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Explore_RevGSP(9).R
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# This script explore the relationship between GSP and state gov revenue and does exploratory modeling
# It is also for the first draft of the policy brief.
#**********************************************************************
# Notes ####
#**********************************************************************
# To-do list for GSP and tax revenue section
# What to model:
# 1. Personal income tax: relationship with GSP growth and returns (stock and/or portfolio)
# 2. General Sales tax: relationship with GSP, how the relationship changes during recessions
# 3. Other taxes (non-PIT-non-sales taxes): relationship with GSP
# 4. Construct stylized government types
# Notes on inflation adustment
# - Use national level GDP price index or GDP deflator to recalculate real tax revenue.
# a. The real tax revenue data from Urban are calculated using CPI-U,
# which is not consistent with the inflation adjustment method for GDP (by chain-type indices)
# b. GDP price index may work better than CPI-U for our purpose. (the basket of goods is fixed in CPI, but changes over time in GDP price index)
# - Use real values for now. When integrated with macro model, which uses real GDP,
# add the assumed inflation rate to the GSP growth rate. So we do not model inflation
# in this version.
# Notes on data
# 1. National GDP from FRED, 2nd quarter data for annual data. This may be more consistent with the timing of FY for most states
# Notes on decomposing GDP and tax revenue
# Other relationships to examine:
# 1. GSP growth and total own soure revenue growth,
# 2. GSP growth and total tax revenue growth
# 3. GSP growth and personal income tax growth
# 4. GSP growth and sales tax growth
# 5. GSP growth and property tax growth
# 6. GSP growth and corporate income tax growth
# 7. GSP growth and growth of total tax revenue minus PIT and sales
# 8. national GDP growth and GSP growth
# Notes on real and nominal variables
# - results for nominal variables for 1995-2015 can be compared with Pew results
# - GDP/GSP are modeled in real terms in the macro model, so the GDP-revenue relationship should be
# modeld using real term. But note that pension contributions are modeled in nominal terms, which
# must be compared against nominal gov revenue.
# - Real revenue variables are in 2015 dollar and adjustment factor for inflation is the same across
# all states, gov levels and types of tax, while real GSPs are in 2009 dollar and adjustment factors
# for inflation differ across states.
# How sales taxes are divided between state and local governments:
# https://taxfoundation.org/state-and-local-sales-tax-rates-in-2017/
# Five states do not have statewide sales taxes: Alaska, Delaware, Montana, New Hampshire, and Oregon.
# Of these, Alaska and Montana allow localities to charge local sales taxes.
#**********************************************************************
# Packages ####
#**********************************************************************
library(tidyverse)
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(mFilter) # HP filter "hpfilter"
#library(MSBVAR)
# packages for ts
library(zoo)
library(xts)
library(timetk)
library(tidyquant)
library(lubridate)
library(feather)
library(psych) # describe
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 <- "policyBrief_out/"
# 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/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)
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)
)
}
color_PIT <- RIG.green
color_salesgen <- "blue"
color_salessel <- "deepskyblue2"
color_other <- RIG.purple
color_propertyLoc <- RIG.yellow.dark
color_GDP <- "gray50"
#**********************************************************************
# Data preparation 1: tax revenue and GSP ####
#**********************************************************************
# 1. Loading saved data
load(paste0(dir_data_out, "data_RevGSP.RData"))
load(paste0(dir_data_out, "dataAll.RData"))
