Techsalerator’s Import/Export Trade Data for United States's $26.9 trillion economy provides a detailed and insightful collection of information on international trade activities involving companies in United States.

To obtain Techsalerator’s Import/Export Trade Data for the United States, please reach out to info@techsalerator.com with your requirements. Techsalerator will provide a customized quote based on your data needs, with delivery available within 24 hours. Ongoing access options can also be discussed.

Techsalerator's Import/Export Trade Data for the United States offers a rich and detailed collection of information crucial for businesses, investors, and trade analysts. This dataset provides a thorough examination of trade activities, documenting and classifying import and export transactions across various U.S. industries. By integrating data from customs reports, trade agreements, and shipping records, the dataset delivers a comprehensive view of the U.S. trade landscape.

Key Data Fields

Company Name: Lists companies involved in trade transactions, helping identify potential partners or competitors and track industry-specific trade patterns. Trade Volume: Details the quantity or value of goods traded, offering insights into the scale and economic impact of trade activities. Product Category: Specifies the types of goods traded, such as raw materials or consumer products, aiding in understanding market demand and supply chain dynamics. Import/Export Country: Identifies the countries of origin or destination for traded goods, providing information on regional trade relationships and market access. Transaction Date: Records the date of transactions, revealing seasonal trends and shifts in trade dynamics over time.

Top Trade Trends in the U.S.

Trade Deficit Dynamics: The U.S. continues to face a notable trade deficit, particularly with major partners like China and the European Union. Efforts are ongoing to address these imbalances through various policy measures and agreements. China-U.S. Trade Relations: The trade relationship with China remains pivotal, characterized by negotiations, tariffs, and agreements that impact global trade flows and supply chains. Shift Towards Regional Trade Agreements: There is a growing emphasis on regional agreements, such as the USMCA, which replaces NAFTA, reflecting a trend toward localized trade solutions. Growth in Technology and E-Commerce: Increased trade in technology products and a surge in e-commerce are reshaping trade patterns and logistics. Sustainability and Environmental Regulations: The U.S. is incorporating sustainability into trade policies, focusing on reducing carbon emissions and promoting green technologies. Notable Companies in U.S. Trade Data Apple Inc.: A major exporter of electronics and software, including iPhones and MacBooks, highlighting its significant role in U.S. trade. Amazon.com, Inc.: A leading e-commerce company with a substantial impact on international trade through its global sales and logistics network. Boeing Company: A key player in aerospace, exporting aircraft and components, contributing significantly to U.S. trade. Microsoft Corporation: Exporter of software, cloud services, and hardware, reflecting the importance of tech exports in the U.S. economy. ExxonMobil Corporation: A major exporter of energy products, including crude oil and refined products, impacting the energy sector of U.S. trade. Accessing Techsalerator’s Data

To obtain Techsalerator’s Import/Export Trade Data for the United States, please reach out to info@techsalerator.com with your requirements. Techsalerator will provide a customized quote based on your data needs, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields:

Company Name Trade Volume Product Category Import/Export Country Transaction Date Shipping Details Customs Codes Trade Value

For detailed insights into U.S. import and export activities and trends, Techsalerator’s dataset is an invaluable resource for staying informed and making strategic decisions.

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

The United Nations General Assembly (UNGA) serves as a unique platform where countries have equal representation and can voice their positions on major global issues. Voting patterns in the UNGA often reveal underlying trends in political alliances, economic interdependencies, and shared values.

This project aims to uncover patterns in UNGA voting alignment with two major global powers: the United States and China. The final dataset used in this analysis was sourced from the United Nations Digital Library, which hosts official UN voting records ( https://digitallibrary.un.org ). By scraping data from the UN’s digital repository, this project focused specifically on the Voting Resolutions of the General Assembly.

The analysis compares the voting behavior of a target country to that of China and the United States on a yearly basis. For each resolution, I recorded how China and the U.S. voted—categorizing each as “Yes,” “No,” or “Abstain”—and then documented how the target country voted. Resolutions labeled "ADOPTED WITHOUT VOTE" were excluded from the analysis. This methodology enabled a detailed comparison of voting alignments (or discordances) between the target country and each of the two major powers.

To illustrate, consider the example of “Mexico 2019.” For each UNGA resolution in 2019, I recorded Mexico’s vote and compared it to the votes of both China and the United States. I then calculated the proportion of resolutions where Mexico’s vote aligned with China’s, and separately, the proportion aligned with the U.S. These proportions were calculated by dividing the number of aligned votes by the total number of resolutions Mexico voted on that year.

