This dataset provides a historical overview of key global indicators, including Gross Domestic Product (GDP), population growth, and CO2 emissions. It captures economic trends, demographic shifts, and environmental impacts over multiple decades, making it useful for researchers, analysts, and policymakers.

The dataset includes Real GDP (inflation-adjusted), allowing for economic trend analysis while accounting for inflation effects. Additionally, it incorporates CO2 emissions data, enabling studies on the relationship between economic growth and environmental impact.

This dataset is valuable for multiple research areas:

✅ Macroeconomic Analysis – Study global economic growth, recessions, and recovery trends.

✅ Inflation & Monetary Policy – Compare nominal vs. real GDP to assess inflationary trends.

✅ Climate Change Research – Analyze CO2 emissions alongside economic growth to identify sustainability challenges.

✅ Predictive Modeling – Train machine learning models for forecasting GDP, population, or emissions.

✅ Public Policy & Development – Evaluate the impact of economic and environmental policies over time.

This dataset is shared for educational and analytical purposes only.

United States US: GDP: Growth: Gross Value Added: Services data was reported at 2.621 % in 2015. This records an increase from the previous number of 2.221 % for 2014. United States US: GDP: Growth: Gross Value Added: Services data is updated yearly, averaging 2.335 % from Dec 1998 (Median) to 2015, with 18 observations. The data reached an all-time high of 4.456 % in 1999 and a record low of -1.772 % in 2009. United States US: GDP: Growth: Gross Value Added: Services data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Gross Domestic Product: Annual Growth Rate. Annual growth rate for value added in services based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Services correspond to ISIC divisions 50-99. They include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average; Note: Data for OECD countries are based on ISIC, revision 4.

This dataset provides information about the number of properties, residents, and average property values for Economy Road cross streets in Economy, IN.

Denton economic data from the American Community Survey (ACS)

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.

About this dataset

Context

The dataset tabulates the population of Economy by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Economy. The dataset can be utilized to understand the population distribution of Economy by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Economy. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Economy.

Key observations

Largest age group (population): Male # 65-69 years (412) | Female # 60-64 years (490). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Economy population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Economy is shown in the following column.
• Population (Female): The female population in the Economy is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Economy for each age group.

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 Economy Population by Gender. You can refer the same here

Today's weakness in the US economy results from lack of aggregate demand, due to high and growing inequality, underinvestment in public infrastructure and technology that is complementary to private capital, continuing mild austerity, difficulties encountered in making the structural transformation from manufacturing to a service-based economy, and a financial sector failing to provide adequate funds to SMEs. An agenda to restore growth includes a carbon price, inducing climate investments; increased public investments in infrastructure and technology; fighting inequality through redistribution and rewriting the rules structuring the economy; and reforming the financial sector and the global reserve system.

Graph and download economic data for Gross Domestic Product: All Industries in Orange County, CA (GDPALL06059) from 2001 to 2023 about Orange County, CA; Los Angeles; CA; industry; GDP; and USA.

Poland Number of National Economy Entities: Registered: Natural Persons Conducting Economic Activity data was reported at 3,081,190.000 Unit in Sep 2018. This records an increase from the previous number of 3,043,282.000 Unit for Jun 2018. Poland Number of National Economy Entities: Registered: Natural Persons Conducting Economic Activity data is updated quarterly, averaging 2,871,457.000 Unit from Sep 2002 (Median) to Sep 2018, with 65 observations. The data reached an all-time high of 3,081,190.000 Unit in Sep 2018 and a record low of 2,677,537.000 Unit in Sep 2002. Poland Number of National Economy Entities: Registered: Natural Persons Conducting Economic Activity data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Poland – Table PL.O013: Number of Economic Entities: Registered: by Ownership and Legal Form.

About the Project The project explores alternative methods of measuring economic diversification and investigating its associated impacts on the Saudi Arabian economy and other GCC countries. By utilizing a financial portfolio framework reconciled with economic growth theory, the economy is viewed as a portfolio of economic sectors, each contributing to the overall output growth. Results demonstrated that diversification policies have been effective, as the economy moves towards higher growth with lower instability. Key Points Evidence confirms that there is a positive correlation between the economic growth rate and its volatility/risk in the Gulf Cooperation Council (GCC) region. In other words, there is a trade-off between the benefits of oil and gas activity and the volatility resulting from unpredictable commodity price swings in such resource dependent economies. Our analysis uses a financial portfolio framework approach (and more specifically an efficient frontier analysis), treating economic sectors as individual investments. We calculate a relative risk measure termed the ‘beta coefficient’ and assemble a portfolio of sectors with varying weights to find the efficient frontier. If the beta of the portfolio representing the economy is above global average, the economy will generally grow faster than the global average but with greater volatility – the upturns will be higher and the downturns deeper. We aim to shed light on diversification policy from this novel, if not yet widely accepted, perspective. The GCC economies exhibit ‘high beta,’ particularly Qatar. Saudi Arabia sits in the middle of the group, but above the global average, while Oman has the lowest coefficient of the group. Saudi Arabia’s National Transformation Plan to 2020 and economic Vision 2030 envisage an economy that is still invested in oil and gas activity at 45 percent of total output. While diversification policies in these plans promote economic growth, it still leaves the economy exposed to the volatility of energy markets. In comparison, the optimal mix of economic sectors could increase the growth rate by more than 1 percent annually and nearly halve the expected volatility (to less than 60 percent of growth rate). Saudi Arabia’s historical economic policies were effective in achieving some diversification. However, their benefits could be increased by policies that balance productive efficiency with diversification of economic activity. The difference between policy-optimized portfolio and non-constrained optimization can be used to estimate the size of the fiscal stabilization fund needed to protect the economy from stop/go risks to diversification objectives.

