In malcalakovalski/fim: Fiscal Impact Measure

knitr::opts_chunk$set(echo = TRUE)

Federal purchases

purchases <-
  read_data() %>% 
  define_variables() %>% 
  select(date, federal_purchases, federal_purchases_growth, state_purchases, state_purchases_growth) %>% 
  filter_index("2020 Q1" ~ "2023 Q1") %>% 
  project(federal_purchases, with = federal_purchases_growth) %>% 
  left_join(arp %>% select(date, federal_non_health_grants_arp)) %>% 
  mutate(fedral_purchases_fim  = federal_purchases + federal_non_health_grants_arp)

purchases

purchases %>% 
  pivot_longer(c(federal_purchases, federal_non_health_grants_arp, fedral_purchases_fim)) %>% 
  ggplot(aes(x = date, y = value, color = name)) +
  geom_line()

State purchases

state_purchases <- purchases %>% 
  select(date, state_purchases, cbo_growth = state_purchases_growth) %>% 
  mutate(fim_growth = cbo_growth) %>% 
  relocate(id) %>% 
    create_override(
    var = fim_growth,
    start = yearquarter("2021 Q2"),
    end = yearquarter("2023 Q1"),
    values = c(
      0.024113689,
      0.019426547,
      0.015868285,
      0.015868285,
      0.015868285,
      0.014673846,
      0.013475174,
      0.012272234
    )
  ) %>%
  mutate(state_purchases_fim = state_purchases,
         state_purchases_cbo = state_purchases) %>% 
  select(-state_purchases) %>% 
  project(state_purchases_fim, with = fim_growth) %>% 
  project(state_purchases_cbo, with = cbo_growth) 

read_data() %>% 
  define_variables() %>% 
  select(date, federal_purchases, federal_purchases_growth, state_purchases, state_purchases_growth) %>% 
  filter_index("2020 Q1" ~ "2023 Q1") %>% 
  select(date, state_purchases, cbo_growth = state_purchases_growth) %>% 
  mutate(fim_growth = cbo_growth) %>% 
  relocate(id) %>% 
    create_override(
    var = fim_growth,
    start = yearquarter("2021 Q2"),
    end = yearquarter("2023 Q1"),
    values = c(
      0.024113689,
      0.019426547,
      0.015868285,
      0.015868285,
      0.015868285,
      0.014673846,
      0.013475174,
      0.012272234
    )
  ) %>%
  mutate(state_purchases_fim = state_purchases,
         state_purchases_cbo = state_purchases) %>% 

  project(state_purchases, with = state_purchases_baseline_growth)

baseline_purchases <-
  read_data() %>% 
  select(date, state_purchases = gs) %>% 
  filter_index("2020 Q1" ~ .) %>% 
  left_join(pre_pandemic_baseline) %>% 
  mutate(id = if_else(date >  yearquarter("2020 Q1"), 
                      "projection", 
                      "historical")) %>% 
  mutate_where(id == 'projection', 
               state_purchases = NA) %>% 
  project(state_purchases, with = state_purchases_growth) %>% 
    as_tibble() %>% 
  select(-id) 

state_purchases %>% 
  left_join(baseline_purchases, by = 'date') %>% 
  rename(state_purchases_pre_pandemic =  state_purchases) %>% 
  pivot_longer(c(state_purchases_cbo, state_purchases_fim, state_purchases_pre_pandemic)) %>% 
  ggplot(aes(x = date, y = value, color = name))  +
  geom_line(size = 1.3) +
  gghutchins::theme_hutchins()+
  gghutchins::scale_color_hutchins() +
  theme(legend.position = 'none') + 
  directlabels::geom_dl(aes(label = snakecase::to_title_case(name)), method = 'smart.grid') +
  scale_y_continuous(labels = scales::label_dollar(suffix = ' B'),
                     name = '')  +
  labs(title = 'State purchases projections')

state_purchases %>% 
  left_join(baseline_purchases, by = 'date') %>% 
  project2(state_purchases, )

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