This statistic shows the results of a survey among young adults in the United States on how they rank their own living standards compared to those of their parents at the same age. ** percent think their own living standards nowadays are better than those of their parents when they were the same age.

Abstract

Starting June 1999, after the intervention of NATO in the conflict between Kosovo and Serbia (FRY), the United Nations provided interim administration for the province. The consequences of the conflict on the living standards of the population were severe, with the collapse of the industrial sector, the paralysis of agriculture, and extensive damage to private housing, education and health facilities and other infrastructure. In addition, the conflict brought massive population displacement both within Kosovo and abroad.

A year later, Kosovo was in a process of transition from emergency relief to long-term economic development. The purpose of the survey was to provide crucial information for policy and program design for use by the United Nations Interim Administration Mission in Kosovo (UNMIK), international donors, non-governmental organizations (NGOs), and the Kosovar community at large for poverty alleviation and inequality reduction.

During the same period, the Food and Agriculture Organization (FAO) was planning an agriculture and livestock survey. It was decided to join both surveys, in order to pool resources and provide better assistance to the newly re-formed Statistical Office of Kosovo (SOK) and to take into account the extensive Kosovar peasant household economy. Therefore the agriculture and food aid modules are more developed than those of a standard LSMS survey.

The International Organization for Migration (IOM) also was interested in information related to labor force and employment. They had run a socio-demographic and reproductive health survey with the United Nations Population Fund, covering approximately 10,000 households at the end of 1999. IOM provided the urban sampling frame for the present survey.

Geographic coverage

Kosovo. Domains: Urban/rural; Area of Responsibility (American, British, French, German, Italian); Serbian minority

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN

The sample design used in the Kosovo LSMS 2000 had to contend with the fact that the last census, conducted in 1991, was rendered obsolete by the boycott of the Albanian population and by the massive displacements since March 1998. A Housing Damage Assessment Survey (HDAS) was conducted in February 1999 and updated in June 1999 by the International Management Group (IMG) and the United Nations High Commissioner for Refugees (UNHCR) in the rural areas. The survey covered 95 percent of the Albanian rural areas and provided the basis for the rural sampling frame, after updating. The updating and household listings in selected villages were conducted by FAO.

Since the HDAS did not cover Serbian villages, a quick counting4 of housing units was performed in these villages, following a procedure similar to the one in the urban areas. In urban areas, the original plan was to use the information from the on-going individual voters’ registration conducted by the Organization for Security and Cooperation in Europe (OSCE). Since the registration was limited to individuals above 16 years old, it was then decided to conduct a quick counting of households in the 22 urban areas. The quick counting and subsequent listing of households was performed by IOM, under the supervision of the sampling expert hired by the World Bank. . FRAMEWORK

UNMIK divided Kosovo into 5 areas of responsibility (AR), roughly equivalent to the former regions (American – Southeast, British – East including Pristina, French – North, German-South, Italian – West). The rural frame used the IMG/UNHCR Housing Damage Assessment Survey. It was updated with the collaboration of FAO and provided much better information on which to build the sample for the survey. Aerial pictures of the villages selected in the survey were used to help identifying housing units. Only one household was interviewed in each housing unit. For the Serbian villages, counting households and making listings had to be elaborated by the survey team.

In urban areas, IOM contracted the quick counting to SOK in the Albanian cities and to firms in the Serb areas. These firms updated existing lists, or performed some quick counting. Using the updated information IOM created enumeration areas of size 150-200 housing units. Based on this quick counting, a full listing took place in all the selected EAs and 12 households were randomly selected. Given safety issues and quality problems discovered at the enumeration stage, the Serb urban listings were revised after the end of the survey, by the Serb survey team, who had performed the rural listings.

The sample was preset at 2,880 households in order to allow analyses in the following breakdowns: (a) Kosovo as a whole; (b) by area of responsibility, (c) by urban/rural locations. In addition, the survey data can be used to derive separate estimates for the Serbian minority.

In the rural area, 30 Albanian villages were randomly selected in each AR and a listing of all households in the village was established.5 In each village, 12 households were then randomly selected (8 for interviewing and 4 reserve households). Similarly, 30 urban enumeration areas (between 150 and 200 households lie in each urban EA) were randomly selected in the Albanian part of each AR. Twelve households were then selected in each EA. In the rural area, 30 Serb villages were selected from the three municipalities in the northern part of Kosovo, the enclaves and the municipality of Strepce. Thirty urban EA were selected in the same region. In each village and urban area, 12 households were then randomly selected.

