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Daily Vegetable and Fruits Prices in Nepal

Dataset Overview

This dataset provides daily updated prices for various commodities in Nepal, initially sourced from the Open Data Nepal platform. The data includes information such as commodity name, unit, minimum price, maximum price, and average price. Starting from November 1, this dataset has been updated on a daily basis, providing timely and accurate information for tracking price trends across different commodities.

Data Source

• Initial Data Source: Open Data Nepal
• Ongoing Updates: This dataset is automatically updated daily to ensure that the most recent data is always available.

Dataset Features

• Commodity: The name of the commodity (e.g., rice, potatoes, onions).
• Unit: The measurement unit (e.g., kg, dozen).
• Min Price: The minimum price recorded.
• Max Price: The maximum price recorded.
• Avg Price: The average price computed for each commodity.

Usage and Applications

This dataset is ideal for: - Market Analysis: Track and analyze price fluctuations and trends for commodities in Nepal. - Economic Studies: Gain insights into inflation and supply-demand impacts on daily prices. - Agricultural and Trade Planning: Use real-time price data for planning in agricultural and trade sectors.

Acknowledgments

Credits to Open Data Nepal for the initial data. This dataset is maintained and updated daily to facilitate ongoing data needs in various sectors.

Contact

If you have any questions or need further information, feel free to reach out at nabinoli2004@gmail.com.

Skills for Care's National Minimum Data Set for Social Care (NMDS-SC) is recognised as the leading source of robust workforce intelligence for adult social care. The NMDS-SC collects information online about providers offering a social care service and their employees. Social care providers can register, maintain and access their business information at www.nmds-sc-online.org.uk.

The Open Data analysis files contain aggregate level, anonymised information on all establishments held in the NMDS-SC system. This file allows analysis of raw NMDS-SC data and contains information on issues such as recruitment and retention, sickness, pay rates, qualifications and demographics to be accessed.

The main purposes of Skills for Care’s publication of raw NMDS-SC information are to promote transparency in the collected data and also to further encourage use of the data by audiences such as government policymakers, academics, researchers, local authorities and workforce planners, as well as any other user with an interest in social care and labour markets.

As ChatGPT emerges as a potential ally in healthcare decision-making, it is imperative to investigate how users leverage and perceive it. The repurposing of technology is innovative but brings risks, especially since AI’s effectiveness depends on the data it’s fed. In healthcare, ChatGPT might provide sound advice based on current medical knowledge, which could turn into misinformation if its data sources later include erroneous information. Our study assesses user perceptions of ChatGPT, particularly of those who used ChatGPT for healthcare-related queries. By examining factors such as competence, reliability, transparency, trustworthiness, security, and persuasiveness of ChatGPT, the research aimed to understand how users rely on ChatGPT for health-related decision-making. A web-based survey was distributed to U.S. adults using ChatGPT at least once a month. Bayesian Linear Regression was used to understand how much ChatGPT aids in informed decision-making. This analysis was conducted on subsets of respondents, both those who used ChatGPT for healthcare decisions and those who did not. Qualitative data from open-ended questions were analyzed using content analysis, with thematic coding to extract public opinions on urban environmental policies. Six hundred and seven individuals responded to the survey. Respondents were distributed across 306 US cities of which 20 participants were from rural cities. Of all the respondents, 44 used ChatGPT for health-related queries and decision-making. In the healthcare context, the most effective model highlights ’Competent + Trustworthy + ChatGPT for healthcare queries’, underscoring the critical importance of perceived competence and trustworthiness specifically in the realm of healthcare applications of ChatGPT. On the other hand, the non-healthcare context reveals a broader spectrum of influential factors in its best model, which includes ’Trustworthy + Secure + Benefits outweigh risks + Satisfaction + Willing to take decisions + Intent to use + Persuasive’. In conclusion our study findings suggest a clear demarcation in user expectations and requirements from AI systems based on the context of their use. We advocate for a balanced approach where technological advancement and user readiness are harmonized.

This dataset presents an extensive record of daily historical stock prices for Tesla, Inc. (TSLA), one of the world’s most innovative and closely watched electric vehicle and clean energy companies. The data was sourced from Yahoo Finance, a widely used and trusted provider of financial market data, and covers a significant period spanning from Tesla’s initial public offering (IPO) to the most recent date available at the time of extraction.

The dataset includes critical trading metrics for each market day, such as the opening price, highest and lowest prices of the day, closing price, adjusted closing price (accounting for dividends and splits), and total trading volume. This rich dataset supports a variety of use cases, including financial market analysis, investment research, time series forecasting, development and backtesting of trading algorithms, and educational projects in data science and finance.

Dataset Features

• Date: The calendar date for each trading session (in YYYY-MM-DD format)
• Open: The opening price of TSLA shares at the start of the trading day
• High: The highest price reached during the trading session
• Low: The lowest price reached during the trading session
• Close: The last price at which the stock traded during the day
• Adj Close: The closing price adjusted for corporate actions (splits, dividends, etc.)
• Volume: The total number of TSLA shares traded on that day

Source and Collection Details

• Source: Yahoo Finance - Tesla (TSLA) Historical Data
• Collection Method: Data was downloaded using Yahoo Finance's CSV export feature for accuracy and completeness.
• Time Range: Covers from Tesla’s IPO (June 2010) to the most recent available trading day.
• Data Integrity: Minimal cleaning was performed—dates were standardized, and any duplicate or empty rows were removed; all values remain as originally reported by Yahoo Finance.

