Cost of food in the United States increased 3.10 percent in September of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Food Prices Year By Year

Findings and Implications

About this dataset

In 2022, the world may face a global food crisis. This dataset includes information on food prices, meat prices, dairy prices, cereal prices, oil prices, and sugar prices. This data is of utmost importance to researchers as it will help inform their work on finding solutions to this potential crisis. With this data, we can better understand the factors that may contribute to the crisis and work towards finding solutions that could help prevent or mitigate its effects

How to use the dataset

This dataset contains information on food prices, meat prices, dairy prices, cereal prices, oil prices, and sugar prices. This data is of utmost importance to researchers as it will help inform their work on finding solutions to this potential crisis.

To use this dataset effectively, researchers should focus on the trends in food prices over time. Additionally, they should look at the relationships between different types of food prices. For example, does an increase in meat price lead to a corresponding increase in dairy price? Finally, researchers should also consider how other factors such as oil price or sugar price may impact food prices

Research Ideas

1. Identifying choke points in the global food supply chain
2. Estimating the economic impact of a potential global food crisis
3. Developing policies to mitigate the impact of a potential global food crisis

Acknowledgements

We would like to thank the Department of Agriculture for their data on food prices, meat prices, dairy prices, cereal prices, oil prices, and sugar prices. This dataset is of utmost importance to researchers as it will help inform their work on finding solutions to this potential crisis

License

See the dataset description for more information.

Columns

File: FAOFP1990_2022.csv

Inflation Rate in the United States increased to 3 percent in September from 2.90 percent in August of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Dataset Summary

Inflation is a critical economic indicator that reflects the overall increase in prices of goods and services within an economy over a specific period. Understanding inflation trends on a global scale is crucial for economists, policymakers, investors, and businesses. This dataset provides comprehensive insights into the inflation rates of various countries for the year 2022. The data is sourced from reputable international organizations and government reports… See the full description on the dataset page: https://huggingface.co/datasets/aswin1906/countries-inflation.

Context:

Inflation is a critical economic indicator that reflects the overall increase in prices of goods and services within an economy over a specific period. Understanding inflation trends on a global scale is crucial for economists, policymakers, investors, and businesses. This dataset provides comprehensive insights into the inflation rates of various countries for the year 2022. The data is sourced from reputable international organizations and government reports, making it a valuable resource for economic analysis and research.

Content:

This dataset includes four essential columns:

1.**Countries:** The names of countries for which inflation data is recorded. Each row represents a specific country.

2.**Inflation, 2022:** The inflation rate for each country in the year 2022. Inflation rates are typically expressed as a percentage and indicate the average increase in prices for that year.

3.**Global Rank:** The rank of each country based on its inflation rate in 2022. Countries with the highest inflation rates will have a lower rank, while those with lower inflation rates will have a higher rank.

4.**Available Data:** A binary indicator (Yes/No) denoting whether complete and reliable data for inflation in 2022 is available for a particular country. This column helps users identify the data quality and coverage.

Potential Use Cases:

-**Economic Analysis:** Researchers and economists can use this dataset to analyze inflation trends globally, identify countries with high or low inflation rates, and make comparisons across regions.

-**Investment Decisions:** Investors and financial analysts can incorporate inflation data into their risk assessments and investment strategies.

-**Business Planning:** Companies operating in multiple countries can assess the impact of inflation on their costs and pricing strategies, helping them make informed decisions.

Data Accuracy: Efforts have been made to ensure the accuracy and reliability of the data; however, users are encouraged to cross-reference this dataset with official sources for critical decision-making processes.

Updates: This dataset will be periodically updated to include the latest available inflation data, making it an ongoing resource for tracking global inflation trends.

Acknowledgments: We would like to express our gratitude to the numerous agencies and organizations that collect and publish inflation data, contributing to the transparency and understanding of economic conditions worldwide.

License: This dataset is provided under an open data license, allowing users to freely use and share the data while adhering to the specified licensing terms.

Feel free to adapt and expand upon this template to create a comprehensive and informative dataset description for your Kaggle publication on global inflation rates for 2022.