STRATIFICATION

In addition to the explicit stratification of the areas of responsibility and the ethnic composition in each rural and urban category, an implicit stratification of geographic ordering in a serpentine method in the villages and urban enumeration areas was followed. In order to be able to provide estimates for the separate domains described above, it was recommended that 240 households be interviewed in each domain. We had very little prior knowledge of response rates. In the rural villages, it was decided to select 12 households and identify 4 of them as “reserve households”. These reserve households were to be used only in specific cases, described at length to the logistics person/driver of the interviewing team. The final sample size was 1,200 rural and urban Albanian households and 240 rural and urban Serb households, for a total sample size of 2,880 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used to collect the information: a household questionnaire and a community questionnaire. No anthropometric information was collected as malnutrition problems, facing Kosovar children and women, would not be detected by these procedures.

Since FAO and SOK were conducting a price survey in 7 cities of Kosovo, on a monthly basis, it was decided to not include a separate price questionnaire but use the data from the FAO-SOK price survey. The Kosovo LSMS 2000 collected information using a household questionnaire, which was based in part on the standard LSMS questionnaire developed in Grosh and Glewwe (2000).

The standard questionnaire was adapted to the specifics of the Kosovar environment and special modules about displacement, food aid and social protection were added. Individual modules were administered as much as possible to most informed respondents. Box 1 contains a summary of the content of the questionnaire.

The community questionnaire was designed to collect information on community-level infrastructure, with a special emphasis on school and health facilities as well as displaced persons issues. Box 2 contains a summary of the content of the community questionnaire. [Note: Community is defined as the Primary Sampling Unit (PSU) of the survey. In rural areas, it generally encompasses villages unless these are less than 50 households (in which case, they were grouped with a neighboring village) or more than 200 households (in which case, they were broken-up in PSUs of 50-200 households). In urban areas, community is defined as the Enumeration Area but includes the larger city when referring to secondary school and university, hospitals and factories.]

Response rate

Households from the original sample selection which could not be interviewed were replaced by reserve households to reach the final sample size. The non-response rate among households originally selected for inclusion in the sample in rural Albanian areas was 11.8 percent and 20.8 percent in urban Albanian areas. These rates in the Serbian areas were 14.2 percent among rural households and 39.2 percent among urban households.

In the rural Albanian areas, non-response came mostly from households having moved outside of the village. A few refusals were due to the fact that households were in mourning or celebrating other religious occasions (wedding, baptisms, circumcisions, etc…), or the household head was a women alone. There were only 20 actual refusals of the originally selected households, only 2 percent of the 1,200 households originally contacted.

In the Serbian rural areas, half of the non-responses were due to households having traveled to Serbia for visits (holidays, health care issues, indefinite travel….). Other reasons included: interviewer’s safety (houses too isolated) and households refusing to respond in the absence of the head. There were only 5 such cases, again only 2 percent of the 240 households originally contacted. In the urban areas, 10 percent of the non-responses were linked to listings problems (non-existent addresses).

This paper revisits the assessment of living standards in the United States from its founding to the present, challenging the conventional portrayal of economic well-being during wartime periods. Reflecting multiple criticisms made of the quality of national accounts which include defense spending during times of both peace and war, we employ the methodological framework established by Higgs (1992) and extended by Geloso and Pender (2023) to correct national accounts by subtracting military expenditures from GDP and GNP data. This rectifies the overstatement of living standards attributed to defense spending. Our analysis uses comprehensive data from the Historical Statistics of the United States and the Measuring Worth database, adjusting for price controls during World Wars I and II, the Korean War, and the Vietnam War using a corrected price deflator based on a regression model of economic indicators. The study finds that traditional measures significantly overstate living standards during the Civil War, World War I, and World War II. Post-World War II analysis reveals a persistent overestimation of living standards, particularly pronounced during the Vietnam War years. More importantly, our results provide nuanced insights into certain stylized facts of trends in American improvements of living standards (notably inequality and the Great Depression).

