How we calculate a living wage

A transparent, needs-based estimate of the take-home pay one worker needs to give their family a basic but decent standard of living — built with a transparent, needs-based methodology on openly-licensed data.

Calculated from public data — transparently and consistently. We don’t license living-wage figures from anyone; we calculate them. We start from reputable, openly published data — the World Bank’s cost of a healthy diet, available for roughly 170 countries — and apply one documented methodology to every country, so the results are comparable, reproducible and fully traceable. For example, a healthy diet for one person in Kenya costs about KES 189/day; scaled to a typical family and divided across its earners, that works out to roughly KES 249/hour (about KES 43,000/month). It is an independent estimate, intended to complement published benchmarks.

The whole calculation, in one place

Every figure is built bottom-up from the cost of a decent life for a reference family, then shared across the family’s earners. End to end, with the real data source behind each input:

NET living wage  (per earner, per month)
   =   cost of a healthy diet / person / day      ← World Bank Food Prices for Nutrition
       × family size × 30.44 days                =  monthly food cost for the family
       ÷ food-expenditure share                  ← USDA ERS, then World Bank ICP
                                                    (recovers the FULL budget — the Engel method)
       × 1.05                                      ← + 5% sustainability margin
       ÷ full-time-equivalent earners             ← 1.6 × labour-force participation (World Bank), clamped 1.4–1.8

then   × 12                  = per year
       ÷ annual hours (ILO)     = per hour
       + income tax & employee social security (OECD) = GROSS (pre-tax)

Every input is openly-licensed and measured per country (not assumed). Housing sits inside that budget and is also broken out separately (ICP housing share × budget). Each figure is produced for a family of 2, 3 or 4, in local currency, USD and PPP, with a low/high uncertainty band and a confidence score.

How the number is built, step by step

Now each step in detail, with the real data source behind every number. A living wage is built bottom-up from the cost of a decent life for a reference family, then shared across the family’s earners.

1Food — the data anchor

We take the cost of a healthy diet — the least-cost set of local foods meeting energy needs and dietary guidelines — from the World Bank Food Prices for Nutrition database, then scale it to a family for a month:

food_month = cost_of_healthy_diet_per_person_day × household_size × 30.44

2From food to a full budget

A decent budget is recovered from the food cost using the Engel coefficient — the share of household spending that goes to food (the classic Orshansky-multiplier / needs-based approach):

total_month = food_month / food_share

We use each country’s real food-expenditure share — USDA ERS first, then World Bank ICP (together ~170 countries) — so this key number is measured, not assumed. It matches our food anchor (both are at-home food). Examples: USA 6.8%, UK 8.7%, Germany 11.6%, Brazil 16.2%, India 29.9%, Kenya 42.1%, Nigeria 59.4%. Where neither has a value, a coarse GDP-income-band default fills the gap (each figure records which was used).

3Sustainability margin

A 5% buffer is added for unexpected events: total_month × 1.05.

4Divide across earners

The family cost is divided by the number of full-time-equivalent earners — a typical 1.6, scaled by each country’s labour-force participation (World Bank), clamped 1.4–1.8:

net_living_wage_month = total_month / earners

Gross (pre-tax) is then recovered by adding back income tax + employee social security (OECD countries only), and the hourly figure uses each country’s real average weekly hours (ILO) where available.

5Choose your family size

Every figure is produced for a household of 2, 3 or 4 so you can match your workforce’s demographics (default 4). Housing is already inside the budget recovered in step 2; we additionally break it out separately (World Bank ICP housing share × budget) so you can see it, rather than adding it on top. Each figure is also tagged with a confidence score reflecting how data-grounded its inputs are.

Worked example (USA, real inputs)

Healthy diet = $2.79/person/day (FPN), food share = 6.8% (USDA), 40h week:

food_month   = 2.79 × 4 × 30.44   = 339.71
total_month  = 339.71 / 0.068        = 4,995.7
+ 5% margin                          = 5,245.5
÷ 1.6 earners                       = 3,278    → net living wage / month
× 12                                 = 39,341   → net living wage / year
÷ 2,080 h (40h × 52wk)            = 18.9     → net living wage / hour

Validated against independent benchmarks. Compared to published, field-researched living-wage benchmarks, our national estimate sits within ~±13% of their urban / regional values (Vietnam 1.04, Côte d’Ivoire 1.12, Kenya-urban 0.89, Mexico City 1.13, Colombia-Bogotá 0.91) and above their rural farm-region values (~1.7×) — the expected national-vs-rural gap. Every figure also carries an explicit uncertainty band (_low/_high) and a confidence score; third-party benchmark values are used for comparison only, never redistributed.

