Cocoa fell to 5,359.52 USD/T on December 1, 2025, down 0.82% from the previous day. Over the past month, Cocoa's price has fallen 18.29%, and is down 41.61% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Cocoa - values, historical data, forecasts and news - updated on December of 2025.

This dataset covers the daily cocoa prices from 03 October 1994 to 07 August 2025, sourced directly from the International Cocoa Organization (ICCO).

It integrates: - London futures prices (British pounds per tonne) - New York futures prices (US dollars per tonne) - ICCO reference prices (US dollars and euros per tonne)

The data has been cleaned to handle missing values and harmonize currency conversions based on official ICCO exchange rates.

Primary applications include: 1. Statistical and econometric modeling of price dynamics 2. Analysis of correlations between regional markets and ICCO reference prices 3. Forecasting price movements using machine learning techniques

This dataset is ideal for academic research, development of commodity trading strategies, and public policy analysis in the agricultural sector.

Notebook Ideas

• Trend and seasonality analysis using rolling averages and STL decomposition
• Price forecasting with ARIMA or Prophet models
• Interactive visualizations with Plotly or Altair, including currency conversion comparisons
• Correlation analysis between cocoa prices and macroeconomic indicators
• Backtesting a simple trading strategy on cocoa futures data
• Machine learning case study using random forests or XGBoost for price prediction
• Building a real-time price dashboard with Dash or Streamlit

Daily cocoa exchange price (CC=F) from ICE New York, in USD per metric ton.

Graph and download economic data for Global price of Cocoa (PCOCOUSDM) from Jan 1990 to Jun 2025 about cocoa, World, and price.

Monthly and long-term cocoa price data (US$/mt): historical series and analyst forecasts curated by FocusEconomics.

From a Zindi-hosted challenge: Amini Cocoa Contamination Challenge.

The image dataset includes images of cocoa plants sourced from farms across multiple African countries, including Uganda, Tanzania, Ghana, and Namibia.

Data license: CC-BY 1.0

Each image in the training set is annotated with bounding boxes indicating the presence and type of diseases. Some images contain multiple diseases, each annotated separately.

The test set includes images that may contain new diseases not present in the training set, with bounding boxes removed.

This dataset supports the development of mobile-friendly machine-learning models for real-time cocoa disease identification, helping smallholder farmers mitigate crop losses.

Learn more.

MONTHLY AVERAGE PRICE OF COCOA BEANS (SMC 1) : 2021

This dataset provides values for COCOA reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Imports of Cocoa Beans in the United States decreased to 127.42 USD Million in February from 128.57 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Imports of Cocoa Beans.

MALAYSIA : ANNUAL AVERAGE PRICE OF COCOA BEANS (SMC 1), 2017 - 2021

Ghana Commodity Price: Cocoa LIFFE data was reported at 1,805.800 GBP/Tonne in Apr 2018. This records an increase from the previous number of 1,756.570 GBP/Tonne for Mar 2018. Ghana Commodity Price: Cocoa LIFFE data is updated monthly, averaging 1,569.320 GBP/Tonne from Dec 2003 (Median) to Apr 2018, with 173 observations. The data reached an all-time high of 2,419.860 GBP/Tonne in Jul 2016 and a record low of 774.800 GBP/Tonne in Jun 2004. Ghana Commodity Price: Cocoa LIFFE data remains active status in CEIC and is reported by Bank of Ghana. The data is categorized under Global Database’s Ghana – Table GH.P001: Commodity Price.

Background During the years 2019-2023, Tony’s Chocolonely commissioned five annual household surveys among farmers in all of their partner cooperatives in Ghana and Côte d’Ivoire. In both countries, a research company was tasked with conducting the face-to-face interviews. The main aim of these surveys was to assess the multidimensional poverty rates, using the global Multidimensional Poverty Index (MPI). Besides the MPI-relevant items on health, education and standard of living, questionnaires also included a range of other questions. These covered various topics such as the use of external labour, livestock ownership or the ratio of cocoa Vs. non-cocoa income. The surveys were set up as a panel. In as much as possible, enumerators returned to the same respondents each year. Out of the initial 2019 sample of farmers, 45% participated in all five rounds of the panel. Sampling Simple random samples were drawn in each coop, using cooperative membership lists as sample frames. As new cooperatives in Côte d’Ivoire joined Tony’s Open Chain in 2021, 2022 and 2023, those were added to the panel. During the first two survey rounds, sample sizes were determined in proportion to the total number of farmers in the coop: in each coop, a sample of about 10% of total coop size was drawn. As Tony’s Chocolonely data needs changed in 2021, it was decided to switch to non-proportionate sampling: during the last three rounds of the panel, about 150 farmers per coop were interviewed, irrespective of coop size. For these rounds, a weight variable per country is included in the dataset for country-level analyses. MPI and HFIAS scales The provided dataset includes the ten MPI deprivation indicators, the sum of weighted MPI deprivations, as well as a binary variable to distinguish MPI-poor from MPI-non-poor households. Furthermore, the dataset includes a variable to categorize households as ‘food secure’, ‘mildly food insecure’, ‘moderately food insecure’ or ‘severely food insecure’, based on the nine items of the Household Food Insecurity Access Scale (HFIAS). Data anonymization All data have been anonymized by removing personally identifiable information, such as names, phone numbers, GPS data, detailed household composition and responses to open questions. Each respondent has been assigned a unique ID to be identifiable across rounds (famer_hash). To further increase confidentiality, coop names have been replaced by pseudonyms. Across time consistency of farmer codes Users of the dataset should note that some farmer codes may have different demographic information associated with them across different years. In most cases, this will mean that a different household member has been interviewed by the enumerator. In some cases it may be that the farmer code has been reassigned to a different household (likely due to farmers leaving the cooperative). This should not affect the representativeness of the sample per year but will introduce some noise when running time series analyses. Using the data Tony’s Open Chain encourage open use of this data and are excited to learn from how others use and analyse the data – if you carry out an analysis of this data, please do share it with us by contacting emmavandam@tonyschocolonely.com.