# Variables in df_RevGSP
# Indices: state, state_abb, year
# GSP variables (calendar year)
# - RGSP_SIC: From BEA, 1987-1997, based on SIC
# - RGSP_NAICS: From BEA, 1997-2016, based on NAICS
# - NGSP_SIC: From BEA, 1963-1997, based on SIC
# - NGSP_NAICS: From BEA, 1997-2016, based on NAICS
# - NGSP: From FRED, 1997-2016 (no national level)
# Tax and revenue variables: 1977-2015 (Fiscal year)
# - suffix for gov levels:
# - local
# - state
# - SL: state and local
# - suffix for real or nominal: real / nom
#
# - variables:
# urban code Var name Var description
# 'R01', 'rev_tot', 'Total revenue',
# 'R02', 'rev_tot_ownSrc', 'Total Rev-Own Sources',
# 'R03', 'rev_gen', 'General Revenue',
# 'R04', 'rev_gen_ownSrc', 'Gen Rev-Own Sources',
# 'R05', 'tax_tot', 'Total Taxes',
# 'R06', 'tax_property', 'Property Taxes',
# 'R08', 'tax_sales_tot', 'Tot Sales & Gr Rec Tax',
# 'R09', 'tax_sales_gen', 'Total Gen Sales Tax (T09)',
# 'R10', 'tax_sales_select', 'Total select Sales Tax',
# 'R27', 'tax_indivIncome', 'Individual Income Tax (T40)',
# 'R28', 'tax_corpIncome', 'Corp Net Income Tax',
# 'R36', 'chgs_Misc', 'Tot Chgs and Misc Rev'
# - with missing values
# 2. Produce real tax revenue by applying GDP price index
# Notes: GDP price deflator and GDP chain-type price index are very close.
# For now, we stick with GDP chain-type price index
# df_dataAll_y %>%
# select(year, GDPdeflator_FRED, GDPCTPI_FRED) %>%
# mutate_at(vars(-year), funs(100 * log(./lag(.)))) %>%
# mutate(diff = GDPdeflator_FRED - GDPCTPI_FRED)
df_RevGDP_GDPCTPI <-
df_RevGSP %>% select(state_abb, year, NGSP_SIC,NGSP_NAICS, contains("_nom_")) %>%
left_join(df_dataAll_y %>% select(year, GDPCTPI_FRED)) %>%
mutate_at(vars(-year, -state_abb), funs(./(GDPCTPI_FRED/100))) %>%
rename_all(funs(str_replace(., "_nom_", "_real2_"))) %>%
rename_all(funs(str_replace(., "NGSP", "RGSP2")))
# 3. Real terms based on CPI-U (Urban's calculation BEA real GSP starts from 1987)
df_real <-
df_RevGSP %>%
select(state_abb, year,
RGSP_SIC, RGSP_NAICS,
tax_tot_real_state,
tax_indivIncome_real_state,
tax_sales_tot_real_state,
tax_sales_gen_real_state,
tax_sales_select_real_state,
tax_corpIncome_real_state,
tax_property_real_state,
tax_property_real_local) %>%
mutate(tax_other_real_state = tax_tot_real_state - tax_indivIncome_real_state -
tax_sales_tot_real_state - tax_corpIncome_real_state - tax_property_real_state,
tax_nonPITsalesgen_real_state = tax_tot_real_state - tax_indivIncome_real_state - tax_sales_gen_real_state,
tax_nonPITsalestot_real_state = tax_tot_real_state - tax_indivIncome_real_state - tax_sales_tot_real_state,
RGSPc = ifelse(year >= 1997, RGSP_NAICS, RGSP_SIC),
#RGSPc = 1000 * RGSPc,
PIT_tax_real = 100 * tax_indivIncome_real_state/tax_tot_real_state,
tax_GSP_real = 100 * tax_tot_real_state / RGSPc)
df_dlreal <-
df_real %>%
group_by(state_abb) %>%
mutate_at(vars(-year), funs(log(./lag(.)))) %>%
mutate(RGSP = ifelse(year > 1997, RGSP_NAICS, RGSP_SIC)) %>%
ungroup() %>%
rename_at(vars(-year, -state_abb), funs(paste0("dl", .) ))
df_real
df_dlreal
# 4. Real terms based on GDP price index (SIC from 1963-1997, NAICS from 1997-2016)
df_real2 <-
df_RevGDP_GDPCTPI %>%
select(state_abb, year,
RGSP2_SIC, RGSP2_NAICS,
tax_tot_real2_state,
tax_indivIncome_real2_state,
tax_sales_tot_real2_state,
tax_sales_gen_real2_state,
tax_sales_select_real2_state,
tax_corpIncome_real2_state,
tax_property_real2_state,
tax_property_real2_local) %>%
mutate(tax_other_real2_state = tax_tot_real2_state - tax_indivIncome_real2_state -
tax_sales_tot_real2_state - tax_corpIncome_real2_state - tax_property_real2_state,
tax_nonPITsalesgen_real2_state = tax_tot_real2_state - tax_indivIncome_real2_state - tax_sales_gen_real2_state,
tax_nonPITsalestot_real2_state = tax_tot_real2_state - tax_indivIncome_real2_state - tax_sales_tot_real2_state,
RGSP2c = ifelse(year >= 1997, RGSP2_NAICS, RGSP2_SIC),
#RGSP2c = 1000 * RGSP2c,
PIT_tax_real2 = 100 * tax_indivIncome_real2_state/tax_tot_real2_state,
tax_GSP_real2 = 100 * tax_tot_real2_state / RGSP2c)
df_dlreal2 <-
df_real2 %>%
group_by(state_abb) %>%
mutate_at(vars(-year), funs(log(./lag(.)))) %>%
mutate(RGSP = ifelse(year > 1997, RGSP2_NAICS, RGSP2_SIC)) %>%
ungroup() %>%
rename_at(vars(-year, -state_abb), funs(paste0("dl", .) ))
df_real2
df_dlreal2
# Nominal terms (BEA nominal GSP starts from 1963)
df_nom <-
df_RevGSP %>%
select(state_abb, year,
NGSP_SIC, NGSP_NAICS,
tax_tot_nom_state,
tax_indivIncome_nom_state,
tax_sales_tot_nom_state,
tax_sales_gen_nom_state,
tax_sales_select_nom_state,
tax_corpIncome_nom_state,
tax_property_nom_state,
tax_property_nom_local) %>%
mutate(tax_other_nom_state = tax_tot_nom_state - tax_indivIncome_nom_state -
tax_sales_tot_nom_state - tax_corpIncome_nom_state - tax_property_nom_state,
tax_nonPITsalesgen_nom_state = tax_tot_nom_state - tax_indivIncome_nom_state - tax_sales_gen_nom_state,
tax_nonPITsalestot_nom_state = tax_tot_nom_state - tax_indivIncome_nom_state - tax_sales_tot_nom_state,
NGSPc = ifelse(year > 1997, NGSP_NAICS, NGSP_SIC),
# NGSPc = 1000 * NGSPc,
PIT_tax_nom = 100 * tax_indivIncome_nom_state/tax_tot_nom_state,
tax_GSP_nom = 100 * tax_tot_nom_state / NGSPc)
df_dlnom <-
df_nom %>%
group_by(state_abb) %>%
mutate_at(vars(-year), funs(log(./lag(.)))) %>%
mutate(NGSP = ifelse(year > 1997, NGSP_NAICS, NGSP_SIC)) %>%
ungroup() %>%
rename_at(vars(-year, -state_abb), funs(paste0("dl", .)))
