Mexico Labor Force Participation Rate

Name: Mexico Labor Force Participation Rate
Creator: Agentic Investor

Mexico · Monthly series · EC-MX-LFPR

Latest, September 2026

58.5%

-0.82pp from August 2026

Open interactive chartMexico Labor Force Participation Rate in the app

History through September 2026, the forecast path to September 2027 (dashed) and its 80% interval (shaded).

Forecast

Forecast for September 2027: 59.1%, with an 80% interval of 57.6% to 60.5%.

Forecast path. Intervals are shown where available.
Period	Forecast	80% low	80% high
October 2026	58.9%	58.5%	59.3%
November 2026	58.9%	58.1%	59.7%
December 2026	58.5%	57.6%	59.3%
January 2027	58.4%	58.0%	58.7%
February 2027	58.6%	58.2%	59.0%
March 2027	58.6%	58.1%	59.1%
April 2027	58.8%	57.4%	60.1%
May 2027	59.0%	58.3%	59.7%
June 2027	59.1%	57.8%	60.3%
July 2027	59.5%	58.3%	60.6%
August 2027	59.2%	58.0%	60.4%
September 2027	59.1%	57.6%	60.5%

Model: Agentic Investor TSFM (foundation model). Forecast generated 2026-11-14.

AI confidence

Supported evidence

The point forecast beat the no-change baseline overall and in a majority of the evaluated tests.

Model: Agentic Investor TSFM (foundation model)

Accuracy vs naive: +25%Beats the no-change baseline over 12 tests (12 tests, 8 won)
Direction: 70%No clear edge over a coin flip (101 of 144 calls right in 12 tests)
Turning points: 44 of 80Anticipated most turns (80 turns in 12 tests, 0 without a turn)

Error vs baseline: 25% lowerError ratio 0.75; 1.00 is a tie.
Tests won: 8 / 12Complete rolling backtests; ties count as no win.
Test period: 2014-10-01 to 2026-09-0112 observations ahead per test.
80% band coverage: 82%Observed outcomes inside the band across all stored test windows.

Band coverage uses all stored test windows, including model-selection windows when present; it does not determine the confidence label.
Evaluated 2026-11-14 against the cached history through 2026-09-01.
This is an evaluation of the serving model; it does not establish a winner across four model classes.

How this assessment is made

AI confidence describes historical evidence for this forecast, not a probability of being correct. Each backtest fits on data available before its test window. The baseline carries the last value forward. Supported evidence requires at least six complete, non-overlapping tests, an error ratio below 0.90 (rounded to two decimals), and wins in a majority of tests. Ratios from 0.90 to below 1.10 are on par; 1.10 or above is weak evidence. Inconsistent wins produce mixed evidence. For a selected model, only the recent tests excluded from model selection determine this label. These are historical simulations using the available data, not a live trading track record; revisions and foundation-model pretraining can limit how closely they reproduce a past decision. Past performance can change.

Replay past tests

Model comparison

Comparison pendingA new ticker first receives its serving model's forecast and evaluation. An eligible nightly batch can compare both candidates, subject to call budgets and retry limits.

Reading

It is above 12% of the 119 earlier readings since October 2016.

Written from the figures by a fixed template; no AI model is involved.

Recent history (24 observations)

The 24 most recent observations, newest first.
Period	Value
September 2026	58.5%
August 2026	59.3%
July 2026	59.2%
June 2026	58.8%
May 2026	59.1%
April 2026	59.1%
March 2026	58.6%
February 2026	59.0%
January 2026	58.5%
December 2025	59.1%
November 2025	59.0%
October 2025	59.9%
September 2025	59.6%
August 2025	58.8%
July 2025	60.2%
June 2025	59.8%
May 2025	59.5%
April 2025	59.4%
March 2025	59.3%
February 2025	58.7%
January 2025	59.4%
December 2024	59.3%
November 2024	60.0%
October 2024	60.2%

Source

Data: public market data feeds.