Example Use Cases

• Stock Price Prediction: Train and test time series models (ARIMA, LSTM, Prophet, etc.) to forecast Tesla’s stock prices.
• Algorithmic Trading: Backtest and evaluate trading strategies using historical price and volume data.
• Market Trend Analysis: Analyze price trends, volatility, and return rates over different periods.
• Event Study: Investigate the impact of major announcements (e.g., product launches, earnings releases) on TSLA stock price.
• Educational Projects: Use as a hands-on resource for learning finance, statistics, or machine learning.

License & Acknowledgments

• Intended Use: This dataset is provided for academic, research, and personal projects. For commercial or investment use, please verify data accuracy and consult Yahoo Finance’s terms of use.
• Acknowledgment: Data sourced from Yahoo Finance. All trademarks and copyrights belong to their respective owners.

This dataset is about stocks per day. It has 169 rows and is filtered where the stock is MLMAB.PA and the date is after the 3rd of September 2024. It features 3 columns: stock, and lowest price.

This dataset aggregates daily wholesale price data for a wide spectrum of agricultural commodities traded across India’s regulated markets (mandis). It captures minimum, maximum, and modal prices, enabling detailed analysis of price dispersion and volatility over time. Data is sourced directly from the AGMARKNET portal and made available under the National Data Sharing and Accessibility Policy (NDSAP). With over 165,000 views and nearly 400,000 downloads, it’s a cornerstone resource for economists, agronomists, and data scientists studying India’s commodity markets.

This dataset provides daily wholesale minimum, maximum, and modal prices for a wide variety of agricultural commodities across India’s mandis, sourced from the AGMARKNET portal and published on Data.gov.in under NDSAP, with records dating back to 2013 and updated as of 19 May 2025 via a REST API; it includes key fields like Arrival_Date, State, District, Market, Commodity, Variety, Min_Price, Max_Price, and Modal_Price, making it ideal for time-series analysis, price-trend visualizations, and commodity forecasting.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Price, UT, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 11,467, while the mean income for the highest quintile (20% of households with the highest income) is 148,423. This indicates that the top earners earn 13 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 200,861, which is 135.33% higher compared to the highest quintile, and 1751.64% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Price median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Price County, WI, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 15,849, while the mean income for the highest quintile (20% of households with the highest income) is 164,114. This indicates that the top earners earn 10 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 273,328, which is 166.55% higher compared to the highest quintile, and 1724.58% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Price County median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Price, Wisconsin, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 17,919, while the mean income for the highest quintile (20% of households with the highest income) is 204,380. This indicates that the top earners earn 11 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 335,077, which is 163.95% higher compared to the highest quintile, and 1869.95% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Price town median household income. You can refer the same here

Context

Netflix, Inc. is an American media company engaged in paid streaming and the production of films and series.

Market cap

Market capitalization of Netflix (NFLX)

Market cap: $517.08 Billion USD

As of June 2025 Netflix has a market cap of $517.08 Billion USD. This makes Netflix the world's 19th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Revenue

Revenue for Netflix (NFLX)

Revenue in 2025: $40.17 Billion USD

According to Netflix's latest financial reports the company's current revenue (TTM ) is $40.17 Billion USD. In 2024 the company made a revenue of $39.00 Billion USD an increase over the revenue in the year 2023 that were of $33.72 Billion USD. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.

Earnings

Earnings for Netflix (NFLX)

Earnings in 2025 (TTM): $11.31 Billion USD

According to Netflix's latest financial reports the company's current earnings are $40.17 Billion USD. In 2024 the company made an earning of $10.70 Billion USD, an increase over its 2023 earnings that were of $7.02 Billion USD. The earnings displayed on this page is the company's Pretax Income.

End of Day market cap according to different sources

On Jun 12th, 2025 the market cap of Netflix was reported to be:

$517.08 Billion USD by Yahoo Finance

$517.08 Billion USD by CompaniesMarketCap

$517.21 Billion USD by Nasdaq

Content

Geography: USA

Time period: May 2002- June 2025

Unit of analysis: Netflix Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Price Township, Pennsylvania, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 32,346, while the mean income for the highest quintile (20% of households with the highest income) is 211,893. This indicates that the top earners earn 7 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 293,107, which is 138.33% higher compared to the highest quintile, and 906.16% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Price township median household income. You can refer the same here

The dataset shows Year-wise and Daily Weighted Average of Call Money Rates

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Big Rock, IL, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 39,445, while the mean income for the highest quintile (20% of households with the highest income) is 318,639. This indicates that the top earners earn 8 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 578,151, which is 181.44% higher compared to the highest quintile, and 1465.71% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Big Rock median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Lake Town, Price County, Wisconsin, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 23,958, while the mean income for the highest quintile (20% of households with the highest income) is 233,302. This indicates that the top earners earn 10 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 422,284, which is 181% higher compared to the highest quintile, and 1762.60% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Lake town median household income. You can refer the same here

This data table has been removed. The information can now be found on the Minimum Wage Database. The dataset for historical minimum wage rates is regularly updated and can be found at Historical minimum wage rates in Canada.