About Dataset

https://www.tbsnews.net/sites/default/files/styles/big_2/public/images/2021/03/12/inflation_1.jpg" alt="Inflation hits nine-year high in June | undefined">###

Global Energy, Food, Consumer, and Producer Price Inflation: A Comprehensive Dataset for Understanding Economic Trends

Key Concepts:

1. Energy Consumer Price Inflation data.
2. Food Consumer Price Inflation data.
3. Headline Consumer Price Inflation data.
4. Official Core Consumer Price Inflation data.
5. Producer Price Inflation data.
6. 206 Countries name, Country code and IMF code.
7. 52 Years data from 1970 to 2022.

The global economy is highly complex, and understanding economic trends and patterns is crucial for making informed decisions about investments, policies, and more. One key factor that impacts the economy is inflation, which refers to the rate at which prices increase over time. The Global Energy, Food, Consumer, and Producer Price Inflation dataset provides a comprehensive collection of inflation rates across 206 countries from 1970 to 2022, covering four critical sectors of the economy.

Finally, the Global Producer Price Inflation dataset provides a detailed look at price changes at the producer level, providing insights into supply chain dynamics and trends. This data can be used to make informed decisions about investments in various sectors of the economy and to develop effective policies to manage producer price inflation.

In conclusion, the Global Energy, Food, Consumer, and Producer Price Inflation dataset provides a comprehensive resource for understanding economic trends and patterns across 206 countries. By examining this data, analysts can gain insights into the complex factors that impact the economy and make informed decisions about investments, policies, and more.

Potential User:

1. Economists and economic researchers
2. Policy makers and government officials
3. Investors and financial analysts
4. Agricultural researchers and policymakers
5. Energy analysts and policy makers
6. Food industry professionals
7. Business leaders and decision makers
8. Academics and students in economics, finance, and related fields

Acknowledgements:

The data were collected from the official website of worldbank.org

About this dataset

Context

The dataset tabulates the Price population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Price across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Price was 8,262, a 1.00% increase year-by-year from 2021. Previously, in 2021, Price population was 8,180, a decline of 0.64% compared to a population of 8,233 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Price decreased by 243. In this period, the peak population was 8,716 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Price is shown in this column.
• Year on Year Change: This column displays the change in Price population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Price Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Price town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Price town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Price town was 226, a 0.89% increase year-by-year from 2021. Previously, in 2021, Price town population was 224, an increase of 0.45% compared to a population of 223 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Price town decreased by 17. In this period, the peak population was 250 in the year 2007. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Price town is shown in this column.
• Year on Year Change: This column displays the change in Price town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Price town Population by Year. You can refer the same here

What is Diamonds Prices Dataset?

This document explores a dataset containing prices and attributes for approximately 54,000 round-cut diamonds. There are 53,940 diamonds in the dataset with 10 features (carat, cut, color, clarity, depth, table, price, x, y, and z). Most variables are numeric in nature, but the variables cut, color, and clarity are ordered factor variables with the following levels.

About the currency for the price column: it is Price ($)

And About the columns x,y, and z they are diamond measurements as (( x: length in mm, y: width in mm,z: depth in mm ))

.

https://user-images.githubusercontent.com/36210723/182397020-a1bcc086-d086-4e37-9975-99a762f328c6.png" alt="2022-08-02_171709">

.

Acknowledgments

When we use this dataset in our research, we credit the authors as :

• License : CC BY 4.0.

• The dataset published to reuse in google research dataset

The main idea for uploading this dataset is to practice data analysis with my students, as I am working in college and want my student to train our studying ideas in a big dataset, It may be not up to date and I mention the collecting years, but it is a good resource of data to practice

Measures of monthly UK inflation data including CPIH, CPI and RPI. These tables complement the consumer price inflation time series dataset.

Average weekly earnings for the whole economy, for total and regular pay, in real terms (adjusted for consumer price inflation), UK, monthly, seasonally adjusted.

