Name: Domestic Food Prices After COVID-19
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Domestic Food Prices After COVID-19

Analyzing Impact on Developing Countries' Food Security

By [source]

About this dataset

This dataset looks at the effect of the COVID-19 pandemic on food prices in both domestic and international markets, particularly in developing countries. It contains data on monthly changes in food prices, categorised by country, market, price type (domestic or international) and commodities. In particular, this dataset provides insight into how the pandemic has impacted food security for those living in poorer countries where price increases may be more acutely felt. This dataset gives us a greater understanding of these changing dynamics of global food systems to enable more efficient interventions and support for those who are most vulnerable

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How to use the dataset

This dataset is an excellent resource for anyone looking to analyze the impact of COVID-19 on domestic food prices in developing countries. With this dataset, you can get an up-to-date overview of changes in the costs of various commodities in a given market and by a given price type. Additionally, you can filter data by commodity, country and price type.

In order to use this dataset effectively, here are some steps:

Identify your research question(s)

Filter the dataset by selecting specific columns that best answer your research question (ex: month, country, commodity)

Analyze the data accordingly (for example: Sorting the results then calculating averages).

Interpret results into actionable insights or visualizations

Research Ideas

Analyzing trends in the cost of food items across different countries to understand regional disparities in food insecurity.

Comparing pre- and post-COVID international food prices to study how nations altered their trade policies in response to the pandemic, indicating a shift towards or away from trading with other nations for food procurement.

Using sentiment analysis to study consumer sentiment towards purchasing certain items based on their market prices, allowing businesses and governments alike to better target interventions aimed at improving access and availability of food supplies

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: dom_clean_data.csv

Column name	Description
month	The month in which the data was collected. (Date)
country	The country in which the data was collected. (String)
price_type	The type of price (domestic or international) that was collected. (String)
market	The market in which the data was collected. (String)
commodity	The type of commodity that was collected. (String)

File: int_clean_data.csv

Column name	Description
country	The country in which the data was collected. (String)
commodity	The type of commodity that was collected. (String)
price_type	The type of price (domestic or international) that was collected. (String)
time	The month in which the data was collected. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Dom Change Percent

@kaggle.thedevastator_domestic_food_prices_after_covid_19.dom_change_percent

33.01 kB
1,144 rows
8 columns

CREATE TABLE dom_change_percent (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "country" VARCHAR,
  "price_type" VARCHAR,
  "market" VARCHAR,
  "commodity" VARCHAR,
  "percent" VARCHAR,
  "post_covid" DOUBLE,
  "yearly" DOUBLE
);

Dom Clean Data

@kaggle.thedevastator_domestic_food_prices_after_covid_19.dom_clean_data

1.21 MB
170,934 rows
7 columns

CREATE TABLE dom_clean_data (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "month" TIMESTAMP,
  "country" VARCHAR,
  "price_type" VARCHAR,
  "market" VARCHAR,
  "commodity" VARCHAR,
  "price" DOUBLE
);

Int Change Percent

@kaggle.thedevastator_domestic_food_prices_after_covid_19.int_change_percent

6.14 kB
60 rows
5 columns

CREATE TABLE int_change_percent (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "country" VARCHAR,
  "commodity" VARCHAR,
  "post_covid" DOUBLE,
  "yearly" DOUBLE
);

Int Clean Data

@kaggle.thedevastator_domestic_food_prices_after_covid_19.int_clean_data

220.47 kB
25,496 rows
5 columns

CREATE TABLE int_clean_data (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "time" TIMESTAMP,
  "country" VARCHAR,
  "commodity" VARCHAR,
  "price" DOUBLE
);