to3","prec_obs_1to3","gddHigh_obs_1to3","dd_obs_1to3"),
weather_obs_4to10 = c("gdd_obs_4to10","prec_obs_4to10","gddHigh_obs_4to10","dd_obs_4to10"),
weather_obs_endwith= c("_gdd_obs","_prec_obs","_gddHigh_obs","_dd_obs"),
weather_1to3_endwith= c("_gdd_1to3","_prec_1to3","_gddHigh_1to3","_dd_1to3"),
weather_4to10_endwith=c("_gdd_4to10","_prec_4to10","_gddHigh_4to10","_dd_4to10"),
weather_obs_1to3_endwith = c("_gdd_obs_1to3","_prec_obs_1to3","_gddHigh_obs_1to3","_dd_obs_1to3"),
weather_obs_4to10_endwith = c("_gdd_obs_4to10","_prec_obs_4to10","_gddHigh_obs_4to10","_dd_obs_4to10"))
n_weather <- list_weather %>% count() %>% as.numeric()
coefs_fert <- dplyr::as_tibble(t(coef_linear)) %>% select(starts_with("NXfert"))
# Loop over all weather variables
for (j in 1:n_weather) {
# Weather-interaction terms
coef_fert_weather_obs_1to3 <- coefs_fert %>% select(ends_with(list_weather[j,"weather_obs_1to3_endwith"]))
coef_fert_weather_obs_4to10 <- coefs_fert %>% select(ends_with(list_weather[j,"weather_obs_4to10_endwith"]))
# Mean weather for derivatives
mean_weather_obs <- dat_sm %>% select(list_weather[j,"weather_obs"])
mean_weather_1to3 <- dat_sm %>% select(list_weather[j,"weather_1to3"])
mean_weather_4to10 <- dat_sm %>% select(list_weather[j,"weather_4to10"])
# Observed weather (note: observed weather only affects structural equations, not crop selection; also note that PHI_* is treated like a constant term )
coef_fert_weather <- coefs_fert %>% select(ends_with(list_weather[j,"weather_obs_endwith"]))
elast_weather[j,7] <- (coef_fert_weather + coef_fert_weather_obs_1to3*mean_weather_1to3 + coef_fert_weather_obs_4to10*mean_weather_4to10) * (1/dat_sm$nx_fert)
# Weather_1to3
coef_fert_weather <- coefs_fert %>% select(ends_with(list_weather[j,"weather_1to3_endwith"]))
elast_weather_1to3 <- (coef_fert_weather + coef_fert_weather_obs_1to3*mean_weather_obs) * (1/dat_sm$nx_fert)
# Weather_4to10
coef_fert_weather <- coefs_fert %>% select(ends_with(list_weather[j,"weather_4to10_endwith"]))
elast_weather_4to10 <- (coef_fert_weather + coef_fert_weather_obs_4to10*mean_weather_obs) * (1/dat_sm$nx_fert)
# Weather_past
elast_weather[j+4,7] <- elast_weather_1to3 + elast_weather_4to10
}
# --------------------------------------- #
# Store weather elasticities in bootstrap #
# ----------------------------------------#
# Rescale results to obtain the units days, mm, days, days
elast_weather[,2:7] <- elast_weather[,2:7]*100
#qcer
# obs
sel_qcer_gdd_obs <- c('sel_qcer_gdd_obs' = as.numeric(elast_weather[1,2]))
sel_qcer_prec_obs <- c('sel_qcer_prec_obs' = as.numeric(elast_weather[2,2]))
sel_qcer_gddHigh_obs <- c('sel_qcer_gddHigh_obs' = as.numeric(elast_weather[3,2]))
sel_qcer_dd_obs <- c('sel_qcer_dd_obs' = as.numeric(elast_weather[4,2]))
#past
sel_qcer_gdd_past <- c('sel_qcer_gdd_past' = as.numeric(elast_weather[5,2]))
sel_qcer_prec_past <- c('sel_qcer_prec_past' = as.numeric(elast_weather[6,2]))
sel_qcer_gddHigh_past <- c('sel_qcer_gddHigh_past' = as.numeric(elast_weather[7,2]))
sel_qcer_dd_past <- c('sel_qcer_dd_past' = as.numeric(elast_weather[8,2]))