This dataset contains replication files for "The Fading American Dream: Trends in Absolute Income Mobility Since 1940" by Raj Chetty, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. For more information, see https://opportunityinsights.org/paper/the-fading-american-dream/. A summary of the related publication follows. One of the defining features of the “American Dream” is the ideal that children have a higher standard of living than their parents. We assess whether the U.S. is living up to this ideal by estimating rates of “absolute income mobility” – the fraction of children who earn more than their parents – since 1940. We measure absolute mobility by comparing children’s household incomes at age 30 (adjusted for inflation using the Consumer Price Index) with their parents’ household incomes at age 30. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Absolute income mobility has fallen across the entire income distribution, with the largest declines for families in the middle class. These findings are unaffected by using alternative price indices to adjust for inflation, accounting for taxes and transfers, measuring income at later ages, and adjusting for changes in household size. Absolute mobility fell in all 50 states, although the rate of decline varied, with the largest declines concentrated in states in the industrial Midwest, such as Michigan and Illinois. The decline in absolute mobility is especially steep – from 95% for children born in 1940 to 41% for children born in 1984 – when we compare the sons’ earnings to their fathers’ earnings. Why have rates of upward income mobility fallen so sharply over the past half-century? There have been two important trends that have affected the incomes of children born in the 1980s relative to those born in the 1940s and 1950s: lower Gross Domestic Product (GDP) growth rates and greater inequality in the distribution of growth. We find that most of the decline in absolute mobility is driven by the more unequal distribution of economic growth rather than the slowdown in aggregate growth rates. When we simulate an economy that restores GDP growth to the levels experienced in the 1940s and 1950s but distributes that growth across income groups as it is distributed today, absolute mobility only increases to 62%. In contrast, maintaining GDP at its current level but distributing it more broadly across income groups – at it was distributed for children born in the 1940s – would increase absolute mobility to 80%, thereby reversing more than two-thirds of the decline in absolute mobility. These findings show that higher growth rates alone are insufficient to restore absolute mobility to the levels experienced in mid-century America. Under the current distribution of GDP, we would need real GDP growth rates above 6% per year to return to rates of absolute mobility in the 1940s. Intuitively, because a large fraction of GDP goes to a small fraction of high-income households today, higher GDP growth does not substantially increase the number of children who earn more than their parents. Of course, this does not mean that GDP growth does not matter: changing the distribution of growth naturally has smaller effects on absolute mobility when there is very little growth to be distributed. The key point is that increasing absolute mobility substantially would require more broad-based economic growth. We conclude that absolute mobility has declined sharply in America over the past half-century primarily because of the growth in inequality. If one wants to revive the “American Dream” of high rates of absolute mobility, one must have an interest in growth that is shared more broadly across the income distribution.

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.

Using data from a randomized experiment, we find that poor rural Mexican households invested part of their cash transfers from the Oportunidades program in productive assets, increasing agricultural income by almost 10 percent after 18 months of benefits. We estimate that for each peso transferred, households consume 74 cents and invest the rest, permanently increasing long-term consumption by about 1.6 cents. Results suggest that cash transfers can achieve long-term increases in consumption through investment in productive activities, thereby permitting beneficiary households to attain higher living standards that are sustained even after transitioning off the program. (JEL D14, H23, I38, O12)

The US Family Budget Dataset provides insights into the cost of living in different US counties based on the Family Budget Calculator by the Economic Policy Institute (EPI).

This dataset offers community-specific estimates for ten family types, including one or two adults with zero to four children, in all 1877 counties and metro areas across the United States.

Interesting Task Ideas:

1. See how family budgets compare to the federal poverty line and the Supplemental Poverty Measure in different counties.
2. Look into the money challenges faced by different types of families using the budgets provided.
3. Find out which counties have the most affordable places to live, food, transportation, healthcare, childcare, and other things people need.
4. Explore how the average income of families relates to the overall cost of living in different counties.
5. Investigate how family size affects the estimated budget and find counties where bigger families have higher costs.
6. Create visuals showing how the cost of living varies across different states and big cities.
7. Check whether specific counties are affordable for families of different sizes and types.
8. Use the dataset to compare living standards and economic security in different US counties.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

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Photo by Alev Takil on Unsplash

This statistic depicts the age distribution in the United States from 2013 to 2023. In 2023, about 17.59 percent of the U.S. population fell into the 0-14 year category, 64.97 percent into the 15-64 age group and 17.43 percent of the population were over 65 years of age. The increasing population of the United States The United States of America is one of the most populated countries in the world, trailing just behind China and India. A total population count of around 320 million inhabitants and a more-or-less steady population growth over the past decade indicate that the country has steadily improved its living conditions and standards for the population. Leading healthier lifestyles and improved living conditions have resulted in a steady increase of the life expectancy at birth in the United States. Life expectancies of men and women at birth in the United States were at a record high in 2012. Furthermore, a constant fertility rate in recent years and a decrease in the death rate and infant mortality, all due to the improved standard of living and health care conditions, have helped not only the American population to increase but as a result, the share of the population younger than 15 and older than 65 years has also increased in recent years, as can be seen above.

