Cost of Living Index (Excl. Rent) is a relative indicator of consumer goods prices, including groceries, restaurants, transportation and utilities. Cost of Living Index does not include accommodation expenses such as rent or mortgage. If a city has a Cost of Living Index of 120, it means Numbeo has estimated it is 20% more expensive than New York (excluding rent).

Please refer further to: https://www.numbeo.com/cost-of-living/cpi_explained.jsp for motivation and methodology.

All credits to https://www.numbeo.com .

This dataset would surely help socio-economic researchers to analyse and get deeper insights regarding the life of people country-wise.

Thanks to @andradaolteanu for the motivation! Upwards and onwards...

Cost of Living - Country Rankings Dataset

Context:

The "Cost of Living - Country Rankings Dataset" provides comprehensive information on the cost of living in various countries around the world. Understanding the cost of living is crucial for individuals, businesses, and policymakers alike, as it impacts decisions related to travel, relocation, investment, and economic analysis. This dataset is intended to serve as a valuable resource for researchers, data analysts, and anyone interested in exploring and comparing the cost of living across different nations.

Content:

This dataset comprises four primary columns:

1. Countries: This column contains the names of various countries included in the dataset. Each country is identified by its official name.

2. Cost of Living: The "Cost of Living" column represents the cost of living index or score for each country. This index is typically calculated by considering various factors, such as housing, food, transportation, healthcare, and other essential expenses. A higher index value indicates a higher cost of living in that particular country, while a lower value suggests a more affordable cost of living.

3. 2017 Global Rank: This column provides the global ranking of each country's cost of living in the year 2017. The ranking is based on the cost of living index mentioned earlier. A lower rank indicates a lower cost of living relative to other countries, while a higher rank suggests a higher cost of living position.

4. Available Data: The "Available Data" column indicates whether or not data for a specific country and year is available.

This dataset is designed to support various data analysis and visualization tasks. Users can explore trends in the cost of living, identify countries with high or low cost of living, and analyze how rankings have changed over time. Researchers can use this dataset to conduct in-depth studies on the factors influencing the cost of living in different regions and the economic implications of such variations.

Please note that the dataset includes information for the year 2017, and users are encouraged to consider this when interpreting the data, as economic conditions and the cost of living may have changed since then. Additionally, this dataset aims to provide a snapshot of cost of living rankings for countries in 2017 and may not cover every country in the world.

Link: https://www.theglobaleconomy.com/rankings/cost_of_living_wb/

Disclaimer: The accuracy and completeness of the data provided in this dataset are subject to the source from which it was obtained. Users are advised to cross-reference this data with authoritative sources and exercise discretion when making decisions based on it. The dataset creator and Kaggle assume no responsibility for any actions taken based on the information provided herein.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

This dataset provides insights into the cost of living and average monthly income across various countries and regions worldwide from 2000 to 2023. It includes critical economic indicators such as housing costs, taxes, healthcare, education, transportation expenses, and savings rates. The data is ideal for analyzing economic trends, regional comparisons, and financial planning.

Column Descriptions: Country: The name of the country where the data was recorded. Region: The geographical region to which the country belongs (e.g., Asia, Europe). Year: The year when the data was recorded. Average_Monthly_Income: The average monthly income of individuals in USD. Cost_of_Living: The average monthly cost of living in USD, including essentials like housing, food, and utilities. Housing_Cost_Percentage: The percentage of income spent on housing expenses. Tax_Rate: The average tax rate applied to individuals' income, expressed as a percentage. Savings_Percentage: The portion of income saved monthly, expressed as a percentage. Healthcare_Cost_Percentage: The percentage of income spent on healthcare services. Education_Cost_Percentage: The percentage of income allocated to educational expenses. Transportation_Cost_Percentage: The percentage of income spent on transportation costs.

We adjust SNAP maximum allotments, deductions, and income eligibility standards at the beginning of each Federal fiscal year. The changes are based on changes in the cost of living. COLAs take effect on October 1 each year. Maximum allotments are calculated from the cost of a market basket based on the Thrifty Food Plan for a family of four, priced in June that year. The maximum allotments for households larger and smaller than four persons are determined using formulas that account for economies of scale. Smaller households get slightly more per person than the four-person household. Larger households get slightly less. Income eligibility standards are set by law. Gross monthly income limits are set at 130 percent of the poverty level for the household size. Net monthly income limits are set at 100 percent of poverty.

The ACCRA Cost of Living Index (COLI) is a measure of living cost differences among urban areas compiled by the Council for Community and Economic Research. Conducted quarterly, the index compares the price of goods and services among approximately 300 communities in the United States and Canada. This Microsoft Excel file contains the average prices of goods and services published in the ACCRA Cost of Living Index since 1990.

This dataset contains data on monthly cost of living index in Estonia in 1919-1939. Dataset "Monthly Cost of Living Index in Estonia, 1919-1939" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

Characteristics of sampled households in the Living Costs and Food Survey.

People in Great Britain's experiences of and actions following increases in their costs of living, and how these differed by a range of personal characteristics.