#qprot
# obs
sel_qprot_gdd_obs <- c('sel_qprot_gdd_obs' = as.numeric(elast_weather[1,3]))
sel_qprot_prec_obs <- c('sel_qprot_prec_obs' = as.numeric(elast_weather[2,3]))
sel_qprot_gddHigh_obs <- c('sel_qprot_gddHigh_obs' = as.numeric(elast_weather[3,3]))
sel_qprot_dd_obs <- c('sel_qprot_dd_obs' = as.numeric(elast_weather[4,3]))
#past
sel_qprot_gdd_past <- c('sel_qprot_gdd_past' = as.numeric(elast_weather[5,3]))
sel_qprot_prec_past <- c('sel_qprot_prec_past' = as.numeric(elast_weather[6,3]))
sel_qprot_gddHigh_past <- c('sel_qprot_gddHigh_past' = as.numeric(elast_weather[7,3]))
sel_qprot_dd_past <- c('sel_qprot_dd_past' = as.numeric(elast_weather[8,3]))
#qoil
# obs
sel_qoil_gdd_obs <- c('sel_qoil_gdd_obs' = as.numeric(elast_weather[1,4]))
sel_qoil_prec_obs <- c('sel_qoil_prec_obs' = as.numeric(elast_weather[2,4]))
sel_qoil_gddHigh_obs <- c('sel_qoil_gddHigh_obs' = as.numeric(elast_weather[3,4]))
sel_qoil_dd_obs <- c('sel_qoil_dd_obs' = as.numeric(elast_weather[4,4]))
#past
sel_qoil_gdd_past <- c('sel_qoil_gdd_past' = as.numeric(elast_weather[5,4]))
sel_qoil_prec_past <- c('sel_qoil_prec_past' = as.numeric(elast_weather[6,4]))
sel_qoil_gddHigh_past <- c('sel_qoil_gddHigh_past' = as.numeric(elast_weather[7,4]))
sel_qoil_dd_past <- c('sel_qoil_dd_past' = as.numeric(elast_weather[8,4]))
#qroots
# obs
sel_qroots_gdd_obs <- c('sel_qroots_gdd_obs' = as.numeric(elast_weather[1,5]))
sel_qroots_prec_obs <- c('sel_qroots_prec_obs' = as.numeric(elast_weather[2,5]))
sel_qroots_gddHigh_obs <- c('sel_qroots_gddHigh_obs' = as.numeric(elast_weather[3,5]))
sel_qroots_dd_obs <- c('sel_qroots_dd_obs' = as.numeric(elast_weather[4,5]))
#past
sel_qroots_gdd_past <- c('sel_qroots_gdd_past' = as.numeric(elast_weather[5,5]))
sel_qroots_prec_past <- c('sel_qroots_prec_past' = as.numeric(elast_weather[6,5]))
sel_qroots_gddHigh_past <- c('sel_qroots_gddHigh_past' = as.numeric(elast_weather[7,5]))
sel_qroots_dd_past <- c('sel_qroots_dd_past' = as.numeric(elast_weather[8,5]))
#corn
# obs
sel_qcorn_gdd_obs <- c('sel_qcorn_gdd_obs' = as.numeric(elast_weather[1,6]))
sel_qcorn_prec_obs <- c('sel_qcorn_prec_obs' = as.numeric(elast_weather[2,6]))
sel_qcorn_gddHigh_obs <- c('sel_qcorn_gddHigh_obs' = as.numeric(elast_weather[3,6]))
sel_qcorn_dd_obs <- c('sel_qcorn_dd_obs' = as.numeric(elast_weather[4,6]))
#past
sel_qcorn_gdd_past <- c('sel_qcorn_gdd_past' = as.numeric(elast_weather[5,6]))
sel_qcorn_prec_past <- c('sel_qcorn_prec_past' = as.numeric(elast_weather[6,6]))
sel_qcorn_gddHigh_past <- c('sel_qcorn_gddHigh_past' = as.numeric(elast_weather[7,6]))
sel_qcorn_dd_past <- c('sel_qcorn_dd_past' = as.numeric(elast_weather[8,6]))
#xfert
# obs
sel_xfert_gdd_obs <- c('sel_xfert_gdd_obs' = as.numeric(elast_weather[1,7]))
sel_xfert_prec_obs <- c('sel_xfert_prec_obs' = as.numeric(elast_weather[2,7]))
sel_xfert_gddHigh_obs <- c('sel_xfert_gddHigh_obs' = as.numeric(elast_weather[3,7]))
sel_xfert_dd_obs <- c('sel_xfert_dd_obs' = as.numeric(elast_weather[4,7]))
#past
sel_xfert_gdd_past <- c('sel_xfert_gdd_past' = as.numeric(elast_weather[5,7]))
