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GovFin_simulation(5)_forward.R
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GovFin_simulation(5)_forward.R
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# This script is for modeling tax revenue of stylized governments
# 10/30/2019:
# (5) generate tax revenue using total GDP growth the elasticities
#**********************************************************************
# Packages ####
#**********************************************************************
library(broom)
library(readxl)
library(magrittr)
library(ggrepel)
library(stringr)
library(forcats)
library(grid)
library(gridExtra)
library(scales)
library(knitr)
library(xlsx)
# packages for econometric and time series modeling
# library(plm)
library(astsa) # companion package
library(TSA) # companion package; arimax: flexible transfer function model
library(tseries) #
library(forecast) # Arima
library(MSwM)
library(TTR)
library(dynlm)
library(broom)
#library(MSBVAR)
# packages for ts
library(zoo)
library(xts)
library(timetk)
library(tidyquant)
library(lubridate)
library(feather)
library(psych) # describe
library(tidyverse)
options(tibble.print_max = 60, tibble.print_min = 60)
# check tidyquant, timetk, sweep (broom ), tibbletime
# Intro to zoo cran.r-project.org/web/packages/zoo/vignettes/zoo-quickref.pdf
# sweep: http://www.business-science.io/code-tools/2017/07/09/sweep-0-1-0.html
#**********************************************************************
# Global settings and tools ####
#**********************************************************************
dir_data_raw <- "data_raw/"
dir_data_out <- "data_out/"
dir_fig_out <- "outputs_report/"
# NBER recession periods, post-WWII
recessionPeriods <-
matrix(c(
1953+2/4, 1954+2/4,
1957+3/4, 1958+2/4,
1960+2/4, 1961+1/4,
1969+4/4, 1970+4/4,
1973+4/4, 1975+1/4,
1980+1/4, 1980+3/4,
1981+3/4, 1982+4/4,
1990+3/4, 1991+1/4,
2001+1/4, 2001+4/4,
2007+4/4, 2009+2/4
) , ncol = 2, byrow = T) %>%
as.data.frame() %>%
rename(peak = V1,
trough = V2) %>%
mutate(peak = peak - 1/4,
trough = trough - 1/4)
get_logReturn <- function(x){
if(any(x <= 0, na.rm = TRUE)) stop("Nagative value(s)")
log(x/dplyr::lag(x))
}
# RIG colors and theme
RIG.blue <- "#003598"
RIG.red <- "#A50021"
RIG.green <- "#009900"
RIG.yellow <- "#FFFF66"
RIG.purple <- "#9966FF"
RIG.yellow.dark <- "#ffc829"
RIG.orange <- "#fc9272"
demo.color6 <- c(RIG.red,
RIG.orange,
RIG.purple,
RIG.green ,
RIG.blue,
RIG.yellow.dark)
color_GDP = "darkgrey"
RIG.theme <- function() {
theme(
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_line(size = 0.5, color = "gray80"),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0, size = 9)
)
}
RIG.themeLite <- function() {
theme(
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0, size = 9)
)
}
get_geoReturn <- function(x) prod(1 + x)^(1/length(x)) - 1
#**********************************************************************
# Outline ####
#**********************************************************************
# Goals:
# Path of state tax revenue derived from simulated GDP, stock returns, and estimated elasticities.
# What to have in the results
# 1. a deterministic simulation with constant GDP growth and stock return
# 2. A single stochastic simulation, how different stylized governments respond to GDP and stocks differently
# 3. A scenario, if hard to find a stochastic simulation that makes sense, construct a scenario that is similar to history.
# 4. Distribution of 2000 simulations (quanitles)
# 5. Risk measures: probability of sharp decline in tax revenue, and difference stylized governments.
# Calculating growth rates of tax revenues
# Trend growth rates:
# - Trend GDP growth rates (real)
# - CBO projection: 1.9%
# - Need to determined mean (and SD) in recession and expansion. (currently we may want to use SD estimated using historical data)
# - Trend stock returns (real)
# - Trend real return = Assumed overall nominal mean stock return (expansion and recession) - assumed inflation
# - Issue:
# 1. Estimated mean stock return is different from projected mean capital return. When using projected mean, how should the SD be adjusted?
# 2. With a target overall mean stock return (and SD), how to determine mean return (and SD) in recession and expansion periods.
# - Trend growth of taxes:
# - Use assumed trend growth rates
# - To ensure that the share of each major tax category stays stable over the long run,
# we assume that the trend growth rates of all tax categories equal the trend GDP growth rates (CBO projection: 1.9%)
# Cyclical growth rates:
# - Cyclical GDP growth rate: (real)
# - Cyclical GDP growth = simulated real rate - assumed overall trend
# - Potential issue: Growth will be above trend in expansion periods. (Since recessions are not very often, overall trend should be close to rate in expansion)