df_nom
df_dlnom
#**********************************************************************
# Data preparation 2: Asset returns ####
#**********************************************************************
# GSP and real tax revenue data
# Notes:
# GSP from 1988-2016
# tax from 1978-2015
# Common period:1988-2015
# Nominal GSP goes back to 1964, may want to extend the real GSP series by adjusting pre-1988 nominal GSP for inflation
# df_dlRevGSP_reg <-
# df_dlreal %>%
# select(state_abb, year,
# RGSP,
# tax_tot_real_state,
# tax_indivIncome_real_state,
# tax_sales_gen_real_state,
# tax_sales_select_real_state,
# tax_sales_tot_real_state,
# tax_corpIncome_real_state,
# tax_property_real_state,
# tax_other_real_state,
# tax_nonPITsalestot_real_state) %>%
# filter(year %in% 1978:2015, state_abb %in% df_us_states$state_abb) %>%
# mutate_at(vars(-year), funs(./100))
# df_dlRevGSP_reg
# 1. Asset index data from SBBI and national GDP from FRED (check it against the aggregate GSP for all states)
df_SBBI <-
df_dataAll_y %>%
select(year, month, yearMon,
GDP_FRED, # National GDP from FRED, in billions
CPIU_NA_FRED, # CPI, urban resident, not seasonally adjusted. FRED
GDPCTPI_FRED, # GDP chain-type price index
Inflation_Index, # SBBI, inflation index
LCapStock_TRI, # SBBI, large cap stock total return index (SP500 total return index)
LCapStock_CAI, # SBBI, large cap stock capital appreciation index (SP500 price index)
CBond_TRI, # SBBI, corp bond total return
LTGBond_TRI, # SBBI, long-term gov bond total return (20y?)
MTGBond_TRI, # SBBI, medium-term gov bond total return
LTGBond_Yield # SBBI, long-term gov bond yield
) %>%
mutate(GDP_FRED = GDP_FRED * 1e6) %>% # unit from $billion to $thousand, (tax revenues are in $thousand)
filter(year %in% 1977:2015)
# 1.1 Compute asset return and GDP growth (FRED) from indexes
df_dlSBBI <- df_SBBI %>%
mutate_at(vars(-year, -month, -yearMon, -LTGBond_Yield), funs(log(./lag(.)))) %>%
rename_at(vars(-year, -month, -yearMon, -LTGBond_Yield), funs(paste0("dl", .)) )
# 2. Asset return data from NYU Stern
df_assetReturn_nyu <- read_excel(paste0(dir_data_raw, "/histretSP_201801.xls"), sheet = "Returns by year", skip = 17)[,1:4]
names(df_assetReturn_nyu) <- c("year", "SP500_return_nyu", "TBill_return_nyu", "TBond_return_nyu")
df_assetReturn_nyu %<>% mutate(year = as.numeric(year)) %>% filter(year <= 2017)
df_assetReturn_nyu
# 3. Captial gain / losses data for states (From Don)
# level in $billions
rlzCapGains_nom <- read_excel(paste0(dir_data_raw, "/NationalCapitalGains_yy.xlsx"), sheet = "CapGains", range = c("A5:C67"))
names(rlzCapGains_nom) <- c("year", "capgains", "capgains_chg")
rlzCapGains_nom %<>% mutate(capgains = capgains * 1e6)
rlzCapGains_nom_rev <- read_excel(paste0(dir_data_raw, "/NationalCapitalGains_yy.xlsx"), sheet = "CapGains_rev", range = c("A8:E30"))[,-(2:3)]
rlzCapGains_nom_rev
# 4. combine data
# Notes: 1977 to 2015, except for tax_gain and tax_gain_chg
df_returns <-
df_SBBI %>%
left_join(df_dlSBBI) %>%
left_join(df_assetReturn_nyu) %>%
left_join(rlzCapGains_nom) %>%
left_join(rlzCapGains_nom_rev)
## 5. getting real variables
df_returns %<>%
mutate(# real changes based on CPI
dlLCapStock_TRI_real = dlLCapStock_TRI - dlInflation_Index,
dlLCapStock_CAI_real = dlLCapStock_CAI - dlInflation_Index,
dlCBond_TRI_real = dlCBond_TRI - dlInflation_Index,
dlLTGBond_TRI_real = dlLTGBond_TRI - dlInflation_Index,
dlMTGBond_TRI_real = dlMTGBond_TRI - dlInflation_Index,
SP500_return_nyu_real = SP500_return_nyu - dlInflation_Index,
TBill_return_nyu_real = TBill_return_nyu - dlInflation_Index,
TBond_return_nyu_real = TBond_return_nyu - dlInflation_Index,
capgains_chg_real = capgains_chg - dlInflation_Index,
tax_gains_chg_real = tax_gains_chg - dlInflation_Index,
# real changes based on GDP price index
dlLCapStock_TRI_real2 = dlLCapStock_TRI - dlGDPCTPI_FRED,
dlLCapStock_CAI_real2 = dlLCapStock_CAI - dlGDPCTPI_FRED,
dlCBond_TRI_real2 = dlCBond_TRI - dlGDPCTPI_FRED,
dlLTGBond_TRI_real2 = dlLTGBond_TRI - dlGDPCTPI_FRED,
dlMTGBond_TRI_real2 = dlMTGBond_TRI - dlGDPCTPI_FRED,
SP500_return_nyu_real2 = SP500_return_nyu - dlGDPCTPI_FRED,
TBill_return_nyu_real2 = TBill_return_nyu - dlGDPCTPI_FRED,