sel_xfert_prec_past <- c('sel_xfert_prec_past' = as.numeric(elast_weather[6,7]))
sel_xfert_gddHigh_past <- c('sel_xfert_gddHigh_past' = as.numeric(elast_weather[7,7]))
sel_xfert_dd_past <- c('sel_xfert_dd_past' = as.numeric(elast_weather[8,7]))
# ------------------------------- #
#### Store results of interest ####
# ------------------------------- #
result <- c(model_linear$coefficients, #structural coefficients
el_qcer_pcer, #price elasticities
el_qcer_pprot,
el_qcer_poil,
el_qcer_proots,
el_qcer_pcorn,
el_qcer_wfert,
el_qcer_wotherinp,
el_qprot_pcer,
el_qprot_pprot,
el_qprot_poil,
el_qprot_proots,
el_qprot_pcorn,
el_qprot_wfert,
el_qprot_wotherinp,
el_qoil_pcer,
el_qoil_pprot,
el_qoil_poil,
el_qoil_proots,
el_qoil_pcorn,
el_qoil_wfert,
el_qoil_wotherinp,
el_qroots_pcer,
el_qroots_pprot,
el_qroots_poil,
el_qroots_proots,
el_qroots_pcorn,
el_qroots_wfert,
el_qroots_wotherinp,
el_qcorn_pcer,
el_qcorn_pprot,
el_qcorn_poil,
el_qcorn_proots,
el_qcorn_pcorn,
el_qcorn_wfert,
el_qcorn_wotherinp,
el_xfert_pcer,
el_xfert_pprot,
el_xfert_poil,
el_xfert_proots,
el_xfert_pcorn,
el_xfert_wfert,
el_xfert_wotherinp,
el_xotherinp_pcer,
el_xotherinp_pprot,
el_xotherinp_poil,
el_xotherinp_proots,
el_xotherinp_pcorn,
el_xotherinp_wfert,
el_xotherinp_wotherinp,
sel_qcer_gdd_obs, #weather elasticities
sel_qcer_prec_obs,
sel_qcer_gddHigh_obs,
sel_qcer_dd_obs,
sel_qprot_gdd_obs,
sel_qprot_prec_obs,
sel_qprot_gddHigh_obs,
sel_qprot_dd_obs,
sel_qoil_gdd_obs,
sel_qoil_prec_obs,
sel_qoil_gddHigh_obs,
sel_qoil_dd_obs,
sel_qroots_gdd_obs,
sel_qroots_prec_obs,
sel_qroots_gddHigh_obs,
sel_qroots_dd_obs,
sel_qcorn_gdd_obs,
sel_qcorn_prec_obs,
sel_qcorn_gddHigh_obs,
sel_qcorn_dd_obs,
sel_xfert_gdd_obs,
sel_xfert_prec_obs,
sel_xfert_gddHigh_obs,
sel_xfert_dd_obs,
sel_qcer_gdd_past,
sel_qcer_prec_past,
sel_qcer_gddHigh_past,
sel_qcer_dd_past,
sel_qprot_gdd_past,
sel_qprot_prec_past,
sel_qprot_gddHigh_past,
sel_qprot_dd_past,
sel_qoil_gdd_past,
sel_qoil_prec_past,
sel_qoil_gddHigh_past,
sel_qoil_dd_past,
sel_qroots_gdd_past,
sel_qroots_prec_past,
sel_qroots_gddHigh_past,
sel_qroots_dd_past,
sel_qcorn_gdd_past,
sel_qcorn_prec_past,
sel_qcorn_gddHigh_past,
sel_qcorn_dd_past,
sel_xfert_gdd_past,
sel_xfert_prec_past,
sel_xfert_gddHigh_past,
sel_xfert_dd_past)
result
}, error=function(err) {rep(NA,475)} ) # insert NA's if there is an optimization error
}
}
#-------------------------------------------#
#### Obtain and store the actual results ####
#-------------------------------------------#
# run one draw with the original data
system.time(boot_actresults <- my.boot(data=df_farm, nrep=1, cluster=df_farm$key, nCores=1, actual=TRUE))
# save actual result
save(boot_actresults, file="rOutput/bootresults_wosel_actual.Rda")
boot_actresults <- NULL
#------------------------------------------------#
#### Run bootstrap: Cluster at the farm-level ####
#------------------------------------------------#
# find number of cores
nCores <- parallel::detectCores() -1
# run the bootstrap
set.seed(1234)
system.time(boot_results <- my.boot(data=df_farm, nrep=1000, cluster=df_farm$key, nCores=nCores, actual=FALSE))
