TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

U.S. Public Debt vs. GDP

Trends and Comparisons

By Charlie Hutcheson [source]

About this dataset

This dataset contains quarterly data on the US Gross Domestic Product (GDP) and Total Public Debt from 1947 through 2020. It provides a comprehensive view into the development of debt versus GDP over the years, offering insights into how our economy has grown and changed since The Great Depression. Explore this valuable information to answer questions such as: How do debt and GDP relate to one another? Has US government spending been outpacing wealth throughout history? From what sources does our national debt originate? This dataset can be utilized by economists, governments, researchers, investors, financial institutions, journalists — anyone looking to gain a better understanding of where our economy stands today compared to past decades

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How to use the dataset

This dataset, U.S. GDP vs Debt Over Time, contains quarterly data on the Gross Domestic Product (GDP) and Total Public Debt of the United States between 1947 to 2020. This can be useful for conducting research into how the total public debt relates to economic growth in the US.

The dataset includes 4 columns: Quarter , Gross Domestic Product ($mil), Total Public Debt ($mil). The Quarter column consists of strings that represent each quarter from 1947-2020 with a corresponding number (e.g., “Q1-1947”). The Gross Domestic Product ($mil) and Total Public Debt ($mil) columns consist of numbers that indicate the respective amounts in millions for each quarter during this same time period.

By analyzing this dataset you can explore various trends over different periods as it relates to public debt versus economic growth in America and make informed decisions about how certain policies may affect future outcomes. Additionally, you could also compare these two values with other variables such as unemployment rate or inflation rate to gain deeper insights into America’s economy over time

Research Ideas

• Comparing the quarterly growth in GDP with public debt to show the correlation between economic growth and government spending.
• Creating a bar or line visualization that compares the US’s total public debt to comparable economic powers like China, Japan, and Europe over time.
• Examining how changes in government deficit have contributed towards an increase in public debt by analyzing which quarters saw significant leaps of growth from one year to the next

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: US GDP vs Debt.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------------------------------------------| | Quarter | The quarter of the year in which the data was collected. (String) | | Gross Domestic Product ($mil) | The total value of all goods and services produced by the US in a given quarter. (Integer) | | Total Public Debt ($mil) | The total amount owed by the federal government. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Charlie Hutcheson.

The China agricultural and economic database is a collection of agricultural-related data from official statistical publications of the People's Republic of China. Analysts and policy professionals around the world need information about the rapidly changing Chinese economy, but statistics are often published only in China and sometimes only in Chinese-language publications. This product assembles a wide variety of data items covering agricultural production, inputs, prices, food consumption, output of industrial products relevant to the agricultural sector, and macroeconomic data.

This dataset provides key economic indicators for five of the world's largest economies, based on their nominal Gross Domestic Product (GDP) in 2022. It includes the GDP values, population, GDP growth rates, per capita GDP, and each country's share of the global economy.

Columns: Country: Name of the country. GDP (nominal, 2022): The total nominal GDP in 2022, represented in USD. GDP (abbrev.): The abbreviated GDP in trillions of USD. GDP growth: The percentage growth in GDP compared to the previous year. Population: Total population of each country in 2022. GDP per capita: The GDP per capita, representing average economic output per person in USD. Share of world GDP: The percentage of global GDP contributed by each country. Key Highlights: The dataset includes some of the largest global economies, such as the United States, China, Japan, Germany, and India. The data can be used to analyze the economic standing of countries in terms of overall GDP and per capita wealth. It offers insights into the relative growth rates and population sizes of these leading economies. This dataset is ideal for exploring economic trends, performing country-wise comparisons, or studying the relationship between population size and GDP growth.

About this dataset

Context

The dataset presents median household incomes for various household sizes in China, Maine, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, China town did not include 6, or 7-person households. Across the different household sizes in China town the mean income is $96,500, and the standard deviation is $16,933. The coefficient of variation (CV) is 17.55%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $81,583. It then further increased to $117,098 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/china-me-median-household-income-by-household-size.jpeg" alt="China, Maine median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China town median household income. You can refer the same here

CONTENT

The figures are based on GDP (Nominal) and sector composition ratios provided by the CIA World Fact Book. Agriculture includes farming, fishing, and forestry. Industry includes mining, manufacturing, energy production, and construction. Services cover government activities, communications, transportation, finance, and all other private economic activities that do not produce material goods.

CONTEXT

• Agriculture Sector : Agriculture Sector contributes 6.4 percent of total world's economic production. Total production of sector is $5,084,800 million. China is the largest contributer followed by India. China and India accounts for 19.49 and 7.39 percent of total global agricultural output. World's largest economy United States is at third place. Next in line come Brazil and Indonesia

• **Industry Sector : **With GDP of $23,835 billion, Industry Sector holds a share of 30% of total GDP nominal. China is the largest contributor followed by US. Japan is at 3rd and Germany is at 4th place. These four countries contributes 45.84 of total global industrial output.