# - Issue to think about: do we need use a different trend for each single simulation?
# - Cyclical Stock returns (real)
# - Cyclical real stock return = simulated real rate - assumed overall trend
# - Cyclical tax growth
# - cyclical tax growth = e_GDP*GDP_cycle + e_stock*stock_cycle + recession adjustment
# Converting to nominal rates:
# - adding assumed inflation rates to simulated real rates.
# - Need to check whether the nominal numbers make sense, especially the asset returns (will be used in pension finance model)
#**********************************************************************
# Importing simulations of GDP growth and stock return ####
#**********************************************************************
# Loading simulation outputs:
# load("policyBrief_out/sim_results_historical.RData")
# sim_results <- sim_results_historical
load(paste0(dir_data_out, "MacroModel_sim_results_forward_real.RData"))
#sim_results <- sim_results_forward
# Notes
# 1. Simulations are generated by Model_simulation(3).R
# 2. What inputs to include:
# - simulated path of real GDP growth
# - Recession and expansion periods in each simulation
# - simulated path stock return
# - simulated path of bond return
# Assumptions:
infl_hist <- 0.022 #(CBO, GDP price index: 1987-2016)
infl_proj <- 0.02 #(CBO, GDP price index: 2017-2047)
infl <- infl_proj
# dfs to use:
# df_sim_gdp_y
# df_sim_stockreturn_y
# df_sim_bondreturn_y
sim_results$df_sim_gdp_y
df_sim <-
sim_results$df_sim_gdp_regimes_y %>%
left_join(sim_results$df_sim_gdp_y %>% rename(gdp_chg = return_y)) %>%
left_join(sim_results$df_sim_stockreturn_y %>% rename(stockreturn_real = return_y)) %>%
left_join(sim_results$df_sim_bondreturn_y %>% rename(bondreturn_real = return_y)) %>%
ungroup() %>%
mutate(sim = str_extract(sim, "\\d+") %>% as.numeric) %>%
mutate(#stockreturn_real = stockreturn - infl,
#bondreturn_real = bondreturn - infl,
recessionYear = recession_nqtr != 0 )
df_sim %>% head
# Calculating geometric mean of GDP growth and stock return
sim_geoMeans <-
df_sim %>%
group_by(sim) %>%
summarise(gdp_chg_geoMean = get_geoReturn(gdp_chg),
stockReturn_real_geoMean = get_geoReturn(stockreturn_real),
bondReturn_real_geoMean = get_geoReturn(bondreturn_real))
trend_growth_gdp <- sim_geoMeans$gdp_chg_geoMean %>% mean
trend_growth_stock_real <- sim_geoMeans$stockReturn_real_geoMean %>% mean
trend_growth_bond_real <- sim_geoMeans$bondReturn_real_geoMean %>% mean
trend_growth_gdp
trend_growth_stock_real
trend_growth_bond_real
df_sim$stockreturn %>% mean
#**********************************************************************
# Assumptions of stylized governments ####
#**********************************************************************
# Structure of tax revenue for stylized governments
taxShare_PITState <- c(PIT = 0.55,
salesGen = 0.2,
salesSel = 0.1,
other = 0.15,
propertyLoc = 0,
taxGDP = 0)
taxShare_salesState <- c(PIT = 0,
salesGen = 0.6,
salesSel = 0.25,
other = 0.15,
propertyLoc = 0,
taxGDP = 0)
taxShare_GDPState <- c(PIT = 0,
salesGen = 0,
salesSel = 0,
other = 0,
propertyLoc = 0,
taxGDP = 1)
taxShare_local <- c(PIT = 0,
salesGen = 0,
salesSel = 0,
other = 0,
propertyLoc = 1,
taxGDP = 0)
# Assumptions on tax growth
# Assumption 1
#df_sim_gdp_y$return_y %>% mean
trend_growth_gdp
trend_growth_stock_real
taxGrowth_trend_1 <-
c(PIT = -0.01,# trend_growth_gdp,
salesGen = -0.005,# trend_growth_gdp,
salesSel = 0.01,# trend_growth_gdp,
other = 0,# trend_growth_gdp,
propertyLoc = 0,# trend_growth_gdp,
taxGDP = 0 # rend_growth_gdp
)
taxGrowth_elastGDP_1 <-
c(PIT = 1.1,
salesGen = 1.3,
salesSel = 0.5,
other = 1.3,
propertyLoc = 0,
taxGDP = 1)
taxGrowth_elastStock_1 <-
c(PIT = 0.2,
salesGen = 0,
salesSel = 0,
other = 0,
propertyLoc = 0,
taxGDP = 0)
taxGrowth_adjRecession_1 <-
c(PIT = 0,
salesGen = 0,
salesSel = 0,
other = 0,
propertyLoc = 0,
taxGDP = 0)
#
# # Assumption 2: sales tax with a constant base growth rate and low elasticity wrt GDP
#
# taxGrowth_trend_2 <-
# c(PIT = trend_growth_gdp,
# salesGen = trend_growth_gdp,
# salesSel = trend_growth_gdp,
# other = trend_growth_gdp,
# propertyLoc = trend_growth_gdp,
# taxGDP = trend_growth_gdp)
#
# taxGrowth_elastGDP_2 <-
# c(PIT = 1,
# salesGen = 0.9,
# salesSel = 0.5,
# other = 1.3,
# propertyLoc = 0,
# taxGDP = 1)
#
# taxGrowth_elastStock_2 <-
# c(PIT = 0.2,
# salesGen = 0,
# salesSel = 0,
# other = 0,
# propertyLoc = 0,
# taxGDP = 0)
#
# taxGrowth_adjRecession_2 <-
# c(PIT = 0,
# salesGen = 0,
# salesSel = 0,
# other = 0,
# propertyLoc = 0,
# taxGDP = 0)
#**********************************************************************
# Calculating tax revenue for stylized governments ####
#**********************************************************************
df_sim %>% head
# Compute trend and cycle growth for GDP and stock return
df_sim %<>%
mutate(gdp_chg_trend = trend_growth_gdp,
gdp_chg_cycle = gdp_chg - gdp_chg_trend,
stockreturn_real_trend = trend_growth_stock_real,
stockreturn_real_cycle = stockreturn_real - stockreturn_real_trend)
# Calculate tax growth
df_sim %<>%
mutate(
# Real growth rate for tax revenue under A1
growthReal_PIT_a1 = taxGrowth_trend_1["PIT"] + taxGrowth_elastGDP_1["PIT"] * gdp_chg + taxGrowth_elastStock_1["PIT"] * stockreturn_real + taxGrowth_adjRecession_1["PIT"],
growthReal_salesGen_a1 = taxGrowth_trend_1["salesGen"] + taxGrowth_elastGDP_1["salesGen"] * gdp_chg + taxGrowth_adjRecession_1["salesGen"],
growthReal_salesSel_a1 = taxGrowth_trend_1["salesSel"] + taxGrowth_elastGDP_1["salesSel"] * gdp_chg + taxGrowth_adjRecession_1["salesSel"],
growthReal_other_a1 = taxGrowth_trend_1["other"] + taxGrowth_elastGDP_1["other"] * gdp_chg + taxGrowth_adjRecession_1["other"],
growthReal_taxGDP_a1 = taxGrowth_trend_1["taxGDP"] + taxGrowth_elastGDP_1["taxGDP"] * gdp_chg,
growthReal_propertyLoc_a1 = trend_growth_gdp # taxGrowth_trend_1["propertyLoc"] + taxGrowth_elastGDP_1["propertyLoc"] * gdp_chg + taxGrowth_adjRecession_1["propertyLoc"],