TBond_return_nyu_real2 = TBond_return_nyu - dlGDPCTPI_FRED,
capgains_chg_real2 = capgains_chg - dlGDPCTPI_FRED,
tax_gains_chg_real2 = tax_gains_chg - dlGDPCTPI_FRED,
# real index/amount based on CPI
LCapStock_TRI_real = LCapStock_TRI / (Inflation_Index/Inflation_Index[year == 2009]),
LCapStock_CAI_real = LCapStock_CAI / (Inflation_Index/Inflation_Index[year == 2009]),
CBond_TRI_real = CBond_TRI / (Inflation_Index/Inflation_Index[year == 2009]),
LTGBond_TRI_real = LTGBond_TRI / (Inflation_Index/Inflation_Index[year == 2009]),
MTGBond_TRI_real = MTGBond_TRI / (Inflation_Index/Inflation_Index[year == 2009]),
capgains_real = capgains / (Inflation_Index/Inflation_Index[year == 2009]),
tax_gains_real = tax_gains / (Inflation_Index/Inflation_Index[year == 2009]),
# real index/amount based on GDP price index
LCapStock_TRI_real2 = LCapStock_TRI /(GDPCTPI_FRED/100),
LCapStock_CAI_real2 = LCapStock_CAI /(GDPCTPI_FRED/100),
CBond_TRI_real2 = CBond_TRI /(GDPCTPI_FRED/100),
LTGBond_TRI_real2 = LTGBond_TRI /(GDPCTPI_FRED/100),
MTGBond_TRI_real2 = MTGBond_TRI /(GDPCTPI_FRED/100),
capgains_real2 = capgains /(GDPCTPI_FRED/100),
tax_gains_real2 = tax_gains /(GDPCTPI_FRED/100)
) %>%
ungroup
#**********************************************************************
# Data preparation 3: Combine data ####
#**********************************************************************
df_real %<>% left_join(df_returns)
df_dlreal %<>% left_join(df_returns)
df_real2 %<>% left_join(df_returns)
df_dlreal2 %<>% left_join(df_returns)
df_nom %<>% left_join(df_returns)
df_dlnom %<>% left_join(df_returns)
#**********************************************************************
# Regression analysis: decomposition of GDP and tax revenue ####
#**********************************************************************
# Variables to decompose (national)
# Tax in real terms (GDP price index)
# PIT, state
# general sales, state
# selective sales, state
# non-PIT-non-sales, state
# Property, local
# National GDP (FRED)
# Real total stock return (LCapStock_TRI)
# capital gains
df_decomp_real2 <-
df_real2 %>%
filter(state_abb == "US", year %in% 1977:2015) %>%
select(year, state_abb, year,
tax_indivIncome_real2_state,
tax_sales_gen_real2_state,
tax_sales_tot_real2_state,
tax_sales_select_real2_state,
tax_nonPITsalestot_real2_state,
tax_property_real2_local,
GDP_FRED,
RGSP2c,
LCapStock_TRI_real2,
capgains_real2
) %>%
mutate(
GDP_log = log(GDP_FRED),
GDP_logtrend = hpfilter(GDP_log, freq = 100)$trend,
GDP_logcycle = hpfilter(GDP_log, freq = 100)$cycle,
GDP_dlog = GDP_log - lag(GDP_log),
GDP_dlogtrend= GDP_logtrend - lag(GDP_logtrend),
GDP_dlogcycle = GDP_logcycle - lag(GDP_logcycle),
# GDP_cycle_fct = exp(GDP_logcycle),
PIT_log = log(tax_indivIncome_real2_state),
PIT_logtrend = hpfilter(PIT_log, freq = 100)$trend,
PIT_logcycle = hpfilter(PIT_log, freq = 100)$cycle,
PIT_dlog = PIT_log - lag(PIT_log),
PIT_dlogtrend= PIT_logtrend - lag(PIT_logtrend),
PIT_dlogcycle = PIT_logcycle - lag(PIT_logcycle),
salesgen_log = log(tax_sales_gen_real2_state),
salesgen_logtrend = hpfilter(salesgen_log, freq = 100)$trend,
salesgen_logcycle = hpfilter(salesgen_log, freq = 100)$cycle,
salesgen_dlog = salesgen_log - lag(salesgen_log),
salesgen_dlogtrend= salesgen_logtrend - lag(salesgen_logtrend),
salesgen_dlogcycle = salesgen_logcycle - lag(salesgen_logcycle),
salessel_log = log(tax_sales_select_real2_state),
salessel_logtrend = hpfilter(salessel_log , freq = 100)$trend,
salessel_logcycle = hpfilter(salessel_log , freq = 100)$cycle,
salessel_dlog = salessel_log - lag(salessel_log),
salessel_dlogtrend= salessel_logtrend - lag(salessel_logtrend),
salessel_dlogcycle = salessel_logcycle - lag(salessel_logcycle),
nonPITsalestot_log = log(tax_nonPITsalestot_real2_state),
nonPITsalestot_logtrend = hpfilter(nonPITsalestot_log, freq = 100)$trend,
nonPITsalestot_logcycle = hpfilter(nonPITsalestot_log, freq = 100)$cycle,
nonPITsalestot_dlog = nonPITsalestot_log - lag(nonPITsalestot_log),
nonPITsalestot_dlogtrend= nonPITsalestot_logtrend - lag(nonPITsalestot_logtrend),
nonPITsalestot_dlogcycle = nonPITsalestot_logcycle - lag(nonPITsalestot_logcycle),
propertyLoc_log = log(tax_property_real2_local),
propertyLoc_logtrend = hpfilter(propertyLoc_log, freq = 100)$trend,