• Services Sector : Services sector is the largest sector of the world as 63 percent of total global wealth comes from services sector. United States is the largest producer of services sector with around 15.53 trillion USD. Services sector is the leading sector in 201 countries/economies. 30 countries receive more than 80 percent of their GDP from services sector. Chad has lowest 27% contribution by services sector in its economy.

Explore GDP per capita data and national accounts information with this comprehensive dataset. Gain insights into economic trends and comparisons across various countries. Click now to access the data!

GDP, National Accounts, ITEM

Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cuba, Cyprus, Czechia, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, ZimbabweFollow data.kapsarc.org for timely data to advance energy economics research..GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current international dollars based on the 2011 ICP round.

China Exports to United States was US$525.65 Billion during 2024, according to the United Nations COMTRADE database on international trade. China Exports to United States - data, historical chart and statistics - was last updated on November of 2025.

China GDP: PPP: 2017 Price data was reported at 25,684,414.532 Intl $ mn in 2022. This records an increase from the previous number of 24,938,967.814 Intl $ mn for 2021. China GDP: PPP: 2017 Price data is updated yearly, averaging 7,839,613.420 Intl $ mn from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 25,684,414.532 Intl $ mn in 2022 and a record low of 1,616,385.776 Intl $ mn in 1990. China GDP: PPP: 2017 Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2017 international dollars.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;Gap-filled total;

Time series data for the statistic Gross_Domestic_Product_Current_USD and country China. Indicator Definition:GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.The statistic "Gross Domestic Product Current USD" stands at 18,743,803,170,827.20 United States Dollars as of 12/31/2024, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.59 percent compared to the value the year prior.The 1 year change in percent is 2.59.The 3 year change in percent is 2.98.The 5 year change in percent is 28.73.The 10 year change in percent is 75.59.The Serie's long term average value is 3,590,131,888,959.60 United States Dollars. It's latest available value, on 12/31/2024, is 422.09 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1962, to it's latest available value, on 12/31/2024, is +39,518.50%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

We analyze whether--and, if so, how--Americans reacted to the escalation of the trade war between the United States and China in June 2018. To address this issue, we leverage surveys conducted in the U.S. during this phase of the economic clash. We find a significant reduction in support for Donald Trump and his trade policy immediately following the announcement of retaliatory tariffs by the Chinese government. Moreover, respondents’ economic concerns about the trade war were primarily sociotropic and only weakly related to personal pocketbook considerations or local exposure to Chinese retaliatory tariffs. We also find that the trade war's intensification was politically consequential, decreasing support for Republican candidates in the 2018 midterm elections. Our findings indicate that trade wars can be politically costly for incumbent politicians, even among voters who are not directly affected by retaliatory tariffs.

About this dataset

Context

The dataset presents median household incomes for various household sizes in China, TX, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, China did not include 4, 5, 6, or 7-person households. Across the different household sizes in China the mean income is $59,611, and the standard deviation is $38,331. The coefficient of variation (CV) is 64.30%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $24,167. It then further increased to $54,375 for 3-person households, the largest household size for which the bureau reported a median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China median household income. You can refer the same here

China recorded a trade surplus of 90.07 USD Billion in October of 2025. This dataset provides - China Balance of Trade - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in China town: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 26(1.40%) households where the householder is under 25 years old, 705(38.01%) households with a householder aged between 25 and 44 years, 693(37.36%) households with a householder aged between 45 and 64 years, and 431(23.23%) households where the householder is over 65 years old.
• In China town, the age group of 25 to 44 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China town median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in China: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 34(8.17%) households where the householder is under 25 years old, 107(25.72%) households with a householder aged between 25 and 44 years, 127(30.53%) households with a householder aged between 45 and 64 years, and 148(35.58%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the city of China, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China median household income by age. You can refer the same here

The database used includes annual frequency data for 43 countries, defined by the IMF as 24 advanced countries and 19 emerging countries, for the years 1992-2018.The database contains the fiscal stress variable and a set of variables that can be classified as follows: macroeconomic and global economy (interest rates in the US, OECD; real GDP in the US, y-o-y, OECD; real GDP in China, y-o-y, World Bank; oil price, y-o-y, BP p.l.c.; VIX, CBOE; real GDP, y-o-y, World Bank, OECD, IMF WEO; GDP per capita in PPS, World Bank); financial (nominal USD exchange rate, y-o-y, IMF IFS; private credit to GDP, change in p.p., IMF IFS, World Bank and OECD); fiscal (general government balance, % GDP, IMF WEO; general government debt, % GDP, IMF WEO, effective interest rate on the g.g. debt, IMF WEO); competitiveness and domestic demand (currency overvaluation, IMF WEO; current account balance, % GDP, IMF WEO; share in global exports, y-o-y, World Bank, OECD; gross fixed capital formation, y-o-y, World Bank, OECD; CPI, IMF IFS, IMF WEO; real consumption, y-o-y, World Bank, OECD); labor market (unemployment rate, change in p.p., IMF WEO; labor productivity, y-o-y, ILO).In line with the convention adopted in the literature, the fiscal stress variable is a binary variable equal to 1 in the case of a fiscal stress event and 0 otherwise. In more recent literature in this field, the dependent variable tends to be defined broadly, reflecting not only outright default or debt restructuring, but also less extreme events. Therefore, following Baldacci et al. (2011), the definition used in the present database is broad, and the focus is on signalling fiscal stress events, in contrast to the narrower event of a fiscal crisis related to outright default or debt restructuring. Fiscal problems can take many forms; in particular, some of the outright defaults can be avoided through timely, targeted responses, like support programs of international institutions. The fiscal stress variable is shifted with regard to the other variables: crisis_next_year – binary variable shifted by 1 year, all years of a fiscal stress coded as 1; crisis_next_period – binary variable shifted by 2 years, all years of a fiscal stress coded as 1; crisis_first_year1 – binary variable shifted by 1 year, only the first year of a fiscal stress coded as 1; crisis_first_year2 - binary variable shifted by 2 years, only the first year of a fiscal stress coded as 1.

