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.

Cost of Living Index data was reported at 7,726.308 1913=1 in 2017. This records an increase from the previous number of 7,642.160 1913=1 for 2016. Cost of Living Index data is updated yearly, averaging 5.167 1913=1 from Dec 1861 (Median) to 2017, with 157 observations. The data reached an all-time high of 7,726.308 1913=1 in 2017 and a record low of 0.766 1913=1 in 1865. Cost of Living Index data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Italy – Table IT.I030: Cost of Living Index: 1913=1.

Cost of living indices are relative to New York City (NYC) which means that for New York City, each index should be 100. If another city has, for example, rent index of 120, it means that on an average in that city rents are 20% more expensive than in New York City. If a city has rent index of 70, that means on an average in that city rents are 30% less expensive than in New York City.

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

Rent Index is an estimation of prices of renting apartments in the city compared to New York City. If Rent index is 80, Numbeo estimates that price of rents in that city is on an average 20% less than the price in New York.

Groceries Index is an estimation of grocery prices in the city compared to New York City. To calculate this section, Numbeo uses weights of items in the "Markets" section for each city.

Restaurants Index is a comparison of prices of meals and drinks in restaurants and bars compared to NYC.

Cost of Living Plus Rent Index is an estimation of consumer goods prices including rent comparing to New York City.

Local Purchasing Power shows relative purchasing power in buying goods and services in a given city for the average wage in that city. If domestic purchasing power is 40, this means that the inhabitants of that city with the average salary can afford to buy on an average 60% less goods and services than New York City residents with an average salary.

Quality of life in a country comparison A total of 36 factors were included in the calculation of the overall index, which were divided into 7 subject areas here. The best achievable value in each division is 100, see below the table to read which individual criteria are included in each division.

Rank
Country Stability(15%) - Political and economic stability Rights(20%) -Legal system and civil rights, freedom of expression Health(15%) - Health and medical services Safety(10%) Climate(15%)
Costs(15%) - Cost of living and average annual income Popularity(10%) - Popularity of the country with foreigners TotalQuality of life(100%)

Acknowledgements- This dataset is taken from https://www.worlddata.info/quality-of-life.php I just shared it to ka kaggle for convenience. ( I will take this down at first request. I am not the owner of this dataset.)

Ireland: Cost of living index, world average = 100: The latest value from 2021 is 175.68 index points, an increase from 157.19 index points in 2017. In comparison, the world average is 79.81 index points, based on data from 165 countries. Historically, the average for Ireland from 2017 to 2021 is 166.44 index points. The minimum value, 157.19 index points, was reached in 2017 while the maximum of 175.68 index points was recorded in 2021.

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

The average for 2021 based on 41 countries was 107.05 index points. The highest value was in Switzerland: 211.98 index points and the lowest value was in Belarus: 40.99 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

This Cost of International Education dataset compiles detailed financial information for students pursuing higher education abroad. It covers multiple countries, cities, and universities around the world, capturing the full tuition and living expenses spectrum alongside key ancillary costs. With standardized fields such as tuition in USD, living-cost indices, rent, visa fees, insurance, and up-to-date exchange rates, it enables comparative analysis across programs, degree levels, and geographies. Whether you’re a prospective international student mapping out budgets, an educational consultant advising on affordability, or a researcher studying global education economics, this dataset offers a comprehensive foundation for data-driven insights.

Description

Potential Uses

• Budget Planning Prospective students can filter by country, program level, or university to forecast total expenses and compare across destinations.
• Policy Analysis Educational policymakers and NGOs can assess the affordability of international education and design support programs.
• Economic Research Economists can correlate living-cost indices and tuition levels with enrollment rates or student demographics.
• University Benchmarking Institutions can benchmark their fees and ancillary costs against peer universities worldwide.

Notes on Data Collection & Quality

• Currency Conversions All monetary values are unified to USD using contemporaneous exchange rates to facilitate direct comparison.
• Living Cost Index Derived from reputable city-index publications (e.g., Numbeo, Mercer) to standardize disparate cost-of-living metrics.
• Data Currency Exchange rates and fee schedules should be periodically updated to reflect market fluctuations and policy changes.

Feel free to explore, visualize, and extend this dataset for deeper insights into the true cost of studying abroad!

This dataset is about countries in Eastern Africa. It has 17 rows. It features 3 columns: expense, and urban population living in areas where elevation is below 5 meters .

Analysis of ‘Socio-Economic Country Profiles’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nishanthsalian/socioeconomic-country-profiles on 30 September 2021.

