In malcalakovalski/fim: Fiscal Impact Measure

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", 
  echo = FALSE,
  fig.align = 'center',
  warning = FALSE,
  message = FALSE,
  cache = TRUE 
)

knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())

devtools::load_all()
librarian::shelf(
  'tidyverse',
  'purrr',
  'tsibble',
  'lubridate',
  'glue',
  'magrittr'

)
comparison_plot <- function(.data, variable){


  plot <- .data %>% 
    filter(variable == {{ variable }}) %>% 
    ggplot(aes(x = date,  y =  value, fill = source)) +
    #geom_col(position=position_dodge2(reverse = TRUE)) +
    geom_col(position=position_dodge2(reverse = TRUE)) +
    labs(title = glue::glue("{snakecase::to_title_case(variable)}"),
         x = NULL,
         y = NULL) +
    ggthemes::theme_hc() +
    gghutchins::scale_fill_hutchins(
      name = "",
      labels = c('Updated', 'Previous'),
      pal = 'qual',
      rev = FALSE
    ) +
    scale_x_yearquarter(breaks = waiver(),
                        date_breaks = '3 months',
                        date_labels = "Q%q") +
    facet_grid( ~ year(date),
                space = "free_x",
                scales = "free_x",
                switch = "x")  +
    theme(legend.position = 'top') +
    guides(fill = guide_legend(reverse = TRUE)) 


  variable_name <- rlang::as_name(rlang::ensym(variable))

  if(str_ends(variable_name, 'contribution')){
    plot + 
      scale_y_continuous(name = '', 
                         labels = scales::label_percent(scale = 1))
  } else {
    plot +
      scale_y_continuous(name = '', 
                         labels = scales::label_comma())
  }

}

# Load previous months results
previous <-
  readxl::read_xlsx('results/5-2021/fim-5-2021.xlsx') %>%
  mutate(date = yearquarter(date)) %>%
  drop_na(date) %>%
  as_tsibble(index = date) %>%
  filter_index("2020 Q2" ~ "2023 Q1") %>% 
  mutate(federal_non_health_grants_arp = mpc_non_health_grants_arp(federal_non_health_grants_arp)) %>% 
  mutate(federal_purchases = federal_purchases + federal_non_health_grants_arp,
         federal_purchases_contribution = federal_purchases_contribution + federal_non_health_grants_arp_contribution) %>% 
  mutate(federal_ui = federal_ui,
         federal_ui_contribution = federal_ui_contribution + federal_ui_arp_contribution) %>% 
  mutate(federal_ui = federal_ui + federal_ui_arp,
         federal_ui_contribution = federal_ui_contribution + federal_ui_arp_contribution) %>% 
  mutate(federal_health_outlays= federal_health_outlays + federal_health_grants_arp,
         federal_health_outlays_contribution = federal_health_outlays_contribution + federal_health_grants_arp_contribution)


forecast <- readxl::read_xlsx('data/forecast_06_2021.xlsx',
                              sheet = 'forecast') %>% 
  select(-name) %>% 
  pivot_longer(-variable) %>% 
  pivot_wider(names_from = 'variable',
              values_from = 'value') %>% 
  rename(date = name) %>% 
  mutate(date = yearquarter(date))


current <- readRDS('data/contributions.RDS') %>%
  mutate(date = yearquarter(date)) %>%
  drop_na(date) %>%
  as_tsibble(index = date) %>%
  filter_index("2020 Q2" ~ "2023 Q1")

previous_long <- pivot_longer(previous, cols = where(is.numeric), values_to = 'previous')
current_long <- pivot_longer(current, cols = where(is.numeric), values_to = 'current')

comparison <- inner_join(previous_long,
                         current_long,
                         by = c('date', 'name', 'id')) %>%
  pivot_longer(c(previous, current),
               names_to = 'source') %>%
  rename(variable = name)

comparison_nested <-
  comparison %>%
  group_by(variable) %>%
  nest() %>%
  mutate(plot = map2(.x = variable,
                     .y = data,
                     .f = ~comparison_plot(.data = .y,
                                  variable = .x)))


plots <- rlang::set_names(comparison_nested$plot, comparison_nested$variable)

plots$fiscal_impact

NIPA Consistent Purchases {.tabset .tabset-pills}

library('magrittr')
library('tidyverse')

During the last update, we were counting all of the grants and purchases from the American Rescue Plan as federal purchases. Hence, of the $r previous %>% filter_index('2021 Q2') %>% pull(federal_purchases) billion in federal purchases in 2021 Q2, $r previous %>% filter_index('2021 Q2') %>% pull(federal_non_health_grants_arp) were from the American Rescue Plan. In 2021 Q3, federal purchases were $r previous %>% filter_index('2021 Q2') %>% pull(federal_purchases) of which $r previous %>% filter_index('2021 Q2') %>% pull(federal_non_health_grants_arp) were from the American Rescue Plan.

However, the federal purchases portion from ARP was actually lower last time since we applied an MPC to it. Now, we only apply that MPC to the portion that we put into consumption grants. Therefore, our federal purchases are r current$federal_purchases[5] - previous$federal_purchases[5] billion higher this time.

Contributions

plots$federal_purchases_contribution + labs(subtitle = "NIPA Consistent")
plots$state_purchases_contribution +  labs(subtitle = "NIPA Consistent")

Levels

plots$federal_purchases  + labs(subtitle = "NIPA Consistent")
plots$state_purchases + labs(subtitle = "NIPA Consistent")

Grants {.tabset .tabset-pills}

Likewise, consumption grants are higher now given that we reattributed some of the ARP grants away from federal purchases.