# save
save(boot_results, file="rOutput/bootresults_linear_wosel.Rda")
boot_results <- NULL
#----------------------------------#
#### 2) Presentation of results ####
#----------------------------------#
load("rOutput/bootresults_wosel.Rda")
load("rOutput/bootresults_wosel_actual.Rda")
# Turn matrix into data frame
boot_results <- as.data.frame(boot_results)
summary(boot_results$sel_qcer_gdd_obs)
# --------------------------------- #
# Create list with original results #
# --------------------------------- #
names(boot_results)
boot_summary <- data.frame("Variable"=c(names(boot_results)),
"Original"=boot_actresults)
# ------------------------ #
# Add confidence intervals #
# ------------------------ #
# Confidence intervals
CIs <- t(sapply(boot_results,function(i) quantile(i,c(0.025, 0.975), na.rm=TRUE)))
CIs <- as.data.frame(CIs)
colnames(CIs) <- c("L95","U95")
# Add CIs to boot_summary
boot_summary <- cbind(boot_summary, CIs)
# Organize
qqCereals <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "QQcereals_")),]
qqProtein <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "QQprotein_")),]
qqOilseed <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "QQoilseed_")),]
qqRoots <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "QQroots_")),]
qqCorn <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "QQcorn_")),]
nxFert <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "NXfert_")),]
PriceElast_cereals <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "el_qcer_")),]
PriceElast_protein <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "el_qprot_")),]
PriceElast_oilseed <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "el_qoil_")),]
PriceElast_roots <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "el_qroots_")),]
PriceElast_corn <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "el_qcorn_")),]
PriceElast_fert <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "el_xfert_")),]
PriceElast_others <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "el_xotherinp_")),]
WeatherElast_cereals <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "sel_qcer_")),]
WeatherElast_protein <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "sel_qprot_")),]
WeatherElast_oilseed <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "sel_qoil_")),]
WeatherElast_roots <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "sel_qroots_")),]
WeatherElast_corn <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "sel_qcorn_")),]
WeatherElast_fert <- boot_summary[(startsWith(x = boot_summary$Variable, prefix = "sel_xfert_")),]
#------------------------------------------------------#
#### Table: Regression results structural equations ####
#------------------------------------------------------#
#Take original tables without Mundlak terms
tab_structural <- rbind(qqCereals,qqProtein,qqOilseed,qqRoots,qqCorn,nxFert) %>%
filter(!grepl("_fm",Variable)) %>%
select("Variable","Coeff"="Original", "L95", "U95")
#Numbers smaller than 0.005 should be in scientific format; others in 0.01 etc.
