A curated list of food prices in South Africa, reported monthly on http://www.pacsa.org.za "What is the PACSA Food Basket? The PACSA Food Basket is an index for food price inflation. It provides insight into the affordability of food and other essential household requirements for working class households in a context of low wages, social grants and high levels of unemployment. The PACSA Food Basket tracks the prices of a basket of 36 basic foods which working class poor households, with 7 members, said they buy every month (based on conversations with women). The food basket is not nutritionally complete; it is a reflection of reality - what people are buying. Data is collected on the same day between the 21st and 24th of each month from six different retail stores which service the lower-income market in Pietermaritzburg, KwaZulu-Natal. Women have told us that they base their purchasing decisions on price and whether the quality of the food is not too poor. Women are savy shoppers and so foods and their prices in each store are selected on this basis. The PACSA Food Basket tracks the foods working class households buy, in the quantities they buy them in and from the supermarkets they buy them from. PACSA has been tracking the price of the basket since 2006. We release our Food Price Barometer monthly and consolidate the data for an annual report to coincide with World Food Day annually on the 16th October." - PACSA website

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

Analysis of ‘Grocery Store Prices, Mongolia’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/robertritz/ub-market-prices on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The National Statistics Office of Mongolia goes to each major market to record food prices each week in Ulaanbaatar, the capital city of Mongolia. The main purpose for this is to monitor a common basket of goods for use in consumer price index (CPI) calculations.

Content

The data is in a long-form, with date, market, product, and price recorded. All prices are in Mongolian Tugriks. As of 2021 the USD to MNT is about 2850 MNT = 1 USD.

Acknowledgements

This dataset is possible thanks to the hard work of the people of the National Statistics Office of Mongolia.

Inspiration

Often people choose supermarkets over the open markets (called a "zakh"). Mostly this is for convenience, but it is notable how much money people could save by choosing a different market!

This would be a great dataset for EDA or looking at how prices change over time.

--- Original source retains full ownership of the source dataset ---

The Retail Price Index (RPI) is a tool that helps us understand how the prices of everyday items change over time in Trinidad and Tobago. Imagine you have a shopping basket filled with various items people commonly buy, like food, gas, and other services. The RPI keeps track of how the prices of these items in the basket change each month. To do this, experts regularly check the prices of these items in fifteen (15) different areas across Trinidad and Tobago. They visit local stores, markets, and gas stations to note the current prices of food and gas, which tend to change often. For items whose prices do not change as quickly, they check the prices every three (3) months. This way, the RPI gives a clear picture of how much more or less it costs to buy the same set of items over time.

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

Introduction

The collected data sets come from the multi-branch store computer system. The data shows: stocking, sales, sales statistics, characteristics of products sold from January 2018 - December 2018.

About the store:

Store was open in 2009 and is located in Poland. The shop area is 120m2. We offer general food-and basic chemistry, hygienic articles. We have fresh bread from 4 different bakers,sweets, local vegetables, dairy, basic meat(ham,sausages), newspaper, home chemistry etc. Interior is basic.

Location: Shop is located in city that population is around 28 000 people. Shop is placed in mid of house estate( block of flats), near is sports field. The store is open every day: Monday-Saturday from 06:00 to 22:00, Sunday from 10:00 to 20:00. The store has 4 employees. Work in the store takes place on 3 shifts. First: 06:00- 12:00, second: 10:00-16: 00/18:00 and third: 16: 00-22: 00.

Competition

1. The nearest competition: There is another grocery store nearby (30 m). The second store is smaller - also a delicatessen, but half smaller. They offer similar products for daily use- bread, dairy, some meat and general foods. I'm not sure about alcohol and how wide their offer is. However in our store the offer is richer(bread is delivered from 4 different bakers). To know exactly what are the differences I need check details.

2. Grocery stores in the town:

3. 1 hypermarket

4. 8 supermarkets

5. 25 groceries stores

6. Shopping trends in Poland Connected to our location: People tend to do general food shopping in supermarkets. If they need daily fresh things, something is missing or they need some special product (not valid at supermarket) they do shopping at groceries like ours. Still in Poland people prefer to go to shop in the neighborhood to do: quicker shopping/talk to people/or just throw out rubbish and do shop at once. To do bigger shopping they go by car to supermarket e.g. after work or on weekend.

Online shopping: E-commerce are 1% of the sales of the FMCG goods market in Poland. It is starting to be popular in bigger cities like Warsaw, Krakow etc. Not popular in our city.

