About Income Trends

Introduction
What you should know

Introduction

Income Trends in Canada is an extensive collection of income statistics, covering topics such as income distribution, income tax, government transfers, and low income. The data are drawn from two household surveys: the Survey of Consumer Finances (SCF) and the Survey of Labour and Income Dynamics (SLID). Historical data prior to 1996 are drawn from the SCF and data since 1996 are taken from SLID.

Income Trends in Canada provides a complete list of the tables and directions for getting started. It also contains notes and definitions, a description of the data sources, survey methodology and data quality, and a section called “Related Products and Services”.

In addition to provincial detail, many of the tables present estimates for the 15 largest Census Metropolitan Areas (CMAs), as follows: Halifax, Quebec, Montreal, Ottawa-Hull, Toronto, St.-Catharines-Niagara, Hamilton-Burlington, Kitchener-Waterloo, London, Windsor, Winnipeg, Calgary, Edmonton, Vancouver, Victoria. Due to the sample size limitations and sampling variability, estimates for urban areas are less reliable and are subject to larger errors than provincial and national estimates. Given the variability of the annual estimates, users are cautioned against drawing conclusions from single year-to-year comparisons alone.

Income Trends in Canada uses the Beyond 20/20 Browser software for accessing and manipulating tables.

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What you should know

A. Cell suppression and reliability of small data cells

Cell suppression has been applied to the tables as necessary to takeinto account the lower reliability of data for small population groups. (Refer to the section “Sources, Methods and Estimation Procedures” for more information on suppression and data quality). In certain cases, particularly where the geographic area covered is small in terms of population, or where any other characteristic used to define the population is rare, the reliability of the statistic may be too low to publish. Suitable rules of reliability were used to screen the data.

In a few table views, the number of suppressed data points may make the entire table view somewhat unusable. While the tables could have been designed to eliminate certain categories or dimensions from the outset, this would most likely mean simultaneously eliminating some usable content. Instead, the approach has been to include all views of the table (no elimination of any categories of any dimensions), and rely on cell-by-cell data suppression to screen data that are not reliable.

B. Geographic detail below the province level

There is a difference in the CMA boundaries used by the two surveys. The latest revised data from the SCF used definitions of CMA areas based on the 1996 Census of Population. The SLID uses, for the time being, the 1991 Census CMA boundaries. Not all CMAs appearing in the standard tables changed in the 1996 Census as compared to the 1991 Census. For those that did, the boundary changes accounted for population changes of less than 1% except in three cases: Montreal, +2.6%; Ottawa-Hull, +2.3%; and Winnipeg, +1.2%. Excluding any other factors, this would tend to make the SLID counts for these CMAs lower than the SCF counts.

C. Missing values for characteristics other than income

While both the SCF and SLID have complete data on all respondents for age, sex, family relationships and income characteristics, only the SCF has complete data without missing values for all labour market characteristics and education. (The SCF was conducted as a supplement to the Labour Force Survey, which provided the base labour data entirely edited and imputed.) To the extent that there are missing values in a dimension, identified as the category “don’t know”, there could be an undercount in any or all of the other categories. If the undercount in SLID is substantial, a break could appear between the two periods (the period up to 1995 and the period since 1996).