) %>%
group_by(sim) %>%
mutate(gdpLevel = cumprod(1 + gdp_chg),
# Future real tax revenue for $1 revenue in t_0.
taxLevelReal_PIT_a1 = cumprod(1 + growthReal_PIT_a1),
taxLevelReal_salesGen_a1 = cumprod(1 + growthReal_salesGen_a1),
taxLevelReal_salesSel_a1 = cumprod(1 + growthReal_salesSel_a1),
taxLevelReal_other_a1 = cumprod(1 + growthReal_other_a1),
taxLevelReal_propertyLoc_a1 = cumprod(1 + growthReal_propertyLoc_a1),
taxLevelReal_taxGDP_a1 = cumprod(1 + growthReal_taxGDP_a1),
# taxLevelReal_PIT_a2 = cumprod(1 + growthReal_PIT_a2),
# taxLevelReal_salesGen_a2 = cumprod(1 + growthReal_salesGen_a2),
# taxLevelReal_salesSel_a2 = cumprod(1 + growthReal_salesSel_a2),
# taxLevelReal_other_a2 = cumprod(1 + growthReal_other_a2),
# taxLevelReal_propertyLoc_a2 = cumprod(1 + growthReal_propertyLoc_a2),
# taxLevelReal_taxGDP_a2 = cumprod(1 + growthReal_taxGDP_a2),
# For PIT state
# tax revenue for each category, A1
taxLevelReal_PITState_PIT_a1 = taxShare_PITState["PIT"] * taxLevelReal_PIT_a1,
taxLevelReal_PITState_salesGen_a1 = taxShare_PITState["salesGen"] * taxLevelReal_salesGen_a1,
taxLevelReal_PITState_salesSel_a1 = taxShare_PITState["salesSel"] * taxLevelReal_salesSel_a1,
taxLevelReal_PITState_other_a1 = taxShare_PITState["other"] * taxLevelReal_other_a1,
taxLevelReal_PITState_propertyLoc_a1 = taxShare_PITState["propertyLoc"] * taxLevelReal_propertyLoc_a1,
# total tax, A1
taxLevelReal_PITState_tot_a1 = taxLevelReal_PITState_PIT_a1 + taxLevelReal_PITState_salesGen_a1 + taxLevelReal_PITState_salesSel_a1 +
taxLevelReal_PITState_other_a1 + taxLevelReal_PITState_propertyLoc_a1,
growthReal_tot_PITState_a1 = ifelse(year == 1, taxLevelReal_PITState_tot_a1 - 1, taxLevelReal_PITState_tot_a1/dplyr::lag(taxLevelReal_PITState_tot_a1) - 1),
# Share of each tax category, A1
taxShare_PITState_PIT_a1 = taxLevelReal_PITState_PIT_a1 / taxLevelReal_PITState_tot_a1,
taxShare_PITState_salesGen_a1 = taxLevelReal_PITState_salesGen_a1 / taxLevelReal_PITState_tot_a1,
taxShare_PITState_salesSel_a1 = taxLevelReal_PITState_salesSel_a1 / taxLevelReal_PITState_tot_a1,
taxShare_PITState_other_a1 = taxLevelReal_PITState_other_a1 / taxLevelReal_PITState_tot_a1,
taxShare_PITState_propertyLoc_a1 = taxLevelReal_PITState_propertyLoc_a1/taxLevelReal_PITState_tot_a1,
# # tax revenue for each category, A2
# taxLevelReal_PITState_PIT_a2 = taxShare_PITState["PIT"] * taxLevelReal_PIT_a2,
# taxLevelReal_PITState_salesGen_a2 = taxShare_PITState["salesGen"] * taxLevelReal_salesGen_a2,
# taxLevelReal_PITState_salesSel_a2 = taxShare_PITState["salesSel"] * taxLevelReal_salesSel_a2,
# taxLevelReal_PITState_other_a2 = taxShare_PITState["other"] * taxLevelReal_other_a2,
# taxLevelReal_PITState_propertyLoc_a2 = taxShare_PITState["propertyLoc"] * taxLevelReal_propertyLoc_a2,
# # total tax, A2
# taxLevelReal_PITState_tot_a2 = taxLevelReal_PITState_PIT_a2 + taxLevelReal_PITState_salesGen_a2 + taxLevelReal_PITState_salesSel_a2 +
# taxLevelReal_PITState_other_a2 + taxLevelReal_PITState_propertyLoc_a2,
# growthReal_tot_PITState_a2 = ifelse(year == 1, taxLevelReal_PITState_tot_a2 - 1, taxLevelReal_PITState_tot_a2/dplyr::lag(taxLevelReal_PITState_tot_a2) - 1),
# # Share of each tax category, A2
# taxShare_PITState_PIT_a1 = taxLevelReal_PITState_PIT_a1 / taxLevelReal_PITState_tot_a1,
# taxShare_PITState_salesGen_a1 = taxLevelReal_PITState_salesGen_a1 / taxLevelReal_PITState_tot_a1,
# taxShare_PITState_salesSel_a1 = taxLevelReal_PITState_salesSel_a1 / taxLevelReal_PITState_tot_a1,
# taxShare_PITState_other_a1 = taxLevelReal_PITState_other_a1 / taxLevelReal_PITState_tot_a1,
# taxShare_PITState_propertyLoc_a1 = taxLevelReal_PITState_propertyLoc_a1/taxLevelReal_PITState_tot_a1,
# For sales tax state
# tax revenue for each category, A1
taxLevelReal_salesState_PIT_a1 = taxShare_salesState["PIT"] * taxLevelReal_PIT_a1,
taxLevelReal_salesState_salesGen_a1 = taxShare_salesState["salesGen"] * taxLevelReal_salesGen_a1,
taxLevelReal_salesState_salesSel_a1 = taxShare_salesState["salesSel"] * taxLevelReal_salesSel_a1,
taxLevelReal_salesState_other_a1 = taxShare_salesState["other"] * taxLevelReal_other_a1,
taxLevelReal_salesState_propertyLoc_a1 = taxShare_salesState["propertyLoc"] * taxLevelReal_propertyLoc_a1,
# total tax, A1
taxLevelReal_salesState_tot_a1 = taxLevelReal_salesState_PIT_a1 + taxLevelReal_salesState_salesGen_a1 + taxLevelReal_salesState_salesSel_a1 +
taxLevelReal_salesState_other_a1 + taxLevelReal_salesState_propertyLoc_a1,
growthReal_tot_salesState_a1 = ifelse(year == 1, taxLevelReal_salesState_tot_a1 - 1, taxLevelReal_salesState_tot_a1/dplyr::lag(taxLevelReal_salesState_tot_a1) - 1),
# Share of each tax category, A1
taxShare_salesState_PIT_a1 = taxLevelReal_salesState_PIT_a1 / taxLevelReal_salesState_tot_a1,
taxShare_salesState_salesGen_a1 = taxLevelReal_salesState_salesGen_a1 / taxLevelReal_salesState_tot_a1,
taxShare_salesState_salesSel_a1 = taxLevelReal_salesState_salesSel_a1 / taxLevelReal_salesState_tot_a1,
