In malcalakovalski/fim: Fiscal Impact Measure

Note to self: Keep checking totals vs contributions. They're all slightly off.

09/08 Realized that for some reason the order of operation matters. Should be net then mpc, not mpc then net.

knitr::opts_chunk$set(echo = TRUE)

# Setup -------------------------------------------------------------------
librarian::shelf(
  tidyverse,
  tsibble,
  fabletools,
  lubridate,
  glue,
  fim,
  ggbrookings,
  dplyover,
  gt,
  gtExtras)

devtools::load_all()

Prepare the FIM as usual.

# Read code until forecast object is created
# Setup -------------------------------------------------------------------
Sys.setenv(TZ = 'UTC')
librarian::shelf(
  "tidyverse",
  "zoo",
  "TTR",
  "tsibble",
  "lubridate",
  "glue",
  "fim",
  "dplyover",
  gt
)
options(digits = 4)
options(scipen = 20)
devtools::load_all()

# Set dates for current and previous months
month_year <- glue('{format.Date(today(), "%m")}-{year(today())}')
last_month_year <- glue('{format.Date(today()-months(1), "%m")}-{year(today())}')

if(!dir.exists(glue('results/{month_year}'))) {
  dir.create(glue('results/{month_year}'))
}

# Wrangle data ------------------------------------------------------------

# Since BEA put all CARES act grants to S&L in Q2 2020 we need to
# override the historical data and spread it out based on our best guess
# for when the money was spent.
overrides <- readxl::read_xlsx('data/forecast.xlsx',
                               sheet = 'historical overrides') %>% 
  select(-name) %>% 
  pivot_longer(-variable) %>% 
  pivot_wider(names_from = 'variable',
              values_from = 'value') %>% 
  rename(date = name) %>% 
  mutate(date = yearquarter(date)) %>% 
   filter(date <= yearquarter('2021 Q2'))

# Load national accounts data from BEA
usna <-
  read_data() %>%
  # Rename Haver codes for clarity
  define_variables() %>%
  # Specify time series structure:
  # Key is historical or forecast period
  # Indexed by date
  as_tsibble(key = id, index = date) %>%
  # Calculate GDP growth for data but take CBO for projection
  mutate_where(id == 'historical',
               real_potential_gdp_growth = q_g(real_potential_gdp)) %>% 
  # Net out unemployment insurance, rebate checks, and Medicare to apply different MPC's
  mutate(
    federal_social_benefits = federal_social_benefits - ui - rebate_checks - medicare,
    state_social_benefits = state_social_benefits - medicaid,
    social_benefits = federal_social_benefits + state_social_benefits,
    consumption_grants = gross_consumption_grants - medicaid_grants,
  ) %>% 
  mutate(rebate_checks_arp = if_else(date == yearquarter("2021 Q1"),
                                     1348.1,
                                     0)) %>%
  mutate_where(id == 'projection',
               rebate_checks_arp = NA,
               federal_ui = NA,
               state_ui = NA) %>% 
  mutate_where(date == yearquarter('2021 Q1'),
               rebate_checks = rebate_checks - rebate_checks_arp,
               federal_social_benefits = federal_social_benefits + 203
            ) %>% 
  mutate(consumption_grants = gross_consumption_grants - medicaid_grants,

         # Aggregate taxes
         corporate_taxes = federal_corporate_taxes + state_corporate_taxes,
         non_corporate_taxes = federal_non_corporate_taxes + state_non_corporate_taxes) %>% 
  mutate_where(id == 'projection',
               consumption_grants_deflator_growth = state_purchases_deflator_growth,
               investment_grants_deflator_growth = state_purchases_deflator_growth) %>% 
  mutate_where(date >= yearquarter('2020 Q2') & date <= yearquarter('2021 Q2'),
               consumption_grants = overrides$consumption_grants_override) 

# Forecast ----------------------------------------------------------------
forecast <- 
  readxl::read_xlsx('data/forecast.xlsx',
                    sheet = 'forecast') %>% 
  select(-15:-17, -name) %>% 
  pivot_longer(-variable) %>% 
  pivot_wider(names_from = 'variable',
              values_from = 'value') %>% 
  rename(date = name) %>% 
  mutate(date = yearquarter(date))


projections <- coalesce_join(usna, forecast, by = 'date') %>%

  mutate(# Coalesce NA's to 0
    across(where(is.numeric),
           ~ coalesce(.x, 0))) %>%
  mutate(
    health_outlays = medicare + medicaid,
    federal_health_outlays = medicare + medicaid_grants,
    state_health_outlays = medicaid - medicaid_grants
  )

