About the Dataset

This dataset contains Quality of Life indices for various countries around the globe, extracted from the Numbeo website. The data provides valuable metrics for comparing countries based on several aspects of living standards, which can assist in decisions such as choosing a place to live or analyzing global trends in quality of life.

OBS: The code to generate this dataset is presented on: https://www.kaggle.com/code/marcelobatalhah/web-scrapping-quality-of-life-index

Columns in the Dataset

1. Rank:
   The global rank of the country based on its Quality of Life Index according to Year (1 = highest quality of life).

2. Country:
   The name of the country.

3. Quality of Life Index:
   A composite index that evaluates the overall quality of life in a country by combining other indices, such as Safety, Purchasing Power, and Health Care.

4. Purchasing Power Index:
   Measures the relative purchasing power of the average consumer in a country compared to New York City (baseline = 100).

5. Safety Index:
   Indicates the safety level of a country. A higher score suggests a safer environment.

6. Health Care Index:
   Evaluates the quality and accessibility of healthcare in the country.

7. Cost of Living Index:
   Measures the relative cost of living in a country compared to New York City (baseline = 100).

8. Property Price to Income Ratio:
   Compares the affordability of real estate by dividing the average property price by the average income.

9. Traffic Commute Time Index:
   Reflects the average time spent commuting due to traffic.

10. Pollution Index:
    Rates the level of pollution in the country (air, water, etc.).

11. Climate Index:
    Rates the favorability of the climate in the country (higher = more favorable).

12. Year:
    Year when the metrics were extracted.

Key Insights from the Dataset

• The Quality of Life Index aggregates multiple indicators, making it a useful single metric to compare countries.
• Specific indices such as Safety Index or Health Care Index allow for focused analysis on areas like security or healthcare quality.
• Cost of Living Index and Purchasing Power Index can help determine the affordability of living in each country.

How the Data Was Collected

• The dataset was built using web scraping techniques in Python.
• The data was extracted from the "Quality of Life Rankings by Country" page on Numbeo.
• Libraries used:
  • requests for retrieving webpage content.
  • BeautifulSoup for parsing the HTML and extracting relevant information.
  • pandas for organizing and storing the data in a structured format.

Possible Applications

1. Relocation Decision Making:
   Use the dataset to compare countries and identify destinations with high quality of life, safety, and healthcare.

2. Global Analysis:
   Perform exploratory data analysis (EDA) to identify trends and correlations across quality of life metrics.

3. Visualization:
   Plot global maps, bar charts, or other visualizations to better understand the data.

4. Predictive Modeling:
   Use this dataset as a base for machine learning tasks, like predicting Quality of Life Index based on other metrics.

Luxembourg stands out as the European leader in quality of life for 2025, achieving a score of 220 on the Quality of Life Index. The Netherlands follows closely behind with 211 points, while Albania and Ukraine rank at the bottom with scores of 104 and 115 respectively. This index provides a thorough assessment of living conditions across Europe, reflecting various factors that shape the overall well-being of populations and extending beyond purely economic metrics. Understanding the quality of life index The quality of life index is a multifaceted measure that incorporates factors such as purchasing power, pollution levels, housing affordability, cost of living, safety, healthcare quality, traffic conditions, and climate, to measure the overall quality of life of a Country. Higher overall index scores indicate better living conditions. However, in subindexes such as pollution, cost of living, and traffic commute time, lower values correspond to improved quality of life. Challenges affecting life satisfaction Despite the fact that European countries register high levels of life quality by for example leading the ranking of happiest countries in the world, life satisfaction across the European Union has been on a downward trend since 2018. The EU's overall life satisfaction score dropped from 7.3 out of 10 in 2018 to 7.1 in 2022. This decline can be attributed to various factors, including the COVID-19 pandemic and economic challenges such as high inflation. Rising housing costs, in particular, have emerged as a critical concern, significantly affecting quality of life. This issue has played a central role in shaping voter priorities for the European Parliamentary Elections in 2024 and becoming one of the most pressing challenges for Europeans, profoundly influencing both daily experiences and long-term well-being.

Cost of Living Index by Country, 2024 Mid Year data Data scraped from Numbeo: www.numbeo.com/cost-of-living/rankings_by_country.jsp All credits to Numbeo: www.numbeo.com/cost-of-living/

An index of 100 reflects the same living cost as in New York City, United States. As of 2024 Mid Year data, in NYC, A family of four estimated monthly costs are $6,074.40 without rent. A single person's estimated monthly costs are $1,640.90 without rent.

The average for 2021 based on 165 countries was 79.81 index points. The highest value was in Bermuda: 212.7 index points and the lowest value was in Syria: 33.25 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

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.

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.