taxShare_salesState_other_a1 = taxLevelReal_salesState_other_a1 / taxLevelReal_salesState_tot_a1,
taxShare_salesState_propertyLoc_a1 = taxLevelReal_salesState_propertyLoc_a1/taxLevelReal_salesState_tot_a1,
# # tax revenue for each category, A2
# taxLevelReal_salesState_PIT_a2 = taxShare_salesState["PIT"] * taxLevelReal_PIT_a2,
# taxLevelReal_salesState_salesGen_a2 = taxShare_salesState["salesGen"] * taxLevelReal_salesGen_a2,
# taxLevelReal_salesState_salesSel_a2 = taxShare_salesState["salesSel"] * taxLevelReal_salesSel_a2,
# taxLevelReal_salesState_other_a2 = taxShare_salesState["other"] * taxLevelReal_other_a2,
# taxLevelReal_salesState_propertyLoc_a2 = taxShare_salesState["propertyLoc"] * taxLevelReal_propertyLoc_a2,
#
# # total tax, A2
# taxLevelReal_salesState_tot_a2 = taxLevelReal_salesState_PIT_a2 + taxLevelReal_salesState_salesGen_a2 + taxLevelReal_salesState_salesSel_a2 +
# taxLevelReal_salesState_other_a2 + taxLevelReal_salesState_propertyLoc_a2,
# growthReal_tot_salesState_a2 = ifelse(year == 1, taxLevelReal_salesState_tot_a2 - 1, taxLevelReal_salesState_tot_a2/dplyr::lag(taxLevelReal_salesState_tot_a2) - 1),
#
#
# # Share of each tax category, A2
# taxShare_salesState_PIT_a1 = taxLevelReal_salesState_PIT_a1 / taxLevelReal_salesState_tot_a1,
# taxShare_salesState_salesGen_a1 = taxLevelReal_salesState_salesGen_a1 / taxLevelReal_salesState_tot_a1,
# taxShare_salesState_salesSel_a1 = taxLevelReal_salesState_salesSel_a1 / taxLevelReal_salesState_tot_a1,
# taxShare_salesState_other_a1 = taxLevelReal_salesState_other_a1 / taxLevelReal_salesState_tot_a1,
# taxShare_salesState_propertyLoc_a1 = taxLevelReal_salesState_propertyLoc_a1/taxLevelReal_salesState_tot_a1,
# For local gov
# tax revenue for each category, A1
taxLevelReal_local_PIT_a1 = taxShare_local["PIT"] * taxLevelReal_PIT_a1,
taxLevelReal_local_salesGen_a1 = taxShare_local["salesGen"] * taxLevelReal_salesGen_a1,
taxLevelReal_local_salesSel_a1 = taxShare_local["salesSel"] * taxLevelReal_salesSel_a1,
taxLevelReal_local_other_a1 = taxShare_local["other"] * taxLevelReal_other_a1,
taxLevelReal_local_propertyLoc_a1 = taxShare_local["propertyLoc"] * taxLevelReal_propertyLoc_a1,
# total tax, A1
taxLevelReal_local_tot_a1 = taxLevelReal_local_PIT_a1 + taxLevelReal_local_salesGen_a1 + taxLevelReal_local_salesSel_a1 +
taxLevelReal_local_other_a1 + taxLevelReal_local_propertyLoc_a1,
growthReal_tot_local_a1 = ifelse(year == 1, taxLevelReal_local_tot_a1 - 1, taxLevelReal_local_tot_a1/dplyr::lag(taxLevelReal_local_tot_a1) - 1),
# Share of each tax category, A1
taxShare_local_PIT_a1 = taxLevelReal_local_PIT_a1 / taxLevelReal_local_tot_a1,
taxShare_local_salesGen_a1 = taxLevelReal_local_salesGen_a1 / taxLevelReal_local_tot_a1,
taxShare_local_salesSel_a1 = taxLevelReal_local_salesSel_a1 / taxLevelReal_local_tot_a1,
taxShare_local_other_a1 = taxLevelReal_local_other_a1 / taxLevelReal_local_tot_a1,
taxShare_local_propertyLoc_a1 = taxLevelReal_local_propertyLoc_a1/taxLevelReal_local_tot_a1,
# # tax revenue for each category, A2
# taxLevelReal_local_PIT_a2 = taxShare_local["PIT"] * taxLevelReal_PIT_a2,
# taxLevelReal_local_salesGen_a2 = taxShare_local["salesGen"] * taxLevelReal_salesGen_a2,
# taxLevelReal_local_salesSel_a2 = taxShare_local["salesSel"] * taxLevelReal_salesSel_a2,
# taxLevelReal_local_other_a2 = taxShare_local["other"] * taxLevelReal_other_a2,
# taxLevelReal_local_propertyLoc_a2 = taxShare_local["propertyLoc"] * taxLevelReal_propertyLoc_a2,
#
# # total tax, A2
# taxLevelReal_local_tot_a2 = taxLevelReal_local_PIT_a2 + taxLevelReal_local_salesGen_a2 + taxLevelReal_local_salesSel_a2 +
# taxLevelReal_local_other_a2 + taxLevelReal_local_propertyLoc_a2,
# growthReal_tot_local_a2 = ifelse(year == 1, taxLevelReal_local_tot_a2 - 1, taxLevelReal_local_tot_a2/dplyr::lag(taxLevelReal_local_tot_a2) - 1),
#
# # Share of each tax category, A2
# taxShare_local_PIT_a1 = taxLevelReal_local_PIT_a1 / taxLevelReal_local_tot_a1,
# taxShare_local_salesGen_a1 = taxLevelReal_local_salesGen_a1 / taxLevelReal_local_tot_a1,
# taxShare_local_salesSel_a1 = taxLevelReal_local_salesSel_a1 / taxLevelReal_local_tot_a1,
# taxShare_local_other_a1 = taxLevelReal_local_other_a1 / taxLevelReal_local_tot_a1,
# taxShare_local_propertyLoc_a1 = taxLevelReal_local_propertyLoc_a1/taxLevelReal_local_tot_a1,
# For tax rev that grow proportionally with GDP
# tax revenue for each category, A1
# taxLevelReal_local_PIT_a1 = taxShare_local["PIT"] * taxLevelReal_PIT_a1,
# taxLevelReal_local_salesGen_a1 = taxShare_local["salesGen"] * taxLevelReal_salesGen_a1,
# taxLevelReal_local_salesSel_a1 = taxShare_local["salesSel"] * taxLevelReal_salesSel_a1,
# taxLevelReal_local_other_a1 = taxShare_local["other"] * taxLevelReal_other_a1,
taxLevelReal_GDPState_taxGDP_a1 = taxShare_GDPState["taxGDP"] * taxLevelReal_taxGDP_a1,
# total tax, A1
taxLevelReal_GDPState_tot_a1 = taxLevelReal_GDPState_taxGDP_a1,
growthReal_tot_GDPState_a1 = ifelse(year == 1, taxLevelReal_GDPState_tot_a1 - 1, taxLevelReal_GDPState_tot_a1/dplyr::lag(taxLevelReal_GDPState_tot_a1) - 1)