New methodology to get the consumption levels and the counterfactual

# Consumption levels ------------------------------------------------------
mpc_data <- read_mpc_file()
consumption <-
  projections %>% 
  rename(federal_rebate_checks = rebate_checks,
         federal_rebate_checks_arp = rebate_checks_arp) %>% 
  # Create counterfactual
  mutate(
    across(
      c(matches('corporate|non_corporate|social_benefits|health_outlays|ui$|subsidies|aid_to_small_businesses|rebate_checks|direct_aid|vulnerable')) & !contains('provider_relief') & !ends_with('growth'),
      ~ counterfactual(.x, consumption_deflator_growth),
      .names = '{.col}_counterfactual'
    )) %>% 
  # ACTUAL CONSUMPTION
  mpc_tidy(mpc_data,
           c(matches('corporate|non_corporate|social_benefits|health_outlays|subsidies|aid_to_small_businesses|rebate_checks|direct_aid|vulnerable') &
               !ends_with('growth') &
               !starts_with('provider_relief')))  %>% 
  # UI MPC depends on quarter so the code above won't help
mutate(across(c(federal_ui, federal_ui_counterfactual,  state_ui, state_ui_counterfactual),
              .fns = ~ if_else(date < yearquarter("2021 Q2"),
                               mpc_ui(.x),
                               mpc_ui_arp(.x)),
              .names = '{.col}_consumption')) %>% 
  mutate(across(c(federal_purchases, state_purchases, consumption_grants, investment_grants),
                .names = '{.col}_consumption')) %>% 
  # COUNTERFACTUAL CONSUMPTION FOR PURCHASES
  mutate(
    federal_purchases_counterfactual_consumption = counterfactual(federal_purchases_consumption, deflator = federal_purchases_deflator_growth),
    state_purchases_counterfactual_consumption = counterfactual(state_purchases_consumption, deflator = state_purchases_deflator_growth),
    consumption_grants_counterfactual_consumption = counterfactual(consumption_grants_consumption, consumption_grants_deflator_growth),
    investment_grants_counterfactual_consumption = counterfactual(investment_grants_consumption, investment_grants_deflator_growth)
  )

consumption_summary <-
  consumption %>%
   select(date,
         gdp,
         matches('federal|state') & matches('_consumption') | matches('consumption_grants_|investment_grants_')) %>% 
  rename_with(
    .cols = ends_with('counterfactual_consumption'),
    .fn = ~ str_replace(
      string = .x,
      pattern = 'counterfactual_consumption',
      replacement = "counterfactual"
    )
  ) %>% 
  mutate(
    federal_purchases_consumption = federal_purchases_consumption + consumption_grants_consumption + investment_grants_consumption,
    federal_purchases_counterfactual = federal_purchases_counterfactual + consumption_grants_counterfactual + investment_grants_counterfactual,
    state_purchases_consumption = state_purchases_consumption - consumption_grants_consumption - investment_grants_consumption,
    state_purchases_counterfactual = state_purchases_counterfactual - consumption_grants_counterfactual - investment_grants_counterfactual
  ) %>% 
  select(-matches('consumption_grants_|investment_grants_')) %>% 
  pivot_longer(
    -c(date, id, gdp),
    names_to = c('government', 'variable', 'level'),
    names_pattern = '(federal|state)_(.*)_(.*)'
  ) %>%
  pivot_wider(names_from = 'level',
              values_from = value) %>% 
  mutate(net = consumption - counterfactual) %>% 
  group_by(variable, government) %>% 
  mutate(contribution = 400 * net / dplyr::lag(gdp)) %>% 
  ungroup()