The Leading Economic Index Final in Japan is a composite index that aggregates various economic indicators to forecast the future direction of the economy.-2025-02-25

ZIP Code Business Patterns (ZBP)

County Business Patterns (CBP) is an annual series that provides subnational economic data by industry. This series includes the number of establishments, employment during the week of March 12, first quarter payroll, and annual payroll. This data is useful for studying the economic activity of small areas; analyzing economic changes over time; and as a benchmark for other statistical series, surveys, and databases between economic censuses. Businesses use the data for analyzing market potential, measuring the effectiveness of sales and advertising programs, setting sales quotas, and developing budgets. Government agencies use the data for administration and planning.

The set contains information on the financial statements in 2021 form 1-ds for the first half of the year

We analyzed the formation mechanism of digital economic gap (DEG), measured the DEGs at four levels (the gaps in information and communication technology accessibility, application skill, digital economic outcome, and efficiency), and explored its spatiotemporal evolution in China by using DEA–Malmquist index method, Gini Coefficent method, Kernel density, and Geodetector. Data from 263 cities in China between 2011 and 2019 were collected. The results demonstrated that (1) The four levels of DEGs showed different trends. The first-, second- and third- level DEGs showed ceiling effects, and the fourth-level DEG oscillated upward. (2) The distribution location of the four levels of DEGs varied. The first- and second-level DEGs shifted at a stable low degree. The third-level DEG increased steadily and polarized. The fourth-level DEG increased steadily and formed a multi-polarization trend, with one strong polar. (3) The long-term transfer trend of the DEGs at four levels changed little, an..., A total of 263 cities in 30 provinces (cities or regions) in China were selected as the study subjects. Data were obtained from the China Statistical Yearbook, China Science and Technology Statistical Yearbook, Provincial and City Statistical Yearbooks, White Paper on China City DE Index, and the Mark Data website (https://www.macrodatas.cn/). The expedition period for this study was from 2011 to 2019. , , The dataset -- data.dta (city = 263, year = 8) -- was compiled from the peer-reviewed literature. This was from study sites in 263 cities, China.

The dataset was compiled by co-authors Shujuan Wu (jane333444@126.com), Jinting Li (1311028217@qq.com), Daqian Huang (1953836900@qq.com), Jianhua Xiao (1312655857@qq.com) of Wuyi University.

For any questions regarding the dataset, please send an email to Shujuan Wu (jane333444@126.com)Â and Jinting Li (1311028217@qq.com).

Filename: data.dta

â—ˆyear: The year of the data

â—ˆcity: City No.

â—ˆregion: The No. Of the region

◈rndexp: R&D expenditure (10000 Yuan)

◈exgebudget:Total financial expenditure (10000 Yuan)

â—ˆfixass: Â Fixed asset investment (10000 Yuan)

◈fstdeg: First-level of digital economy (/)

◈library: The collection of books in public libraries per capi...

PIA01 - Overall Economy Productivity Indicators. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Overall Economy Productivity Indicators...

The Leading Economic Index Final in Japan is a composite index that aggregates various economic indicators to forecast the future direction of the economy.-2024-06-24

Merchandise imports from low- and middle-income economies within region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in the same World Bank region according to the World Bank classification of economies. Data are as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data. No figures are shown for high-income economies, because they are a separate category in the World Bank classification of economies.

This statistic shows the share of economic sectors in gross domestic product (GDP) in Portugal from 2013 to 2023. In 2023, the share of agriculture in Portugal's gross domestic product was 2.12 percent, industry contributed approximately 18.15 percent and the services sector contributed about 66.76 percent.

United States NLB: saar: Capital Account: Addendum: Rest of the World data was reported at 569.211 USD bn in Mar 2018. This records an increase from the previous number of 524.026 USD bn for Dec 2017. United States NLB: saar: Capital Account: Addendum: Rest of the World data is updated quarterly, averaging 43.283 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 859.012 USD bn in Sep 2006 and a record low of -42.129 USD bn in Mar 1991. United States NLB: saar: Capital Account: Addendum: Rest of the World data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB074: Integrated Macroeconomic Accounts: Total Economy and Sectors: Selected Aggregates.