Health trend: -Three-quarters of Polish consumers agree with the statement that "you are what you eat". Therefore, we pay more attention to what we eat and do not save on food products

Convenience trend: According to the expert, the habits of buyers will not change so quickly, and the fact is that Poles like to shop flat - Polish shoppers visit 4 shops a month on average. Also the vast majority of them tend to make smaller purchases, which confirms the most popular shopping mission - replenishing stocks. However, the shopping experience is pleasant in the third place among buyers' motivation and selection of the store. 8 out of 10 buyers prefer to shop in a well-organized store with a nice atmosphere. This is one of the reasons for the development of the convenience channel. He also responds very well to other needs of Polish consumers, because Poles definitely have less and less time, so shopping must be fast and convenient. In this situation, the price is not the most important - 30% of Polish buyers declare that anything that saves their time is worth the higher price.

Costumer

Our costumer is located in the neighborhood leave in house estate (block of flats). During events of the sport field our opening hours are adjusted to get more costumers from event. Moreover, during trade free Sundays we have costumers from City. Some of the costumer work abroad and come to our shop when they are at home and have special order- e.g. cigarettes packages.

Demographic of city and wellness of inhabitants

Average age of people is 40 years old. Gender split is equal between men and women. Majority of population are marriages 60% and city has positive natural increase. Unemployment rate is low and similar to country rate- around 7%. Average monthly gross salary is around 3800 PLN gross .This is between minimum and average salary in Poland. (Minimum wage in Poland is :2250 PLN gross and average wage is : 4272 PLN gross.) Occupation split of people is : 40 % industry and construction, 30% agricultural sector, 11%service sector and other. Companies in the city are micro and small ( only few big companies). City is not touristic. In general situation in city is good-budget revenues are growing year to year. Additionally, polish government gives social funds for every second children starts from 2017 and now in 2019 it is going to be extended to every children, without limits. This should boost economy.

In general- Costumer in the city has good shopping condition.

The main problems faced by the owners are:

• • Overhaul of the owners - the store employs 4 employees, but the owners' great involvement in the current operation of the store means that they are unable to assess the situation and take actions to...

Analysis of ‘US Public Food Assistance’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jpmiller/publicassistance on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset focuses on public assistance programs in the United States that provide food, namely SNAP and WIC. If you are interested in a broader picture of food security across the world, please see Food Security Indicators for the World 2016-2020.

Initial coverage was for the Special Supplemental Nutrition Program for Women, Infants, and Children Program, or simply WIC. The program allocates Federal and State funds to help low-income women and children up to age five who are at nutritional risk. Funds are used to provide supplemental foods, baby formula, health care, and nutrition education.

Starting with version 5, the dataset also covers the US Supplemental Nutrition Assistance Program, more commonly known as SNAP. The program is the successor to the Food Stamps program previously in place. The program provides food assistance to low-income families in the form of a debit card. A 2016 study using POS data from SNAP-eligible vendors showed the three most purchased types of food to be meats, sweetened beverages, and vegetables.

Content

Files may include participation data and spending for state programs, and poverty data for each state. Data for WIC covers fiscal years 2013-2016, which is actually October 2012 through September 2016. Data for SNAP covers 2015 to 2020.

Motivation

My original purpose here is two-fold:

• Explore various aspects of US Public Assistance. Show trends over recent years and better understand differences across state agencies. Although the federal government sponsors the program and provides funding, program are administered at the state level and can widely vary. Indian nations (native Americans) also administer their own programs.

• Share with the Kaggle Community the joy - and pain - of working with government data. Data is often spread across numerous agency sites and comes in a variety of formats. Often the data is provided in Excel, with the files consisting of multiple tabs. Also, files are formatted as reports and contain aggregated data (sums, averages, etc.) along with base data.

As of March 2nd, I am expanding the purpose to support the M5 Forecasting Challenges here on Kaggle. Store sales are partly driven by participation in Public Assistance programs. Participants typically receive the items free of charge. The store then recovers the sale price from the state agencies administering the program.

Additional Content Ideas

The dataset can benefit greatly from additional content. Economics, additional demographics, administrative costs and more. I'd like to eventually explore the money trail from taxes and corporate subsidies, through the government agencies, and on to program participants. All community ideas are welcome!

--- Original source retains full ownership of the source dataset ---

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

This study was undertaken to understand why some SNAP participants shop at farmers markets and others in the same geographic area do not. Results suggest that SNAP participants buy most of their fresh fruits and vegetables at farmers markets. Of those who shop at farmers markets, overall value including quality and price are major reasons for shopping at markets. Of those who do not, reasons for not shopping at farmers markets centered on convenience.