Plots

Headline

theme_set(theme_brookings())
update_geom_defaults('line',
                     list(size = 1.5))
contributions <- read_rds('data/contributions.rds' )

totals <-
  consumption_summary %>% 
  as_tsibble(index = date, key = c(government, variable)) %>% 
  filter_index("2019 Q4" ~ "2023 Q1") %>% 
  mutate(category = case_when(variable %in% c('rebate_checks', 'rebate_checks_arp') ~ 'rebate_checks',
                              variable %in% c('ui') ~ 'unemployment_insurance',
                              variable %in% c('subsidies', 'aid_to_small_businesses_arp') ~ 'subsidies',
                              variable %in% c('social_benefits', 'other_direct_aid_arp', 'other_vulnerable_arp') ~ 'social_benefits',
                              variable %in% c('corporate_taxes', 'non_corporate_taxes') ~ 'taxes',
                              variable %in% c('purchases') ~ 'purchases',
         TRUE ~ 'health_outlays')) %>% 
    mutate(category = snakecase::to_title_case(category)) %>% 
  as_tsibble(index = date, key = c(government, variable, category)) %>% 
  aggregate_key(category, 
                total = sum(consumption), counterfactual = sum(counterfactual), contribution = sum(contribution)) %>% 
  slice(15:98) %>% 
  as_tibble() %>% 
  mutate(category = forcats::as_factor(as.character(category))) 


totals %>% 
    left_join(contributions) %>%   
    # Ungroup or `complete` won't work as expected
  ggplot(aes(x = date, y = contribution,  fill = category, group = category)) +
  geom_col(alpha = 0.5) +
  geom_line(aes(x = date, y = fiscal_impact)) +
  geom_point(aes(x = date, y = fiscal_impact), size = 2, show.legend = FALSE) +
  scale_fill_brookings('categorical') +
  scale_y_continuous(labels = scales::label_percent(accuracy = 0.1, scale = 1)) +
  scale_x_yearquarter(breaks = '3 months') +
  labs(title = 'Headline FIM by components <br>',
       x = NULL,
       y = NULL) +
  geom_hline(yintercept = 0) +
  theme(legend.key.height = unit(.5, 'lines'),
        legend.key.width = unit(1.75, 'lines'),
        legend.margin = margin(7, 0, 0, 0)) 

ggsave(here::here('headline_pieces.png'),
       width = 85 * (14 / 5),
       height = 53 * (14 / 5),
       units = 'mm',
       device = ragg::agg_png(),
       dpi = 300,
       type = 'cairo',
       bg = '#FAFAFA')

Levels of consumption

consumption_summary %>% 
  as_tsibble(index = date, key = c(government, variable)) %>% 
  filter_index("2018 Q4" ~ "2024 Q2") %>% 
    mutate(variable = snakecase::to_title_case(variable)) %>% 
  aggregate_key(  variable,total = sum(consumption), counterfactual = sum(counterfactual)) %>% 

  autoplot(show.legend = FALSE) +
    facet_wrap(.~ variable, scales = 'free') +
  ggbrookings::scale_color_brookings('misc') +
  scale_y_continuous(labels = scales::label_dollar()) +
  labs(title = 'Consumption from transfers<br>',
       x = NULL,
       y = NULL) 
ggsave(here::here('explainers/levels/consumption.png'),
       width = 85 * (14 / 5),
       height = 53 * (14 / 5),
       units = 'mm',
       device = ragg::agg_png(),
       dpi = 300,
       type = 'cairo',
       bg = '#FAFAFA')

totals %>% 
  as_tsibble(index = date, key = c( category)) %>% 
  filter_index("2018 Q4" ~ "2024 Q2") %>% 
  autoplot(show.legend = FALSE) +
  facet_wrap(. ~ category, scales = 'free') +
  ggbrookings::scale_color_brookings('misc') +
  scale_y_continuous(labels = scales::label_dollar()) +
  labs(title = 'Consumption from transfers<br>',
       x = NULL,
       y = NULL) 

ggsave(here::here('explainers/levels/consumption_v2.png'),
       width = 85 * (14 / 5),
       height = 53 * (14 / 5),
       units = 'mm',
       device = ragg::agg_png(),
       dpi = 300,
       type = 'cairo',
       bg = '#FAFAFA')