# Share of each tax category, A1
# taxShare_local_PIT_a1 = taxLevelReal_local_PIT_a1 / taxLevelReal_local_tot_a1,
# taxShare_local_salesGen_a1 = taxLevelReal_local_salesGen_a1 / taxLevelReal_local_tot_a1,
# taxShare_local_salesSel_a1 = taxLevelReal_local_salesSel_a1 / taxLevelReal_local_tot_a1,
# taxShare_local_other_a1 = taxLevelReal_local_other_a1 / taxLevelReal_local_tot_a1,
# taxShare_local_propertyLoc_a1 = taxLevelReal_local_propertyLoc_a1/taxLevelReal_local_tot_a1,
# # tax revenue for each category, A2
# # taxLevelReal_local_PIT_a2 = taxShare_local["PIT"] * taxLevelReal_PIT_a2,
# # taxLevelReal_local_salesGen_a2 = taxShare_local["salesGen"] * taxLevelReal_salesGen_a2,
# # taxLevelReal_local_salesSel_a2 = taxShare_local["salesSel"] * taxLevelReal_salesSel_a2,
# # taxLevelReal_local_other_a2 = taxShare_local["other"] * taxLevelReal_other_a2,
# taxLevelReal_GDPState_taxGDP_a2 = taxShare_GDPState["taxGDP"] * taxLevelReal_taxGDP_a2,
#
# # total tax, A2
# taxLevelReal_GDPState_tot_a2 = taxLevelReal_GDPState_taxGDP_a2,
# growthReal_tot_GDPState_a2 = ifelse(year == 1, taxLevelReal_GDPState_tot_a2 - 1, taxLevelReal_GDPState_tot_a2/dplyr::lag(taxLevelReal_GDPState_tot_a2) - 1)
# Share of each tax category, A2
# taxShare_local_PIT_a2 = taxLevelReal_local_PIT_a2 / taxLevelReal_local_tot_a2,
# taxShare_local_salesGen_a2 = taxLevelReal_local_salesGen_a2 / taxLevelReal_local_tot_a2,
# taxShare_local_salesSel_a2 = taxLevelReal_local_salesSel_a2 / taxLevelReal_local_tot_a2,
# taxShare_local_other_a2 = taxLevelReal_local_other_a2 / taxLevelReal_local_tot_a2,
# taxShare_local_propertyLoc_a2 = taxLevelReal_local_propertyLoc_a2/taxLevelReal_local_tot_a2
) %>%
ungroup()
#df_sim %>% select(year, taxLevel_PITState_a1)
# Save results
save(df_sim, file = paste0(dir_data_out, "GovFin_sim_forward_tot.RData"))
#**********************************************************************
# Illustration of a single simulation ####
#**********************************************************************
# Example simulation #2:
rec_year <- (df_sim %>% filter(sim == 2, recession_nqtr !=0))$year
rec_year
df_sim2 <- df_sim %>% filter(sim == 2)
save(df_sim2, file = paste0(dir_data_out, "df_sim2_tot.RData"))
fig_singleSim_GDP <-
df_sim %>% filter(sim == 2) %>%
select(sim, year, gdp_chg, stockreturn_real) %>%
gather(var, value, -sim, - year) %>%
mutate(var = factor(var, levels = c("gdp_chg", "stockreturn_real"), labels = c("Real GDP growth", "Real stock return"))) %>%
ggplot(aes(x = year, y = 100 * value, color = var)) + theme_bw() + RIG.themeLite() +
geom_line() +
geom_point() +
geom_vline(xintercept = rec_year, linetype = 2, color = "grey") +
geom_hline(yintercept = 0, linetype = 2) +
scale_x_continuous(breaks = c(1, seq(0, 30, 5)[-1]) ) +
scale_color_manual(values = c("darkgrey", RIG.red)) +
labs(title = "Illustration of a single simulation (#2): real GDP growth and real stock return",
x = NULL,
y = "Percent",
color = NULL) +
theme(legend.position = "bottom")
fig_singleSim_GDP
fig_singleSim_tax_a1 <-
df_sim %>% filter(sim == 2) %>%
select(sim, year, growthReal_tot_PITState_a1, growthReal_tot_salesState_a1) %>%
gather(var, value, -sim, - year) %>%
mutate(var = factor(var, levels = c("growthReal_tot_PITState_a1", "growthReal_tot_salesState_a1"),
labels = c("Income tax dominant state", "Sales tax dominant state"))) %>%
ggplot(aes(x = year, y = 100 * value, color = var, shape = var)) + theme_bw() + RIG.themeLite() +
geom_line() +
geom_point() +
geom_vline(xintercept = rec_year, linetype = 2, color = "grey") +
geom_hline(yintercept = 0, linetype = 2) +
coord_cartesian(ylim = c(-10, 10)) +
scale_x_continuous(breaks = c(1, seq(0, 30, 5)[-1]) ) +
scale_color_manual(values = c(RIG.blue, "deepskyblue1" )) +
labs(title = "Illustration of a single simulation (#2): \nGrowth of real tax revenue for two types of stylized government",
x = NULL,
y = "Percent",
color = NULL,
shape = NULL) +
theme(legend.position = "bottom")
fig_singleSim_tax_a1
df_sim %>% filter(sim == 2) %>%
select(sim, year, gdp_chg, growthReal_tot_PITState_a1, growthReal_tot_salesState_a1) %>%
gather(var, value, -sim, - year) %>%
mutate(var = factor(var, levels = c("gdp_chg", "growthReal_tot_PITState_a1", "growthReal_tot_salesState_a1"),
labels = c("GDP growth", "Income tax dominant state", "Sales tax dominant state"))) %>%
ggplot(aes(x = year, y = 100 * value, color = var, shape = var)) + theme_bw() + RIG.themeLite() +
geom_line() +
geom_point() +
geom_vline(xintercept = rec_year, linetype = 2, color = "grey") +
geom_hline(yintercept = 0, linetype = 2) +
coord_cartesian(ylim = c(-10, 10)) +
scale_x_continuous(breaks = c(1, seq(0, 30, 5)[-1]) ) +
scale_color_manual(values = c(color_GDP, RIG.blue, "deepskyblue1" )) +
labs(title = "Illustration of a single simulation (#2): \nGrowth of real tax revenue for two types of stylized government",
x = NULL,
y = "Percent",
color = NULL,
shape = NULL) +
theme(legend.position = "bottom")