An increase in a currency internationalization levels can positively impact its credibility in international economic activities, and expand the effective demand and optimize the supply structure for the country’s financial service trade. In this way, a state can improve its financial service trade competitiveness in the international market. This study builds a vector autoregressive model based on time-series data of China-US financial services trade from 2010 to 2021, analyzes the impact of different quantitative indicators of RMB internationalization on this trade from the impulse response results, and validates the conclusions using various inspection methods. The results show that the increase in RMB internationalization helps to narrow the China-US financial services trade balance, but with a significant lag. And this effect is heterogeneous in different dimensions, demonstrated by the fact that the development of overseas RMB securities business is more important for the level of RMB internationalization to narrow the China-US financial services trade balance. Finally, among the specific measures to improve its financial services trade, China should focus on developing the international competitiveness of the traditional RMB deposit and loan financial sector, while the competition in the overseas market for high value-added financial businesses must also not be neglected. Furthermore, China needs to implement more targeted RMB internationalization development policies at different levels in the future to provide high-quality financial services to the rest of the world and aid in the economic recovery of the world in the "post-pandemic" era.

Context

Time use can vary greatly by country and between genders, be it by it's location, cultural differences, or economic situation. The data provided is by no means exhaustive but contains some interesting information on leisure time by gender, as well as historic data (1950-2017) on Avg. work hours and GDP in different countries and continents.

Content

Datasets from two sources are provided: 1. OECD Time use country statistics: Based on a collection of different questionnaires for different countries, it provides a distribution for time spent on different activities for both men and women in different countries. 2. Penn World Table (PWT) with information on RGDPO (in mil. 2017US$), work hours and population (in millions) actively working. Covering 183 countries between 1950 and 2019.

*RGDPO: Output-side real GDP at chained PPPs, to compare relative productive capacity across countries and over time. Example: Productive capacity of China today compared to the US at some point in the past.

If you'd like, you can see an exploration of the data on my notebook: Data exploration

Acknowledgements

These databases provide additional indicators and may be of interest: - https://stats.oecd.org/Index.aspx?DataSetCode=TIME_USE - https://www.rug.nl/ggdc/productivity/pwt/

Inspiration

It is an interesting, easy to handle dataset which provides a great opportunity for interesting visuals and identifying relationships or trends between indicators.

Some questions to answer: - How to annual working hours relate to GDP per capita. - Is there a specific trend in working hours vs GDP per capita % change? Is it different for any specific region? - Is there any relationship between leisure time use and location, GDP or religion? - Is there a time use discrepancy by gender?

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in East China township: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 15(0.86%) households where the householder is under 25 years old, 229(13.06%) households with a householder aged between 25 and 44 years, 706(40.25%) households with a householder aged between 45 and 64 years, and 804(45.84%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the township of East China township, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for East China township median household income by age. You can refer the same here

This dataset contains panel data for a sample of 15 countries (Australia, Austria, Canada, China, Denmark, France, Germany, Israel, Italy, Japan, Republic of Korea, Spain, Sweden, Switzerland and United States) over the period 2006-2015. The series used are available for a small number of developed countries and for a relatively short time period. Solar PV module prices, imports of solar PV panels and public budget for R&D in PV are in real terms and were obtained by dividing them by the United States GDP deflator. The series are obtained from five main sources. Imports value of solar PV panels series are taken from Commodity Trade Statistics database (COMTRADE). PV panels (cells and modules) are a part of the category HS 854140, "Photosensitive Semiconductor Devices, Photovoltaic Cells and Light-Emitting Diodes". Solar PV module prices, cumulative installed PV capacity and public budget for R&D in PV series are constructed from the PVPS report Trends in Photovoltaic Applications of the International Energy Agency (IEA). Population density, political stability index, renewable energy consumption and per capita carbon dioxide emissions series are all obtained from the World Bank (WB). Real GDP per capita series is taken from Federal Reserve Bank of St. Louis (FRED). Technological development in PV and crude oil import price series are drawn from the Organisation for Economic Co-operation and Development (OECD) database. Since crude oil import price series are not available for China and Israel, we use the West Texas Intermediate spot crude oil price as a proxy. The dummy for presence of feed-in tariff is constructed from the OECD database.