Counterfactual GDP

Let $$ \text{counterfactualGDP}= real GDP_{t-1} * (real GDP growth_t - FIM_t)$$. That is,

counterfct <-
  contributions %>% 
  select(date, real_gdp, real_gdp_growth, fiscal_impact) %>% 
  mutate(fiscal_impact_quarterly = fiscal_impact / 400) %>% 
  mutate(Counterfactual = lag(real_gdp) * ( 1 + real_gdp_growth - fiscal_impact_quarterly))

library('ggrepel')
library('ggtext')
library('ggannotate')
counterfct_long <-
  counterfct %>% 
  rename(`Real GDP` = real_gdp) %>% 
  pivot_longer(c(`Real GDP`, Counterfactual)) %>% 
  filter_index("2019 Q4" ~ "2021 Q2")

counterfct_long %>% 
  pivot_wider(values_from = value)
counterfct_long %>% 
  ggplot(aes(x = date, y = value, color = name)) +
  geom_line(size = 1.5) +
  geom_point(size = 5) +
    coord_cartesian(clip = 'off') +
  geom_label_repel(data = . %>% arrange(date) %>% filter(date == last(date)),
             aes(label = name, 
                 x = as_date(date) , 
                 y = value - 50,
                 color = name),
             family = 'Roboto',
             fontface = 'bold',
             hjust = 1,
             size = 5,
             min.segment.length = unit(2000, 'lines'),
             nudge_x = 100,
             na.rm = TRUE) + 
  scale_x_yearquarter(breaks = counterfct_long$date,
                      expand = expansion(c(0, 0.29))) +
  scale_y_continuous(labels = scales::label_dollar(),
                     breaks = scales::pretty_breaks()) +
  scale_color_brookings() +
  labs(x = NULL,
       y = NULL,
       title = "<br><span style = 'color:#FF9E1B;'>Real GDP</span> vs <span style = 'color:#003A79;'>Counterfactual GDP</span> without Fiscal Policy",
       subtitle = "Billions SAAR (Chained 2012 dollars)") +
  theme(legend.position = 'none',
        plot.title = element_textbox_simple(color = 'black',
                                             hjust = 0,
      size = rel(1),
      vjust = 1,
      margin = margin(b = 4)),
        plot.margin = margin(0.1, 2.6, 0.1, 0.1, "cm")) +

  geom_richtext(aes(label = "Positive<br>Fiscal Impact"), 
                        x = as_date("2019/12/20"),
                        y = 17000,
                        family = 'Roboto',
                        fontface = 'bold',
                        color = brookings_colors['orange_60'],
                        fill = NA, label.color = NA, # remove background and outline
    label.padding = grid::unit(rep(0, 4), "pt"), # remove padding
    size = 6,
                        y = 18500) +
    geom_richtext(aes(label = "Negative<br>Fiscal Impact"), 
                        x = as_date("2020/09/20"),

                        family = 'Roboto',
                        fontface = 'bold',
                        fill = NA, label.color = NA, # remove background and outline
    label.padding = grid::unit(rep(0, 4), "pt"), # remove padding
size = 6,
                        y = 19500) +
  # Negative impact
    annotate(
    geom = "curve", x = as_date("2020/10/01"), y = 18840, xend = as_date("2020/10/10"), yend = 19250, 
    size = 1.5,
    curvature =  -.1, arrow = arrow(length = unit(5, "mm"))
  ) +
  # Positive impact
      annotate(
    geom = "curve", x = as_date("2020/04/10"), y = 16900, xend = as_date("2020/02/15"), yend = 17100, 
    size = 1.5,
    curvature = .3, arrow = arrow(length = unit(5, "mm"))
  )  

ggsave(here::here('explainers/levels/gdp_counterfactual.png'),
       width = 85 * (14 / 5),
       height = 53 * (14 / 5),
       units = 'mm',
       device = ragg::agg_png(),
       dpi = 300,
       type = 'cairo',
       bg = '#FAFAFA')

aggregated <-
  consumption_summary %>% 
  as_tsibble(index = date, key = c(government, variable)) %>% 
  filter_index("2018 Q4" ~ "2024 Q2")  
  mutate(variable = snakecase::to_title_case(variable)) %>% 
  aggregate_key(variable,total = sum(consumption), counterfactual = sum(counterfactual)) %>% 
  filter(variable == '<aggregated>')

left_join(aggregated, contributions %>% select(date, gdp)) %>% 
  mutate(gdp_cfct = gdp - total) %>% 
  pivot_longer(c(gdp, gdp_cfct)) %>% 
  ggplot(aes(x = date, y = value, color = name)) +
  geom_line()