tab_structural <- as.data.frame(cbind(tab_structural[,1], sapply(tab_structural[,2:ncol(tab_structural)], function(x) ifelse(abs(x)<0.005&is.numeric(x),format(x,scientific=TRUE,digits=1),format(round(x, 2), nsmall = 2, scientific=FALSE)) )) )
tab_structural <- dplyr::rename(tab_structural, "Variable" = "V1")
# remove white space
tab_structural <- as.data.frame(sapply(tab_structural, function(x) (gsub(" ", "", x))))
#Put CIs together
tab_structural$L95 <- paste0(paste0("(",tab_structural$L95,";"),
paste0(tab_structural$U95,")"))
tab_structural$U95 <- NULL
tab_structural <- dplyr::rename(tab_structural,"CI" = "L95")
#Use nice variable names
tab_structural$Variable <- c("Intercept", "P Cereals", "P Protein", "P Oilseed crops", "P Root crops", "P Corn", "R Fert",
"K Land", "K Labor", "K Capital", "Trend", "Trend^2",
"Gdd", "Prec", "GddHigh", "DD",
"Gdd_1to3", "Prec_1to3", "GddHigh_1to3", "DD_1to3",
"Gdd_4to10", "Prec_4to10", "GddHigh_4to10", "DD_4to10",
"Gdd x Gdd_1to3", "Prec x Prec_1to3", "GddHigh x GddHigh_1to3", "DD x DD_1to3",
"Gdd x Gdd_4to10", "Prec x Prec_4to10", "GddHigh x GddHigh_4to10", "DD x DD_4to10",
"Intercept", "P Cereals", "P Protein", "P Oilseed crops", "P Root crops", "P Corn", "R Fert",
"K Land", "K Labor", "K Capital", "Trend", "Trend^2",
"Gdd", "Prec", "GddHigh", "DD",
"Gdd_1to3", "Prec_1to3", "GddHigh_1to3", "DD_1to3",
"Gdd_4to10", "Prec_4to10", "GddHigh_4to10", "DD_4to10",
"Gdd x Gdd_1to3", "Prec x Prec_1to3", "GddHigh x GddHigh_1to3", "DD x DD_1to3",
"Gdd x Gdd_4to10", "Prec x Prec_4to10", "GddHigh x GddHigh_4to10", "DD x DD_4to10",
"Intercept", "P Cereals", "P Protein", "P Oilseed crops", "P Root crops", "P Corn", "R Fert",
"K Land", "K Labor", "K Capital", "Trend", "Trend^2",
"Gdd", "Prec", "GddHigh", "DD",
"Gdd_1to3", "Prec_1to3", "GddHigh_1to3", "DD_1to3",
"Gdd_4to10", "Prec_4to10", "GddHigh_4to10", "DD_4to10",
"Gdd x Gdd_1to3", "Prec x Prec_1to3", "GddHigh x GddHigh_1to3", "DD x DD_1to3",
"Gdd x Gdd_4to10", "Prec x Prec_4to10", "GddHigh x GddHigh_4to10", "DD x DD_4to10",
"Intercept", "P Cereals", "P Protein", "P Oilseed crops", "P Root crops", "P Corn", "R Fert",
"K Land", "K Labor", "K Capital", "Trend", "Trend^2",
"Gdd", "Prec", "GddHigh", "DD",
"Gdd_1to3", "Prec_1to3", "GddHigh_1to3", "DD_1to3",
"Gdd_4to10", "Prec_4to10", "GddHigh_4to10", "DD_4to10",
"Gdd x Gdd_1to3", "Prec x Prec_1to3", "GddHigh x GddHigh_1to3", "DD x DD_1to3",
"Gdd x Gdd_4to10", "Prec x Prec_4to10", "GddHigh x GddHigh_4to10", "DD x DD_4to10",
"Intercept", "P Cereals", "P Protein", "P Oilseed crops", "P Root crops", "P Corn", "R Fert",
"K Land", "K Labor", "K Capital", "Trend", "Trend^2",
"Gdd", "Prec", "GddHigh", "DD",
"Gdd_1to3", "Prec_1to3", "GddHigh_1to3", "DD_1to3",