# sales tax state is extremely close to the GDP state.
# fig_singleSim_tax_a2 <-
# df_sim %>% filter(sim == 2) %>%
# select(sim, year, growthReal_tot_PITState_a2, growthReal_tot_salesState_a2) %>%
# gather(var, value, -sim, - year) %>%
# mutate(var = factor(var, levels = c("growthReal_tot_PITState_a2", "growthReal_tot_salesState_a2"),
# labels = c("PIT dominant state", "Sales tax dominant state"))) %>%
# ggplot(aes(x = year, y = 100 * value, color = var)) + theme_bw() + RIG.themeLite() +
# geom_line() +
# geom_point() +
# geom_vline(xintercept = rec_year, linetype = 2, color = "grey") +
# geom_hline(yintercept = 0, linetype = 2) +
# coord_cartesian(ylim = c(-10, 10)) +
# scale_x_continuous(breaks = c(1, seq(0, 30, 5)[-1]) ) +
# scale_color_manual(values = c(RIG.blue, "deepskyblue1")) +
# labs(title = "Illustration of a single simulation (#2): \nGrowth of real tax revenue of two types of stylized government (Assumption 2)",
# x = NULL,
# y = "Percent",
# color = NULL) +
# theme(legend.position = "bottom")
# fig_singleSim_tax_a2
ggsave(paste0(dir_fig_out, "fig_singleSim_GDP.png"), fig_singleSim_GDP, width = 10*0.8, height = 5*0.8)
ggsave(paste0(dir_fig_out, "fig_singleSim_tax_a1_tot.png"), fig_singleSim_tax_a1, width = 10*0.8, height = 5*0.8)
# ggsave(paste0(dir_fig_out, "fig_singleSim_tax_a2.png"), fig_singleSim_tax_a2, width = 10*0.8, height = 5*0.8)
#**********************************************************************
# Distribution of simulated variables ####
#**********************************************************************
df_sim %>% head
# quantiles
{ df_sim_quantiles <-
df_sim %>%
group_by(year) %>%
summarise(
gdp_chg_q10 = quantile(gdp_chg, 0.1),
gdp_chg_q25 = quantile(gdp_chg, 0.25),
gdp_chg_q50 = quantile(gdp_chg, 0.50),
gdp_chg_q75 = quantile(gdp_chg, 0.75),
gdp_chg_q90 = quantile(gdp_chg, 0.90),
# stockreturn_q10 = quantile(stockreturn, 0.1),
# stockreturn_q25 = quantile(stockreturn, 0.25),
# stockreturn_q50 = quantile(stockreturn, 0.50),
# stockreturn_q75 = quantile(stockreturn, 0.75),
# stockreturn_q90 = quantile(stockreturn, 0.90),
#
GrowthReal_tot_PITState_a1_q10 = quantile(growthReal_tot_PITState_a1, 0.1),
GrowthReal_tot_PITState_a1_q25 = quantile(growthReal_tot_PITState_a1, 0.25),
GrowthReal_tot_PITState_a1_q50 = quantile(growthReal_tot_PITState_a1, 0.50),
GrowthReal_tot_PITState_a1_q75 = quantile(growthReal_tot_PITState_a1, 0.75),
GrowthReal_tot_PITState_a1_q90 = quantile(growthReal_tot_PITState_a1, 0.90),
# GrowthReal_tot_PITState_a2_q10 = quantile(growthReal_tot_PITState_a2, 0.1),
# GrowthReal_tot_PITState_a2_q25 = quantile(growthReal_tot_PITState_a2, 0.25),
# GrowthReal_tot_PITState_a2_q50 = quantile(growthReal_tot_PITState_a2, 0.50),
# GrowthReal_tot_PITState_a2_q75 = quantile(growthReal_tot_PITState_a2, 0.75),
# GrowthReal_tot_PITState_a2_q90 = quantile(growthReal_tot_PITState_a2, 0.90),
#
GrowthReal_tot_salesState_a1_q25 = quantile(growthReal_tot_salesState_a1, 0.25),
GrowthReal_tot_salesState_a1_q50 = quantile(growthReal_tot_salesState_a1, 0.50),
GrowthReal_tot_salesState_a1_q75 = quantile(growthReal_tot_salesState_a1, 0.75),
GrowthReal_tot_salesState_a1_q90 = quantile(growthReal_tot_salesState_a1, 0.90),
GrowthReal_tot_salesState_a1_q10 = quantile(growthReal_tot_salesState_a1, 0.1),
# GrowthReal_tot_salesState_a2_q10 = quantile(growthReal_tot_salesState_a2, 0.1),
# GrowthReal_tot_salesState_a2_q25 = quantile(growthReal_tot_salesState_a2, 0.25),
# GrowthReal_tot_salesState_a2_q50 = quantile(growthReal_tot_salesState_a2, 0.50),
# GrowthReal_tot_salesState_a2_q75 = quantile(growthReal_tot_salesState_a2, 0.75),
# GrowthReal_tot_salesState_a2_q90 = quantile(growthReal_tot_salesState_a2, 0.90),
taxLevelReal_PITState_tot_a1_q10 = quantile(taxLevelReal_PITState_tot_a1, 0.1),
taxLevelReal_PITState_tot_a1_q25 = quantile(taxLevelReal_PITState_tot_a1, 0.25),
taxLevelReal_PITState_tot_a1_q50 = quantile(taxLevelReal_PITState_tot_a1, 0.50),
taxLevelReal_PITState_tot_a1_q75 = quantile(taxLevelReal_PITState_tot_a1, 0.75),
taxLevelReal_PITState_tot_a1_q90 = quantile(taxLevelReal_PITState_tot_a1, 0.90),
# taxLevelReal_PITState_tot_a2_q10 = quantile(taxLevelReal_PITState_tot_a2, 0.1),
# taxLevelReal_PITState_tot_a2_q25 = quantile(taxLevelReal_PITState_tot_a2, 0.25),
# taxLevelReal_PITState_tot_a2_q50 = quantile(taxLevelReal_PITState_tot_a2, 0.50),
# taxLevelReal_PITState_tot_a2_q75 = quantile(taxLevelReal_PITState_tot_a2, 0.75),
# taxLevelReal_PITState_tot_a2_q90 = quantile(taxLevelReal_PITState_tot_a2, 0.90),
taxLevelReal_salesState_tot_a1_q10 = quantile(taxLevelReal_salesState_tot_a1, 0.1),