This dataset contains data for food prices on several key food items in the Canadian province of Newfoundland and Labrador collected using citizen science, from October 2020 to December 2021. Data were collected in different places in the province on different timelines, with some locations receiving regular biweekly data and others having a single date inputted. The goal of this dataset was to gain high-resolution temporal data on food pricing to see variations over time, place, stores, online vs in-person shopping, sales, and food items.

This data is the basis of the report: Liboiron, Max, Willa Neilsen, Morgan Davidson, Sarah Crocker, Amanda Asiamah, Kaitlyn Hawkins, Brittany Marie Schaefer, Patricia Johnson-Castle, Kerri Claire Neil, Lynn Blackwood, Corinne Neil, Sarah Sauvé, and Charlotte Florian. (2023). Comparative Food Pricing in Newfoundland and Labrador using Citizen Science, 2020-2021. Civic Laboratory for Environmental Action Research (CLEAR). St. John’s: Memorial University.

Report, figures, etc are available at https://civiclaboratory.nl/nl-food-pricing-project/

The dataset contains information collected from 122 K-12 public school food service directors in Mississippi, USA, who completed an online survey designed for Mississippi school food service directors. The survey was created using Snap Surveys Desktop software. Information includes school size (number of enrolled students), percent of students participating in free or reduced-price lunch, foods sourced locally (defined as grown or produced in Mississippi), desire to purchase more or start purchasing locally sourced foods, fresh fruit and vegetable purchasing practices, experience purchasing fruits and vegetables from farmers, challenges purchasing from farmers, and interest in other farm to school (F2S) activities. School food service directors' demographic characteristics collected include gender, age, ethnicity/race, marital status, and education level. The data were collected from October 2021 to January 2022 using an online mobile and secure survey management system called Snap Online. The data were collected to obtain updated demographic and school purchasing characteristics from school food service directors in Mississippi and to determine their current abilities, experiences, and desires to engage in F2S activities. The dataset can be used to learn about K-12 public school food service directors in Mississippi but results should not be generalized to all school food service directors in Mississippi or elsewhere in the USA. Resources in this dataset:Resource Title: Mississippi Farm to School Food Service Director Dataset. File Name: MS F2S School Data Public.csvResource Description: The dataset contains information collected from 122 K-12 public school food service directors in Mississippi regarding their experience with and interest in farm to school, including purchasing local foods. It also contains demographic characteristics of the school food service directors and their fresh fruit and vegetable purchasing practices.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Mississippi Farm to School Food Service Director Data Dictionary. File Name: MS F2S School Data Dictionary Public.csvResource Description: The file contains information for variables contained in the associated dataset including names, brief descriptions, types, lengths, and values.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

Retail Scanner Data consist of weekly pricing, volume, and store environment information generated by point-of-sale systems from more than 90 participating retail chains across all US markets.

Store Demographics: Includes store chain code, channel type, and area location. Retailer names are masked to protect identity.

Weekly Product Data: For each UPC code, participating stores report units, price, price multiplier, baseline units, baseline price, feature indicator, and display indicator. Products: Weekly product data for 2.6-4.5* million UPCs including food, nonfood grocery items, health and beauty aids, and select general merchandise aggregated into 1,100 product categories store environment variables (i.e., feature and display indicators) from a subset of stores. The 1,100 product categories are categorized into 125 product groups and 10 departments. The structure matches that of the consumer panel data. All private-label goods have a masked UPC to protect the identity of the retailers.

Product Characteristics: All products include UPC code and description, brand, multipack, and size, as well as NielsenIQ codes for department, product group, and product module. Some products contain additional characteristics (e.g., flavor).

Geographies: Scanner Data from 35,000-50,000* participating grocery, drug, mass merchandiser, and other stores, covering more than half the total sales volume of US grocery and drug stores and more than 30 percent of all US mass merchandiser sales volume. Data cover the entire United States, divided into 52 major markets, and include the same codes as those used in the consumer panel data.

Retail Channels: Food, drug, mass merchandise, convenience, and liquor.

Household food insecurity is a serious public health issue that can negatively affect the health of individuals, families, and our communities. For this reason, the City of Kingston is providing a list of local food resources as part of the Community Development and Well-being team. This dataset includes resources for low to no cost food resources, including but not limited to Community Food Programs, location of Community Gardens, Cultural Food Stores, Farm stands and low cost grocery stores. The Corporation of the City of Kingston assumes no responsibility for inaccurate or inconsistent data set out in this map product. The City does not accept any responsibility for reliance thereon. The City does not make any representations or warranty, express or implied, in relation to the website including without limitations, as to the quality, merchantability and fitness for any use. Further, the City does not accept any responsibility for the accuracy of this information, nor is it responsible for any expenses or damages incurred, directly or indirectly, resulting from the use of this information.

