Tea Price History: Monthly Global Price Since 1992

Tea is the world’s most consumed beverage after water, and the black-tea trade is dominated by Kenya, India, and Sri Lanka, priced largely through the Mombasa, Kolkata, and Colombo auctions. This dataset tracks the IMF Primary Commodity Prices tea benchmark, an auction average published monthly in US cents per kilogram and distributed via FRED under the code PTEAUSDM, with continuous coverage since 1992. Tea is far less financialised and much less volatile than coffee or cocoa, with no major futures market.

Dataset: Tea Price (1992–2026) · Updated 2026-03-01

Source: IMF Primary Commodity Prices · International Monetary Fund (via FRED)

Macro Takeaway

Dataset Overview

Dataset Variables

The CSV and Excel files contain the following columns. Each row represents one calendar month.

Column names match the CSV headers exactly.

Download the Complete Dataset

The full dataset is available in CSV and Excel formats — monthly observations covering more than three decades.

FRED Direct CSV Access

The underlying data is published in the Federal Reserve Economic Data (FRED) database under the series code PTEAUSDM, sourced from the IMF Primary Commodity Prices dataset:

https://fred.stlouisfed.org/graph/fredgraph.csv?id=PTEAUSDM

The Eco3min dataset mirrors the same monthly series, packaged in a stable, versionable CSV with consistent column names — designed for direct ingestion in Python, R, or any data pipeline. The URL never changes, making it suitable for automated scripts.

Direct CSV Access — Eco3min Structured Dataset

https://eco3min.fr/dataset/tea-price.csv

This URL returns the complete dataset in CSV format. It can be used directly in pandas, R, curl, or any data tool.

Using the Dataset in Python

import pandas as pd

url = "https://eco3min.fr/dataset/tea-price.csv"
df = pd.read_csv(url, parse_dates=["date"])

print(df.head())
print(df["tea_price"].describe())

df.plot(x="date", y="tea_price", title="Tea Price", legend=False)

Using the Dataset in R

library(readr)

url <- "https://eco3min.fr/dataset/tea-price.csv"
df <- read_csv(url)

head(df)
summary(df$tea_price)

Both examples load the dataset directly from the URL — no download or API key required.

Methodology

The IMF reports an average tea price in US cents per kilogram, based on the major black-tea auctions in Mombasa (Kenya), Colombo (Sri Lanka), and Kolkata (India).

Values are monthly averages, which smooth the intra-month swings visible in daily futures and physical quotes. The series begins in 1992.

This dataset is updated monthly via an automated pull from the FRED API (series PTEAUSDM) by an Eco3min pipeline running on GitHub Actions, which regenerates the cleaned CSV and Excel files and refreshes the page metadata.

Data Quality & Provider Notes

The IMF/benchmark price is a widely cited physical-market reference. A few provider-specific points matter when using this series.

• Release latency. The IMF publishes Primary Commodity Prices monthly, typically in the first week of the following month. FRED ingests the update shortly after, and Eco3min mirrors it with a monthly pull. The series is not a real-time price.
• Monthly average vs futures spot. This series is a monthly average. It will differ from any single exchange settlement, and a monthly average necessarily understates intra-month peaks.
• Revisions. Prices are market-derived and not subject to the vintage revisions of survey-based macro series, though the IMF can restate recent observations.
• Alternative sources. ICE futures and the originating auction or indicator bodies provide higher-frequency or contract-specific quotes.

Common Pitfalls When Using This Series

1. Confusing nominal and real prices. This series is nominal. Comparing an early-1990s reading to a recent one without adjusting for cumulative inflation overstates the real change. Deflating by CPI gives the true purchasing-power move.
2. Reading the monthly average as a market price. Headlines quote exchange futures; this dataset reports the monthly benchmark average. The two diverge most during fast-moving rallies.
3. Unit confusion. This series is in US cents per kilogram, not per pound — note the different base unit from coffee, sugar, and cotton when comparing soft commodities.

Historical Regimes

1992–2008 — Gradual, low volatility. Tea traded in a slowly rising band as auction supply from Kenya, India, and Sri Lanka stayed broadly adequate.

2009–2017 — Firmer demand. Growing consumption within producing countries and periodic East African droughts supported prices.

2018–2020 — Oversupply pressure. Strong Kenyan crops weighed on Mombasa auction prices.

2021–2022 — Sri Lanka shock. Sri Lanka’s abrupt 2021 ban on chemical fertiliser slashed yields, and the 2022 economic crisis disrupted exports, tightening global black-tea supply.

2023–2026 — Weather-driven firmness. Recurrent Kenyan drought and uneven South Asian harvests kept prices firm, though without the parabolic moves seen in coffee or cocoa.

Related Macroeconomic Datasets

Commodity Price Hub

This dataset is part of the Eco3min commodity price repository — energy, metals, agricultural softs, and grains, all sourced from IMF Primary Commodity Prices via FRED.

Explore the Commodity Price Hub

Sources

• International Monetary Fund — Primary Commodity Prices, Global Price of Tea
• Federal Reserve Bank of St. Louis — FRED database, series PTEAUSDM
• Mombasa, Colombo, and Kolkata tea auctions — price basis underlying the IMF series

Dataset Reference

Eco3min Research. (2026). Tea Price — Global Monthly Price (1992-2026). Source: IMF Primary Commodity Prices via FRED.<br> https://eco3min.fr/en/tea-price-history-dataset/