August 2022 update

1. The updated database has monthly prices from over 400 supermarkets across six chains (all Shufersal stores, plus ~20 per chain for the other chains), from March 2022-August 2022. Before march there is a steady increase in the number of stores.
2. The current database has combined all the supermarket chains, which required adding two columns - 'ChainName' is the supermarket chain, and 'store_code' is a concatenation of the store_id and the beginning of the chain name, to solve the duplicate store_ids.
3. A new table was constructed to try and identify the manufacturers of each product, given the significant discrepancies in the retailers' reporting. This table ("manufacturer_finder") has identified the likely supplier of around half of the products in the database, and can be used to track specific suppliers (producers/importers).

This dataset was created to analyze changes in prices in the Israeli grocery retail market. It was created based on the files retailers are legally required to upload, available here: https://www.gov.il/he/departments/legalInfo/cpfta_prices_regulations

The data is not complete and downloads increased gradually. Beginning in May 2020 there are sporadic files for three specific Shufersal stores. Starting in November 2021 Downloads increased, ~20-50 stores downloaded at various times from Shufersal, and ~5-10 stores downloaded from a few other retailers.

Different table for each retailer. The table "snifim" specifies the names for stores for Shufersal (in the main table you can find store_id which can be joined to the names).

Description of columns in the Prices tables: Filename - original file name (without the xml extension) store_id - ID of the store upload_date - date of file download. Upload dates before 2020 - unclear what they are, probably of stores which shut down.
PriceUpdateDate - Last date of price change of the item. ItemCode - a unique ID of the item. ItemName - name. ManufacturerName - manufacturer. These data are messy. ManufactureCountry - country of production. ManufacturerItemDescription - similar to ItemName UnitQty - unit of measure Quantity - quantity. UnitOfMeasure - also unit of measure ItemPrice - price (NIS) UnitOfMeasurePrice - price divided by quantity AllowDiscount - boolean/dummy variable.

Supplementary data can be found here: https://docs.google.com/spreadsheets/d/1LYyCt3BTJ-QInja-4iN1vqZ91xV6TAwhywgJxecSOkM/edit?usp=sharing Including: - Analysis of suppliers - different labels associated with each supplier - A table linking Shufersal stores with their store_id - A table with details on how many price files (stores) were downloaded each date.

What are we looking for? - Price collusion - producers raising prices at the same time. - Which producers saw the greatest price increase? - Which is the most expensive store? - Which products are most promoted? You can go to the source and find "promo" tables. - Can you create a user-friendly tool to analyze these data for non-data scientists?

Dataset Structure & Description

https://imgur.com/AYzsmYU.jpg" alt="Dataset Structure">

Context and Inspiration

I read an article yesterday which got my mind storming, A article by Worldbank on August 15th, 2022 better explains it, It has been quoted below,
I already have a project i'm working on since Feb 2021, trying to solving this problem, listed in my datasets

This dataset showcases the statistics over the past 6-7 decades which covers the production of 150+ unique crops, 50+ livestock elements, Land distribution by usage and population, As aspiring data scientists one can try to extract insights incentivizing the optimal use of natural resources and distribution of resources

August 15, 2022 - Worldbank

Record high food prices have triggered a global crisis that will drive millions more into extreme poverty, magnifying hunger and malnutrition, while threatening to erase hard-won gains in development. The war in Ukraine, supply chain disruptions, and the continued economic fallout of the COVID-19 pandemic are reversing years of development gains and pushing food prices to all-time highs. Rising food prices have a greater impact on people in low- and middle-income countries, since they spend a larger share of their income on food than people in high-income countries. This brief looks at rising food insecurity and World Bank responses to date.

Please leave a upvote if you found this helpful ☮️

Hello 👋, If you are enjoying so far, Please checkout my other Datasets, I would love to hear your support & feedback on it, Thank you !

<---	(❁´◡`❁)	--->

Checkout my other Datasets & Notebooks

Monthly average retail prices for gasoline and fuel oil for Canada, selected provincial cities, Whitehorse and Yellowknife. Prices are presented for the current month and previous four months. Includes fuel type and the price in cents per litre.