taxLevelReal_salesState_tot_a1_q25 = quantile(taxLevelReal_salesState_tot_a1, 0.25),
taxLevelReal_salesState_tot_a1_q50 = quantile(taxLevelReal_salesState_tot_a1, 0.50),
taxLevelReal_salesState_tot_a1_q75 = quantile(taxLevelReal_salesState_tot_a1, 0.75),
taxLevelReal_salesState_tot_a1_q90 = quantile(taxLevelReal_salesState_tot_a1, 0.90),
# taxLevelReal_salesState_tot_a2_q10 = quantile(taxLevelReal_salesState_tot_a2, 0.1),
# taxLevelReal_salesState_tot_a2_q25 = quantile(taxLevelReal_salesState_tot_a2, 0.25),
# taxLevelReal_salesState_tot_a2_q50 = quantile(taxLevelReal_salesState_tot_a2, 0.50),
# taxLevelReal_salesState_tot_a2_q75 = quantile(taxLevelReal_salesState_tot_a2, 0.75),
# taxLevelReal_salesState_tot_a2_q90 = quantile(taxLevelReal_salesState_tot_a2, 0.90)
)
}
df_sim_quantiles
# Box plots
fig_taxGrowth_box_a1 <-
df_sim %>%
# filter(year == 15) %>%
select(year, sim, growthReal_tot_PITState_a1, growthReal_tot_salesState_a1) %>%
gather(var, value, -year, -sim) %>%
mutate(var = factor(var, levels = c("growthReal_tot_PITState_a1", "growthReal_tot_salesState_a1", "growthReal_tot_local_a1"),
labels = c("Income tax dominant state", "Sales tax dominant state", "Local government"))) %>%
ggplot(aes(x = var, y = 100 * value, fill = var)) + theme_bw() + RIG.themeLite() +
geom_violin() +
geom_boxplot(width = 0.1, outlier.shape = NA, color = "white") +
scale_fill_manual(values = c(RIG.blue, "deepskyblue1", RIG.yellow.dark)) +
scale_y_continuous(breaks = seq(-20, 20, 2)) +
theme(legend.position = "none") +
labs(title = "Distributions of simulated growth in real total tax revenue \nfor the two stylized governments",
fill = NULL,
x = NULL,
y = "Percent (%)")
fig_taxGrowth_box_a1
# df_sim$growthReal_tot_PITState_a1 %>% median # 2.44%
ggsave(paste0(dir_fig_out, "fig_GovFin_taxGrowth_box_a1_tot.png"), fig_taxGrowth_box_a1, width = 8*0.8, height = 7*0.8)
fig_taxGrowth_box_a1 <-
df_sim %>%
# filter(year == 15) %>%
select(year, sim, growthReal_tot_PITState_a1, growthReal_tot_salesState_a1, growthReal_tot_GDPState_a1) %>%
gather(var, value, -year, -sim) %>%
mutate(var = factor(var, levels = c("growthReal_tot_PITState_a1", "growthReal_tot_salesState_a1", "growthReal_tot_GDPState_a1"),
labels = c("Income tax dominant state", "Sales tax dominant state", "GDP growth government"))) %>%
ggplot(aes(x = var, y = 100 * value, fill = var)) + theme_bw() + RIG.themeLite() +
geom_violin() +
geom_boxplot(width = 0.1, outlier.shape = NA, color = "white") +
scale_fill_manual(values = c(RIG.blue, "deepskyblue1", color_GDP)) +
scale_y_continuous(breaks = seq(-20, 20, 2)) +
theme(legend.position = "none") +
labs(title = "Distributions of simulated growth in real total tax revenue \nfor the two stylized governments",
fill = NULL,
x = NULL,
y = "Percent (%)")
fig_taxGrowth_box_a1
# Tax growth in recessions
# Box plots
df_sim %>%
filter(recessionYear == TRUE) %>%
select(year, sim, growthReal_tot_PITState_a1, growthReal_tot_salesState_a1) %>%
gather(var, value, -year, -sim) %>%
ggplot(aes(x = var, y = value)) +
geom_boxplot(width = 0.5, outlier.shape = NA)
df_sim %>%
#filter(recessionYear == TRUE) %>%
select(year, sim, growthReal_tot_PITState_a2, growthReal_tot_salesState_a2) %>%
gather(var, value, -year, -sim) %>%
ggplot(aes(x = var, y = value)) +
geom_boxplot(width = 0.5, outlier.shape = NA)
# Distribution of tax level
fig_taxLevel_PITState_a1 <-
df_sim_quantiles %>%
select(year,
taxLevelReal_PITState_tot_a1_q10,
taxLevelReal_PITState_tot_a1_q25,
taxLevelReal_PITState_tot_a1_q50,
taxLevelReal_PITState_tot_a1_q75,
taxLevelReal_PITState_tot_a1_q90
) %>%
gather(var, value, -year) %>%
mutate(var = factor(var,
levels = c("taxLevelReal_PITState_tot_a1_q90",
"taxLevelReal_PITState_tot_a1_q75",
"taxLevelReal_PITState_tot_a1_q50",
"taxLevelReal_PITState_tot_a1_q25",
"taxLevelReal_PITState_tot_a1_q10"),
labels = c("90th percentile",
"75th percentile",
"50th percentile",
"25th percentile",
"10th percentile"
))) %>%
ggplot(aes(x = year, y = value, color = var)) +theme_bw() + RIG.themeLite() +
geom_line() +
geom_point() +
coord_cartesian(ylim = c(1, 3)) +
labs(title = "Distribution of simulated tax revenue relative to year 0 \n PIT dominant state (Assumption 1)",
x = "Year",
y = "Tax revenue relative to year 0",
color = NULL)
fig_taxLevel_PITState_a1
fig_taxLevel_salesState_a1 <-
df_sim_quantiles %>%
select(year,