"Gdd_4to10", "Prec_4to10", "GddHigh_4to10", "DD_4to10",
"Gdd x Gdd_1to3", "Prec x Prec_1to3", "GddHigh x GddHigh_1to3", "DD x DD_1to3",
"Gdd x Gdd_4to10", "Prec x Prec_4to10", "GddHigh x GddHigh_4to10", "DD x DD_4to10",
"Intercept", "P Cereals", "P Protein", "P Oilseed crops", "P Root crops", "P Corn", "R Fert",
"K Land", "K Labor", "K Capital", "Trend", "Trend^2",
"Gdd", "Prec", "GddHigh", "DD",
"Gdd_1to3", "Prec_1to3", "GddHigh_1to3", "DD_1to3",
"Gdd_4to10", "Prec_4to10", "GddHigh_4to10", "DD_4to10",
"Gdd x Gdd_1to3", "Prec x Prec_1to3", "GddHigh x GddHigh_1to3", "DD x DD_1to3",
"Gdd x Gdd_4to10", "Prec x Prec_4to10", "GddHigh x GddHigh_4to10", "DD x DD_4to10")
# Insert LHS indicators
tab_structural <- berryFunctions::insertRows(tab_structural, 1 , new = c("Cereal supply","",""))
tab_structural <- berryFunctions::insertRows(tab_structural, 2+nrow(qqCereals %>% filter(!grepl("_fm",Variable))), new = c("Protein supply","",""))
tab_structural <- berryFunctions::insertRows(tab_structural, 3+2*nrow(qqCereals %>% filter(!grepl("_fm",Variable))) , new = c("Oilseed supply","",""))
tab_structural <- berryFunctions::insertRows(tab_structural, 4+3*+nrow(qqCereals %>% filter(!grepl("_fm",Variable))) , new = c("Roots supply","",""))
tab_structural <- berryFunctions::insertRows(tab_structural, 5+4*nrow(qqCereals %>% filter(!grepl("_fm",Variable))) , new = c("Corn supply","",""))
tab_structural <- berryFunctions::insertRows(tab_structural, 6+5*nrow(qqCereals %>% filter(!grepl("_fm",Variable))) , new = c("Fertilizer demand","",""))
# Print
write_xlsx(tab_structural,"Tables/Table_S8.1.xlsx")
#------------------------------------------------#
#### Table: Own- and cross-price elasticities ####
#------------------------------------------------#
#combine qs and xs
tab_PriceElast <- cbind(PriceElast_cereals[,c("Variable","Original","L95","U95")],
PriceElast_protein[,c("Original","L95","U95")],
PriceElast_oilseed[,c("Original","L95","U95")],
PriceElast_roots[,c("Original","L95","U95")],
PriceElast_corn[,c("Original","L95","U95")],
PriceElast_fert[,c("Original","L95","U95")],
PriceElast_others[,c("Original","L95","U95")])
#name columns
names(tab_PriceElast) <- c("na",
"Q_Cereals", "L95_cereals", "U95_cereals",
"Q_Protein", "L95_protein", "U95_protein",
"Q_Oilseed", "L95_oilseed", "U95_oilseed",
"Q_Roots", "L95_roots", "U95_roots",
"Q_Corn", "L95_corn", "U95_corn",
"X_Fertilizer", "L95_fert", "U95_fert",
"X_Others", "L95_others", "U95_others")
#name rows
tab_PriceElast$na <- c("P_Cereals","P_Protein","P_Oilseed","P_Roots","P_Corn",
"X_Fertilizer", "X_Others")
#Numbers smaller than 0.0005 should be in scientific format; others in 0.001 etc.
