Inflators for Australian Policy Analysis

Using Australian Bureau of Statistics indices, provides functions that convert historical, nominal statistics to real, contemporary values without worrying about date input quality, performance, or the ABS catalogue.

grattanInflators

Utility package for CPI and other inflators.

Average weekly earnings

awe_inflator() uses all employees' average weekly total earnings; awote_inflator() uses full-time adults' average weekly ordinary time earnings. Both cover persons, Australia. Original, seasonally adjusted and trend data are available through awe_original(), awe_seasonal(), awe_trend() and the corresponding awote_*() functions.

awe <- awe_original()  # date and value (dollars per week)
awote_inflator("2024-05-15", "2025-05-15")
awe_inflator("2024-05-15", "2025-05-15", series = awe_seasonal())
awote_inflator("2030-05-15", "2031-05-15", series = awote_original("3%"))

The ABS May 2026 release is bundled for offline use; download_data() refreshes it through the package's ABS mirror. Original series begin in November 1994; adjusted and trend series begin in May 2012. Inflators use May observations for May to October and November observations for November to April, with the usual endpoint checks. Earnings changes include workforce composition effects; wage_inflator() uses the WPI.

Benchmarks

x <- rep_len(fy::yr2fy(1999:2020), 1e7)

system_time(grattan::cpi_inflator(, x, "2019-20"))

## process    real 
##   531ms   482ms

system_time(cpi_inflator(x, "2019-20"))

## process    real 
##   438ms   439ms

system_time(cpi_inflator(x, "2019-20", nThread = 4L))

## process    real 
##   391ms   114ms

y <- dqrng::dqsample(x)
system_time(grattan::cpi_inflator(, x, y))

## process    real 
##   24.3s   21.1s

system_time(cpi_inflator(x, y, nThread = 4L))

## process    real 
##   984ms   241ms

x <- rep_len(x, 1e8)
system_time(cpi_inflator(x, "2019-20", nThread = 4L))

## process    real 
##   4.61s   1.25s

x <- y <- NULL

x <- seq(as.Date("1999-01-01"), as.Date("2020-01-01"), by = "1 day")
x <- rep_len(x, 1e7)
system_time(cpi_inflator(x, "2019-01-01"))

## process    real 
##   297ms   311ms

x <- rep_len(x, 1e8)
system_time(cpi_inflator(x, "2019-01-01", nThread = 4L))

## process    real 
##   3.67s   1.29s

x <- as.IDate(x)
system_time(cpi_inflator(x, as.IDate("2019-01-01"), nThread = 4L))

##  process     real 
##    2.66s 905.37ms