Notably, the analysis found that, on average, countries tend to align more closely with China than with the United States in UNGA voting.

The final output of this process was a dataset that quantifies each country’s diplomatic alignment with China and the U.S. within the United Nations, providing a useful measure for further geopolitical analysis.

Code and Processing Tools

All code can be found on https://github.com/jasonding15/cosIW

The Gross Domestic Product per capita in China was last recorded at 13121.68 US dollars in 2024. The GDP per Capita in China is equivalent to 104 percent of the world's average. This dataset provides - China GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Supplementary Material 4：The ASIR of CKD attributed to diabetes and the ASIR of diabetes

Techsalerator’s Import/Export Trade Data for the United States

Techsalerator’s Import/Export Trade Data for the United States offers a comprehensive and insightful collection of information on international trade activities involving U.S. companies. This dataset provides a detailed examination of trade transactions, documenting and classifying imports and exports across various industries within the U.S.

To obtain Techsalerator’s Import/Export Trade Data for the United States, please reach out to info@techsalerator.com or visit Techsalerator Contact Us with your specific requirements. Techsalerator will provide a customized quote based on your data needs, with delivery available within 24 hours. Ongoing access options can also be discussed.

Techsalerator's Import/Export Trade Data for the United States delivers a thorough analysis of trade activities, integrating data from customs reports, trade agreements, and shipping records. This comprehensive dataset helps businesses, investors, and trade analysts understand the U.S. trade landscape in detail.

Key Data Fields

• Company Name: Lists the companies involved in trade transactions. This information helps identify potential partners or competitors and track industry-specific trade patterns.
• Trade Volume: Details the quantity or value of goods traded, providing insights into the scale and economic impact of trade activities.
• Product Category: Specifies the types of goods traded, such as raw materials or finished products, aiding in understanding market demand and supply chain dynamics.
• Import/Export Country: Identifies the countries of origin or destination for traded goods, offering insights into regional trade relationships and market access.
• Transaction Date: Records the date of transactions, revealing seasonal trends and shifts in trade dynamics over time.

Top Trade Trends in the United States

• Trade Balance Dynamics: The U.S. trade balance fluctuates with major partners such as China, Canada, and Mexico. Ongoing trade negotiations and policy adjustments aim to address imbalances and foster more equitable trade relationships.
• U.S.-China Trade Relations: The trade relationship with China remains central, influenced by agreements and tariffs. This partnership shapes significant aspects of the U.S. trade policy and practices.
• Expansion of Global Trade Networks: The United States continues to diversify its trade partners and markets beyond traditional partners, reflecting a trend toward broader global trade engagement.
• Growth in Technology Exports: The U.S. sees substantial trade in technology products, including electronics and software, which play a critical role in its export economy.
• Emphasis on Sustainable Trade Practices: There is a growing focus on integrating sustainability into trade policies, promoting environmentally friendly practices and technologies.

Notable Companies in U.S. Trade Data

• Apple Inc.: A leading technology company involved in exporting electronics and importing components from various global suppliers.
• Boeing: A major aerospace manufacturer engaged in importing and exporting aircraft and aerospace products, impacting U.S. trade in the transportation sector.
• Cargill: A key player in agriculture, known for exporting and importing agricultural products, impacting the U.S. trade in commodities.
• Amazon: A significant e-commerce operator involved in the import and export of a wide range of goods, reflecting its role in the U.S. trade dynamics.
• General Motors: An important automotive manufacturer that engages in global trade of vehicles and automotive parts, highlighting the U.S. role in the automotive sector.

Accessing Techsalerator’s Data

To obtain Techsalerator’s Import/Export Trade Data for the United States, please contact us at info@techsalerator.com with your requirements. We will provide a customized quote based on the number of data fields and records needed, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields:

• Company Name
• Trade Volume
• Product Category
• Import/Export Country
• Transaction Date
• Shipping Details
• Customs Codes
• Trade Value

For detailed insights into the United States’ import and export activities and trends, Techsalerator’s dataset is an invaluable resource for staying informed and making strategic decisions.

Success.ai’s Consumer Behavior Data for Consumer Goods & Electronics Industry Leaders in Asia, the US, and Europe offers a robust dataset designed to empower businesses with actionable insights into global consumer trends and professional profiles. Covering executives, product managers, marketers, and other professionals in the consumer goods and electronics sectors, this dataset includes verified contact information, professional histories, and geographic business data.