tab_PriceElast <- as.data.frame(cbind(tab_PriceElast[,1], sapply(tab_PriceElast[,-1], function(x) ifelse(abs(x)<0.0005&is.numeric(x),format(x,scientific=TRUE,digits=1),format(round(x, 3), nsmall = 3, scientific=FALSE)) )) )
# remove white space
tab_PriceElast <- as.data.frame(sapply(tab_PriceElast, function(x) (gsub(" ", "", x))))
# Merge CIs
tab_PriceElast$L95_cereals <- paste0(paste0("(",tab_PriceElast$L95_cereals,";"),
paste0(tab_PriceElast$U95_cereals,")"))
tab_PriceElast$U95_cereals <- NULL
tab_PriceElast <- dplyr::rename(tab_PriceElast,"CI_cereals" = "L95_cereals")
tab_PriceElast$L95_protein <- paste0(paste0("(",tab_PriceElast$L95_protein,";"),
paste0(tab_PriceElast$U95_protein,")"))
tab_PriceElast$U95_protein <- NULL
tab_PriceElast <- dplyr::rename(tab_PriceElast,"CI_protein" = "L95_protein")
tab_PriceElast$L95_oilseed <- paste0(paste0("(",tab_PriceElast$L95_oilseed,";"),
paste0(tab_PriceElast$U95_oilseed,")"))
tab_PriceElast$U95_oilseed <- NULL
tab_PriceElast <- dplyr::rename(tab_PriceElast,"CI_oilseed" = "L95_oilseed")
tab_PriceElast$L95_roots <- paste0(paste0("(",tab_PriceElast$L95_roots,";"),
paste0(tab_PriceElast$U95_roots,")"))
tab_PriceElast$U95_roots <- NULL
tab_PriceElast <- dplyr::rename(tab_PriceElast,"CI_roots" = "L95_roots")
tab_PriceElast$L95_corn <- paste0(paste0("(",tab_PriceElast$L95_corn,";"),
paste0(tab_PriceElast$U95_corn,")"))
tab_PriceElast$U95_corn <- NULL
tab_PriceElast <- dplyr::rename(tab_PriceElast,"CI_corn" = "L95_corn")
tab_PriceElast$L95_fert <- paste0(paste0("(",tab_PriceElast$L95_fert,";"),
paste0(tab_PriceElast$U95_fert,")"))
tab_PriceElast$U95_fert <- NULL
tab_PriceElast <- dplyr::rename(tab_PriceElast,"CI_fert" = "L95_fert")
tab_PriceElast$L95_others <- paste0(paste0("(",tab_PriceElast$L95_others,";"),
paste0(tab_PriceElast$U95_others,")"))
tab_PriceElast$U95_others <- NULL
tab_PriceElast <- dplyr::rename(tab_PriceElast,"CI_others" = "L95_others")
#Insert a blank row to every other row
na.df <- data.frame("na" = NA, "Q_Cereals" = NA, "CI_cereals" = NA, "Q_Protein" = NA,
"CI_protein" = NA, "Q_Oilseed" = NA, "CI_oilseed" = NA, "Q_Roots" = NA,
"CI_roots" = NA, "Q_Corn" = NA, "CI_corn" = NA, "X_Fertilizer" = NA,
"CI_fert" = NA, "X_Others" = NA, "CI_others" = NA)
tab_PriceElast <- do.call(rbind, apply(tab_PriceElast, 1, function(x) {rbind(x, na.df)}))
## Shift CIs down one line
tab_PriceElast$CI_cereals <- c(NA,tab_PriceElast$CI_cereals[1:nrow(tab_PriceElast)-1])
tab_PriceElast$CI_protein <- c(NA,tab_PriceElast$CI_protein[1:nrow(tab_PriceElast)-1])
tab_PriceElast$CI_oilseed <- c(NA,tab_PriceElast$CI_oilseed[1:nrow(tab_PriceElast)-1])
tab_PriceElast$CI_roots <- c(NA,tab_PriceElast$CI_roots[1:nrow(tab_PriceElast)-1])
tab_PriceElast$CI_corn <- c(NA,tab_PriceElast$CI_corn[1:nrow(tab_PriceElast)-1])
tab_PriceElast$CI_fert <- c(NA,tab_PriceElast$CI_fert[1:nrow(tab_PriceElast)-1])