propertyLoc_logcycle = hpfilter(propertyLoc_log, freq = 100)$cycle,
propertyLoc_dlog = propertyLoc_log - lag(propertyLoc_log),
propertyLoc_dlogtrend= propertyLoc_logtrend - lag(propertyLoc_logtrend),
propertyLoc_dlogcycle = propertyLoc_logcycle - lag(propertyLoc_logcycle),
stockIdx_log = log(LCapStock_TRI_real2),
stockIdx_logtrend = hpfilter(stockIdx_log, freq = 100)$trend,
stockIdx_logcycle = hpfilter(stockIdx_log, freq = 100)$cycle,
stockIdx_dlog = stockIdx_log - lag(stockIdx_log),
stockIdx_dlogtrend= stockIdx_logtrend - lag(stockIdx_logtrend),
stockIdx_dlogcycle = stockIdx_logcycle - lag(stockIdx_logcycle),
LagstockIdx_dlog = lag(stockIdx_dlog),
LagstockIdx_dlogtrend = lag(stockIdx_dlogtrend),
LagstockIdx_dlogcycle = lag(stockIdx_dlogcycle),
capgains_log = log(capgains_real2),
capgains_logtrend = hpfilter( capgains_log , freq = 100)$trend,
capgains_logcycle = hpfilter( capgains_log , freq = 100)$cycle,
capgains_dlog = capgains_log - lag(capgains_log),
capgains_dlogtrend= capgains_logtrend - lag(capgains_logtrend),
capgains_dlogcycle = capgains_logcycle - lag(capgains_logcycle),
Lagcapgains_dlog = lag(capgains_dlog),
Lagcapgains_dlogtrend = lag(capgains_dlogtrend),
Lagcapgains_dlogcycle = lag(capgains_dlogcycle),
PIT_GDP_trend = exp(PIT_logtrend - GDP_logtrend),
salesgen_GDP_trend = exp(salesgen_logtrend - GDP_logtrend),
salessel_GDP_trend = exp(salessel_logtrend - GDP_logtrend),
nonPITsalestot_GDP_trend = exp(nonPITsalestot_logtrend - GDP_logtrend),
propertyLoc_GDP_trend = exp(propertyLoc_logtrend - GDP_logtrend)
)
# level and trend
df_decomp_real2 %>% # GDP
select(year, GDP_dlog, GDP_dlogtrend) %>%
gather(var, value, -year) %>%
qplot(x = year, y = value, color = var, data = ., geom = c("line", "point")) +
geom_hline(yintercept = 0, linetype = 2)
df_decomp_real2 %>% #PIT
select(year, PIT_dlog, PIT_dlogtrend) %>%
gather(var, value, -year) %>%
qplot(x = year, y = value, color = var, data = ., geom = c("line", "point"))
df_decomp_real2 %>% #gen sales
select(year, salesgen_dlog, salesgen_dlogtrend) %>%
gather(var, value, -year) %>%
qplot(x = year, y = value, color = var, data = ., geom = c("line", "point"))
df_decomp_real2 %>% #gen sales
select(year, stockIdx_dlog, stockIdx_dlogtrend) %>%
gather(var, value, -year) %>%
qplot(x = year, y = value, color = var, data = ., geom = c("line", "point"))
# compare cycles
# log cycle
df_decomp_real2 %>% # GDP, PIT, and gen sales
select(year, GDP_dlogcycle, PIT_dlogcycle, salesgen_dlogcycle) %>%
gather(var, value, -year) %>%
qplot(x = year, y = value, color = var, data = ., geom = c("line", "point")) +
geom_hline(yintercept = 0, linetype = 2)
df_decomp_real2 %>% # GDP, gen sales and sel sales
select(year, GDP_dlogcycle, salesgen_dlogcycle, salessel_dlogcycle) %>%
gather(var, value, -year) %>%
qplot(x = year, y = value, color = var, data = ., geom = c("line", "point")) +
geom_hline(yintercept = 0, linetype = 2)
df_decomp_real2 %>% # GDP, nonPITsales, and local property
select(year, GDP_dlogcycle, nonPITsalestot_dlogcycle, propertyLoc_dlogcycle) %>%
gather(var, value, -year) %>%
qplot(x = year, y = value, color = var, data = ., geom = c("line", "point")) +
geom_hline(yintercept = 0, linetype = 2)
df_decomp_real2 %>% # GDP, PIT, and stock
select(year, GDP_dlogcycle, PIT_dlogcycle, stockIdx_dlogcycle) %>%
gather(var, value, -year) %>%
qplot(x = year, y = value, color = var, data = ., geom = c("line", "point")) +
geom_hline(yintercept = 0, linetype = 2)
df_decomp_real2 %>% # PIT, and capgains and stock
select(year, PIT_dlogcycle, stockIdx_dlogcycle, capgains_dlogcycle) %>%
gather(var, value, -year) %>%
qplot(x = year, y = value, color = var, data = ., geom = c("line", "point")) +
geom_hline(yintercept = 0, linetype = 2)
# df_decomp_real2 %>% # PIT, and capgains and stock
# mutate() %>%
# select(year, diff_PIT_logcycle, diff_stockIdx_logcycle, diff_capgains_logcycle) %>%
# gather(var, value, -year) %>%
# qplot(x = year, y = value, color = var, data = ., geom = c("line", "point")) +
# geom_hline(yintercept = 0, linetype = 2)
# Trends
df_decomp_real2 %>% # PIT vs gen sales
select(year, PIT_logtrend, salesgen_logtrend) %>%
gather(var, value, -year) %>%
