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...

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.

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.

This dataset provides an analysis of average monthly prices for four essential food items, namely Eggs, Milk, Bread, and Potatoes, in five different countries: Australia, Japan, Canada, South Africa, and Sweden. The dataset spans a five-year period, from 2018 to 2022, offering a comprehensive overview of how food prices have evolved over time in these nations.

The dataset includes information on the average monthly prices of each food item in the respective countries. This information can be valuable for studying and comparing the cost of living, assessing economic trends, and understanding variations in food price dynamics across different regions.

Use Cases:

Comparative Analysis: Researchers and analysts can compare food prices across the five countries over the five-year period to identify patterns, trends, and variations. This analysis can help understand differences in purchasing power and economic factors impacting food costs.

Cost of Living Studies: The dataset can be used to examine the cost of living in different countries, specifically focusing on the expenses related to basic food items. This information can be beneficial for individuals considering relocation or policymakers aiming to evaluate living standards.

Economic Studies: Economists and policymakers can utilize this dataset to analyze the impact of economic factors, such as inflation or currency fluctuations, on food prices in different countries. It can provide insights into the stability and volatility of food markets in each region.

Forecasting and Planning: Businesses in the food industry can leverage the dataset to forecast future food price trends and plan their operations accordingly. The historical data can serve as a foundation for predictive models and assist in optimizing pricing strategies and supply chain management.

Note: The dataset is based on average monthly prices and does not capture individual variations or specific regions within each country. Further analysis and interpretation should consider additional factors like seasonal influences, local market dynamics, and consumer preferences.

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

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.

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 .

Average monthly disposable salary Years: 2013-2014 DEFINITION: Average Monthly Disposable Salary (After Tax). Based on 0-50 contributions for Afghanistan, Aland Islands, Andorra and 81 more countries and 50-100 contributions for Albania, Algeria, Armenia and 19 more countries and over 100 contributions for Argentina, Australia, Austria and 82 more countries. The surveys were conducted by numbeo.com from May, 2011 to February, 2014. See this sample survey for the United States, respondents were asked "Average Monthly Disposable Salary (After Tax)". Prices in current USD.

Source: https://www.nationmaster.com/country-info/stats/Cost-of-living/Average-monthly-disposable-salary/After-tax#

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 .

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 28 January 2022.

--- 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 ---

Context

Movehub city ranking as published on http://www.movehub.com/city-rankings

Content

movehubqualityoflife.csv

Cities ranked by
Movehub Rating: A combination of all scores for an overall rating for a city or country.
Purchase Power: This compares the average cost of living with the average local wage.
Health Care: Compiled from how citizens feel about their access to healthcare, and its quality.
Pollution: Low is good. A score of how polluted people find a city, includes air, water and noise pollution.
Quality of Life: A balance of healthcare, pollution, purchase power, crime rate to give an overall quality of life score.
Crime Rating: Low is good. The lower the score the safer people feel in this city.

movehubcostofliving.csv

Unit: GBP
City
Cappuccino
Cinema
Wine
Gasoline
Avg Rent
Avg Disposable Income

cities.csv

Cities to countries as parsed from Wikipedia https://en.wikipedia.org/wiki/List_of_towns_and_cities_with_100,000_or_more_inhabitants/cityname:_A (A-Z)

Acknowledgements

Movehub

http://www.movehub.com/city-rankings

Wikipedia

https://en.wikipedia.org/wiki/List_of_towns_and_cities_with_100,000_or_more_inhabitants/cityname:_A

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 .

Analysis of ‘GapMinder - Income Inequality’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/psterk/income-inequality on 28 January 2022.

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

Content

This analysis focuses on income inequailty as measured by the Gini Index* and its association with economic metrics such as GDP per capita, investments as a % of GDP, and tax revenue as a % of GDP. One polical metric, EIU democracy index, is also included.

The data is for years 2006 - 2016

This investigation can be considered a starting point for complex questions such as:

1. Is a higher tax revenue as a % of GDP associated with less income inequality?
2. Is a higher EIU democracy index associated with less income inequality?
3. Is higher GDP per capita associated with less income inequality?
4. Is higher investments as a % of GDP associated with less income inequality?

This analysis uses the gapminder dataset from the Gapminder Foundation. The Gapminder Foundation is a non-profit venture registered in Stockholm, Sweden, that promotes sustainable global development and achievement of the United Nations Millennium Development Goals by increased use and understanding of statistics and other information about social, economic and environmental development at local, national and global levels.

*The Gini Index is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents, and is the most commonly used measurement of inequality. It was developed by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper Variability and Mutability.