Hungary, Czechia, Poland, and Portugal were the countries in Europe where house prices increased the most between 2015 and 2024. The EMF house price index for all four countries measured more than 200 index points, indicating that home prices more than doubled since 2015 — the base year. Property prices are tightly connected with the supply of new homes. France, Poland, and Denmark are some of the countries with the most dwellings completed per 1,000 citizens in Europe.

"Cost of living and purchasing power related to average income

We adjusted the average cost of living inside the USA (based on 2021 and 2022) to an index of 100. All other countries are related to this index. Therefore with an index of e.g. 80, the usual expenses in another country are 20% less then in the United States.

The monthly income (please do not confuse this with a wage or salary) is calculated from the gross national income per capita.

The calculated purchasing power index is again based on a value of 100 for the United States. If it is higher, people can afford more based on the cost of living in relation to income. If it is lower, the population is less wealthy.

The example of Switzerland: With a cost of living index of 142 all goods are on average about 42% more expensive than in the USA. But the average income in Switzerland of 7,550 USD is also 28% higher, which means that citizens can also afford more goods. Now you calculate the 42% higher costs against the 28% higher income. In the result, people in Switzerland can afford about 10 percent less than a US citizen."

Source: https://www.worlddata.info/cost-of-living.php

As of 2022, Israel had the highest price level index among listed countries, amounting to 138, with 100 being the average of OECD countries. Switzerland and Iceland followed on the places behind. On the other hand, Turkey and India had the lowest price levels compared to the OECD average. This price index shows differences in price levels in different countries. Another very popular index indicating the value of money is the Big Mac index, showing how much a Big Mac costs in different countries. This list was also topped by Switzerland in 2023.

As of September 2025, Mumbai had the highest cost of living among other cities in the country, with an index value of ****. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of ****.  What is cost of living? The cost of living varies depending on geographical regions and factors that affect the cost of living in an area include housing, food, utilities, clothing, childcare, and fuel among others. The cost of living is calculated based on different measures such as the consumer price index (CPI), living cost indexes, and wage price index. CPI refers to the change in the value of consumer goods and services. The wage price index, on the other hand, measures the change in labor services prices due to market pressures. Lastly, the living cost indexes calculate the impact of changing costs on different households. The relationship between wages and costs determines affordability and shifts in the cost of living. Mumbai tops the list Mumbai usually tops the list of most expensive cities in India. As the financial and entertainment hub of the country, Mumbai offers wide opportunities and attracts talent from all over the country. It is the second-largest city in India and has one of the most expensive real estates in the world.

Table showing the country rankings based in the different metrics analysed

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...

Russia Living Cost: Average per Month data was reported at 10,213.000 RUB in Dec 2018. This records a decrease from the previous number of 10,451.000 RUB for Sep 2018. Russia Living Cost: Average per Month data is updated quarterly, averaging 3,050.000 RUB from Mar 1992 (Median) to Dec 2018, with 108 observations. The data reached an all-time high of 10,451.000 RUB in Sep 2018 and a record low of 1.423 RUB in Jun 1992. Russia Living Cost: Average per Month data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Abstract

Timor-Leste experienced a fundamental social and economic upheaval after its people voted for independence from Indonesia in a referendum in August 1999. Population was displaced, and public and private infrastructure was destroyed or rendered inoperable. Soon after the violence ceased, the country began rebuilding itself with the support from UN agencies, the international donor community and NGOs. The government laid out a National Development Plan (NDP) with two central goals: to promote rapid, equitable and sustainable economic growth and to reduce poverty.

Formulating a national plan and poverty reduction strategy required data on poverty and living standards, and given the profound changes experienced, new data collection had to be undertaken to accurately assess the living conditions in the country. The Planning Commission of the Timor-Leste Transitional Authority undertook a Poverty Assessment Project along with the World Bank, the Asian Development Bank, the United Nations Development Programme and the Japanese International Cooperation Agency (JICA).

This project comprised three data collection activities on different aspects of living standards, which taken together, provide a comprehensive picture of well-being in Timor-Leste. The first component was the Suco Survey, which is a census of all 498 sucos (villages) in the country. It provides an inventory of existing social and physical infrastructure and of the economic characteristics of each suco, in addition to aldeia (hamlet) level population figures. It was carried out between February and April 2001.

A second element was the Timor-Leste Living Standards Measurement Survey (TLSS). This is a household survey with a nationally representative sample of 1,800 families from 100 sucos. It was designed to diagnose the extent, nature and causes of poverty, and to analyze policy options facing the country. It assembles comprehensive information on household demographics, housing and assets, household expenditures and some components of income, agriculture, labor market data, basic health and education, subjective perceptions of poverty and social capital.