How this differs from the Anker methodology

The gold-standard reference for living wages is the Anker methodology, used by the Global Living Wage Coalition. We deliberately apply the same public, needs-based recipe it pioneered — cost of a decent diet → recover the full budget via the food share → add a margin → divide across earners → gross up for tax. A methodology isn’t something anyone owns; what differs is the data behind each input and the granularity:

Local field surveys vs. open global data. Anker prices each component from primary local fieldwork — market-price surveys of the foods workers actually buy, physical inspections of worker housing, and worker/employer interviews — for one specific locality. We use globally-consistent, openly-licensed datasets (World Bank, USDA, ILO, OECD) applied identically to every country, at national granularity.
Housing. Anker measures an absolute decent rent from local housing surveys. No openly-licensed global rent source exists, so we recover housing inside the food-share budget and additionally break it out using the World Bank ICP housing share.
We never reuse Anker’s published figures. Their benchmark values are licensed for non-commercial use only, so we do not store or redistribute them — we use a handful purely to validate our estimates (see above), never in the product.

The result is an independent national estimate with an uncertainty band, reproducible from public data and comparable across 160+ countries — meant to complement Anker’s deep, locality-specific benchmarks, not replace them. Where an official Anker living-wage benchmark exists for a location, treat it as authoritative.

What you get

Net and gross living wage, per hour, per month and per year, in three currencies:

Field pattern	Unit	Notes
`living_wage_net_{hourly,monthly,annual}`	local currency	take-home; all countries
`living_wage_gross_{hourly,monthly,annual}`	local currency	pre-tax; OECD only
`living_wage_{net,gross}_..._usd`	USD (market FX)	at the market exchange rate
`living_wage_net_{hourly,monthly}_ppp`	PPP intl $	comparable across countries; net only
`living_wage_housing_monthly` (+ `_usd`)	local / USD	explicit housing component
`living_wage_net_monthly_low` / `_high`	local currency	uncertainty band (± `uncertainty_pct`)

All of the above exist for family size 2, 3 and 4 (the family_size dimension; API defaults to 4 — add ?family_size=2).

Hourly figures assume a standard full-time week of 40 hours (2,080 hours/year) — the same convention as the MIT Living Wage Calculator — using a country’s statutory standard week where it is well-established (e.g. 35h in France). Every data point also returns the full calculation breakdown, the parameters used and the source list in its metadata.

Data sources & licences

Cost of a healthy diet — World Bank, Food Prices for Nutrition — CC BY 4.0
Food-expenditure share (Engel coefficient) — USDA ERS Food Expenditure Series — US public domain / CC BY 4.0
Food + housing share (2nd source) — World Bank ICP — CC BY 4.0
Earners, GDP-PPP, exchange rate — World Bank — CC BY 4.0
Working hours — ILO; gross deduction rate — OECD Taxing Wages — CC BY 4.0

Known limitations

National granularity only. We do not (yet) produce sub-national benchmarks the way field-researched local studies do.
Housing is share-derived, not absolute rent. No openly-licensed global rent source exists, so housing uses the real ICP housing share; very high-rent cities can still be under-stated.
Family size scales by headcount — children aren’t discounted against adults.
Gross is OECD-only (~38 countries), and its deduction rate is measured at the average wage (may slightly over-state the gross-up).

We're actively working to address these limitations — adding sub-national granularity, an absolute-rent source where licensing allows, child/adult cost weighting, and broader gross-pay coverage as suitable openly-licensed data becomes available.

Every living-wage figure is a Rewardata estimate, produced with the method described on this page and validated against published Anker living-wage benchmarks. For the underlying data sources and their licences, see the data attributions page.