This dashboard brings together 158 quarterly labour productivity time series for the Canadian business sector and a range of industries, drawn from Statistics Canada tables 36-10-0206-01 and 36-10-0207-01. Indicators are organized by industry group — broad aggregates first (Business sector), followed by goods-producing and services-producing industries — so you can scroll through all measures for one industry before moving to the next.
For each industry, the dashboard covers up to eight measures: real gross domestic product, total number of jobs, average hours worked, hours worked, labour productivity, total compensation per hour worked, unit labour cost, and unit labour cost in United States dollars. All series are expressed as indexes with 2017 = 100.
All data are retrieved through the Statistics Canada Web Data Service (WDS). Each morning, shortly after 8:30 am Eastern time, the app automatically fetches fresh data from Statistics Canada the first time any user opens it. That fetch is then cached on the server and served to all subsequent users for the rest of the day. The cycle repeats the following morning.
Statistics Canada typically releases updated quarterly productivity data once per quarter. The dashboard will reflect the most recent release automatically on the morning after it appears.
A sticky navigation bar near the top lets you jump directly to any industry group with a single click. The ⬆ Top and ⬇ Bottom buttons scroll to the extremes of the page. # Indicator prompts for a number (1–158) and scrolls directly to that panel. ⊗ Ratio jumps to the Ratio Explorer at the bottom of the page.
A search bar sits just above the navigation strip. Searching for a term like "productivity" or "wholesale" highlights every matching panel, with Prev/Next buttons to step through matches. This is a quick way to compare the same measure across different industries.
Two global controls in the header let you set a default chart mode and a default time range that apply simultaneously to all 158 panels.
Each panel offers four views of its time series:
The global "Default chart" selector in the header applies a chosen mode to all panels at once. Individual panels can be switched independently using their own mode buttons.
Each panel also has independent year-range selectors for both the chart and the data table, so you can focus on any sub-period of interest.
The 5-QMA is a 5-quarter centred moving average: the simple mean of the two quarters before, the current quarter, and the two quarters after each observation. It smooths out short-term quarter-to-quarter noise and makes the underlying trend easier to see.
At the trailing edge of the series — where future quarters are not yet available — the last known value is repeated as a stand-in for the missing observations. This keeps the MA line running right up to the most recent data point rather than stopping two quarters short, at the cost of a slight flattening of the very end of the MA line. The data table always shows the underlying level alongside the MA, so the distinction is clear.
The Ratio Explorer at the bottom of the page lets you compute and chart the ratio of any two series in the dashboard. This is particularly useful for comparing productivity measures across industries — for example, dividing Manufacturing labour productivity by Business sector labour productivity to see how manufacturing has performed relative to the economy-wide benchmark.
Type the name of any indicator in the Numerator and Denominator search boxes, select the series you want, and click Calculate ratio. Up to three ratios are displayed at once; older ones are pushed off the top as new ones are added. Each ratio result has its own Level / % Change / YoY % / 5-QMA controls and data table.
A caution: the ratio is only meaningful when both series use the same units and base year. All series in this dashboard are indexes with 2017 = 100, so cross-industry ratios of the same measure (e.g. labour productivity in two industries) are directly interpretable. Ratios of different measures (e.g. unit labour cost divided by hours worked) may not have a straightforward economic interpretation.