The Cocoa Sector Expenditure and Revenue in Ghana dataset contains expenditure and revenue data from cocoa sector in Ghana. The dataset aggregates information from various sources including Food and Agriculture Organization (FAO), Ghana Cocoa Board (COCOBOD), and Producer Price Review Committee (PPRC). These data are available from 1996-2012.

Dataset include total cocoa exports and production in Ghana and world averages, world cocoa prices, gross domestic product growth rate in Ghana and real exchange rate in Ghana. The objective of the research is to measure the impact of cocoa rehabilitation project on cocoa export performance in Ghana. Data used is time series.

Note: This dataset is not yet peer-reviewed. Please see this GitHub README for more information. This image collection provides estimated per-pixel probability that the underlying area is occupied by the commodity. The probability estimates are provided at 10 meter resolution, and have been generated by a machine learning model. For details, see the technical documentation on the Forest Data Partnership repo on Github. The primary purpose of this image collection is to support the mission of the Forest Data Partnership which aims to halt and reverse forest loss from commodity production by collaboratively improving global monitoring, supply chain tracking, and restoration. This dataset currently covers the following countries: Côte d'Ivoire, Ghana, Indonesia, Ecuador, Peru, Colombia. This community data product is meant to evolve over time, as more data becomes available from the community and the model used to produce the maps continuously improves. If you would like to provide general feedback or additional datasets to improve these layers, please reach out through this form. Limitations: Model output is limited to selected countries as calendar year composites for 2020 and 2023. Not all regions of the output are well represented by training data. Accuracy is reported in aggregate, and will vary geographically and with user chosen thresholds. Sensor artifacts based on data availability, cross-track nonuniformity, or cloudiness may be visually apparent in output probabilities and result in classification errors at some thresholds. Note that this dataset has separate terms of use for commercial users of Earth Engine. Please see "Terms of Use" tab for details.

this dataset provides a comprehensive overview of consumer preferences and willingness to pay for fair trade chocolate versus normal chocolate the data is sourced from a survey conducted in 2023 and offers detailed insights into buying behavior attitudes towards fair trade products and demographic factors the dataset is structured to include key metrics such as buying frequency reasons for selecting fair trade or normal chocolate important purchase criteria willingness to pay more for fair trade products and demographic information providing a robust foundation for analyzing consumer behavior and market trends it delves into buying frequency highlighting how often respondents purchase bar chocolate thereby uncovering their buying patterns it explores reasons for selecting either fair trade or normal chocolate giving a deeper understanding of consumer attitudes motivations and ethical considerations it also presents purchase criteria segmented into aspects like brand taste country of origin price and sustainable behavior helping businesses comprehend what drives consumer choices in addition the dataset measures willingness to pay showcasing how much more respondents are inclined to spend on fair trade chocolate and thus reflecting their price sensitivity and commitment to supporting sustainable products furthermore it captures demographic information segmented by gender income and country of origin allowing for nuanced analysis of how these factors influence consumer behavior this dataset has a broad range of potential uses in market research it can provide businesses with an understanding of consumer preferences and market trends crucial for product development and marketing strategies it can support consumer behavior analysis by revealing patterns attitudes and motivations enabling businesses and researchers to craft more effective approaches additionally the dataset is valuable for policymakers as it can inform the development and adjustment of policies related to fair trade and sustainable products strategic planners can rely on these insights to align business strategies with consumer behavior and market trends finally it is a rich resource for academic research offering data for studies on consumer behavior market dynamics and the broader impact of fair trade products ensuring its utility across sectors and disciplines.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Cocoa. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Cocoa. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Cocoa, householders within the 45 to 64 years age group have the highest median household income at $71,990, followed by those in the 25 to 44 years age group with an income of $51,373. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $48,000. Notably, householders within the under 25 years age group, had the lowest median household income at $2,499.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cocoa median household income by age. You can refer the same here

2018 - WORLD : INTERNATIONAL COCOA PRICES, NEW YORK

This is a synthetic dataset based on real data showing NIR spectral intensity of varied cocoa samples on different wavelengths. The moisture content and fat content of each sample has also been provided.

The dataset has 72 rows and 1560 columns. Each row is a different cocoa sample, and each column represents the respective wavelength. The wavelength starts at 999.9nm and goes up to 2500.2nm in increasing order with a difference of 0.4nm in each column. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13696381%2Fa2ee4889db2b9b564ddd7998b4cd4d94%2FScreenshot%202024-06-27%20164221.png?generation=1719487517661032&alt=media" alt="">

The dataset was synthetically generated from MostlyAI API. The original data can be found here https://doi.org/10.17632/7734j4fd98.1

Scope of data :- It is signal processing data. The data can be used to do chemometric analysis and then differentiate the cocoa and produced chocolate quality. It can help you in analyzing and understanding major and minor peaks of intensity. Signal processing data is treated differently from normal data so there are going to be totally different techniques to treat the data.

Prediction of moisture and fat content through regression analysis is an important application as well as studying their variedness.

The data can be visualized in many great forms including boxplots, biplots etc..

No. of Views : 37