How is the Average Weekly Hours Worked ranking compiled?

A ranking is a snapshot of relative position, not a fixed property of a country, and a few habits make it far more useful to read. Every country shown has a non-null observation for its most recent reporting year, and that year is not synchronised across the table, so two neighbouring rows may describe different points in time. The size of the spread between the top and the bottom tells you whether an indicator is structurally uneven across the world or broadly universal, and that shape is often more informative than any single rank. Where a value is expressed per capita or as a share, currency revisions and population updates can shift positions between releases. Treat the order as a starting point for questions, then open the underlying country profiles to understand why each sits where it does.

This ranking orders 168 countries by Average Weekly Hours Worked, measured in hours/week. Bhutan leads with 54.4 hrs/wk (2022), while Vanuatu sits at the bottom with 24.7 hrs/wk. The midpoint country reports 39.6 hrs/wk, so any country below that mark falls in the lower half of the distribution and any above sits in the upper half. The spread between the top and bottom gives you an immediate sense of how unevenly this indicator is distributed across the Labor & Wages picture.

Average Weekly Hours Worked is part of the Labor & Wages topic and is collected by ILO ILOSTAT. Average actual weekly hours worked per employed person, both sexes, all sectors. Source: ILO ILOSTAT. The most recent observations shown here are from 2022, reflecting the latest release cycle for this series. Because definitions, base years, and methodologies can change, the "Year" column is shown for every row — always check it before comparing two countries whose values come from different vintages.

Click any country name to open its full profile with hundreds more indicators in context, or use the Compare tool to pair any two countries from this table side by side. You can also browse all indicators inside the Labor & Wages topic from the breadcrumbs above to see which other measures move together with Average Weekly Hours Worked. Data is licensed under CC BY 4.0 from ILO ILOSTAT, which means you may reuse the figures freely in articles, reports, and research so long as you credit the original agency.

How rankings are constructed: every country with a non-null observation for Average Weekly Hours Worked in its most recent reporting year is included; countries with no data for that indicator are excluded from the ranking rather than imputed or interpolated. Ranks are dense (1, 2, 3 with no skips on ties) and ties break alphabetically by country name. The "Year" column carries the observation vintage because the world is not synchronous: some countries publish a 2024 figure for this indicator while others only have a 2021 or 2019 reading, depending on each statistical agency's release cycle and the country's own reporting compliance. We never carry-forward a stale year to make the ranking look complete.

What the spread tells you: when the gap between the top and bottom of a ranking is wide — say a 50× ratio between the leader and the median — the indicator is structurally uneven across the global income gradient. When the spread is narrow — a 2-3× ratio — the indicator is more universal, reaching most economies regardless of GDP per capita. Comparing the spread of Average Weekly Hours Worked against peer indicators in the Labor & Wages topic is the fastest way to see which dimensions of development are converging globally and which remain stubbornly polarised.

Cross-checks before citing: if you plan to cite a figure from this ranking, open the source country's profile and confirm the year, the unit of measurement, and whether the underlying definition has changed in recent revisions. ILO ILOSTAT publishes definition notes alongside every series; the Labor & Wages chapter of the WDI metadata document is a good place to verify the boundaries of the variable. Be especially careful with per-capita figures (population denominators get revised after each census), GDP figures (PPP vs current-USD vs constant-USD make order-of-magnitude differences), and health indicators that switch between crude rates and age-standardised rates between releases.