What would transfers be if they had grown with potential since 2020 Q1

options(scipen=999)
transfers <- 
  consumption_summary %>% 
  left_join(contributions %>% select(date, real_potential_gdp_growth)) %>% 
  filter_index("2020 Q1" ~ "2023 Q2") %>% 
  mutate(period = if_else(date > yearquarter("2021 Q2"),
                          "forecast",
                          "history")) %>% 
  arrange(variable, government) %>% 
  mutate(counterfactual = if_else(date == min(date),
                                  consumption,
                                  1 + real_potential_gdp_growth)) %>% 
  mutate(category = case_when(variable %in% c('rebate_checks', 'rebate_checks_arp') ~ 'rebate_checks',
                              variable %in% c('ui') ~ 'unemployment_insurance',
                              variable %in% c('subsidies', 'aid_to_small_businesses_arp') ~ 'subsidies',
                              variable %in% c('social_benefits', 'other_direct_aid_arp', 'other_vulnerable_arp') ~ 'social_benefits',
                              variable %in% c('purchases') ~ 'purchases',
                              variable %in% c("corporate_taxes", "non_corporate_taxes") ~ "taxes",
         TRUE ~ 'health_outlays')) %>% 
  filter(category %in% c("rebate_checks", "unemployment_insurance", "subsidies", "social_benefits", "health_outlays")) %>% 
  mutate(category = snakecase::to_title_case(category)) %>% 
  as_tibble() %>% 

  group_by(category, date) %>% 
  summarise(period, consumption = sum(consumption),
            real_potential_gdp_growth) %>% 
  distinct(date, period, consumption, real_potential_gdp_growth) %>% 
  group_by(category) %>% 
  mutate(counterfactual = if_else(date == min(date),
                                  consumption,
                                  1 + real_potential_gdp_growth),
         counterfactual = purrr::accumulate(counterfactual, `*`))

transfers %>% 
  pivot_longer(c(consumption, counterfactual)) %>% 
  ggplot(aes(x = date, y = value, color = name)) +
  geom_line() +
  facet_wrap(. ~ category, scales = "free") +
  scale_color_brookings() +
  scale_y_continuous(labels = scales::label_dollar()) +
  labs(title = "Consumption from transfers and counterfactual spending if they had grown with potential since Q1 2020<br>",
       x = NULL,  
       y = NULL)

transfers %>% 
  mutate(net = consumption - counterfactual) %>% 
  mutate_where(category == "Unemployment Insurance",
               category = "UI") %>% 
  ggplot(mapping = aes(x = date, y = net, color = category, lty = period)) +
  geom_line(data = . %>% filter(date <= yearquarter("2021 Q2'"))) +
  geom_line(data = . %>% filter(date > yearquarter("2021 Q2"),
                                mapping = aes(lty = "dashed line"))) 
  geom_label(data = . %>% arrange(date) %>% filter(date == last(date)),
             aes(label = category,
                 x = date ,
                 y = net + 0.1,
                 color = category),
             family = 'Roboto',
             fontface = 'bold',
             nudge_x = 30,
             size = 3.4) +
  coord_cartesian(clip = "off") +
  scale_y_continuous(breaks = scales::pretty_breaks(),

                     labels = scales::label_dollar()) +

  scale_color_brookings('categorical') +
  scale_linetype_manual(values = c("history" = "solid",  "forecast" = "dashed")) + 
  theme(legend.position = "none") +
  labs(title = "Government transfers minus what they would be if they had grown with potential since Q1 2020<br>",
       x = NULL,
       y = NULL)

transfers %>% 
  mutate(net = consumption - counterfactual) %>% 

  ggplot(., (aes(lty = period))) +
  geom_line(data = . %>% filter(date <= yearquarter("2021 Q2'")),
            mapping = aes(x = date, y = net, color = category)) +
  geom_line(data = . %>% filter(date >= yearquarter("2021 Q2")),
            mapping = aes(x = date, y = net, color = category),  lty = "dashed") +
   geom_label_repel(data = . %>% arrange(date) %>% filter(date == last(date)),
             aes(label = category,
                 x = as_date(date) + month(10),
                 y = net + 1,
                 color = category),
             family = 'Roboto',
             fontface = 'bold',
             nudge_x = 500,
             hjust = 1,

             size = 3.4) +
  coord_cartesian(clip = "off") +
  scale_x_yearquarter(expand = expansion(0.1, 100),
                      breaks = yearquarter(seq(as.Date("2020-01-01"), as.Date("2023-06-30"), by = "2 quarter"))) +
  scale_y_continuous(breaks = scales::pretty_breaks(),