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

Context

There can be multiple motivations for analyzing country specific data, ranging from identifying successful approaches in healthcare policy to identifying business investment opportunities, and many more. Often, all these various goals would have to analyze a substantially overlapping set of parameters. Thus, it would be very good to have a broad set of country specific indicators at one place.

This data-set is an effort in that direction. Of-course there are still plenty more parameters out there. If anyone is interested to integrate more parameters to this dataset, you are more than welcome.

Content

This dataset contains about 95 statistical indicators of the 66 countries. It covers a broad spectrum of areas including

General Information Broader Economic Indicators Social Indicators Environmental & Infrastructure Indicators Military Spending Healthcare Indicators Trade Related Indicators e.t.c.

This data-set for the year 2017 is an amalgamation of data from SRK's Country Statistics - UNData, Numbeo and World Bank.

The entire data-set is contained in one file described below:

soci_econ_country_profiles.csv - The first column contains the country names followed by 95 columns containing the various indicator variables.

Acknowledgements

This is a data-set built on top of SRK's Country Statistics - UNData which was primarily sourced from UNData.

Additional data such as "Cost of living index", "Property price index", "Quality of life index" have been extracted from Numbeo and a number of metrics related to "trade", "healthcare", "military spending", "taxes" etc are extracted from World Bank data source. Given that this is an amalgamation of data from three different sources, only those countries(about 66) which have sufficient data across all the three sources are considered.

Please read the Numbeo terms of use and policieshere Please read the WorldBank terms of use and policies here Please read the UN terms of use and policies here

Photo Credits : Louis Maniquet on Unsplash

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

This dataset is about countries per year in Lithuania. It has 64 rows. It features 4 columns: country, expense, and urban population living in areas where elevation is below 5 meters .

This dataset is about countries per year in Luxembourg. It has 64 rows. It features 4 columns: country, expense, and urban population living in areas where elevation is below 5 meters .