SPSS data file

SPSS output file

Excel data and sources file

Excel data only file for use with python processing (program on Github and archived on Zenodo)

Comprehensive cost of living dataset for Dover including housing, transport, food, utilities, and entertainment costs

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

This dataset contains information about the cost of living in almost 5000 cities across the world. The data were gathered by scraping Numbeo's website (https://www.numbeo.com).

Data dictionary

Explore the Consumer Price Index dataset for United Arab Emirates, covering various categories such as Food and Beverages, Transportation, Housing, and more. Stay informed about the cost of living trends with this valuable resource.

Consumer price index, Recreational and culture, Food and Beverages, Restaurants and Hotels, Education, Transportation, Communications, Medical care, Miscellaneous goods and services, Textiles, clothing and footwear, Furniture, household goods, Tobacco, Housing, Water, Electricity, Gas, CPI, Cost of living, Household, Food, Transportation, Price

United Arab EmiratesFollow data.kapsarc.org for timely data to advance energy economics research..(2014=100)

In his survey about wage development in Germany from 1871 to 1945 Gerhard Bry (1960) composes an overview about different estimations for index series for costs of living for the period from 1871 to 1913. The index series differ in their regional ties (German Empire, Silesia, Bavaria, Ruhr area, Berlin, Essen, Braunschweig, Lübeck, Mannheim, Stuttgart and Prussia) and also in the composition of the different components (food and rent, only food, costs of living). The indices are in general quite similar to each other, as Bry already noticed. To what extent differences in the details are due to local differences is difficult to assess. The only index, which differs significantly compared to other series from 1877 to 1892, is the board of trade for Krupp, Essen. “In order to test Kuczynski’s index, we compared it with the major available independent food cost measures – whether included in his index or not. This comparison is carried through in the table A-11. In spite of differences in detail there is a rather striking similarity in the behavior of all these series – particularly with respect to trends” (Bry, G., 1960, a. a. O., Appendix A-11). Data tables in HISTAT: A.01 Index – Series of costs of living, comparison of estimations (1871-1913) In seiner Untersuchung zur Lohnentwicklung in Deutschland von 1871 bis 1945 hat Gerhard Bry (1960) eine Übersicht zu verschiedenen Schätzungen für Index-Reihen der Lebenshaltungskosten für die Zeit 1871 bis 1913 gegenübergestellt. Die Index-Reihen unterscheiden sich sowohl in dem regionalen Bezug (Deutsches Reich, Schlesien, Bayern, Ruhrgebiet, Berlin, Essen, Braunschweig, Lübeck, Mannheim, Stuttgart und Preußen) als auch in der Zusammensetzung ihrer Komponenten (Ernährung und Miete, lediglich Ernährung, Lebenshaltungskosten). Im Allgemeinen verlaufen die Indices, wie Bry bemerkt, recht ähnlich. Inwiefern Unterschiede im Einzelnen auf lokale Verschiedenheiten zurückzuführen sind ist schwer einzuschätzen. Der einzige Index, der in den Jahren 1877 bis 1892 deutliche Unterschiede im Vergleich zu den übrigen Reihen aufweist, ist der des Board of Trade für Krupp (Essen. „In order to test Kuczynski’s index, we compared it with the major available independent food cost measures – whether included in his index or not. This comparison is carried through in the table A-11. In spite of differences in detail there is a rather striking similarity in the behavior of all these series – particularly with respect to trends” (Bry, G., 1960, a. a. O., Appendix A-11). Datentabellen in HISTAT: A.01 Index – Reihen der Lebenshaltungskosten, Schätzungen im Vergleich (1871-1913)

Comprehensive cost of living dataset for Canterbury including housing, transport, food, utilities, and entertainment costs

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

This dataset contains data on the monthly cost of living in Lithuania in 1913 and 1919-1939.

Comprehensive cost of living dataset for Eastleigh including housing, transport, food, utilities, and entertainment costs

The City of Toronto monitors food affordability every year using the Ontario Nutritious Food Basket (ONFB) costing tool. Food prices, among other essential needs, have increased considerably in the last several years. People receiving social assistance and earning low wages often do not have enough money to cover the cost of basic expenses, including food. As such, ONFB data is best used to assess the cost of living in Toronto by analyzing food affordability in relation to income, alongside other local basic expenses. The dataset describes the affordability of food and other basic expenses relative to income for 13 household scenarios. Scenarios were selected to reflect household characteristics that increase the risk of being food insecure, including reliance on social assistance as the main source of income, single-parent households, and rental housing. A median income scenario has also been included as a comparator. Income, including federal and provincial tax benefits, and the cost of four basic living expenses - rent food, childcare, and transportation - are estimated for each scenario. Results show the estimated amount of money remaining at the end of the month for each household. Three versions of the scenarios were created to describe: Income scenarios with subsidies: Subsidies can substantially reduce a households’ monthly expenses. Local subsidies for rent (Rent-Geared-to-Income), childcare (Childcare Fee Subsidy), and transit (Fair Pass) are accounted for in this file. Income scenarios without subsidies + average market rent: In this file, rental costs are based on average market rent, as measured by the Canadian Mortgage and Housing Corporation (CMHC). Income scenarios without subsidies + current market rent: Rental costs are based on current market rent (as of October 2023), as measured by the Toronto Regional Real Estate Board (TRREB). All values are rounded to the nearest dollar.