Average yearly pay, adjusted for local prices in Mexico: our forecast to 2035
Satoria projects average yearly pay, adjusted for local prices in Mexico at about 23,240 USD in 2035, from 24,463 USD recorded in 2025.
How far the evidence reaches
Beyond tested horizon. Tests cover up to 5 years ahead (through 2030 from this reference), across 27 country series. Longer horizons extend beyond those tests. Colours show evidence, not certainty.
The full name of this series is Average yearly pay, adjusted for local prices (average annual wages, constant prices, USD at purchasing power parity).
The path
- 2026: Satoria forecast 24,398 USD (80% range 23,042 to 25,804)
- 2027: Satoria forecast 24,238 USD (80% range 22,233 to 26,263)
- 2031: Satoria forecast 23,449 USD (80% range 20,427 to 26,752)
- 2035: Satoria forecast 23,240 USD (80% range 20,023 to 27,080)
Where it starts
The latest source value is 24,463 USD recorded in 2025, published by OECD Data Explorer. Everything after that year is Satoria's forecast, with the range printed next to it.
The same measure elsewhere
- Average yearly pay, adjusted for local prices in United States
- Average yearly pay, adjusted for local prices in Germany
- Average yearly pay, adjusted for local prices in India
Every forecast we have for Mexico · Average yearly pay, adjusted for local prices in every country
This forecast as data: https://satoria-pipeline-production.up.railway.app/api/forecast/oecd.avg_annual_wages?country=MEX
Conditions and limits
What this number can establish
The published record supports a statistical projection and a model range. It does not establish why a future event will happen. Connections between measurements are predictive associations, not proof of causation. Source measurements, estimates and Satoria forecasts must be read separately.
The model range is not a guarantee. Historical test coverage is not the probability of the central forecast happening.
All stories · Countries · The map of the future · Why trust Satoria · Engine · About · Satoria vs the IMF · How accurate we are · Method · Indicators · For AI agents