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2023, at 90,730 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 39,102 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 214,000 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

This statistic depicts the age distribution in the United States from 2014 to 2024. In 2024, about 17.32 percent of the U.S. population fell into the 0-14 year category, 64.75 percent into the 15-64 age group and 17.93 percent of the population were over 65 years of age. The increasing population of the United States The United States of America is one of the most populated countries in the world, trailing just behind China and India. A total population count of around 320 million inhabitants and a more-or-less steady population growth over the past decade indicate that the country has steadily improved its living conditions and standards for the population. Leading healthier lifestyles and improved living conditions have resulted in a steady increase of the life expectancy at birth in the United States. Life expectancies of men and women at birth in the United States were at a record high in 2012. Furthermore, a constant fertility rate in recent years and a decrease in the death rate and infant mortality, all due to the improved standard of living and health care conditions, have helped not only the American population to increase but as a result, the share of the population younger than 15 and older than 65 years has also increased in recent years, as can be seen above.

This map compares the number of people living above the poverty line to the number of people living below. Why do this?There are people living below the poverty line everywhere. Nearly every area of the country has a balance of people living above the poverty line and people living below it. There is not an "ideal" balance, so this map makes good use of the national ratio of 6 persons living above the poverty line for every 1 person living below it. Please consider that there is constant movement of people above and below the poverty threshold, as they gain better employment or lose a job; as they encounter a new family situation, natural disaster, health issue, major accident or other crisis. There are areas that suffer chronic poverty year after year. This map does not indicate how long people in the area have been below the poverty line. "The poverty rate is one of several socioeconomic indicators used by policy makers to evaluate economic conditions. It measures the percentage of people whose income fell below the poverty threshold. Federal and state governments use such estimates to allocate funds to local communities. Local communities use these estimates to identify the number of individuals or families eligible for various programs." Source: U.S. Census BureauIn the U.S. overall, there are 6 people living above the poverty line for every 1 household living below. Green areas on the map have a higher than normal number of people living above compared to below poverty. Orange areas on the map have a higher than normal number of people living below the poverty line compared to those above in that same area.The map shows the ratio for counties and census tracts, using these layers, created directly from the U.S. Census Bureau's American Community Survey (ACS)For comparison, an older layer using 2013 ACS data is also provided.The layers are updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Poverty status is based on income in past 12 months of survey. Current Vintage: 2014-2018ACS Table(s): B17020Data downloaded from: Census Bureau's API for American Community Survey National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

This statistic shows the results of a survey among American adults on their happiness with their current living standards, by social class. The survey was conducted in July 2012, shortly before the presidential election. 32 percent of Americans who define themselves as members of the middle class stated they were very happy with their life nowadays, while 20 percent of Americans belonging to the lower class stated the same.

GDP per capita (current US$) is an economic indicator that measures the average economic output per person in a country. It is calculated by dividing the total Gross Domestic Product (GDP) of a country by its population, both measured in current US dollars. GDP per capita provides a useful metric for comparing the economic well-being and living standards between different countries.

There are various sources where you can find GDP per capita data, including international organizations, government agencies, and financial institutions. Some prominent sources for GDP per capita data include:

1. World Bank: The World Bank provides comprehensive data on GDP per capita for countries around the world. They maintain the World Development Indicators (WDI) database, which includes GDP per capita figures for different years.

2. International Monetary Fund (IMF): The IMF also offers GDP per capita data through their World Economic Outlook (WEO) database. It provides economic indicators and forecasts, including GDP per capita figures for various countries.

3. National Statistical Agencies: Many countries have their own national statistical agencies that publish GDP per capita data. These agencies collect and analyze economic data, including GDP and population figures, to calculate GDP per capita.

4. Central Banks: In some cases, central banks may also provide GDP per capita data for their respective countries. They often publish economic indicators and reports that include GDP per capita figures.

When using GDP per capita data, it's important to note that it represents an average measure and does not necessarily reflect the distribution of wealth within a country. Additionally, GDP per capita figures are often adjusted for inflation to provide real GDP per capita, which accounts for changes in the purchasing power of money over time.

To access the most up-to-date and accurate GDP per capita data, it is recommended to refer to reputable sources mentioned above or consult the official websites of international organizations, government agencies, or central banks that specialize in economic data and analysis.

