Industrial Product Price Index (IPPI)
Summary of changes
Activity on this program started: January 1956
With the release of November 2013 data, Statistics Canada converted the Industrial Product Price Index (IPPI) and the Raw Materials Price Index (RMPI) series to 2010=100, with 2010 as the base year. These indexes have also been updated using a weighting pattern based on the 2010 production values of Canadian manufacturers.
At the same time, the classification system was converted to the North American Product Classification System (NAPCS) developed by Canada, the United States and Mexico.
The Industrial Product Price Index (IPPI) and the Raw Materials Price Index (RMPI) series have been converted to 2002=100, with 2002 as the base year. These indexes have also been updated using a 2002 weighting pattern based on the production values of 2002.
Effective with the January 2004 release, the monthly average exchange rate as determined by the Bank of Canada will now be used to convert prices received in currencies other than the Canadian dollar. Previously, the exchange rate conversion of such prices was carried out using the rate for the 15th of the month. The decision to switch to the monthly rate reflects the fact that the Industrial Product Price Index is intended to measure the change in the average monthly price for these goods. The monthly average exchange rate is a better estimator of the actual exchange rates used in transactions than a point in time exchange rate.
With the October 2001 data, Statistics Canada converted its economic series to a 1997=100 base year. Series for the Industrial Product Price Indexes have been converted. The indexes have also been updated using a 1997 weighting pattern.
The release of October data also includes an important classification change. The indexes covering industries based on the 1980 Standard Industrial Classification (SIC) are now classified using the 1997 North American Industrial Classification System (NAICS). The conversion from SIC to NAICS caused significant changes in the time series.