BLS API Inflation and Prices

The BLS Data Landscape

The most difficult thing about working with BLS data is gaining a clear understanding on what data are available and what they represent. Some of the more popular data sets can be found on the BLS Databases, Tables & Calculations website. The selected examples below do not include all series or databases.

##Categories you will find useful are:

CPI (Consumer Price Index)

The CPI data tracks the prices paid by urban consumers for a representative basket of goods and services. The CPI is the primary metric used to calculate inflation.

• CPI All Urban Consumers (Current Series)

• CPI Urban Wage Earners and Clerical Workers (Current Series)

• CPI All Urban Consumers (Chained CPI)

• CPI Average Price Data

IPP (International Price Program)

• IPP Import/Export Price Indexes

Note: The hyperlinks above link to lists of the most popular seriesIDs, but are only a small sample of all the data tracked by the BLS.

##CPI: Tracking Inflation

Although there are many measures of inflation, the CPI’s “Consumer Price Index for All Urban Consumers: All Items” is normally the headline inflation rate one would hear about on the news (see FRED).

Getting these data from the blscrapeR package is easy enough:

library(blscrapeR)
df <- bls_api("CUSR0000SA0")
head(df)

Due to the limitations of the API, we are only able to gather twenty years of data per request. However the formula for calculating inflation is based on the 1980 dollar, so the data from the API aren’t sufficient.

The package includes a function that collects information form the CPI beginning at 1947 and calculates inflation.

To find out the value of a January 2015 dollar in January 2023, we just make a simple function call.

df <- inflation_adjust("2015-01-01") %>%
    arrange(desc(date))
head(df)

If we want to check our results, we can head over to the CPI Inflation Calculator on the BLS website.

library(blscrapeR)
df <- bls_api("CUSR0000SA0",
              startyear = 2014, endyear = 2015)
head(df)
# Set base value.
base_value <- 100
# Get CPI from base period (January 2014).
base_cpi <- subset(df, year==2014 & periodName=="January", select = "value")
# Get the CPI for the new period (February 2015).
new_cpi <- subset(df, year==2015 & periodName=="February", select = "value")
# Calculate the updated value of our $100 investment.
(base_value / base_cpi) * new_cpi
# Woops, looks like we lost a penny!