                     labels = scales::label_dollar()) +

  scale_color_brookings('categorical') +
  theme(legend.position = 'none')+
  scale_linetype_manual(values = c("history" = "solid",  "forecast" = "dashed")) + 
  labs(title = "Government transfers minus what they would be if they had grown with potential since Q1 2020<br>",
       x = NULL,
       y = NULL)

transfers %>% 
 mutate(net = consumption - counterfactual) %>% 
  group_by(date) %>% 
  summarise(net = sum(net)) %>%  
  left_join(contributions %>% select(date, gdp, real_gdp), by = 'date') %>% 
  mutate(gdp_cfct = gdp - net) %>% 
  pivot_longer(c(gdp, gdp_cfct)) %>% 
  ggplot(aes(x = date, y = value, color = name)) +
  geom_line(alpha = 0.7) +
  geom_point(size = 3.5) +
  geom_label_repel(data = . %>% arrange(date) %>% filter(date == last(date)),
             aes(label = c("GDP", "Counterfactual\nGDP"),
                 x = date ,
                 y = value + 0.1,
                 color = name),
             family = 'Roboto',
             fontface = 'bold',
             nudge_x = 35,
             size = 3.4) +
  coord_cartesian(clip = "off") +
  scale_y_continuous(breaks = scales::pretty_breaks(),

                     labels = scales::label_dollar()) +

  scale_color_brookings('categorical') +
  theme(legend.position = "none") +
    scale_x_yearquarter(expand = expansion(0.1, 100),
                      breaks = yearquarter(seq(as.Date("2020-01-01"), as.Date("2023-06-30"), by = "2 quarter"))) +
  labs(title = "Actual GDP and counterfactual GDP without fiscal policy",
       x = NULL,
       y = NULL) +
        geom_richtext(aes(x = yearquarter(today()) + 1, y = 25000), 
                    label = "Projection",
                    size = 4,
                    cex = 2, 
                    color = 'black',
                    family = 'Roboto',
                    fill = NA, label.color = NA, # remove background and outline
      ) +
 annotate("rect", xmin = yearquarter("2021 Q3"), xmax = yearquarter('2024 Q1'),
               ymin = -Inf, ymax = Inf, alpha = 0.1, fill = 'yellow')

fim <-
  consumption_summary %>% 
  as_tsibble(index = date, key = c(government, variable)) %>% 
  filter_index("2019 Q4" ~ "2023 Q1") %>% 
  mutate(category = case_when(variable %in% c('rebate_checks', 'rebate_checks_arp') ~ 'rebate_checks',
                              variable %in% c('ui') ~ 'unemployment_insurance',
                              variable %in% c('subsidies', 'aid_to_small_businesses_arp') ~ 'subsidies',
                              variable %in% c('social_benefits', 'other_direct_aid_arp', 'other_vulnerable_arp', 'corporate_taxes', 'non_corporate_taxes') ~ 'other',
                              variable %in% c('purchases') ~ 'purchases',
         TRUE ~ 'health_outlays')) %>% 
    mutate(category = snakecase::to_title_case(category)) %>% 
  as_tsibble(index = date, key = c(government, variable, category)) %>% 
  aggregate_key(category, 
                total = sum(consumption), counterfactual = sum(counterfactual), contribution = sum(contribution)) %>% 
  slice(15:98) %>% 
  as_tibble() %>% 
  mutate(category = forcats::as_factor(as.character(category))) 

totals %>% 
  filter(date >= yearquarter("2019 Q4"),
         date <= yearquarter("2021 Q2")) %>% 
  mutate(net = total - counterfactual) %>% 
  group_by(date) %>% 
  summarise(net = sum(net)) %>% 
  left_join(contributions %>% select(date, gdp), by = 'date') %>% 
  mutate(gdp_cfct = gdp - net) %>% 
   pivot_longer(c(gdp, gdp_cfct)) %>% 
  ggplot(aes(x = date, y = value, color = name)) +
  geom_line() +
  geom_point(size = 3.5) +
  coord_cartesian(clip = "off") +
  scale_y_continuous(breaks = scales::pretty_breaks(),

                     labels = scales::label_dollar()) +
  scale_x_yearquarter(breaks = scales::pretty_breaks()) +

  scale_color_brookings('brand1',
                        labels = c("GDP", "Counterfactual GDP")) +
  labs(title = "Actual GDP and counterfactual GDP used in the FIM",
       x = NULL,
       y = NULL)