With access to over 700 million verified global profiles and firmographic data from leading companies, Success.ai ensures your outreach, market analysis, and strategic planning efforts are powered by accurate, continuously updated, and GDPR-compliant data. Backed by our Best Price Guarantee, this solution is ideal for businesses aiming to navigate and lead in these fast-paced industries.

Why Choose Success.ai’s Consumer Behavior Data?

1. Verified Contact Data for Precision Engagement

   • Access verified email addresses, phone numbers, and LinkedIn profiles of professionals in the consumer goods and electronics industries.
   • AI-driven validation ensures 99% accuracy, optimizing communication efficiency and minimizing data gaps.

2. Comprehensive Global Coverage

   • Includes profiles from key markets in Asia, the US, and Europe, covering regions such as China, India, Germany, and the United States.
   • Gain insights into region-specific consumer trends, product preferences, and purchasing behaviors.

3. Continuously Updated Datasets

   • Real-time updates capture career progressions, company expansions, market shifts, and consumer trend data.
   • Stay aligned with evolving market dynamics and seize emerging opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible use and legal compliance for all data-driven campaigns.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with industry leaders, marketers, and decision-makers in consumer goods and electronics industries worldwide.
• Consumer Trend Insights: Gain detailed insights into product preferences, purchasing patterns, and demographic influences.
• Business Locations: Access geographic data to identify regional markets, operational hubs, and emerging consumer bases.
• Professional Histories: Understand career trajectories, skills, and expertise of professionals driving innovation and strategy.

Key Features of the Dataset:

1. Decision-Maker Profiles in Consumer Goods and Electronics

   • Identify and engage with professionals responsible for product development, marketing strategy, and supply chain optimization.
   • Target individuals making decisions on consumer engagement, distribution, and market entry strategies.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (consumer electronics, FMCG, luxury goods), geographic location, or job function.
   • Tailor campaigns to align with specific industry trends, market demands, and regional preferences.

3. Consumer Trend Data and Insights

   • Access data on regional product preferences, spending behaviors, and purchasing influences across key global markets.
   • Leverage these insights to shape product development, marketing campaigns, and customer engagement strategies.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing and Demand Generation

   • Design campaigns tailored to consumer preferences, regional trends, and target demographics in the consumer goods and electronics industries.
   • Leverage verified contact data for multi-channel outreach, including email, social media, and direct marketing.

2. Market Research and Competitive Analysis

   • Analyze global consumer trends, spending patterns, and product preferences to refine your product portfolio and market positioning.
   • Benchmark against competitors to identify gaps, emerging needs, and growth opportunities in target regions.

3. Sales and Partnership Development

   • Build relationships with key decision-makers at companies specializing in consumer goods or electronics manufacturing and distribution.
   • Present innovative solutions, supply chain partnerships, or co-marketing opportunities to grow your market share.

4. Product Development and Innovation

   • Utilize consumer trend insights to inform product design, pricing strategies, and feature prioritization.
   • Develop offerings that align with regional preferences and purchasing behaviors to maximize market impact.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality consumer behavior data at competitive prices, ensuring maximum ROI for your outreach, research, and ma...

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

More Datasets

For more datasets, click here.

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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 USD/CNY exchange rate fell to 7.0696 on December 2, 2025, down 0.05% from the previous session. Over the past month, the Chinese Yuan has strengthened 0.81%, and is up by 3.15% over the last 12 months. Chinese Yuan - values, historical data, forecasts and news - updated on December of 2025.

The United States recorded a trade deficit of 59.55 USD Billion in August of 2025. This dataset provides the latest reported value for - United States Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The "Chinese Music" AI dataset is a pioneering collection meticulously curated to push the boundaries of music generation within the realm of Chinese traditional and classical music. This comprehensive dataset encompasses a wide range of melodies, scales, instruments, and rhythmic patterns that encapsulate the rich cultural heritage of China.

With detailed metadata accompanying each sample, including scale mode, instrument type, tempo, key, and regional influence, this dataset offers a valuable resource for exploring and innovating within the domain of the Chinese music generation.

Delve into the vast diversity of Chinese music genres, from traditional folk tunes to classical compositions, and embark on a journey of cultural discovery. With its comprehensive selection of tracks, this dataset catalyzes innovation, perfect for delving into the intricacies of Chinese music and developing cutting-edge AI models that can generate original and evocative Chinese compositions.

This exceptional AI Music Dataset encompasses an array of vital data categories, contributing to its excellence. It encompasses Machine Learning (ML) Data, serving as the foundation for training intricate algorithms that generate musical pieces. Music Data, offering a rich collection of melodies, harmonies, and rhythms that fuel the AI's creative process. AI & ML Training Data continuously hone the dataset's capabilities through iterative learning. Copyright Data ensures the dataset's compliance with legal standards, while Intellectual Property Data safeguards the innovative techniques embedded within, fostering a harmonious blend of technological advancement and artistic innovation.

