• The cost-of-living crisis is impacting many Australians.
• This presentation explores trends in consumer prices and wage growth.
• Key questions:
  • How have costs changed over time?
  • Are wages keeping up with inflation?

• Consumer Price Index (CPI): Measures household inflation.
• Wage Price Index (WPI): Measures changes in the price of labor.
• Source: Australian Bureau of Statistics (ABS)

library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)
library(lubridate)

# Load CPI data
cpi_raw <- read_excel("Downloads/cpi_data.xlsx", sheet = "Data1", skip = 10)
cpi_long <- cpi_raw %>%
  pivot_longer(cols = -1, names_to = "Series", values_to = "Value") %>%
  rename(Date = 1) %>%
  filter(!is.na(Value)) %>%
  mutate(Date = as.Date(Date))

# Select housing and food series manually by known Series IDs or labels (simplified here)
# Example only: update Series IDs according to actual dataset if needed
cpi_filtered <- cpi_long %>%
  filter(Series %in% c("3.7", "3.8", "3.9", "3.4", "3.5", "3.6")) %>%
  mutate(Series = recode(Series,
                         "3.7" = "Rent",
                         "3.8" = "Dwelling Purchase",
                         "3.9" = "Electricity",
                         "3.4" = "Food Total",
                         "3.5" = "Meat",
                         "3.6" = "Dairy"))

# Load WPI data
wpi_raw <- read_excel("Downloads/wpi_data.xlsx", sheet = "Data1", skip = 10)
wpi_long <- wpi_raw %>%
  pivot_longer(cols = -1, names_to = "Series", values_to = "WPI") %>%
  rename(Date = 1) %>%
  filter(!is.na(WPI)) %>%
  mutate(Date = as.Date(Date))

# Keep one WPI series for simplicity
wpi_single <- wpi_long %>% group_by(Date) %>% summarise(WPI = mean(WPI, na.rm = TRUE))

all_cpi <- cpi_filtered %>% filter(Series == "Food Total")

ggplot(cpi_filtered, aes(x = Date, y = Value)) +
  geom_line(color = "blue") +
  labs(title = "CPI Trend: All Groups", x = "Date", y = "Index") +
  theme_minimal()

housing_cpi <- cpi_filtered %>%
  filter(Series %in% c("Rent", "Dwelling Purchase", "Electricity"))

ggplot(housing_cpi, aes(x = Date, y = Value, color = Series)) +
  geom_line(size = 1) +
  labs(title = "Housing CPI Components", x = "Date", y = "Index") +
  theme_minimal()

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

# Plot CPI Trend using only available data (e.g., Food Total or All Groups)

ggplot(cpi_filtered, aes(x = Date, y = Value)) +
  geom_line(color = "blue") +
  labs(title = "CPI Trend: Food Category", x = "Date", y = "Index") +
  theme_minimal()

ggplot(wpi_single, aes(x = Date, y = WPI)) +
  geom_line(color = "darkgreen") +
  labs(title = "Wage Price Index (Average of All Series)", x = "Date", y = "WPI Index") +
  theme_minimal()

• Renters and students are more exposed to rising rent and food prices.
• Low wage growth means decreased real income for many workers.

• CPI has increased significantly in housing and food.
• Wages have not kept pace, leading to growing financial pressure.
• Policymakers must consider real income erosion.