qplot(x = year, y = value, color = var, data = ., geom = c("line", "point"))
# geom_hline(yintercept = 1, linetype = 2)
df_decomp_real2 %>% # PIT vs gen sales as % of GDP
select(year,
PIT_GDP_trend,
salesgen_GDP_trend,
salessel_GDP_trend,
nonPITsalestot_GDP_trend,
propertyLoc_GDP_trend) %>%
gather(var, value, -year) %>%
qplot(x = year, y = value, color = var, data = ., geom = c("line", "point")) +
coord_cartesian(ylim = c(0, 0.035))
# geom_hline(yintercept = 1, linetype = 2)
# Check CPI inflation and GDP price index inflation
df_dlreal2 %>%
filter(state_abb == "US") %>%
select(state_abb, year, dlInflation_Index, dlGDPCTPI_FRED) %>%
gather(var, value, -year, -state_abb) %>%
qplot(x = year, y = value, color = var, data = ., geom = c("line","point"))
# GDP price index inflation is generally lower and less volatile.
#*******************************************************************************
# Regression analysis: data prep 2 asset return and PIT ####
#*******************************************************************************
# Goal: Estimate the relationship between real PIT growth, real GSP growth, and asset return (nominal),
# and see if there is structure breek around 2000. (if the recent two recessions are different.)
# Notes:
# Need to first examine the relationship between realized capgains, tax revenue from realized capgains, and asset returns.
# Need an argument for using investment returns in the PIT regression
# 1. Asset returns and captial gains, realized captial gains and PIT
df_temp <-
df_dlreal2 %>%
select(state_abb, year,
dlLCapStock_CAI,
dlLCapStock_TRI,
dlLTGBond_TRI,
LTGBond_Yield,
SP500_return_nyu,
TBond_return_nyu,
capgains_chg,
dlLCapStock_CAI_real2,
dlLCapStock_TRI_real2,
dlLTGBond_TRI_real2,
SP500_return_nyu_real2,
TBond_return_nyu_real2,
capgains_chg_real2,
tax_gains_chg,
dltax_indivIncome_real2_state) %>%
filter(state_abb == "US")
# quick look at how the total bond returns from SBBI and NYU Stern differ
df_temp %>%
select(state_abb, year, dlLTGBond_TRI, TBond_return_nyu) %>%
gather(var, value, -state_abb, -year) %>%
qplot(x = year, y = value, color = var, data=., geom = c("line", "point")) + theme_bw() +
scale_x_continuous(breaks = seq(1950, 2020, 5))
# The two sources show very different returns, especially after 2000.
# We may want to stick with the SBBI return. (SBBI's calculation looks more rigorous)
df_temp %>%
select(state_abb, year, dlLCapStock_TRI, SP500_return_nyu) %>%
gather(var, value, -state_abb, -year) %>%
qplot(x = year, y = value, color = var, data=., geom = c("line", "point")) + theme_bw() +
scale_x_continuous(breaks = seq(1950, 2020, 5))
# It seems there is a lag in SBBI SP500 return
# Plotting asset returns and realized capital gains
# Stock
fig_returnCapGains_nom <-
df_temp %>%
filter(year >=1977) %>%
select(state_abb, year, dlLCapStock_TRI, capgains_chg_real2) %>%
gather(var, value, -state_abb, -year) %>%
mutate(var = factor(var, levels = c("dlLCapStock_TRI", "capgains_chg_real2"),
labels = c("SP500 Total Return", "Change in realized capital gains"))) %>%
qplot(x = year, y = 100 * value, color = var, data=., geom = c("line", "point")) +
theme_bw() + RIG.themeLite() +
scale_x_continuous(breaks = seq(1950, 2020, 5)) +
scale_color_manual(values = c(RIG.green, "blue")) +
labs(x = NULL, y = "Percent", color = NULL,
title = "Total stock returns and realized capital gains") +
theme(legend.position = "bottom")
fig_returnCapGains_nom
fig_returnCapGains_real <-
df_temp %>%
filter(year >=1977) %>%
select(state_abb, year, dlLCapStock_TRI_real2, capgains_chg_real2) %>%
gather(var, value, -state_abb, -year) %>%
mutate(var = factor(var, levels = c("dlLCapStock_TRI_real2", "capgains_chg_real2"),
labels = c("SP500 real total Return", "Change in real realized capital gains"))) %>%
qplot(x = year, y = 100 * value, color = var, data=., geom = c("line", "point")) +
theme_bw() + RIG.themeLite() +
scale_x_continuous(breaks = seq(1950, 2020, 5)) +
scale_color_manual(values = c(RIG.green, "blue")) +
labs(x = NULL, y = "Percent", color = NULL,
title = "Total stock returns and realized capital gains (in real terms)") +
theme(legend.position = "bottom")
fig_returnCapGains_real
# Long-term gov bond, SBBI
df_temp %>%