Gasoline fell to 1.86 USD/Gal on December 2, 2025, down 0.53% from the previous day. Over the past month, Gasoline's price has fallen 2.79%, and is down 4.95% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on December of 2025.

Second-Hand Fashion Dataset

Overview

The dataset originates from projects focused on the sorting of used clothes within a sorting facility. The primary objective is to classify each garment into one of several categories to determine its ultimate destination: reuse, reuse outside Sweden (export), recycling, repair, remake, or thermal waste.

The dataset has 31,997 clothing items, a massive update from the 3,000 items in version 1. The dataset collection started under the Vinnova funded project "AI for resource-efficient circular fashion" in Spring, 2022 and involves collaboration among three institutions: RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB. The dataset has received further support through the EU project, CISUTAC (cisutac.eu).

Project page

- Webpage: https://fnauman.github.io/second-hand-fashion/">second-hand-fashion
- Contact: farrukh.nauman@ri.se

Dataset Details

- The dataset contains 31,997 clothing items, each with a unique item ID in a datetime format. The items are divided into three stations: `station1`, `station2`, and `station3`. The `station1` and `station2` folders contain images and annotations from Wargön Innovation AB, while the `station3` folder contains data from Myrorna AB. Each clothing item has three images and a JSON file containing annotations.

- Three images are provided for each clothing item:
1. Front view.
2. Back view.
3. Brand label close-up. About 4000-5000 brand images are missing because of privacy concerns: people's hands, faces, etc. Some clothing items did not have a brand label to begin with.

- Image resolutions are primarily in two sizes: `1280x720` and `1920x1080`. The background of the images is a table that used a measuring tape prior to January 2023, but later images have a square grid pattern with each square measuring `10x10` cm.

- Each JSON file contains a list of annotations, some of which require nuanced interpretation (see `labels.py` for the options):
- `usage`: Arguably the most critical label, usage indicates the garment's intended pathway. Options include 'Reuse,' 'Repair,' 'Remake,' 'Recycle,' 'Export' (reuse outside Sweden), and 'Energy recovery' (thermal waste). About 99% of the garments fall into the 'Reuse,' 'Export,' or 'Recycle' categories.
- `price`: The price field should be viewed as suggestive rather than definitive. Pricing models in the second-hand industry vary widely, including pricing by weight, brand, demand, or fixed value. Wargön Innovation AB does not determine actual pricing.
- `trend`: This field refers to the general style of the garment, not a time-dependent trend as in some other datasets (e.g., Visuelle 2.0). It might be more accurately labeled as 'style.'
- `material`: Material annotations are mostly based on the readings from a Near Infrared (NIR) scanner and in some cases from the garment's brand label.
- Damage-related attributes include:
- `condition` (1-5 scale, 5 being the best)
- `pilling` (1-5 scale, 5 meaning no pilling)
- `stains`, `holes`, `smell` (each with options 'None,' 'Minor,' 'Major').

Note: 'holes' and 'smell' were introduced after November 17th, 2022, and stains previously only had 'Yes'/'No' options. For `station1` and `station2`, we introduced additional damage location labels to assist in damage detection:

      "damageimage": "back",
      "damageloc": "bottom left",
      "damage": "stain ",
      "damage2image": "front",
      "damage2loc": "None",
      "damage2": "",
      "damage3image": "back",
      "damage3loc": "bottom right",
      "damage3": "stain"

Taken from `labels_2024_04_05_08_47_35.json` file. Additionally, we annotated a few hundred images with bounding box annotations that we aim to release at a later date.
- `comments`: The comments field is mostly empty, but sometimes contains important information about the garment, such as a detailed text description of the damage.

- Whenever possible, ISO standards have been followed to define these attributes on a 1-5 scale (e.g., `pilling`).

- Gold dataset: `Test` inside the comments field is meant for garments that were annotated multiple times by different annotators for annotator agreement comparisons. These 100 garments were annotated twice at Wargön Innovation AB (search within `station1/[dec2022,feb2023]`)and once at Myrorna AB (see `station3/test100` folder for JSON files containing their annotations).

- The data has been annotated by a group of expert second-hand sorters at Wargön Innovation AB and Myrorna AB.