This table contains 214 series, with data for years 1961 - 2011 (not all combinations necessarily have data for all years), and was last released on 2012-10-01. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Prices (4 items: Current prices; 1992 constant prices (terminated); Chained (2002) dollars; Contributions to percent change ...), Consumer goods and services (61 items: Personal expenditure on consumer goods and services; Food and non-alcoholic beverages; Alcoholic beverages bought in stores; Food; beverages and tobacco ...).

Motivation

In the European Union, there are many foods that have long histories of being produced in one specific region. You may be familiar with champagne, produced in the historical province of Champagne in northeastern France; products made outside this region, even if they are very similar to champagne, cannot be called “champagne” and are usually called “sparkling wine”.

To protect these local foods, European Union regulations recognize certain “geographical indications” (GIs) for food, and restrict the names and labels that can be used for certain food items. There are two categories of GI:

A Protected Designation of Origin (PDO) product must be completely produced within a specific region. For example, only prosciutto from a specific region of Italy can be called “prosciutto di Parma”. A Protected Geographic Indication (PGI) product must have at least one stage of production (but not necessarily all) within a specific region. For example, a product can only be called Black Forest ham if part of the production is done within the Black Forest region of Germany. Additionally, a Traditional Speciality Guaranteed (TSG) product does not have to be from a specific region, but must be produced using certain traditional techniques or materials. For example, pizza napoletana (Neapolitan pizza) must be produced using a traditional method, but can be made anywhere.

Naturally, economists are interested in how this system affects the price of protected products. One theory is that a geographically indicated product’s price is inversely related to the area of the geographic region it can be produced in. If the area is very small, there can only be few producers, and the exclusivity of the product makes it seem more desirable or higher quality; if the area is large, there can be more producers, and the product seems less exclusive.

This dataset comes from a study meant to test this theory by using 22 types of GI ham (Höhn, Huysmans, and Crombez, 2023).

Data

The authors write:

We manually gathered data from online store websites operating in 11 EU countries, namely Austria, Belgium, France, Germany, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Slovenia and Spain. These countries were chosen based on the following criteria. Our study encompasses all countries of origin of an eligible GI ham that have the euro as currency. Also, it includes the Netherlands as a major pig meat producer (Augère-Granier, 2020) and Ireland as a major ham importer (Török & Jambor, 2016).

We selected 36 online stores of established supermarkets that are present with physical stores in the respective city chosen for delivery, for example, Monoprix in Paris, REWE in Berlin or Coop in Rome. To avoid strong price differences due to strongly different store types we excluded specialty shops focusing on specific product categories. […] To ensure consistency we only gathered observations on supermarket websites providing home delivery in the respective capital’s centre. We collected these cross-sectional data in April 2021.

Each row of the data represents one type of ham from one particular online store. Of the 768 observations, 190 are GI hams (either PDO or PGI), while the rest have no geographic restrictions.

Variable Description

• COO Country of origin of geographical indication (GI) ham, or NA if ham is not GI
• GI If ham has geographical indication (GI), 1, otherwise 0
• GI_cluster Cluster at GI level, or NA if not GI. Cluster is the specific geographic indication, such as “Prosciutto di Parma”
• GI_label Whether the ham is Non-GI, PDO (protected designation of origin), or PGI (protected geographical indication)
• NonGI 1 if not labeled PDO or PGI, 0 otherwise
• PDO 1 if the ham is PDO, 0 otherwise
• PGI 1 if the ham is PGI, 0 otherwise
• TSG 1 if the ham is TSG (traditional specialities guaranteed), 0 otherwise
• breed 1 if a special pig breed is used for the ham, 0 otherwise
• country Country in which the ham is sold
• domestic 1 if the ham is domestic to the country, 0 if it is foreign
• lnarea Natural logarithm of the GI area, square kilometers; NA for non-GI ham
• lnproduction Natural logarithm of the total production of GI ham by the consortium, tons; only available for certain Italian GI hams, and NA otherwise
• lnsourcing Natural logarithm of the area where the pig meat can be sourced, square kilometers
• longevity Years passed since the PDO, PGI, or TSG designation was registered in the European Union, as of April 1, 2021
• maturation The time the ham is matured, in months
• national 1 if the ham is a national brand, 0 if it is a private label brand (i.e. made for the supermarket under their brand name)
• organic 1 if the product is labeled/certified organic, 0 otherwise
• packsize Size of the ham package, in grams
• price Price of the ham per 100 grams, in Euros
• producers Number of producers ...