The Living Conditions and Social Development Survey (LCSDS) aims to evaluate and monitor the economic and social conditions, as well as poverty levels, in the communes near the Caracol Industrial Park (PIC). Located 25 km southeast of Cap-Haïtien (Haiti’s second most populous city), the PIC is a flagship economic development initiative that, by 2017, had created 10,000 new jobs in the light manufacturing sector. Between 2001 and 2003, the Haitian Institute of Statistics and Informatics (IHSI) conducted its first and only comprehensive survey on living conditions. The purpose of that survey was to establish baseline development indicators on topics such as housing and infrastructure, demographics, migration, education, labor, health, household income, domestic and public life, and agriculture. No similar comprehensive survey on living conditions has been conducted since. The LCSDS adapted the IHSI’s 2001-2003 survey questionnaire, adding specific modules on perceptions and opinions about the PIC and associated changes in the area. The survey was conducted between 2014 and 2015 as part of the implementation of the Emerging and Sustainable Cities Initiative in Northern Haiti.

1199 persons were interviewed in the FRG, 1228 in France, 1178 in Great Britain, 1164 in Italy and 500 in Greece. The study has the USIA-designation XX-17. The USIA-Studies of the XX-Series (international relations) from XX-2 to XX-18 are archived under ZA Study Nos. 1969-1976 as well as 2069-2074 and 2124-2127.

The report on North America Analytical Standards for Life Sciences covers a summarized study of several factors supporting market growth, such as market size, market type, major regions, and end-user applications. The report enables customers to recognize key drivers that influence and govern the market.

American citizens of African descent, living in Maryland.

In 2024, the U.S. GDP increased from the previous year to about 29.18 trillion U.S. dollars. Gross domestic product (GDP) refers to the market value of all goods and services produced within a country. In 2024, the United States has the largest economy in the world. What is GDP? Gross domestic product is one of the most important indicators used to analyze the health of an economy. GDP is defined by the BEA as the market value of goods and services produced by labor and property in the United States, regardless of nationality. It is the primary measure of U.S. production. The OECD defines GDP as an aggregate measure of production equal to the sum of the gross values added of all resident, institutional units engaged in production (plus any taxes, and minus any subsidies, on products not included in the value of their outputs). GDP and national debt Although the United States had the highest Gross Domestic Product (GDP) in the world in 2022, this does not tell us much about the quality of life in any given country. GDP per capita at purchasing power parity (PPP) is an economic measurement that is thought to be a better method for comparing living standards across countries because it accounts for domestic inflation and variations in the cost of living. While the United States might have the largest economy, the country that ranked highest in terms of GDP at PPP was Luxembourg, amounting to around 141,333 international dollars per capita. Singapore, Ireland, and Qatar also ranked highly on the GDP PPP list, and the United States ranked 9th in 2022.

Questions on security policy. Attitude to the Common Market. East-West comparison and preferred economic system.

Topics: Attitude to selected countries; preferred political position of one´s own country; judgement on American and Soviet foreign policy; agreement in principle of the interests of one´s own country with the interests of the USA and the USSR; confidence in the ability of the USA to solve international problems; judgement on the peace efforts of the USA and the USSR; alliance loyalty of the USA to Western Europe; attitude to nuclear tests and to a test-ban treaty even without a surveillance system; judgement on one´s own standard of living in the past, at present, and in future; country with the highest standard of living; superiority of the communist or anti-communist countries in military, economic and scientific areas as well as in the area of space flight; attitude to use of nuclear weapons in case of attack on one´s own country; judgement on the American peace corps program; attitude to a possible peace corps program of one´s own country, to a united Western Europe and to the membership of one´s own country in the Common Market; assumed effect of the Common Market on the standard of living, on the political unification of Western Europe and on the influence of the USA on European affairs; approval for a change of American influence on Europe; judgement on American trade policy; supposed attitude of the USA and the USSR to the Common Market and effect of the Common Market on US imports; attitude to socialism, communism and capitalism as well as to a nationalization of industrial establishments; outstanding characteristics of a social, capitalist or communist economic system; preferred economic system; classification of the economic systems of selected countries as more socialist or capitalist and attitude to the economic systems of these countries; judgement on American capitalism (scale); TV viewing; attitude to American television programs and films; judgement on American films, books, newspapers, music and television programs as agent of American life-style; trips to the movies; film preferences relative to individual countries; frequency of viewing American films; judgement on American, British, French and Italian films as an image of life in these countries; impression of different aspects of American life mediated by American films.

In France the following questions were also asked: income, employment of household help; ownership of automobile, radio and real estate.

Interviewer rating: duration of interview (Great Britain only); location of interview (Italy only); respondent´s willingness to cooperate (FRG only).

This dataset contains statistics on a wide range of key indicators related to the living conditions of the population in Latin America and the Caribbean, organized by life cycle. This information is based on microdata from the Harmonized Household Surveys of 22 countries in the region and covers the period from 1999 to 2016.