- Some attributes, such as `price`, should be considered with caution. Many distinct pricing models exist in the second-hand industry:
- Price by weight
- Price by brand and demand (similar to first-hand fashion)
- Generic pricing at a fixed value (e.g., 1 Euro or 10 SEK)

Wargön Innovation AB does not set the prices in practice and their prices are suggestive only (`station1` and `station2`). Myrorna AB (`station3`), in contrast, does resale and sets the prices.

Comments

- We received feedback on our version 1 that some images were too blurry or had poor lighting. The image quality has slightly improved, but largely remains similar to release 1.
- We further learned that a handful of data items were duplicates. Several duplicate images were removed, but about 400 still remain.
- Some users did not prefer a `tar.gz` format that we uploaded in version 1 of the dataset. We have now switched to `.zip` for convenience.
- Most JSON files parse fine using any standard JSON reader, but a handful that are problematic have been set aside in the `json_errors` folder.
- Extra care was taken not to leak personal information. This is why you will not see any entries for `annotator` attribute in the JSON files in station1/sep2023 since people used their real names. Since then, we used internally assigned IDs.
- Many brand images contained people's hands, faces, or other personal information. We have removed about 4000-5000 brand images for privacy reasons.
- Please inform us immediately if you find any personal information revelations in the dataset:
- Farrukh Nauman (RISE AB): `farrukh.nauman@ri.se`,
- Susanne Eriksson (Wargön Innovation AB): `susanne.eriksson@wargoninnovation.se`,
- Gabriella Engstrom (Wargön Innovation AB): `gabriella.engstrom@wargoninnovation.se`.

We went through 100k images three times to ensure no personal information is leaked, but we are human and can make mistakes.

Partners

The data collection for this dataset has been carried out in collaboration with the following partners:

1. RISE Research Institutes of Sweden AB: RISE is a leading research institute dedicated to advancing innovation and sustainability across various sectors, including fashion and textiles.

2. Wargön Innovation AB: Wargön Innovation is an expert in sustainable and circular fashion solutions, contributing valuable insights and expertise to the dataset creation.

3. Myrorna AB: Myrorna is Sweden's oldest chain of stores for collecting clothes and furnishings that can be reused.

License

CC-BY 4.0. Please refer to the LICENSE file for more details.

Acknowledgments

This dataset was made possible through the collaborative efforts of RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB, with funding from Vinnova and support from the EU project CISUTAC. We extend our gratitude to all the expert second-hand sorters and annotators who contributed their expertise to this project.

Context

The dataset includes sectors that play an essential role in the Pakistani economy. Economic conditions are deteriorating as FY 2022 (July 2021-June 2022) draws close. Rising commodity prices and a large fiscal deficit have inflated the import bill, putting the country on the verge of a balance of payments crisis. The currency has sunk to an all-time low, while international reserves have dwindled to barely two months' import cover.

Content

This dataset contain columns:'Year', ' Crops ', 'Livestock', 'Forestry', ' Fishing', 'total Agricultural sectors', ' Mining and Quarrying, ' Manufacturing ', ' Large Scale', 'Small Scale', 'Slaughtering', 'Electricity generation & distribution and Gas distribution, , , , 'Construction', ' total Industrial Sectors ', 'Wholesale & Retail trade', 'Transport, Storage & Communication, 'Finance & Insurance', 'Housing Services ', 'General Government Services', 'Other Services, , 'total Services Sector ', 'GDP', 'Per Capita', 'Growth rate'

Acknowledgement

You can download, copy and share this dataset for analysis and can easily find Contributions of various sectors to Pakistan's GDP by data we can predict better and can analyze our community problems and solve them.

About this dataset

Context

The dataset tabulates the Price township population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Price township across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Price township was 3,741, a 0.38% increase year-by-year from 2021. Previously, in 2021, Price township population was 3,727, an increase of 0.98% compared to a population of 3,691 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Price township increased by 1,065. In this period, the peak population was 3,741 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Price township is shown in this column.
• Year on Year Change: This column displays the change in Price township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Price township Population by Year. You can refer the same here