taxLevelReal_salesState_tot_a1_q10,
taxLevelReal_salesState_tot_a1_q25,
taxLevelReal_salesState_tot_a1_q50,
taxLevelReal_salesState_tot_a1_q75,
taxLevelReal_salesState_tot_a1_q90
) %>%
gather(var, value, -year) %>%
mutate(var = factor(var,
levels = c("taxLevelReal_salesState_tot_a1_q90",
"taxLevelReal_salesState_tot_a1_q75",
"taxLevelReal_salesState_tot_a1_q50",
"taxLevelReal_salesState_tot_a1_q25",
"taxLevelReal_salesState_tot_a1_q10"),
labels = c("90th percentile",
"75th percentile",
"50th percentile",
"25th percentile",
"10th percentile"
))) %>%
ggplot(aes(x = year, y = value, color = var)) +theme_bw() + RIG.themeLite() +
geom_line() +
geom_point() +
coord_cartesian(ylim = c(1, 2.5)) +
labs(title = "Distribution of simulated tax revenue relative to year 0 \n sales tax dominant state (Assumption 1)",
x = "Year",
y = "Tax revenue relative to year 0",
color = NULL)
fig_taxLevel_salesState_a1
ggsave(paste0(dir_fig_out, "fig_GovFin_taxLevel_PITState_a1_tot.png"), fig_taxLevel_PITState_a1, width = 8*0.8, height = 6*0.8)
ggsave(paste0(dir_fig_out, "fig_GovFin_taxLevel_salesState_a1_tot.png"), fig_taxLevel_salesState_a1, width = 8*0.8, height = 6*0.8)
# ggsave(paste0(dir_fig_out, "fig_GovFin_taxLevel_PITState_a2.png"), fig_taxLevel_PITState_a2, width = 8*0.8, height = 6*0.8)
# ggsave(paste0(dir_fig_out, "fig_GovFin_taxLevel_salesState_a2.png"), fig_taxLevel_salesState_a2, width = 8*0.8, height = 6*0.8)
#**********************************************************************
# Changes in tax structure ####
#**********************************************************************
# Because of the "volatility drag", taxes with high volatility (income tax) may grow
# slower than taxes with lower volatility (sales tax, especially selective sales tax)
df_sim %>%
select(sim, year, taxShare_PITState_PIT_a1,
taxShare_PITState_salesGen_a1,
taxShare_PITState_salesSel_a1,
taxShare_PITState_other_a1) %>%
group_by(year) %>%
summarise(
share_PIT_q10 = quantile(taxShare_PITState_PIT_a1, 0.1),
#share_PIT_q25 = quantile(taxShare_PITState_PIT_a1, 0.25),
share_PIT_q50 = quantile(taxShare_PITState_PIT_a1, 0.50),
#share_PIT_q75 = quantile(taxShare_PITState_PIT_a1, 0.75),
share_PIT_q90 = quantile(taxShare_PITState_PIT_a1, 0.90),
share_salesGen_q10 = quantile(taxShare_PITState_salesGen_a1, 0.1),
#share_salesGen_q25 = quantile(taxShare_PITState_salesGen_a1, 0.25),
share_salesGen_q50 = quantile(taxShare_PITState_salesGen_a1, 0.50),
#share_salesGen_q75 = quantile(taxShare_PITState_salesGen_a1, 0.75),
share_salesGen_q90 = quantile(taxShare_PITState_salesGen_a1, 0.90),
share_salesSel_q10 = quantile(taxShare_PITState_salesSel_a1, 0.1),
#share_salesGen_q25 = quantile(taxShare_PITState_salesSel_a1, 0.25),
share_salesSel_q50 = quantile(taxShare_PITState_salesSel_a1, 0.50),
#share_salesGen_q75 = quantile(taxShare_PITState_salesSel_a1, 0.75),
share_salesSel_q90 = quantile(taxShare_PITState_salesSel_a1, 0.90)
)
df_sim %>%
select(sim, year, taxShare_salesState_PIT_a1,
taxShare_salesState_salesGen_a1,
taxShare_salesState_salesSel_a1,
taxShare_salesState_other_a1) %>%
group_by(year) %>%
summarise(
share_other_q10 = quantile(taxShare_salesState_other_a1, 0.1),
#share_other_q25 = quantile(taxShare_salesState_other_a1, 0.25),
share_other_q50 = quantile(taxShare_salesState_other_a1, 0.50),
#share_other_q75 = quantile(taxShare_salesState_other_a1, 0.75),
share_other_q90 = quantile(taxShare_salesState_other_a1, 0.90),
share_salesGen_q10 = quantile(taxShare_salesState_salesGen_a1, 0.1),
#share_salesGen_q25 = quantile(taxShare_salesState_salesGen_a1, 0.25),
share_salesGen_q50 = quantile(taxShare_salesState_salesGen_a1, 0.50),
#share_salesGen_q75 = quantile(taxShare_salesState_salesGen_a1, 0.75),
share_salesGen_q90 = quantile(taxShare_salesState_salesGen_a1, 0.90),
share_salesSel_q10 = quantile(taxShare_salesState_salesSel_a1, 0.1),
#share_salesGen_q25 = quantile(taxShare_salesState_salesSel_a1, 0.25),
share_salesSel_q50 = quantile(taxShare_salesState_salesSel_a1, 0.50),
#share_salesGen_q75 = quantile(taxShare_salesState_salesSel_a1, 0.75),
share_salesSel_q90 = quantile(taxShare_salesState_salesSel_a1, 0.90)
)
df_salesState <-
df_sim %>%
group_by(sim) %>%
summarise(geoMean_gdp = get_geoReturn(gdp_chg),
share_salesGen_salesState = taxShare_salesState_salesGen_a1[30],
share_salesSel_salesState = taxShare_salesState_salesSel_a1[30],
share_other_salesState = taxShare_salesState_other_a1[30]
) %>%
arrange((geoMean_gdp))
df_PITState <-
df_sim %>%