tab_PriceElast$CI_others <- c(NA,tab_PriceElast$CI_others[1:nrow(tab_PriceElast)-1])
# Shifts CIs to the left
tab_PriceElast$Q_Cereals[is.na(tab_PriceElast$Q_Cereals)] <- tab_PriceElast$CI_cereals[is.na(tab_PriceElast$Q_Cereals)]
tab_PriceElast$CI_cereals <- NULL
tab_PriceElast$Q_Protein[is.na(tab_PriceElast$Q_Protein)] <- tab_PriceElast$CI_protein[is.na(tab_PriceElast$Q_Protein)]
tab_PriceElast$CI_protein <- NULL
tab_PriceElast$Q_Oilseed[is.na(tab_PriceElast$Q_Oilseed)] <- tab_PriceElast$CI_oilseed[is.na(tab_PriceElast$Q_Oilseed)]
tab_PriceElast$CI_oilseed <- NULL
tab_PriceElast$Q_Roots[is.na(tab_PriceElast$Q_Roots)] <- tab_PriceElast$CI_roots[is.na(tab_PriceElast$Q_Roots)]
tab_PriceElast$CI_roots <- NULL
tab_PriceElast$Q_Corn[is.na(tab_PriceElast$Q_Corn)] <- tab_PriceElast$CI_corn[is.na(tab_PriceElast$Q_Corn)]
tab_PriceElast$CI_corn <- NULL
tab_PriceElast$X_Fertilizer[is.na(tab_PriceElast$X_Fertilizer)] <- tab_PriceElast$CI_fert[is.na(tab_PriceElast$X_Fertilizer)]
tab_PriceElast$CI_fert <- NULL
tab_PriceElast$X_Others[is.na(tab_PriceElast$X_Others)] <- tab_PriceElast$CI_others[is.na(tab_PriceElast$X_Others)]
tab_PriceElast$CI_others <- NULL
# Print
write_xlsx(tab_PriceElast,"Tables/Table_S8.2.xlsx")
#---------------------------------------------------#
#### Table: Weather effects on output and inputs ####
#---------------------------------------------------#
#(compare obs vs. past)
#combine qs and xs
tab_WeatherElast <- cbind(WeatherElast_cereals[,c("Variable","Original","L95","U95")],
WeatherElast_protein[,c("Original","L95","U95")],
WeatherElast_oilseed[,c("Original","L95","U95")],
WeatherElast_roots[,c("Original","L95","U95")],
WeatherElast_corn[,c("Original","L95","U95")],
WeatherElast_fert[,c("Original","L95","U95")])
#name columns
names(tab_WeatherElast) <- c("na",
"Q_Cereals", "L95_cereals", "U95_cereals",
"Q_Protein", "L95_protein", "U95_protein",
"Q_Oilseed", "L95_oilseed", "U95_oilseed",
"Q_Roots", "L95_roots", "U95_roots",
"Q_Corn", "L95_corn", "U95_corn",
"X_Fertilizer", "L95_fert", "U95_fert")
#name rows
tab_WeatherElast$na <- c("gdd_obs","prec_obs","gddHigh_obs","dd_obs",
"gdd_past","prec_past","gddHigh_past","dd_past")
#Numbers smaller than 0.0005 should be in scientific format; others in 0.001 etc.
tab_WeatherElast <- as.data.frame(cbind(tab_WeatherElast[,1], sapply(tab_WeatherElast[,-1], function(x) ifelse(abs(x)<0.0005&is.numeric(x),format(x,scientific=TRUE,digits=1),format(round(x, 3), nsmall = 3, scientific=FALSE)) )) )
# remove white space
tab_WeatherElast <- as.data.frame(sapply(tab_WeatherElast, function(x) (gsub(" ", "", x))))
# Merge CIs
tab_WeatherElast$L95_cereals <- paste0(paste0("(",tab_WeatherElast$L95_cereals,";"),
paste0(tab_WeatherElast$U95_cereals,")"))
tab_WeatherElast$U95_cereals <- NULL
tab_WeatherElast <- dplyr::rename(tab_WeatherElast,"CI_cereals" = "L95_cereals")