--- DATASET OVERVIEW --- This dataset provides critical insights into market pricing dynamics, availability patterns, and booking trends with AI-enhanced forecasting capabilities for vacation rental properties across global markets. With daily updates and extensive coverage, it provides a detailed view of pricing strategies, demand patterns, and market positioning for properties across different segments and regions.

The data is sourced directly from major OTA platforms using advanced collection methodologies that ensure high accuracy and comprehensive coverage. Our proprietary algorithms enhance the raw data with AI and machine learning driven booking probability predictions, enabling users to anticipate future booking patterns and occupancy levels with increased precision.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Identifiers: Unique identifiers for each property with OTA-specific IDs - Geographic Information: Location data including neighborhood, city, region, and country - Property Characteristics: Property type, bedroom count, bathroom count, and capacity - Quoted Rates: Price points for each available date - Minimum Stay Requirements: Minimum night requirements for different booking periods - Availability Status: Available/unavailable including guest stay detection for each calendar date - Key Pricing Patterns: Price variations across different seasons and months as well as event driven and other high-demand periods. - Price Positioning: Relative price positioning compared to similar properties in the same area - Historical Price Trends: Price changes over time for the same property and dates

--- USE CASES --- Revenue Management Optimization: Property managers and revenue specialists can leverage this dataset to develop sophisticated dynamic pricing strategies. By analyzing how similar properties adjust pricing based on seasonality, day of week, and market demand, managers can optimize their own pricing to maximize revenue without sacrificing occupancy. The AI-detected guest bookings provide the best context for expected demand, allowing for more precise rate adjustments during different booking windows.

Demand Forecasting and Trend Analysis: Market analysts and tourism organizations can use this dataset to forecast demand patterns across different destinations. The comprehensive availability data, coupled with AI-detected guest bookings, enables accurate prediction of occupancy trends, booking pace, and seasonal fluctuations. These insights support capacity planning, marketing timing, and resource allocation decisions.

Competitive Benchmarking: Property owners and managers can benchmark their pricing and availability strategies against competitors in the same market. The dataset allows for detailed comparison of pricing strategies, minimum stay requirements, and booking pace across similar properties. This competitive intelligence helps identify opportunities for market positioning adjustments and pricing optimization.

Investment Decision Support Real estate investors focused on the vacation rental sector can analyze pricing and occupancy patterns across different markets to identify investment opportunities. The dataset provides insights into rate potential, seasonal demand variations, and overall market performance, supporting data-driven acquisition and portfolio expansion decisions.

Market Entry Analysis Companies considering entering new vacation rental markets can utilize this dataset to understand pricing dynamics, seasonality impacts, and demand patterns before committing resources. The comprehensive pricing and availability data reduces market entry risk by providing clear visibility into potential revenue opportunities and competitive positioning requirements.

Economic Impact Studies Researchers and economic development organizations can leverage this dataset to analyze the economic impact of vacation rentals on local communities. By tracking pricing trends, occupancy patterns, and overall inventory utilization, researchers can quantify the contribution of the vacation rental sector to local economies and tax bases.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: daily | weekly | monthly • Delivery Method: scheduled file deliveries • File Formats: csv | parquet • Large File Format: partitioned parquet • Delivery Channels: Google Cloud | Amazon S3 | Azure Blob • Data Refreshes: daily

Dataset Options: • Coverage: Global (most countries) • Historic Data: Available (2021 for most areas) • Future Looking Data: Available (Current date + 180 days+) • Point-in-Time: Not Available • Aggregation and Filtering Options: • Area/Market • Time Scales (daily, weekly) • Listing Source • Property Characteristics (property types, bedroom counts, amenities, etc.) • Management Practices (professionally managed, by o...

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Big Rapids charter Township, Michigan, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 9,042, while the mean income for the highest quintile (20% of households with the highest income) is 231,383. This indicates that the top earners earn 26 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 434,773, which is 187.90% higher compared to the highest quintile, and 4808.37% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Big Rapids charter township median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Big Lake, TX, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 21,903, while the mean income for the highest quintile (20% of households with the highest income) is 175,427. This indicates that the top earners earn 8 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 248,093, which is 141.42% higher compared to the highest quintile, and 1132.69% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Big Lake median household income. You can refer the same here

Minimum Wages in Hungary remained unchanged at 707 EUR/Month in the second quarter of 2025 from 707 EUR/Month in the first quarter of 2025. This dataset provides - Hungary Minimum Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Minimum Wages in Belarus increased to 726 BYN/Month in 2025 from 626 BYN/Month in 2024. This dataset provides - Belarus Minimum Wages- actual values, historical data, forecast, chart, statistics, economic calendar and news.