select(state_abb, year, dlLTGBond_TRI_real2, capgains_chg_real2) %>%
gather(var, value, -state_abb, -year) %>%
qplot(x = year, y = value, color = var, data=., geom = c("line", "point")) + theme_bw() +
scale_x_continuous(breaks = seq(1950, 2020, 5))
# No obvious correlation
df_temp %>%
select(state_abb, year, dlLTGBond_TRI, capgains_chg) %>%
gather(var, value, -state_abb, -year) %>%
qplot(x = year, y = value, color = var, data=., geom = c("line", "point")) + theme_bw() +
scale_x_continuous(breaks = seq(1950, 2020, 5))
# No obvious correlation
# Long-term gov bond, NYU
df_temp %>%
select(state_abb, year, TBond_return_nyu, capgains_chg) %>%
gather(var, value, -state_abb, -year) %>%
qplot(x = year, y = value, color = var, data=., geom = c("line", "point")) + theme_bw() +
scale_x_continuous(breaks = seq(1950, 2020, 5))
#
# Plotting realized captial gains
# High correlation between 1-year lag of realized captial gain and capital gains tax receipts
# High correlation between captial gain tax receipt and state PIT
# Question: the share of capital gain tax in total PIT
# fig_captainsTax <-
# df_temp %>%
# select(state_abb, year, capgains_chg, tax_gains_chg, tax_indivIncome_real_state) %>%
# mutate(capgains_chg = lag(capgains_chg)) %>%
# gather(var, value, -state_abb, -year) %>%
# mutate(var = factor(var, levels = c("capgains_chg", "tax_gains_chg", "tax_indivIncome_real_state"),
# labels = c("Change in realized capital gains (1 year lag)", "Change in capital gains tax receipts", "change in state individual tax"))) %>%
# qplot(x = year, y = 100 * value, color = var, data=., geom = c("line", "point")) +
# theme_bw() + RIG.themeLite() +
# geom_hline(yintercept = 0, linetype = 2) +
# scale_x_continuous(breaks = seq(1950, 2020, 5)) +
# scale_color_manual(values = c("blue", RIG.red, "darkgrey")) +
# labs(x = NULL, y = "Percent", color = NULL,
# title = "Realized capital gains and tax revenues") +
# theme(legend.position = "bottom")
# fig_captainsTax
fig_capgainsTax_real <-
df_temp %>%
filter(year >= 1988) %>%
select(state_abb, year, capgains_chg_real2, dlLCapStock_TRI_real2, dltax_indivIncome_real2_state) %>%
mutate(capgains_chg_real2 = lag(capgains_chg_real2),
dlLCapStock_TRI_real2 = lag(dlLCapStock_TRI_real2)) %>%
gather(var, value, -state_abb, -year) %>%
mutate(var = factor(var, levels = c("capgains_chg_real2", "dlLCapStock_TRI_real2", "dltax_indivIncome_real2_state"),
labels = c("Real change in realized capital gains (1 year lag)", "SP500 total real return (1 year lag)", "Real change in state individual tax"))) %>%
qplot(x = year, y = 100 * value, color = var, data=., geom = c("line", "point")) +
theme_bw() + RIG.themeLite() +
geom_hline(yintercept = 0, linetype = 2) +
scale_x_continuous(breaks = seq(1950, 2020, 5)) +
scale_color_manual(values = c("blue", RIG.red, "darkgrey")) +
labs(x = NULL, y = "Percent", color = NULL,
title = "Realized capital gains and tax revenues (in real terms)") +
theme(legend.position = "bottom")
fig_capgainsTax_real
#*******************************************************************************
# Figures:tax revenues, GDP and asset returns ####
#*******************************************************************************
# PIT and sales
df_decomp_real2 %>%
filter(year >= 1988) %>%
select(state_abb, year, GDP_dlogcycle, PIT_dlogcycle, salesgen_dlogcycle) %>%
gather(var, value, -state_abb, -year) %>%
mutate(var = factor(var, levels = c("PIT_dlogcycle", "salesgen_dlogcycle", "GDP_dlogcycle"),
labels = c("Real change in state individual tax", "Real change in general sales tax","Real GDP growth" ))) %>%
qplot(x = year, y = 100 * value, color = var, data=., geom = c("line", "point")) +
theme_bw() + RIG.themeLite() +
geom_hline(yintercept = 0, linetype = 2) +
scale_x_continuous(breaks = seq(1950, 2020, 5)) +
scale_color_manual(values = c("blue", RIG.red, "darkgrey")) +
labs(x = NULL, y = "Percent", color = NULL,
title = "Cyclical components of real growth rates of individual income tax, general sales tax, \nand GDP growth and capital gain tax") +
theme(legend.position = "bottom")
# PIT 1: GDP growth, PIT growth and capgains
fig_cycle_PIT <-
df_decomp_real2 %>%
filter(year >= 1988) %>%