Data collection was undertaken between end August and November 2001.

The final component was the Participatory Potential Assessment (PPA), which is a qualitative community survey in 48 aldeias in the 13 districts of the country to take stock of their assets, skills and strengths, identify the main challenges and priorities, and formulate strategies for tackling these within their communities. It was completed between November 2001 and January 2002.

Geographic coverage

National coverage. Domains: Urban/rural; Agro-ecological zones (Highlands, Lowlands, Western Region, Eastern Region, Central Region)

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE SIZE AND ANALYTIC DOMAINS

A survey relies on identifying a subgroup of a population that is representative both for the underlying population and for specific analytical domains of interest. The main objective of the TLSS is to derive a poverty profile for the country and salient population groups. The fundamental analytic domains identified are the Major Urban Centers (Dili and Baucau), the Other Urban Centers and the Rural Areas. The survey represents certain important sub-divisions of the Rural Areas, namely two major agro-ecologic zones (Lowlands and Highlands) and three broad geographic regions (West, Center and East). In addition to these domains, we can separate landlocked sucos (Inland) from those with sea access (Coast), and generate categories merging rural and urban strata along the geographic, altitude, and sea access dimensions. However, the TLSS does not provide detailed indicators for narrow geographic areas, such as postos or even districts. [Note: Timor-Leste is divided into 13 major units called districts. These are further subdivided into 67 postos (subdistricts), 498 sucos (villages) and 2,336 aldeias (sub-villages). The administrative structure is uniform throughout the country, including rural and urban areas.]

The survey has a sample size of 1,800 households, or about one percent of the total number of households in Timor-Leste. The experience of Living Standards Measurement Surveys in many countries - most of them substantially larger than Timor-Leste - has shown that samples of that size are sufficient for the requirements of a poverty assessment.

The survey domains were defined as follows. The Urban Area is divided into the Major Urban Centers (the 31 sucos in Dili and the 6 sucos in Baucau) and the Other Urban Centers (the remaining 34 urban sucos outside Dili and Baucau). The rest of the country (427 sucos in total) comprises the Rural Area. The grouping of sucos into urban and rural areas is based on the Indonesian classification. In addition, we separated rural sucos both by agro-ecological zones and geographic areas. With the help of the Geographic Information System developed at the Department of Agriculture, sucos were subsequently qualified as belonging to the Highlands or the Lowlands depending on the share of their surface above and below the 500 m level curve. The three westernmost districts (Oecussi, Bobonaro and Cova Lima) constitute the Western Region, the three easternmost districts (Baucau, Lautem and Viqueque) the Eastern Region, and the remaining seven districts (Aileu, Ainaro, Dili, Ermera, Liquica, Manufahi and Manatuto) belong to the Central Region.

SAMPLING STRATA AND SAMPLE ALLOCATION

Our next step was to ensure that each analytical domain contained a sufficient number of households. Assuming a uniform sampling fraction of approximately 1/100, a non-stratified 1,800-household sample would contain around 240 Major Urban households and 170 Other Urban households -too few to sustain representative and significant analyses. We therefore stratified the sample to separate the two urban areas from the rural areas. The rural strata were large enough so that its implicit stratification along agro-ecological and geographical dimensions was sufficient to ensure that these dimensions were represented proportionally to their share of the population. The final sample design by strata was as follows: 450 households in the Major Urban Centers (378 in Dili and 72 in Baucau), 252 households in the Other Urban Centers and 1,098 households in the Rural Areas.

SAMPLING STRATEGY

The sampling of households in each stratum, with the exception of Urban Dili, followed a 3-stage procedure. In the first stage, a certain number of sucos were selected with probability proportional to size (PPS). Hence 4 sucos were selected in Urban Baucau, 14 in Other Urban Centers and 61 in the Rural Areas. In the second stage, 3 aldeias in each suco were selected, again with probability proportional to size (PPS). In the third stage, 6 households were selected in each aldeia with equal probability (EP). This implies that the sample is approximately selfweighted within the stratum: all households in the stratum had the same chance of being visited by the survey.

A simpler and more efficient 2-stage process was used for Urban Dili. In the first stage, 63 aldeias were selected with PPS and in the second stage 6 households with equal probability in each aldeia (for a total sample of 378 households). This procedure reduces sampling errors since the sample will be spread more than with the standard 3-stage process, but it can only be applied to Urban Dili as only there it was possible to sort the selected aldeias into groups of 3 aldeias located in close proximity of each other.

