min_wage_productivity.qmd

---
title: "Minimum wage productivity"
format: html
theme: cosmo
date: "`r Sys.Date()`"
date-format: long
execute:
  echo: false
---

\captionsetup[table]{labelformat=empty}
\vspace{-3em}
```{r}
#| output: false
library(tidyverse)
library(scales)
library(gt)
library(kableExtra)
library(hrbrthemes)
library(bea.R)

bea_key <- Sys.getenv("BEA_API_KEY")

bea_grab <- function(tablename) {
  bea_specs <- list(
    'UserID' = bea_key,
    'Method' = 'GetData',
    'datasetname' = 'NIPA',
    'TableName' = tablename,
    'Frequency' = 'A',
    'Year' = 'X'
  )
  beaGet(bea_specs, asWide = FALSE) %>% 
    as_tibble()
}

bea_ndp <- bea_grab("T10705") %>% 
  filter(LineNumber == 14) %>% 
  transmute(year = as.numeric(TimePeriod), ndp = DataValue)

bls_hours <- read_csv("inputs_raw/total-economy-hours-employment.csv") %>% 
  filter(
    Component == "Total U.S. economy",
    Basis == "All workers",
    Measure == "Hours worked"
  ) %>% 
  select(year = Year, hours = Value) %>% 
  filter(year >= 1948, year <= 2022) %>% 
  mutate(hours = as.numeric(hours)) %>% 
  summarize(hours = mean(hours), .by = year)

productivity <- bea_ndp %>% 
  full_join(bls_hours, by = "year") %>% 
  mutate(prod = ndp / hours)
```

```{r}
prod_base <- productivity %>% 
  filter(year == 1967) %>% 
  pull(prod)

mw_data <- productivity %>% 
  add_row(year = 2023) %>% 
  mutate(prod_mw = case_when(
    year == 1968 ~ 1.60,
    year >= 1969 ~ 1.60 * lag(prod) / prod_base 
  )) %>% 
  filter(!is.na(prod_mw))

 mw_data %>% 
  ggplot(aes(x = year, y = prod_mw)) +
  geom_line() +
  scale_y_continuous(labels = label_dollar(), breaks = seq(0,24,2)) +
  hrbrthemes::theme_ipsum() +
  labs(x = NULL, y = NULL)
 
 mw_data %>%
   mutate(prod_mw = scales::label_dollar()(prod_mw)) %>% 
   select(Year = year, 'Productivity-indexed minimum' = prod_mw) %>% 
   gt()
```