Contributions

plots$consumption_grants_contribution
plots$investment_grants_contribution

Levels

plots$consumption_grants
plots$investment_grants
plots$federal_non_health_grants_arp

FIM Consistent Purchases

Contributions

plots$federal_contribution + labs(subtitle = "NIPA Consistent")
plots$state_contribution  + labs(subtitle = "NIPA Consistent")

Taxes

Corporate taxes {.tabset .tabset-pills}

Contributions

plots$federal_corporate_taxes_contribution
plots$state_corporate_taxes_contribution

Levels

plots$federal_corporate_taxes
plots$state_corporate_taxes

Non-corporate taxes {.tabset .tabset-pills}

"Updates to Fourth-Quarter Wages and Salaries In addition to presenting updated estimates for the first quarter, today's release presents revised estimates of fourth-quarter wages and salaries, personal taxes, and contributions for government social insurance based on updated data from the BLS Quarterly Census of Employment and Wages program. Wages and salaries are now estimated to have increased $360.5 billion in the fourth quarter of 2020, an upward revision of $157.8 billion. The revision to fourth-quarter wages and salaries resulted in a revision to GDI; real GDI increased 19.4 percent (annual rate) in the fourth quarter, an upward revision of 3.7 percentage points from the previously published estimate."

Our forecast is taking these revisions until the end of the forecast period. So these revisions lead to lower contributions than last time in the forecast period.

Contributions

plots$federal_non_corporate_taxes_contribution
plots$state_non_corporate_taxes_contribution

Levels

plots$federal_non_corporate_taxes
plots$state_non_corporate_taxes

Transfers

Total

Contributions

plots$transfers_contribution
plots$federal_transfers_contribution
plots$state_transfers_contribution

Health Outlays {.tabset .tabset-pills}

While our total medicaid forecast is unchanged, we bumped up our forecast for the federal portion of medicaid (Medicaid grants) to reflect the increased FMAP due to the ARP. This increases federal health outlays and decreases state health outlays a litte bit.

We assume that the pandemic will end in 2022 Q3.

Moreover, our medicare forecast is unchanged. The difference of r current$medicare[5] - previous$medicare[5] is due to the Medicare sequester which we previously included in our federal health outlays add factor instead of directly into our Medicare forecast.

Finally, the bulk of the difference in federal health outlays comes from double counting revenues from Medicaid, Medicare, and CHIP in CBO's ARP score. We had previously said that health grants from the ARP in Q2 2021 and Q3 2021 were r previous$federal_health_grants_arp[5]. Afterwards, we had r previous$federal_health_grants_arp[7] for 4 quarters. Since the MPC for health outlays is high (90% over four quarters), this overestimated the contribution of federal health outlays considerably.

Contributions

plots$federal_health_outlays_contribution
plots$state_health_outlays_contribution

Levels

plots$federal_health_outlays
plots$state_health_outlays

Components

plots$medicaid
plots$medicaid_grants
plots$medicare

Subsidies (non-ARP) {.tabset .tabset-pills}

We took out the business meals deductions from subsidies since we believe they're actually a tax break instead. Moreover, we changed our PPP forecast to more accurately predict the timing of how BEA will write PPP disbursements down. In particular, we had $568 billion in PPP for Q2 2021 and Q3 2021 previously and now we have 488, 322 and 32 in Q1 through Q3 of 2021. Hence, our PPP forecast is 294 lower.

Otherwise, our forecast is the same (nothing from the ARP changed)

Contributions

plots$subsidies_contribution

Levels

plots$subsidies

Subsidies (ARP) {.tabset .tabset-pills}

ARP PPP and Provider Relief Fund

Contribution

plots$federal_aid_to_small_businesses_arp_contribution

Levels

plots$federal_aid_to_small_businesses_arp

Unemployment Insurance {.tabset .tabset-pills}

Previously, we guessed that regular federal UI would be $20 billion and that the ARP UI was r previous$federal_ui_arp[5]. Since our ARP score was based on an outdated unemployment rate forecast from CBO, we changed it to reflect higher employment growth due to reopenings.

Contributions

plots$federal_ui_contribution
plots$state_ui_contribution

Levels

plots$federal_ui
plots$state_ui

Other aid to vulnerable households {.tabset .tabset-pills}

Premium Tax Credits, Ratepayer protection, Assistance for older Americans, COBRA, Emergency Assistance

Contribution

plots$federal_other_vulnerable_arp_contribution

Level

plots$federal_other_vulnerable_arp

Rebate Checks (1st and 2nd round){.tabset .tabset-pills}

Contribution

plots$rebate_checks_contribution

Level

plots$rebate_checks

Rebate Checks (ARP) {.tabset .tabset-pills}

Last time, we forgot to include the rebate checks from ARP that will go out in the next tax year.

Contribution

plots$rebate_checks_arp_contribution

Level

plots$rebate_checks_arp

Direct aid to households {.tabset .tabset-pills}

Includes Child tax credit, EITC, Childcare for workers, Dependent care for families

Contribution

plots$federal_other_direct_aid_arp_contribution

Level

plots$federal_other_direct_aid_arp

Social Benefits Remainder {.tabset .tabset-pills}

Level and impact of social benefits net of health outlays, rebate checks, and unemployment insurance.

The social benefits remainder came in 3.6 billion lower than we had last time. Almost half of this is due to Federal UI which came in 1.63 billion lower.

Contribution

plots$federal_social_benefits_contribution + labs(subtitle = "Remainder of social benefits")
plots$state_social_benefits_contribution+ labs(subtitle = "Remainder of social benefits")

Level

plots$federal_social_benefits+ labs(subtitle = "Remainder of social benefits")
plots$state_social_benefits+ labs(subtitle = "Remainder of social benefits")

malcalakovalski/fim documentation built on July 30, 2024, 8:37 a.m.