HOUSEHOLD LISTING

The final sampling stage requires choosing a certain number of households at random with equal probability in each of the aldeias selected by the previous sampling stages. This requires establishing the complete inventory of all households in these aldeias - a field task known as the household listing operation. The household listing operation also acquires importance as a benchmark for assessing the quality of the population data collected by the Suco Survey, which was conducted in February-March 2001. At that time, the number of households currently living in each aldeia was asked from the suco and aldeia chiefs, but there are reasons to suspect that these figures are biased. Specifically, certain suco and aldeia chiefs may have answered about households belonging, rather than currently living, in the aldeias, whereas others may have faced perverse incentives to report figures different from the actual ones. These biases are believed to be more serious in Dili than in the rest of the country.

Two operational approaches were considered for the household listing. One is the classical doorto-door (DTD) method that is generally used in most countries for this kind of operations. The second approach - which is specific of Timor-Leste - depends on the lists of families that are kept by most suco and aldeia chiefs in their offices. The prior-list-dependent (PLD) method is much faster, since it can be completed by a single enumerator in each aldeia, working most of the time in the premises of the suco or aldeia chief; however, it can be prone to biases depending on the accuracy and timeliness of the family lists.

After extensive empirical testing of the weaknesses and strengths of the two alternatives, we decided to use the DTD method in Dili and an improved version of the PLD method elsewhere. The improvements introduced to the PLD consisted in clarifying the concept of a household "currently living in the aldeia", both by intensive training and supervision of the enumerators and by making its meaning explicit in the form's wording (it means that the household members are regularly eating and sleeping in the aldeia at the time of the operation). In addition,

Kazakhstan Cost of Living: Average per Capita data was reported at 28,620.000 KZT in Oct 2018. This records a decrease from the previous number of 28,690.000 KZT for Sep 2018. Kazakhstan Cost of Living: Average per Capita data is updated monthly, averaging 13,073.000 KZT from Oct 2000 (Median) to Oct 2018, with 217 observations. The data reached an all-time high of 29,146.000 KZT in Aug 2018 and a record low of 3,983.000 KZT in Oct 2000. Kazakhstan Cost of Living: Average per Capita data remains active status in CEIC and is reported by The Agency of Statistics of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.H012: Cost of Living: Average per Capita.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and five are in Asia, alongside the U.S. and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

According to a recent study, Colombia had the lowest monthly cost of living in Latin America with 546 U.S. dollars needed for basic living. In contrast, four countries had a cost of living above one thousand dollars, Costa Rica, Chile, Panama and Uruguay. In 2022, the highest minimum wage in the region was recorded by Ecuador with 425 dollars per month.

Can Latin Americans survive on a minimum wage? Even if most countries in Latin America have instated laws to guarantee citizens a basic income, these minimum standards are often not enough to meet household needs. For instance, it was estimated that almost 22 million people in Mexico lacked basic housing services. Salary levels also vary greatly among Latin American economies. In 2022, the average net monthly salary in Brazil was lower than Ecuador's minimum wage.

What can a minimum wage afford in Latin America? Latin American real wages have generally risen in the past decade. However, consumers in this region still struggle to afford non-basic goods, such as tech products. Recent estimates reveal that, in order to buy an iPhone, Brazilian residents would have to work more than two months to be able to pay for it. A gaming console, on the other hand, could easily cost a Latin American worker several minimum wages.

Gross domestic product (GDP) per capita is a crucial economic indicator that represents the average economic output per person in a given country or region. It is calculated by dividing the total GDP by the population size. This metric is often used to compare the economic performance of different countries and assess the relative prosperity of their citizens. Two commonly used versions of this indicator are GDP per capita at current prices and GDP per capita adjusted for purchasing power parity (PPP). GDP per capita at current prices reflects the total economic output of a country divided by its population, using the market prices of goods and services at the time of measurement. This metric provides a snapshot of the economic activity within a country without adjusting for inflation or differences in the cost of living across regions. Global GDP per capita at current prices (PPP) provides a measure of the average economic output per person, adjusted for the differences in the cost of living between countries. This adjustment allows for a more accurate comparison of living standards and economic productivity across different nations.

CONTENT

The figures presented here do not take into account differences in the cost of living in different countries, and the results vary greatly from one year to another based on fluctuations in the exchange rates of the country's currency. Such fluctuations change a country's ranking from one year to the next, even though they often make little or no difference to the standard of living of its population.

GDP per capita is often considered an indicator of a country's standard of living; however, this is inaccurate because GDP per capita is not a measure of personal income.

Comparisons of national income are also frequently made on the basis of purchasing power parity (PPP), to adjust for differences in the cost of living in different countries. (See List of countries by GDP (PPP) per capita.) PPP largely removes the exchange rate problem but not others; it does not reflect the value of economic output in international trade, and it also requires more estimation than GDP per capita. On the whole, PPP per capita figures are more narrowly spread than nominal GDP per capita figures.