The dataset contains data from the following GapMinder datasets:

EIU Democracy Index:

"This democracy index is using the data from the Economist Inteligence Unit to express the quality of democracies as a number between 0 and 100. It's based on 60 different aspects of societies that are relevant to democracy universal suffrage for all adults, voter participation, perception of human rights protection and freedom to form organizations and parties. The democracy index is calculated from the 60 indicators, divided into five ""sub indexes"", which are:

1. Electoral pluralism index;
2. Government index;
3. Political participation indexm;
4. Political culture index;
5. Civil liberty index.

The sub-indexes are based on the sum of scores on roughly 12 indicators per sub-index, converted into a score between 0 and 100. (The Economist publishes the index with a scale from 0 to 10, but Gapminder has converted it to 0 to 100 to make it easier to communicate as a percentage.)" https://docs.google.com/spreadsheets/d/1d0noZrwAWxNBTDSfDgG06_aLGWUz4R6fgDhRaUZbDzE/edit#gid=935776888

Income: GDP per capita, constant PPP dollars

GDP per capita measures the value of everything produced in a country during a year, divided by the number of people. The unit is in international dollars, fixed 2011 prices. The data is adjusted for inflation and differences in the cost of living between countries, so-called PPP dollars. The end of the time series, between 1990 and 2016, uses the latest GDP per capita data from the World Bank, from their World Development Indicators. To go back in time before the World Bank series starts in 1990, we have used several sources, such as Angus Maddison. https://www.gapminder.org/data/documentation/gd001/

Investments (% of GDP)

Capital formation is a term used to describe the net capital accumulation during an accounting period for a particular country. The term refers to additions of capital goods, such as equipment, tools, transportation assets, and electricity. Countries need capital goods to replace the older ones that are used to produce goods and services. If a country cannot replace capital goods as they reach the end of their useful lives, production declines. Generally, the higher the capital formation of an economy, the faster an economy can grow its aggregate income.

Tax revenue (% of GDP)

refers to compulsory transfers to the central governement for public purposes. Does not include social security. https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS

Context

Gapminder is an independent Swedish foundation with no political, religious or economic affiliations. Gapminder is a fact tank, not a think tank. Gapminder fights devastating misconceptions about global development. Gapminder produces free teaching resources making the world understandable based on reliable statistics. Gapminder promotes a fact-based worldview everyone can understand. Gapminder collaborates with universities, UN, public agencies and non-governmental organizations. All Gapminder activities are governed by the board. We do not award grants. Gapminder Foundation is registered at Stockholm County Administration Board. Our constitution can be found here.

Acknowledgements

Thanks to gapminder.org for organizing the above datasets.

Inspiration

Below are some research questions associated with the data and some initial conclusions:

Research Question 1 - Is Income Inequality Getting Worse or Better in the Last 10 Years?

Answer:

Yes, it is getting better, improving from 38.7 to 37.3

On a continent basis, all were either declining or mostly flat, except for Africa.

Research Question 2 - What Top 10 Countries Have the Lowest and Highest Income Inequality?

Answer:

Lowest: Slovenia, Ukraine, Czech Republic, Norway, Slovak Republic, Denmark, Kazakhstan, Finland, Belarus,Kyrgyz Republic

Highest: Colombia, Lesotho, Honduras, Bolivia, Central African Republic, Zambia, Suriname, Namibia, Botswana, South Africa

Research Question 3 Is a higher tax revenue as a % of GDP associated with less income inequality?

Answer: No

Research Question 4 - Is Higher Income Per Person - GDP Per Capita associated with less income inequality?

Answer: No, but weak negative correlation.

Research Question 5 - Is Higher Investment as % GDP associated with less income inequality?

Answer: No

Research Question 6 - Is Higher EIU Democracy Index associated with less income inequality?

Answer: No, but weak negative correlation.

The above results suggest that there are other drivers for the overall reduction in income inequality. Futher analysis of additional factors should be undertaken.

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

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.

https://cdn.internetadvisor.com/1612521728046-1._Total_Internet_Users_Worldwide_Statistic.jpg" alt="">

GapMinder collects data from a handful of sources, including the Institute for Health Metrics and Evaluation, the US Census Bureau’s International Database, the United Nations Statistics Division, and the World Bank.