Soziales Klima. Wahrnehmung von Wissenschaft, Forschung und Innovation. Themen: 1. Soziales Klima: Lebenszufriedenheit; Einschätzung der aktuellen Situation in ausgewählten Bereichen (Wohnraum, Gesundheitsversorgung im Land, Rentensystem, Arbeitslosenunterstützung, Lebenshaltungskosten im Land, die Beziehungen zwischen Menschen unterschiedlicher kultureller oder religiöser Herkunft oder Nationalität, Umgang mit sozialer Benachteiligung und Armut im Land, Bezahlbarkeit von Energie und Wohnen, Funktionieren der öffentlichen Verwaltung, nationale Wirtschaftslage und Beschäftigungslage, persönliche Arbeitssituation und finanzielle Lage des Haushaltes); Erwartungen für die nächsten zwölf Monate auf diesen Gebieten und Vergleich mit der Situation vor fünf Jahren; geschätzte Verbreitung von Armut im Land; vermutete Gründe für Armut; zu große Einkommensunterschiede zwischen Menschen; Personenvertrauen; Bereitstellung von Arbeitsplätzen als Aufgabe der nationalen Regierung oder von privaten Unternehmen und Märkten im Allgemeinen; kostenfreie Bildung trotz eines eventuell qualitativ geringeren Bildungsniveaus versus Studiengebühren für qualitativ hochwertige Bildung; präferierter Weg zur Lösung sozialer und wirtschaftlicher Probleme: garantiertes hohes Niveau von Gesundheitsversorgung, Bildung und Sozialausgaben, auch bei Steuererhöhungen versus Steuersenkungen mit der Folge eines geringeren Niveaus von Gesundheitsversorgung, Bildung und Sozialausgaben; Verantwortung der nationalen Regierung versus Eigenverantwortung des Einzelnen. 2. Wahrnehmung von Wissenschaft, Forschung und Innovation: erwartetes Ausmaß der Auswirkungen menschlichen Handelns und Verhaltens auf ausgewählte Bereiche (Kampf gegen den Klimawandel, Umweltschutz, Sicherheit der Bürger, Schaffung von Arbeitsplätzen, Energieversorgung, Gesundheit und medizinische Versorgung, Schutz personenbezogener Daten, Abbau von Ungleichheiten, Anpassung der Gesellschaft an eine alternde Bevölkerung, Verfügbarkeit und Qualität von Lebensmitteln, Transport- und Verkehrsinfrastruktur, Bildung und Qualifikationen, Wohnqualität); erwartetes Ausmaß der Auswirkungen von Wissenschaft und technologischer Innovation auf die vorgenannten Bereiche; priorisierte Bereiche zukünftiger Wissenschaft und technologischer Innovationen; Befragter hat Wissenschaft und Technik in der Schule, an der Universität oder Fachhochschule studiert. Demographie: Nationalität; Familiensituation; Alter bei Ende der Schulbildung; Geschlecht; Alter; Beruf; berufliche Position; Urbanisierungsgrad; Haushaltsgröße und Haushaltszusammensetzung: Anzahl der Personen im Haushalt im Alter von 15 Jahren und älter, Anzahl der Kinder im Haushalt unter 10 Jahren und zwischen 10 und 14 Jahren; Besitz langlebiger Wirtschaftsgüter (Unterhaltungselektronik, Internet-Anschluss, Autobesitz, abbezahltes bzw. noch abzuzahlendes Wohnungs- bzw. Hauseigentum); finanzielle Schwierigkeiten im letzten Jahr; Selbsteinschätzung der sozialen Position (Skalometer); Internetnutzung (zu Hause, am Arbeitsplatz, in der Schule etc.); Selbsteinstufung zur Arbeiterklasse, Mittelklasse oder der höheren Klasse der Gesellschaft; Häufigkeit von Diskussionen über nationale, europäische und lokale politische Angelegenheiten; eigene Stimme zählt, im eigenen Land und in der EU (politischer Einfluss); allgemeine Richtung der Dinge im eigenen Land und in der EU. Zusätzlich verkodet wurde: Ortsgröße; Region. Social climate. Perceptions of science, research, and innovation. Topics: 1. Social climate: life satisfaction; assessment of the current situation in selected fields (living area, health care provision in the country, the pension system, unemployment benefits, the cost of living in the country, relations in the country between people from different cultural or religious backgrounds or nationalities, the way inequalities and poverty are addressed in the country, affordability of energy and housing in the country, the way public administration runs, the situation of the national economy, the employment situation in the country, the personal job situation and the financial situation of the household); expectations for the next twelve months in these fields and comparison with the situation five years ago; estimated spread of poverty in the country; reasons for poverty; income differences between people are far too large; trust in people; providing jobs for the unemployed as a task of the national government or of private companies and markets in general; education should be totally free, even if this means lower quality versus tuition fees are necessary for providing high quality education, even if this means that some people won´t be able to afford it; preferred way of solving social and economic problems: guaranteed high level of health care, education and social spending, even if it means that taxes might increase versus taxes should be decreased even if it means a lower level of health care, education and social spending; responsibility of the national government versus people should take more responsibility to provide for themselves. 2. Perceptions of science, research, and innovation: expected extent of impact of people´s actions and behaviour on selected areas (fight against climate change, protection of the environment, security of citizens, job creation, energy supply, health and medical care, protection of personal data, reduction of inequalities, adaption of society to an ageing population, availability and quality of food, transport and transport infrastructure, education and skills, quality of housing); expected extent of impact of science and technological innovation on these aforementioned areas; prioritized fields of future science and technological innovations; respondent has studied science or technology at school, at university, or college. Demography: nationality; family situation; age at end of education; sex; age; occupation; professional position; degree of urbanization; household size and household composition: number of persons in the household aged 15 years and older; number of children in household less 10 years and 10 to 14 years; possession of durable goods (entertainment electronics, Internet connection, possession of a car, a flat/a house have finished paying for or still paying for); financial difficulties during the last year; self-rated social position (scale); Internet use (at home, at work, at school etc.); self-reported belonging to the working class, the middle class or the higher class of society; frequency of discussions about political matters on national, European and local level; own voice counts in the own country and in the EU (political influence); general direction things are going in the own country and in the EU. Additionally coded was: size of locality; region.

We present here a new dataset of per capita disposable income for 42 European countries (and more than 120,000 administrative units at the subnational level), over the 2010-2020 period (with few additional years for some countries). This dataset was created by harmonizing disparate income data (net earnings, gross income, disposable income, etc.) gathered from national statistical institutes across Europe. Disposable income was converted to constant 2015 EU27 PPP€ to adjust for the costs of living and inflation across countries and to allow comparability over time. Total population and a measure of income inequality (Gini index) are also provided for subnational administrative units. Users can download the aggregated dataset covering the whole years (Disposable_Inc_DB.gpkg) or yearly files.

This dataset is about countries per year in Poland. It has 64 rows. It features 4 columns: country, expense, and urban population living in areas where elevation is below 5 meters .

This dataset is about countries per year in Spain. It has 64 rows. It features 4 columns: country, expense, and urban population living in areas where elevation is below 5 meters .

Data set for respondents, and txt for generating tables and figures.

This dataset is about countries per year in Turkey. It has 64 rows. It features 4 columns: country, expense, and urban population living in areas where elevation is below 5 meters .