This dataset can also be useful as Advertising Data to generate music tailored to resonate with specific target audiences, enhancing the effectiveness of advertisements by evoking emotions and capturing attention. It can be a valuable source of Social Media Data as well. Users can post, share, and interact with the music, leading to increased user engagement and virality. The music's novelty and uniqueness can spark discussions, debates, and trends across social media communities, amplifying its reach and impact.

Money Supply M2 in China decreased to 335105.40 CNY Billion in October from 335377.10 CNY Billion in September of 2025. This dataset provides - China Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States Imports from China was US$462.62 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from China - data, historical chart and statistics - was last updated on December of 2025.

Dataset Description

This dataset is sourced from FAOSTAT, the comprehensive statistical database maintained by the Food and Agriculture Organization (FAO) of the United Nations. It provides detailed and reliable data on global agriculture, food security, nutrition, and related topics. The dataset covers the period from 1971 to 2022, offering a 50-year perspective on trends and changes in agricultural production, trade, resource use, and environmental impacts.

Visit the FAOSTAT website: https://www.fao.org/faostat/.

Variables :

• Year: The year for which the data is recorded (e.g., 1971, 2022).
• China: A metric (likely percentage change or growth rate) for China in the given year.
• India: A metric for India in the given year.
• Pakistan: A metric for Pakistan in the given year.
• United Arab Emirates: A metric for the UAE in the given year.
• United Kingdom: A metric for the UK in the given year.
• United States of America: A metric for the USA in the given year.

Each column (except Year) represents a country and contains numerical values, possibly indicating growth rates, percentage changes, or other metrics over time.

Possible Sources International Organizations: FAOSTAT (Food and Agriculture Organization): Provides data on agriculture, food security, and related metrics. World Bank: Offers economic, demographic, and environmental data. United Nations (UN): Publishes data on global development indicators. IMF (International Monetary Fund): Provides financial and economic data. Government Agencies: National statistical offices (e.g., Census Bureau, Ministry of Agriculture). Central banks or economic departments. Research Institutions: Universities or think tanks that collect and analyze data for specific studies

Exports to United States in China decreased to 30786817 USD Thousand in February from 42633059 USD Thousand in January of 2024. This dataset includes a chart with historical data for China Exports To Us.

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.

This dataset contains detailed specifications and official launch prices of various mobile phone models from different companies. It provides insights into smartphone hardware, pricing trends, and brand competitiveness across multiple countries. The dataset includes key features such as RAM, camera specifications, battery capacity, processor details, and screen size.

One important aspect of this dataset is the pricing information. The recorded prices represent the official launch prices of the mobile phones at the time they were first introduced in the market. Prices vary based on the country and the launch period, meaning older models reflect their original launch prices, while newer models include their most recent launch prices. This makes the dataset valuable for studying price trends over time and comparing smartphone affordability across different regions.

Features:

• Company Name: The brand or manufacturer of the mobile phone.
• Model Name: The specific model of the smartphone.
• Mobile Weight: The weight of the mobile phone (in grams).
• RAM: The amount of Random Access Memory (RAM) in the device (in GB).
• Front Camera: The resolution of the front (selfie) camera (in MP).
• Back Camera: The resolution of the primary rear camera (in MP).
• Processor: The chipset or processor used in the device.
• Battery Capacity: The battery size of the smartphone (in mAh).
• Screen Size: The display size of the smartphone (in inches).
• Launched Price: (Pakistan, India, China, USA, Dubai): The official launch price of the mobile in the respective country at the time of its release. Prices vary based on the year the mobile was launched.
• Launched Year: The year the mobile phone was officially launched.

Context:

The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.

Number of Countries & Index:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">

Content:

Unit of analysis: Stock Market Index Analysis

This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.

There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.

The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.

Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.

To extract the data provided in the attachment, various criteria were applied:

1. Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.

2. Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.

In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).

Annualized Return:

As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">

Compound Annual Growth Rate (CAGR):

The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.

The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">

Geography: Stock Market Index of the World Top Economies

Time period: Jan 01, 2003 – June 30, 2023

Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR

File Type: CSV file

Inspiration:

• Time series prediction model
• Investment opportunities in world best economies
• Comparative Analysis of past data with other stock market indices or other indices

Disclaimer:

This is not a financial advice; due diligence is required in each investment decision.

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