Variable Name & Description of Indicator:

• country: Unique Identifier
• incomeperperson: Gross Domestic Product per capita in constant 2000 US$. The inflation but not the differences in the cost of living between countries has been taken into account.
• Internetuserate: Internet users (per 100 people) Internet users are people with access to the worldwide network.
• urbanrate: Urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects)

More information is available at www.gapminder.org

This dataset provides a comprehensive view of key macroeconomic indicators across various entities (countries or regions) over time. It includes annual data for the following variables:

Entity: The name of the country or region for which the data is recorded. Code: A standardized three-letter country or region code, facilitating easier identification and merging with other datasets. Year: The calendar year for which the economic indicators are reported. GDP per capita: The gross domestic product (GDP) divided by the midyear population. It represents the average economic output per person and is a common measure of living standards and economic development. Value of global merchandise exports as a share of GDP: This indicates the proportion of a country's total economic output that is represented by the value of its exported goods. It highlights the importance of international trade in the economy. Government expenditure (% of GDP): The total spending by the government as a percentage of the country's GDP. This reflects the size and scope of government involvement in the economy. Trade as a Share of GDP: The sum of a country's total exports and imports of goods and services, expressed as a percentage of its GDP. This metric indicates the overall openness of an economy to international trade. ****Inflation, consumer prices (annual %)****: The percentage change in the average prices of goods and services typically purchased by households over a one-year period. It measures the rate at which the cost of living is changing.

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

The dataset provides the price indices computed for the academic paper "Price and Global Inequality", available at https://www.xavierjaravel.com/papers. The data has been created as part of the project addressing two questions: (1) What are the implications of prices changes for inequality and standards of living? (2) To what extent do the price effects induced by policies alter the cost-benefit analysis of these policies? Despite extensive research, we currently lack detailed data as well as various empirical and theoretical tools to appropriately answer these questions. These questions are fundamental because it is well-known that individuals across the income distribution purchase different baskets of goods and services. Therefore, changes in prices or product availability over time can potentially have an important impact on inequality.

This project asks two questions:

(1) What are the implications of prices changes for inequality and standards of living?

(2) To what extent do the price effects induced by policies alter the cost-benefit analysis of these policies?

Despite extensive research, we currently lack detailed data as well as various empirical and theoretical tools to appropriately answer these questions.

These questions are fundamental because it is well-known that individuals across the income distribution purchase different baskets of goods and services. Therefore, changes in prices or product availability over time can potentially have an important impact on inequality. Likewise, differences in prices across countries can have a profound impact on standards of living across countries.

The few studies that have investigated these questions have used "macro" data (at a high level of aggregation), but I have shown in previous work (Jaravel 2017) that it is crucial to use "micro" data (i.e. very disaggregated data, at the product level) to accurately answer these questions.

We know that policies may have large price effects (see Jaravel 2018 on the price effects of food stamps). For instance, increasing import tariffs is likely to result in higher prices for domestic consumers (which I have started investigating in ongoing work: Borusyak and Jaravel 2017 and Jaravel and Sager 2018). But we do not have a good understanding of how large this effect might be. Likewise, other important policies like income redistribution schemes or monetary policy could have significant effects on prices, which are not well understood currently.

There are two main challenges to answer the two fundamental questions asked in this project. First, it is not easy to properly measure how prices change over time and across countries, because the set of available goods and services is always changing and detailed micro data is required. Second, it is challenging to understand the impact of policies on prices because of feedback loops. For instance, if a given policy makes a particular group of individuals richer, they might change their consumption patterns and start buying a different set of goods or services, which may have an impact on the income of other agents, who in turn will change their consumption patterns, etc.

In this project, I propose to proceed in two steps, tackling each of these two major challenges in turn to advance our understanding of the effects of price changes and of their implications for major policies. The first part of the project aims at addressing three fundamental limitations in the literature on the measurement of "quality-adjusted" price changes (building on Jaravel 2017): (i) limited availability of scanner data across countries; (ii) limited use of hedonic regressions; and (iii) limited understanding of the welfare impact of house prices changes. Using new models and new empirical tools, the second part of the project aims at shedding new light on the welfare impact of three important types of policies, given their price effects: (i) optimal income and commodity taxation; (ii) trade policy (building on Borusyak and Jaravel 2017 and Jaravel and Sager 2018); and (iii) monetary policy.

The various parts of this project constitute a cohesive whole. Taking a multi-faceted approach is the only way of making significant progress on understanding the effects of prices and their policy implications.

This project has a strong potential for impact. In particular, it could change the type of inflation statistics published by national government agencies, as well as the type of standards-of-living statistics across countries published by international organisations. To ensure that the new data and new findings from the project are easily accessible by other researchers, policymakers, think tanks, as well as by the general public, the results and data will be made available online on a dedicated, user-friendly website.

This dashboard highlights the living situation in Syria by showing the prices of basic market items. How to use this product: The first three pages track price change chronologically on governorate level, with ability to compare between them by choosing one or more. The subsequent pages show the prices of market items on the governorate and sub-district level with an item availability heat map of any selected item on any selected level and period. You can select one of the listed items in one sub-district or more. When you choose a governorate its subdistrict(s) will be highlighted according to the availability of the selected item in the selected governorate(s).