select(state_abb, year, GDP_dlogcycle, PIT_dlogcycle, capgains_dlogcycle) %>%
mutate(capgains_dlogcycle = lag(capgains_dlogcycle)) %>%
gather(var, value, -state_abb, -year) %>%
mutate(var = factor(var, levels = c("PIT_dlogcycle", "GDP_dlogcycle", "capgains_dlogcycle"),
labels = c("Real change in state individual income tax", "Real GDP growth", "Real change in capital gains (1 year lag)"))) %>%
qplot(x = year, y = 100 * value, color = var, data=., geom = c("line", "point")) +
theme_bw() + RIG.themeLite() +
geom_hline(yintercept = 0, linetype = 2) +
scale_x_continuous(breaks = seq(1950, 2020, 5)) +
scale_y_continuous(breaks = seq(-100, 100, 10)) +
scale_color_manual(values = c(color_PIT, color_GDP, RIG.red)) +
labs(x = NULL, y = "Percent", color = NULL,
title = "Cyclical changes in state personal income tax, GDP and capital gains",
subtitle = "(Real growth rate calculated based on 2009 dollar)") +
theme(legend.position = "bottom")
fig_cycle_PIT
df_decomp_real2 %>%
filter(year >= 1988) %>%
select(state_abb, year, GDP_dlog, PIT_dlog, capgains_dlog) %>%
mutate(capgains_dlog = lag(capgains_dlog)) %>%
gather(var, value, -state_abb, -year) %>%
mutate(var = factor(var, levels = c("PIT_dlog", "GDP_dlog", "capgains_dlog"),
labels = c("Real change in state individual tax", "Real GDP growth", "Real change in capital gains tax (1 year lag)"))) %>%
qplot(x = year, y = 100 * value, color = var, data=., geom = c("line", "point")) +
theme_bw() + RIG.themeLite() +
geom_hline(yintercept = 0, linetype = 2) +
scale_x_continuous(breaks = seq(1950, 2020, 5)) +
scale_color_manual(values = c("blue", "darkgrey", RIG.red)) +
labs(x = NULL, y = "Percent", color = NULL,
title = "Real growth rates of individual income tax, GDP growth and capital gain tax") +
theme(legend.position = "bottom")
# PIT 2: cyclical component (?) of Stock return and capgains
fig_stockCaptains <-
df_decomp_real2 %>%
filter(year >= 1988) %>%
select(state_abb, year, capgains_dlog, stockIdx_dlog) %>%
mutate(capgains_dlog = lag(capgains_dlog),
stockIdx_dlog = lag(stockIdx_dlog)
) %>%
gather(var, value, -state_abb, -year) %>%
mutate(var = factor(var, levels = c("capgains_dlog", "stockIdx_dlog"),
labels = c("Real change in capital gains (1-year lag)", "SP500 total return (1-year lag)"))) %>%
qplot(x = year, y = 100 * value, color = var, data=., geom = c("line", "point")) +
theme_bw() + RIG.themeLite() +
geom_hline(yintercept = 0, linetype = 2) +
scale_x_continuous(breaks = seq(1950, 2020, 5)) +
scale_y_continuous(breaks = seq(-100, 100, 10)) +
scale_color_manual(values = c("blue", RIG.red)) +
labs(x = NULL, y = "Percent", color = NULL,
title = "Real changes in capital gains and real stock returns",
subtitle = "(Real growth rate calculated based on 2009 dollar)") +
theme(legend.position = "bottom")
fig_stockCaptains
ggsave(paste0("policyBrief_out/", "fig_cycle_PIT.png"), fig_cycle_PIT , width = 10*0.8, height = 6*0.8)
ggsave(paste0("policyBrief_out/", "fig_stockCaptains.png"), fig_stockCaptains , width = 10*0.8, height = 6*0.8)
# Sales: gdp growth, gen sales growth and selective sales growth
df_decomp_real2 %>%
filter(year >= 1985) %>%
select(state_abb, year, GDP_dlog, salesgen_dlog, salessel_dlog) %>%
gather(var, value, -state_abb, -year) %>%
mutate(var = factor(var, levels = c( "salesgen_dlog", "salessel_dlog", "GDP_dlog"),
labels = c("Real growth of general sales tax", "Real growth of selective sales tax", "Real GDP growth"))) %>%
qplot(x = year, y = 100 * value, color = var, data=., geom = c("line", "point")) +
theme_bw() + RIG.themeLite() +
geom_hline(yintercept = 0, linetype = 2) +
scale_x_continuous(breaks = seq(1950, 2020, 5)) +
scale_y_continuous(breaks = seq(-100, 100, 2)) +
scale_color_manual(values = c(color_salesgen, color_salessel, color_GDP)) +
labs(x = NULL, y = "Percent", color = NULL,
title = "Changes in sales taxes and GDP",
subtitle = "(Real growth rate calculated based on 2009 dollar)") +
theme(legend.position = "bottom")
fig_cycle_sales <-
df_decomp_real2 %>%
#filter(year >= 1988) %>%
select(state_abb, year, GDP_dlogcycle, salesgen_dlogcycle, salessel_dlogcycle) %>%
gather(var, value, -state_abb, -year) %>%
mutate(var = factor(var, levels = c( "salesgen_dlogcycle", "salessel_dlogcycle", "GDP_dlogcycle"),